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177 Great Artificial Intelligence Research Paper Topics to Use

In this top-notch post, we will look at the definition of artificial intelligence, its applications, and writing tips on how to come up with AI topics. Finally, we shall lock at top artificial intelligence research topics for your inspiration.

What Is Artificial Intelligence?

It refers to intelligence as demonstrated by machines, unlike that which animals and humans display. The latter involves emotionality and consciousness. The field of AI has gained proliferation in recent days, with many scientists investing their time and effort in research.

How To Develop Topics in Artificial Intelligence

Developing AI topics is a critical thinking process that also incorporates a lot of creativity. Due to the ever-dynamic nature of the discipline, most students find it hard to develop impressive topics in artificial intelligence. However, here are some general rules to get you started:

Read widely on the subject of artificial intelligence Have an interest in news and other current updates about AI Consult your supervisor

Once you are ready with these steps, nothing is holding you from developing top-rated topics in artificial intelligence. Now let’s look at what the pros have in store for you.

Artificial Intelligence Research Paper Topics

• The role of artificial intelligence in evolving the workforce
• Are there tasks that require unique human abilities apart from machines?
• The transformative economic impact of artificial intelligence
• Managing a global autonomous arms race in the face of AI
• The legal and ethical boundaries of artificial intelligence
• Is the destructive role of AI more than its constructive role in society?
• How to build AI algorithms to achieve the far-reaching goals of humans
• How privacy gets compromised with the everyday collection of data
• How businesses and governments can suffer at the hands of AI
• Is it possible for AI to devolve into social oppression?
• Augmentation of the work humans do through artificial intelligence
• The role of AI in monitoring and diagnosing capabilities

Artificial Intelligence Topics For Presentation

• How AI helps to uncover criminal activity and solve serial crimes
• The place of facial recognition technologies in security systems
• How to use AI without crossing an individual’s privacy
• What are the disadvantages of using a computer-controlled robot in performing tasks?
• How to develop systems endowed with intellectual processes
• The challenge of programming computers to perform complex tasks
• Discuss some of the mathematical theorems for artificial intelligence systems
• The role of computer processing speed and memory capacity in AI
• Can computer machines achieve the performance levels of human experts?
• Discuss the application of artificial intelligence in handwriting recognition
• A case study of the key people involved in developing AI systems
• Computational aesthetics when developing artificial intelligence systems

Topics in AI For Tip-Top Grades

• Describe the necessities for artificial programming language
• The impact of American companies possessing about 2/3 of investments in AI
• The relationship between human neural networks and A.I
• The role of psychologists in developing human intelligence
• How to apply past experiences to analogous new situations
• How machine learning helps in achieving artificial intelligence
• The role of discernment and human intelligence in developing AI systems
• Discuss the various methods and goals in artificial intelligence
• What is the relationship between applied AI, strong AI, and cognitive simulation
• Discuss the implications of the first AI programs
• Logical reasoning and problem-solving in artificial intelligence
• Challenges involved in controlled learning environments

AI Research Topics For High School Students

• How quantum computing is affecting artificial intelligence
• The role of the Internet of Things in advancing artificial intelligence
• Using Artificial intelligence to enable machines to perform programming tasks
• Why do machines learn automatically without human hand holding
• Implementing decisions based on data processing in the human mind
• Describe the web-like structure of artificial neural networks
• Machine learning algorithms for optimal functions through trial and error
• A case study of Google’s AlphaGo computer program
• How robots solve problems in an intelligent manner
• Evaluate the significant role of M.I.T.’s artificial intelligence lab
• A case study of Robonaut developed by NASA to work with astronauts in space
• Discuss natural language processing where machines analyze language and speech

Argument Debate Topics on AI

• How chatbots use ML and N.L.P. to interact with the users
• How do computers use and understand images?
• The impact of genetic engineering on the life of man
• Why are micro-chips not recommended in human body systems?
• Can humans work alongside robots in a workplace system?
• Have computers contributed to the intrusion of privacy for many?
• Why artificial intelligence systems should not be made accessible to children
• How artificial intelligence systems are contributing to healthcare problems
• Does artificial intelligence alleviate human problems or add to them?
• Why governments should put more stringent measures for AI inventions
• How artificial intelligence is affecting the character traits of children born
• Is virtual reality taking people out of the real-world situation?

Quality AI Topics For Research Paper

• The use of recommender systems in choosing movies and series
• Collaborative filtering in designing systems
• How do developers arrive at a content-based recommendation
• Creation of systems that can emulate human tasks
• How IoT devices generate a lot of data
• Artificial intelligence algorithms convert data to useful, actionable results.
• How AI is progressing rapidly with the 5G technology
• How to develop robots with human-like characteristics
• Developing Google search algorithms
• The role of artificial intelligence in developing autonomous weapons
• Discuss the long-term goal of artificial intelligence
• Will artificial intelligence outperform humans at every cognitive task?

Computer Science AI Topics

• Computational intelligence magazine in computer science
• Swarm and evolutionary computation procedures for college students
• Discuss computational transactions on intelligent transportation systems
• The structure and function of knowledge-based systems
• A review of the artificial intelligence systems in developing systems
• Conduct a review of the expert systems with applications
• Critique the various foundations and trends in information retrieval
• The role of specialized systems in transactions on knowledge and data engineering
• An analysis of a journal on ambient intelligence and humanized computing
• Discuss the various computer transactions on cognitive communications and networking
• What is the role of artificial intelligence in medicine?
• Computer engineering applications of artificial intelligence

AI Ethics Topics

• How the automation of jobs is going to make many jobless
• Discuss inequality challenges in distributing wealth created by machines
• The impact of machines on human behavior and interactions
• How artificial intelligence is going to affect how we act accordingly
• The process of eliminating bias in Artificial intelligence: A case of racist robots
• Measures that can keep artificial intelligence safe from adversaries
• Protecting artificial intelligence discoveries from unintended consequences
• How a man can stay in control despite the complex, intelligent systems
• Robot rights: A case of how man is mistreating and misusing robots
• The balance between mitigating suffering and interfering with set ethics
• The role of artificial intelligence in negative outcomes: Is it worth it?
• How to ethically use artificial intelligence for bettering lives

Advanced AI Topics

• Discuss how long it will take until machines greatly supersede human intelligence
• Is it possible to achieve superhuman artificial intelligence in this century?
• The impact of techno-skeptic prediction on the performance of A.I
• The role of quarks and electrons in the human brain
• The impact of artificial intelligence safety research institutes
• Will robots be disastrous for humanity shortly?
• Robots: A concern about consciousness and evil
• Discuss whether a self-driving car has a subjective experience or not
• Should humans worry about machines turning evil in the end?
• Discuss how machines exhibit goal-oriented behavior in their functions
• Should man continue to develop lethal autonomous weapons?
• What is the implication of machine-produced wealth?

