artificial intelligence thesis statement

What are effective thesis statements for an artificial intelligence research paper?

Effective thesis statements for an artificial intelligence research paper should be clear, specific, arguable, researchable, and relevant to the field of artificial intelligence. Here are some examples:

"The ethical implications of artificial intelligence in autonomous vehicles: balancing safety, privacy, and decision-making algorithms" [2]

• This thesis statement focuses on the ethical considerations surrounding the use of AI in autonomous vehicles, specifically addressing safety, privacy, and decision-making algorithms.

"Exploring the impact of artificial intelligence on job displacement and the future of work: a comparative analysis of industries" [1]

• This thesis statement examines the effects of AI on job displacement and the future of work, comparing its impact across different industries.

"Enhancing healthcare delivery through the integration of artificial intelligence: a case study of AI-powered diagnosis systems" [1]

• This thesis statement investigates how the integration of AI in healthcare can improve the delivery of medical services, with a specific focus on AI-powered diagnosis systems.

"Understanding the role of artificial intelligence in cybersecurity: analyzing the effectiveness of AI-based threat detection and prevention" [2]

• This thesis statement explores the role of AI in cybersecurity, specifically examining the effectiveness of AI-based threat detection and prevention methods.

"The implications of bias in artificial intelligence algorithms: addressing fairness and accountability in decision-making processes" [2]

• This thesis statement delves into the issue of bias in AI algorithms, highlighting the importance of fairness and accountability in decision-making processes.

Learn more:

• Artificial Intelligence & Machine Learning Thesis Statement Examples | AcademicHelp.net
• Thesis Statement Examples - Learn The Art From The Experts.
• How to Write a Better Thesis Statement Using AI (2023 Updated)

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Artificial Intelligence (AI) and Machine Learning (ML) are pioneering technologies driving innovation across various sectors. When composing a thesis in this dynamic field, it is essential to commence with a concise and precise thesis statement that encapsulates your research’s essence. Below are examples of good and bad thesis statements, each followed by an analysis illustrating their effectiveness or shortcomings.

Good Thesis Statement Examples

Specific and Clear: “This thesis will investigate the application of machine learning algorithms in predicting stock prices with a focus on the technology sector.” Unclear: “Machine learning can be used to predict stock prices.”

The good example is clear and specific, detailing the application area (stock price prediction) and narrowing the focus to the technology sector. In contrast, the bad statement is vague, lacking both specificity and a defined scope.

Arguable and Debatable: “Despite its benefits, the implementation of AI in hiring processes can inadvertently reinforce existing biases, thus exacerbating workplace inequality.” Dull: “AI in hiring has pros and cons.”

The good statement is debatable and presents a clear argument, highlighting the potential downside of AI in hiring. Meanwhile, the bad statement is indecisive and fails to present a clear argument or stance.

Researchable and Measurable: “This study explores the efficacy of deep learning in the early detection of breast cancer through the analysis of mammographic images.” Uninspiring: “AI can help detect diseases early.”

A good example is researchable and measurable, specifying the AI type (deep learning), application (early detection of breast cancer), and method (analysis of mammographic images). Conversely, the bad statement is too general and lacks specificity.

Bad Thesis Statement Examples

Overly Broad: “Artificial intelligence is changing the world.”

While true, this statement is overly broad, providing no clear direction or focus for research.

Lack of Clear Argument: “AI and ML are important in data analysis.”

This statement, while factual, lacks a clear argument or focus, not providing the reader with an understanding of the research’s purpose or direction.

Unoriginal and Unengaging: “AI is used in many areas like healthcare, finance, and technology.”

Though factual, this statement is unoriginal and unengaging, lacking a specific focus or claim to guide the research.

Crafting an effective thesis statement for AI and ML research necessitates clarity, specificity, and a well-defined argument. Good thesis statements serve as a robust foundation, guiding both the researcher and the reader through the research journey. Conversely, bad thesis statements are vague, broad, and lack a clear focus, which might misguide the research process. By considering the examples provided, students can adeptly craft thesis statements that not only encapsulate their research focus but also engage readers with compelling arguments in the ever-evolving fields of Artificial Intelligence and Machine Learning.

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Crafting the Perfect Thesis Statement for Artificial Intelligence with WriteGo

How to craft the perfect thesis statement for artificial intelligence.

Writing a strong thesis statement is a crucial step in any academic paper, especially when tackling complex topics like artificial intelligence. With WriteGo , creating a compelling thesis statement has never been easier. This article will guide you through the process and introduce you to WriteGo, our innovative AI-powered writing tool designed to enhance your academic writing experience.

Understanding the Thesis Statement

A thesis statement is a concise summary of the main point or claim of an essay or research paper. For a topic as expansive as artificial intelligence, crafting a clear and focused thesis statement is essential. It sets the direction of your paper and informs your readers about what to expect.

Key Elements of a Strong Thesis Statement

• Clarity : Your thesis statement should be clear and specific. Avoid vague language and ensure that your main point is easily understood.
• Arguability : A good thesis statement should present a claim that others might dispute. It should encourage discussion and not just state a fact.
• Scope : The statement should be manageable within the constraints of your paper. Avoid overly broad or overly narrow topics.

Steps to Create a Thesis Statement for Artificial Intelligence

• Choose a Focused Topic : Identify a specific area within the broad field of artificial intelligence. For example, you might focus on AI ethics, machine learning, or the impact of AI on employment.
• Conduct Preliminary Research : Gather information on your chosen topic to understand the existing discourse and identify gaps or areas of contention.
• Formulate Your Main Argument : Based on your research, decide what specific claim or argument you want to make about your topic.
• Refine Your Statement : Ensure that your thesis statement is clear, arguable, and appropriately scoped.

Example Thesis Statements for Artificial Intelligence

• "The integration of artificial intelligence in healthcare can significantly enhance patient outcomes while reducing operational costs."
• "Ethical considerations in artificial intelligence development are crucial to ensure technology benefits society without infringing on individual rights."
• "The rise of artificial intelligence will transform the job market, necessitating new skills and education systems to prepare the workforce."

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WriteGo is an advanced AI-powered writing tool designed to help you generate high-quality academic content. Whether you're struggling with your thesis statement or need assistance with the entire paper, WriteGo is here to help.

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Creating a strong thesis statement for artificial intelligence topics is a critical step in your academic writing journey. By following the steps outlined above, you can craft a clear, arguable, and well-scoped thesis statement.

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8 Best Topics for Research and Thesis in Artificial Intelligence

Imagine a future in which intelligence is not restricted to humans!!! A future where machines can think as well as humans and work with them to create an even more exciting universe. While this future is still far away, Artificial Intelligence has still made a lot of advancement in these times. There is a lot of research being conducted in almost all fields of AI like Quantum Computing, Healthcare, Autonomous Vehicles, Internet of Things , Robotics , etc. So much so that there is an increase of 90% in the number of annually published research papers on Artificial Intelligence since 1996.

Keeping this in mind, if you want to research and write a thesis based on Artificial Intelligence, there are many sub-topics that you can focus on. Some of these topics along with a brief introduction are provided in this article. We have also mentioned some published research papers related to each of these topics so that you can better understand the research process.

Table of Content

1. Machine Learning

2. deep learning, 3. reinforcement learning, 4. robotics, 5. natural language processing (nlp), 6. computer vision, 7. recommender systems, 8. internet of things.

So without further ado, let’s see the different Topics for Research and Thesis in Artificial Intelligence!

Machine Learning involves the use of Artificial Intelligence to enable machines to learn a task from experience without programming them specifically about that task. (In short, Machines learn automatically without human hand holding!!!) This process starts with feeding them good quality data and then training the machines by building various machine learning models using the data and different algorithms. The choice of algorithms depends on what type of data do we have and what kind of task we are trying to automate.

However, generally speaking, Machine Learning Algorithms are generally divided into 3 types: Supervised Machine Learning Algorithms , Unsupervised Machine Learning Algorithms , and Reinforcement Machine Learning Algorithms . If you are interested in gaining practical experience and understanding these algorithms in-depth, check out the Data Science Live Course by us.

Deep Learning is a subset of Machine Learning that learns by imitating the inner working of the human brain in order to process data and implement decisions based on that data. Basically, Deep Learning uses artificial neural networks to implement machine learning. These neural networks are connected in a web-like structure like the networks in the human brain (Basically a simplified version of our brain!).

This web-like structure of artificial neural networks means that they are able to process data in a nonlinear approach which is a significant advantage over traditional algorithms that can only process data in a linear approach. An example of a deep neural network is RankBrain which is one of the factors in the Google Search algorithm.

Reinforcement Learning is a part of Artificial Intelligence in which the machine learns something in a way that is similar to how humans learn. As an example, assume that the machine is a student. Here the hypothetical student learns from its own mistakes over time (like we had to!!). So the Reinforcement Machine Learning Algorithms learn optimal actions through trial and error.

This means that the algorithm decides the next action by learning behaviors that are based on its current state and that will maximize the reward in the future. And like humans, this works for machines as well! For example, Google’s AlphaGo computer program was able to beat the world champion in the game of Go (that’s a human!) in 2017 using Reinforcement Learning.

Robotics is a field that deals with creating humanoid machines that can behave like humans and perform some actions like human beings. Now, robots can act like humans in certain situations but can they think like humans as well? This is where artificial intelligence comes in! AI allows robots to act intelligently in certain situations. These robots may be able to solve problems in a limited sphere or even learn in controlled environments.

An example of this is Kismet , which is a social interaction robot developed at M.I.T’s Artificial Intelligence Lab. It recognizes the human body language and also our voice and interacts with humans accordingly. Another example is Robonaut , which was developed by NASA to work alongside the astronauts in space.

It’s obvious that humans can converse with each other using speech but now machines can too! This is known as Natural Language Processing where machines analyze and understand language and speech as it is spoken (Now if you talk to a machine it may just talk back!). There are many subparts of NLP that deal with language such as speech recognition, natural language generation, natural language translation , etc. NLP is currently extremely popular for customer support applications, particularly the chatbot . These chatbots use ML and NLP to interact with the users in textual form and solve their queries. So you get the human touch in your customer support interactions without ever directly interacting with a human.

Some Research Papers published in the field of Natural Language Processing are provided here. You can study them to get more ideas about research and thesis on this topic.

The internet is full of images! This is the selfie age, where taking an image and sharing it has never been easier. In fact, millions of images are uploaded and viewed every day on the internet. To make the most use of this huge amount of images online, it’s important that computers can see and understand images. And while humans can do this easily without a thought, it’s not so easy for computers! This is where Computer Vision comes in.

Computer Vision uses Artificial Intelligence to extract information from images. This information can be object detection in the image, identification of image content to group various images together, etc. An application of computer vision is navigation for autonomous vehicles by analyzing images of surroundings such as AutoNav used in the Spirit and Opportunity rovers which landed on Mars.

When you are using Netflix, do you get a recommendation of movies and series based on your past choices or genres you like? This is done by Recommender Systems that provide you some guidance on what to choose next among the vast choices available online. A Recommender System can be based on Content-based Recommendation or even Collaborative Filtering.

Content-Based Recommendation is done by analyzing the content of all the items. For example, you can be recommended books you might like based on Natural Language Processing done on the books. On the other hand, Collaborative Filtering is done by analyzing your past reading behavior and then recommending books based on that.

Artificial Intelligence deals with the creation of systems that can learn to emulate human tasks using their prior experience and without any manual intervention. Internet of Things , on the other hand, is a network of various devices that are connected over the internet and they can collect and exchange data with each other.

Now, all these IoT devices generate a lot of data that needs to be collected and mined for actionable results. This is where Artificial Intelligence comes into the picture. Internet of Things is used to collect and handle the huge amount of data that is required by the Artificial Intelligence algorithms. In turn, these algorithms convert the data into useful actionable results that can be implemented by the IoT devices.

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Home — Essay Samples — Information Science and Technology — Modern Technology — Artificial Intelligence

Essays on Artificial Intelligence

Artificial intelligence essay topics for college students.

Welcome, college students! Writing an essay on artificial intelligence can be an exciting and challenging task. The key to a successful essay lies in selecting the right topic that sparks your interest and allows you to showcase your creativity. In this resource page, we will provide you with a variety of essay types and topics to help you get started on your AI essay journey.

Argumentative Essay Topic for Artificial Intelligence Essays

• The ethical implications of AI technology
• The impact of AI on job automation
• Regulating AI development for societal benefits

Introduction Paragraph Example: Artificial intelligence has revolutionized the way we interact with technology, raising important ethical questions about its implications on society. In this essay, we will explore the ethical challenges of AI technology and discuss the need for regulations to ensure its responsible development.

Conclusion Paragraph Example: In conclusion, it is evident that the ethical implications of AI technology are multifaceted and require careful consideration. By implementing regulations and ethical guidelines, we can harness the benefits of AI while minimizing its potential risks.

Compare and Contrast Essay Topics for Artificial Intelligence

• The differences between narrow AI and general AI
• Comparing AI in science fiction to real-world applications
• The impact of AI on different industries
• AI vs. human intelligence: Strengths and weaknesses
• Machine learning vs. deep learning
• AI in healthcare vs. AI in finance
• AI-driven automation vs. traditional automation
• Cloud-based AI vs. edge AI
• The role of AI in developed vs. developing countries
• AI in education vs. AI in entertainment

Introduction Paragraph Example: The field of artificial intelligence encompasses a wide range of technologies, from narrow AI systems designed for specific tasks to the hypothetical concept of general AI capable of human-like intelligence. In this essay, we will compare and contrast the characteristics of narrow and general AI to understand their implications on society.

Conclusion Paragraph Example: Through this comparison, we have gained insights into the diverse applications of AI technology and the potential challenges it poses to various industries. By understanding the differences between narrow and general AI, we can better prepare for the future of artificial intelligence.

Descriptive Essay Essay Topics for Artificial Intelligence

• The role of AI in healthcare advancements
• The development of AI algorithms for autonomous vehicles
• The applications of AI in natural language processing
• The architecture of neural networks
• The evolution of AI from the 20th century to today
• The ethical implications of AI decision-making
• The process of training an AI model
• The impact of AI on the job market
• The future potential of quantum AI
• The role of AI in personalized marketing

Introduction Paragraph Example: AI technology has transformed the healthcare industry, enabling innovative solutions that improve patient care and diagnosis accuracy. In this essay, we will explore the role of AI in healthcare advancements and its impact on the future of medicine.

Conclusion Paragraph Example: In conclusion, the integration of AI technology in healthcare has the potential to revolutionize the way we approach patient care and medical research. By leveraging AI algorithms and machine learning capabilities, we can achieve significant advancements in the field of medicine.

Persuasive Essay Essay Topics for Artificial Intelligence

• Promoting diversity and inclusion in AI development
• The importance of ethical AI education in schools
• Advocating for AI transparency and accountability
• The necessity of regulating AI technology
• Why AI should be used to combat climate change
• The benefits of AI in improving public safety
• Encouraging responsible AI usage in social media
• The potential of AI to revolutionize education
• Why businesses should invest in AI technology
• The role of AI in enhancing cybersecurity

Introduction Paragraph Example: As artificial intelligence continues to permeate various aspects of our lives, it is essential to prioritize diversity and inclusion in AI development to ensure equitable outcomes for all individuals. In this essay, we will discuss the importance of promoting diversity and inclusion in AI initiatives and the benefits it brings to society.

Conclusion Paragraph Example: By advocating for diversity and inclusion in AI development, we can create a more equitable and socially responsible future for artificial intelligence. Through ethical education and transparent practices, we can build a foundation of trust and accountability in AI technology.

Narrative Essay Essay Topics for Artificial Intelligence

• A day in the life of an AI researcher
• The journey of building your first AI project
• An imaginary conversation with a sentient AI being
• The story of a world transformed by AI
• How AI solved a major global problem
• A personal encounter with AI technology
• The evolution of AI in your lifetime
• The challenges faced while developing an AI startup
• A future where AI coexists with humans
• Your experience learning about AI for the first time

Introduction Paragraph Example: Imagine a world where artificial intelligence blurs the lines between human and machine, offering new possibilities and ethical dilemmas. In this narrative essay, we will embark on a journey through the eyes of an AI researcher, exploring the challenges and discoveries that come with pushing the boundaries of technology.

Conclusion Paragraph Example: Through this narrative journey, we have delved into the complexities of artificial intelligence and the ethical considerations that accompany its development. By embracing the possibilities of AI technology while acknowledging its limitations, we can shape a future that balances innovation with ethical responsibility.

Hooks for Artificial Intelligence Essay

• "Imagine a world where machines not only perform tasks but also think, learn, and make decisions just like humans. Welcome to the era of Artificial Intelligence (AI), a revolutionary force reshaping our future."
• "From self-driving cars to smart personal assistants, AI is seamlessly integrating into our daily lives. But what lies beneath this cutting-edge technology, and how will it transform the way we live and work?"
• "As AI continues to advance at an unprecedented pace, questions about its ethical implications and impact on society become more urgent. Can we control the intelligence we create, or will it control us?"
• "AI is not just a futuristic concept confined to science fiction. It’s here, and it’s real, influencing industries, healthcare, education, and even our personal lives. How prepared are we for this technological revolution?"
• "The debate over AI is heating up: Will it lead to a utopian society with endless possibilities, or is it a Pandora's box with risks we have yet to fully understand? The answers may surprise you."

