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A curated list of the most impressive AI papers
aimerou/awesome-ai-papers
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Awesome ai papers ⭐️, description.
This repository is an up-to-date list of significant AI papers organized by publication date. It covers five fields : computer vision, natural language processing, audio processing, multimodal learning and reinforcement learning. Feel free to give this repository a star if you enjoy the work.
Maintainer: Aimerou Ndiaye
Table of Contents
Computer vision.
- Natural Language Processing
Audio Processing
Multimodal learning, reinforcement learning, other papers, historical papers.
To select the most relevant papers, we chose subjective limits in terms of number of citations. Each icon here designates a paper type that meets one of these criteria.
🏆 Historical Paper : more than 10k citations and a decisive impact in the evolution of AI.
⭐ Important Paper : more than 50 citations and state of the art results.
⏫ Trend : 1 to 50 citations, recent and innovative paper with growing adoption.
📰 Important Article : decisive work that was not accompanied by a research paper.
2023 Papers
- ⭐ 01/2023: Muse: Text-To-Image Generation via Masked Generative Transformers (Muse)
- ⭐ 02/2023: Structure and Content-Guided Video Synthesis with Diffusion Models (Gen-1)
- ⭐ 02/2023: Scaling Vision Transformers to 22 Billion Parameters (ViT 22B)
- ⭐ 02/2023: Adding Conditional Control to Text-to-Image Diffusion Models (ControlNet)
- ⭐ 03/2023: Visual ChatGPT: Talking, Drawing and Editing with Visual Foundation Models (Visual ChatGPT)
- ⭐ 03/2023: Scaling up GANs for Text-to-Image Synthesis (GigaGAN)
- ⭐ 04/2023: Segment Anything (SAM)
- ⭐ 04/2023: DINOv2: Learning Robust Visual Features without Supervision (DINOv2)
- ⭐ 04/2023: Visual Instruction Tuning
- ⭐ 04/2023: Align your Latents: High-Resolution Video Synthesis with Latent Diffusion Models (VideoLDM)
- ⭐ 04/2023: Synthetic Data from Diffusion Models Improves ImageNet Classification
- ⭐ 04/2023: Segment Anything in Medical Images (MedSAM)
- ⭐ 05/2023: Drag Your GAN: Interactive Point-based Manipulation on the Generative Image Manifold (DragGAN)
- ⭐ 06/2023: Neuralangelo: High-Fidelity Neural Surface Reconstruction (Neuralangelo)
- ⭐ 07/2023: SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis (SDXL)
- ⭐ 08/2023: 3D Gaussian Splatting for Real-Time Radiance Field Rendering
- ⭐ 08/2023: Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization... (Qwen-VL)
- ⏫ 08/2023: MVDream: Multi-view Diffusion for 3D Generation (MVDream)
- ⏫ 11/2023: Florence-2: Advancing a Unified Representation for a Variety of Vision Tasks (Florence-2)
- ⏫ 12/2023: VideoPoet: A Large Language Model for Zero-Shot Video Generation (VideoPoet)
- ⭐ 01/2023: DetectGPT: Zero-Shot Machine-Generated Text Detection using Probability Curvature (DetectGPT)
- ⭐ 02/2023: Toolformer: Language Models Can Teach Themselves to Use Tools (Toolformer)
- ⭐ 02/2023: LLaMA: Open and Efficient Foundation Language Models (LLaMA)
- 📰 03/2023: GPT-4
- ⭐ 03/2023: Sparks of Artificial General Intelligence: Early experiments with GPT-4 (GPT-4 Eval)
- ⭐ 03/2023: HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in HuggingFace (HuggingGPT)
- ⭐ 03/2023: BloombergGPT: A Large Language Model for Finance (BloombergGPT)
- ⭐ 04/2023: Instruction Tuning with GPT-4
- ⭐ 04/2023: Generative Agents: Interactive Simulacra of Human (Gen Agents)
- ⭐ 05/2023: PaLM 2 Technical Report (PaLM-2)
- ⭐ 05/2023: Tree of Thoughts: Deliberate Problem Solving with Large Language Models (ToT)
- ⭐ 05/2023: LIMA: Less Is More for Alignment (LIMA)
- ⭐ 05/2023: QLoRA: Efficient Finetuning of Quantized LLMs (QLoRA)
- ⭐ 05/2023: Voyager: An Open-Ended Embodied Agent with Large Language Models (Voyager)
- ⭐ 07/2023: ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs (ToolLLM)
- ⭐ 08/2023: MetaGPT: Meta Programming for Multi-Agent Collaborative Framework (MetaGPT)
- ⭐ 08/2023: Code Llama: Open Foundation Models for Code (Code Llama)
- ⏫ 09/2023: RLAIF: Scaling Reinforcement Learning from Human Feedback with AI Feedback (RLAIF)
- ⭐ 09/2023: Large Language Models as Optimizers (OPRO)
- ⏫ 10/2023: Eureka: Human-Level Reward Design via Coding Large Language Models (Eureka)
- ⏫ 12/2023: Mathematical discoveries from program search with large language models (FunSearch)
- ⭐ 01/2023: Neural Codec Language Models are Zero-Shot Text to Speech Synthesizers (VALL-E)
- ⭐ 01/2023: MusicLM: Generating Music From Text (MusicLM)
- ⭐ 01/2023: AudioLDM: Text-to-Audio Generation with Latent Diffusion Models (AudioLDM)
- ⭐ 03/2023: Google USM: Scaling Automatic Speech Recognition Beyond 100 Languages (USM)
- ⭐ 05/2023: Scaling Speech Technology to 1,000+ Languages (MMS)
- ⏫ 06/2023: Simple and Controllable Music Generation (MusicGen)
- ⏫ 06/2023: AudioPaLM: A Large Language Model That Can Speak and Listen (AudioPaLM)
- ⏫ 06/2023: Voicebox: Text-Guided Multilingual Universal Speech Generation at Scale (Voicebox)
- ⭐ 02/2023: Language Is Not All You Need: Aligning Perception with Language Models (Kosmos-1)
- ⭐ 03/2023: PaLM-E: An Embodied Multimodal Language Model (PaLM-E)
- ⭐ 04/2023: AudioGPT: Understanding and Generating Speech, Music, Sound, and Talking Head (AudioGPT)
- ⭐ 05/2023: ImageBind: One Embedding Space To Bind Them All (ImageBind)
- ⏫ 07/2023: Scaling Autoregressive Multi-Modal Models: Pretraining and Instruction Tuning (CM3Leon)
- ⏫ 07/2023: Meta-Transformer: A Unified Framework for Multimodal Learning (Meta-Transformer)
- ⏫ 08/2023: SeamlessM4T: Massively Multilingual & Multimodal Machine Translation (SeamlessM4T)
- ⭐ 01/2023: Mastering Diverse Domains through World Models (DreamerV3)
- ⏫ 02/2023: Grounding Large Language Models in Interactive Environments with Online RL (GLAM)
- ⏫ 02/2023: Efficient Online Reinforcement Learning with Offline Data (RLPD)
- ⏫ 03/2023: Reward Design with Language Models
- ⭐ 05/2023: Direct Preference Optimization: Your Language Model is Secretly a Reward Model (DPO)
- ⏫ 06/2023: Faster sorting algorithms discovered using deep reinforcement learning (AlphaDev)
- ⏫ 08/2023: Retroformer: Retrospective Large Language Agents with Policy Gradient Optimization (Retroformer)
- ⭐ 02/2023: Symbolic Discovery of Optimization Algorithms (Lion)
- ⭐ 07/2023: RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control (RT-2)
- ⏫ 11/2023: Scaling deep learning for materials discovery (GNoME)
- ⏫ 12/2023: Discovery of a structural class of antibiotics with explainable deep learning
2022 Papers
- ⭐ 01/2022: A ConvNet for the 2020s (ConvNeXt)
- ⭐ 01/2022: Patches Are All You Need (ConvMixer)
- ⭐ 02/2022: Block-NeRF: Scalable Large Scene Neural View Synthesis (Block-NeRF)
- ⭐ 03/2022: DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection (DINO)
- ⭐ 03/2022: Scaling Up Your Kernels to 31×31: Revisiting Large Kernel Design in CNNs (Large Kernel CNN)
- ⭐ 03/2022: TensoRF: Tensorial Radiance Fields (TensoRF)
- ⭐ 04/2022: MaxViT: Multi-Axis Vision Transformer (MaxViT)
- ⭐ 04/2022: Hierarchical Text-Conditional Image Generation with CLIP Latents (DALL-E 2)
- ⭐ 05/2022: Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding (Imagen)
- ⭐ 05/2022: GIT: A Generative Image-to-text Transformer for Vision and Language (GIT)
- ⭐ 06/2022: CMT: Convolutional Neural Network Meet Vision Transformers (CMT)
- ⭐ 07/2022: Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors... (Swin UNETR)
- ⭐ 07/2022: Classifier-Free Diffusion Guidance
- ⭐ 08/2022: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation (DreamBooth)
- ⭐ 09/2022: DreamFusion: Text-to-3D using 2D Diffusion (DreamFusion)
- ⭐ 09/2022: Make-A-Video: Text-to-Video Generation without Text-Video Data (Make-A-Video)
- ⭐ 10/2022: On Distillation of Guided Diffusion Models
- ⭐ 10/2022: LAION-5B: An open large-scale dataset for training next generation image-text models (LAION-5B)
- ⭐ 10/2022: Imagic: Text-Based Real Image Editing with Diffusion Models (Imagic)
- ⭐ 11/2022: Visual Prompt Tuning
- ⭐ 11/2022: Magic3D: High-Resolution Text-to-3D Content Creation (Magic3D)
- ⭐ 11/2022: DiffusionDet: Diffusion Model for Object Detection (DiffusionDet)
- ⭐ 11/2022: InstructPix2Pix: Learning to Follow Image Editing Instructions (InstructPix2Pix)
- ⭐ 12/2022: Multi-Concept Customization of Text-to-Image Diffusion (Custom Diffusion)
- ⭐ 12/2022: Scalable Diffusion Models with Transformers (DiT)
- ⭐ 01/2022: LaMBDA: Language Models for Dialog Applications (LaMBDA)
- ⭐ 01/2022: Chain-of-Thought Prompting Elicits Reasoning in Large Language Models (CoT)
- ⭐ 02/2022: Competition-Level Code Generation with AlphaCode (AlphaCode)
- ⭐ 02/2022: Finetuned Language Models Are Zero-Shot Learners (FLAN)
- ⭐ 03/2022: Training language models to follow human instructions with human feedback (InstructGPT)
- ⭐ 03/2022: Multitask Prompted Training Enables Zero-Shot Task Generalization (T0)
- ⭐ 03/2022: Training Compute-Optimal Large Language Models (Chinchilla)
- ⭐ 04/2022: Do As I Can, Not As I Say: Grounding Language in Robotic Affordances (SayCan)
- ⭐ 04/2022: GPT-NeoX-20B: An Open-Source Autoregressive Language Model (GPT-NeoX)
- ⭐ 04/2022: PaLM: Scaling Language Modeling with Pathways (PaLM)
- ⭐ 06/2022: Beyond the Imitation Game: Quantifying and extrapolating the capabilities of lang... (BIG-bench)
- ⭐ 06/2022: Solving Quantitative Reasoning Problems with Language Models (Minerva)
- ⭐ 10/2022: ReAct: Synergizing Reasoning and Acting in Language Models (ReAct)
- ⭐ 11/2022: BLOOM: A 176B-Parameter Open-Access Multilingual Language Model (BLOOM)
- 📰 11/2022: Optimizing Language Models for Dialogue (ChatGPT)
- ⭐ 12/2022: Large Language Models Encode Clinical Knowledge (Med-PaLM)
- ⭐ 02/2022: mSLAM: Massively multilingual joint pre-training for speech and text (mSLAM)
- ⭐ 02/2022: ADD 2022: the First Audio Deep Synthesis Detection Challenge (ADD)
- ⭐ 03/2022: Efficient Training of Audio Transformers with Patchout (PaSST)
- ⭐ 04/2022: MAESTRO: Matched Speech Text Representations through Modality Matching (Maestro)
- ⭐ 05/2022: SpeechT5: Unified-Modal Encoder-Decoder Pre-Training for Spoken Language... (SpeechT5)
- ⭐ 06/2022: WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing (WavLM)
- ⭐ 07/2022: BigSSL: Exploring the Frontier of Large-Scale Semi-Supervised Learning for ASR (BigSSL)
- ⭐ 08/2022: MuLan: A Joint Embedding of Music Audio and Natural Language (MuLan)
- ⭐ 09/2022: AudioLM: a Language Modeling Approach to Audio Generation (AudioLM)
- ⭐ 09/2022: AudioGen: Textually Guided Audio Generation (AudioGen)
- ⭐ 10/2022: High Fidelity Neural Audio Compression (EnCodec)
- ⭐ 12/2022: Robust Speech Recognition via Large-Scale Weak Supervision (Whisper)
- ⭐ 01/2022: BLIP: Boostrapping Language-Image Pre-training for Unified Vision-Language... (BLIP)
- ⭐ 02/2022: data2vec: A General Framework for Self-supervised Learning in Speech, Vision and... (Data2vec)
- ⭐ 03/2022: VL-Adapter: Parameter-Efficient Transfer Learning for Vision-and-Language Tasks (VL-Adapter)
- ⭐ 04/2022: Winoground: Probing Vision and Language Models for Visio-Linguistic... (Winoground)
- ⭐ 04/2022: Flamingo: a Visual Language Model for Few-Shot Learning (Flamingo)
- ⭐ 05/2022: A Generalist Agent (Gato)
- ⭐ 05/2022: CoCa: Contrastive Captioners are Image-Text Foundation Models (CoCa)
- ⭐ 05/2022: VLMo: Unified Vision-Language Pre-Training with Mixture-of-Modality-Experts (VLMo)
- ⭐ 08/2022: Image as a Foreign Language: BEiT Pretraining for All Vision and Vision-Language Tasks (BEiT)
- ⭐ 09/2022: PaLI: A Jointly-Scaled Multilingual Language-Image Model (PaLI)
- ⭐ 01/2022: Learning robust perceptive locomotion for quadrupedal robots in the wild
- ⭐ 02/2022: BC-Z: Zero-Shot Task Generalization with Robotic Imitation Learning
- ⭐ 02/2022: Outracing champion Gran Turismo drivers with deep reinforcement learning (Sophy)
- ⭐ 02/2022: Magnetic control of tokamak plasmas through deep reinforcement learning
- ⭐ 08/2022: Learning to Walk in Minutes Using Massively Parallel Deep Reinforcement Learning (ANYmal)
- ⭐ 10/2022: Discovering faster matrix multiplication algorithms with reinforcement learning (AlphaTensor)
- ⭐ 02/2022: FourCastNet: A Global Data-driven High-resolution Weather Model... (FourCastNet)
- ⭐ 05/2022: ColabFold: making protein folding accessible to all (ColabFold)
- ⭐ 06/2022: Measuring and Improving the Use of Graph Information in GNN
- ⭐ 10/2022: TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis (TimesNet)
- ⭐ 12/2022: RT-1: Robotics Transformer for Real-World Control at Scale (RT-1)
- 🏆 1958: Perceptron: A probabilistic model for information storage and organization in the brain (Perceptron)
- 🏆 1986: Learning representations by back-propagating errors (Backpropagation)
- 🏆 1986: Induction of decision trees (CART)
- 🏆 1989: A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition (HMM)
- 🏆 1989: Multilayer feedforward networks are universal approximators
- 🏆 1992: A training algorithm for optimal margin classifiers (SVM)
- 🏆 1996: Bagging predictors
- 🏆 1998: Gradient-based learning applied to document recognition (CNN/GTN)
- 🏆 2001: Random Forests
- 🏆 2001: A fast and elitist multiobjective genetic algorithm (NSGA-II)
- 🏆 2003: Latent Dirichlet Allocation (LDA)
- 🏆 2006: Reducing the Dimensionality of Data with Neural Networks (Autoencoder)
- 🏆 2008: Visualizing Data using t-SNE (t-SNE)
- 🏆 2009: ImageNet: A large-scale hierarchical image database (ImageNet)
- 🏆 2012: ImageNet Classification with Deep Convolutional Neural Networks (AlexNet)
- 🏆 2013: Efficient Estimation of Word Representations in Vector Space (Word2vec)
- 🏆 2013: Auto-Encoding Variational Bayes (VAE)
- 🏆 2014: Generative Adversarial Networks (GAN)
- 🏆 2014: Dropout: A Simple Way to Prevent Neural Networks from Overfitting (Dropout)
- 🏆 2014: Sequence to Sequence Learning with Neural Networks
- 🏆 2014: Neural Machine Translation by Jointly Learning to Align and Translate (RNNSearch-50)
- 🏆 2014: Adam: A Method for Stochastic Optimization (Adam)
- 🏆 2015: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Cov... (BatchNorm)
- 🏆 2015: Going Deeper With Convolutions (Inception)
- 🏆 2015: Human-level control through deep reinforcement learning (Deep Q Network)
- 🏆 2015: Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks (Faster R-CNN)
- 🏆 2015: U-Net: Convolutional Networks for Biomedical Image Segmentation (U-Net)
- 🏆 2015: Deep Residual Learning for Image Recognition (ResNet)
- 🏆 2016: You Only Look Once: Unified, Real-Time Object Detection (YOLO)
- 🏆 2017: Attention is All you Need (Transformer)
- 🏆 2018: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (BERT)
- 🏆 2020: Language Models are Few-Shot Learners (GPT-3)
- 🏆 2020: An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale (ViT)
- 🏆 2021: Highly accurate protein structure prediction with AlphaFold (Alphafold)
- 📰 2022: ChatGPT: Optimizing Language Models For Dialogue (ChatGPT)
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siyuanliii/masa • 6 Jun 2024
The robust association of the same objects across video frames in complex scenes is crucial for many applications, especially Multiple Object Tracking (MOT).
LLaVA-UHD: an LMM Perceiving Any Aspect Ratio and High-Resolution Images
To address the challenges, we present LLaVA-UHD, a large multimodal model that can efficiently perceive images in any aspect ratio and high resolution.
Buffer of Thoughts: Thought-Augmented Reasoning with Large Language Models
yangling0818/buffer-of-thought-llm • 6 Jun 2024
We introduce Buffer of Thoughts (BoT), a novel and versatile thought-augmented reasoning approach for enhancing accuracy, efficiency and robustness of large language models (LLMs).
Vision-LSTM: xLSTM as Generic Vision Backbone
Transformers are widely used as generic backbones in computer vision, despite initially introduced for natural language processing.
Fast Timing-Conditioned Latent Audio Diffusion
Generating long-form 44. 1kHz stereo audio from text prompts can be computationally demanding.
StreamSpeech: Simultaneous Speech-to-Speech Translation with Multi-task Learning
Simultaneous speech-to-speech translation (Simul-S2ST, a. k. a streaming speech translation) outputs target speech while receiving streaming speech inputs, which is critical for real-time communication.
DeTikZify: Synthesizing Graphics Programs for Scientific Figures and Sketches with TikZ
Creating high-quality scientific figures can be time-consuming and challenging, even though sketching ideas on paper is relatively easy.
Scalable MatMul-free Language Modeling
Our experiments show that our proposed MatMul-free models achieve performance on-par with state-of-the-art Transformers that require far more memory during inference at a scale up to at least 2. 7B parameters.
AgentGym: Evolving Large Language Model-based Agents across Diverse Environments
woooodyy/agentgym • 6 Jun 2024
Building generalist agents that can handle diverse tasks and evolve themselves across different environments is a long-term goal in the AI community.
Flash Diffusion: Accelerating Any Conditional Diffusion Model for Few Steps Image Generation
In this paper, we propose an efficient, fast, and versatile distillation method to accelerate the generation of pre-trained diffusion models: Flash Diffusion.
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The Best of Applied Artificial Intelligence, Machine Learning, Automation, Bots, Chatbots
Top 10 Influential AI Research Papers in 2023 from Google, Meta, Microsoft, and More
December 5, 2023 by Mariya Yao
From Generative Agents research paper
In this article, we delve into ten transformative research papers from diverse domains, spanning language models, image processing, image generation, and video editing. As discussions around Artificial General Intelligence (AGI) reveal that AGI seems more approachable than ever, it’s no wonder that some of the featured papers explore various paths to AGI, such as extending language models or harnessing reinforcement learning for domain-spanning mastery.
If you’d like to skip around, here are the research papers we featured:
- Sparks of AGI by Microsoft
- PALM-E by Google
- LLaMA 2 by Meta AI
- LLaVA by University of Wisconsin–Madison, Microsoft, and Columbia University
- Generative Agents by Stanford University and Google
- Segment Anything by Meta AI
- DALL-E 3 by OpenAI
- ControlNet by Stanford University
- Gen-1 by Runway
- DreamerV3 by DeepMind and University of Toronto
If this in-depth educational content is useful for you, subscribe to our AI mailing list to be alerted when we release new material.
Top 10 AI Research Papers 2023
1. sparks of agi by microsoft.
In this research paper, a team from Microsoft Research analyzes an early version of OpenAI’s GPT-4, which was still under active development at the time. The team argues that GPT-4 represents a new class of large language models, exhibiting more generalized intelligence compared to previous AI models. Their investigation reveals GPT-4’s expansive capabilities across various domains, including mathematics, coding, vision, medicine, law, and psychology. They highlight that GPT-4 can solve complex and novel tasks without specialized prompting, often achieving performance close to human level.