AI Essay Topics Technology

• Discuss the implication of the fourth technological revelation in cloud computing
• Big database technologies used in sensors
• The combination of technologies typical of the technological revolution
• Key determinants of the civilization process of industry 4.0
• Discuss some of the concepts of technological management
• Evaluate the creation of internet-based companies in the U.S.
• The most dominant scientific research in the field of artificial intelligence
• Discuss the application of artificial intelligence in the literature
• How enterprises use artificial intelligence in blockchain business operations
• Discuss the various immersive experiences as a result of digital AI
• Elaborate on various enterprise architects and technology innovations
• Mega-trends that are future impacts on business operations

Interesting Topics in AI

• The role of the industrial revolution of the 18 th century in A.I
• The electricity era of the late 19 th century and its contribution to the development of robots
• How the widespread use of the internet contributes to the AI revolution
• The short-term economic crisis as a result of artificial intelligence business technologies
• Designing and creating artificial intelligence production processes
• Analyzing large collections of information for technological solutions
• How biotechnology is transforming the field of agriculture
• Innovative business projects that work using artificial intelligence systems
• Process and marketing innovations in the 21 st century
• Medical intelligence in the era of smart cities
• Advanced data processing technologies in developed nations
• Discuss the development of stelliform technologies

Good Research Topics For AI

• Development of new technological solutions in I.T
• Innovative organizational solutions that develop machine learning
• How to develop branches of a knowledge-based economy
• Discuss the implications of advanced computerized neural network systems
• How to solve complex problems with the help of algorithms
• Why artificial intelligence systems are predominating over their creator
• How to determine artificial emotional intelligence
• Discuss the negative and positive aspects of technological advancement
• How internet technology companies like Facebook are managing large social media portals
• The application of analytical business intelligence systems
• How artificial intelligence improves business management systems
• Strategic and ongoing management of artificial intelligence systems

Graduate AI NLP Research Topics

• Morphological segmentation in artificial intelligence
• Sentiment analysis and breaking machine language
• Discuss input utterance for language interpretation
• Festival speech synthesis system for natural language processing
• Discuss the role of the Google language translator
• Evaluate the various analysis methodologies in N.L.P.
• Native language identification procedure for deep analytics
• Modular audio recognition framework
• Deep linguistic processing techniques
• Fact recognition and extraction techniques
• Dialogue and text-based applications
• Speaker verification and identification systems

Controversial Topics in AI

• Ethical implication of AI in movies: A case study of The Terminator
• Will machines take over the world and enslave humanity?
• Does human intelligence paint a dark future for humanity?
• Ethical and practical issues of artificial intelligence
• The impact of mimicking human cognitive functions
• Why the integration of AI technologies into society should be limited
• Should robots get paid hourly?
• What if AI is a mistake?
• Why did Microsoft shut down chatbots immediately?
• Should there be AI systems for killing?
• Should machines be created to do what they want?
• Is the computerized gun ethical?

Hot AI Topics

• Why predator drones should not exist
• Do the U.S. laws restrict meaningful innovations in AI
• Why did the campaign to stop killer robots fail in the end?
• Fully autonomous weapons and human safety
• How to deal with rogues artificial intelligence systems in the United States
• Is it okay to have a monopoly and control over artificial intelligence innovations?
• Should robots have human rights or citizenship?
• Biases when detecting people’s gender using Artificial intelligence
• Considerations for the adoption of a particular artificial intelligence technology

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Research Topics & Ideas

Artifical Intelligence (AI) and Machine Learning (ML)

If you’re just starting out exploring AI-related research topics for your dissertation, thesis or research project, you’ve come to the right place. In this post, we’ll help kickstart your research topic ideation process by providing a hearty list of research topics and ideas , including examples from past studies.

PS – This is just the start…

We know it’s exciting to run through a list of research topics, but please keep in mind that this list is just a starting point . To develop a suitable research topic, you’ll need to identify a clear and convincing research gap , and a viable plan  to fill that gap.

If this sounds foreign to you, check out our free research topic webinar that explores how to find and refine a high-quality research topic, from scratch. Alternatively, if you’d like hands-on help, consider our 1-on-1 coaching service .

AI-Related Research Topics & Ideas

Below you’ll find a list of AI and machine learning-related research topics ideas. These are intentionally broad and generic , so keep in mind that you will need to refine them a little. Nevertheless, they should inspire some ideas for your project.

• Developing AI algorithms for early detection of chronic diseases using patient data.
• The use of deep learning in enhancing the accuracy of weather prediction models.
• Machine learning techniques for real-time language translation in social media platforms.
• AI-driven approaches to improve cybersecurity in financial transactions.
• The role of AI in optimizing supply chain logistics for e-commerce.
• Investigating the impact of machine learning in personalized education systems.
• The use of AI in predictive maintenance for industrial machinery.
• Developing ethical frameworks for AI decision-making in healthcare.
• The application of ML algorithms in autonomous vehicle navigation systems.
• AI in agricultural technology: Optimizing crop yield predictions.
• Machine learning techniques for enhancing image recognition in security systems.
• AI-powered chatbots: Improving customer service efficiency in retail.
• The impact of AI on enhancing energy efficiency in smart buildings.
• Deep learning in drug discovery and pharmaceutical research.
• The use of AI in detecting and combating online misinformation.
• Machine learning models for real-time traffic prediction and management.
• AI applications in facial recognition: Privacy and ethical considerations.
• The effectiveness of ML in financial market prediction and analysis.
• Developing AI tools for real-time monitoring of environmental pollution.
• Machine learning for automated content moderation on social platforms.
• The role of AI in enhancing the accuracy of medical diagnostics.
• AI in space exploration: Automated data analysis and interpretation.
• Machine learning techniques in identifying genetic markers for diseases.
• AI-driven personal finance management tools.
• The use of AI in developing adaptive learning technologies for disabled students.

AI & ML Research Topic Ideas (Continued)

• Machine learning in cybersecurity threat detection and response.
• AI applications in virtual reality and augmented reality experiences.
• Developing ethical AI systems for recruitment and hiring processes.
• Machine learning for sentiment analysis in customer feedback.
• AI in sports analytics for performance enhancement and injury prevention.
• The role of AI in improving urban planning and smart city initiatives.
• Machine learning models for predicting consumer behaviour trends.
• AI and ML in artistic creation: Music, visual arts, and literature.
• The use of AI in automated drone navigation for delivery services.
• Developing AI algorithms for effective waste management and recycling.
• Machine learning in seismology for earthquake prediction.
• AI-powered tools for enhancing online privacy and data protection.
• The application of ML in enhancing speech recognition technologies.
• Investigating the role of AI in mental health assessment and therapy.
• Machine learning for optimization of renewable energy systems.
• AI in fashion: Predicting trends and personalizing customer experiences.
• The impact of AI on legal research and case analysis.
• Developing AI systems for real-time language interpretation for the deaf and hard of hearing.
• Machine learning in genomic data analysis for personalized medicine.
• AI-driven algorithms for credit scoring in microfinance.
• The use of AI in enhancing public safety and emergency response systems.
• Machine learning for improving water quality monitoring and management.
• AI applications in wildlife conservation and habitat monitoring.
• The role of AI in streamlining manufacturing processes.
• Investigating the use of AI in enhancing the accessibility of digital content for visually impaired users.

Recent AI & ML-Related Studies

While the ideas we’ve presented above are a decent starting point for finding a research topic in AI, they are fairly generic and non-specific. So, it helps to look at actual studies in the AI and machine learning space to see how this all comes together in practice.

Below, we’ve included a selection of AI-related studies to help refine your thinking. These are actual studies,  so they can provide some useful insight as to what a research topic looks like in practice.