Artificial Intelligence: a Threat Or a Potential Boon

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Artificial Intelligence: Good and Bad Effects for Humanity

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Artificial Intelligence: Applications, Advantages and Disanvantages

The possibility of machines to be able to think and feel, artificial intelligence: what really makes us human, how artificial intelligence is transforming the world, risks and benefits of ai in the future, the possibility of artificial intelligence to replace teachers, artificial intelligence, machine learning and deep learning, the ethical challenges of artificial intelligence, will artificial intelligence have a progressive or retrogressive impact on our society, artificial intelligence in medicine, impact of technology: how artificial intelligence will change the future, artificial intelligence in home automation, artificial intelligence and the future of human rights, artificial intelligence (ai) and its impact on our life, impact of artificial intelligence on hr jobs, the ability of artificial intelligence to make society more sustainable, deep learning for artificial intelligence, the role of artificial intelligence in future technology, artificial intelligence against homelessness and hiv, artificial intelligence in radiology.

Artificial intelligence (AI) refers to the intellectual capabilities exhibited by machines, contrasting with the innate intelligence observed in living beings, such as animals and humans.

The inception of artificial intelligence research as an academic field can be traced back to its establishment in 1956. It was during the renowned Dartmouth conference of the same year that artificial intelligence acquired its distinctive name, definitive purpose, initial accomplishments, and notable pioneers, thereby earning its reputation as the birthplace of AI. The esteemed figures of Marvin Minsky and John McCarthy are widely recognized as the founding fathers of this discipline.

• The term "artificial intelligence" was coined in 1956 by computer scientist John McCarthy.
• McKinsey Global Institute estimates that by 2030, automation and AI technologies could contribute to a global economic impact of $13 trillion.
• AI is used in various industries, including healthcare, finance, and transportation.
• The healthcare industry is leveraging AI for improved patient care. A study published in the journal Nature Medicine reported that an AI model was able to detect breast cancer with an accuracy of 94.5%, outperforming human radiologists.
• Ethical concerns surrounding AI include privacy issues, bias in algorithms, and the potential for job displacement.

Artificial Intelligence is an important topic because it has the potential to revolutionize industries, improve efficiency, and enhance decision-making processes. As AI technology continues to advance, it is crucial for society to understand its implications, both positive and negative, in order to harness its benefits while mitigating its risks.

1. Russell, S. J., & Norvig, P. (2016). Artificial Intelligence: A Modern Approach (3rd ed.). Prentice Hall. 2. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press. 3. Kurzweil, R. (2005). The Singularity Is Near: When Humans Transcend Biology. Viking. 4. Bostrom, N. (2014). Superintelligence: Paths, Dangers, Strategies. Oxford University Press. 5. Chollet, F. (2017). Deep Learning with Python. Manning Publications. 6. Domingos, P. (2018). The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World. Basic Books. 7. Ng, A. (2017). Machine Learning Yearning. deeplearning.ai. 8. Marcus, G. (2018). Rebooting AI: Building Artificial Intelligence We Can Trust. Vintage. 9. Winfield, A. (2018). Robotics: A Very Short Introduction. Oxford University Press. 10. Shalev-Shwartz, S., & Ben-David, S. (2014). Understanding Machine Learning: From Theory to Algorithms. Cambridge University Press.

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The Future of AI Research: 20 Thesis Ideas for Undergraduate Students in Machine Learning and Deep Learning for 2023!

A comprehensive guide for crafting an original and innovative thesis in the field of ai..

By Aarafat Islam on 2023-01-11

“The beauty of machine learning is that it can be applied to any problem you want to solve, as long as you can provide the computer with enough examples.” — Andrew Ng

This article provides a list of 20 potential thesis ideas for an undergraduate program in machine learning and deep learning in 2023. Each thesis idea includes an  introduction , which presents a brief overview of the topic and the  research objectives . The ideas provided are related to different areas of machine learning and deep learning, such as computer vision, natural language processing, robotics, finance, drug discovery, and more. The article also includes explanations, examples, and conclusions for each thesis idea, which can help guide the research and provide a clear understanding of the potential contributions and outcomes of the proposed research. The article also emphasized the importance of originality and the need for proper citation in order to avoid plagiarism.

1. Investigating the use of Generative Adversarial Networks (GANs) in medical imaging:  A deep learning approach to improve the accuracy of medical diagnoses.

Introduction:  Medical imaging is an important tool in the diagnosis and treatment of various medical conditions. However, accurately interpreting medical images can be challenging, especially for less experienced doctors. This thesis aims to explore the use of GANs in medical imaging, in order to improve the accuracy of medical diagnoses.

2. Exploring the use of deep learning in natural language generation (NLG): An analysis of the current state-of-the-art and future potential.

Introduction:  Natural language generation is an important field in natural language processing (NLP) that deals with creating human-like text automatically. Deep learning has shown promising results in NLP tasks such as machine translation, sentiment analysis, and question-answering. This thesis aims to explore the use of deep learning in NLG and analyze the current state-of-the-art models, as well as potential future developments.

3. Development and evaluation of deep reinforcement learning (RL) for robotic navigation and control.

Introduction:  Robotic navigation and control are challenging tasks, which require a high degree of intelligence and adaptability. Deep RL has shown promising results in various robotics tasks, such as robotic arm control, autonomous navigation, and manipulation. This thesis aims to develop and evaluate a deep RL-based approach for robotic navigation and control and evaluate its performance in various environments and tasks.

4. Investigating the use of deep learning for drug discovery and development.

Introduction:  Drug discovery and development is a time-consuming and expensive process, which often involves high failure rates. Deep learning has been used to improve various tasks in bioinformatics and biotechnology, such as protein structure prediction and gene expression analysis. This thesis aims to investigate the use of deep learning for drug discovery and development and examine its potential to improve the efficiency and accuracy of the drug development process.

5. Comparison of deep learning and traditional machine learning methods for anomaly detection in time series data.

Introduction:  Anomaly detection in time series data is a challenging task, which is important in various fields such as finance, healthcare, and manufacturing. Deep learning methods have been used to improve anomaly detection in time series data, while traditional machine learning methods have been widely used as well. This thesis aims to compare deep learning and traditional machine learning methods for anomaly detection in time series data and examine their respective strengths and weaknesses.

Photo by  Joanna Kosinska  on  Unsplash

6. Use of deep transfer learning in speech recognition and synthesis.

Introduction:  Speech recognition and synthesis are areas of natural language processing that focus on converting spoken language to text and vice versa. Transfer learning has been widely used in deep learning-based speech recognition and synthesis systems to improve their performance by reusing the features learned from other tasks. This thesis aims to investigate the use of transfer learning in speech recognition and synthesis and how it improves the performance of the system in comparison to traditional methods.

7. The use of deep learning for financial prediction.

Introduction:  Financial prediction is a challenging task that requires a high degree of intelligence and adaptability, especially in the field of stock market prediction. Deep learning has shown promising results in various financial prediction tasks, such as stock price prediction and credit risk analysis. This thesis aims to investigate the use of deep learning for financial prediction and examine its potential to improve the accuracy of financial forecasting.

8. Investigating the use of deep learning for computer vision in agriculture.

Introduction:  Computer vision has the potential to revolutionize the field of agriculture by improving crop monitoring, precision farming, and yield prediction. Deep learning has been used to improve various computer vision tasks, such as object detection, semantic segmentation, and image classification. This thesis aims to investigate the use of deep learning for computer vision in agriculture and examine its potential to improve the efficiency and accuracy of crop monitoring and precision farming.

9. Development and evaluation of deep learning models for generative design in engineering and architecture.

Introduction:  Generative design is a powerful tool in engineering and architecture that can help optimize designs and reduce human error. Deep learning has been used to improve various generative design tasks, such as design optimization and form generation. This thesis aims to develop and evaluate deep learning models for generative design in engineering and architecture and examine their potential to improve the efficiency and accuracy of the design process.

10. Investigating the use of deep learning for natural language understanding.

Introduction:  Natural language understanding is a complex task of natural language processing that involves extracting meaning from text. Deep learning has been used to improve various NLP tasks, such as machine translation, sentiment analysis, and question-answering. This thesis aims to investigate the use of deep learning for natural language understanding and examine its potential to improve the efficiency and accuracy of natural language understanding systems.

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11. Comparing deep learning and traditional machine learning methods for image compression.

Introduction:  Image compression is an important task in image processing and computer vision. It enables faster data transmission and storage of image files. Deep learning methods have been used to improve image compression, while traditional machine learning methods have been widely used as well. This thesis aims to compare deep learning and traditional machine learning methods for image compression and examine their respective strengths and weaknesses.

12. Using deep learning for sentiment analysis in social media.

Introduction:  Sentiment analysis in social media is an important task that can help businesses and organizations understand their customers’ opinions and feedback. Deep learning has been used to improve sentiment analysis in social media, by training models on large datasets of social media text. This thesis aims to use deep learning for sentiment analysis in social media, and evaluate its performance against traditional machine learning methods.

13. Investigating the use of deep learning for image generation.

Introduction:  Image generation is a task in computer vision that involves creating new images from scratch or modifying existing images. Deep learning has been used to improve various image generation tasks, such as super-resolution, style transfer, and face generation. This thesis aims to investigate the use of deep learning for image generation and examine its potential to improve the quality and diversity of generated images.

14. Development and evaluation of deep learning models for anomaly detection in cybersecurity.

Introduction:  Anomaly detection in cybersecurity is an important task that can help detect and prevent cyber-attacks. Deep learning has been used to improve various anomaly detection tasks, such as intrusion detection and malware detection. This thesis aims to develop and evaluate deep learning models for anomaly detection in cybersecurity and examine their potential to improve the efficiency and accuracy of cybersecurity systems.

15. Investigating the use of deep learning for natural language summarization.

Introduction:  Natural language summarization is an important task in natural language processing that involves creating a condensed version of a text that preserves its main meaning. Deep learning has been used to improve various natural language summarization tasks, such as document summarization and headline generation. This thesis aims to investigate the use of deep learning for natural language summarization and examine its potential to improve the efficiency and accuracy of natural language summarization systems.

Photo by  Windows  on  Unsplash

16. Development and evaluation of deep learning models for facial expression recognition.

Introduction:  Facial expression recognition is an important task in computer vision and has many practical applications, such as human-computer interaction, emotion recognition, and psychological studies. Deep learning has been used to improve facial expression recognition, by training models on large datasets of images. This thesis aims to develop and evaluate deep learning models for facial expression recognition and examine their performance against traditional machine learning methods.

17. Investigating the use of deep learning for generative models in music and audio.

Introduction:  Music and audio synthesis is an important task in audio processing, which has many practical applications, such as music generation and speech synthesis. Deep learning has been used to improve generative models for music and audio, by training models on large datasets of audio data. This thesis aims to investigate the use of deep learning for generative models in music and audio and examine its potential to improve the quality and diversity of generated audio.

18. Study the comparison of deep learning models with traditional algorithms for anomaly detection in network traffic.

Introduction:  Anomaly detection in network traffic is an important task that can help detect and prevent cyber-attacks. Deep learning models have been used for this task, and traditional methods such as clustering and rule-based systems are widely used as well. This thesis aims to compare deep learning models with traditional algorithms for anomaly detection in network traffic and analyze the trade-offs between the models in terms of accuracy and scalability.

19. Investigating the use of deep learning for improving recommender systems.

Introduction:  Recommender systems are widely used in many applications such as online shopping, music streaming, and movie streaming. Deep learning has been used to improve the performance of recommender systems, by training models on large datasets of user-item interactions. This thesis aims to investigate the use of deep learning for improving recommender systems and compare its performance with traditional content-based and collaborative filtering approaches.

20. Development and evaluation of deep learning models for multi-modal data analysis.

Introduction:  Multi-modal data analysis is the task of analyzing and understanding data from multiple sources such as text, images, and audio. Deep learning has been used to improve multi-modal data analysis, by training models on large datasets of multi-modal data. This thesis aims to develop and evaluate deep learning models for multi-modal data analysis and analyze their potential to improve performance in comparison to single-modal models.

I hope that this article has provided you with a useful guide for your thesis research in machine learning and deep learning. Remember to conduct a thorough literature review and to include proper citations in your work, as well as to be original in your research to avoid plagiarism. I wish you all the best of luck with your thesis and your research endeavors!

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Cathie Wood Thinks This Artificial Intelligence (AI) Stock Can Soar by 1,082%. Here's Why I Think It Could Run Even Higher Than That.

• Cathie Wood sees Tesla's planned robotaxi fleet as its next major catalyst.
• Beyond self-driving car technology, Tesla is investing heavily in other areas of artificial intelligence (AI) such as humanoid robotics.
• The energy storage business is the fastest growing part of the company, yet it feels as if it's being ignored by the prognosticators.
• Motley Fool Issues Rare “All In” Buy Alert

NASDAQ: TSLA

Ark Invest's iconic boss just raised her five-year price target on Tesla stock to $2,600 per share.

2024 has been a tumultuous year for Tesla ( TSLA 5.24% ) investors.

For the first six months of the year, its shares drastically underperformed the S&P 500 and Nasdaq Composite as the stock cratered by roughly 20%. Nevertheless, longtime Tesla bull Cathie Wood of Ark Invest recently revised her price target for the EV stock. Wood is calling for Tesla shares to reach $2,600 by 2029 -- 1,082% higher than its recent level.

While some investors may argue such a forecast is outlandish, I personally think Wood may be underestimating Tesla's prospects.

Wood's thesis hinges on robotaxis, but...

Tesla is a pioneer among electric vehicle (EV) manufacturers. After making considerable headway over the last several years, the company is now laser-focused on what it expects will be its next chapter -- autonomous driving technology.

CEO Elon Musk is so compelled by the potential of autonomous driving that during the company's latest earnings call, he said: "I recommend anyone who doesn't believe that Tesla will solve vehicle autonomy should not hold Tesla stock. They should sell their Tesla stock. If you believe Tesla will solve autonomy, you should buy Tesla stock."

That's a polarizing statement, but those are pretty much par for the course with Musk. However, he isn't the only one with a strong conviction that autonomous driving is an enormous opportunity.

In Wood's latest model, she is projecting that subscriptions to Tesla's autonomous driving software -- dubbed full-self driving (FSD) -- will be the major catalyst for the company's future growth. In essence, FSD represents a source of high-margin recurring revenue for its EV operation.

Moreover, if the company ends up as the superior developer of self-driving technology , Tesla will have an enormous opportunity to disrupt areas such as rental car businesses, delivery services, and ride-hailing applications.

But while I understand the opportunities FSD presents, I think Wood is underestimating Tesla's long-run potential.

Image Source: Getty Images

...what about robotics and energy storage?

Autonomous driving is merely an extension of the EV business. But on a deeper level, FSD is an advanced application of artificial intelligence (AI). While Musk and his team remain steadfast in its FSD pursuits, Tesla has far more ambitions in the broader AI realm. In particular, it is developing a line of humanoid robots called Optimus. The idea is that it could enhance its efficiency and productivity by augmenting its human workforce with Optimus robots.

Should this effort prove successful, Tesla would have an incredibly lucrative opportunity to commercialize Optimus and sell robots to other companies. This could revolutionize the labor market -- and Wood does not seem to be accounting for that potential in her model.

The main reason why Wood isn't banking much on Optimus at the moment is that she believes commercialization of the robots is more than five years away. However, at the risk of being overly Pollyanna-ish, I think Wood may want to rethink that position. On Tesla's second-quarter earnings call , Musk said "we expect to have several thousand Optimus robots produced and doing useful things by the end of next year in the Tesla factories." He further predicted that in 2026 the company would be "ramping up production quite a bit" and selling them to external customers.

Beyond its AI initiatives, Tesla has already built a thriving energy storage business that I think is criminally underappreciated.

The table below breaks down the financial results for Tesla's energy generation and storage segment over the last several quarters.

Data source: Tesla.

Tesla has doubled its revenues from the energy storage business over the last year. But even more impressive, it has achieved strong unit economics, underpinned by a consistently expanding gross margin profile.

These dynamics are important because the growth in the energy storage segment is helping offset the stagnation and decline of its core EV business. Moreover, because it's generating strong and growing profits from its energy generation unit, Tesla is still able to invest in important growth initiatives -- particularly in AI.

Looking at Tesla's valuation

One thing that Wall Street analysts can't seem to agree on is Tesla's valuation. And on the surface, I totally get it. Its market capitalization is currently around $700 billion -- nearly 16 times that of Ford and 14 times the size of General Motors . Fundamentally, that doesn't really check out.

However, like Wood and Musk, I see Tesla as much more than a car company. Investors need to look at the different components of its overall operation and analyze their valuations individually. This is commonly referred to as a sum-of-the-parts valuation.

Beyond its EV segment, the company is making inroads in two areas with strong secular tailwinds : AI and sustainable energy. Admittedly, it has yet to really monetize its AI products at scale. For that reason, I understand if bearish investors remain skeptical. But as a Tesla shareholder for many years, I have a strong belief that the company will continue making progress toward FSD. Moreover, I think success in FSD will lead to a surge of demand as the company attempts to differentiate its fleet from the competition.