The Microsoft team also emphasizes the potential of GPT-4 to be considered an early, albeit incomplete, form of artificial general intelligence (AGI). They focus on identifying GPT-4’s limitations and discuss the challenges in progressing towards more advanced and comprehensive AGI versions. This includes considering new paradigms beyond the current next-word prediction model.
Where to learn more about this research?
- Sparks of Artificial General Intelligence: Early experiments with GPT-4 (research paper)
- Sparks of AGI: early experiments with GPT-4 (a talk by the paper’s first author Sébastien Bubeck)
Where can you get implementation code?
- Not applicable
2. PALM-E by Google
The research paper introduces PaLM-E , a novel approach to language models that bridges the gap between words and percepts in the real world by directly incorporating continuous sensor inputs. This embodied language model seamlessly integrates multi-modal sentences containing visual, continuous state estimation, and textual information. These inputs are trained end-to-end with a pre-trained LLM and applied to various embodied tasks, including sequential robotic manipulation planning, visual question answering, and captioning.
PaLM-E, particularly the largest model with 562B parameters, demonstrates remarkable performance on a wide range of tasks and modalities. Notably, it excels in embodied reasoning tasks, exhibits positive transfer from joint training across language, vision, and visual-language domains, and showcases state-of-the-art capabilities in OK-VQA benchmarking. Despite its focus on embodied reasoning, PaLM-E-562B also exhibits an array of capabilities, including zero-shot multimodal chain-of-thought reasoning, few-shot prompting, OCR-free math reasoning, and multi-image reasoning, despite being trained on only single-image examples.
- PaLM-E: An Embodied Multimodal Language Model (research paper)
- PaLM-E (demos)
- PaLM-E (blog post)
- Code implementation of the PaLM-E model is not available.
3. LLaMA 2 by Meta AI
Summary .
LLaMA 2 is an enhanced version of its predecessor, trained on a new data mix, featuring a 40% larger pretraining corpus, doubled context length, and grouped-query attention. The LLaMA 2 series of models includes LLaMA 2 and LLaMA 2-Chat , optimized for dialogue, with sizes ranging from 7 to 70 billion parameters. These models exhibit superior performance in helpfulness and safety benchmarks compared to open-source counterparts and are comparable to some closed-source models. The development process involved rigorous safety measures, including safety-specific data annotation and red-teaming. The paper aims to contribute to the responsible development of LLMs by providing detailed descriptions of fine-tuning methodologies and safety improvements.
- Llama 2: Open Foundation and Fine-Tuned Chat Models (research paper)
- Llama 2: open source, free for research and commercial use (blog post)
- Meta AI released LLaMA 2 models to individuals, creators, researchers, and businesses of all sizes. You can access model weights and starting code for pretrained and fine-tuned LLaMA 2 language models through GitHub .
4. LLaVA by University of Wisconsin–Madison, Microsoft, and Columbia University
The research paper introduces LLaVA , L arge L anguage a nd V ision A ssistant, a groundbreaking multimodal model that leverages language-only GPT-4 to generate instruction-following data for both text and images. This novel approach extends the concept of instruction tuning to the multimodal space, enabling the development of a general-purpose visual assistant.
The paper addresses the challenge of a scarcity of vision-language instruction-following data by presenting a method to convert image-text pairs into the appropriate instruction-following format, utilizing GPT-4. They construct a large multimodal model (LMM) by integrating the open-set visual encoder of CLIP with the language decoder LLaMA. The fine-tuning process on generated instructional vision-language data proves effective, and practical insights are offered for building a general-purpose instruction-following visual agent.
The paper’s contributions include the generation of multimodal instruction-following data, the development of large multimodal models through end-to-end training on generated data, and the achievement of state-of-the-art performance on the Science QA multimodal reasoning dataset. Additionally, the paper demonstrates a commitment to open-source principles by making the generated multimodal instruction data, codebase for data generation and model training, model checkpoint, and a visual chat demo available to the public.
- Visual Instruction Tuning (research paper)
- LLaVA: Large Language and Vision Assistant (blog post with demos)
- The LLaVa code implementation is available on GitHub .
5. Generative Agents by Stanford University and Google
The paper introduces a groundbreaking concept – generative agents that can simulate believable human behavior. These agents exhibit a wide range of actions, from daily routines like cooking breakfast to creative endeavors such as painting and writing. They form opinions, engage in conversations, and remember past experiences, creating a vibrant simulation of human-like interactions.
To achieve this, the paper presents an architectural framework that extends large language models, allowing agents to store their experiences in natural language, synthesize memories over time, and retrieve them dynamically for behavior planning. These generative agents find applications in various domains, from role-play scenarios to social prototyping in virtual worlds. The research validates their effectiveness through evaluations, emphasizing the importance of memory, reflection, and planning in creating convincing agent behavior while addressing ethical and societal considerations.
- Generative Agents: Interactive Simulacra of Human Behavior (research paper)
- Generative Agents (video presentation of the research by the paper’s first author, Joon Sung Park)
- The core simulation module for generative agents was released on GitHub .
6. Segment Anything by Meta AI
In this paper, the Meta AI team introduced a groundbreaking task, model, and dataset for image segmentation. Leveraging an efficient model in a data collection loop, the project has created the most extensive segmentation dataset to date, featuring over 1 billion masks for 11 million licensed and privacy-respecting images. To achieve their goal of building a foundational model for image segmentation, the project focuses on promptable models trained on a diverse dataset. SAM, the Segment Anything Model , employs a straightforward yet effective architecture comprising an image encoder, a prompt encoder, and a mask decoder. The experiments demonstrate that SAM competes favorably with fully supervised results on a diverse range of downstream tasks, including edge detection, object proposal generation, and instance segmentation.
- Segment Anything (research paper)
- Segment Anything (the research website with demos, datasets, etc)
- The Segment Anything Model (SAM) and corresponding dataset (SA-1B) of 1B masks and 11M images have been released here .
7. DALL-E 3 by OpenAI
The research paper presents a groundbreaking approach to addressing one of the most significant challenges in text-to-image models: prompt following. Text-to-image models have historically struggled with accurately translating detailed image descriptions into visuals, often misinterpreting prompts or overlooking critical details. The authors of the paper hypothesize that these issues come from noisy and inaccurate image captions in the training dataset. To overcome this limitation, they developed a specialized image captioning system capable of generating highly descriptive and precise image captions. These enhanced captions are then used to recaption the training dataset for text-to-image models. The results are remarkable, with the DALL-E model trained on the improved dataset showcasing significantly enhanced prompt-following abilities.
Note: The paper does not cover training or implementation details of the DALL-E 3 model and only focuses on evaluating the improved prompt following of DALL-E 3 as a result of training on highly descriptive generated captions.
- Improving Image Generation with Better Captions (research paper)
- DALL-E 3 (blog post by OpenAI)
- The code implementation of DALL-E 3 is not available, but the authors released text-to-image samples collected for the evaluations of DALL-E against the competitors.
8. ControlNet by Stanford University
ControlNet is a neural network structure designed by the Stanford University research team to control pretrained large diffusion models and support additional input conditions. ControlNet learns task-specific conditions in an end-to-end manner and demonstrates robust learning even with small training datasets. The training process is as fast as fine-tuning a diffusion model and can be performed on personal devices or scaled to handle large amounts of data using powerful computation clusters. By augmenting large diffusion models like Stable Diffusion with ControlNets, the researchers enable conditional inputs such as edge maps, segmentation maps, and keypoints, thereby enriching methods to control large diffusion models and facilitating related applications.
- Adding Conditional Control to Text-to-Image Diffusion Models (research paper)
- Ablation Study: Why ControlNets use deep encoder? What if it was lighter? Or even an MLP? (blog post by ControlNet developers)
- The official implementation of this paper is available on GitHub .
9. Gen-1 by Runway
The Gen-1 research paper introduced a groundbreaking advancement in the realm of video editing through the fusion of text-guided generative diffusion models. While such models had previously revolutionized image creation and manipulation, extending their capabilities to video editing had remained a formidable challenge. Existing methods either required laborious re-training for each input or resorted to error-prone techniques to propagate image edits across frames. In response to these limitations, the researchers presented a structure and content-guided video diffusion model that allowed seamless video editing based on textual or visual descriptions of the desired output. The suggested solution was to leverage monocular depth estimates with varying levels of detail to gain precise control over structure and content fidelity.
Gen-1 was trained jointly on images and videos, paving the way for versatile video editing capabilities. It empowered users with fine-grained control over output characteristics, enabling customization based on a few reference images. Extensive experiments demonstrated its prowess, from preserving temporal consistency to achieving user preferences in editing outcomes.
- Structure and Content-Guided Video Synthesis with Diffusion Models (research paper)
- Gen-1: The Next Step Forward for Generative AI (blog post by Runway)
- Gen-2: The Next Step Forward for Generative AI (blog post by Runway)
- The code implementation of Gen-1 is not available.
10. DreamerV3 by DeepMind and University of Toronto
The paper introduces DreamerV3 , a pioneering algorithm, based on world models, that showcases remarkable performance across a wide spectrum of domains, encompassing both continuous and discrete actions, visual and low-dimensional inputs, 2D and 3D environments, varied data budgets, reward frequencies, and reward scales. At the heart of DreamerV3 lies a world model that learns from experience, combining rich perception and imagination training. This model incorporates three neural networks: one for predicting future outcomes based on potential actions, another for assessing the value of different situations, and a third for learning how to navigate toward valuable situations. The algorithm’s generalizability across domains with fixed hyperparameters is achieved through the transformation of signal magnitudes and robust normalization techniques.
A particularly noteworthy achievement of DreamerV3 is its ability to conquer the challenge of collecting diamonds in the popular video game Minecraft entirely from scratch, without any reliance on human data or curricula. DreamerV3 also demonstrates scalability, where larger models directly translate to higher data efficiency and superior final performance.
- Mastering Diverse Domains through World Models (research paper)
- DreamerV3 (project website)
- A reimplementation of DreamerV3 is available on GitHub .
In 2023, the landscape of AI research witnessed remarkable advancements, and these ten transformative papers have illuminated the path forward. From innovative language models to groundbreaking image generation and video editing techniques, these papers have pushed the boundaries of AI capabilities. As we reflect on these achievements, we anticipate even more transformative discoveries and applications on the horizon, shaping the AI landscape for years to come.
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Ten Noteworthy AI Research Papers of 2023
This year has felt distinctly different. I've been working in, on, and with machine learning and AI for over a decade, yet I can't recall a time when these fields were as popular and rapidly evolving as they have been this year.
To conclude an eventful 2023 in machine learning and AI research, I'm excited to share 10 noteworthy papers I've read this year. My personal focus has been more on large language models, so you'll find a heavier emphasis on large language model (LLM) papers than computer vision papers this year.
I resisted labeling this article "Top AI Research Papers of 2023" because determining the "best" paper is subjective. The selection criteria were based on a mix of papers I either particularly enjoyed or found impactful and worth noting. (The sorting order is a recommended reading order, not an ordering by perceived quality or impact.)
By the way, if you scroll down to the end of this article, you'll find a little surprise. Thanks for all your support, and I wish you a great start to the new year!
1) Pythia — Insights from Large-Scale Training Runs
With Pythia: A Suite for Analyzing Large Language Models Across Training and Scaling , the researchers originally released 8 LLMs ranging from 70M to 12B parameters (with both weights and data publicly released, which is rare).
But in my opinion, the standout feature of this paper is that they also released the training details, analyses, and insights (some of them shown in the annotated figure below).
Here are some questions that the Pythia paper addresses:
Does pretraining on duplicated data (i.e., training for >1 epoch) make a difference? It turns out that deduplication does not benefit or hurt performance.
Does training order influence memorization? Unfortunately, it turns out that it does not. "Unfortunately," because if this was true, we could mitigate undesirable verbatim memorization issues by reordering the training data.
Does pretrained term frequency influence task performance? Yes, few-shot accuracy tends to be higher for terms that occur more frequently.
Does increasing the batch size affect training efficiency and model convergence? Doubling the batch size halves the training time but doesn't hurt convergence.
Today, only six months later, the LLMs are by no means groundbreaking. However, I am including this paper because it not only tries to answer interesting questions about training settings but is also a positive example regarding details and transparency. Moreover, the small LLMs in the <1B range are nice templates for small studies and tinkering, or starters for pretraining experiments (here's a link to their GitHub repository ).
My wish for 2024 is that we see more studies like this and well-written papers in the coming year!
2) Llama 2: Open Foundation and Fine-Tuned Chat Models
Llama 2: Open Foundation and Fine-Tuned Chat Models is the follow-up paper to Meta's popular first Llama paper.
Llama 2 models, which range from 7B to 70B parameters, are one of the reasons this paper made it onto this list: these are still among the most capable and widely used openly available models. Worth noting is that the Llama 2 license also permits use in commercial applications (see the Request to Access page for details).
On the model side, what differentiates the Llama 2 suite from many other LLMs is that the models come as standard pretrained models and chat models that have been finetuned via reinforcement learning with human feedback (RLHF, the method used to create ChatGPT) to follow human instructions similar to ChatGPT — RLHF-finetuned models are still rare.
For more details on RLHF and how it's used in Llama 2, see my more comprehensive standalone article below.
LLM Training: RLHF and Its Alternatives
Next to the fact that Llama 2 models are widely used and come with RLHF instruction-finetuned variants, the other reason I decided to include the paper on this list is the accompanying in-depth 77-page research report.
Here, the authors also nicely illustrated the evolution of the Llama 2 70B Chat models, tracing their journey from the initial supervised finetuning (SFT-v1) to the final RLHF finetuning stage with PPO (RLHF-v5). The chart reflects consistent improvements in both the harmlessness and helpfulness axes, as shown in the annotated plots below.
Even though models such as Mistral-8x7B (more later), DeepSeek-67B, and YI-34B top the larger Llama-2-70B models in public benchmarks, Llama 2 still remains a common and popular choice when it comes to openly available LLMs and developing methods on top of it.
Furthermore, even though some benchmarks indicate that there may be better models, one of the bigger challenges this year has been the trustworthiness of benchmarks. For instance, how do we know that the models haven't been trained on said benchmarks and the scores aren't inflated? In classic machine learning, when someone proposed a new gradient boosting model, it was relatively easy to reproduce the results and check. Nowadays, given how expensive and complex it is to train LLMs (and the fact that most researchers either don't disclose the architecture or the training data details), it is impossible to tell.
To conclude, it's refreshing to see Meta doubling down on open source even though every other major company is now rolling out its own proprietary large language models (Google's Bard and Gemini, Amazon's Q, and Twitter/X's Grok, and OpenAI's ChatGPT).
3) QLoRA: Efficient Finetuning of Quantized LLMs
QLoRA: Efficient Finetuning of Quantized LLMs has been one of the favorite techniques in the LLM research and finetuning community this year because it makes the already popular LoRA (low-rank adaptation) technique more memory efficient. In short, this means that you can fit larger models onto smaller GPUs.
QLoRA stands for quantized LoRA (low-rank adaptation). The standard LoRA method modifies a pretrained LLM by adding low-rank matrices to the weights of the model's layers. These matrices are smaller and, therefore, require fewer resources to update during finetuning.
In QLoRA, these low-rank matrices are quantized, meaning their numerical precision is reduced. This is done by mapping the continuous range of values in these matrices to a limited set of discrete levels. This process reduces the model's memory footprint and computational demands, as operations on lower-precision numbers are less memory-intensive
According to the QLoRA paper , QLoRA reduces the memory requirements of a 65B Llama model to fit onto a single 48 GB GPU (like an A100). The 65B Guanaco model, obtained from quantized 4-bit training of 65B Llama, maintains full 16-bit finetuning task performance, reaching 99.3% of the ChatGPT performance after only 24 hours of finetuning.
I've also run many QLoRA experiments this year and found QLoRA a handy tool for reducing GPU memory requirements during finetuning. There's a trade-off, though: the extra quantization step results in an additional computation overhead, meaning the training will be a bit slower than regular LoRA.
LLM finetuning remains as relevant as ever as researchers and practitioners aim to create custom LLMs. And I appreciate techniques like QLoRA that help make this process more accessible by lowering the GPU memory-requirement barrier.
4) BloombergGPT: A Large Language Model for Finance
Looking at all the papers published this year, BloombergGPT: A Large Language Model for Finance may look like an odd choice for a top-10 list because it didn't result in a groundbreaking new insight, methodology, or open-source model.
I include it because it's an interesting case study where someone pretrained a relatively large LLM on a domain-specific dataset. Moreover, the description was pretty thorough, which is becoming increasingly rare. This is especially true when it comes to papers with authors employed at companies -- one of the trends this year was that major companies are becoming increasingly secretive about architecture or dataset details to preserve trade secrets in this competitive landscape (PS: I don't fault them for that).
Also, BloombergGPT made me think of all the different ways we can pretrain and finetune models on domain-specific data, as summarized in the figure below (note that this was not explored in the BloombergGPT paper, but it would be interesting to see future studies on that).
In short, BloombergGPT is a 50-billion parameter language model for finance, trained on 363 billion tokens from finance data and 345 billion tokens from a general, publicly available dataset. For comparison, GPT-3 is 3.5x larger (175 billion parameters) but was trained on 1.4x fewer tokens (499 billion).
Why did the authors use an architecture with "only" 50 billion parameters since GPT-3 is 3.5x larger? That's easier to answer. They adopted the Chinchilla scaling laws and found this to be a good size given the available size of the finance data.
Is it worth (pre)training the LLM on the combined dataset from scratch? Based on the paper, the model performs really well in the target domain. However, we don't know whether it's better than a) further pretraining a pretrained model on domain-specific data or b) finetuning a pretrained model on domain-specific data.
Despite the little criticism above, overall, this is an interesting paper that serves as an interesting case study and example for domain-specific LLMs; plus, it leaves room for further research on pretraining versus finetuning to instill knowledge into an LLM.
(PS: For those curious about a comparison to finetuning, as Rohan Paul shared with me, the "small" AdaptLLM-7B model outperforms BloombergGPT on one dataset and nearly matches its performance on three other finance datasets. Although BloombergGPT appears to be slightly better overall, it's worth noting that training AdaptLLM-7B cost about $100, in contrast to BloombergGPT's multi-million dollar investment.)
5) Direct Preference Optimization: Your Language Model is Secretly a Reward Model
Before discussing the Direct Preference Optimization: Your Language Model is Secretly a Reward Model paper, let's take a short step back and discuss the method it aims to replace, Reinforcement Learning from Human Feedback (RLHF).
RLHF is the main technique behind ChatGPT and Llama 2 Chat models. In RLHF, which I described in more detail in a separate article , we use a multi-step procedure:
Supervised finetuning: The model is initially trained on a dataset containing instructions and the desired responses.
Reward modeling: Human raters provide feedback on the model's outputs. This feedback is used to create a reward model, which learns to predict what kinds of outputs are to be preferred.
Proximal policy optimization (PPO): The model generates outputs, and the reward model scores each output. The PPO algorithm uses these scores to adjust the model's policy toward
generating higher-quality outputs. (This is a reinforcement learning algorithm used to finetune the model's policy.
While RLHF is popular and effective, as we've seen with ChatGPT and Llama 2, it's also pretty complex to implement and finicky.
The Direct Preference Optimization (DPO) paper introduces an algorithm that optimizes language models to align with human preferences without explicit reward modeling or reinforcement learning. Instead, DPO uses a simple classification objective.
In DPO, we still keep the supervised finetuning step (step 1 above), but we replace steps 2 and 3 with a single step to further finetune the model on the preference data. In other words, DPO skips the reward model creation required by RLHF entirely, which significantly simplifies the finetuning process.
How well does it work? There haven't been many models trained with DPO until very recently. (This makes sense because DPO is also a relatively recent method.) However, one recent example is the Zephyr 7B model described in Zephyr: Direct Distillation of LM Alignment . Zephyr-7B is based on a Mistral-7B base LLM that has been finetuned using DPO. (There will be more on Mistral later.)
As the performance tables below reveal, the 7B-parameter Zephyr model outperformed all other models in its size class at the time of its release. Even more impressively, Zephyr-7B even surpassed the 10 times larger 70B-parameter Llama 2 chat model on the conversational MT-Bench benchmark as well.
In summary, the appeal of the DPO paper lies in the simplicity of its method. The scarcity of chat models trained using RLHF, with Llama 2 as a notable exception, can likely be attributed to the complexity of the RLHF approach. Given this, I think it's reasonable to anticipate an increase in the adoption of DPO models in the coming year.
6) Mistral 7B
I must admit that the Mistral 7B paper wasn't among my favorites due to its brevity. However, the model it proposed was quite impactful.
I decided to include the paper on this list because the Mistral 7B model was not only very popular upon release, but also served as the base model, leading to the development of two other notable models: Zephyr 7B and the latest Mistral Mixture of Experts (MoE) approach. These models are good examples of the trend I foresee for small LLMs in (at least) the early half of 2024.
Before we discuss the Zephyr 7B and Mistral MoE models, let's briefly talk about Mistral 7B itself.
In short, The Mistral 7B paper introduces a compact yet powerful language model that, despite its relatively modest size of 7 billion tokens, outperforms its larger counterparts, such as the 13B Llama 2 model, in various benchmarks. (Next to the two-times larger Qwen 14B , Mistral 7B was also the base model used in the winning solutions of this year's NeurIPS LLM Finetuning & Efficiency challenge .)