• An overview of artificial intelligence in diabetic retinopathy and other ocular diseases (Sheng et al., 2022)
• HOW DOES ARTIFICIAL INTELLIGENCE HELP ASTRONOMY? A REVIEW (Patel, 2022)
• Editorial: Artificial Intelligence in Bioinformatics and Drug Repurposing: Methods and Applications (Zheng et al., 2022)
• Review of Artificial Intelligence and Machine Learning Technologies: Classification, Restrictions, Opportunities, and Challenges (Mukhamediev et al., 2022)
• Will digitization, big data, and artificial intelligence – and deep learning–based algorithm govern the practice of medicine? (Goh, 2022)
• Flower Classifier Web App Using Ml & Flask Web Framework (Singh et al., 2022)
• Object-based Classification of Natural Scenes Using Machine Learning Methods (Jasim & Younis, 2023)
• Automated Training Data Construction using Measurements for High-Level Learning-Based FPGA Power Modeling (Richa et al., 2022)
• Artificial Intelligence (AI) and Internet of Medical Things (IoMT) Assisted Biomedical Systems for Intelligent Healthcare (Manickam et al., 2022)
• Critical Review of Air Quality Prediction using Machine Learning Techniques (Sharma et al., 2022)
• Artificial Intelligence: New Frontiers in Real–Time Inverse Scattering and Electromagnetic Imaging (Salucci et al., 2022)
• Machine learning alternative to systems biology should not solely depend on data (Yeo & Selvarajoo, 2022)
• Measurement-While-Drilling Based Estimation of Dynamic Penetrometer Values Using Decision Trees and Random Forests (García et al., 2022).
• Artificial Intelligence in the Diagnosis of Oral Diseases: Applications and Pitfalls (Patil et al., 2022).
• Automated Machine Learning on High Dimensional Big Data for Prediction Tasks (Jayanthi & Devi, 2022)
• Breakdown of Machine Learning Algorithms (Meena & Sehrawat, 2022)
• Technology-Enabled, Evidence-Driven, and Patient-Centered: The Way Forward for Regulating Software as a Medical Device (Carolan et al., 2021)
• Machine Learning in Tourism (Rugge, 2022)
• Towards a training data model for artificial intelligence in earth observation (Yue et al., 2022)
• Classification of Music Generality using ANN, CNN and RNN-LSTM (Tripathy & Patel, 2022)

As you can see, these research topics are a lot more focused than the generic topic ideas we presented earlier. So, in order for you to develop a high-quality research topic, you’ll need to get specific and laser-focused on a specific context with specific variables of interest.  In the video below, we explore some other important things you’ll need to consider when crafting your research topic.

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If you’re still unsure about how to find a quality research topic, check out our Research Topic Kickstarter service, which is the perfect starting point for developing a unique, well-justified research topic.

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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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65+ Topics In Artificial Intelligence: A Comprehensive Guide To The Field

Jane Ng • 24 July, 2023 • 8 min read

Welcome to the world of AI. Are you ready to dive into the 65+ best topics in artificial intelligenc e and make an impact with your research, presentations, essay, or thought-provoking debates?

In this blog post, we present a curated list of cutting-edge topics in AI that are perfect for exploration. From the ethical implications of AI algorithms to the future of AI in healthcare and the societal impact of autonomous vehicles, this "topics in artificial intelligence" collection will equip you with exciting ideas to captivate your audience and navigate the forefront of AI research.  

Table of Contents

Artificial intelligence research topics, artificial intelligence topics for presentation, ai projects for the final year, artificial intelligence seminar topics, artificial intelligence debate topics, artificial intelligence essay topics, interesting topics in artificial intelligence.

• Key Takeaways

FAQs About Topics In Artificial Intelligence

Here are topics in artificial intelligence that cover various subfields and emerging areas:

• AI in Healthcare: Applications of AI in medical diagnosis, treatment recommendation, and healthcare management.
• AI in Drug Discovery : Applying AI methods to accelerate the process of drug discovery, including target identification and drug candidate screening.
• Transfer Learning: Research methods to transfer knowledge learned from one task or domain to improve performance on another.
• Ethical Considerations in AI: Examining the ethical implications and challenges associated with the deployment of AI systems.
• Natural Language Processing: Developing AI models for language understanding, sentiment analysis, and language generation.
• Fairness and Bias in AI: Examining approaches to mitigate biases and ensure fairness in AI decision-making processes.
• AI applications to address societal challenges.
• Multimodal Learning: Exploring techniques for integrating and learning from multiple modalities, such as text, images, and audio.
• Deep Learning Architectures: Advancements in neural network architectures, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs).

Here are topics in artificial intelligence suitable for presentations:

• Deepfake Technology: Discussing the ethical and societal consequences of AI-generated synthetic media and its potential for misinformation and manipulation.
• Cybersecurity: Presenting the applications of AI in detecting and mitigating cybersecurity threats and attacks.
• AI in Game Development: Discuss how AI algorithms are used to create intelligent and lifelike behaviors in video games.
• AI for Personalized Learning: Presenting how AI can personalize educational experiences, adapt content, and provide intelligent tutoring.
• Smart Cities: Discuss how AI can optimize urban planning, transportation systems, energy consumption, and waste management in cities.
• Social Media Analysis: Utilizing AI techniques for sentiment analysis, content recommendation, and user behavior modeling in social media platforms.
• Personalized Marketing: Presenting how AI-driven approaches improve targeted advertising, customer segmentation, and campaign optimization.
• AI and Data Ownership: Highlighting the debates around the ownership, control, and access to data used by AI systems and the implications for privacy and data rights.

• AI-Powered Chatbot for Customer Support: Building a chatbot that uses natural language processing and machine learning to provide customer support in a specific domain or industry.
• AI-Powered Virtual Personal Assistant: A virtual assistant that uses natural language processing and machine learning to perform tasks, answer questions, and provide recommendations.
• Emotion Recognition : An AI system that can accurately recognize and interpret human emotions from facial expressions or speech.
• AI-Based Financial Market Prediction: Creating an AI system that analyzes financial data and market trends to predict stock prices or market movements.
• Traffic Flow Optimization: Developing an AI system that analyzes real-time traffic data to optimize traffic signal timings and improve traffic flow in urban areas.
• Virtual Fashion Stylist: An AI-powered virtual stylist that provides personalized fashion recommendations and assists users in selecting outfits.

Here are the topics in artificial intelligence for the seminar:

• How Can Artificial Intelligence Assist in Natural Disaster Prediction and Management?
• AI in Healthcare: Applications of artificial intelligence in medical diagnosis, treatment recommendation, and patient care.
• Ethical Implications of AI: Examining the ethical considerations and responsible development of AI Systems.
• AI in Autonomous Vehicles: The role of AI in self-driving cars, including perception, decision-making, and safety.
• AI in Agriculture: Discussing AI applications in precision farming, crop monitoring, and yield prediction.
• How Can Artificial Intelligence Help Detect and Prevent Cybersecurity Attacks?
• Can Artificial Intelligence Assist in Addressing Climate Change Challenges?
• How Does Artificial Intelligence Impact Employment and the Future of Work?
• What Ethical Concerns Arise with the Use of Artificial Intelligence in Autonomous Weapons?

Here are topics in artificial intelligence that can generate thought-provoking discussions and allow participants to critically analyze different perspectives on the subject.