In addition, I'm compelled by the case around Optimus and am aligned with Musk on the premise that these robots will be commercialized sooner rather than later. Lastly, the energy storage business is growing faster than any other part of the company, and I don't see demand in that area slowing down anytime soon.

Whether or not the stock reaches Wood's price target on her forecast schedule is moot. In the long run, Tesla has a lot of opportunities to disrupt several more markets other than EVs, and I am bullish about its prospects for executing on its vision. For these reasons, I think Tesla is a strong buy for investors with a long-term time horizon.

Adam Spatacco has positions in Tesla. The Motley Fool has positions in and recommends Tesla. The Motley Fool recommends General Motors and recommends the following options: long January 2025 $25 calls on General Motors. The Motley Fool has a disclosure policy .

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12 Best Artificial Intelligence Topics for Research in 2024

Explore the "12 Best Artificial Intelligence Topics for Research in 2024." Dive into the top AI research areas, including Natural Language Processing, Computer Vision, Reinforcement Learning, Explainable AI (XAI), AI in Healthcare, Autonomous Vehicles, and AI Ethics and Bias. Stay ahead of the curve and make informed choices for your AI research endeavours.

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Table of Contents  

1) Top Artificial Intelligence Topics for Research 

     a) Natural Language Processing 

     b) Computer vision 

     c) Reinforcement Learning 

     d) Explainable AI (XAI) 

     e) Generative Adversarial Networks (GANs) 

     f) Robotics and AI 

     g) AI in healthcare 

     h) AI for social good 

     i) Autonomous vehicles 

     j) AI ethics and bias 

2) Conclusion 

Top Artificial Intelligence Topics for Research   

This section of the blog will expand on some of the best Artificial Intelligence Topics for research.

Natural Language Processing   

Natural Language Processing (NLP) is centred around empowering machines to comprehend, interpret, and even generate human language. Within this domain, three distinctive research avenues beckon: 

1) Sentiment analysis: This entails the study of methodologies to decipher and discern emotions encapsulated within textual content. Understanding sentiments is pivotal in applications ranging from brand perception analysis to social media insights. 

2) Language generation: Generating coherent and contextually apt text is an ongoing pursuit. Investigating mechanisms that allow machines to produce human-like narratives and responses holds immense potential across sectors. 

3) Question answering systems: Constructing systems that can grasp the nuances of natural language questions and provide accurate, coherent responses is a cornerstone of NLP research. This facet has implications for knowledge dissemination, customer support, and more. 

Computer Vision   

Computer Vision, a discipline that bestows machines with the ability to interpret visual data, is replete with intriguing avenues for research: 

1) Object detection and tracking: The development of algorithms capable of identifying and tracking objects within images and videos finds relevance in surveillance, automotive safety, and beyond. 

2) Image captioning: Bridging the gap between visual and textual comprehension, this research area focuses on generating descriptive captions for images, catering to visually impaired individuals and enhancing multimedia indexing. 

3) Facial recognition: Advancements in facial recognition technology hold implications for security, personalisation, and accessibility, necessitating ongoing research into accuracy and ethical considerations. 

Reinforcement Learning   

Reinforcement Learning revolves around training agents to make sequential decisions in order to maximise rewards. Within this realm, three prominent Artificial Intelligence Topics emerge: 

1) Autonomous agents: Crafting AI agents that exhibit decision-making prowess in dynamic environments paves the way for applications like autonomous robotics and adaptive systems. 

2) Deep Q-Networks (DQN): Deep Q-Networks, a class of reinforcement learning algorithms, remain under active research for refining value-based decision-making in complex scenarios. 

3) Policy gradient methods: These methods, aiming to optimise policies directly, play a crucial role in fine-tuning decision-making processes across domains like gaming, finance, and robotics.  

Explainable AI (XAI)   

The pursuit of Explainable AI seeks to demystify the decision-making processes of AI systems. This area comprises Artificial Intelligence Topics such as: 

1) Model interpretability: Unravelling the inner workings of complex models to elucidate the factors influencing their outputs, thus fostering transparency and accountability. 

2) Visualising neural networks: Transforming abstract neural network structures into visual representations aids in comprehending their functionality and behaviour. 

3) Rule-based systems: Augmenting AI decision-making with interpretable, rule-based systems holds promise in domains requiring logical explanations for actions taken. 

Generative Adversarial Networks (GANs)   

The captivating world of Generative Adversarial Networks (GANs) unfolds through the interplay of generator and discriminator networks, birthing remarkable research avenues: 

1) Image generation: Crafting realistic images from random noise showcases the creative potential of GANs, with applications spanning art, design, and data augmentation. 

2) Style transfer: Enabling the transfer of artistic styles between images, merging creativity and technology to yield visually captivating results. 

3) Anomaly detection: GANs find utility in identifying anomalies within datasets, bolstering fraud detection, quality control, and anomaly-sensitive industries. 

Robotics and AI   

The synergy between Robotics and AI is a fertile ground for exploration, with Artificial Intelligence Topics such as: 

1) Human-robot collaboration: Research in this arena strives to establish harmonious collaboration between humans and robots, augmenting industry productivity and efficiency. 

2) Robot learning: By enabling robots to learn and adapt from their experiences, Researchers foster robots' autonomy and the ability to handle diverse tasks. 

3) Ethical considerations: Delving into the ethical implications surrounding AI-powered robots helps establish responsible guidelines for their deployment. 

AI in healthcare   

AI presents a transformative potential within healthcare, spurring research into: 

1) Medical diagnosis: AI aids in accurately diagnosing medical conditions, revolutionising early detection and patient care. 

2) Drug discovery: Leveraging AI for drug discovery expedites the identification of potential candidates, accelerating the development of new treatments. 

3) Personalised treatment: Tailoring medical interventions to individual patient profiles enhances treatment outcomes and patient well-being. 

AI for social good   

Harnessing the prowess of AI for Social Good entails addressing pressing global challenges: 

1) Environmental monitoring: AI-powered solutions facilitate real-time monitoring of ecological changes, supporting conservation and sustainable practices. 

2) Disaster response: Research in this area bolsters disaster response efforts by employing AI to analyse data and optimise resource allocation. 

3) Poverty alleviation: Researchers contribute to humanitarian efforts and socioeconomic equality by devising AI solutions to tackle poverty. 

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Autonomous vehicles   

Autonomous Vehicles represent a realm brimming with potential and complexities, necessitating research in Artificial Intelligence Topics such as: 

1) Sensor fusion: Integrating data from diverse sensors enhances perception accuracy, which is essential for safe autonomous navigation. 

2) Path planning: Developing advanced algorithms for path planning ensures optimal routes while adhering to safety protocols. 

3) Safety and ethics: Ethical considerations, such as programming vehicles to make difficult decisions in potential accident scenarios, require meticulous research and deliberation. 

AI ethics and bias   

Ethical underpinnings in AI drive research efforts in these directions: 

1) Fairness in AI: Ensuring AI systems remain impartial and unbiased across diverse demographic groups. 

2) Bias detection and mitigation: Identifying and rectifying biases present within AI models guarantees equitable outcomes. 

3) Ethical decision-making: Developing frameworks that imbue AI with ethical decision-making capabilities aligns technology with societal values. 

Future of AI  

The vanguard of AI beckons Researchers to explore these horizons: 

1) Artificial General Intelligence (AGI): Speculating on the potential emergence of AI systems capable of emulating human-like intelligence opens dialogues on the implications and challenges. 

2) AI and creativity: Probing the interface between AI and creative domains, such as art and music, unveils the coalescence of human ingenuity and technological prowess. 

3) Ethical and regulatory challenges: Researching the ethical dilemmas and regulatory frameworks underpinning AI's evolution fortifies responsible innovation. 

AI and education   

The intersection of AI and Education opens doors to innovative learning paradigms: 

1) Personalised learning: Developing AI systems that adapt educational content to individual learning styles and paces. 

2) Intelligent tutoring systems: Creating AI-driven tutoring systems that provide targeted support to students. 

3) Educational data mining: Applying AI to analyse educational data for insights into learning patterns and trends. 

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Conclusion  

The domain of AI is ever-expanding, rich with intriguing topics about Artificial Intelligence that beckon Researchers to explore, question, and innovate. Through the pursuit of these twelve diverse Artificial Intelligence Topics, we pave the way for not only technological advancement but also a deeper understanding of the societal impact of AI. By delving into these realms, Researchers stand poised to shape the trajectory of AI, ensuring it remains a force for progress, empowerment, and positive transformation in our world. 

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163 Unique Artificial Intelligence Topics For Your Dissertation

The artificial intelligence industry is an industry of the future, but it’s also a course many students find difficult to write about. According to some students, the main reason is that there are many research topics on artificial intelligence. Several topics are already covered, and they claim not to know what to write about.

However, one of the interesting things about writing a dissertation or thesis is that you don’t need to be the number one author of an idea. It would be best if you write about the idea from a unique perspective instead. Writing from a unique perspective also means coupling your ideas with original research, giving your long essay quality and value to your professors and other students who may want to cover the same topic in the future.

This blog post will cover basic advanced AI topics and interesting ones for your next research paper or debate. This will help prepare you for your next long essay or presentation.

What is Artificial Intelligence?

Artificial Intelligence (AI) is the concept that enables humans to perform their tasks more smartly and faster through automated systems. AI is human intelligence packed in machines.

AI facilitates several computer systems such as voice recognition, machine vision, natural language processing, robotics engineering, and many others. All these systems revolutionize how work is done in today’s world.

Now that you know what artificial intelligence is, here are some advanced AI topics for your college research.

Writing Tips to Create a Good Thesis or Dissertation

Every student wants to create the best thesis and dissertation in their class. The first step to creating or researching the perfect dissertation is to write a great thesis. What are the things to be on the lookout for?

• Create a Strong Thesis Statement You need this to have a concise approach to your research. Your thesis statement should, therefore, be specific, precise, factual, debatable, and logical enough to be an assertive point. Afterwards, the only way to create a competitive dissertation is to draw from existing research in journals and other sources.
• Strong Arguments You can create a good dissertation if you have strong arguments. Your arguments must be backed by reputed sources such as academics, government, reputed media organizations, or statistic-oriented websites. All these make your arguments recognizable and accepted.
• Well Organized and Logically Structured Your dissertation has different subsections, including an abstract, thesis statement, background to the study, chapters (where your body is), and concluding arguments. If you’ve embarked on quantitative data analysis, you must report the data you got and what it means for your discourse. You can even add recommendations for future research. The information you want to convey must be well structured to improve its reception by your university professors.
• Concise and Free of Errors Your essay must also be straightforward. Your ideas must not be complex to understand, and you must always explain ambiguous industry terms. Revising your draft to check for grammatical errors several times is also important. Editing can be difficult, but it’s integral to determining whether your professors will love your dissertation or otherwise.

Artificial Intelligence Research Topics

Artificial intelligence is here to stay in several industries and sectors worldwide. It is the technology of the present and the future, and here are some AI topics to write about:

• How will artificial intelligence contribute to the flight to Mars?
• Machine learning and the challenges it poses to scientists
• How can retail stores maximize machine learning?
• Expatiate on what is meant by deep learning
• General AI and Narrow AI: what does it mean?
• AI changes the world: a case study of the gambling industry
• AI improved business: a case study of SaaS industries
• AI in homes: how smart homes change how humans live
• The critical challenges scientists have not yet solved with AI
• How students can contribute to both research and development of AI systems
• Is automation the way forward for the interconnected world: an overview of the ethical issues in AI
• How does cybernetics connect with AI?
• How do artificial intelligence systems manifest in healthcare?
• A case for artificial intelligence in how it facilitates the use of data in the criminal department
• What are the innovations in the vision system applications
• The inductive logic program: meaning and origin
• Brain simulation and AI: right or wrong
• How to maximize AI in Big data
• How AI can increase cybersecurity threat
• AI in companies: a case study of Telegram

Hot Topics in Artificial Intelligence

If you’d love to be one of the few who will cover hot topics in AI, researching some sub-sectors could be a way to go. There are several subsections of AI, some of which are hot AI topics causing several arguments among scholars and moralists today. Some of these are:

• How natural language is generated and how AI maximizes it
• Speech recognition: a case study of Alexa and how it works
• How AI makes its decisions
• What are known as virtual agents?
• Key deep learning platforms for governments
• Text analytics and the future of text-to-speech systems
• How marketing automation works
• Do robots operate based on rules?
• AI and emotion recognition
• AI and the future of biometrics
• AI in content creation
• AI and how data is used to create social media addiction
• What can be considered core problems with AI?
• What do five pieces of literature say about AI taking over the world?
• How does AI help with predictive sales?
• Motion planning and how AI is used in video editing
• Distinguish between data science vs. artificial intelligence
• Account for five failed AI experiments in the past decade
• The world from the machine’s view
• Project management systems from the machine’s view

Artificial Intelligence Topics for Presentation

Students are sometimes fond of presentations to show knowledge or win debates. If you’re in a debate club and would love to add a presentation to your AI topics, here are topics in artificial intelligence for you.

You can even expand these for your artificial intelligence research paper topics:

• How AI has penetrated all industries
• The future of cloud technologies
• The future of AI in military equipment
• The evolution of AI in a security application
• Industrial robots: an account of Tesla’s factory
• Industrial robots: an account of Amazon’s factories
• An overview of deep generative models and what they mean
• What are the space travel ideas fueling the innovation of AI?
• What is amortized inference?
• Examine the Monte Carlo methods in AI
• How technology has improved maps
• Comment on how AI is used to find fresh craters on the moon
• Comment on two previous papers from your professor about AI

AI Research Topics

If you’d like to take a general perspective on AI, here are some topics in AI to discuss amongst your friends or for your next essay:

• Are robots a threat to human jobs?
• How automation has changed the world since 2020
• Would you say Tesla produces robot cars?
• What are the basic violations of artificial intelligence?
• Account for the evolution of AI models
• Weapon systems and the future of weaponry
• Account for the interaction between machines and humans
• Basic principles of AI risk management
• How AI protects people against spam
• Can AI predict election results?
• What are the limits of AI?
• Detailed reports on image recognition algorithms in two companies of your choice
• How is AI used in customer service?
• Telehealth and its significance
• Can AI help predict the future?
• How to measure water quality and cleanness through AI
• Analyze the technology used for the Breathometer products
• Key trends in AI and robotics research and development
• How AI helps with fraud detection in a bank of your choice
• How AI helps the academic industry.

Argument Debate Topics in AI

You’d expect controversial topics in AI, and here are some of them. These are topics for friendly debates in class or topics to start a conversation with industry leaders:

• Will humans end all work when AI replaces them?
• Who is liable for AI’s misdoing?
• AI is smarter than humans: can it be controlled?
• Machines will affect human interactions: discuss
• AI bias exists and is here to stay
• Artificial Intelligence cannot be humanized even if it understands emotions
• New wealth and AI: how will it be distributed?
• Can humans prevent AI bias?
• Can AI be protected from hackers?
• What will happen with the unintended consequences of using AI?

Computer Science AI Topics

Every computer science student also needs AI topics for research papers, presentations or scientific thesis . Whatever it is, here are some helpful ideas:

• AI and machine learning: how does it help healthcare systems?
• What does hierarchical deep learning neural network mean
• AI in architecture and engineering: explain
• Can robots safely perform surgery?
• Can robots help with teaching?
• Recent trends in machine learning
• Recent trends in big data that will affect the future of the internet of things
• How does AI contribute to the excavation management Industry?
• Can AI help spot drug distribution?
• AI and imaging system: Trends since 1990
• Explain five pieces of literature on how AI can be contained
• Discuss how AI reduced the escalation of COVID-19
• How can natural language processing help interpret sign languages?
• Review a recent book about AI and cybersecurity
• Discuss the key discoveries from a recent popular seminar on AI and cybercrime
• What does Stephen Hawking think about AI?
• How did AI make Tesla a possibility?
• How recommender systems work in the retail industry
• What is the artificial Internet of Things (A-IoT)?
• Explain the intricacies of enhanced AI in the pharmaceutical industry

AI Ethics Topics

There are always argumentative debate topics on AI, especially on the ethical and moral components. Here are a few ethical topics in artificial intelligence to discuss:

• Is AI the end of all jobs?
• Is artificial intelligence in concert with patent law?
• Do humans understand machines?
• What happens when robots gain self-control?
• Can machines make catastrophic mistakes?
• What happens when AI reads minds and executes actions even if they’re violent?
• What can be done about racist robots?
• Comments on how science can mediate human-machine interactions
• What does Google CEO mean when he said AI would be the world’s saviour?
• What are robots’ rights?
• How does power balance shift with a rise in AI development?
• How can human privacy be assured when robots are used as police?
• What is morality for AI?
• Can AI affect the environment?
• Discuss ways to keep robots safe from enemies.

AI Essay Topics Technology

Technology is already intertwined with AI, but you may need hot AI topics that focus on the tech side of the innovation. Here are 20 custom topics for you:

• How can we understand autonomous driving?
• Pros and cons of artificial intelligence to the world?
• How does modern science interact with AI?
• Account for the scandalous innovations in AI in the 21st century
• Account for the most destructive robots ever built
• Review a documentary on AI
• Review three books or journals that express AI as a threat to humans and draw conclusions based on your thoughts
• What do non-experts think about AI?
• Discuss the most ingenious robots developed in the past decade
• Can the robotic population replace human significance?
• Is it possible to be ruled by robots?
• What would world domination look like: from the machine perspective
• He who controls AI controls the world: discuss
• Key areas in AI engineering that man must control
• How Apple is using AI for its products
• Would you say AI is a positive or negative invention?
• AI and video gaming: how it changed the arcade Industry
• Would you say eSports is toxic?
• How AI helps in the hospitality industry
• AI and its use in sustainable energy.