Why exactly it is so good is unclear, but it might likely be due to its training data. Neither Llama 2 nor Mistral discloses the training data, so we can only speculate.
Architecture-wise, the model shares group-query attention with Llama 2. While being very similar to Llama 2, one interesting addition to the Mistral architecture is sliding window attention to save memory and improve computational throughput for faster training. (Sliding window attention was previously proposed in Child et al. 2019 and Beltagy et al. 2020 .)
The sliding window attention mechanism used in Mistral is essentially a fixed-sized attention block that allows a current token to attend only a specific number of previous tokens (instead of all previous tokens), which is illustrated in the figure below.
In the specific case of 7B Mistral, the attention block size is 4096 tokens, and the researchers were training the model with up to 100k token context sizes. To provide a concrete example, in regular self-attention, a model at the 50,000th token can attend all previous 49,999 tokens. In sliding window self-attention, the Mistral model can only attend tokens 45,904 to 50,000 (since 50,000 - 4,096 = 45,904).
However, sliding window attention is mainly used to improve computational performance. The fact that Mistral outperforms larger Llama 2 models is likely not because of sliding window attention but rather despite sliding window attention.
Zephyr and Mixtral
One reason Mistral 7B is an influential model is that it served as the base model for Zephyr 7B, as mentioned earlier in the DPO section. Zephyr 7B, the first popular model trained with DPO to outperform other alternatives, has potentially set the stage for DPO to become the preferred method for finetuning chat models in the coming months.
Another noteworthy model derived from Mistral 7B is the recently released Mistral Mixture of Experts (MoE) model , also known as Mixtral-8x7B. This model matches or exceeds the performance of the larger Llama-2-70B on several public benchmarks.
For more benchmarks, also see the official Mixtral blog post announcement . The team also released a Mixtral-8x7B-Instruct model that has been finetuned with DPO (but as of this writing there are no benchmarks comparing it to Llama-2-70-Chat, the RLHF-finetuned model).
GPT-4 is also rumored to be an MoE consisting of 16 submodules. Each of these 16 submodules is rumored to have 111 billion parameters (for reference, GPT-3 has 175 billion parameters). If you read my AI and Open Source in 2023 article approximately two months ago, I mentioned that "It will be interesting to see if MoE approaches can lift open-source models to new heights in 2024". It looks like Mixtral started this trend early, and I am sure that this is just the beginning.
Mixture of Experts 101
If you are new to MoE models, here's a short explanation.
The figure above shows the architecture behind the Switch Transformer, which uses 1 expert per token with 4 experts in total. Mixtral-8x-7B, on the other hand, consists of 8 experts and uses 2 experts per token.
Why MoEs? Combined, the 8 experts in a 7B model like Mixtral are still ~56B parameters. Actually, it's less than 56B, because the MoE approach is only applied to the FFN (feed forward network, aka fully-connected) layers, not the self-attention weight matrices. So, it's likely closer to 40-50B parameters.
Note that the router reroutes the tokens such that only <14B parameters (2x <7B, instead of all <56B) are used at a time for the forward pass, so the training (and especially inference) will be faster compared to the traditional non-MoE approach.
If you want to learn more about MoEs, here's a reading list recommended by Sophia Yang :
The Sparsely-Gated Mixture-of-Experts Layer (2017)
GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding (2020)
MegaBlocks: Efficient Sparse Training with Mixture-of-Experts (2022)
Mixture-of-Experts Meets Instruction Tuning (2023)
Furthermore, if you are interested in trying MoE LLMs, also check out the OpenMoE repository, which implemented and shared MoE LLMs earlier this year.
Other Small but Competitive LLMs
Mistral 7B, Zephyr 7B, and Mixtral-8x7B are excellent examples of the progress made in 2023 with small yet capable models featuring openly available weights. Another notable model, a runner-up on my favorite papers list, is Microsoft's phi series.
The secret sauce of phi is training on high-quality data (referred to as “textbook quality data”) obtained by filtering web data.
Released in stages throughout 2023, the phi models include phi-1 (1.3B parameters), phi-1.5 (1.3B parameters), and phi-2 (2.7B parameters). The latter, released just two weeks ago, is already said to match or outperform Mistral 7B, despite being only half its size.
For more information about the phi models, I recommend the following resources:
Textbooks Are All You Need -- the phi-1 paper
Textbooks Are All You Need II: phi-1.5 Technical Report
The Phi-2: The Surprising Power of Small Language Models announcement
7) Orca 2: Teaching Small Language Models How to Reason
Orca 2: Teaching Small Language Models How to Reason is a relatively new paper, and time will tell whether it has a lasting impact on how we train LLMs in the upcoming months or years.
I decided to include it because it combines several concepts and ideas.
One is the idea of distilling data from large, capable models such as GPT-4 to create a synthetic dataset to train small but capable LLMs. This idea was described in the Self-Instruct paper, which came out last year. Earlier this year, Alpaca (a Llama model finetuned on ChatGPT outputs) really popularized this approach.
How does this work? In a nutshell, it's a 4-step process:
Seed task pool with a set of human-written instructions (175 in this case) and sample instructions;
Use a pretrained LLM (like GPT-3) to determine the task category;
Given the new instruction, let a pretrained LLM generate the response;
Collect, prune, and filter the responses before adding them to the task pool.
The other idea may not be surprising but worth highlighting: high-quality data is important for finetuning. For instance, the LIMA paper proposed a human-generated high-quality dataset consisting of only 1k training examples that can be used to finetuning to outperform the same model finetuned on 50k ChatGPT-generated responses.
Unlike previous research that heavily relied on imitation learning to replicate outputs from larger models, Orca 2 aims to teach "small" (i.e., 7B and 13B) LLMs various reasoning techniques (like step-by-step reasoning, recall-then-generate, etc.) and to help them determine the most effective strategy for each task. This approach has led Orca 2 to outperform similar-sized models noticeably and even achieve results comparable to models 5-10 times larger.
While we haven't seen any extensive studies on this, the Orca 2 approach might also be able to address the issue of using synthetic data that was highlighted in the The False Promise of Imitating Proprietary LLMs paper. Here, the researchers investigated the finetuning weaker language models to imitate stronger proprietary models like ChatGPT, using examples such as Alpaca and Self-Instruct. Initially, the imitation models showed promising results, performing well in following instructions and receiving competitive ratings from crowd workers compared to ChatGPT. However, more follow-up evaluations revealed that these imitation models only seemed to perform well to a human observer but often generated factually incorrect responses.
8) ConvNets Match Vision Transformers at Scale
In recent years, I've almost exclusively worked with large language transformers or vision transformers (ViTs) due to their good performance.
Switching gears from language to computer vision papers for the last three entries, what I find particularly appealing about transformers for computer vision is that pretrained ViTs are even easier to finetune than convolutional neural networks. (I summarized a short hands-on talk at CVPR earlier this year here: https://magazine.sebastianraschka.com/p/accelerating-pytorch-model-training).
To my surprise, I stumbled upon the ConvNets Match Vision Transformers at Scale paper showing that convolutional neural networks (CNNs) are in fact, competitive with ViTs when given access to large enough datasets.
Here, researchers invested compute budgets of up to 110k TPU hours to do a fair comparison between ViTs and CNNs. The outcome was that when CNNs are pretrained with a compute budget similar to what is typically used for ViTs, they can match the performance of ViTs. For this, they pretrained on 4 billion labeled images from JFT and subsequently finetuned the models on ImageNet.
9) Segment Anything
Object recognition and segmentation in images and videos, along with classification and generative modeling, are the main research fields in computer vision.
To briefly highlight the difference between these two tasks: object detection about predicting bounding boxes and the associated labels; segmentation classifies each pixel to distinguish between foreground and background objects.
Meta's Segment Anything paper is a notable milestone for open source and image segmentation research. The paper introduces a new task, model, and dataset for image segmentation. The accompanying image datasets the largest segmentation dataset to date with over 1 billion masks on 11 million images.
However, what's rare and especially laudable is that the researchers used licensed and privacy-respecting images, so the model can be open-sourced without major copyright concerns.
The Segment Anything Model (SAM) consists of three main components, as summarized in the annotated figure above.
In slightly more details, the three components can be summarized as follows:
An image encoder utilizing a masked autoencoder based on a pretrained vision transformer (ViT) that can handle high-resolution inputs. This encoder is run once per image and can be applied before prompting the model.
A prompt encoder that handles two types of prompts: sparse (points, boxes, text) and dense (masks). Points and boxes are represented by positional encodings combined with learned embeddings for each prompt type. And free-form text uses an off-the-shelf text encoder from CLIP. Dense prompts, i.e., masks, are embedded using convolutions and summed element-wise with the image embedding.
A mask decoder maps the image embedding, prompt embeddings, and an output token to a mask. This is a decoder-style transformer architecture that computes the mask foreground probability at each image location.
Image segmentation is important for applications like self-driving cars, medical imaging, and many others. In the short amount of 6 months, the paper has already been cited more than 1500 times , and there have already been many projects that have been built on top of this paper.
10) Align your Latents: High-Resolution Video Synthesis with Latent Diffusion Models
Emu Video: Factorizing Text-to-Video Generation by Explicit Image Conditioning is another notable computer vision project from Meta's research division.
Emu is a text-to-video model that can generate entire videos from text prompts.
While it's not the first model for impressive text-to-video generation, it compares very favorably to previous works.
As the authors note, the Emu architecture setup is relatively simple compared to previous approaches. One of the main ideas here is that Emu factorizes the generation process into two steps: first, generating an image based on text (using a diffusion model), then creating a video conditioned on both the text and the generated image (using another diffusion model).
2022 has been a big year for text-to-image models like DALL-E 2, Stable Diffusion, and Midjourney. While text-to-image models remain very popular in 2023 (even though LLMs got most of the attention throughout the year), I think that text-to-video models are just about to become more prevalent in online communities in the upcoming year.
Since I am not an image or video designer, I don't have use cases for these tools at the moment; however, text-to-image and text-to-video models are nonetheless interesting to watch as a general measure of progress regarding computer vision.
This magazine is personal passion project that does not offer direct compensation. However, for those who wish to support me, please consider purchasing a copy of one of my books . If you find them insightful and beneficial, please feel free to recommend them to your friends and colleagues.
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| Liked by Sebastian Raschka, PhD It shows the potential we have yet to explore with such LLMs that can be applied to smaller models as well, substantially boosting their performance at a fraction of the size, cost, and latency.
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| Liked by Sebastian Raschka, PhD PS: SUPER!!! Another most-excellent textbook from SR. I got it! Minor note... Your 45% discount was not accepted since Manning already discounts the ebook by 50%. PSS: You are missing an opportunity with this new textbook. What about a chapter on 'Beyond Language To Multi-Modal'? The term LLM is aging; it should LxM for both pretraining inputs and generative outputs.
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- Computer Science (miscellaneous)
- Computer Vision and Pattern Recognition
- Computers in Earth Sciences
- Condensed Matter Physics
- Conservation
- Control and Optimization
- Control and Systems Engineering
- Critical Care and Intensive Care Medicine
- Critical Care Nursing
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- Dental Assisting
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- Developmental Biology
- Developmental Neuroscience
- Discrete Mathematics and Combinatorics
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- Earth-Surface Processes
- Ecological Modeling
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- Economics, Econometrics and Finance (miscellaneous)
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- Emergency Medicine
- Emergency Nursing
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- Endocrinology
- Endocrinology, Diabetes and Metabolism
- Energy Engineering and Power Technology
- Energy (miscellaneous)
- Engineering (miscellaneous)
- Environmental Chemistry
- Environmental Engineering
- Environmental Science (miscellaneous)
- Epidemiology
- Experimental and Cognitive Psychology
- Family Practice
- Filtration and Separation
- Fluid Flow and Transfer Processes
- Food Animals
- Food Science
- Fuel Technology
- Fundamentals and Skills
- Gastroenterology
- Gender Studies
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- Management Information Systems
- Management, Monitoring, Policy and Law
- Management of Technology and Innovation
- Management Science and Operations Research
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- Materials Science (miscellaneous)
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- Citable Docs. (3years)
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| Title | Type | --> | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | journal | 37.044 Q1 | 39 | 3 | 13 | 897 | 955 | 13 | 100.11 | 299.00 | 27.78 | ||
| 2 | book series | 9.129 Q1 | 30 | 13 | 37 | 234 | 370 | 4 | 0.36 | 18.00 | 0.00 | ||
| 3 | journal | 8.119 Q1 | 16 | 21 | 46 | 1045 | 1720 | 44 | 18.17 | 49.76 | 30.68 | ||
| 4 | journal | 6.668 Q1 | 221 | 218 | 471 | 15857 | 8320 | 457 | 16.03 | 72.74 | 26.08 | ||
| 5 | journal | 6.601 Q1 | 101 | 94 | 297 | 3891 | 5224 | 233 | 16.40 | 41.39 | 24.66 | ||
| 6 | journal | 6.158 Q1 | 417 | 1132 | 1248 | 79813 | 26320 | 1236 | 19.32 | 70.51 | 25.30 | ||
| 7 | journal | 5.940 Q1 | 67 | 176 | 428 | 8504 | 7872 | 304 | 14.59 | 48.32 | 25.08 | ||
| 8 | journal | 5.775 Q1 | 177 | 95 | 464 | 9818 | 13119 | 450 | 24.28 | 103.35 | 28.01 | ||
| 9 | journal | 4.696 Q1 | 83 | 240 | 510 | 10198 | 6783 | 458 | 14.59 | 42.49 | 29.34 | ||
| 10 | journal | 4.633 Q1 | 36 | 18 | 70 | 2223 | 1203 | 70 | 13.64 | 123.50 | 21.88 | ||
| 11 | journal | 4.608 Q1 | 27 | 63 | 110 | 3855 | 1648 | 106 | 19.84 | 61.19 | 32.10 | ||
| 12 | journal | 4.346 Q1 | 190 | 92 | 216 | 5524 | 2531 | 208 | 9.24 | 60.04 | 17.11 | ||
| 13 | journal | 4.204 Q1 | 218 | 426 | 1070 | 16592 | 11768 | 1062 | 10.99 | 38.95 | 29.27 | ||
| 14 | journal | 4.170 Q1 | 251 | 1818 | 1894 | 46076 | 23364 | 1884 | 11.83 | 25.34 | 28.16 | ||
| 15 | journal | 4.006 Q1 | 56 | 97 | 232 | 5911 | 5002 | 232 | 15.63 | 60.94 | 29.27 | ||
| 16 | journal | 3.371 Q1 | 54 | 156 | 351 | 5084 | 2955 | 346 | 8.00 | 32.59 | 24.09 | ||
| 17 | journal | 3.260 Q1 | 115 | 447 | 490 | 54420 | 9187 | 489 | 18.42 | 121.74 | 28.68 | ||
| 18 | journal | 3.227 Q1 | 29 | 79 | 106 | 5426 | 2196 | 105 | 18.85 | 68.68 | 31.47 | ||
| 19 | journal | 2.866 Q1 | 140 | 36 | 133 | 1634 | 546 | 122 | 3.53 | 45.39 | 15.38 | ||
| 20 | journal | 2.796 Q1 | 261 | 2 | 892 | 129 | 8472 | 892 | 5.56 | 64.50 | 25.00 | ||
| 21 | journal | 2.732 Q1 | 245 | 776 | 1594 | 35674 | 15242 | 1591 | 9.32 | 45.97 | 29.43 | ||
| 22 | journal | 2.605 Q1 | 173 | 546 | 1124 | 29315 | 9914 | 1119 | 7.03 | 53.69 | 28.39 | ||
| 23 | journal | 2.602 Q1 | 35 | 59 | 208 | 1355 | 1735 | 169 | 7.21 | 22.97 | 36.45 | ||
| 24 | journal | 2.516 Q1 | 10 | 20 | 15 | 966 | 318 | 15 | 21.20 | 48.30 | 41.30 | ||
| 25 | journal | 2.469 Q1 | 57 | 513 | 204 | 19021 | 2703 | 202 | 14.68 | 37.08 | 25.35 | ||
| 26 | journal | 2.430 Q1 | 80 | 118 | 223 | 5582 | 2332 | 223 | 8.77 | 47.31 | 23.11 | ||
| 27 | journal | 2.238 Q1 | 227 | 1367 | 3554 | 61452 | 29548 | 3543 | 7.89 | 44.95 | 30.59 | ||
| 28 | journal | 2.219 Q1 | 169 | 897 | 2840 | 49776 | 25794 | 2839 | 8.47 | 55.49 | 30.32 | ||
| 29 | journal | 2.195 Q1 | 140 | 51 | 207 | 913 | 1526 | 197 | 6.51 | 17.90 | 31.64 | ||
| 30 | journal | 2.160 Q1 | 33 | 100 | 164 | 5416 | 2080 | 160 | 11.94 | 54.16 | 20.27 | ||
| 31 | journal | 2.137 Q1 | 34 | 65 | 137 | 5733 | 2127 | 137 | 15.53 | 88.20 | 25.13 | ||
| 32 | journal | 2.119 Q1 | 104 | 1093 | 3600 | 34552 | 21816 | 3595 | 5.46 | 31.61 | 21.36 | ||
| 33 | journal | 2.085 Q1 | 71 | 48 | 125 | 1400 | 684 | 77 | 6.20 | 29.17 | 29.84 | ||
| 34 | journal | 2.071 Q1 | 104 | 351 | 400 | 20495 | 4585 | 393 | 8.96 | 58.39 | 24.75 | ||
| 35 | journal | 2.042 Q1 | 168 | 142 | 309 | 8968 | 2352 | 306 | 8.77 | 63.15 | 14.64 | ||
| 36 | journal | 1.945 Q1 | 51 | 38 | 95 | 2358 | 1112 | 86 | 4.78 | 62.05 | 21.13 | ||
| 37 | journal | 1.942 Q1 | 172 | 38 | 171 | 2877 | 809 | 168 | 3.32 | 75.71 | 50.98 | ||
| 38 | journal | 1.933 Q1 | 38 | 41 | 74 | 1894 | 875 | 73 | 11.80 | 46.20 | 28.47 | ||
| 39 | journal | 1.926 Q1 | 42 | 80 | 238 | 3621 | 1318 | 238 | 4.56 | 45.26 | 20.69 | ||
| 40 | journal | 1.894 Q1 | 41 | 172 | 241 | 7254 | 1596 | 232 | 6.14 | 42.17 | 24.92 | ||
| 41 | journal | 1.882 Q1 | 78 | 110 | 264 | 7074 | 1822 | 258 | 5.89 | 64.31 | 29.11 | ||
| 42 | journal | 1.875 Q1 | 271 | 2272 | 4192 | 128885 | 41492 | 4192 | 9.29 | 56.73 | 28.93 | ||
| 43 | journal | 1.836 Q1 | 28 | 34 | 89 | 1470 | 554 | 83 | 7.23 | 43.24 | 32.08 | ||
| 44 | journal | 1.815 Q1 | 196 | 975 | 4641 | 57031 | 33170 | 4590 | 6.79 | 58.49 | 30.59 | ||
| 45 | journal | 1.805 Q1 | 48 | 5 | 57 | 313 | 110 | 57 | 1.93 | 62.60 | 42.86 | ||
| 46 | journal | 1.771 Q1 | 19 | 312 | 154 | 7780 | 1075 | 153 | 5.94 | 24.94 | 23.67 | ||
| 47 | journal | 1.749 Q1 | 137 | 1554 | 1209 | 92424 | 11904 | 1206 | 9.22 | 59.47 | 26.10 | ||
| 48 | journal | 1.731 Q1 | 110 | 378 | 650 | 22451 | 6399 | 644 | 9.16 | 59.39 | 29.65 | ||
| 49 | journal | 1.723 Q1 | 110 | 180 | 437 | 11318 | 4081 | 431 | 7.50 | 62.88 | 33.55 | ||
| 50 | journal | 1.720 Q1 | 169 | 219 | 368 | 11327 | 2427 | 356 | 5.09 | 51.72 | 20.16 |
Follow us on @ScimagoJR Scimago Lab , Copyright 2007-2024. Data Source: Scopus®
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The best AI tools for research papers and academic research (Literature review, grants, PDFs and more)
As our collective understanding and application of artificial intelligence (AI) continues to evolve, so too does the realm of academic research. Some people are scared by it while others are openly embracing the change.
Make no mistake, AI is here to stay!
Instead of tirelessly scrolling through hundreds of PDFs, a powerful AI tool comes to your rescue, summarizing key information in your research papers. Instead of manually combing through citations and conducting literature reviews, an AI research assistant proficiently handles these tasks.
These aren’t futuristic dreams, but today’s reality. Welcome to the transformative world of AI-powered research tools!
This blog post will dive deeper into these tools, providing a detailed review of how AI is revolutionizing academic research. We’ll look at the tools that can make your literature review process less tedious, your search for relevant papers more precise, and your overall research process more efficient and fruitful.
I know that I wish these were around during my time in academia. It can be quite confronting when trying to work out what ones you should and shouldn’t use. A new one seems to be coming out every day!
Here is everything you need to know about AI for academic research and the ones I have personally trialed on my YouTube channel.
My Top AI Tools for Researchers and Academics – Tested and Reviewed!