• Can AI ever truly understand and possess consciousness?
• Can Artificial Intelligence Algorithms be Unbiased and Fair in Decision-Making?
• Is it ethical to use AI for facial recognition and surveillance?
• Can AI effectively replicate human creativity and artistic expression?
• Does AI pose a threat to job security and the future of employment?
• Should there be legal liability for AI errors or accidents caused by autonomous systems?
• Is it ethical to use AI for social media manipulation and personalized advertising?
• Should there be a universal code of ethics for AI developers and researchers?
• Should there be strict regulations on the development and deployment of AI technologies?
• Is artificial general intelligence (AGI) a realistic possibility in the near future?
• Should AI algorithms be transparent and explainable in their decision-making processes?
• Does AI have the potential to solve global challenges, such as climate change and poverty?
• Does AI have the potential to surpass human intelligence, and if so, what are the implications?
• Should AI be used for predictive policing and law enforcement decision-making?

Here are 30 essay topics in artificial intelligence:

• AI and the Future of Work: Reshaping Industries and Skills
• AI and Human Creativity: Companions or Competitors?
• AI in Agriculture: Transforming Farming Practices for Sustainable Food Production
• Artificial Intelligence in Financial Markets: Opportunities and Risks
• The Impact of Artificial Intelligence on Employment and the Workforce
• AI in Mental Health: Opportunities, Challenges, and Ethical Considerations
• The Rise of Explainable AI: Necessity, Challenges, and Impacts
• The Ethical Implications of AI-Based Humanoid Robots in Elderly Care
• The Intersection of Artificial Intelligence and Cybersecurity: Challenges and Solutions
• Artificial Intelligence and the Privacy Paradox: Balancing Innovation with Data Protection
• The Future of Autonomous Vehicles and the Role of AI in Transportation

Here topics in artificial intelligence cover a broad spectrum of AI applications and research areas, providing ample opportunities for exploration, innovation, and further study.

• What are the ethical considerations for using AI in educational assessments?
• What are the potential biases and fairness concerns in AI algorithms for criminal sentencing?
• Should AI algorithms be used to influence voting decisions or electoral processes?
• Should AI models be used for predictive analysis in determining creditworthiness?
• What are the challenges of integrating AI with augmented reality (AR) and virtual reality (VR)?
• What are the challenges of deploying AI in developing countries?
• What are the risks and benefits of AI in healthcare?
• Is AI a solution or a hindrance to addressing social challenges?
• How can we address the issue of algorithmic bias in AI systems?
• What are the limitations of current deep learning models?
• Can AI algorithms be completely unbiased and free from human bias?
• How can AI contribute to wildlife conservation efforts?

Key Takeaways 

The field of artificial intelligence encompasses a vast range of topics that continue to shape and redefine our world. In addition, AhaSlides offers a dynamic and engaging way to explore these topics. With AhaSlides, presenters can captivate their audience through interactive slide templates , live polls , quizzes , and other features allowing for real-time participation and feedback. By leveraging the power of AhaSlides, presenters can enhance their discussions on artificial intelligence and create memorable and impactful presentations. 

As AI continues to evolve, the exploration of these topics becomes even more critical, and AhaSlides provides a platform for meaningful and interactive conversations in this exciting field.

What are the 8 types of artificial intelligence?

Here are some commonly recognized types of artificial intelligence:

• Reactive Machines
• Limited Memory AI
• Theory of Mind AI
• Self-Aware AI
• Superintelligent AI
• Artificial Superintelligence

What are the five big ideas in artificial intelligence?

The five big ideas in artificial intelligence, as outlined in the book " Artificial Intelligence: A Modern Approach " by Stuart Russell and Peter Norvig, are as follows:

• Agents are AI systems that interact with and impact the world. 
• Uncertainty deals with incomplete information using probabilistic models. 
• Learning enables AI systems to improve performance through data and experience. 
• Reasoning involves logical inference to derive knowledge. 
• Perception involves interpreting sensory inputs like vision and language.

Are there 4 basic AI concepts?

The four fundamental concepts in artificial intelligence are problem-solving, knowledge representation, learning, and perception. 

These concepts form the foundation for developing AI systems that can solve problems, store and reason with information, improve performance through learning, and interpret sensory inputs. They are essential in building intelligent systems and advancing the field of artificial intelligence.

Ref: Towards Data Science | Forbes | Thesis RUSH  

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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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Our research covers a wide range of topics of this fast-evolving field, advancing how machines learn, predict, and control, while also making them secure, robust and trustworthy. Research covers both the theory and applications of ML. This broad area studies ML theory (algorithms, optimization, etc.); statistical learning (inference, graphical models, causal analysis, etc.); deep learning; reinforcement learning; symbolic reasoning ML systems; as well as diverse hardware implementations of ML.

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The present and future of AI

Finale doshi-velez on how ai is shaping our lives and how we can shape ai.

Finale Doshi-Velez, the John L. Loeb Professor of Engineering and Applied Sciences. (Photo courtesy of Eliza Grinnell/Harvard SEAS)

How has artificial intelligence changed and shaped our world over the last five years? How will AI continue to impact our lives in the coming years? Those were the questions addressed in the most recent report from the One Hundred Year Study on Artificial Intelligence (AI100), an ongoing project hosted at Stanford University, that will study the status of AI technology and its impacts on the world over the next 100 years.

The 2021 report is the second in a series that will be released every five years until 2116. Titled “Gathering Strength, Gathering Storms,” the report explores the various ways AI is  increasingly touching people’s lives in settings that range from  movie recommendations  and  voice assistants  to  autonomous driving  and  automated medical diagnoses .

Barbara Grosz , the Higgins Research Professor of Natural Sciences at the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS) is a member of the standing committee overseeing the AI100 project and Finale Doshi-Velez , Gordon McKay Professor of Computer Science, is part of the panel of interdisciplinary researchers who wrote this year’s report. 

We spoke with Doshi-Velez about the report, what it says about the role AI is currently playing in our lives, and how it will change in the future.  

Q: Let's start with a snapshot: What is the current state of AI and its potential?

Doshi-Velez: Some of the biggest changes in the last five years have been how well AIs now perform in large data regimes on specific types of tasks.  We've seen [DeepMind’s] AlphaZero become the best Go player entirely through self-play, and everyday uses of AI such as grammar checks and autocomplete, automatic personal photo organization and search, and speech recognition become commonplace for large numbers of people.  

In terms of potential, I'm most excited about AIs that might augment and assist people.  They can be used to drive insights in drug discovery, help with decision making such as identifying a menu of likely treatment options for patients, and provide basic assistance, such as lane keeping while driving or text-to-speech based on images from a phone for the visually impaired.  In many situations, people and AIs have complementary strengths. I think we're getting closer to unlocking the potential of people and AI teams.

There's a much greater recognition that we should not be waiting for AI tools to become mainstream before making sure they are ethical.

Q: Over the course of 100 years, these reports will tell the story of AI and its evolving role in society. Even though there have only been two reports, what's the story so far?

There's actually a lot of change even in five years.  The first report is fairly rosy.  For example, it mentions how algorithmic risk assessments may mitigate the human biases of judges.  The second has a much more mixed view.  I think this comes from the fact that as AI tools have come into the mainstream — both in higher stakes and everyday settings — we are appropriately much less willing to tolerate flaws, especially discriminatory ones. There's also been questions of information and disinformation control as people get their news, social media, and entertainment via searches and rankings personalized to them. So, there's a much greater recognition that we should not be waiting for AI tools to become mainstream before making sure they are ethical.