Interesting Topics in AI

There are interesting ways to look at the subject of AI in today’s world. Here are some good research topics for AI to answer some questions:

• AI can be toxic: Should a high school student pursue a career in artificial intelligence?
• Prediction vs. judgment: experimenting with AI
• What makes AI know what’s right or wrong?
• Human judgment in AI: explain
• Effects of AI on businesses
• Will AI play critical roles in human future affairs?
• Tech devices and AI
• Search application and AI: account for how AI maximizes programming languages
• The history of artificial intelligence
• How AI impacts market design
• Data management and AI: discuss
• How can AI influence the future of computing
• How AI has changed the video viewing industry
• How can AI contribute to the global economy?
• How smart would you say artificial intelligence is?

Graduate AI NLP Research Topics

NLP (Natural Language Processing) is the aspect of artificial intelligence or computer science that deals with the ability of machines to understand spoken words and simplify them as humans can. It’s as simple as saying NLP is how computers understand human language.

If you’d like to focus your research topics on artificial intelligence on NLP, here are some topics for you:

• How did natural language processing help with Twitter Space discussions?
• How language is essential for regulatory and legal texts
• NLP in the eCommerce industry: top trends
• How NLP is used in language modelling and occlusion
• How does AI manoeuvre semantic analysis in natural language processing?
• History and top trends in NLP conference video call apps
• Text mining techniques and the role of NLP
• How physicians detected stroke since 2020 through NLP of radiology results
• How does big data contribute to understanding medical acronyms in the NLP section of AI?
• What does applied natural language processing mean in the mental health world?

Get Thesis Help Today

These 163 custom artificial intelligence topics are carefully selected and written by dissertation writers to help you prepare a quality dissertation. However, there are instances where you may be unable to come up with a quality dissertation by yourself.

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• Artificial Intelligence Thesis [List of Top 10 Tools]

Artificial intelligence is the technology where humans’ intelligence is replicated by the supercomputers in the network . Artificial intelligence is often called AI. It is the main branch of computer science to stimulate smart devices with human analytical behaviours.

“This article is completely contented with the interesting concepts related to doing the artificial intelligence thesis”

At the end of this article, you can able to do your thesis by learning the AI concepts ranging from basic to advance . This would be possible by paying your kind attention throughout the article. Generally, artificial intelligence is an emerging technology and it cannot be replaced by any other technology. So it has so many areas to explore. Let us begin this handout with an overview of artificial intelligence.

What is Artificial Intelligence?

• Artificial intelligence is imitating human behaviors to perform
• They perform utilizing data manipulation, problem-solving, reasoning & learning
• They permit human-computer interactions & enhances the processes

This is the overview of artificial intelligence . Researchers in the world are supposing to improve artificial intelligence technology by the way of understanding our emotions and sentiments to respond logically. Here, we thought that it would be nice to list the application areas of artificial intelligence to make you understand . Are you interested to step into the next section? Come on guys let us sail with the flow of the article.

What are the Applications of Artificial Intelligence?

• Facial Recognition
• Video & Photo Influences
• Image Processing , Computer vision & Virtualization
• Artificial Inventiveness
• Speech Recognition
• Handwriting / Text Recognition
• Optical Feature Recognition

The above listed are the technical application of artificial intelligence . On the other hand, in day-to-day life, it is also giving their impacts. Some of the examples of artificial intelligence application, in reality , are mentioned below,

• Security Surveillance Systems
• Demand & Supply Forecasting
• Automated Mechanisms
• Digital Buyer Support & Robotic Responders
• Smart E-mail Classifiers
• E-mail, Message & Call Spam Filters

The listed above are the technical & real-time application areas of artificial intelligence in modeling and simulation . On the other hand, artificial intelligence is pillared by some of the other technologies and they are otherwise known as the main themes or topics of AI . Yes, guys, we are going to let you know about the main topics that are involved in artificial intelligence for the ease of your understanding . Shall we move on to that section? Come let’s learn together.

What are the Main Topics in Artificial Intelligence?

• Machine Learning
• Automated Programs
• Computer Vision
• Natural Language Processing
• Planning & Reasoning
• Problem Solving

The above listed are the major topics that get involved in artificial intelligence so far. Moreover, it can be stated that it is the technologies and processes are enriched by the application of artificial intelligence concepts.

As this article is focused on giving the facts about the artificial intelligence thesis , we first wanted to let you know about the list of top 10 frameworks and tools for the ease of your understanding. Our researchers in the institute are very much familiar with the foregoing areas. As proficiency, it reveals our capacities. Let us start to learn about the tools and frameworks with their features for your better understanding .

List of 10 Tools in AI

• Torch Description
• Torch is a kind of programming language & a scientific computation tool
• Torch Features
• Large bio network with developer communities
• Linear algebra techniques
• Numerical optimization procedures
• Neural network & energy models
• Lua program based C user interfaces
• Multi-layer segmenting, normalizing & transferring
• Sound N-dimensional ranges
• Efficient graphic processing unit
• NET Description
• net is the machine learning & .NET oriented commercial AI toolkit
• It has various numbers of libraries for audio & image preprocessing
• It is suggested for the large scale industries as it has the high capacity
• NET Features
• Audio signals parsing, filtering, saving, and loading
• Signal application in spatial domain & frequency
• Clustering technique application in arbitrary data inputs
• DT, LR & SVM based classification
• AutoML Description
• AutoML is a machine learning & Google based AI tool
• It has dynamic and effective features to handle the massive inputs
• AutoML Features
• Great performance with high accuracy levels
• Effective graphical user interfaces
• Fast and easy tool configurations
• Lenient operational ML model training
• Model developments & evaluations

4.Microsoft CNTK

• Microsoft CNTK Description
• CNTK refers to the Computational Network Toolkit
• CNTK is the Microsoft and deep learning oriented toolkit
• Neural networks computational based graphs are described by CNTK
• It has similarities among the various devices & graphical processing units
• Microsoft CNTK Features
• Dynamic adaption regards input formats (audio, text, video) & ideas
• Superlative performance & complex task management
• Fast & précised training of the models/systems
• MXNET is the deep learning-based application framework
• It has notable features like lightweight, large scalability & flexibility
• It also trains the models in a fast manner
• In addition, it is compatible with numerous programming languages
• Computer vision & NLP based libraries and tools
• SVM deep learning-based compilers are used to test programs by test running
• High scalability in supporting the graphic processing units & devices
• Multi-host training & GPU is differentiated by the MXNET features
• Symbolic & gluon’s eager imperative modes with hybrid front end transitions
• They are compatible with the R, Java, C ++, Clojure, Scala & Julia languages
• Keras is the neural network & an open-source library
• They are capable of running upper on the Tensor flow & Theano
• It has an efficient neural network’s API & focused to offer fast empiricism
• It is a good suit in both GPU & CPU and RNN & CNN
• Effortless debugging & expansions by python codes
• Independent modules with complete configurations
• Effortless integration of regularization, activation, initialization & optimization
• Minimized reasoning loads & curtails the chances for common use case tries
• It is mainly designed for humans whereas others are designed for machines
• Enhances the user experiences & allows module extensive possibilities
• It is a kind of python allied library for estimating numerical expression
• Optimizes and defines the multi-dimensional arrays statistical expressions too
• They endowing the scientifically based empiricism
• In addition, it can integrate linear algebra with its compilers
• As well as minimizes the analytical overhead & assimilations
• It is possible to minimize the same even over symbolic features variation
• Symbol based distinctions by evaluating derivatives
• Numpy arrays integration with Theano
• Fast evaluation of the expressions by C code creations
• Translucent & high-speed graphic processing units
• Caffe refers to Convolutional Architecture for Fast Feature Embedding
• It is an artificial intelligence-based development framework
• It is very thoughtful, modulations & speed in nature
• It is scripted in C++ & has python user interfaces
• Large developer communities based out from users hub & Github
• Animated and innovated architecture & coding-free configurations
• Speed processes & implementations in image processing
• Concurrent development in the codes and state of models

9.Tensor Flow

• Tensor Flow Description
• Tensor flow is an open-source AI & machine learning-based framework
• It is meant to perform the statistical evaluations
• In addition, it has simplified architecture & simple deployment procedures
• It is subject to habitual product updates & points issues faced by developers
• Tensor Flow Features
• Manages and controls networks utilizing allowing developers in few areas
• Programming with easy syntaxes & reduces the time for distribution
• Permits the users to run various programs simultaneously from other servers
• It is compatible with the influential programs & experiments
• Resilient output production & simplified deployments

10.Scikit Learn

• Scikit learn is a machine learning-based open-source AI toolkit
• It has the graphical user interfaces which are based out from python
• In addition, it deals with unsupervised & supervised methodologies
• It is mainly distributed with the Linux operating systems
• It can be used for both academic & commercial purposes
• It is presented with the supervised models & methods
• It suppresses the visualized attributes dimensionalities
• Experimentation of dataset properties & test datasets
• Selection of complete attributes & supervised models generation
• Unlabeled data can be clustered & cross-validated performance

Before installing the scikit tool consider the following aspects,

• Complete 2D or 3D plotting
• Structural design & analysis of the data
• N-dimensional array or ranges
• Emblematic statistics
• Scientific computing
• Communicative consoles

In the above-listed areas, we have been used some of the terms as acronyms. Hence, we wanted to list out those expansions here for the ease of your understanding.

• DT- Decision Trees
• SVM- Support Vector Machine
• TVM- Tensor Virtual Machine
• GPU- Graphics Processing Units
• SGD- Stochastic Gradient Descent
• BPA- Back Propagation Algorithm
• RNN- Recurrent Neural Network
• CNN- Convolutional Neural Network

The aforesaid are major and top 10 tools & frameworks aided with artificial intelligence. As of now, we have come up with the overview, application areas, real-time examples , and the top 10 tools and frameworks used for artificial intelligence with brief explanations. So that, we hope you have understood the concepts as of now listed. If you do have any doubts about the above-listed areas you are always welcomed to have our opinions at any time.

Before going to the next phase, we would like to state about our researchers and technicians . In a matter of fact, our technical team does have unique methodologies and techniques for the artificial intelligence base thesis, proposals, projects, and researches . As a point of fact, every work related to the researches is being examined through various quality checks. If you work with us! You might get wonder about our skills. We are a company with 40+ expert researchers who can help the students throughout the research or thesis proposed.

As this article is titled with the artificial intelligence thesis ideas , we felt that it would be the right time to state about the same. Yes, you people guessed right here we are going to mention to you what makes a thesis good. Are you ready to know about that? Come on guys let us we have the section with crisp contents.

What makes Good Thesis Writing?

• Clear & succinct statements of the main theme, research purpose & paper argument
• Relevant thesis statements & discussions according to the selected topics
• Concisely points out to the particular audience
• Closure arrangements of statements and basing it for introduction

These are the aspects that should present to make the thesis best comparing to others. Generally, best thesis writing needs experts’ advice. Besides you can have our experts’ pieces of advice in the needed areas and the areas in which you are struggling. We are delighted to guide the students in the fields of artificial intelligence thesis and so on. In this regard, let us discuss how should a thesis be developed for the ease of your understanding.

How Should a Thesis be developed?

• Introduction
• Literature reviews
• Problem findings
• Methodologies & techniques
• Discussion on outcomes
• Absolute conclusions

The above listed are the stages that are should be predetermined before framing your thesis writing . So far, we have discussed the artificial intelligence concepts that are needed to frame the effective thesis. We hope that you would have enjoyed the article completely. Do you interested to explore more about the artificial intelligence thesis? Then here is a suggestion, approach our technical team at any time (24/7).

Let’s inject your innovations & thought processes into the research development eras

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Before sit down to research proposal writing, we need to decide exact journals. For e.g. SCI, SCI-E, ISI, SCOPUS.

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We helping you with right and perfect topic selection, which sound interesting to the other fellows of your committee. For e.g. if your interest in networking, the research topic is VANET / MANET / any other

Literature Survey Writing

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Case Study Writing

After literature survey, we get the main issue/problem that your research topic will aim to resolve and elegant writing support to identify relevance of the issue.

Problem Statement

Based on the research gaps finding and importance of your research, we conclude the appropriate and specific problem statement.

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Writing a good research proposal has need of lot of time. We only span a few to cover all major aspects (reference papers collection, deficiency finding, drawing system architecture, highlights novelty)

MILESTONE 2: System Development

Fix implementation plan.

We prepare a clear project implementation plan that narrates your proposal in step-by step and it contains Software and OS specification. We recommend you very suitable tools/software that fit for your concept.

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Pseudocode Description

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Comparison/Experiments

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Graphs, Results, Analysis Table

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Project Deliverables

For every project order, we deliver the following: reference papers, source codes screenshots, project video, installation and running procedures.

MILESTONE 3: Paper Writing

Choosing right format.

We intend to write a paper in customized layout. If you are interesting in any specific journal, we ready to support you. Otherwise we prepare in IEEE transaction level.

Collecting Reliable Resources

Before paper writing, we collect reliable resources such as 50+ journal papers, magazines, news, encyclopedia (books), benchmark datasets, and online resources.

Writing Rough Draft

We create an outline of a paper at first and then writing under each heading and sub-headings. It consists of novel idea and resources

Proofreading & Formatting

We must proofread and formatting a paper to fix typesetting errors, and avoiding misspelled words, misplaced punctuation marks, and so on

Native English Writing

We check the communication of a paper by rewriting with native English writers who accomplish their English literature in University of Oxford.

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We examine the paper quality by top-experts who can easily fix the issues in journal paper writing and also confirm the level of journal paper (SCI, Scopus or Normal).

Plagiarism Checking

We at phdservices.org is 100% guarantee for original journal paper writing. We never use previously published works.

MILESTONE 4: Paper Publication

Finding apt journal.

We play crucial role in this step since this is very important for scholar’s future. Our experts will help you in choosing high Impact Factor (SJR) journals for publishing.

Lay Paper to Submit

We organize your paper for journal submission, which covers the preparation of Authors Biography, Cover Letter, Highlights of Novelty, and Suggested Reviewers.

Paper Submission

We upload paper with submit all prerequisites that are required in journal. We completely remove frustration in paper publishing.

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We track your paper status and answering the questions raise before review process and also we giving you frequent updates for your paper received from journal.

Revising Paper Precisely

When we receive decision for revising paper, we get ready to prepare the point-point response to address all reviewers query and resubmit it to catch final acceptance.

Get Accept & e-Proofing

We receive final mail for acceptance confirmation letter and editors send e-proofing and licensing to ensure the originality.

Publishing Paper

Paper published in online and we inform you with paper title, authors information, journal name volume, issue number, page number, and DOI link

MILESTONE 5: Thesis Writing

Identifying university format.

We pay special attention for your thesis writing and our 100+ thesis writers are proficient and clear in writing thesis for all university formats.

Gathering Adequate Resources

We collect primary and adequate resources for writing well-structured thesis using published research articles, 150+ reputed reference papers, writing plan, and so on.

Writing Thesis (Preliminary)

We write thesis in chapter-by-chapter without any empirical mistakes and we completely provide plagiarism-free thesis.

Skimming & Reading

Skimming involve reading the thesis and looking abstract, conclusions, sections, & sub-sections, paragraphs, sentences & words and writing thesis chorological order of papers.

Fixing Crosscutting Issues

This step is tricky when write thesis by amateurs. Proofreading and formatting is made by our world class thesis writers who avoid verbose, and brainstorming for significant writing.

Organize Thesis Chapters

We organize thesis chapters by completing the following: elaborate chapter, structuring chapters, flow of writing, citations correction, etc.

Writing Thesis (Final Version)

We attention to details of importance of thesis contribution, well-illustrated literature review, sharp and broad results and discussion and relevant applications study.

How PhDservices.org deal with significant issues ?

1. novel ideas.

Novelty is essential for a PhD degree. Our experts are bringing quality of being novel ideas in the particular research area. It can be only determined by after thorough literature search (state-of-the-art works published in IEEE, Springer, Elsevier, ACM, ScienceDirect, Inderscience, and so on). SCI and SCOPUS journals reviewers and editors will always demand “Novelty” for each publishing work. Our experts have in-depth knowledge in all major and sub-research fields to introduce New Methods and Ideas. MAKING NOVEL IDEAS IS THE ONLY WAY OF WINNING PHD.

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To improve the quality and originality of works, we are strictly avoiding plagiarism since plagiarism is not allowed and acceptable for any type journals (SCI, SCI-E, or Scopus) in editorial and reviewer point of view. We have software named as “Anti-Plagiarism Software” that examines the similarity score for documents with good accuracy. We consist of various plagiarism tools like Viper, Turnitin, Students and scholars can get your work in Zero Tolerance to Plagiarism. DONT WORRY ABOUT PHD, WE WILL TAKE CARE OF EVERYTHING.

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We intended to keep your personal and technical information in secret and it is a basic worry for all scholars.