There are many different tools now available on the market but there are only a handful that are specifically designed with researchers and academics as their primary user.
These are my recommendations that’ll cover almost everything that you’ll want to do:
| Find literature using semantic search. I use this almost every day to answer a question that pops into my head. | |
| An increasingly powerful and useful application, especially effective for conducting literature reviews through its advanced semantic search capabilities. | |
| An AI-powered search engine specifically designed for academic research, providing a range of innovative features that make it extremely valuable for academia, PhD candidates, and anyone interested in in-depth research on various topics. | |
| A tool designed to streamline the process of academic writing and journal submission, offering features that integrate directly with Microsoft Word as well as an online web document option. | |
| A tools that allow users to easily understand complex language in peer reviewed papers. The free tier is enough for nearly everyone. | |
| A versatile and powerful tool that acts like a personal data scientist, ideal for any research field. It simplifies data analysis and visualization, making complex tasks approachable and quick through its user-friendly interface. |
Want to find out all of the tools that you could use?
Here they are, below:
AI literature search and mapping – best AI tools for a literature review – elicit and more
Harnessing AI tools for literature reviews and mapping brings a new level of efficiency and precision to academic research. No longer do you have to spend hours looking in obscure research databases to find what you need!
AI-powered tools like Semantic Scholar and elicit.org use sophisticated search engines to quickly identify relevant papers.
They can mine key information from countless PDFs, drastically reducing research time. You can even search with semantic questions, rather than having to deal with key words etc.
With AI as your research assistant, you can navigate the vast sea of scientific research with ease, uncovering citations and focusing on academic writing. It’s a revolutionary way to take on literature reviews.
- Elicit – https://elicit.org
- Litmaps – https://www.litmaps.com
- Research rabbit – https://www.researchrabbit.ai/
- Connected Papers – https://www.connectedpapers.com/
- Supersymmetry.ai: https://www.supersymmetry.ai
- Semantic Scholar: https://www.semanticscholar.org
- Laser AI – https://laser.ai/
- Inciteful – https://inciteful.xyz/
- Scite – https://scite.ai/
- System – https://www.system.com
If you like AI tools you may want to check out this article:
- How to get ChatGPT to write an essay [The prompts you need]
AI-powered research tools and AI for academic research
AI research tools, like Concensus, offer immense benefits in scientific research. Here are the general AI-powered tools for academic research.
These AI-powered tools can efficiently summarize PDFs, extract key information, and perform AI-powered searches, and much more. Some are even working towards adding your own data base of files to ask questions from.
Tools like scite even analyze citations in depth, while AI models like ChatGPT elicit new perspectives.
The result? The research process, previously a grueling endeavor, becomes significantly streamlined, offering you time for deeper exploration and understanding. Say goodbye to traditional struggles, and hello to your new AI research assistant!
- Consensus – https://consensus.app/
- Iris AI – https://iris.ai/
- Research Buddy – https://researchbuddy.app/
- Mirror Think – https://mirrorthink.ai
AI for reading peer-reviewed papers easily
Using AI tools like Explain paper and Humata can significantly enhance your engagement with peer-reviewed papers. I always used to skip over the details of the papers because I had reached saturation point with the information coming in.
These AI-powered research tools provide succinct summaries, saving you from sifting through extensive PDFs – no more boring nights trying to figure out which papers are the most important ones for you to read!
They not only facilitate efficient literature reviews by presenting key information, but also find overlooked insights.
With AI, deciphering complex citations and accelerating research has never been easier.
- Aetherbrain – https://aetherbrain.ai
- Explain Paper – https://www.explainpaper.com
- Chat PDF – https://www.chatpdf.com
- Humata – https://www.humata.ai/
- Lateral AI – https://www.lateral.io/
- Paper Brain – https://www.paperbrain.study/
- Scholarcy – https://www.scholarcy.com/
- SciSpace Copilot – https://typeset.io/
- Unriddle – https://www.unriddle.ai/
- Sharly.ai – https://www.sharly.ai/
- Open Read – https://www.openread.academy
AI for scientific writing and research papers
In the ever-evolving realm of academic research, AI tools are increasingly taking center stage.
Enter Paper Wizard, Jenny.AI, and Wisio – these groundbreaking platforms are set to revolutionize the way we approach scientific writing.
Together, these AI tools are pioneering a new era of efficient, streamlined scientific writing.
- Jenny.AI – https://jenni.ai/ (20% off with code ANDY20)
- Yomu – https://www.yomu.ai
- Wisio – https://www.wisio.app
AI academic editing tools
In the realm of scientific writing and editing, artificial intelligence (AI) tools are making a world of difference, offering precision and efficiency like never before. Consider tools such as Paper Pal, Writefull, and Trinka.
Together, these tools usher in a new era of scientific writing, where AI is your dedicated partner in the quest for impeccable composition.
- PaperPal – https://paperpal.com/
- Writefull – https://www.writefull.com/
- Trinka – https://www.trinka.ai/
AI tools for grant writing
In the challenging realm of science grant writing, two innovative AI tools are making waves: Granted AI and Grantable.
These platforms are game-changers, leveraging the power of artificial intelligence to streamline and enhance the grant application process.
Granted AI, an intelligent tool, uses AI algorithms to simplify the process of finding, applying, and managing grants. Meanwhile, Grantable offers a platform that automates and organizes grant application processes, making it easier than ever to secure funding.
Together, these tools are transforming the way we approach grant writing, using the power of AI to turn a complex, often arduous task into a more manageable, efficient, and successful endeavor.
- Granted AI – https://grantedai.com/
- Grantable – https://grantable.co/
Best free AI research tools
There are many different tools online that are emerging for researchers to be able to streamline their research processes. There’s no need for convience to come at a massive cost and break the bank.
The best free ones at time of writing are:
- Elicit – https://elicit.org
- Connected Papers – https://www.connectedpapers.com/
- Litmaps – https://www.litmaps.com ( 10% off Pro subscription using the code “STAPLETON” )
- Consensus – https://consensus.app/
Wrapping up
The integration of artificial intelligence in the world of academic research is nothing short of revolutionary.
With the array of AI tools we’ve explored today – from research and mapping, literature review, peer-reviewed papers reading, scientific writing, to academic editing and grant writing – the landscape of research is significantly transformed.
The advantages that AI-powered research tools bring to the table – efficiency, precision, time saving, and a more streamlined process – cannot be overstated.
These AI research tools aren’t just about convenience; they are transforming the way we conduct and comprehend research.
They liberate researchers from the clutches of tedium and overwhelm, allowing for more space for deep exploration, innovative thinking, and in-depth comprehension.
Whether you’re an experienced academic researcher or a student just starting out, these tools provide indispensable aid in your research journey.
And with a suite of free AI tools also available, there is no reason to not explore and embrace this AI revolution in academic research.
We are on the precipice of a new era of academic research, one where AI and human ingenuity work in tandem for richer, more profound scientific exploration. The future of research is here, and it is smart, efficient, and AI-powered.
Before we get too excited however, let us remember that AI tools are meant to be our assistants, not our masters. As we engage with these advanced technologies, let’s not lose sight of the human intellect, intuition, and imagination that form the heart of all meaningful research. Happy researching!
Thank you to Ivan Aguilar – Ph.D. Student at SFU (Simon Fraser University), for starting this list for me!
Dr Andrew Stapleton has a Masters and PhD in Chemistry from the UK and Australia. He has many years of research experience and has worked as a Postdoctoral Fellow and Associate at a number of Universities. Although having secured funding for his own research, he left academia to help others with his YouTube channel all about the inner workings of academia and how to make it work for you.
Thank you for visiting Academia Insider.
We are here to help you navigate Academia as painlessly as possible. We are supported by our readers and by visiting you are helping us earn a small amount through ads and affiliate revenue - Thank you!
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Analyze research papers at superhuman speed
Search for research papers, get one sentence abstract summaries, select relevant papers and search for more like them, extract details from papers into an organized table.
Find themes and concepts across many papers
Don't just take our word for it.
.webp)
Tons of features to speed up your research
Upload your own pdfs, orient with a quick summary, view sources for every answer, ask questions to papers, research for the machine intelligence age, pick a plan that's right for you, get in touch, enterprise and institutions, custom pricing, common questions. great answers., how do researchers use elicit.
Over 2 million researchers have used Elicit. Researchers commonly use Elicit to:
- Speed up literature review
- Find papers they couldn’t find elsewhere
- Automate systematic reviews and meta-analyses
- Learn about a new domain
Elicit tends to work best for empirical domains that involve experiments and concrete results. This type of research is common in biomedicine and machine learning.
What is Elicit not a good fit for?
Elicit does not currently answer questions or surface information that is not written about in an academic paper. It tends to work less well for identifying facts (e.g. “How many cars were sold in Malaysia last year?”) and theoretical or non-empirical domains.
What types of data can Elicit search over?
Elicit searches across 125 million academic papers from the Semantic Scholar corpus, which covers all academic disciplines. When you extract data from papers in Elicit, Elicit will use the full text if available or the abstract if not.
How accurate are the answers in Elicit?
A good rule of thumb is to assume that around 90% of the information you see in Elicit is accurate. While we do our best to increase accuracy without skyrocketing costs, it’s very important for you to check the work in Elicit closely. We try to make this easier for you by identifying all of the sources for information generated with language models.
What is Elicit Plus?
Elicit Plus is Elicit's subscription offering, which comes with a set of features, as well as monthly credits. On Elicit Plus, you may use up to 12,000 credits a month. Unused monthly credits do not carry forward into the next month. Plus subscriptions auto-renew every month.
What are credits?
Elicit uses a credit system to pay for the costs of running our app. When you run workflows and add columns to tables it will cost you credits. When you sign up you get 5,000 credits to use. Once those run out, you'll need to subscribe to Elicit Plus to get more. Credits are non-transferable.
How can you get in contact with the team?
Please email us at [email protected] or post in our Slack community if you have feedback or general comments! We log and incorporate all user comments. If you have a problem, please email [email protected] and we will try to help you as soon as possible.
What happens to papers uploaded to Elicit?
When you upload papers to analyze in Elicit, those papers will remain private to you and will not be shared with anyone else.
How accurate is Elicit?
Training our models on specific tasks, searching over academic papers, making it easy to double-check answers, save time, think more. try elicit for free..
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- NEWS FEATURE
- 28 May 2024
- Correction 31 May 2024
The AI revolution is coming to robots: how will it change them?
- Elizabeth Gibney
You can also search for this author in PubMed Google Scholar
Humanoid robots developed by the US company Figure use OpenAI programming for language and vision. Credit: AP Photo/Jae C. Hong/Alamy
You have full access to this article via your institution.
For a generation of scientists raised watching Star Wars, there’s a disappointing lack of C-3PO-like droids wandering around our cities and homes. Where are the humanoid robots fuelled with common sense that can help around the house and workplace?
Rapid advances in artificial intelligence (AI) might be set to fill that hole. “I wouldn’t be surprised if we are the last generation for which those sci-fi scenes are not a reality,” says Alexander Khazatsky, a machine-learning and robotics researcher at Stanford University in California.
From OpenAI to Google DeepMind, almost every big technology firm with AI expertise is now working on bringing the versatile learning algorithms that power chatbots, known as foundation models, to robotics. The idea is to imbue robots with common-sense knowledge, letting them tackle a wide range of tasks. Many researchers think that robots could become really good, really fast. “We believe we are at the point of a step change in robotics,” says Gerard Andrews, a marketing manager focused on robotics at technology company Nvidia in Santa Clara, California, which in March launched a general-purpose AI model designed for humanoid robots.
At the same time, robots could help to improve AI. Many researchers hope that bringing an embodied experience to AI training could take them closer to the dream of ‘artificial general intelligence’ — AI that has human-like cognitive abilities across any task . “The last step to true intelligence has to be physical intelligence,” says Akshara Rai, an AI researcher at Meta in Menlo Park, California.
But although many researchers are excited about the latest injection of AI into robotics, they also caution that some of the more impressive demonstrations are just that — demonstrations, often by companies that are eager to generate buzz. It can be a long road from demonstration to deployment, says Rodney Brooks, a roboticist at the Massachusetts Institute of Technology in Cambridge, whose company iRobot invented the Roomba autonomous vacuum cleaner.
There are plenty of hurdles on this road, including scraping together enough of the right data for robots to learn from, dealing with temperamental hardware and tackling concerns about safety. Foundation models for robotics “should be explored”, says Harold Soh, a specialist in human–robot interactions at the National University of Singapore. But he is sceptical, he says, that this strategy will lead to the revolution in robotics that some researchers predict.
Firm foundations
The term robot covers a wide range of automated devices, from the robotic arms widely used in manufacturing, to self-driving cars and drones used in warfare and rescue missions. Most incorporate some sort of AI — to recognize objects, for example. But they are also programmed to carry out specific tasks, work in particular environments or rely on some level of human supervision, says Joyce Sidopoulos, co-founder of MassRobotics, an innovation hub for robotics companies in Boston, Massachusetts. Even Atlas — a robot made by Boston Dynamics, a robotics company in Waltham, Massachusetts, which famously showed off its parkour skills in 2018 — works by carefully mapping its environment and choosing the best actions to execute from a library of built-in templates.
For most AI researchers branching into robotics, the goal is to create something much more autonomous and adaptable across a wider range of circumstances. This might start with robot arms that can ‘pick and place’ any factory product, but evolve into humanoid robots that provide company and support for older people , for example. “There are so many applications,” says Sidopoulos.
The human form is complicated and not always optimized for specific physical tasks, but it has the huge benefit of being perfectly suited to the world that people have built. A human-shaped robot would be able to physically interact with the world in much the same way that a person does.
However, controlling any robot — let alone a human-shaped one — is incredibly hard. Apparently simple tasks, such as opening a door, are actually hugely complex, requiring a robot to understand how different door mechanisms work, how much force to apply to a handle and how to maintain balance while doing so. The real world is extremely varied and constantly changing.
The approach now gathering steam is to control a robot using the same type of AI foundation models that power image generators and chatbots such as ChatGPT. These models use brain-inspired neural networks to learn from huge swathes of generic data. They build associations between elements of their training data and, when asked for an output, tap these connections to generate appropriate words or images, often with uncannily good results.
Likewise, a robot foundation model is trained on text and images from the Internet, providing it with information about the nature of various objects and their contexts. It also learns from examples of robotic operations. It can be trained, for example, on videos of robot trial and error, or videos of robots that are being remotely operated by humans, alongside the instructions that pair with those actions. A trained robot foundation model can then observe a scenario and use its learnt associations to predict what action will lead to the best outcome.
Google DeepMind has built one of the most advanced robotic foundation models, known as Robotic Transformer 2 (RT-2), that can operate a mobile robot arm built by its sister company Everyday Robots in Mountain View, California. Like other robotic foundation models, it was trained on both the Internet and videos of robotic operation. Thanks to the online training, RT-2 can follow instructions even when those commands go beyond what the robot has seen another robot do before 1 . For example, it can move a drink can onto a picture of Taylor Swift when asked to do so — even though Swift’s image was not in any of the 130,000 demonstrations that RT-2 had been trained on.
In other words, knowledge gleaned from Internet trawling (such as what the singer Taylor Swift looks like) is being carried over into the robot’s actions. “A lot of Internet concepts just transfer,” says Keerthana Gopalakrishnan, an AI and robotics researcher at Google DeepMind in San Francisco, California. This radically reduces the amount of physical data that a robot needs to have absorbed to cope in different situations, she says.
But to fully understand the basics of movements and their consequences, robots still need to learn from lots of physical data. And therein lies a problem.
Data dearth
Although chatbots are being trained on billions of words from the Internet, there is no equivalently large data set for robotic activity. This lack of data has left robotics “in the dust”, says Khazatsky.
Pooling data is one way around this. Khazatsky and his colleagues have created DROID 2 , an open-source data set that brings together around 350 hours of video data from one type of robot arm (the Franka Panda 7DoF robot arm, built by Franka Robotics in Munich, Germany), as it was being remotely operated by people in 18 laboratories around the world. The robot-eye-view camera has recorded visual data in hundreds of environments, including bathrooms, laundry rooms, bedrooms and kitchens. This diversity helps robots to perform well on tasks with previously unencountered elements, says Khazatsky.
When prompted to ‘pick up extinct animal’, Google’s RT-2 model selects the dinosaur figurine from a crowded table. Credit: Google DeepMind
Gopalakrishnan is part of a collaboration of more than a dozen academic labs that is also bringing together robotic data, in its case from a diversity of robot forms, from single arms to quadrupeds. The collaborators’ theory is that learning about the physical world in one robot body should help an AI to operate another — in the same way that learning in English can help a language model to generate Chinese, because the underlying concepts about the world that the words describe are the same. This seems to work. The collaboration’s resulting foundation model, called RT-X, which was released in October 2023 3 , performed better on real-world tasks than did models the researchers trained on one robot architecture.
Many researchers say that having this kind of diversity is essential. “We believe that a true robotics foundation model should not be tied to only one embodiment,” says Peter Chen, an AI researcher and co-founder of Covariant, an AI firm in Emeryville, California.
Covariant is also working hard on scaling up robot data. The company, which was set up in part by former OpenAI researchers, began collecting data in 2018 from 30 variations of robot arms in warehouses across the world, which all run using Covariant software. Covariant’s Robotics Foundation Model 1 (RFM-1) goes beyond collecting video data to encompass sensor readings, such as how much weight was lifted or force applied. This kind of data should help a robot to perform tasks such as manipulating a squishy object, says Gopalakrishnan — in theory, helping a robot to know, for example, how not to bruise a banana.
Covariant has built up a proprietary database that includes hundreds of billions of ‘tokens’ — units of real-world robotic information — which Chen says is roughly on a par with the scale of data that trained GPT-3, the 2020 version of OpenAI's large language model. “We have way more real-world data than other people, because that’s what we have been focused on,” Chen says. RFM-1 is poised to roll out soon, says Chen, and should allow operators of robots running Covariant’s software to type or speak general instructions, such as “pick up apples from the bin”.
Another way to access large databases of movement is to focus on a humanoid robot form so that an AI can learn by watching videos of people — of which there are billions online. Nvidia’s Project GR00T foundation model, for example, is ingesting videos of people performing tasks, says Andrews. Although copying humans has huge potential for boosting robot skills, doing so well is hard, says Gopalakrishnan. For example, robot videos generally come with data about context and commands — the same isn’t true for human videos, she says.
Virtual reality
A final and promising way to find limitless supplies of physical data, researchers say, is through simulation. Many roboticists are working on building 3D virtual-reality environments, the physics of which mimic the real world, and then wiring those up to a robotic brain for training. Simulators can churn out huge quantities of data and allow humans and robots to interact virtually, without risk, in rare or dangerous situations, all without wearing out the mechanics. “If you had to get a farm of robotic hands and exercise them until they achieve [a high] level of dexterity, you will blow the motors,” says Nvidia’s Andrews.
But making a good simulator is a difficult task. “Simulators have good physics, but not perfect physics, and making diverse simulated environments is almost as hard as just collecting diverse data,” says Khazatsky.
Meta and Nvidia are both betting big on simulation to scale up robot data, and have built sophisticated simulated worlds: Habitat from Meta and Isaac Sim from Nvidia. In them, robots gain the equivalent of years of experience in a few hours, and, in trials, they then successfully apply what they have learnt to situations they have never encountered in the real world. “Simulation is an extremely powerful but underrated tool in robotics, and I am excited to see it gaining momentum,” says Rai.
Many researchers are optimistic that foundation models will help to create general-purpose robots that can replace human labour. In February, Figure, a robotics company in Sunnyvale, California, raised US$675 million in investment for its plan to use language and vision models developed by OpenAI in its general-purpose humanoid robot. A demonstration video shows a robot giving a person an apple in response to a general request for ‘something to eat’. The video on X (the platform formerly known as Twitter) has racked up 4.8 million views.
Exactly how this robot’s foundation model has been trained, along with any details about its performance across various settings, is unclear (neither OpenAI nor Figure responded to Nature ’s requests for an interview). Such demos should be taken with a pinch of salt, says Soh. The environment in the video is conspicuously sparse, he says. Adding a more complex environment could potentially confuse the robot — in the same way that such environments have fooled self-driving cars. “Roboticists are very sceptical of robot videos for good reason, because we make them and we know that out of 100 shots, there’s usually only one that works,” Soh says.
Hurdles ahead
As the AI research community forges ahead with robotic brains, many of those who actually build robots caution that the hardware also presents a challenge: robots are complicated and break a lot. Hardware has been advancing, Chen says, but “a lot of people looking at the promise of foundation models just don't know the other side of how difficult it is to deploy these types of robots”, he says.
Another issue is how far robot foundation models can get using the visual data that make up the vast majority of their physical training. Robots might need reams of other kinds of sensory data, for example from the sense of touch or proprioception — a sense of where their body is in space — say Soh. Those data sets don’t yet exist. “There’s all this stuff that’s missing, which I think is required for things like a humanoid to work efficiently in the world,” he says.
Releasing foundation models into the real world comes with another major challenge — safety. In the two years since they started proliferating, large language models have been shown to come up with false and biased information. They can also be tricked into doing things that they are programmed not to do, such as telling users how to make a bomb. Giving AI systems a body brings these types of mistake and threat to the physical world. “If a robot is wrong, it can actually physically harm you or break things or cause damage,” says Gopalakrishnan.