Q: What is the responsibility of institutes of higher education in preparing students and the next generation of computer scientists for the future of AI and its impact on society?

First, I'll say that the need to understand the basics of AI and data science starts much earlier than higher education!  Children are being exposed to AIs as soon as they click on videos on YouTube or browse photo albums. They need to understand aspects of AI such as how their actions affect future recommendations.

But for computer science students in college, I think a key thing that future engineers need to realize is when to demand input and how to talk across disciplinary boundaries to get at often difficult-to-quantify notions of safety, equity, fairness, etc.  I'm really excited that Harvard has the Embedded EthiCS program to provide some of this education.  Of course, this is an addition to standard good engineering practices like building robust models, validating them, and so forth, which is all a bit harder with AI.

I think a key thing that future engineers need to realize is when to demand input and how to talk across disciplinary boundaries to get at often difficult-to-quantify notions of safety, equity, fairness, etc. 

Q: Your work focuses on machine learning with applications to healthcare, which is also an area of focus of this report. What is the state of AI in healthcare? 

A lot of AI in healthcare has been on the business end, used for optimizing billing, scheduling surgeries, that sort of thing.  When it comes to AI for better patient care, which is what we usually think about, there are few legal, regulatory, and financial incentives to do so, and many disincentives. Still, there's been slow but steady integration of AI-based tools, often in the form of risk scoring and alert systems.

In the near future, two applications that I'm really excited about are triage in low-resource settings — having AIs do initial reads of pathology slides, for example, if there are not enough pathologists, or get an initial check of whether a mole looks suspicious — and ways in which AIs can help identify promising treatment options for discussion with a clinician team and patient.

Q: Any predictions for the next report?

I'll be keen to see where currently nascent AI regulation initiatives have gotten to. Accountability is such a difficult question in AI,  it's tricky to nurture both innovation and basic protections.  Perhaps the most important innovation will be in approaches for AI accountability.

Topics: AI / Machine Learning , Computer Science

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[ go to the annotated version ]

Until the turn of the millennium, AI’s appeal lay largely in its promise to deliver, but in the last fifteen years, much of that promise has been redeemed. [15]  AI already pervades our lives. And as it becomes a central force in society, the field is now shifting from simply building systems that are intelligent to building intelligent systems that are human-aware and trustworthy.

Several factors have fueled the AI revolution. Foremost among them is the maturing of machine learning, supported in part by cloud computing resources and wide-spread, web-based data gathering. Machine learning has been propelled dramatically forward by “deep learning,” a form of adaptive artificial neural networks trained using a method called backpropagation. [16]  This leap in the performance of information processing algorithms has been accompanied by significant progress in hardware technology for basic operations such as sensing, perception, and object recognition. New platforms and markets for data-driven products, and the economic incentives to find new products and markets, have also contributed to the advent of AI-driven technology.

All these trends drive the “hot” areas of research described below. This compilation is meant simply to reflect the areas that, by one metric or another, currently receive greater attention than others. They are not necessarily more important or valuable than other ones. Indeed, some of the currently “hot” areas were less popular in past years, and it is likely that other areas will similarly re-emerge in the future.

Large-scale machine learning

Many of the basic problems in machine learning (such as supervised and unsupervised learning) are well-understood. A major focus of current efforts is to scale existing algorithms to work with extremely large data sets. For example, whereas traditional methods could afford to make several passes over the data set, modern ones are designed to make only a single pass; in some cases, only sublinear methods (those that only look at a fraction of the data) can be admitted.

Deep learning

The ability to successfully train convolutional neural networks has most benefited the field of computer vision, with applications such as object recognition, video labeling, activity recognition, and several variants thereof. Deep learning is also making significant inroads into other areas of perception, such as audio, speech, and natural language processing.

Reinforcement learning

Whereas traditional machine learning has mostly focused on pattern mining, reinforcement learning shifts the focus to decision making, and is a technology that will help AI to advance more deeply into the realm of learning about and executing actions in the real world. It has existed for several decades as a framework for experience-driven sequential decision-making, but the methods have not found great success in practice, mainly owing to issues of representation and scaling. However, the advent of deep learning has provided reinforcement learning with a “shot in the arm.” The recent success of AlphaGo, a computer program developed by Google Deepmind that beat the human Go champion in a five-game match, was due in large part to reinforcement learning. AlphaGo was trained by initializing an automated agent with a human expert database, but was subsequently refined by playing a large number of games against itself and applying reinforcement learning.

Robotic navigation, at least in static environments, is largely solved. Current efforts consider how to train a robot to interact with the world around it in generalizable and predictable ways. A natural requirement that arises in interactive environments is manipulation, another topic of current interest. The deep learning revolution is only beginning to influence robotics, in large part because it is far more difficult to acquire the large labeled data sets that have driven other learning-based areas of AI. Reinforcement learning (see above), which obviates the requirement of labeled data, may help bridge this gap but requires systems to be able to safely explore a policy space without committing errors that harm the system itself or others. Advances in reliable machine perception, including computer vision, force, and tactile perception, much of which will be driven by machine learning, will continue to be key enablers to advancing the capabilities of robotics.

Computer vision

Computer vision is currently the most prominent form of machine perception. It has been the sub-area of AI most transformed by the rise of deep learning. Until just a few years ago, support vector machines were the method of choice for most visual classification tasks. But the confluence of large-scale computing, especially on GPUs, the availability of large datasets, especially via the internet, and refinements of neural network algorithms has led to dramatic improvements in performance on benchmark tasks (e.g., classification on ImageNet [17] ). For the first time, computers are able to perform some (narrowly defined) visual classification tasks better than people. Much current research is focused on automatic image and video captioning.

Natural Language Processing

Often coupled with automatic speech recognition, Natural Language Processing is another very active area of machine perception. It is quickly becoming a commodity for mainstream languages with large data sets. Google announced that 20% of current mobile queries are done by voice, [18]  and recent demonstrations have proven the possibility of real-time translation. Research is now shifting towards developing refined and capable systems that are able to interact with people through dialog, not just react to stylized requests.

Collaborative systems

Research on collaborative systems investigates models and algorithms to help develop autonomous systems that can work collaboratively with other systems and with humans. This research relies on developing formal models of collaboration, and studies the capabilities needed for systems to become effective partners. There is growing interest in applications that can utilize the complementary strengths of humans and machines—for humans to help AI systems to overcome their limitations, and for agents to augment human abilities and activities.

Crowdsourcing and human computation

Since human abilities are superior to automated methods for accomplishing many tasks, research on crowdsourcing and human computation investigates methods to augment computer systems by utilizing human intelligence to solve problems that computers alone cannot solve well. Introduced only about fifteen years ago, this research now has an established presence in AI. The best-known example of crowdsourcing is Wikipedia, a knowledge repository that is maintained and updated by netizens and that far exceeds traditionally-compiled information sources, such as encyclopedias and dictionaries, in scale and depth. Crowdsourcing focuses on devising innovative ways to harness human intelligence. Citizen science platforms energize volunteers to solve scientific problems, while paid crowdsourcing platforms such as Amazon Mechanical Turk provide automated access to human intelligence on demand. Work in this area has facilitated advances in other subfields of AI, including computer vision and NLP, by enabling large amounts of labeled training data and/or human interaction data to be collected in a short amount of time. Current research efforts explore ideal divisions of tasks between humans and machines based on their differing capabilities and costs.