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CONFIDENTIALITY AND PRIVACY OF INFORMATION HELD IS OF VITAL IMPORTANCE AT PHDSERVICES.ORG. WE HONEST FOR ALL CUSTOMERS.

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The latest sell-off may be the beginning of the end for the tech bubble, strategist says

• The AI-fueled tech bubble could be approaching its end date, according to Paul Dietrich.
• The market strategist pointed to similarities between the recent tech sell-off and the dot-com crash.
• The economy also looks poised to enter a downturn, which will likely fuel more downside, he said.

The recent bloodbath in stocks might mark the beginning of the end of the artificial intelligence-fueled craze among investors.

Paul Dietrich, chief investment strategist of B. Riley Wealth Portfolio advisors, has been warning for months of an impending recession and stock market crash, particularly as enthusiasm for AI bleeds out of the market.

In a note on Tuesday, Dietrich said that his thesis has been bolstered by the recent sell-off sparked by weak economic data, which pushed the Nasdaq Composite into correction territory.

He pointed to the similarities between the dot-com crash and the latest drop in the stock market. Apple , which lost 79% of its total market value in the early 2000s, has dropped 8% over the past month. Amazon , which lost 93% of its total value in the dot-com era, has plunged 18% over the last month.

The flow of "smart money" in the market also suggests more downside could be on the way for tech stocks, Dietrich noted. He pointed to large stock sales initiated by tech CEOs, like Jeff Bezos, whose total sales of Amazon stock have totaled $13.5 billion so far this year. Meta CEO Mark Zuckerberg has sold off around $1 billion in company stock, while Nvidia CEO Jensen Huang has sold nearly half a billion worth of company shares so far this summer, according to securities filings.

"These investors do not think their companies are bad investments; they merely believe the stock market is currently valuing them far above their worth," Dietrich said. "I feel sorry for many average investors still piling into the stock market chasing the Artificial Intelligence (AI) hype and other tech stocks when many of those founders are selling out."

The economy, meanwhile, looks poised to enter a recession, Dietrich said, posing more bad news for stocks. Historically, stocks have declined 36% when the economy enters a recession, even if the downturn proves to be mild, Dietrich has said in previous notes.

He pointed to a slew of indicators that could suggest a downturn is on its way. Markets are coming off of one of the longest bull markets of all time, he noted. Corporate earnings have been "spotty." Interest rates in the economy remain at their highest levels since 2001. Meanwhile, the economy has triggered multiple recession warnings with near-perfect track records, like the yield curve inverting , and the unemployment rate rising past a key threshold typically associated with recessions.

"What kind of evidence does one need to see that we are moving into a business cycle recession," Dietrich said. "Eventually, we will have another long-term bear market recession."

Though he didn't have an exact prediction for when a downturn could strike, Dietrich said the economy could start entering a mild recession by the end of the year. That could fuel as much as a 40% downside in the S&P 500, he predicted, pointing to historical losses in the stock market when the economy entered a recession.

Recession fears spiked last week after the job market was found to slow more than expected in the month of July, fueling concerns that the Fed may have made a mistake keeping interest rates too-high for too-long. Investors are ramping up bets for steep rate cuts and even an emergency rate cut by the end of the year, a move central bankers have typically only employed during times of extreme volatility.

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The STEM-designated M.S. program in Computer Science is designed to provide students with the knowledge and skills they need to succeed in research and industry. With a focus on both theoretical and applied aspects of the discipline, the program offers a core foundation as well as two specialized tracks: Artificial Intelligence and Software Development.

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Why Utica University

Taught by Utica University's highly accomplished computer science faculty, the M.S. program offers many opportunities for research and real-world learning.

Artificial Intelligence Track The Artificial Intelligence track is designed for students who want to explore the latest developments in machine learning, natural language processing, and more. Students will learn from experts in the field and gain hands-on experience working on real-world projects.

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Computer Science (M.S.) at Utica

Projected growth for computer and information research scientists through 2032, median annual salary for computer research scientists, career opportunities.

• Computer and Information Research Scientist
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• Systems Analyst
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• Computer and Information Systems Manager

Program Curriculum

Academic requirements (30 credit hours), core (9 credit hours).

CSC 603 - Theory of Computation (3) CSC 623 - Design and Analysis of Algorithms (3) CSC 633 - Advanced Programming Languages (3)

Specializations (12 credit hours)

Artificial Intelligence

CSC 607 - Artificial Intelligence (3) CSC 617 - Machine Learning (3) CSC 627 - Deep Learning (3) CSC 637 - Natural Language Processing (3)

Software Development

CSC 605 - Software Modeling and Architecture (3) CSC 615 - Software Testing (3) CSC 625 - Software Project Management (3) CSC 635 - Data Science for Software Development (3)

Electives (3 credits)

One of the following:

CSC 608 - Secure Software Development (3) CSC 618 - Information Retrieval (3) CSC 628 - Database Systems (3) CSC 638 - Compiler Design (3) CSC 648 - Formal Methods (3)

Thesis or Project (6 credits)

CSC 698 – Project (3,6) CSC 699 – Thesis (3,6)

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Applicants to the Master’s in Computer Science program, especially those with a baccalaureate degree outside computer science, may need to complete additional undergraduate coursework to prepare for the mathematical and technical aspects of graduate study in computer science.

Prerequisites for admissions include courses in:

• Probability/statistics
• Discrete Mathematics
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• Data Structures
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• Associate Professor of Computer Science

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How to Write a Better Thesis Statement Using AI (2023 Updated)

Table of contents

Meredith Sell

With the exceptions of poetry and fiction, every piece of writing needs a thesis statement. 

- Opinion pieces for the local newspaper? Yes. 

- An essay for a college class? You betcha.

- A book about China’s Ming Dynasty? Absolutely.

All of these pieces of writing need a thesis statement that sums up what they’re about and tells the reader what to expect, whether you’re making an argument, describing something in detail, or exploring ideas.

But how do you write a thesis statement? How do you even come up with one?

This step-by-step guide will show you exactly how — and help you make sure every thesis statement you write has all the parts needed to be clear, coherent, and complete.

Let’s start by making sure we understand what a thesis is (and what it’s not).

What Is a Thesis Statement?

A thesis statement is a one or two sentence long statement that concisely describes your paper’s subject, angle or position — and offers a preview of the evidence or argument your essay will present.

A thesis is not:

• An exclamation
• A simple fact

Think of your thesis as the road map for your essay. It briefly charts where you’ll start (subject), what you’ll cover (evidence/argument), and where you’ll land (position, angle). 

Writing a thesis early in your essay writing process can help you keep your writing focused, so you won’t get off-track describing something that has nothing to do with your central point. Your central point is your thesis, and the rest of your essay fleshes it out.

Get help writing your thesis statement with this FREE AI tool > Get help writing your thesis statement with this FREE AI tool >

Different Kinds of Papers Need Different Kinds of Theses

How you compose your thesis will depend on the type of essay you’re writing. For academic writing, there are three main kinds of essays:

• Persuasive, aka argumentative
• Expository, aka explanatory

A persuasive essay requires a thesis that clearly states the central stance of the paper , what the rest of the paper will argue in support of. 

Paper books are superior to ebooks when it comes to form, function, and overall reader experience.

An expository essay’s thesis sets up the paper’s focus and angle — the paper’s unique take, what in particular it will be describing and why . The why element gives the reader a reason to read; it tells the reader why the topic matters.

Understanding the functional design of physical books can help ebook designers create digital reading experiences that usher readers into literary worlds without technological difficulties.

A narrative essay is similar to that of an expository essay, but it may be less focused on tangible realities and more on intangibles of, for example, the human experience.

The books I’ve read over the years have shaped me, opening me up to worlds and ideas and ways of being that I would otherwise know nothing about.

As you prepare to craft your thesis, think through the goal of your paper. Are you making an argument? Describing the chemical properties of hydrogen? Exploring your relationship with the outdoors? What do you want the reader to take away from reading your piece?

Make note of your paper’s goal and then walk through our thesis-writing process.

Now that you practically have a PhD in theses, let’s learn how to write one:

How to Write (and Develop) a Strong Thesis

If developing a thesis is stressing you out, take heart — basically no one has a strong thesis right away. Developing a thesis is a multi-step process that takes time, thought, and perhaps most important of all: research . 

Tackle these steps one by one and you’ll soon have a thesis that’s rock-solid.

1. Identify your essay topic.

Are you writing about gardening? Sword etiquette? King Louis XIV?

With your assignment requirements in mind, pick out a topic (or two) and do some preliminary research . Read up on the basic facts of your topic. Identify a particular angle or focus that’s interesting to you. If you’re writing a persuasive essay, look for an aspect that people have contentious opinions on (and read our piece on persuasive essays to craft a compelling argument).

If your professor assigned a particular topic, you’ll still want to do some reading to make sure you know enough about the topic to pick your specific angle.

For those writing narrative essays involving personal experiences, you may need to do a combination of research and freewriting to explore the topic before honing in on what’s most compelling to you.

Once you have a clear idea of the topic and what interests you, go on to the next step.

2. Ask a research question.

You know what you’re going to write about, at least broadly. Now you just have to narrow in on an angle or focus appropriate to the length of your assignment. To do this, start by asking a question that probes deeper into your topic. 

This question may explore connections between causes and effects, the accuracy of an assumption you have, or a value judgment you’d like to investigate, among others.

For example, if you want to write about gardening for a persuasive essay and you’re interested in raised garden beds, your question could be:

What are the unique benefits of gardening in raised beds versus on the ground? Is one better than the other?

Or if you’re writing about sword etiquette for an expository essay , you could ask:

How did sword etiquette in Europe compare to samurai sword etiquette in Japan?

How does medieval sword etiquette influence modern fencing?

Kickstart your curiosity and come up with a handful of intriguing questions. Then pick the two most compelling to initially research (you’ll discard one later).

3. Answer the question tentatively.

You probably have an initial thought of what the answer to your research question is. Write that down in as specific terms as possible. This is your working thesis . 

Gardening in raised beds is preferable because you won’t accidentally awaken dormant weed seeds — and you can provide more fertile soil and protection from invasive species.

Medieval sword-fighting rituals are echoed in modern fencing etiquette.

Why is a working thesis helpful?

Both your research question and your working thesis will guide your research. It’s easy to start reading anything and everything related to your broad topic — but for a 4-, 10-, or even 20-page paper, you don’t need to know everything. You just need the relevant facts and enough context to accurately and clearly communicate to your reader.

Your working thesis will not be identical to your final thesis, because you don’t know that much just yet.

This brings us to our next step:

4. Research the question (and working thesis).

What do you need to find out in order to evaluate the strength of your thesis? What do you need to investigate to answer your research question more fully? 

Comb through authoritative, trustworthy sources to find that information. And keep detailed notes.

As you research, evaluate the strengths and weaknesses of your thesis — and see what other opposing or more nuanced theses exist. 

If you’re writing a persuasive essay, it may be helpful to organize information according to what does or does not support your thesis — or simply gather the information and see if it’s changing your mind. What new opinion do you have now that you’ve learned more about your topic and question? What discoveries have you made that discredit or support your initial thesis?

Raised garden beds prevent full maturity in certain plants — and are more prone to cold, heat, and drought.

If you’re writing an expository essay, use this research process to see if your initial idea holds up to the facts. And be on the lookout for other angles that would be more appropriate or interesting for your assignment.

Modern fencing doesn’t share many rituals with medieval swordplay.

With all this research under your belt, you can answer your research question in-depth — and you’ll have a clearer idea of whether or not your working thesis is anywhere near being accurate or arguable. What’s next?

5. Refine your thesis.

If you found that your working thesis was totally off-base, you’ll probably have to write a new one from scratch. 

For a persuasive essay , maybe you found a different opinion far more compelling than your initial take. For an expository essay , maybe your initial assumption was completely wrong — could you flip your thesis around and inform your readers of what you learned?

Use what you’ve learned to rewrite or revise your thesis to be more accurate, specific, and compelling.

Raised garden beds appeal to many gardeners for the semblance of control they offer over what will and will not grow, but they are also more prone to changes in weather and air temperature and may prevent certain plants from reaching full maturity. All of this makes raised beds the worse option for ambitious gardeners. 

While swordplay can be traced back through millennia, modern fencing has little in common with medieval combat where swordsmen fought to the death.

If you’ve been researching two separate questions and theses, now’s the time to evaluate which one is most interesting, compelling, or appropriate for your assignment. Did one thesis completely fall apart when faced with the facts? Did one fail to turn up any legitimate sources or studies? Choose the stronger question or the more interesting (revised) thesis, and discard the other.

6. Get help from AI

To make the process even easier, you can take advantage of Wordtune's generative AI capabilities to craft an effective thesis statement. You can take your current thesis statement and try the paraphrase tool to get suggestions for better ways of articulating it. WordTune will generate a set of related phrases, which you can select to help you refine your statement. You can also use Wordtune's suggestions to craft the thesis statement. Write your initial introduction sentence, then click '+' and select the explain suggestion. Browse through the suggestions until you have a statement that captures your idea perfectly.

Thesis Check: Look for These Three Elements

At this point, you should have a thesis that will set up an original, compelling essay, but before you set out to write that essay, make sure your thesis contains these three elements:

• Topic: Your thesis should clearly state the topic of your essay, whether swashbuckling pirates, raised garden beds, or methods of snow removal.
• Position or angle: Your thesis should zoom into the specific aspect of your topic that your essay will focus on, and briefly but boldly state your position or describe your angle.
• Summary of evidence and/or argument: In a concise phrase or two, your thesis should summarize the evidence and/or argument your essay will present, setting up your readers for what’s coming without giving everything away.

The challenge for you is communicating each of these elements in a sentence or two. But remember: Your thesis will come at the end of your intro, which will already have done some work to establish your topic and focus. Those aspects don’t need to be over explained in your thesis — just clearly mentioned and tied to your position and evidence.

Let’s look at our examples from earlier to see how they accomplish this:

Notice how:

• The topic is mentioned by name. 
• The position or angle is clearly stated. 
• The evidence or argument is set up, as well as the assumptions or opposing view that the essay will debunk.

Both theses prepare the reader for what’s coming in the rest of the essay: 

• An argument to show that raised beds are actually a poor option for gardeners who want to grow thriving, healthy, resilient plants.
• An exposition of modern fencing in comparison with medieval sword fighting that shows how different they are.

Examine your refined thesis. Are all three elements present? If any are missing, make any additions or clarifications needed to correct it.

It’s Essay-Writing Time!

Now that your thesis is ready to go, you have the rest of your essay to think about. With the work you’ve already done to develop your thesis, you should have an idea of what comes next — but if you need help forming your persuasive essay’s argument, we’ve got a blog for that.

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The impact of artificial intelligence on human society and bioethics

Michael cheng-tek tai.

Department of Medical Sociology and Social Work, College of Medicine, Chung Shan Medical University, Taichung, Taiwan

Artificial intelligence (AI), known by some as the industrial revolution (IR) 4.0, is going to change not only the way we do things, how we relate to others, but also what we know about ourselves. This article will first examine what AI is, discuss its impact on industrial, social, and economic changes on humankind in the 21 st century, and then propose a set of principles for AI bioethics. The IR1.0, the IR of the 18 th century, impelled a huge social change without directly complicating human relationships. Modern AI, however, has a tremendous impact on how we do things and also the ways we relate to one another. Facing this challenge, new principles of AI bioethics must be considered and developed to provide guidelines for the AI technology to observe so that the world will be benefited by the progress of this new intelligence.

W HAT IS ARTIFICIAL INTELLIGENCE ?

Artificial intelligence (AI) has many different definitions; some see it as the created technology that allows computers and machines to function intelligently. Some see it as the machine that replaces human labor to work for men a more effective and speedier result. Others see it as “a system” with the ability to correctly interpret external data, to learn from such data, and to use those learnings to achieve specific goals and tasks through flexible adaptation [ 1 ].

Despite the different definitions, the common understanding of AI is that it is associated with machines and computers to help humankind solve problems and facilitate working processes. In short, it is an intelligence designed by humans and demonstrated by machines. The term AI is used to describe these functions of human-made tool that emulates the “cognitive” abilities of the natural intelligence of human minds [ 2 ].

Along with the rapid development of cybernetic technology in recent years, AI has been seen almost in all our life circles, and some of that may no longer be regarded as AI because it is so common in daily life that we are much used to it such as optical character recognition or the Siri (speech interpretation and recognition interface) of information searching equipment on computer [ 3 ].

D IFFERENT TYPES OF ARTIFICIAL INTELLIGENCE

From the functions and abilities provided by AI, we can distinguish two different types. The first is weak AI, also known as narrow AI that is designed to perform a narrow task, such as facial recognition or Internet Siri search or self-driving car. Many currently existing systems that claim to use “AI” are likely operating as a weak AI focusing on a narrowly defined specific function. Although this weak AI seems to be helpful to human living, there are still some think weak AI could be dangerous because weak AI could cause disruptions in the electric grid or may damage nuclear power plants when malfunctioned.

The new development of the long-term goal of many researchers is to create strong AI or artificial general intelligence (AGI) which is the speculative intelligence of a machine that has the capacity to understand or learn any intelligent task human being can, thus assisting human to unravel the confronted problem. While narrow AI may outperform humans such as playing chess or solving equations, but its effect is still weak. AGI, however, could outperform humans at nearly every cognitive task.