Valuable work going on in AI safety will transfer to the world of robotics, says Gopalakrishnan. In addition, her team has imbued some robot AI models with rules that layer on top of their learning, such as not to even attempt tasks that involve interacting with people, animals or other living organisms. “Until we have confidence in robots, we will need a lot of human supervision,” she says.
Despite the risks, there is a lot of momentum in using AI to improve robots — and using robots to improve AI. Gopalakrishnan thinks that hooking up AI brains to physical robots will improve the foundation models, for example giving them better spatial reasoning. Meta, says Rai, is among those pursuing the hypothesis that “true intelligence can only emerge when an agent can interact with its world”. That real-world interaction, some say, is what could take AI beyond learning patterns and making predictions, to truly understanding and reasoning about the world.
What the future holds depends on who you ask. Brooks says that robots will continue to improve and find new applications, but their eventual use “is nowhere near as sexy” as humanoids replacing human labour. But others think that developing a functional and safe humanoid robot that is capable of cooking dinner, running errands and folding the laundry is possible — but could just cost hundreds of millions of dollars. “I’m sure someone will do it,” says Khazatsky. “It’ll just be a lot of money, and time.”
Nature 630 , 22-24 (2024)
doi: https://doi.org/10.1038/d41586-024-01442-5
Updates & Corrections
Correction 31 May 2024 : An earlier version of this feature gave the wrong name for Nvidia’s simulated world.
Brohan, A. et al. Preprint at arXiv https://doi.org/10.48550/arXiv.2307.15818 (2023).
Khazatsky, A. et al. Preprint at arXiv https://doi.org/10.48550/arXiv.2403.12945 (2024).
Open X-Embodiment Collaboration et al. Preprint at arXiv https://doi.org/10.48550/arXiv.2310.08864 (2023).
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10 Best AI Tools for Academic Research in 2024 (Free and Paid)
Ayush Chaturvedi
20 min read
Research can be a time-consuming endeavour. Sifting through mountains of literature, analyzing data, and crafting clear arguments can feel overwhelming.
However, you can streamline much of this research process with Artificial Intelligence (AI) tools, some of which are the best for research.
These AI-powered assistants can search vast databases in seconds, pinpoint relevant studies, and customize data to your specific research question.
They can also recommend key research articles and highlight emerging trends within your field, saving you time.
Additionally, with the help of the best AI tools for research, you can improve your writing and streamline your workflow with real-time grammar and punctuation checks, stylistic suggestions, and clear explanations of complex concepts.
But how do you choose?
Don't worry; we've got you covered.
We have created a list of all the best AI tools for research on the internet, filtering based on various factors and handpicked the top 10.
These research AI tools not only assist you in research but also integrate with your workflow and reduce your overall workload.
So let's get started.
Best AI Tools for Research at a Glance
What are research ai tools, benefits of using ai tools for research, factors to consider when choosing the best ai tools for research, top 10 best ai tools for research, key features of elephas , elephas pricing , elepahs reviews, chatgpt key features , chatgpt pricing , chatgpt reviews , typeset.io features:, typeset.io pricing , typeset.io reviews , quillbot key features , quillbot pricing , quillbot review , wordvice.ai features:, wordvice.ai pricing , wordvice.ai reviews , consensus ai key features , consensus ai pricing , consensus ai reviews , scite.ai features , scite.ai pricing , scite.ai reviews , scholarly key features, scholarcy pricing , scholarcy reviews , proofhub key features , proofhub pricing , proofhub reviews , research rabbit key features , research rabbit pricing , research rabbit reviews , limitations of ai tools for research, case study: how a professor used elephas in his lesson research process.
- Conclusion
1. Which AI is better for research?
2. is chatgpt good for research, 3. how can ai be used for research, 4. what is the best ai for phd.
Elephas: Summarize research, rewrite content in different styles, and organize summaries in a central "Super Brain" for easy access.
ChatGPT: Summarize news articles and answer research questions
Typeset.io: Streamline academic writing with templates and citation management.
Quillbot: Rephrase text and summarize complex materials for research.
Wordvice.ai : Ensure clarity, grammar, and originality in your academic writing.
Consensus AI: Search vast databases and filter research papers for quality.
Scite.ai: Get real citations and measure the credibility of research claims.
Scholarcy: Summarize complex articles and build a searchable research library.
ProofHub: Manage research projects with tasks, collaboration tools, and scheduling.
ResearchRabbit: Build a research library and get recommendations for new papers.
Research AI tools are game-changers for students, academics, and researchers, streamlining the entire research process.
With the help of the best AI tools for research as your personal research assistant, they help you find relevant articles, analyze information, and even improve your writing!
Imagine being able to find hundreds of relevant research papers in minutes, or getting a clear summary of a complex article with the click of a button. That's the magic of AI research assistants.
Some specialize in specific areas, like grammar and plagiarism checking, while others focus on broader tasks like literature review and research question development.
No matter your research needs, there's an AI tool out there to help you save time, improve your work, and produce higher-quality research.
Let's look closer at the features that a research AI tool offers
These AI-powered tools offer a variety of features such as:
- Effortless searching: Quickly find high-quality research papers by entering your topic.
- Smarter literature reviews: Get suggestions for key studies, authors, and research trends.
- Enhanced writing: Improve your writing with grammar checks, stylistic suggestions, and help with complex concepts.
- Citation management: Easily manage and format your citations to avoid plagiarism.
- Research organization: Build your research library and organize articles for easy access.
These are just a few examples of how AI research tools can save you time and effort, allowing you to focus on the analysis and critical thinking that truly matters.
Some tools even go beyond and offer a complete suite of AI features that cut down more than half of the research time.
Research can be a time-consuming endeavour. Sifting through mountains of literature, analyzing data, and crafting clear arguments can feel overwhelming. However, you can streamline much of this research process with Artificial Intelligence (AI) tools like Research AI tools.
Here are some benefits you can gain with Research AI tools:
Effortless Information Retrieval: AI tools can search vast databases in seconds, pinpointing relevant studies and data tailored to your specific research question.
Smarter Literature Reviews: No more wading through mountains of papers. AI can recommend key research articles, and influential authors, and highlight emerging trends within your field, saving you time and ensuring a comprehensive review.
Idea Generation: If you struggle to spark new research ideas, then AI can help you. It can brainstorm fresh research questions, and hypotheses, and even suggest innovative experiment designs to propel your research forward.
Writing Assistant & Editor: You can improve your writing and streamline your workflow with AI's editing prowess. Get real-time grammar and punctuation checks , stylistic suggestions, and clear explanations of complex concepts, all designed to elevate the quality of your research writing.
Enhanced Efficiency: AI automates tedious tasks like citation management and formatting, freeing you to focus on the analysis and interpretation of your research findings.
Personalized Research Assistant: AI tools can adapt to your research interests, suggesting relevant articles, recommending new avenues for exploration, and even summarizing complex research papers for a clearer understanding.
There are different AI tools present on the internet for different needs. So with the vast array of AI-powered research assistants available, selecting the most suitable tool can be problematic.
Here are some key factors to consider, when you choosing the best AI Tools for Research:
Your Research Needs: Identify your specific needs. Are you searching for literature, summarizing complex papers, or improving your writing? Different tools excel in various areas.
Features Offered: Align the tool's features with your needs. Do you require real-time citation suggestions or plagiarism checkers?
Data Accuracy and Credibility: Ensure the tool retrieves information from reliable sources. Scite.ai stands out for highlighting the credibility of research claims.
Ease of Use: Consider the platform's user-friendliness. Look for intuitive interfaces and clear instructions.
Cost: AI tools often have varying pricing structures. Some offer free trials or basic plans, while others require subscriptions. Determine your budget and choose a tool that aligns with it.
Integration Capabilities: Does the tool integrate with your existing workflow? Look for options that seamlessly connect with your preferred reference managers or writing platforms.
Most importantly, remember that AI research assistants are only there to increase your productivity in the research process, not to replace it .
|
|
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Elephas | Summarizes research papers, Rewrites content in various tones, organizes your research in its second brain | Premium Plan Starts at $4.99
|
ChatGPT | Summarizes news articles and answers research questions | Premium Plan Starts at $20/month |
Typeset.io | Predefined templates, Citation management | Premium Plan starts at $7.78/month |
Quillbot | Paraphrases text, Summarizes complex materials | Premium Plan starts at $4.17/month |
Wordvice.ai | Grammar and clarity checks, Plagiarism detection | Premium Plan starts at $9.95/month |
Consensus AI | AI-powered search engine, Filters results by quality | Premium Plan Starts at $8.99/month |
Scite.ai | Real citations, Measures claim credibility | Premium Plan starts at $20/month |
Scholarcy | Summarizes complex articles, Builds a searchable database | Premium Plan Starts at $4.99/month |
ProofHub | Project management tools, Centralized collaboration | Premium Plan Starts at $45/month |
ResearchRabbit | Recommends new papers, Visualizes connections | Free Forever |
1. Elephas
Elephas is an innovative AI tool designed to supercharge your research and writing efficiency. It utilizes advanced technology to break down complex research papers, YouTube videos, and other content, extracting the key points and saving you valuable time.
Additionally, Elephas goes beyond summarizing – it can seamlessly integrate with your workflow and rewrite content in various tones, making it a versatile companion for all your writing needs.
Elephas doesn't just summarize research papers; it extracts key points and integrates seamlessly with your workflow. Whether you're a student, researcher, or content creator, Elephas helps you achieve more in less time.
Effortless Sum marization: Extract key points from research papers and YouTube videos with ease.
Centralized Hub: Keep all your research summaries organized in one place with Elephas Super Brain .
Seamless Content Creation: Create professional emails, engaging social media posts, and documents in just a few clicks.
Multiple Rewrite Modes: Choose from a variety of writing styles to make your content more engaging.
Super-Command Bar: Increase your productivity with features like article summarization and data extraction.
|
|
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$4.99/month | $4.17/month | $129 |
$8.99/month | $7.17/month | $199 |
$14.99/month | $12.50/month | $249 |
Elephas is also one of the best AI Tools for Summarizing Research Papers in the market right now. And it bundles up with a powerful iOS app as well.
It works locally and it's 100% privacy friendly!
If you own a Mac, you should definitely try it out.
ChatGPT , the tool behind the existence of many AI tools, is undeniably one of the best AI tools for research. With the right prompts, you can easily summarize any news articles , long notes, etc., in seconds. You can also ask ChatGPT research-related questions to gain a better understanding of research papers. Furthermore, you can improve your writing and avoid any grammar and punctuation mistakes. With the help of ChatGPT, the number of things you can do is endless.
Effortless Information Retrieval: Find the studies and data you need in a flash.
Smarter Literature Reviews: Get suggestions for key papers, authors, and research trends.
Idea Generation on Demand: Spark new research questions, hypotheses, and experiment designs.
Writing Assistant: Improve your writing with grammar checks, stylistic suggestions, and simplified explanations of complex concepts.
- Premium Plan Starts at $20/month
Some users have reported false money deductions and low-quality service provided in the premium subscription.
3. Typeset.io
Typeset.io streamlines the entire academic writing process, saving you time and frustration. This user-friendly platform offers a variety of features to help you write, collaborate, and publish top-notch research. From predefined templates to AI-powered writing assistance, Typeset.io empowers researchers of all levels to achieve their scholarly goals.
Effortless Formatting: Predefined templates ensure your paper meets journal requirements.
Citation Breeze: Manage citations and references effortlessly, with automatic generation.
Seamless Collaboration: Work together on research papers in real time.
Smart Journal Selection: Find the perfect fit for your research with a built-in journal database.
Premium Plan Starts at $7.78/month
Users have reported that the tool doesn't notify at the end of the free trial and sneakily charges for the premium plan. Additionally, once the plan is purchased, the money is non-refundable. Some have claimed that even after cancelling the subscription, the customer service did not cancel it and still charged their cards.
4. Quillbot
Quillbot is your AI research companion, offering several time-saving features to streamline your workflow. It is designed to assist researchers of all levels. This tool utilizes advanced learning algorithms to enhance your writing and comprehension skills. With Quillbot, you can confidently paraphrase text, summarize complex materials, and ensure clear, plagiarism-free writing. Additionally, you can perform citations with high accuracy. Quillbot streamlines your workflow and strengthens your writing.
Paraphrasing & Summarizing: Quillbot rewrites sentences and condenses lengthy passages, saving you time and effort.
Language Enhancement & Learning: Improve your writing with advanced suggestions and explanations, perfect for non-native speakers.
Research Brainstorming: Generate fresh ideas from just a few keywords, overcoming writer's block.
Academic Accuracy & Citation Help: Ensure your writing matches specific citation styles and uses precise academic language.
- Premium Plan starts at $4.17/month
Users have reported that the tool is working slowly when used in Microsoft Word, and it often uses complex words while paraphrasing. Some have also reported that the rephrased content on Quillbot is detected as AI-generated content on various AI detection tools.
5. Wordvice.ai
Wordvice AI is one of the best AI tools for research, it is your one-stop shop for powerful writing assistance. This AI-powered tool uses cutting-edge technology to streamline your research workflow, saving you time and effort. From basic grammar and clarity checks to advanced plagiarism detection, Wordvice AI helps you to write with confidence and produce polished, original academic content.
All-in-one editing: Grammar, style, clarity, and fluency checks with real-time feedback.
Vocabulary booster: Get suggestions for synonyms and alternative phrasing to diversify your writing.
Academic writing companion: Ensures proper citation format, maintains a scholarly tone, and adheres to research conventions.
Originality assured: Scans millions of sources to prevent plagiarism in your work.
Premium Plan starts at $9.95/month
Users have reported that certain sentence patterns generated by AI are already found on existing web pages, which has led to an increase in plagiarism within content.
6. Consensus AI
Consensus AI is an innovative platform that uses artificial intelligence to simplify your search process. In just minutes, Consensus AI can search through vast databases and deliver hundreds of relevant, high-quality research papers directly to you. Also, Consensus AI filters results by date, study type, and journal quality, ensuring you find high-quality, credible sources to strengthen your research.
AI-powered Search Engine: Enter your research question and let Consensus AI scour vast databases to find relevant papers.
Time-Saving Efficiency: Gather hundreds of papers in minutes, freeing you up to focus on analysis and writing.
Comprehensive Results: Access a diverse range of studies, including randomized trials, reviews, and observational studies.
High-Quality Papers: Filter results by journal quality to ensure the credibility of your sources.
- Premium Plan Starts at $8.99/month
Users have reported that when we try to share the live demo over Zoom, the tool becomes slow and hangs. They think it is a hassle to jump between the browser and Zoom. They suggest introducing some integration features in the tool as a good solution.
7. Scite.ai
Scite.ai is one of the best for reliable research assistance powered by Artificial Intelligence. Scite.ai tackles a common problem with AI research tools – unreliable citations. Unlike others, Scite.ai provides you with real citations to published papers, so you can be confident in the information you use. Even better, Scite.ai can analyze the research and tell you how many studies support or challenge a specific claim.
Create Dashboards: Organize your research findings in a user-friendly format.
Journal and Institution Metrics: Gain insights into the reputation of academic sources.
Interactive Visualizations: You can see research trends and connections come through visualizations of the tool.
Measure Claim Credibility: Scite.ai analyzes the strength of a claim by showing you how many studies support or refute it.
Premium Plan starts at $20/month
Users have noticed that sometimes the tool produces inaccurate citations, which can be problematic for researchers who rely on its accuracy. Additionally, some users believe that the tool's pricing is significantly higher compared to its competitors.
8. Scholarcy
Scholarcy is an AI-powered tool that acts like a personal research assistant, summarizing complex articles, reports, and even book chapters for you. Scholarcy quickly helps you understand the key points of any document and assess its relevance to your work, saving you precious time and effort. Whether you're a researcher, student, or just curious about the latest advancements, Scholarcy helps you quickly grasp key findings and identify relevant sources
Key Points at a Glance: Scholarcy extracts crucial information and organizes it into clear categories, making it easy to grasp the main ideas.
Seamless Integration: Scholarcy offers handy Chrome and Edge browser extensions, allowing you to summarize research directly from your web browser.
Visual Aids: Scholarcy can extract figures, tables, and images from articles, providing a more comprehensive understanding of the research.
Organized Knowledge: Build your searchable database of summarized research, making it easy to revisit key information later.
- Premium Plan Starts at $4.99/month
Some users are not satisfied with the complete summaries produced by Scholarcy, as some of the sentences are not actual sentences and need to be corrected. Additionally, some sentences do not make any sense. Other users have claimed that the quality of the tool has significantly dropped in recent months and it feels glitchy while using it.
9. ProofHub
ProofHub is one of the best AI tools for research to streamline research projects. It's an all-in-one project management tool designed specifically to make research teams more efficient and effective. ProofHub centralizes everything your team needs in a single platform, allowing seamless collaboration and communication. Save valuable time and avoid confusion by ditching the scattered emails, documents, and endless meetings.
Effortless Task & Project Management: Organize your research projects with ease using powerful tools like Kanban boards and Gantt charts.
Centralized Hub for Collaboration: Keep your team on the same page with a central platform for file sharing, discussions, and real-time feedback.
Streamlined Time Tracking & Scheduling: Never miss a deadline again! ProofHub's time tracking and scheduling features help you stay on top of your research project's progress.
Automated Workflows: Save even more time by automating repetitive tasks and creating custom workflows perfectly suited to your research needs.
- Premium Plan Starts at $45/month
Users have expressed dissatisfaction with the user interface and email notifications of the tool, stating that they are not up to par. In addition, some have reported that certain features in Proofhub are not as impressive as those of its competitors.
10. Research Rabbit
ResearchRabbit is another best AI tools for research, it helps you navigate through the vast world of scientific literature. Nicknamed the "Spotify for Papers," this innovative tool lets you explore research like never before. Build collections of articles you find interesting, and ResearchRabbit will cleverly suggest new papers that align with your specific interests. No more endless searches – ResearchRabbit becomes your personalized research assistant, saving you time and frustration.
Build your research library: Collect and organize articles you find interesting, all in one place.
Smart recommendations: Never miss a groundbreaking study! ResearchRabbit suggests new papers based on your interests, saving you valuable time.
Visualize connections: See how different research areas, authors, and ideas are linked together.
Collaboration made easy: Share your research collections with colleagues to work together more effectively.
Free Forever
We couldn't find any public reviews for the Research Rabbit. Therefore, we advise users to proceed with caution.
Many best AI tools for research suit different types of people, and these research AI tools have streamlined tasks and uncovered connections. However, they still have many limitations compared to manual research processes. Here's a closer look.
1. Accuracy and Bias: AI tools rely on the data they're trained on. If the data is biased or inaccurate, the results can be misleading. It's crucial to critically evaluate AI outputs and not rely solely on them.
2. Depth vs. Breadth: AI tools can efficiently scan vast amounts of literature, but they may miss nuances or subtleties within research papers. In-depth analysis and critical thinking remain essential for a comprehensive understanding.
3. Overreliance on Automation: AI shouldn't replace the core research process. Researchers should use AI to streamline tasks, not eliminate critical steps like evaluating source credibility and understanding research context.
4. Black Box Problem: Sometimes, AI won't explain its reasoning behind results. This lack of transparency can make it difficult to assess the trustworthiness of findings or suggestions.
5. Limited Scope: AI tools might not cover all relevant sources, especially niche or emerging research areas. Supplement your search with traditional methods like library databases and expert consultations.
In our community, we have found Elephas being used by some professors at a university, and they have shared their experiences on how they used it in their lesson research process. Here is how they did it:
1. Summarization: The professor utilized Elephas' ability to generate concise summaries of different textbooks and research papers. This allowed him to quickly grasp the core arguments and findings of numerous studies, saving him hours of dedicated reading time.
2. Video Research: Then the professor had to gather more knowledge to create a lesson plan, so he searched for some of the best lengthy video lectures. Packed with historical insights, these videos were no longer a trouble because Elephas efficiently summarized key points from them, enabling our professor to include this valuable information in his lessons without spending hours glued to the screen.
3. Building Knowledge Base: Finally, the professor used Elephas Super Brain to create a centralized hub for all his research summaries. This eliminated the need to sift through countless folders and documents, allowing him to access critical information instantly. Additionally, he utilized the Super Brain to better understand the lesson plan through the Super Brain chat feature of Elephas.
Let's see what Elephas was able to do for our professor who is striving to teach his students in-depth subject knowledge:
1. Increased Efficiency: The professor has seen a significant reduction in research time, freeing up valuable hours for lesson planning and development.
2. Deeper Lesson Understanding: With more time at his disposal, our professor was able to delve into the research he found most compelling, leading to a deeper understanding of historical topics.
3. Engaging Lectures: By using key insights from research summaries provided by Elephas, the professor's lectures became more informative and engaging for his students, helping in their understanding of the topic faster than before.
The professor's experience explains how Elephas can revolutionize the research process for academics. By saving time and streamlining workflows, Elephas helps researchers get deeper into their respective fields and create truly impactful learning experiences and also cut their research process to more than half.
Conclusion
In summary, AI research assistants are transforming how researchers approach their work. These tools can summarize complex information, find relevant studies, and even suggest new research ideas. Top choices include Elephas (which summarizes research papers and YouTube videos), ChatGPT (which summarizes articles and answers questions), and Typeset.io (which streamlines academic writing).
However, make sure to pick the best AI tool for research based on your requirements. Also, remember that while AI offers significant time savings and improved efficiency, it shouldn't replace critical thinking and human expertise in research because AI has several limitations that can degrade your research quality.