Algorithmic game theory and computational social choice

New attention is being drawn to the economic and social computing dimensions of AI, including incentive structures. Distributed AI and multi-agent systems have been studied since the early 1980s, gained prominence starting in the late 1990s, and were accelerated by the internet. A natural requirement is that systems handle potentially misaligned incentives, including self-interested human participants or firms, as well as automated AI-based agents representing them. Topics receiving attention include computational mechanism design (an economic theory of incentive design, seeking incentive-compatible systems where inputs are truthfully reported), computational social choice (a theory for how to aggregate rank orders on alternatives), incentive aligned information elicitation (prediction markets, scoring rules, peer prediction) and algorithmic game theory (the equilibria of markets, network games, and parlor games such as Poker—a game where significant advances have been made in recent years through abstraction techniques and no-regret learning).

Internet of Things (IoT)

A growing body of research is devoted to the idea that a wide array of devices can be interconnected to collect and share their sensory information. Such devices can include appliances, vehicles, buildings, cameras, and other things. While it's a matter of technology and wireless networking to connect the devices, AI can process and use the resulting huge amounts of data for intelligent and useful purposes. Currently, these devices use a bewildering array of incompatible communication protocols. AI could help tame this Tower of Babel.

Neuromorphic Computing

Traditional computers implement the von Neumann model of computing, which separates the modules for input/output, instruction-processing, and memory. With the success of deep neural networks on a wide array of tasks, manufacturers are actively pursuing alternative models of computing—especially those that are inspired by what is known about biological neural networks—with the aim of improving the hardware efficiency and robustness of computing systems. At the moment, such “neuromorphic” computers have not yet clearly demonstrated big wins, and are just beginning to become commercially viable. But it is possible that they will become commonplace (even if only as additions to their von Neumann cousins) in the near future. Deep neural networks have already created a splash in the application landscape. A larger wave may hit when these networks can be trained and executed on dedicated neuromorphic hardware, as opposed to simulated on standard von Neumann architectures, as they are today.

[15]  Appendix I offers a short history of AI, including a description of some of the traditionally core areas of research, which have shifted over the past six decades.

[16]  Backpropogation is an abbreviation for "backward propagation of errors,” a common method of training artificial neural networks used in conjunction with an optimization method such as gradient descent. The method calculates the gradient of a loss function with respect to all the weights in the network.

[17]  ImageNet, Stanford Vision Lab, Stanford University, Princeton University, 2016, accessed August 1, 2016,  www.image-net.org/ .

[18]  Greg Sterling, "Google says 20% of mobile queries are voice searches,"  Search Engine Land , May 18, 2016, accessed August 1, 2016,  http://searchengineland.com/google-reveals-20-percent-queries-voice-queries-249917 .

In this section

Overall Trends and the Future of AI Research

Cite This Report

Peter Stone, Rodney Brooks, Erik Brynjolfsson, Ryan Calo, Oren Etzioni, Greg Hager, Julia Hirschberg, Shivaram Kalyanakrishnan, Ece Kamar, Sarit Kraus, Kevin Leyton-Brown, David Parkes, William Press, AnnaLee Saxenian, Julie Shah, Milind Tambe, and Astro Teller.  "Artificial Intelligence and Life in 2030." One Hundred Year Study on Artificial Intelligence: Report of the 2015-2016 Study Panel, Stanford University, Stanford, CA,  September 2016. Doc:  http://ai100.stanford.edu/2016-report . Accessed:  September 6, 2016.

Report Authors

AI100 Standing Committee and Study Panel 

© 2016 by Stanford University. Artificial Intelligence and Life in 2030 is made available under a Creative Commons Attribution-NoDerivatives 4.0 License (International):  https://creativecommons.org/licenses/by-nd/4.0/ .

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• Published: 20 January 2022

AI in health and medicine

• Pranav Rajpurkar   ORCID: orcid.org/0000-0002-8030-3727 1   na1 ,
• Emma Chen 2   na1 ,
• Oishi Banerjee 2   na1 &
• Eric J. Topol   ORCID: orcid.org/0000-0002-1478-4729 3  

Nature Medicine volume  28 ,  pages 31–38 ( 2022 ) Cite this article

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• Computational biology and bioinformatics
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Artificial intelligence (AI) is poised to broadly reshape medicine, potentially improving the experiences of both clinicians and patients. We discuss key findings from a 2-year weekly effort to track and share key developments in medical AI. We cover prospective studies and advances in medical image analysis, which have reduced the gap between research and deployment. We also address several promising avenues for novel medical AI research, including non-image data sources, unconventional problem formulations and human–AI collaboration. Finally, we consider serious technical and ethical challenges in issues spanning from data scarcity to racial bias. As these challenges are addressed, AI’s potential may be realized, making healthcare more accurate, efficient and accessible for patients worldwide.

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Acknowledgements

We thank A. Tamkin and N. Phillips for their feedback. E.J.T. receives funding support from US National Institutes of Health grant UL1TR002550.

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These authors contributed equally: Pranav Rajpurkar, Emma Chen, Oishi Banerjee.

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Department of Biomedical Informatics, Harvard University, Cambridge, MA, USA

Pranav Rajpurkar

Department of Computer Science, Stanford University, Stanford, CA, USA

Emma Chen & Oishi Banerjee

Scripps Translational Science Institute, San Diego, CA, USA

Eric J. Topol

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P.R. and E.J.T. conceptualized this Review. E.C., O.B. and P.R. were responsible for the design and synthesis of this Review. All authors contributed to writing and editing the manuscript.

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Correspondence to Eric J. Topol .

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Rajpurkar, P., Chen, E., Banerjee, O. et al. AI in health and medicine. Nat Med 28 , 31–38 (2022). https://doi.org/10.1038/s41591-021-01614-0

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A quarter of u.s. teachers say ai tools do more harm than good in k-12 education.

High school teachers are more likely than elementary and middle school teachers to hold negative views about AI tools in education.

Many Americans think generative AI programs should credit the sources they rely on

Americans’ use of chatgpt is ticking up, but few trust its election information, what the data says about americans’ views of artificial intelligence, sign up for our internet, science, and tech newsletter.

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22% of Americans say they interact with artificial intelligence almost constantly or several times a day. 27% say they do this about once a day or several times a week.

About one-in-five U.S. adults have used ChatGPT to learn something new (17%) or for entertainment (17%).

Q&A: How we used large language models to identify guests on popular podcasts

We asked researchers how they used the newest generation of large language models to analyze roughly 24,000 podcast episodes.

Striking findings from 2023

Here’s a look back at 2023 through some of our most striking research findings.

Today, 52% of Americans are more concerned than excited about AI in daily life, compared with just 10% who say they are more excited than concerned.

About 1 in 5 U.S. teens who’ve heard of ChatGPT have used it for schoolwork

Roughly one-in-five teenagers who have heard of ChatGPT say they have used it to help them do their schoolwork.

Key findings about Americans and data privacy

71% of adults say they are very or somewhat concerned about how the government uses the data it collects about them, up from 64% in 2019.