Strong AI is a different perception of AI that it can be programmed to actually be a human mind, to be intelligent in whatever it is commanded to attempt, even to have perception, beliefs and other cognitive capacities that are normally only ascribed to humans [ 4 ].

In summary, we can see these different functions of AI [ 5 , 6 ]:

• Automation: What makes a system or process to function automatically
• Machine learning and vision: The science of getting a computer to act through deep learning to predict and analyze, and to see through a camera, analog-to-digital conversion and digital signal processing
• Natural language processing: The processing of human language by a computer program, such as spam detection and converting instantly a language to another to help humans communicate
• Robotics: A field of engineering focusing on the design and manufacturing of cyborgs, the so-called machine man. They are used to perform tasks for human's convenience or something too difficult or dangerous for human to perform and can operate without stopping such as in assembly lines
• Self-driving car: Use a combination of computer vision, image recognition amid deep learning to build automated control in a vehicle.

D O HUMAN-BEINGS REALLY NEED ARTIFICIAL INTELLIGENCE ?

Is AI really needed in human society? It depends. If human opts for a faster and effective way to complete their work and to work constantly without taking a break, yes, it is. However if humankind is satisfied with a natural way of living without excessive desires to conquer the order of nature, it is not. History tells us that human is always looking for something faster, easier, more effective, and convenient to finish the task they work on; therefore, the pressure for further development motivates humankind to look for a new and better way of doing things. Humankind as the homo-sapiens discovered that tools could facilitate many hardships for daily livings and through tools they invented, human could complete the work better, faster, smarter and more effectively. The invention to create new things becomes the incentive of human progress. We enjoy a much easier and more leisurely life today all because of the contribution of technology. The human society has been using the tools since the beginning of civilization, and human progress depends on it. The human kind living in the 21 st century did not have to work as hard as their forefathers in previous times because they have new machines to work for them. It is all good and should be all right for these AI but a warning came in early 20 th century as the human-technology kept developing that Aldous Huxley warned in his book Brave New World that human might step into a world in which we are creating a monster or a super human with the development of genetic technology.

Besides, up-to-dated AI is breaking into healthcare industry too by assisting doctors to diagnose, finding the sources of diseases, suggesting various ways of treatment performing surgery and also predicting if the illness is life-threatening [ 7 ]. A recent study by surgeons at the Children's National Medical Center in Washington successfully demonstrated surgery with an autonomous robot. The team supervised the robot to perform soft-tissue surgery, stitch together a pig's bowel, and the robot finished the job better than a human surgeon, the team claimed [ 8 , 9 ]. It demonstrates robotically-assisted surgery can overcome the limitations of pre-existing minimally-invasive surgical procedures and to enhance the capacities of surgeons performing open surgery.

Above all, we see the high-profile examples of AI including autonomous vehicles (such as drones and self-driving cars), medical diagnosis, creating art, playing games (such as Chess or Go), search engines (such as Google search), online assistants (such as Siri), image recognition in photographs, spam filtering, predicting flight delays…etc. All these have made human life much easier and convenient that we are so used to them and take them for granted. AI has become indispensable, although it is not absolutely needed without it our world will be in chaos in many ways today.

T HE IMPACT OF ARTIFICIAL INTELLIGENCE ON HUMAN SOCIETY

Negative impact.

Questions have been asked: With the progressive development of AI, human labor will no longer be needed as everything can be done mechanically. Will humans become lazier and eventually degrade to the stage that we return to our primitive form of being? The process of evolution takes eons to develop, so we will not notice the backsliding of humankind. However how about if the AI becomes so powerful that it can program itself to be in charge and disobey the order given by its master, the humankind?

Let us see the negative impact the AI will have on human society [ 10 , 11 ]:

• A huge social change that disrupts the way we live in the human community will occur. Humankind has to be industrious to make their living, but with the service of AI, we can just program the machine to do a thing for us without even lifting a tool. Human closeness will be gradually diminishing as AI will replace the need for people to meet face to face for idea exchange. AI will stand in between people as the personal gathering will no longer be needed for communication
• Unemployment is the next because many works will be replaced by machinery. Today, many automobile assembly lines have been filled with machineries and robots, forcing traditional workers to lose their jobs. Even in supermarket, the store clerks will not be needed anymore as the digital device can take over human labor
• Wealth inequality will be created as the investors of AI will take up the major share of the earnings. The gap between the rich and the poor will be widened. The so-called “M” shape wealth distribution will be more obvious
• New issues surface not only in a social sense but also in AI itself as the AI being trained and learned how to operate the given task can eventually take off to the stage that human has no control, thus creating un-anticipated problems and consequences. It refers to AI's capacity after being loaded with all needed algorithm may automatically function on its own course ignoring the command given by the human controller
• The human masters who create AI may invent something that is racial bias or egocentrically oriented to harm certain people or things. For instance, the United Nations has voted to limit the spread of nucleus power in fear of its indiscriminative use to destroying humankind or targeting on certain races or region to achieve the goal of domination. AI is possible to target certain race or some programmed objects to accomplish the command of destruction by the programmers, thus creating world disaster.

P OSITIVE IMPACT

There are, however, many positive impacts on humans as well, especially in the field of healthcare. AI gives computers the capacity to learn, reason, and apply logic. Scientists, medical researchers, clinicians, mathematicians, and engineers, when working together, can design an AI that is aimed at medical diagnosis and treatments, thus offering reliable and safe systems of health-care delivery. As health professors and medical researchers endeavor to find new and efficient ways of treating diseases, not only the digital computer can assist in analyzing, robotic systems can also be created to do some delicate medical procedures with precision. Here, we see the contribution of AI to health care [ 7 , 11 ]:

Fast and accurate diagnostics

IBM's Watson computer has been used to diagnose with the fascinating result. Loading the data to the computer will instantly get AI's diagnosis. AI can also provide various ways of treatment for physicians to consider. The procedure is something like this: To load the digital results of physical examination to the computer that will consider all possibilities and automatically diagnose whether or not the patient suffers from some deficiencies and illness and even suggest various kinds of available treatment.

Socially therapeutic robots

Pets are recommended to senior citizens to ease their tension and reduce blood pressure, anxiety, loneliness, and increase social interaction. Now cyborgs have been suggested to accompany those lonely old folks, even to help do some house chores. Therapeutic robots and the socially assistive robot technology help improve the quality of life for seniors and physically challenged [ 12 ].

Reduce errors related to human fatigue

Human error at workforce is inevitable and often costly, the greater the level of fatigue, the higher the risk of errors occurring. Al technology, however, does not suffer from fatigue or emotional distraction. It saves errors and can accomplish the duty faster and more accurately.

Artificial intelligence-based surgical contribution

AI-based surgical procedures have been available for people to choose. Although this AI still needs to be operated by the health professionals, it can complete the work with less damage to the body. The da Vinci surgical system, a robotic technology allowing surgeons to perform minimally invasive procedures, is available in most of the hospitals now. These systems enable a degree of precision and accuracy far greater than the procedures done manually. The less invasive the surgery, the less trauma it will occur and less blood loss, less anxiety of the patients.

Improved radiology

The first computed tomography scanners were introduced in 1971. The first magnetic resonance imaging (MRI) scan of the human body took place in 1977. By the early 2000s, cardiac MRI, body MRI, and fetal imaging, became routine. The search continues for new algorithms to detect specific diseases as well as to analyze the results of scans [ 9 ]. All those are the contribution of the technology of AI.

Virtual presence

The virtual presence technology can enable a distant diagnosis of the diseases. The patient does not have to leave his/her bed but using a remote presence robot, doctors can check the patients without actually being there. Health professionals can move around and interact almost as effectively as if they were present. This allows specialists to assist patients who are unable to travel.

S OME CAUTIONS TO BE REMINDED

Despite all the positive promises that AI provides, human experts, however, are still essential and necessary to design, program, and operate the AI from any unpredictable error from occurring. Beth Kindig, a San Francisco-based technology analyst with more than a decade of experience in analyzing private and public technology companies, published a free newsletter indicating that although AI has a potential promise for better medical diagnosis, human experts are still needed to avoid the misclassification of unknown diseases because AI is not omnipotent to solve all problems for human kinds. There are times when AI meets an impasse, and to carry on its mission, it may just proceed indiscriminately, ending in creating more problems. Thus vigilant watch of AI's function cannot be neglected. This reminder is known as physician-in-the-loop [ 13 ].

The question of an ethical AI consequently was brought up by Elizabeth Gibney in her article published in Nature to caution any bias and possible societal harm [ 14 ]. The Neural Information processing Systems (NeurIPS) conference in Vancouver Canada in 2020 brought up the ethical controversies of the application of AI technology, such as in predictive policing or facial recognition, that due to bias algorithms can result in hurting the vulnerable population [ 14 ]. For instance, the NeurIPS can be programmed to target certain race or decree as the probable suspect of crime or trouble makers.

T HE CHALLENGE OF ARTIFICIAL INTELLIGENCE TO BIOETHICS

Artificial intelligence ethics must be developed.

Bioethics is a discipline that focuses on the relationship among living beings. Bioethics accentuates the good and the right in biospheres and can be categorized into at least three areas, the bioethics in health settings that is the relationship between physicians and patients, the bioethics in social settings that is the relationship among humankind and the bioethics in environmental settings that is the relationship between man and nature including animal ethics, land ethics, ecological ethics…etc. All these are concerned about relationships within and among natural existences.

As AI arises, human has a new challenge in terms of establishing a relationship toward something that is not natural in its own right. Bioethics normally discusses the relationship within natural existences, either humankind or his environment, that are parts of natural phenomena. But now men have to deal with something that is human-made, artificial and unnatural, namely AI. Human has created many things yet never has human had to think of how to ethically relate to his own creation. AI by itself is without feeling or personality. AI engineers have realized the importance of giving the AI ability to discern so that it will avoid any deviated activities causing unintended harm. From this perspective, we understand that AI can have a negative impact on humans and society; thus, a bioethics of AI becomes important to make sure that AI will not take off on its own by deviating from its originally designated purpose.

Stephen Hawking warned early in 2014 that the development of full AI could spell the end of the human race. He said that once humans develop AI, it may take off on its own and redesign itself at an ever-increasing rate [ 15 ]. Humans, who are limited by slow biological evolution, could not compete and would be superseded. In his book Superintelligence, Nick Bostrom gives an argument that AI will pose a threat to humankind. He argues that sufficiently intelligent AI can exhibit convergent behavior such as acquiring resources or protecting itself from being shut down, and it might harm humanity [ 16 ].

The question is–do we have to think of bioethics for the human's own created product that bears no bio-vitality? Can a machine have a mind, consciousness, and mental state in exactly the same sense that human beings do? Can a machine be sentient and thus deserve certain rights? Can a machine intentionally cause harm? Regulations must be contemplated as a bioethical mandate for AI production.

Studies have shown that AI can reflect the very prejudices humans have tried to overcome. As AI becomes “truly ubiquitous,” it has a tremendous potential to positively impact all manner of life, from industry to employment to health care and even security. Addressing the risks associated with the technology, Janosch Delcker, Politico Europe's AI correspondent, said: “I don't think AI will ever be free of bias, at least not as long as we stick to machine learning as we know it today,”…. “What's crucially important, I believe, is to recognize that those biases exist and that policymakers try to mitigate them” [ 17 ]. The High-Level Expert Group on AI of the European Union presented Ethics Guidelines for Trustworthy AI in 2019 that suggested AI systems must be accountable, explainable, and unbiased. Three emphases are given:

• Lawful-respecting all applicable laws and regulations
• Ethical-respecting ethical principles and values
• Robust-being adaptive, reliable, fair, and trustworthy from a technical perspective while taking into account its social environment [ 18 ].

Seven requirements are recommended [ 18 ]:

• AI should not trample on human autonomy. People should not be manipulated or coerced by AI systems, and humans should be able to intervene or oversee every decision that the software makes
• AI should be secure and accurate. It should not be easily compromised by external attacks, and it should be reasonably reliable
• Personal data collected by AI systems should be secure and private. It should not be accessible to just anyone, and it should not be easily stolen
• Data and algorithms used to create an AI system should be accessible, and the decisions made by the software should be “understood and traced by human beings.” In other words, operators should be able to explain the decisions their AI systems make
• Services provided by AI should be available to all, regardless of age, gender, race, or other characteristics. Similarly, systems should not be biased along these lines
• AI systems should be sustainable (i.e., they should be ecologically responsible) and “enhance positive social change”
• AI systems should be auditable and covered by existing protections for corporate whistleblowers. The negative impacts of systems should be acknowledged and reported in advance.

From these guidelines, we can suggest that future AI must be equipped with human sensibility or “AI humanities.” To accomplish this, AI researchers, manufacturers, and all industries must bear in mind that technology is to serve not to manipulate humans and his society. Bostrom and Judkowsky listed responsibility, transparency, auditability, incorruptibility, and predictability [ 19 ] as criteria for the computerized society to think about.

S UGGESTED PRINCIPLES FOR ARTIFICIAL INTELLIGENCE BIOETHICS

Nathan Strout, a reporter at Space and Intelligence System at Easter University, USA, reported just recently that the intelligence community is developing its own AI ethics. The Pentagon made announced in February 2020 that it is in the process of adopting principles for using AI as the guidelines for the department to follow while developing new AI tools and AI-enabled technologies. Ben Huebner, chief of the Office of Director of National Intelligence's Civil Liberties, Privacy, and Transparency Office, said that “We're going to need to ensure that we have transparency and accountability in these structures as we use them. They have to be secure and resilient” [ 20 ]. Two themes have been suggested for the AI community to think more about: Explainability and interpretability. Explainability is the concept of understanding how the analytic works, while interpretability is being able to understand a particular result produced by an analytic [ 20 ].

All the principles suggested by scholars for AI bioethics are well-brought-up. I gather from different bioethical principles in all the related fields of bioethics to suggest four principles here for consideration to guide the future development of the AI technology. We however must bear in mind that the main attention should still be placed on human because AI after all has been designed and manufactured by human. AI proceeds to its work according to its algorithm. AI itself cannot empathize nor have the ability to discern good from evil and may commit mistakes in processes. All the ethical quality of AI depends on the human designers; therefore, it is an AI bioethics and at the same time, a trans-bioethics that abridge human and material worlds. Here are the principles:

• Beneficence: Beneficence means doing good, and here it refers to the purpose and functions of AI should benefit the whole human life, society and universe. Any AI that will perform any destructive work on bio-universe, including all life forms, must be avoided and forbidden. The AI scientists must understand that reason of developing this technology has no other purpose but to benefit human society as a whole not for any individual personal gain. It should be altruistic, not egocentric in nature
• Value-upholding: This refers to AI's congruence to social values, in other words, universal values that govern the order of the natural world must be observed. AI cannot elevate to the height above social and moral norms and must be bias-free. The scientific and technological developments must be for the enhancement of human well-being that is the chief value AI must hold dearly as it progresses further
• Lucidity: AI must be transparent without hiding any secret agenda. It has to be easily comprehensible, detectable, incorruptible, and perceivable. AI technology should be made available for public auditing, testing and review, and subject to accountability standards … In high-stakes settings like diagnosing cancer from radiologic images, an algorithm that can't “explain its work” may pose an unacceptable risk. Thus, explainability and interpretability are absolutely required
• Accountability: AI designers and developers must bear in mind they carry a heavy responsibility on their shoulders of the outcome and impact of AI on whole human society and the universe. They must be accountable for whatever they manufacture and create.

C ONCLUSION

AI is here to stay in our world and we must try to enforce the AI bioethics of beneficence, value upholding, lucidity and accountability. Since AI is without a soul as it is, its bioethics must be transcendental to bridge the shortcoming of AI's inability to empathize. AI is a reality of the world. We must take note of what Joseph Weizenbaum, a pioneer of AI, said that we must not let computers make important decisions for us because AI as a machine will never possess human qualities such as compassion and wisdom to morally discern and judge [ 10 ]. Bioethics is not a matter of calculation but a process of conscientization. Although AI designers can up-load all information, data, and programmed to AI to function as a human being, it is still a machine and a tool. AI will always remain as AI without having authentic human feelings and the capacity to commiserate. Therefore, AI technology must be progressed with extreme caution. As Von der Leyen said in White Paper on AI – A European approach to excellence and trust : “AI must serve people, and therefore, AI must always comply with people's rights…. High-risk AI. That potentially interferes with people's rights has to be tested and certified before it reaches our single market” [ 21 ].

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Ethical Use of Generative AI

Statement on ethical use of ai for thesis/dissertation work, capstones, and comprehensive exams.

The use of generative artificial intelligence (AI) can support innovative and creative scholarship when used within appropriate guidelines, which may vary by discipline. It is vital that students uphold the core principles of academic and research integrity. As such, transparency around the usage of generative AI is required between the student, their committee and school/department, as well as the student and the audience of their completed thesis/dissertation, capstone, and/or comprehensive exams. Students need to take responsibility for their work, including using their own words and proper citations.

All use of generative AI in the thesis/dissertation/capstone/comprehensive exam process must be disclosed to the student’s committee by the student. Generative AI use should be verified to be within the standards of the discipline and school/department by the student and the student’s committee. Unauthorized use of generative AI may be considered a violation of Policy 1.8 Integrity in Research, Scholarly, and Creative Activities . Usage of generative AI must be appropriately cited following the guidelines of the style manual used by your discipline.