Elephas is the best AI tool for research, offering key features for researchers such as summarizing research papers, articles, and YouTube videos. Additionally, you can upload data to a "super brain" for retrieval and chat with uploaded PDFs for deeper understanding. This makes Elephas a strong AI tool for research tasks
Yes, ChatGPT can be a helpful tool for initial research exploration. It can brainstorm ideas, summarize complex topics, and even find relevant sources. However, for in-depth research, specialized academic databases and citation tools are better suited. These resources provide more reliable and accurate information, often with features like peer-reviewed content and advanced search options.
AI is revolutionizing research by summarizing complex information and assisting with content creation. AI tools can analyze research papers, articles, and even videos to extract key findings, saving researchers time and effort. AI can also rewrite content in different tones, making it a valuable asset for researchers who need to communicate their findings to various audiences.
Elephas is an AI tool designed to boost research and writing efficiency for PhD students and researchers. It summarizes complex research papers, YouTube videos, and other content, saving you time. Elephas also integrates with your workflow and rewrites content in various tones, making it a versatile PhD buddy.
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ChatDOC lets you chat with PDFs and tables to quickly extract information and insights. Sources are cited for fact-checking, this is a ChatGPT based file-reading
Glasp is a free Chrome and Safari extension that lets you easily highlight and annotate text on websites and PDFs. Its key features include syncing
Andi is a next-generation search tool powered by generative AI. It provides answers to questions and explains and summarizes information from the best sources, giving
Ai Summary Generator
Ai Summary Generator is a text summarization tool that can instantly summarize lengthy texts or turn them into bullet point lists. It uses AI to
Exa (formerly known as Metaphor) offers an AI-powered search engine that can connect to the vast knowledge of the internet. Exa delivers highly relevant search
Qonqur is an innovative software that allows you to control your computer and digital content using hand gestures, without the need for expensive virtual reality
Julius is an AI data analysis tool that helps you visualize, analyze, and get insights from all kinds of data. With Julius, you can simply
ChatPDF allows you to talk to your PDF documents as if they were human. It’s perfect for quickly extracting information or answering questions from large
Afforai is an AI research assistant designed to help researchers collect, organize, and analyze academic materials. Afforai allows you to upload papers, automatically extract metadata
Instabooks AI
Instabooks AI instantly generates customized textbooks on any topic you want to explore in depth. Simply type a detailed description of the information you want
scite is an AI-powered research tool that helps researchers discover and evaluate scientific articles. It analyzes millions of citations and shows how each article has
SciSpace is an AI research assistant that simplifies researching papers through AI-generated explanations and a network showing connections between relevant papers. It aims to automate
Discover the latest AI research tools to accelerate your studies and academic research. Search through millions of research papers, summarize articles, view citations, and more.
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AI tools I have found useful w/ research. What do you guys think? What did I miss?
NOTE: A commenter in this sub has a YouTube channel which reviews some of these and other products: Dr Lyndon Walker - YouTube
Need help deciding on the best ones, and to identify ones I've missed:
ASSISTANTS (chatbots, multi-purpose)
Chat with Open Large Language Models ( https://chat.lmsys.org/ )
ChatGPT ( https://chat.openai.com/ )
Krater.ai – The AI SuperApp ( https://www.krater.ai/ )
Hugging Face – The AI community building the future. ( https://huggingface.co/ )
Perplexity AI ( https://www.perplexity.ai/ )
DOCUMENT ANALYSIS (talk to PDFs, identify keywords, etc.)
Dashboard | Explainpaper ( https://www.explainpaper.com/dashboard )
Humata ( https://app.humata.ai/context/888702b6-410d-496c-b184-044d8b1b2f5c )
Visus ( https://app.visus.ai/ )
My AskAI — Your own ChatGPT, with your content ( https://myaskai.com/ )
Discover, Create, and Publish your research paper | SciSpace by Typeset ( https://typeset.io/ )
OpenRead ( https://www.openread.academy/home )
ResearchAIde - Your Personal AI Research Assistant ( https://www.researchaide.org/ )
ADDITION: Online Summarizing Tool | Flashcard Generator & Summarizer | Scholarcy
SEARCH ENGINES (finding papers, TLDRs, etc.)
Talk to Books ( https://books.google.com/talktobooks/ )
Semantic Scholar | AI-Powered Research Tool ( https://www.semanticscholar.org/ )
Consensus - Evidence-Based Answers, Faster ( https://consensus.app/ )
Elicit: The AI Research Assistant ( https://elicit.org/ )
ResearchRabbit ( https://www.researchrabbit.ai/ )
genei | AI-powered summarisation & research tool ( https://www.genei.io/ )
ADDITION: Connected Papers | Find and explore academic papers
ADDITION: scite: see how research has been cited
ADDITION: GitHub - Yiiipu/CustomScholarSearch: Search google scholar and only return the papers published on high h-index journals byu/IWannaChangeUsername
MEETINGS/DIGITAL EVENTS (recording, transcription, summaries, etc.)
Welcome - fireflies.ai ( https://app.fireflies.ai/ )
Pricing ( https://otter.ai/pricing )
Parrot AI - Pricing ( https://parrot.ai/pricing )
COPY (basically anti-plagiarism and translation since GPT> for copy)
Streamlit ( https://hidegpt.app/ )
The UNDETECTABLE AI Writing Tool That Bypasses AI Detectors ( https://undetectable.ai/ )
DeepL Translate: The worlds most accurate translator ( https://www.deepl.com/en/translator )
SCRAPERS (data from documents/websites)
Web Scraping, Data Extraction and Automation · Apify ( https://apify.com/ )
Document types | Sensible ( https://app.sensible.so/document-types/ )
AI image misinformation has surged, Google researchers find
Fake images generated by artificial intelligence have proliferated so quickly that they’re now nearly as common as those manipulated by text or traditional editing tools like Photoshop, according to researchers at Google and several fact-checking organizations.
The findings offer an indication of just how quickly the technology has been embraced by people seeking to spread false information. But researchers warned that AI is still just one way in which pictures are used to mislead the public — the most common continues to be real images taken out of context.
In a paper released online this month but not yet peer-reviewed, the researchers tracked misinformation trends by analyzing nearly 136,000 fact-checks dating back to 1995, with the majority of them published after 2016 and ending in November 2023. They found that AI accounted for very little image-based misinformation until spring of 2023, right around when fake photos of Pope Francis in a puffer coat went viral.
“The sudden prominence of AI-generated content in fact checked misinformation claims suggests a rapidly changing landscape,” the researchers wrote.
The lead researchers and representatives for Google did not comment in time for publication.
Alexios Mantzarlis, who first flagged and reviewed the latest research in his newsletter, Faked Up , said the democratization of generative AI tools has made it easy for almost anyone to spread false information online.
“We go through waves of technological advancements that shock us in their capacity to manipulate and alter reality, and we are going through one now,” said Mantzarlis, who is the director of the Security, Trust, and Safety Initiative at Cornell Tech, Cornell University’s graduate campus in New York City. “The question is, how quickly can we adapt? And then, what safeguards can we put in place to avoid their harms?”
We go through waves of technological advancements that shock us in their capacity to manipulate and alter reality, and we are going through one now.
-Alexios Mantzarlis, director of the Security, Trust, and Safety Initiative at Cornell Tech
The researchers found that about 80% of fact-checked misinformation claims involve media such as images and video, with video increasingly dominating those claims since 2022.
Even with AI, the study found that real images paired with false claims about what they depict or imply continue to spread without the need for AI or even photo-editing.
“While AI-generated images did not cause content manipulations to overtake context manipulations, our data collection ended in late 2023 and this may have changed since,” the researchers wrote. “Regardless, generative-AI images are now a sizable fraction of all misinformation-associated images.”
Text is also a component in about 80% of all image-based misinformation, most commonly seen in screenshots.
“We were surprised to note that such cases comprise the majority of context manipulations,” the paper stated. “These images are highly shareable on social media platforms, as they don’t require that the individual sharing them replicate the false context claim themselves: they’re embedded in the image.”
Cayce Myers, a public relations professor and graduate studies director at Virginia Tech’s School of Communication, said context manipulations can be even harder to detect than AI-generated images because they already look authentic.
“In that sense, that’s a much more insidious problem,” Myers, who reviewed the recent findings prior to being interviewed, said. “Because if you have, let’s say, a totally AI-generated image that someone can look at and say, ‘That doesn’t look quite right,’ that’s a lot different than seeing an actual image that is captioned in a way that is misrepresenting what the image is of.”
Even AI-based misinformation, however, is quickly growing harder to detect as technology advances. Myers said traditional hallmarks of an AI-generated image — abnormalities such as misshapen hands, garbled text or a dog with five legs — have diminished “tremendously” since these tools first became widespread.
Earlier this month, during the Met Gala, two viral AI-generated images of Katy Perry (who wasn’t at the event) looked so realistic at first glance that even her mom mistakenly thought the singer was in attendance.
And while the study stated AI models aren’t typically trained to generate images like screenshots and memes, it’s possible they will quickly learn to reliably produce those types of images as new iterations of advanced language models continue to roll out.
To reliably distinguish misinformation as AI tools grow more sophisticated, Mantzarlis said people will have to learn to question the content’s source or distributor rather than the visuals themselves.
“The content alone is no longer going to be sufficient for us to make an assessment of truthfulness of trustworthiness veracity,” Mantzarlis said. “I think you need to have the full context: Who shared it with you? How was it shared? How do you know it was them?”
But the study noted that relying solely on fact-checked claims doesn’t capture the whole scope of misinformation out there, as it’s often the images that go viral that end up being fact checked. It also relied only on misinformation claims made in English. This leaves out many lesser-viewed or non-English pieces of misinformation that float unchecked in the wild.
Still, Mantzarlis said he believes the study reflects a “good sample” of English-language misinformation cases online, particularly those that have reached a substantial enough audience for fact-checkers to take notice.
For Myers, the bigger limitation affecting any study on disinformation — especially in the age of AI — will be the fast-changing nature of the disinformation itself.
“The problem for people who are looking at how to get a handle on disinformation is that it’s an evolving technological reality,” Myers said. “And capturing that is difficult, because what you study in May of 2024 may already be a very different reality in June of 2024.”
Angela Yang is a culture and trends reporter for NBC News.
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Top 10 Emerging Research Paper Topics in 2024
- June 10, 2024 11:19 AM
In 2024, the landscape of academic research is marked by groundbreaking discoveries and innovations across diverse fields. Researchers are increasingly focusing on interdisciplinary approaches to address complex global challenges. This blog explores the top 10 emerging research paper topics that are expected to dominate academic discourse in 2024, offering insights into their significance, potential impact, and areas of exploration.
1. Artificial Intelligence in Healthcare
overview.
The integration of artificial intelligence (AI) into healthcare is revolutionizing patient care, diagnostics, and treatment plans. Researchers are exploring how AI can enhance the accuracy of diagnoses, predict patient outcomes, and personalize treatment.
Key Areas of Research
– Predictive Analytics: Using AI to predict disease outbreaks, patient deterioration, and treatment responses.
– Medical Imaging: AI algorithms for early detection of diseases through imaging techniques like MRI and CT scans.
– Robotic Surgery: Enhancing precision and reducing recovery times through AI-assisted robotic surgeries.
Potential Impact
AI in healthcare promises to improve patient outcomes, reduce healthcare costs, and streamline hospital operations, making quality care more accessible globally.
2. Quantum Computing Applications
Quantum computing is moving from theoretical physics into practical applications, offering unprecedented computational power. Research paper writing services are investigating its potential to solve complex problems beyond the reach of classical computers.
– Cryptography: Developing quantum-resistant encryption methods to secure digital communications.
– Drug Discovery: Using quantum simulations to accelerate the discovery of new pharmaceuticals.
– Optimization Problems: Solving logistical and resource allocation problems more efficiently.
Quantum computing could revolutionize fields like cryptography, logistics, and pharmaceuticals, leading to faster advancements and more robust security systems.
3. Climate Change Mitigation and Adaptation
As the effects of climate change become increasingly evident, research is focused on innovative strategies for mitigation and adaptation. This includes both technological advancements and policy-driven solutions.
– Renewable Energy: Innovations in solar, wind, and bioenergy technologies.
– Carbon Capture: Developing efficient methods for capturing and storing carbon dioxide.
– Resilient Infrastructure: Designing infrastructure to withstand extreme weather events.
Effective climate change strategies can significantly reduce global carbon emissions, protect ecosystems, and build resilient communities, safeguarding future generations.
4. Neuroscience and Brain-Computer Interfaces
Advancements in neuroscience are paving the way for brain-computer interfaces (BCIs), which enable direct communication between the brain and external devices pay someone to do my assignment . This research has profound implications for medicine, communication, and human augmentation.
– Neuroprosthetics: Developing prosthetic limbs controlled by brain signals.
– Cognitive Enhancement: Enhancing memory, learning, and cognitive functions through BCIs.
– Mental Health: Using BCIs to treat neurological disorders like depression and anxiety.
BCIs have the potential to restore lost functions, enhance human capabilities, and provide new treatments for mental health conditions, improving quality of life for millions.
5. Synthetic Biology and Genetic Engineering
Synthetic biology and genetic engineering are enabling scientists to design and construct new biological parts, devices, and systems. This research is driving innovation in medicine, agriculture, and environmental management.
– Gene Editing: CRISPR and other technologies for precise genetic modifications.
– Synthetic Organisms: Creating microorganisms with tailored functions for industrial applications.
– Biofuels: Engineering organisms to produce sustainable biofuels.
These advancements could lead to breakthroughs in curing genetic diseases, improving agricultural yields, and developing sustainable biofuels, addressing critical global challenges.
6. Space Exploration and Colonization
With renewed interest in space exploration, researchers are exploring the feasibility of colonizing other planets, particularly Mars. This involves addressing technical, biological, and social challenges.
– Life Support Systems: Developing sustainable habitats for long-term space missions.
– Space Farming: Growing food in extraterrestrial environments.
– Astrobiology: Studying the potential for life on other planets and moons.
Successful space colonization could ensure the long-term survival of humanity, provide new resources, and inspire generations to pursue scientific and technological careers.
7. Blockchain and Decentralized Technologies
Blockchain technology, initially known for cryptocurrencies, is finding applications in various sectors, including finance, supply chain, and governance. Research is focused on enhancing security, scalability, and interoperability.
– Decentralized Finance (DeFi): Creating financial systems without intermediaries.
– Supply Chain Transparency: Using blockchain for traceability and accountability.
– Smart Contracts: Automating legal agreements and transactions.
Blockchain technology can democratize access to financial services, enhance supply chain transparency, and reduce fraud, transforming traditional industries.
8. Advanced Materials and Nanotechnology
Nanotechnology and advanced materials are enabling the development of materials with novel properties, such as enhanced strength, flexibility, and conductivity. These materials have applications across various industries.
– Graphene: Exploring applications of this highly conductive and strong material.
– Nano-Medicine: Using nanoparticles for targeted drug delivery and diagnostics.
– Smart Materials: Developing materials that respond to environmental changes.
Advances in nanotechnology and materials science could lead to more efficient electronics, revolutionary medical treatments, and smart infrastructures, driving innovation across sectors.
9. Sustainable Agriculture and Food Security
With a growing global population, ensuring food security through sustainable agriculture practices is a critical research area. Researchers are focusing on improving crop yields, reducing environmental impact, and enhancing food distribution systems.
– Precision Agriculture: Using data and technology to optimize farming practices.
– Genetically Modified Crops: Developing crops resistant to pests and climate change.
– Alternative Proteins: Exploring plant-based and lab-grown meat as sustainable food sources.
Sustainable agriculture can help feed the growing population, reduce environmental degradation, and promote food equity, ensuring that everyone has access to nutritious food.
10. Cybersecurity and Privacy Enhancements
As digital transformation accelerates, ensuring cybersecurity and protecting privacy are paramount. Researchers are developing new methods to safeguard data and systems from ever-evolving threats.
– Quantum-Safe Cryptography: Preparing for the security challenges posed by quantum computers.
– AI in Cybersecurity: Using machine learning to detect and respond to cyber threats.
– Privacy-Preserving Technologies: Enhancing data privacy in digital interactions.
Advanced cybersecurity measures can protect sensitive information, maintain trust in digital systems, and ensure the safe operation of critical infrastructure, safeguarding individuals and organizations.
Conclusion
The emerging research topics of 2024 reflect a world grappling with rapid technological advancements and pressing global challenges. From the integration of AI in healthcare to the potential colonization of space, these topics highlight the transformative power of science and technology. By addressing these issues through innovative research, we can pave the way for a more sustainable, secure, and prosperous future. Whether through mitigating the impacts of climate change or revolutionizing cybersecurity, the research of today will shape the world of tomorrow. Researchers and students diving into these areas have the opportunity to contribute significantly to their fields and to society at large, making 2024 a pivotal year for academic and scientific advancement.
This content is brought to you by the FingerLakes1.com Team. Support our mission by visiting www.patreon.com/fl1 or learn how you send us your local content here .
A generative AI reset: Rewiring to turn potential into value in 2024
It’s time for a generative AI (gen AI) reset. The initial enthusiasm and flurry of activity in 2023 is giving way to second thoughts and recalibrations as companies realize that capturing gen AI’s enormous potential value is harder than expected .
With 2024 shaping up to be the year for gen AI to prove its value, companies should keep in mind the hard lessons learned with digital and AI transformations: competitive advantage comes from building organizational and technological capabilities to broadly innovate, deploy, and improve solutions at scale—in effect, rewiring the business for distributed digital and AI innovation.
About QuantumBlack, AI by McKinsey
QuantumBlack, McKinsey’s AI arm, helps companies transform using the power of technology, technical expertise, and industry experts. With thousands of practitioners at QuantumBlack (data engineers, data scientists, product managers, designers, and software engineers) and McKinsey (industry and domain experts), we are working to solve the world’s most important AI challenges. QuantumBlack Labs is our center of technology development and client innovation, which has been driving cutting-edge advancements and developments in AI through locations across the globe.
Companies looking to score early wins with gen AI should move quickly. But those hoping that gen AI offers a shortcut past the tough—and necessary—organizational surgery are likely to meet with disappointing results. Launching pilots is (relatively) easy; getting pilots to scale and create meaningful value is hard because they require a broad set of changes to the way work actually gets done.
Let’s briefly look at what this has meant for one Pacific region telecommunications company. The company hired a chief data and AI officer with a mandate to “enable the organization to create value with data and AI.” The chief data and AI officer worked with the business to develop the strategic vision and implement the road map for the use cases. After a scan of domains (that is, customer journeys or functions) and use case opportunities across the enterprise, leadership prioritized the home-servicing/maintenance domain to pilot and then scale as part of a larger sequencing of initiatives. They targeted, in particular, the development of a gen AI tool to help dispatchers and service operators better predict the types of calls and parts needed when servicing homes.
Leadership put in place cross-functional product teams with shared objectives and incentives to build the gen AI tool. As part of an effort to upskill the entire enterprise to better work with data and gen AI tools, they also set up a data and AI academy, which the dispatchers and service operators enrolled in as part of their training. To provide the technology and data underpinnings for gen AI, the chief data and AI officer also selected a large language model (LLM) and cloud provider that could meet the needs of the domain as well as serve other parts of the enterprise. The chief data and AI officer also oversaw the implementation of a data architecture so that the clean and reliable data (including service histories and inventory databases) needed to build the gen AI tool could be delivered quickly and responsibly.
Creating value beyond the hype
Let’s deliver on the promise of technology from strategy to scale.
Our book Rewired: The McKinsey Guide to Outcompeting in the Age of Digital and AI (Wiley, June 2023) provides a detailed manual on the six capabilities needed to deliver the kind of broad change that harnesses digital and AI technology. In this article, we will explore how to extend each of those capabilities to implement a successful gen AI program at scale. While recognizing that these are still early days and that there is much more to learn, our experience has shown that breaking open the gen AI opportunity requires companies to rewire how they work in the following ways.
Figure out where gen AI copilots can give you a real competitive advantage
The broad excitement around gen AI and its relative ease of use has led to a burst of experimentation across organizations. Most of these initiatives, however, won’t generate a competitive advantage. One bank, for example, bought tens of thousands of GitHub Copilot licenses, but since it didn’t have a clear sense of how to work with the technology, progress was slow. Another unfocused effort we often see is when companies move to incorporate gen AI into their customer service capabilities. Customer service is a commodity capability, not part of the core business, for most companies. While gen AI might help with productivity in such cases, it won’t create a competitive advantage.
To create competitive advantage, companies should first understand the difference between being a “taker” (a user of available tools, often via APIs and subscription services), a “shaper” (an integrator of available models with proprietary data), and a “maker” (a builder of LLMs). For now, the maker approach is too expensive for most companies, so the sweet spot for businesses is implementing a taker model for productivity improvements while building shaper applications for competitive advantage.
Much of gen AI’s near-term value is closely tied to its ability to help people do their current jobs better. In this way, gen AI tools act as copilots that work side by side with an employee, creating an initial block of code that a developer can adapt, for example, or drafting a requisition order for a new part that a maintenance worker in the field can review and submit (see sidebar “Copilot examples across three generative AI archetypes”). This means companies should be focusing on where copilot technology can have the biggest impact on their priority programs.