How Americans View Data Privacy

The share of Americans who say they are very or somewhat concerned about government use of people’s data has increased from 64% in 2019 to 71% today. Two-thirds (67%) of adults say they understand little to nothing about what companies are doing with their personal data, up from 59%.

Growing public concern about the role of artificial intelligence in daily life

52% of Americans say they feel more concerned than excited about the increased use of artificial intelligence.

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Artificial intelligence (AI) is technology that enables computers and machines to simulate human learning, comprehension, problem solving, decision making, creativity and autonomy.

Applications and devices equipped with AI can see and identify objects. They can understand and respond to human language. They can learn from new information and experience. They can make detailed recommendations to users and experts. They can act independently, replacing the need for human intelligence or intervention (a classic example being a self-driving car). 

But in 2024, most AI researchers and practitioners—and most AI-related headlines—are focused on breakthroughs in generative AI  (gen AI), a technology that can create original text, images, video and other content. To fully understand generative AI, it’s important to first understand the technologies on which generative AI tools are built: machine learning  (ML) and deep learning .

Learn how to choose the right approach in preparing data sets and employing AI models.

A simple way to think about AI is as a series of nested or derivative concepts that have emerged over more than 70 years:  

Directly underneath AI, we have machine learning, which involves creating models by training an algorithm to make predictions or decisions based on data. It encompasses a broad range of techniques that enable computers to learn from and make inferences based on data without being explicitly programmed for specific tasks. 

There are many types of machine learning techniques or algorithms, including linear regression ,  logistic regression , decision trees , random forest , support vector machines   (SVMs) , k-nearest neighbor (KNN), clustering and more. Each of these approaches is suited to different kinds of problems and data.

But one of the most popular types of machine learning algorithm is called a neural network (or artificial neural network). Neural networks are modeled after the human brain's structure and function. A neural network consists of interconnected layers of nodes (analogous to neurons) that work together to process and analyze complex data. Neural networks are well suited to tasks that involve identifying complex patterns and relationships in large amounts of data.

The simplest form of machine learning is called supervised learning , which involves the use of labeled data sets to train algorithms to classify data or predict outcomes accurately. In supervised learning, humans pair each training example with an output label. The goal is for the model to learn the mapping between inputs and outputs in the training data, so it can predict the labels of new, unseen data.  

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Deep learning is a subset of machine learning that uses multilayered neural networks, called deep neural networks, that more closely simulate the complex decision-making power of the human brain.

Deep neural networks include an input layer, at least three but usually hundreds of hidden layers, and an output layer, unlike neural networks used in classic machine learning models, which usually have only one or two hidden layers.

These multiple layers enable unsupervised learning : they can automate the extraction of features from large, unlabeled and unstructured data sets, and make their own predictions about what the data represents.

Because deep learning doesn’t require human intervention, it enables machine learning at a tremendous scale. It is well suited to natural language processing (NLP) , computer vision , and other tasks that involve the fast, accurate identification complex patterns and relationships in large amounts of data. Some form of deep learning powers most of the artificial intelligence (AI) applications in our lives today.  

Deep learning also enables:

• Semi-supervised learning , which combines supervised and unsupervised learning by using both labeled and unlabeled data to train AI models for classification and regression tasks.
• Self-supervised learning , which generates implicit labels from unstructured data, rather than relying on labeled data sets for supervisory signals.
• Reinforcement learning , which learns by trial-and-error and reward functions rather than by extracting information from hidden patterns.
• Transfer learning , in which knowledge gained through one task or data set is used to improve model performance on another related task or different data set.

Generative AI, sometimes called "gen AI" , refers to deep learning models that can create complex original content—such as long-form text, high-quality images, realistic video or audio and more—in response to a user’s prompt or request.

At a high level, generative models encode a simplified representation of their training data, and then draw from that representation to create new work that’s similar, but not identical, to the original data.

Generative models have been used for years in statistics to analyze numerical data. But over the last decade, they evolved to analyze and generate more complex data types. This evolution coincided with the emergence of three sophisticated deep learning model types:

• Variational autoencoders  or VAEs, which were introduced in 2013, and enabled models that could generate multiple variations of content in response to a prompt or instruction.
• Diffusion models, first seen in 2014, which add "noise" to images until they are unrecognizable, and then remove the noise to generate original images in response to prompts.
• Transformers (also called transformer models), which are trained on sequenced data to generate extended sequences of content (such as words in sentences, shapes in an image, frames of a video or commands in software code). Transformers are at the core of most of today’s headline-making generative AI tools, including ChatGPT and GPT-4, Copilot, BERT, Bard and Midjourney. 

In general, generative AI operates in three phases:

• Training, to create a foundation model.
• Tuning, to adapt the model to a specific application.
• Generation, evaluation and more tuning, to improve accuracy.

Generative AI begins with a "foundation model"; a deep learning model that serves as the basis for multiple different types of generative AI applications.

The most common foundation models today are large language models (LLMs) , created for text generation applications. But there are also foundation models for image, video, sound or music generation, and multimodal foundation models that support several kinds of content.

To create a foundation model, practitioners train a deep learning algorithm on huge volumes of relevant raw, unstructured, unlabeled data, such as terabytes or petabytes of data text or images or video from the internet. The training yields a neural network of billions of parameters —encoded representations of the entities, patterns and relationships in the data—that can generate content autonomously in response to prompts. This is the foundation model.

This training process is compute-intensive, time-consuming and expensive. It requires thousands of clustered graphics processing units (GPUs) and weeks of processing, all of which typically costs millions of dollars. Open source foundation model projects, such as Meta's Llama-2, enable gen AI developers to avoid this step and its costs.

Next, the model must be tuned to a specific content generation task. This can be done in various ways, including:

• Fine-tuning, which involves feeding the model application-specific labeled data—questions or prompts the application is likely to receive, and corresponding correct answers in the wanted format.
• Reinforcement learning with human feedback (RLHF), in which human users evaluate the accuracy or relevance of model outputs so that the model can improve itself. This can be as simple as having people type or talk back corrections to a chatbot or virtual assistant.

Generation, evaluation and more tuning  

Developers and users regularly assess the outputs of their generative AI apps, and further tune the model—even as often as once a week—for greater accuracy or relevance. In contrast, the foundation model itself is updated much less frequently, perhaps every year or 18 months.

Another option for improving a gen AI app's performance is retrieval augmented generation (RAG), a technique for extending the foundation model to use relevant sources outside of the training data to refine the parameters for greater accuracy or relevance.

AI offers numerous benefits across various industries and applications. Some of the most commonly cited benefits include:

• Automation of repetitive tasks.
• More and faster insight from data.
• Enhanced decision-making.
• Fewer human errors.
• 24x7 availability.
• Reduced physical risks.

Automation of repetitive tasks  

AI can automate routine, repetitive and often tedious tasks—including digital tasks such as data collection, entering and preprocessing, and physical tasks such as warehouse stock-picking and manufacturing processes. This automation frees to work on higher value, more creative work.

Enhanced decision-making  

Whether used for decision support or for fully automated decision-making, AI enables faster, more accurate predictions and reliable, data-driven decisions . Combined with automation, AI enables businesses to act on opportunities and respond to crises as they emerge, in real time and without human intervention.