While these guidelines serve as the Graduate School’s position on the use of generative AI in the thesis/dissertation/capstone/comprehensive exam process, individual schools/departments may have additional policies, guidance, or restrictions. It is the student’s responsibility to understand and follow these standards and contact their school/department or the Graduate School with any questions or concerns. As generative AI is relatively new and evolving, additional or more detailed guidance may be issued in the future.

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AI for thesis writing — Unveiling 7 best AI tools

Table of Contents

Writing a thesis is akin to piecing together a complex puzzle. Each research paper, every data point, and all the hours spent reading and analyzing contribute to this monumental task.

For many students, this journey is a relentless pursuit of knowledge, often marked by sleepless nights and tight deadlines.

Here, the potential of AI for writing a thesis or research papers becomes clear: artificial intelligence can step in, not to take over but to assist and guide.

Far from being just a trendy term, AI is revolutionizing academic research, offering tools that can make the task of thesis writing more manageable, more precise, and a little less overwhelming.

In this article, we’ll discuss the impact of AI on academic writing process, and articulate the best AI tools for thesis writing to enhance your thesis writing process.

The Impact of AI on Thesis Writing

Artificial Intelligence offers a supportive hand in thesis writing, adeptly navigating vast datasets, suggesting enhancements in writing, and refining the narrative.

With the integration of AI writing assistant, instead of requiring you to manually sift through endless articles, AI tools can spotlight the most pertinent pieces in mere moments. Need clarity or the right phrasing? AI-driven writing assistants are there, offering real-time feedback, ensuring your work is both articulative  and academically sound.

AI tools for thesis writing harness Natural Language Processing (NLP) to generate content, check grammar, and assist in literature reviews. Simultaneously, Machine Learning (ML) techniques enable data analysis, provide personalized research recommendations, and aid in proper citation.

And for the detailed tasks of academic formatting and referencing? AI streamlines it all, ensuring your thesis meets the highest academic standards.

However, understanding AI's role is pivotal. It's a supportive tool, not the primary author. Your thesis remains a testament to your unique perspective and voice.

AI for writing thesis is there to amplify that voice, ensuring it's heard clearly and effectively.

How AI tools supplement your thesis writing

AI tools have emerged as invaluable allies for scholars. With just a few clicks, these advanced platforms can streamline various aspects of thesis writing, from data analysis to literature review.

Let's explore how an AI tool can supplement and transform your thesis writing style and process.

Efficient literature review : AI tools can quickly scan and summarize vast amounts of literature, making the process of literature review more efficient. Instead of spending countless hours reading through papers, researchers can get concise summaries and insights, allowing them  to focus on relevant content.

Enhanced data analysis : AI algorithms can process and analyze large datasets with ease, identifying patterns, trends, and correlations that might be difficult or time-consuming for humans to detect. This capability is especially valuable in fields with massive datasets, like genomics or social sciences.

Improved writing quality : AI-powered writing assistants can provide real-time feedback on grammar, style, and coherence. They can suggest improvements, ensuring that the final draft of a research paper or thesis is of high quality.

Plagiarism detection : AI tools can scan vast databases of academic content to ensure that a researcher's work is original and free from unintentional plagiarism .

Automated citations : Managing and formatting citations is a tedious aspect of academic writing. AI citation generators  can automatically format citations according to specific journal or conference standards, reducing the chances of errors.

Personalized research recommendations : AI tools can analyze a researcher's past work and reading habits to recommend relevant papers and articles, ensuring that they stay updated with the latest in their field.

Interactive data visualization : AI can assist in creating dynamic and interactive visualizations, making it easier for researchers to present their findings in a more engaging manner.

Top 7 AI Tools for Thesis Writing

The academic field is brimming with AI tools tailored for academic paper writing. Here's a glimpse into some of the most popular and effective ones.

Here we'll talk about some of the best ai writing tools, expanding on their major uses, benefits, and reasons to consider them.

If you've ever been bogged down by the minutiae of formatting or are unsure about specific academic standards, Typeset is a lifesaver.

Typeset specializes in formatting, ensuring academic papers align with various journal and conference standards.

It automates the intricate process of academic formatting, saving you from the manual hassle and potential errors, inflating your writing experience.

An AI-driven writing assistant, Wisio elevates the quality of your thesis content. It goes beyond grammar checks, offering style suggestions tailored to academic writing.

This ensures your thesis is both grammatically correct and maintains a scholarly tone. For moments of doubt or when maintaining a consistent style becomes challenging, Wisio acts as your personal editor, providing real-time feedback.

Known for its ability to generate and refine thesis content using AI algorithms, Texti ensures logical and coherent content flow according to the academic guidelines.

When faced with writer's block or a blank page, Texti can jumpstart your thesis writing process, aiding in drafting or refining content.

JustDone is an AI for thesis writing and content creation. It offers a straightforward three-step process for generating content, from choosing a template to customizing details and enjoying the final output.

JustDone AI can generate thesis drafts based on the input provided by you. This can be particularly useful for getting started or overcoming writer's block.

This platform can refine and enhance the editing process, ensuring it aligns with academic standards and is free from common errors. Moreover, it can process and analyze data, helping researchers identify patterns, trends, and insights that might be crucial for their thesis.

Tailored for academic writing, Writefull offers style suggestions to ensure your content maintains a scholarly tone.

This AI for thesis writing provides feedback on your language use, suggesting improvements in grammar, vocabulary, and structure . Moreover, it compares your written content against a vast database of academic texts. This helps in ensuring that your writing is in line with academic standards.

Isaac Editor

For those seeking an all-in-one solution for writing, editing, and refining, Isaac Editor offers a comprehensive platform.

Combining traditional text editor features with AI, Isaac Editor streamlines the writing process. It's an all-in-one solution for writing, editing, and refining, ensuring your content is of the highest quality.

PaperPal , an AI-powered personal writing assistant, enhances academic writing skills, particularly for PhD thesis writing and English editing.

This AI for thesis writing offers comprehensive grammar, spelling, punctuation, and readability suggestions, along with detailed English writing tips.

It offers grammar checks, providing insights on rephrasing sentences, improving article structure, and other edits to refine academic writing.

The platform also offers tools like "Paperpal for Word" and "Paperpal for Web" to provide real-time editing suggestions, and "Paperpal for Manuscript" for a thorough check of completed articles or theses.

Is it ethical to use AI for thesis writing?

The AI for writing thesis has ignited discussions on authenticity. While AI tools offer unparalleled assistance, it's vital to maintain originality and not become overly reliant. Research thrives on unique contributions, and AI should be a supportive tool, not a replacement.

The key question: Can a thesis, significantly aided by AI, still be viewed as an original piece of work?

AI tools can simplify research, offer grammar corrections, and even produce content. However, there's a fine line between using AI as a helpful tool and becoming overly dependent on it.

In essence, while AI offers numerous advantages for thesis writing, it's crucial to use it judiciously. AI should complement human effort, not replace it. The challenge is to strike the right balance, ensuring genuine research contributions while leveraging AI's capabilities.

Wrapping Up

Nowadays, it's evident that AI tools are not just fleeting trends but pivotal game-changers.

They're reshaping how we approach, structure, and refine our theses, making the process more efficient and the output more impactful. But amidst this technological revolution, it's essential to remember the heart of any thesis: the researcher's unique voice and perspective .

AI tools are here to amplify that voice, not overshadow it. They're guiding you through the vast sea of information, ensuring our research stands out and resonates.

Try these tools out and let us know what worked for you the best.

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Frequently Asked Questions

Yes, you can use AI to assist in writing your thesis. AI tools can help streamline various aspects of the writing process, such as data analysis, literature review, grammar checks, and content refinement.

However, it's essential to use AI as a supportive tool and not a replacement for original research and critical thinking. Your thesis should reflect your unique perspective and voice.

Yes, there are AI tools designed to assist in writing research papers. These tools can generate content, suggest improvements, help with formatting, and even provide real-time feedback on grammar and coherence.

Examples include Typeset, JustDone, Writefull, and Texti. However, while they can aid the process, the primary research, analysis, and conclusions should come from the researcher.

The "best" AI for writing papers depends on your specific needs. For content generation and refinement, Texti is a strong contender.

For grammar checks and style suggestions tailored to academic writing, Writefull is highly recommended. JustDone offers a user-friendly interface for content creation. It's advisable to explore different tools and choose one that aligns with your requirements.

To use AI for writing your thesis:

1. Identify the areas where you need assistance, such as literature review, data analysis, content generation, or grammar checks.

2. Choose an AI tool tailored for academic writing, like Typeset, JustDone, Texti, or Writefull.

3. Integrate the tool into your writing process. This could mean using it as a browser extension, a standalone application, or a plugin for your word processor.

4. As you write or review content, use the AI tool for real-time feedback, suggestions, or content generation.

5. Always review and critically assess the suggestions or content provided by the AI to ensure it aligns with your research goals and maintains academic integrity.

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Guest Essay

Will A.I. Be a Creator or a Destroyer of Worlds?

By Thomas B. Edsall

Mr. Edsall contributes a weekly column from Washington, D.C., on politics, demographics and inequality.

The advent of A.I. — artificial intelligence — is spurring curiosity and fear. Will A.I. be a creator or a destroyer of worlds?

In “ Can We Have Pro-Worker A.I. ? Choosing a Path of Machines in Service of Minds,” three economists at M.I.T., Daron Acemoglu , David Autor and Simon Johnson , looked at this epochal innovation last year:

The private sector in the United States is currently pursuing a path for generative A.I. that emphasizes automation and the displacement of labor, along with intrusive workplace surveillance. As a result, disruptions could lead to a potential downward cascade in wage levels, as well as inefficient productivity gains. Before the advent of artificial intelligence, automation was largely limited to blue-collar and office jobs using digital technologies while more complex and better-paying jobs were left untouched because they require flexibility, judgment and common sense.

Now, Acemoglu, Autor and Johnson wrote, A.I. presents a direct threat to those high-skill jobs: “A major focus of A.I. research is to attain human parity in a vast range of cognitive tasks and, more generally, to achieve ‘artificial general intelligence’ that fully mimics and then surpasses capabilities of the human mind.”

The three economists make the case that

There is no guarantee that the transformative capabilities of generative A.I. will be used for the betterment of work or workers. The bias of the tax code, of the private sector generally, and of the technology sector specifically, leans toward automation over augmentation. But there are also potentially powerful A.I.-based tools that can be used to create new tasks, boosting expertise and productivity across a range of skills. To redirect A.I. development onto the human-complementary path requires changes in the direction of technological innovation, as well as in corporate norms and behavior. This needs to be backed up by the right priorities at the federal level and a broader public understanding of the stakes and the available choices. We know this is a tall order.

“Tall” is an understatement.

In an email elaborating on the A.I. paper, Acemoglu contended that artificial intelligence has the potential to improve employment prospects rather than undermine them:

It is quite possible to leverage generative A.I. as an informational tool that enables various different types of workers to get better at their jobs and perform more complex tasks. If we are able to do this, this would help create good, meaningful jobs, with wage growth potential, and may even reduce inequality. Think of a generative A.I. tool that helps electricians get much better at diagnosing complex problems and troubleshoot them effectively.

This, however, “is not where we are heading,” Acemoglu continued:

The preoccupation of the tech industry is still automation and more automation, and the monetization of data via digital ads. To turn generative A.I. pro-worker, we need a major course correction, and this is not something that’s going to happen by itself.

Acemoglu pointed out that unlike the regional trade shock that decimated manufacturing employment after China entered the World Trade Organization in 2001, “The kinds of tasks impacted by A.I. are much more broadly distributed in the population and also across regions.” In other words, A.I. threatens employment at virtually all levels of the economy, including well-paid jobs requiring complex cognitive capabilities.

Four technology specialists — Tyna Eloundou and Pamela Mishkin , both on the staff of OpenAI , with Sam Manning , a research fellow at the Centre for the Governance of A.I., and Daniel Rock at the University of Pennsylvania — provided a detailed case study on the employment effects of artificial intelligence in their 2023 paper, “ GPTs Are GPTs : An Early Look at the Labor Market Impact Potential of Large Language Models.”

“Around 80 percent of the U.S. work force could have at least 10 percent of their work tasks affected by the introduction of large language models,” Eloundou and her co-authors wrote, and “approximately 19 percent of workers may see at least 50 percent of their tasks impacted.”

Large language models have multiple and diverse uses, according to Eloundou and her colleagues, and “can process and produce various forms of sequential data, including assembly language, protein sequences and chess games, extending beyond natural.” In addition, these models “excel in diverse applications like translation, classification, creative writing, and code generation — capabilities that previously demanded specialized, task-specific models developed by expert engineers using domain-specific data.”

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Doctoral Thesis: Techniques for Foundational End-to-End Verification of Systems Stacks

By: Samuel Gruetter

• Date: Wednesday, August 21
• Time: 3:00 pm - 4:30 pm
• Category: Thesis Defense
• Location: 32-G882

Additional Location Details:

Abstract: Today’s software is full of bugs and vulnerabilities. Formal verification provides a promising remedy through mathematical specifications and machine-checked proofs that the implementations conform to the specifications. However, there could still be bugs in the specifications or in the verification tools, which could lead to missed bugs in the software being verified. Therefore, this dissertation advocates for foundational end-to-end verification, a proof-based software development method that can mitigate both of these concerns:

It is end-to-end in the sense that the correctness proofs of individual components are used to discharge the assumptions of adjacent components throughout the whole stack, resulting in end-to-end theorems that only mention the top-most and bottom-most specifications, so that bugs in intermediate specifications cannot invalidate the soundness of the end-to-end statement anymore.

The method is foundational in the sense that the soundness of the proofs only relies on the foundations of mathematics and on the correctness of a small proof-checking kernel, but not on the correctness of other, domain-specific verification tools, because these tools are either proven correct once-and-for-all, or they output proofs that are checked by the kernel.

Ensuring that all the reasoning can be checked by the same small foundational kernel requires considerable effort, and the first part of this dissertation presents techniques to reduce this effort:

Omnisemantics, a new style of semantics that can be used instead of traditional small-step or big-step operational semantics, offer a smooth way of combining undefined behavior and nondeterminism, and enable forward-simulation compiler correctness proofs with nondeterministic languages, whereas previous approaches need to fall back to the much less convenient backward simulations if support for nondeterminism is needed.

Live Verification is proposed, a technique to turn an interactive proof assistant into a programming assistant that displays the symbolic state of the program as the user writes it and allows the user to tweak the symbolic state as long as the tweaks are provably sound. An additional convenience-improving feature is that instead of stating lengthy loop invariants, the user only needs to give the diff between the symbolic state before the loop and the desired loop invariant, resulting in shorter and more maintainable annotations. Finally, in order to make Live Verification practical, a number of additional proof techniques is presented.

The second part of the dissertation shows how these techniques were useful in three collaborative case studies: An embedded system running on a verified processor with an end-to-end proof where the software-hardware interface specification cancels out, a cryptographic server with an end-to-end proof going from high-level elliptic-curve math all the way down to machine code, and a trap handler to catch unsupported-instruction exceptions whose correctness proof combines program-logic proofs about C-level functions, a compiler correctness proof, and proofs about hand-written assembly.

Zoom Link: https://mit.zoom.us/j/94297415474

• Email: [email protected]

The Oppenheimer of AI

Michal Kosinski is pushing the boundaries of what artificial intelligence can do — and it terrifies him

Michal Kosinski knows exactly how he sounds when he talks about his research. And how it sounds is not great.

A psychologist at Stanford University, Kosinski is a specialist in psychometrics — a field that attempts to measure the facets of the human mind. For more than a decade, his work has freaked people right the hell out. In study after study, Kosinski has made scarily plausible claims that machine-learning algorithms of artificial intelligence can discern deeply private things about us — our intelligence, our sexual preferences, our political beliefs — using little more than our Facebook likes and photographs of our faces.

"I'm not even interested in faces," Kosinski insists. "There was nothing in my career that indicated I'd be spending a few years looking at people's appearances."

What Kosinski cares about are data and psychology. And what are photographs if not pixelated data? "Psychological theory kind of shouts in your face that there should be links between facial appearance and intimate traits," he says. That's why he believes that you can judge our inner lives by our outward characteristics.

It's a belief with disturbing implications. Science has been trying to divine truths about personality and behavior from various tests and images for centuries. As far back as the 1700s, physiognomists measured facial features in a search for ineffable qualities like nobility and immorality. Phrenologists used calipers to measure bumps on people's heads, hoping to diagnose mental incapacities or moral deficiencies. Eugenicists used photographs and IQ tests to determine which people were "inferior," and sterilized those who didn't measure up — which usually turned out to be anyone who wasn't white and rich. The methods differed, but the underlying theory remained the same: that measurements could somehow gauge the mind, and a person's value to society.

To be clear, none of these "sciences" worked. In fact, every time someone claimed they'd found a way to measure people's inner traits based on their exterior features, it quickly turned into a tool to discriminate against people based on their race or gender. That's because findings involving individuals almost always get applied to entire populations. It's a short leap from saying "some people are smarter than others" to " some races are smarter than others ." A test can be useful to assess which calculus class your daughter has an aptitude for. But it's malicious and wrong to use those test results to assert that there aren't many female software engineers because girls don't like math . Yet today, intelligence testing and facial recognition continue to be used, and abused, in everything from marketing and job hiring to college admissions and law enforcement.