Copilot examples across three generative AI archetypes
- “Taker” copilots help real estate customers sift through property options and find the most promising one, write code for a developer, and summarize investor transcripts.
- “Shaper” copilots provide recommendations to sales reps for upselling customers by connecting generative AI tools to customer relationship management systems, financial systems, and customer behavior histories; create virtual assistants to personalize treatments for patients; and recommend solutions for maintenance workers based on historical data.
- “Maker” copilots are foundation models that lab scientists at pharmaceutical companies can use to find and test new and better drugs more quickly.
Some industrial companies, for example, have identified maintenance as a critical domain for their business. Reviewing maintenance reports and spending time with workers on the front lines can help determine where a gen AI copilot could make a big difference, such as in identifying issues with equipment failures quickly and early on. A gen AI copilot can also help identify root causes of truck breakdowns and recommend resolutions much more quickly than usual, as well as act as an ongoing source for best practices or standard operating procedures.
The challenge with copilots is figuring out how to generate revenue from increased productivity. In the case of customer service centers, for example, companies can stop recruiting new agents and use attrition to potentially achieve real financial gains. Defining the plans for how to generate revenue from the increased productivity up front, therefore, is crucial to capturing the value.
McKinsey Live Event: Unlocking the full value of gen AI
Join our colleagues Jessica Lamb and Gayatri Shenai on April 8, as they discuss how companies can navigate the ever-changing world of gen AI.
Upskill the talent you have but be clear about the gen-AI-specific skills you need
By now, most companies have a decent understanding of the technical gen AI skills they need, such as model fine-tuning, vector database administration, prompt engineering, and context engineering. In many cases, these are skills that you can train your existing workforce to develop. Those with existing AI and machine learning (ML) capabilities have a strong head start. Data engineers, for example, can learn multimodal processing and vector database management, MLOps (ML operations) engineers can extend their skills to LLMOps (LLM operations), and data scientists can develop prompt engineering, bias detection, and fine-tuning skills.
A sample of new generative AI skills needed
The following are examples of new skills needed for the successful deployment of generative AI tools:
- data scientist:
- prompt engineering
- in-context learning
- bias detection
- pattern identification
- reinforcement learning from human feedback
- hyperparameter/large language model fine-tuning; transfer learning
- data engineer:
- data wrangling and data warehousing
- data pipeline construction
- multimodal processing
- vector database management
The learning process can take two to three months to get to a decent level of competence because of the complexities in learning what various LLMs can and can’t do and how best to use them. The coders need to gain experience building software, testing, and validating answers, for example. It took one financial-services company three months to train its best data scientists to a high level of competence. While courses and documentation are available—many LLM providers have boot camps for developers—we have found that the most effective way to build capabilities at scale is through apprenticeship, training people to then train others, and building communities of practitioners. Rotating experts through teams to train others, scheduling regular sessions for people to share learnings, and hosting biweekly documentation review sessions are practices that have proven successful in building communities of practitioners (see sidebar “A sample of new generative AI skills needed”).
It’s important to bear in mind that successful gen AI skills are about more than coding proficiency. Our experience in developing our own gen AI platform, Lilli , showed us that the best gen AI technical talent has design skills to uncover where to focus solutions, contextual understanding to ensure the most relevant and high-quality answers are generated, collaboration skills to work well with knowledge experts (to test and validate answers and develop an appropriate curation approach), strong forensic skills to figure out causes of breakdowns (is the issue the data, the interpretation of the user’s intent, the quality of metadata on embeddings, or something else?), and anticipation skills to conceive of and plan for possible outcomes and to put the right kind of tracking into their code. A pure coder who doesn’t intrinsically have these skills may not be as useful a team member.
While current upskilling is largely based on a “learn on the job” approach, we see a rapid market emerging for people who have learned these skills over the past year. That skill growth is moving quickly. GitHub reported that developers were working on gen AI projects “in big numbers,” and that 65,000 public gen AI projects were created on its platform in 2023—a jump of almost 250 percent over the previous year. If your company is just starting its gen AI journey, you could consider hiring two or three senior engineers who have built a gen AI shaper product for their companies. This could greatly accelerate your efforts.
Form a centralized team to establish standards that enable responsible scaling
To ensure that all parts of the business can scale gen AI capabilities, centralizing competencies is a natural first move. The critical focus for this central team will be to develop and put in place protocols and standards to support scale, ensuring that teams can access models while also minimizing risk and containing costs. The team’s work could include, for example, procuring models and prescribing ways to access them, developing standards for data readiness, setting up approved prompt libraries, and allocating resources.
While developing Lilli, our team had its mind on scale when it created an open plug-in architecture and setting standards for how APIs should function and be built. They developed standardized tooling and infrastructure where teams could securely experiment and access a GPT LLM , a gateway with preapproved APIs that teams could access, and a self-serve developer portal. Our goal is that this approach, over time, can help shift “Lilli as a product” (that a handful of teams use to build specific solutions) to “Lilli as a platform” (that teams across the enterprise can access to build other products).
For teams developing gen AI solutions, squad composition will be similar to AI teams but with data engineers and data scientists with gen AI experience and more contributors from risk management, compliance, and legal functions. The general idea of staffing squads with resources that are federated from the different expertise areas will not change, but the skill composition of a gen-AI-intensive squad will.
Set up the technology architecture to scale
Building a gen AI model is often relatively straightforward, but making it fully operational at scale is a different matter entirely. We’ve seen engineers build a basic chatbot in a week, but releasing a stable, accurate, and compliant version that scales can take four months. That’s why, our experience shows, the actual model costs may be less than 10 to 15 percent of the total costs of the solution.
Building for scale doesn’t mean building a new technology architecture. But it does mean focusing on a few core decisions that simplify and speed up processes without breaking the bank. Three such decisions stand out:
- Focus on reusing your technology. Reusing code can increase the development speed of gen AI use cases by 30 to 50 percent. One good approach is simply creating a source for approved tools, code, and components. A financial-services company, for example, created a library of production-grade tools, which had been approved by both the security and legal teams, and made them available in a library for teams to use. More important is taking the time to identify and build those capabilities that are common across the most priority use cases. The same financial-services company, for example, identified three components that could be reused for more than 100 identified use cases. By building those first, they were able to generate a significant portion of the code base for all the identified use cases—essentially giving every application a big head start.
- Focus the architecture on enabling efficient connections between gen AI models and internal systems. For gen AI models to work effectively in the shaper archetype, they need access to a business’s data and applications. Advances in integration and orchestration frameworks have significantly reduced the effort required to make those connections. But laying out what those integrations are and how to enable them is critical to ensure these models work efficiently and to avoid the complexity that creates technical debt (the “tax” a company pays in terms of time and resources needed to redress existing technology issues). Chief information officers and chief technology officers can define reference architectures and integration standards for their organizations. Key elements should include a model hub, which contains trained and approved models that can be provisioned on demand; standard APIs that act as bridges connecting gen AI models to applications or data; and context management and caching, which speed up processing by providing models with relevant information from enterprise data sources.
- Build up your testing and quality assurance capabilities. Our own experience building Lilli taught us to prioritize testing over development. Our team invested in not only developing testing protocols for each stage of development but also aligning the entire team so that, for example, it was clear who specifically needed to sign off on each stage of the process. This slowed down initial development but sped up the overall delivery pace and quality by cutting back on errors and the time needed to fix mistakes.
Ensure data quality and focus on unstructured data to fuel your models
The ability of a business to generate and scale value from gen AI models will depend on how well it takes advantage of its own data. As with technology, targeted upgrades to existing data architecture are needed to maximize the future strategic benefits of gen AI:
- Be targeted in ramping up your data quality and data augmentation efforts. While data quality has always been an important issue, the scale and scope of data that gen AI models can use—especially unstructured data—has made this issue much more consequential. For this reason, it’s critical to get the data foundations right, from clarifying decision rights to defining clear data processes to establishing taxonomies so models can access the data they need. The companies that do this well tie their data quality and augmentation efforts to the specific AI/gen AI application and use case—you don’t need this data foundation to extend to every corner of the enterprise. This could mean, for example, developing a new data repository for all equipment specifications and reported issues to better support maintenance copilot applications.
- Understand what value is locked into your unstructured data. Most organizations have traditionally focused their data efforts on structured data (values that can be organized in tables, such as prices and features). But the real value from LLMs comes from their ability to work with unstructured data (for example, PowerPoint slides, videos, and text). Companies can map out which unstructured data sources are most valuable and establish metadata tagging standards so models can process the data and teams can find what they need (tagging is particularly important to help companies remove data from models as well, if necessary). Be creative in thinking about data opportunities. Some companies, for example, are interviewing senior employees as they retire and feeding that captured institutional knowledge into an LLM to help improve their copilot performance.
- Optimize to lower costs at scale. There is often as much as a tenfold difference between what companies pay for data and what they could be paying if they optimized their data infrastructure and underlying costs. This issue often stems from companies scaling their proofs of concept without optimizing their data approach. Two costs generally stand out. One is storage costs arising from companies uploading terabytes of data into the cloud and wanting that data available 24/7. In practice, companies rarely need more than 10 percent of their data to have that level of availability, and accessing the rest over a 24- or 48-hour period is a much cheaper option. The other costs relate to computation with models that require on-call access to thousands of processors to run. This is especially the case when companies are building their own models (the maker archetype) but also when they are using pretrained models and running them with their own data and use cases (the shaper archetype). Companies could take a close look at how they can optimize computation costs on cloud platforms—for instance, putting some models in a queue to run when processors aren’t being used (such as when Americans go to bed and consumption of computing services like Netflix decreases) is a much cheaper option.
Build trust and reusability to drive adoption and scale
Because many people have concerns about gen AI, the bar on explaining how these tools work is much higher than for most solutions. People who use the tools want to know how they work, not just what they do. So it’s important to invest extra time and money to build trust by ensuring model accuracy and making it easy to check answers.
One insurance company, for example, created a gen AI tool to help manage claims. As part of the tool, it listed all the guardrails that had been put in place, and for each answer provided a link to the sentence or page of the relevant policy documents. The company also used an LLM to generate many variations of the same question to ensure answer consistency. These steps, among others, were critical to helping end users build trust in the tool.
Part of the training for maintenance teams using a gen AI tool should be to help them understand the limitations of models and how best to get the right answers. That includes teaching workers strategies to get to the best answer as fast as possible by starting with broad questions then narrowing them down. This provides the model with more context, and it also helps remove any bias of the people who might think they know the answer already. Having model interfaces that look and feel the same as existing tools also helps users feel less pressured to learn something new each time a new application is introduced.
Getting to scale means that businesses will need to stop building one-off solutions that are hard to use for other similar use cases. One global energy and materials company, for example, has established ease of reuse as a key requirement for all gen AI models, and has found in early iterations that 50 to 60 percent of its components can be reused. This means setting standards for developing gen AI assets (for example, prompts and context) that can be easily reused for other cases.
While many of the risk issues relating to gen AI are evolutions of discussions that were already brewing—for instance, data privacy, security, bias risk, job displacement, and intellectual property protection—gen AI has greatly expanded that risk landscape. Just 21 percent of companies reporting AI adoption say they have established policies governing employees’ use of gen AI technologies.
Similarly, a set of tests for AI/gen AI solutions should be established to demonstrate that data privacy, debiasing, and intellectual property protection are respected. Some organizations, in fact, are proposing to release models accompanied with documentation that details their performance characteristics. Documenting your decisions and rationales can be particularly helpful in conversations with regulators.
In some ways, this article is premature—so much is changing that we’ll likely have a profoundly different understanding of gen AI and its capabilities in a year’s time. But the core truths of finding value and driving change will still apply. How well companies have learned those lessons may largely determine how successful they’ll be in capturing that value.
The authors wish to thank Michael Chui, Juan Couto, Ben Ellencweig, Josh Gartner, Bryce Hall, Holger Harreis, Phil Hudelson, Suzana Iacob, Sid Kamath, Neerav Kingsland, Kitti Lakner, Robert Levin, Matej Macak, Lapo Mori, Alex Peluffo, Aldo Rosales, Erik Roth, Abdul Wahab Shaikh, and Stephen Xu for their contributions to this article.
This article was edited by Barr Seitz, an editorial director in the New York office.
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Google’s A.I. Search Leaves Publishers Scrambling
Since Google overhauled its search engine, publishers have tried to assess the danger to their brittle business models while calling for government intervention.
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By Nico Grant and Katie Robertson
Nico Grant reports on Google from San Francisco and Katie Robertson reports on media from New York.
When Frank Pine searched Google for a link to a news article two months ago, he encountered paragraphs generated by artificial intelligence about the topic at the top of his results. To see what he wanted, he had to scroll past them.
That experience annoyed Mr. Pine, the executive editor of Media News Group and Tribune Publishing, which own 68 daily newspapers across the country. Now, those paragraphs scare him.
In May, Google announced that the A.I.-generated summaries, which compile content from news sites and blogs on the topic being searched, would be made available to everyone in the United States. And that change has Mr. Pine and many other publishing executives worried that the paragraphs pose a big danger to their brittle business model, by sharply reducing the amount of traffic to their sites from Google.
“It potentially chokes off the original creators of the content,” Mr. Pine said. The feature, AI Overviews, felt like another step toward generative A.I. replacing “the publications that they have cannibalized,” he added.
Media executives said in interviews that Google had left them in a vexing position. They want their sites listed in Google’s search results, which for some outlets can generate more than half of their traffic. But doing that means Google can use their content in AI Overviews summaries.
Publishers could also try to protect their content from Google by forbidding its web crawler from sharing any content snippets from their sites. But then their links would show up without any description, making people less likely to click.
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B2B Content Marketing Benchmarks, Budgets, and Trends: Outlook for 2024 [Research]
- by Stephanie Stahl
- | Published: October 18, 2023
- | Trends and Research
Creating standards, guidelines, processes, and workflows for content marketing is not the sexiest job.
But setting standards is the only way to know if you can improve anything (with AI or anything else).
Here’s the good news: All that non-sexy work frees time and resources (human and tech) you can apply to bring your brand’s strategies and plans to life.
But in many organizations, content still isn’t treated as a coordinated business function. That’s one of the big takeaways from our latest research, B2B Content Marketing Benchmarks, Budgets, and Trends: Outlook for 2024, conducted with MarketingProfs and sponsored by Brightspot .
A few symptoms of that reality showed up in the research:
- Marketers cite a lack of resources as a top situational challenge, the same as they did the previous year.
- Nearly three-quarters (72%) say they use generative AI, but 61% say their organization lacks guidelines for its use.
- The most frequently cited challenges include creating the right content, creating content consistently, and differentiating content.
I’ll walk you through the findings and share some advice from CMI Chief Strategy Advisor Robert Rose and other industry voices to shed light on what it all means for B2B marketers. There’s a lot to work through, so feel free to use the table of contents to navigate to the sections that most interest you.
Note: These numbers come from a July 2023 survey of marketers around the globe. We received 1,080 responses. This article focuses on answers from the 894 B2B respondents.
Table of contents
- Team structure
- Content marketing challenges
Content types, distribution channels, and paid channels
- Social media
Content management and operations
- Measurement and goals
- Overall success
- Budgets and spending
- Top content-related priorities for 2024
- Content marketing trends for 2024
Action steps
Methodology, ai: 3 out of 4 b2b marketers use generative tools.
Of course, we asked respondents how they use generative AI in content and marketing. As it turns out, most experiment with it: 72% of respondents say they use generative AI tools.
But a lack of standards can get in the way.
“Generative AI is the new, disruptive capability entering the realm of content marketing in 2024,” Robert says. “It’s just another way to make our content process more efficient and effective. But it can’t do either until you establish a standard to define its value. Until then, it’s yet just another technology that may or may not make you better at what you do.”
So, how do content marketers use the tools today? About half (51%) use generative AI to brainstorm new topics. Many use the tools to research headlines and keywords (45%) and write drafts (45%). Fewer say they use AI to outline assignments (23%), proofread (20%), generate graphics (11%), and create audio (5%) and video (5%).
Some marketers say they use AI to do things like generate email headlines and email copy, extract social media posts from long-form content, condense long-form copy into short form, etc.
Only 28% say they don’t use generative AI tools.
Most don’t pay for generative AI tools (yet)
Among those who use generative AI tools, 91% use free tools (e.g., ChatGPT ). Thirty-eight percent use tools embedded in their content creation/management systems, and 27% pay for tools such as Writer and Jasper.
AI in content remains mostly ungoverned
Asked if their organizations have guidelines for using generative AI tools, 31% say yes, 61% say no, and 8% are unsure.
We asked Ann Handley , chief content officer of MarketingProfs, for her perspective. “It feels crazy … 61% have no guidelines? But is it actually shocking and crazy? No. It is not. Most of us are just getting going with generative AI. That means there is a clear and rich opportunity to lead from where you sit,” she says.
“Ignite the conversation internally. Press upon your colleagues and your leadership that this isn’t a technology opportunity. It’s also a people and operational challenge in need of thoughtful and intelligent response. You can be the AI leader your organization needs,” Ann says.
Why some marketers don’t use generative AI tools
While a lack of guidelines may deter some B2B marketers from using generative AI tools, other reasons include accuracy concerns (36%), lack of training (27%), and lack of understanding (27%). Twenty-two percent cite copyright concerns, and 19% have corporate mandates not to use them.
How AI is changing SEO
We also wondered how AI’s integration in search engines shifts content marketers’ SEO strategy. Here’s what we found:
- 31% are sharpening their focus on user intent/answering questions.
- 27% are creating more thought leadership content.
- 22% are creating more conversational content.
Over one-fourth (28%) say they’re not doing any of those things, while 26% say they’re unsure.
AI may heighten the need to rethink your SEO strategy. But it’s not the only reason to do so, as Orbit Media Studios co-founder and chief marketing officer Andy Crestodina points out: “Featured snippets and people-also-ask boxes have chipped away at click-through rates for years,” he says. “AI will make that even worse … but only for information intent queries . Searchers who want quick answers really don’t want to visit websites.
“Focus your SEO efforts on those big questions with big answers – and on the commercial intent queries,” Andy continues. “Those phrases still have ‘visit website intent’ … and will for years to come.”
Will the AI obsession ever end?
Many B2B marketers surveyed predict AI will dominate the discussions of content marketing trends in 2024. As one respondent says: “AI will continue to be the shiny thing through 2024 until marketers realize the dedication required to develop prompts, go through the iterative process, and fact-check output . AI can help you sharpen your skills, but it isn’t a replacement solution for B2B marketing.”
Back to table of contents
Team structure: How does the work get done?
Generative AI isn’t the only issue affecting content marketing these days. We also asked marketers about how they organize their teams .
Among larger companies (100-plus employees), half say content requests go through a centralized content team. Others say each department/brand produces its own content (23%), and the departments/brand/products share responsibility (21%).
Content strategies integrate with marketing, comms, and sales
Seventy percent say their organizations integrate content strategy into the overall marketing sales/communication/strategy, and 2% say it’s integrated into another strategy. Eleven percent say content is a stand-alone strategy for content used for marketing, and 6% say it’s a stand-alone strategy for all content produced by the company. Only 9% say they don’t have a content strategy. The remaining 2% say other or are unsure.
Employee churn means new teammates; content teams experience enlightened leadership
Twenty-eight percent of B2B marketers say team members resigned in the last year, 20% say team members were laid off, and about half (49%) say they had new team members acclimating to their ways of working.
While team members come and go, the understanding of content doesn’t. Over half (54%) strongly agree, and 30% somewhat agree the leader to whom their content team reports understands the work they do. Only 11% disagree. The remaining 5% neither agree nor disagree.
And remote work seems well-tolerated: Only 20% say collaboration was challenging due to remote or hybrid work.
Content marketing challenges: Focus shifts to creating the right content
We asked B2B marketers about both content creation and non-creation challenges.
Content creation
Most marketers (57%) cite creating the right content for their audience as a challenge. This is a change from many years when “creating enough content” was the most frequently cited challenge.
One respondent points out why understanding what audiences want is more important than ever: “As the internet gets noisier and AI makes it incredibly easy to create listicles and content that copy each other, there will be a need for companies to stand out. At the same time, as … millennials and Gen Z [grow in the workforce], we’ll begin to see B2B become more entertaining and less boring. We were never only competing with other B2B content. We’ve always been competing for attention.”
Other content creation challenges include creating it consistently (54%) and differentiating it (54%). Close to half (45%) cite optimizing for search and creating quality content (44%). About a third (34%) cite creating enough content to keep up with internal demand, 30% say creating enough content to keep up with external demand, and 30% say creating content that requires technical skills.
Other hurdles
The most frequently cited non-creation challenge, by far, is a lack of resources (58%), followed by aligning content with the buyer’s journey (48%) and aligning content efforts across sales and marketing (45%). Forty-one percent say they have issues with workflow/content approval, and 39% say they have difficulty accessing subject matter experts. Thirty-four percent say it is difficult to keep up with new technologies/tools (e.g., AI). Only 25% cite a lack of strategy as a challenge, 19% say keeping up with privacy rules, and 15% point to tech integration issues.
We asked content marketers about the types of content they produce, their distribution channels , and paid content promotion. We also asked which formats and channels produce the best results.
Popular content types and formats
As in the previous year, the three most popular content types/formats are short articles/posts (94%, up from 89% last year), videos (84%, up from 75% last year), and case studies/customer stories (78%, up from 67% last year). Almost three-quarters (71%) use long articles, 60% produce visual content, and 59% craft thought leadership e-books or white papers. Less than half of marketers use brochures (49%), product or technical data sheets (45%), research reports (36%), interactive content (33%), audio (29%), and livestreaming (25%).
Effective content types and formats
Which formats are most effective? Fifty-three percent say case studies/customer stories and videos deliver some of their best results. Almost as many (51%) names thought leadership e-books or white papers, 47% short articles, and 43% research reports.
Popular content distribution channels
Regarding the channels used to distribute content, 90% use social media platforms (organic), followed by blogs (79%), email newsletters (73%), email (66%), in-person events (56%), and webinars (56%).
Channels used by the minority of those surveyed include:
- Digital events (44%)
- Podcasts (30%)
- Microsites (29%)
- Digital magazines (21%)
- Branded online communities (19%)
- Hybrid events (18%)
- Print magazines (16%)
- Online learning platforms (15%)
- Mobile apps (8%)
- Separate content brands (5%)
Effective content distribution channels
Which channels perform the best? Most marketers in the survey point to in-person events (56%) and webinars (51%) as producing better results. Email (44%), organic social media platforms (44%), blogs (40%) and email newsletters (39%) round out the list.
Popular paid content channels
When marketers pay to promote content , which channels do they invest in? Eighty-six percent use paid content distribution channels.
Of those, 78% use social media advertising/promoted posts, 65% use sponsorships, 64% use search engine marketing (SEM)/pay-per-click, and 59% use digital display advertising. Far fewer invest in native advertising (35%), partner emails (29%), and print display ads (21%).
Effective paid content channels
SEM/pay-per-click produces good results, according to 62% of those surveyed. Half of those who use paid channels say social media advertising/promoted posts produce good results, followed by sponsorships (49%), partner emails (36%), and digital display advertising (34%).
Social media use: One platform rises way above
When asked which organic social media platforms deliver the best value for their organization, B2B marketers picked LinkedIn by far (84%). Only 29% cite Facebook as a top performer, 22% say YouTube, and 21% say Instagram. Twitter and TikTok see 8% and 3%, respectively.
So it makes sense that 72% say they increased their use of LinkedIn over the last 12 months, while only 32% boosted their YouTube presence, 31% increased Instagram use, 22% grew their Facebook presence, and 10% increased X and TikTok use.
Which platforms are marketers giving up? Did you guess X? You’re right – 32% of marketers say they decreased their X use last year. Twenty percent decreased their use of Facebook, with 10% decreasing on Instagram, 9% pulling back on YouTube, and only 2% decreasing their use of LinkedIn.
Interestingly, we saw a significant rise in B2B marketers who use TikTok: 19% say they use the platform – more than double from last year.
To explore how teams manage content, we asked marketers about their technology use and investments and the challenges they face when scaling their content .
Content management technology
When asked which technologies they use to manage content, marketers point to:
- Analytics tools (81%)
- Social media publishing/analytics (72%)
- Email marketing software (69%)
- Content creation/calendaring/collaboration/workflow (64%)
- Content management system (50%)
- Customer relationship management system (48%)
But having technology doesn’t mean it’s the right technology (or that its capabilities are used). So, we asked if they felt their organization had the right technology to manage content across the organization.
Only 31% say yes. Thirty percent say they have the technology but aren’t using its potential, and 29% say they haven’t acquired the right technology. Ten percent are unsure.
Content tech spending will likely rise
Even so, investment in content management technology seems likely in 2024: 45% say their organization is likely to invest in new technology, whereas 32% say their organization is unlikely to do so. Twenty-three percent say their organization is neither likely nor unlikely to invest.
Scaling content production
We introduced a new question this year to understand what challenges B2B marketers face while scaling content production .
Almost half (48%) say it’s “not enough content repurposing.” Lack of communication across organizational silos is a problem for 40%. Thirty-one percent say they have no structured content production process, and 29% say they lack an editorial calendar with clear deadlines. Ten percent say scaling is not a current focus.
Among the other hurdles – difficulty locating digital content assets (16%), technology issues (15%), translation/localization issues (12%), and no style guide (11%).
For those struggling with content repurposing, content standardization is critical. “Content reuse is the only way to deliver content at scale. There’s just no other way,” says Regina Lynn Preciado , senior director of content strategy solutions at Content Rules Inc.
“Even if you’re not trying to provide the most personalized experience ever or dominate the metaverse with your omnichannel presence, you absolutely must reuse content if you are going to deliver content effectively,” she says.
“How to achieve content reuse ? You’ve probably heard that you need to move to modular, structured content. However, just chunking your content into smaller components doesn’t go far enough. For content to flow together seamlessly wherever you reuse it, you’ve got to standardize your content. That’s the personalization paradox right there. To personalize, you must standardize.
“Once you have your content standards in place and everyone is creating content in alignment with those standards, there is no limit to what you can do with the content,” Regina explains.
Why do content marketers – who are skilled communicators – struggle with cross-silo communication? Standards and alignment come into play.
“I think in the rush to all the things, we run out of time to address scalable processes that will fix those painful silos, including taking time to align on goals, roles and responsibilities, workflows, and measurement,” says Ali Orlando Wert , senior director of content strategy at Appfire. “It takes time, but the payoffs are worth it. You have to learn how to crawl before you can walk – and walk before you can run.”
Measurement and goals: Generating sales and revenue rises
Almost half (46%) of B2B marketers agree their organization measures content performance effectively. Thirty-six percent disagree, and 15% neither agree nor disagree. Only 3% say they don’t measure content performance.
The five most frequently used metrics to assess content performance are conversions (73%), email engagement (71%), website traffic (71%), website engagement (69%), and social media analytics (65%).
About half (52%) mention the quality of leads, 45% say they rely on search rankings, 41% use quantity of leads, 32% track email subscribers, and 29% track the cost to acquire a lead, subscriber, or customer.
The most common challenge B2B marketers have while measuring content performance is integrating/correlating data across multiple platforms (84%), followed by extracting insights from data (77%), tying performance data to goals (76%), organizational goal setting (70%), and lack of training (66%).
Regarding goals, 84% of B2B marketers say content marketing helped create brand awareness in the last 12 months. Seventy-six percent say it helped generate demand/leads; 63% say it helped nurture subscribers/audiences/leads, and 58% say it helped generate sales/revenue (up from 42% the previous year).
Success factors: Know your audience
To separate top performers from the pack, we asked the B2B marketers to assess the success of their content marketing approach.
Twenty-eight percent rate the success of their organization’s content marketing approach as extremely or very successful. Another 57% report moderate success and 15% feel minimally or not at all successful.
The most popular factor for successful marketers is knowing their audience (79%).
This makes sense, considering that “creating the right content for our audience” is the top challenge. The logic? Top-performing content marketers prioritize knowing their audiences to create the right content for those audiences.
Top performers also set goals that align with their organization’s objectives (68%), effectively measure and demonstrate content performance (61%), and show thought leadership (60%). Collaboration with other teams (55%) and a documented strategy (53%) also help top performers reach high levels of content marketing success.
We looked at several other dimensions to identify how top performers differ from their peers. Of note, top performers:
- Are backed by leaders who understand the work they do.
- Are more likely to have the right content management technologies.
- Have better communication across organizational silos.
- Do a better job of measuring content effectiveness.
- Are more likely to use content marketing successfully to generate demand/leads, nurture subscribers/audiences/leads, generate sales/revenue, and grow a subscribed audience.
Little difference exists between top performers and their less successful peers when it comes to the adoption of generative AI tools and related guidelines. It will be interesting to see if and how that changes next year.
Budgets and spending: Holding steady
To explore budget plans for 2024, we asked respondents if they have knowledge of their organization’s budget/budgeting process for content marketing. Then, we asked follow-up questions to the 55% who say they do have budget knowledge.
Content marketing as a percentage of total marketing spend
Here’s what they say about the total marketing budget (excluding salaries):
- About a quarter (24%) say content marketing takes up one-fourth or more of the total marketing budget.
- Nearly one in three (29%) indicate that 10% to 24% of the marketing budget goes to content marketing.
- Just under half (48%) say less than 10% of the marketing budget goes to content marketing.
Content marketing budget outlook for 2024
Next, we asked about their 2024 content marketing budget. Forty-five percent think their content marketing budget will increase compared with 2023, whereas 42% think it will stay the same. Only 6% think it will decrease.
Where will the budget go?
We also asked where respondents plan to increase their spending.
Sixty-nine percent of B2B marketers say they would increase their investment in video, followed by thought leadership content (53%), in-person events (47%), paid advertising (43%), online community building (33%), webinars (33%), audio content (25%), digital events (21%), and hybrid events (11%).
The increased investment in video isn’t surprising. The focus on thought leadership content might surprise, but it shouldn’t, says Stephanie Losee , director of executive and ABM content at Autodesk.
“As measurement becomes more sophisticated, companies are finding they’re better able to quantify the return from upper-funnel activities like thought leadership content ,” she says. “At the same time, companies recognize the impact of shifting their status from vendor to true partner with their customers’ businesses.
“Autodesk recently launched its first global, longitudinal State of Design & Make report (registration required), and we’re finding that its insights are of such value to our customers that it’s enabling conversations we’ve never been able to have before. These conversations are worth gold to both sides, and I would imagine other B2B companies are finding the same thing,” Stephanie says.
Top content-related priorities for 2024: Leading with thought leadership
We asked an open-ended question about marketers’ top three content-related priorities for 2024. The responses indicate marketers place an emphasis on thought leadership and becoming a trusted resource.
Other frequently mentioned priorities include:
- Better understanding of the audience
- Discovering the best ways to use AI
- Increasing brand awareness
- Lead generation
- Using more video
- Better use of analytics
- Conversions
- Repurposing existing content
Content marketing predictions for 2024: AI is top of mind
In another open-ended question, we asked B2B marketers, “What content marketing trends do you predict for 2024?” You probably guessed the most popular trend: AI.
Here are some of the marketers’ comments about how AI will affect content marketing next year:
- “We’ll see generative AI everywhere, all the time.”
- “There will be struggles to determine the best use of generative AI in content marketing.”
- “AI will likely result in a flood of poor-quality, machine-written content. Winners will use AI for automating the processes that support content creation while continuing to create high-quality human-generated content.”
- “AI has made creating content so easy that there are and will be too many long articles on similar subjects; most will never be read or viewed. A sea of too many words. I predict short-form content will have to be the driver for eyeballs.”
Other trends include:
- Greater demand for high-quality content as consumers grow weary of AI-generated content
- Importance of video content
- Increasing use of short video and audio content
- Impact of AI on SEO
Among the related comments:
- “Event marketing (webinars and video thought leadership) will become more necessary as teams rely on AI-generated written content.”
- “AI will be an industry sea change and strongly impact the meaning of SEO. Marketers need to be ready to ride the wave or get left behind.”
- “Excitement around AI-generated content will rise before flattening out when people realize it’s hard to differentiate, validate, verify, attribute, and authenticate. New tools, processes, and roles will emerge to tackle this challenge.”
- “Long-form reports could start to see a decline. If that is the case, we will need a replacement. Logically, that could be a webinar or video series that digs deeper into the takeaways.”
What does this year’s research suggest B2B content marketers do to move forward?
I asked CMI’s Robert Rose for some insights. He says the steps are clear: Develop standards, guidelines, and playbooks for how to operate – just like every other function in business does.
“Imagine if everyone in your organization had a different idea of how to define ‘revenue’ or ‘profit margin,’” Robert says. “Imagine if each salesperson had their own version of your company’s customer agreements and tried to figure out how to write them for every new deal. The legal team would be apoplectic. You’d start to hear from sales how they were frustrated that they couldn’t figure out how to make the ‘right agreement,’ or how to create agreements ‘consistently,’ or that there was a complete ‘lack of resources’ for creating agreements.”
Just remember: Standards can change along with your team, audiences, and business priorities. “Setting standards doesn’t mean casting policies and templates in stone,” Robert says. “Standards only exist so that we can always question the standard and make sure that there’s improvement available to use in setting new standards.”
He offers these five steps to take to solidify your content marketing strategy and execution:
- Direct. Create an initiative that will define the scope of the most important standards for your content marketing. Prioritize the areas that hurt the most. Work with leadership to decide where to start. Maybe it’s persona development. Maybe you need a new standardized content process. Maybe you need a solid taxonomy. Build the list and make it a real initiative.
- Define . Create a common understanding of all the things associated with the standards. Don’t assume that everybody knows. They don’t. What is a white paper? What is an e-book? What is a campaign vs. an initiative? What is a blog post vs. an article? Getting to a common language is one of the most powerful things you can do to coordinate better.
- Develop . You need both policies and playbooks. Policies are the formal documentation of your definitions and standards. Playbooks are how you communicate combinations of policies so that different people can not just understand them but are ready, willing, and able to follow them.
- Distribute . If no one follows the standards, they’re not standards. So, you need to develop a plan for how your new playbooks fit into the larger, cross-functional approach to the content strategy. You need to deepen the integration into each department – even if that is just four other people in your company.
- Distill . Evolve your standards. Make them living documents. Deploy technology to enforce and scale the standards. Test. If a standard isn’t working, change it. Sometimes, more organic processes are OK. Sometimes, it’s OK to acknowledge two definitions for something. The key is acknowledging a change to an existing standard so you know whether it improves things.
For their 14 th annual content marketing survey, CMI and MarketingProfs surveyed 1,080 recipients around the globe – representing a range of industries, functional areas, and company sizes — in July 2023. The online survey was emailed to a sample of marketers using lists from CMI and MarketingProfs.
This article presents the findings from the 894 respondents, mostly from North America, who indicated their organization is primarily B2B and that they are either content marketers or work in marketing, communications, or other roles involving content.
Thanks to the survey participants, who made this research possible, and to everyone who helps disseminate these findings throughout the content marketing industry.
Cover image by Joseph Kalinowski/Content Marketing Institute
About Content Marketing Institute
Content Marketing Institute (CMI) exists to do one thing: advance the practice of content marketing through online education and in-person and digital events. We create and curate content experiences that teach marketers and creators from enterprise brands, small businesses, and agencies how to attract and retain customers through compelling, multichannel storytelling. Global brands turn to CMI for strategic consultation, training, and research. Organizations from around the world send teams to Content Marketing World, the largest content marketing-focused event, the Marketing Analytics & Data Science (MADS) conference, and CMI virtual events, including ContentTECH Summit. Our community of 215,000+ content marketers shares camaraderie and conversation. CMI is organized by Informa Connect. To learn more, visit www.contentmarketinginstitute.com .
About MarketingProfs
Marketingprofs is your quickest path to b2b marketing mastery.
More than 600,000 marketing professionals worldwide rely on MarketingProfs for B2B Marketing training and education backed by data science, psychology, and real-world experience. Access free B2B marketing publications, virtual conferences, podcasts, daily newsletters (and more), and check out the MarketingProfs B2B Forum–the flagship in-person event for B2B Marketing training and education at MarketingProfs.com.
About Brightspot
Brightspot , the content management system to boost your business.
Why Brightspot? Align your technology approach and content strategy with Brightspot, the leading Content Management System for delivering exceptional digital experiences. Brightspot helps global organizations meet the business needs of today and scale to capitalize on the opportunities of tomorrow. Our Enterprise CMS and world-class team solves your unique business challenges at scale. Fast, flexible, and fully customizable, Brightspot perfectly harmonizes your technology approach with your content strategy and grows with you as your business evolves. Our customer-obsessed teams walk with you every step of the way with an unwavering commitment to your long-term success. To learn more, visit www.brightspot.com .
Stephanie Stahl
IMAGES
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COMMENTS
Generative Artificial Intelligence (AI) stands as a transformative paradigm in machine learning, enabling the creation of complex and realistic data from latent representations. This review paper comprehensively surveys the landscape of Generative AI, encompassing its foundational concepts, diverse models, training methodologies, applications, challenges, recent advancements, evaluation ...
Description. This repository is an up-to-date list of significant AI papers organized by publication date. It covers five fields : computer vision, natural language processing, audio processing, multimodal learning and reinforcement learning. Feel free to give this repository a star if you enjoy the work. Maintainer: Aimerou Ndiaye.
In this paper, we propose an efficient, fast, and versatile distillation method to accelerate the generation of pre-trained diffusion models: Flash Diffusion. Papers With Code highlights trending Machine Learning research and the code to implement it.
Title: Promoting Fairness and Diversity in Speech Datasets for Mental Health and Neurological Disorders Research Eleonora Mancini, Ana Tanevska, Andrea Galassi, Alessio Galatolo, Federico ... Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
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Top 10 AI Research Papers 2023. 1. Sparks of AGI by Microsoft Summary. In this research paper, a team from Microsoft Research analyzes an early version of OpenAI's GPT-4, which was still under active development at the time. The team argues that GPT-4 represents a new class of large language models, exhibiting more generalized intelligence ...
Semantic Reader is an augmented reader with the potential to revolutionize scientific reading by making it more accessible and richly contextual. Try it for select papers. Semantic Scholar uses groundbreaking AI and engineering to understand the semantics of scientific literature to help Scholars discover relevant research.
I resisted labeling this article "Top AI Research Papers of 2023" because determining the "best" paper is subjective. The selection criteria were based on a mix of papers I either particularly enjoyed or found impactful and worth noting. (The sorting order is a recommended reading order, not an ordering by perceived quality or impact.)
The corpus of scientific literature grows at an ever-increasing speed. Specifically, in the field of artificial intelligence (AI) and machine learning (ML), the number of papers every month is ...
5. Each year scientists from around the world publish thousands of research papers in AI but only a few of them reach wide audiences and make a global impact in the world. Below are the top-10 most impactful research papers published in top AI conferences during the last 5 years. The ranking is based on the number of citations and includes ...
Reason 2: There is quite a hype over Transformers. However, there is more to these papers than Attention. This paper shows how backporting some of these elements to boring-old models might be all you need. Reason 3: Following the same trend as #1, the buzzword model might not be the best model for your task.
In our top-20 list, only two papers (10%) can be classified as critique papers. These papers (rank 17 and 11) respectively uncover biases in LLMs as evaluation models and criticize the lack of ...
SCImago Journal Country & Rank SCImago Institutions Rankings SCImago Media Rankings SCImago Iber SCImago Research Centers Ranking SCImago Graphica Ediciones Profesionales de la Información. Scimago Journal & Country Rank ... Engineering Applications of Artificial Intelligence: journal: 1.749 Q1: 137: 1554: 1209: 92424: 11904: 1206: 9.22: 59.47 ...
AI for scientific writing and research papers. In the ever-evolving realm of academic research, AI tools are increasingly taking center stage. Enter Paper Wizard, Jenny.AI, and Wisio - these groundbreaking platforms are set to revolutionize the way we approach scientific writing. ... Best free AI research tools.
Pioneering research on the path to AGI. We believe our research will eventually lead to artificial general intelligence, a system that can solve human-level problems. Building safe and beneficial AGI is our mission. "Safely aligning powerful AI systems is one of the most important unsolved problems for our mission.
In a survey of users, 10% of respondents said that Elicit saves them 5 or more hours each week. 2. In pilot projects, we were able to save research groups 50% in costs and more than 50% in time by automating data extraction work they previously did manually. 5. Elicit's users save up to 5 hours per week 1.
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Research. AI publications, tools, and datasets. FEATURED CONTENT. Our largest and most capable AI model. ... best practices, and examples for designing with AI ... Google publishes over 1,000 papers annually. Publishing our work enables us to collaborate and share ideas with, as well as learn from, the broader scientific community. ...
Here are our top 10 picks from the hundreds of research papers published on GenAI. 1. Improving Language Understanding by Generative Pre-Training. This research paper explores a semi-supervised approach for enhancing natural language understanding tasks by combining unsupervised pre-training and supervised fine-tuning.
AI Research Papers. Novel papers are one of the ways Qualcomm Technologies contributes impactful research to the larger community of AI research. Below are papers that Qualcomm AI Research has written or co-authored. Computer vision. Data compression and generative modeling. Machine learning fundamentals. Optimization and reinforcement learning.
AI research has seen a great acceleration lately. This repository brings together the most important papers from 2022 to today in the fields of natural language processing, computer vision, audio processing, multimodal learning and reinforcement learning.
Quillbot: Rephrase text and summarize complex materials for research. Wordvice.ai: Ensure clarity, grammar, and originality in your academic writing. Consensus AI: Search vast databases and filter research papers for quality. Scite.ai: Get real citations and measure the credibility of research claims.
GigaBrain. GigaBrain is a resourceful search engine that uses AI to scan billions of comments across Reddit and other online communities to find the most useful. Discover the latest AI research tools to accelerate your studies and academic research. Analyze research papers, summarize articles, citations, and more.
Here's one that I found really useful: ResearchGPT by SciSpace. It gives accurate citations and reference links that take you directly to the papers. You don't have to worry about AI hallucination with every response GPT generates. Check it out if you haven't yet. Reply reply. Existing-World-2480.
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Google's chief executive, Sundar Pichai, last year. A new A.I.-generated feature in Google search results "is greatly detrimental to everyone apart from Google," a newspaper executive said.
Marketers talk AI, common challenges, best results, and more in the 14th annual B2B Content Marketing Benchmarks, Budgets, and Trends: Outlook for 2024. ... Almost as many (51%) names thought leadership e-books or white papers, 47% short articles, and 43% research reports. Click the image to enlarge. Popular content distribution channels ...