Kosinski is aware of the long, dark history of his chosen field. Like his skull-measuring forebears, he believes that his research is right — that AI, combined with facial recognition, can lay bare our personalities and preferences more accurately than humans can. And to him, that accuracy is what makes his findings so dangerous. In pursuit of this ability, he fears, its creators will violate people's privacy and use it to manipulate public opinion and persecute minority groups. His work, he says, isn't meant to be used as a tool of oppression, like the pseudoscience of the past. It's meant as a warning about the future. In a sense, he's the Oppenheimer of AI, warning us all about the destructive potential of an artificial-intelligence bomb — while he's building it.

"Very soon," he says, "we may find ourselves in a position where these models have properties and capacities that are way ahead of what humans could dream of. And we will not even notice."

When we meet, Kosinski does not brandish any calipers to assess my brow and determine my tendency toward indolence, as the phrenologists of the 19th century did. Instead, dressed in a California-casual flowered shirt and white leather loafers — no socks — he leads me to a sunny Stanford courtyard for coffee. We're surrounded by a happy and diverse crowd of business-school students. Here, on a perfect California day, he lays out the case for what he fears will be the secret algorithmic domination of our world.

Before he worked with photographs, Kosinski was interested in Facebook. When he was a doctoral student at Cambridge in the mid-2000s, the few social scientists who took the emerging online world seriously regarded it as an uncanny valley, a place where people essentially donned fake personalities. How they behaved online didn't reflect their psychology or behavior in the real world.

Kosinski disagreed. "I felt that I'm still myself while using those products and services, and that my friends and people I knew were like this as well," he says. Even people pretending to be dwarf paladins or sex dragons still had the same anxieties, biases, and prejudices they carried around IRL.

Drawing on Facebook likes, Kosinski's model could tell whether a man was gay with 88% accuracy.

Much to the dismay of his thesis advisor, this became the foundation of Kosinski's approach. "That was the first aim, to show that continuity," he says. "And that led me to the second aim, which was: If we are all still ourselves online, that means we can use data collected online — Big Data — to understand humans better." To test his hypothesis, Kosinski and a grad student named David Stillwell at the Cambridge Psychometrics Centre created a Facebook app called myPersonality — an old-school magazine-style quiz that tested for personality traits like "openness" or "introversion" while also hoovering up people's Facebook likes. Then they built a computer model that mapped those likes to specific personality traits for nearly 60,000 people.

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Published in the Proceedings of the National Academy of Sciences in 2013, the results seemed astonishing . Facebook likes alone could predict someone's religion and politics with better than 80% accuracy. The model could tell whether a man was gay with 88% accuracy. Sometimes the algorithm didn't seem to have any particularly magical powers — liking the musical "Wicked," for example, was a leading predictor of male homosexuality. But other connections were baffling. Among the best predictors of high intelligence, for instance, were liking "thunderstorms" and "curly fries."

How did the machine draw such seemingly accurate conclusions from such arbitrary data? "Who knows why?" Stillwell, now the director of the Psychometrics Centre, tells me. "Who cares why? If it's a group of 10,000 individuals, the mistakes cancel out, and it's good enough for a population." Stillwell and Kosinski, in other words, aren't particularly interested in whether their models say anything about actual causation, about an explanation behind the connections. Correlation is enough. Their method enabled a machine to predict human behaviors and preferences. They don't have to know — or even care — how.

It didn't take long for such models to be weaponized. Another University of Cambridge researcher, Aleksandr Kogan, took similar ideas to a political-campaign consultancy called Cambridge Analytica , which sold its services to the 2016 campaign of Donald Trump and to Brexit advocates in the UK. Did the efforts to manipulate social-media feeds and change voting behaviors actually influence those votes? No one knows for sure. But a year later, Stillwell and Kosinski used myPersonality data to create psychologically customized ads that markedly influenced what 3.5 million people bought online, versus those who saw ads that weren't targeted to them. The research was at the forefront of what is today commonplace: using social-media algorithms to sell us stuff based on our every point and click.

Around the same time Kosinski was demonstrating that his research could manipulate online shoppers, a bunch of companies were starting to sell facial-recognition systems. At the time, the systems weren't even good at what they claimed to do: distinguishing among individuals for identification purposes. But Kosinski wondered whether software could use the data embedded in huge numbers of photographs, the same way it had with Facebook likes, to discern things like emotions and personality traits.

Most scientists consider that idea a form of modern physiognomy — a pseudoscience based on the mistaken assumption that our faces reveal something about our minds. Sure, we can tell a lot about someone by looking at them. At a glance we can guess, with a fair degree of accuracy, things like age, gender, and race. Based on simple odds, we can intuit that an older white man is more likely to be politically conservative than a younger Latina woman; an unshaven guy in a filthy hoodie and demolished sneakers probably has less ready cash than a woman in a Chanel suit. But discerning stuff like extroversion, or intelligence, or trustworthiness? Come on.

We can tell things like age, gender, and race just by looking at someone. But extroversion, or intelligence? Come on.

But once again, Kosinski believed that a machine, relying on Big Data, could divine our souls from our faces in a way that humans can't. People judge you based on your face, he says, and treat you differently based on those judgments. That, in turn, changes your psychology. If people constantly reward you with jobs and invitations to parties because they consider you attractive, that will alter your character over time. Your face affects how people treat you, and how people treat you affects who you are. All he needed was an algorithm to read the clues written on our faces — to separate the curly fries from the Broadway musicals.

Kosinski and a colleague scraped a dating site for photographs of 36,360 men and 38,593 women, equally divided between gay and straight (as indicated by their "looking for" fields). Then he used a facial-recognition algorithm called VGG-Face, trained on 2.6 million images, to compare his test subjects based on 500 variables. Presenting the model with photographs in pairs — one gay person and one straight person — he asked it to pick which one was gay.

Presented with at least five photographs of a person, Kosinski's model picked the gay person out of a pair with 91% accuracy. Humans, by contrast, were right only 61% of the time.

The paper gestures at an explanation — hormonal exposure in the womb something something. But once again, Kosinski isn't really interested in why the model works. To him, what's important is that a computer trained on thousands of images can draw accurate conclusions about something like sexual preference by combining multiple invisible details about a person.

Others disagreed. Researchers who study faces and emotions criticized both his math and his conclusions. The Guardian took Kosinski to task for giving a talk about his work in famously homophobic Russia. The Economist called his research " bad news " for anyone with secrets. The Human Rights Campaign and GLAAD issued a statement decrying the study , warning that it could be used by brutal regimes to persecute gay people. "Stanford should distance itself from such junk science," the HRC said, "rather than lending its name and credibility to research that is dangerously flawed and leaves the world — and this case, millions of people's lives — worse and less safe than before."

Kosinski felt blindsided. "People said, 'Stanford professor developed facial-recognition algorithms to build a gaydar.' But I don't even actually care about facial appearance per se. I care about privacy, and the algorithmic power to do stuff that we humans cannot do." He wasn't trying to build a scanner for right-wingers to take to school-board meetings, he says. He wanted policymakers to take action, and gay people to prepare themselves for the world to come.

"We did not create a privacy-invading tool, but rather showed that basic and widely used methods pose serious privacy threats," Kosinski and his coauthor wrote in their paper. "We hope that our findings will inform the public and policymakers, and inspire them to design technologies and write policies that reduce the risks faced by homosexual communities across the world."

Kosinski kept at it. This time, he scraped more than a million photographs of people from Facebook and a dating site, along with the political affiliations they listed in their profiles. Using VGGFace2 — open source, available to anyone who wants to try such a thing — he converted those faces to thousands of data points and averaged together the data for liberals and conservatives. Then he showed a new algorithm hundreds of thousands of pairs of images from the dating site and asked it to separate the MAGA lovers from the Bernie bros. The machine got it right 72% of the time. In pairs matched for age, gender, and race — knocking out the easy cues — accuracy fell, but only by a little.

This might seem like a big scary deal. AI can tell if we have political wrongthink! It can tricorder our sexuality! But most people who study faces and personality think Kosinski is flat-out wrong. "I absolutely do not dispute the fact that you can design an algorithm that can guess much better than chance whether a person is gay or straight," says Alexander Todorov, a psychologist at the University of Chicago. "But that's because all of the images are posted by the users themselves, so there are lots of confounds." Kosinski's model, in other words, isn't picking up microscopically subtle cues from the photos. It's just picking up on the way gay people present themselves on dating sites — which, not surprisingly, is often very different from the way straight people present themselves to potential partners. Control for that in the photos, and the algorithmic gaydar's accuracy ends up little better than chance.

Kosinski has tried to respond to these critiques. In his most recent study on political affiliation , he took his own photos of test subjects, rather than scraping the internet for self-posted photos. That enabled him to control for more variables — cutting out backdrops, keeping hairstyles the same, making sure people looked directly at the camera with a neutral expression. Then, using this new set of photos, he once again asked the algorithm to separate the conservatives from the liberals.

This time, the machine did fractionally worse than humans at accurately predicting someone's political affiliation. And therein lies the problem. It's not just that Kosinski's central finding — that AI can read humans better than humans can — is very possibly wrong. It's that we'll tend to believe it anyway. Computation, the math that a machine has instead of a mind, seems objective and infallible — even if the computer is just operationalizing our own biases.

That faulty belief isn't just at the heart of science's misguided and terrifying attempts to measure human beings over the past three centuries. It's at the heart of the science itself. The way scientists know whether to believe they've found data that confirms a hypothesis is through statistics. And the pioneers of modern statistics — Francis Galton, Ronald Fisher, and Karl Pearson — were among the most egregious eugenicists and physiognomists of the late 19th and early 20th centuries. They believed that Black people were savages, that Jews were a gutter race, that only the "right" kind of people should be allowed to have babies. As the mathematician Aubrey Clayton has argued, they literally invented statistical analysis to give their virulent racial prejudice a veneer of objectivity.

The methods and techniques they pioneered are with us today. They're behind IQ testing and college-admissions exams, the ceaseless racial profiling by police, the systems being used to screen job candidates for things like "soft skills" and "growth mindset." It's no coincidence that Hitler took his cues from the eugenicists — including an infamous 1929 ruling by the US Supreme Court that upheld the forced sterilization of women deemed by science to be "imbeciles." Imagine what a second Trump administration would do with AI-driven facial recognition at a border crossing — or anywhere, really, with the goal of identifying "enemies of the state." Such tools, in fact, are already built into ammunition vending machines (possibly one of the most dystopian phrases I have ever typed). They're also being incorporated into many of the technologies deployed on America's southern border , built by startups founded and funded by the same people supporting the Trump campaign. You think racism is systemic now? Just wait until the system is literally programmed with it.

The various technologies we've taken to calling "artificial intelligence" are basically just statistical engines that have been trained on our biases. Kosinski thinks AI's ability to make the kind of personality judgments he studies will only get better. "Ultimately, we're developing a model that produces outputs like a human mind," he tells me. And once the machine has thoroughly studied and mastered our all-too-human prejudices, he believes, it will then be able to see into our minds and use whatever it finds there to call the shots.

In Kosinski's nightmare, this won't be Skynet bombing us into oblivion. The sophisticated AI of tomorrow will know us so well that it won't need force — it will simply ensure our compliance by giving us exactly what we want. "Think about having a model that has read all the books on the planet, knows you intimately, knows how to talk to you, and is rewarded not only by you but by billions of other people for engaging interactions," he says. "It will become a master manipulator — a master entertainment system." That is the future Kosinski fears — even as he continues to tinker with the very models that prove it will come to pass.

Adam Rogers is a senior correspondent at Business Insider.

Correction: August 7, 2024 — An earlier version of this story misstated the affiliation of Aleksandr Kogan. He was not part of the University of Cambridge's Psychometrics Centre.

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Will the Billions in AI Investment Ever Pay Off?

The big companies succeed. The small ones struggle. That definitely affects ROI.

Over the past 10 years, investment in artificial intelligence has accelerated at a quick pace, reaching hundreds of billions of dollars.

3 Factors Holding Back the Return on AI Investments

• Much of the investment in AI infrastructure is geared toward the future.
• Outside of OpenAI, Claude and a few others, there is limited consumer adoption of AI tech.
• The tech is early in the adoption curve for enterprises, too. Adoption on a large scale is still on the horizon.

Economic returns, though, have yet to match the investments. Nascent technological breakthroughs such as large language models still need to be fully adopted within most enterprises and although the technology has seen one of the fastest adoption curves, currently the technology is costly to develop. 

This pattern is typical for emerging technologies. For instance, sequencing the human genome initially cost $1 billion, whereas it now costs about $100.

While OpenAI has surpassed an estimated $3 billion in revenue, many other AI startups and ventures struggle to exceed the $100 million mark. The current market focuses heavily on developing foundational frontier models and technologies, enabling products like AI companions such as Friend.

In the AI wrappers space, where startups develop products around AI lab APIs, competition is fierce. These startups often struggle to exceed the $100 million revenue mark, even when fine-tuning models for specific use cases. 

A major risk is the emergence of new AI models that can perform these specialized tasks inherently, potentially rendering the fine-tuned solutions of these startups obsolete. For instance, when ChatGPT came out, jasper.ai lost subscribers, resulting in staff cuts, and copy.ai now operates in an extremely crowded market. This challenge underscores the volatility and rapid evolution of the AI industry, making it difficult for smaller ventures to achieve significant market traction and differentiation.

More on Artificial Intelligence Explore Built In’s AI Coverage

AI and the Competition Gap

This big-vs-small company situation creates a considerable gap between major players like OpenAI, MidJjourney and Anthropic and the rest of the ventures within the industry. That’s because there is limited consumer adoption of AI technologies, outside of a few key products such as Claude , ChatGPT, MidJourney and Runway.

However, operating these models is capital-intensive, with rumors suggesting that running ChatGPT costs a staggering $700,000 per day . That’s not even including all the staffing and expenditure that goes into R&D and training costs of new models.  The high costs and investments exclude many companies from competing.

This has raised concerns in the market and provoked certain rumors, one being that OpenAI might run out of cash within a year. While this seems unlikely, the company needs to keep attracting investment and expanding its operation so that it has a clear road to profitability. But that’s not the goal for now.

Investing in AI Infrastructure

Because AI development is still in its early stages, companies like Microsoft, Amazon and Google are leading the charge with substantial investments in AI and data center infrastructure. The VC ecosystem is highly active in AI investments, too. Firms like Sequoia Capital and Andreessen Horowitz are among the most active and prominent investors in the AI space, particularly in generative AI startups.

Investments in infrastructure ensure that AI labs can stay ahead by pushing out the newest models and remaining competitive. Building this infrastructure is crucial for the future, as it enables the development and deployment of even more advanced AI technology.

Investing in Compute

One of the main infrastructure components is compute, with investments potentially surpassing a staggering $1 trillion over the next few years. Major tech companies, including Microsoft, Google and Amazon, are heavily investing in this sector, with each data center costing around $2 billion to build. This field is still nascent, as companies are learning how to set up these specialized GPU data centers . These centers will be equipped with the latest chips, like the H100. However, these chips will quickly become outdated as more powerful chips emerge, requiring ongoing reinvestment to meet the increasing computational demands of new AI models.

While one can argue that certain labs have advantages in models, algorithms or data, competing in this space is challenging. Researchers often move between AI labs, transferring knowledge and reducing competitive edges. One of many examples is Dario Amodel, former vice president of research at OpenAI, who co-founded Anthropic in 2021. When it comes to returns on capital expenditure, what are AI labs and their investors really betting on?

Related Reading Go Ahead. Explore Large Language Model APIs Beyond Open AI.

The Future Cost of Intelligence

Although AI is not yet on the roadmap of all corporations, AI labs are counting on decreasing the cost of intelligence and its value for companies seeking to acquire it. Currently, companies invest heavily in recruiting top talent, which is a significant expense. While current AI models are akin to clumsy interns or junior employees, they are improving and becoming cheaper. 

For example, OpenAI’s GPT-4o-mini is 97 percent cheaper for input tokens and 96 percent cheaper for output tokens compared with GPT-4 . This reduction translates to a 97 percent decrease in the cost of a clumsy intern’s intelligence. Imagine if this intelligence reaches Ph.D.-level capabilities; the implications for cost savings and efficiency would be immense.

In the near future, digital workers, also known as AI agents , will collaborate with humans and other AI agents. Initially, they will automate mundane tasks, but eventually, they will handle higher-value activities. This shift could allow humans to focus on more significant problems , potentially reducing the need for as many human workers. Smaller groups of humans, supported by thousands of digital agents handling non-strategic tasks, could produce more valuable outputs and tackle complex issues more efficiently.

When considering the investment in AI, one might ask if capturing only a small part of the tasks that humans perform today will yield significant returns on investment. We can support this thesis by examining past innovations that optimized human productivity, such as electricity, the personal computer and the internet. These technologies revolutionized industries, leading to substantial efficiency gains and cost reductions. Similarly, AI has the potential to transform various sectors by decreasing the cost of intelligence, thereby creating significant economic value and improving productivity across the board.

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