Fewer human errors  

AI can reduce human errors in various ways, from guiding people through the proper steps of a process, to flagging potential errors before they occur, and fully automating processes without human intervention. This is especially important in industries such as healthcare where, for example, AI-guided surgical robotics enable consistent precision.

Machine learning algorithms can continually improve their accuracy and further reduce errors as they're exposed to more data and "learn" from experience.

Round-the-clock availability and consistency  

AI is always on, available around the clock, and delivers consistent performance every time. Tools such as AI chatbots or virtual assistants can lighten staffing demands for customer service or support. In other applications—such as materials processing or production lines—AI can help maintain consistent work quality and output levels when used to complete repetitive or tedious tasks.

Reduced physical risk  

By automating dangerous work—such as animal control, handling explosives, performing tasks in deep ocean water, high altitudes or in outer space—AI can eliminate the need to put human workers at risk of injury or worse. While they have yet to be perfected, self-driving cars and other vehicles offer the potential to reduce the risk of injury to passengers.

The real-world applications of AI are many. Here is just a small sampling of use cases across various industries to illustrate its potential:

Customer experience, service and support  

Companies can implement AI-powered chatbots and virtual assistants to handle customer inquiries, support tickets and more. These tools use natural language processing (NLP) and generative AI capabilities to understand and respond to customer questions about order status, product details and return policies.

Chatbots and virtual assistants enable always-on support, provide faster answers to frequently asked questions (FAQs), free human agents to focus on higher-level tasks, and give customers faster, more consistent service.

Fraud detection  

Machine learning and deep learning algorithms can analyze transaction patterns and flag anomalies, such as unusual spending or login locations, that indicate fraudulent transactions. This enables organizations to respond more quickly to potential fraud and limit its impact, giving themselves and customers greater peace of mind.

Personalized marketing  

Retailers, banks and other customer-facing companies can use AI to create personalized customer experiences and marketing campaigns that delight customers, improve sales and prevent churn. Based on data from customer purchase history and behaviors, deep learning algorithms can recommend products and services customers are likely to want, and even generate personalized copy and special offers for individual customers in real time.

Human resources and recruitment  

AI-driven recruitment platforms can streamline hiring by screening resumes, matching candidates with job descriptions, and even conducting preliminary interviews using video analysis. These and other tools can dramatically reduce the mountain of administrative paperwork associated with fielding a large volume of candidates. It can also reduce response times and time-to-hire, improving the experience for candidates whether they get the job or not.

Application development and modernization  

Generative AI code generation tools and automation tools can streamline repetitive coding tasks associated with application development, and accelerate the migration and modernization (reformatting and replatorming) of legacy applications at scale. These tools can speed up tasks, help ensure code consistency and reduce errors.

Predictive maintenance  

Machine learning models can analyze data from sensors, Internet of Things (IoT) devices and operational technology (OT) to forecast when maintenance will be required and predict equipment failures before they occur. AI-powered preventive maintenance helps prevent downtime and enables you to stay ahead of supply chain issues before they affect the bottom line.

Organizations are scrambling to take advantage of the latest AI technologies and capitalize on AI's many benefits. This rapid adoption is necessary, but adopting and maintaining AI workflows comes with challenges and risks. 

Data risks  

AI systems rely on data sets that might be vulnerable to data poisoning, data tampering, data bias or cyberattacks that can lead to data breaches. Organizations can mitigate these risks by protecting data integrity and implementing security and availability throughout the entire AI lifecycle, from development to training and deployment and postdeployment.

Model risks  

Threat actors can target AI models for theft, reverse engineering or unauthorized manipulation. Attackers might compromise a model’s integrity by tampering with its architecture, weights or parameters; the core components that determine a model’s behavior, accuracy and performance.

Operational risks  

Like all technologies, models are susceptible to operational risks such as model drift, bias and breakdowns in the governance structure. Left unaddressed, these risks can lead to system failures and cybersecurity vulnerabilities that threat actors can use.

Ethics and legal risks  

If organizations don’t prioritize safety and ethics when developing and deploying AI systems, they risk committing privacy violations and producing biased outcomes. For example, biased training data used for hiring decisions might reinforce gender or racial stereotypes and create AI models that favor certain demographic groups over others.  

AI ethics is a multidisciplinary field that studies how to optimize AI's beneficial impact while reducing risks and adverse outcomes. Principles of AI ethics are applied through a system of AI governance consisted of guardrails that help ensure that AI tools and systems remain safe and ethical.  

AI governance encompasses oversight mechanisms that address risks. An ethical approach to AI governance requires the involvement of a wide range of stakeholders, including developers, users, policymakers and ethicists, helping to ensure that AI-related systems are developed and used to align with society's values.

Here are common values associated with AI ethics and responsible AI :

As AI becomes more advanced, humans are challenged to comprehend and retrace how the algorithm came to a result. Explainable AI is a set of processes and methods that enables human users to interpret, comprehend and trust the results and output created by algorithms.

Although machine learning, by its very nature, is a form of statistical discrimination, the discrimination becomes objectionable when it places privileged groups at systematic advantage and certain unprivileged groups at systematic disadvantage, potentially causing varied harms. To encourage fairness, practitioners can try to minimize algorithmic bias across data collection and model design, and to build more diverse and inclusive teams.

Robust AI effectively handles exceptional conditions, such as abnormalities in input or malicious attacks, without causing unintentional harm. It is also built to withstand intentional and unintentional interference by protecting against exposed vulnerabilities.

Organizations should implement clear responsibilities and governance structures for the development, deployment and outcomes of AI systems. In addition, users should be able to see how an AI service works, evaluate its functionality, and comprehend its strengths and limitations. Increased transparency provides information for AI consumers to better understand how the AI model or service was created.

Many regulatory frameworks, including GDPR, mandate that organizations abide by certain privacy principles when processing personal information. It is crucial to be able to protect AI models that might contain personal information, control what data goes into the model in the first place, and to build adaptable systems that can adjust to changes in regulation and attitudes around AI ethics.

In order to contextualize the use of AI at various levels of complexity and sophistication, researchers have defined several types of AI that refer to its level of sophistication:

Weak AI : Also known as “narrow AI,” defines AI systems designed to perform a specific task or a set of tasks. Examples might include “smart” voice assistant apps, such as Amazon’s Alexa, Apple’s Siri, a social media chatbot or the autonomous vehicles promised by Tesla. 

Strong AI : Also known as “artificial general intelligence” (AGI) or “general AI,” possess the ability to understand, learn and apply knowledge across a wide range of tasks at a level equal to or surpassing human intelligence . This level of AI is currently theoretical and no known AI systems approach this level of sophistication. Researchers argue that if AGI is even possible, it requires major increases in computing power. Despite recent advances in AI development, self-aware AI systems of science fiction remain firmly in that realm. 

The idea of "a machine that thinks" dates back to ancient Greece. But since the advent of electronic computing (and relative to some of the topics discussed in this article) important events and milestones in the evolution of AI include the following:

1950 Alan Turing publishes Computing Machinery and Intelligence (link resides outside ibm.com). In this paper, Turing—famous for breaking the German ENIGMA code during WWII and often referred to as the "father of computer science"—asks the following question: "Can machines think?" 

From there, he offers a test, now famously known as the "Turing Test," where a human interrogator would try to distinguish between a computer and human text response. While this test has undergone much scrutiny since it was published, it remains an important part of the history of AI, and an ongoing concept within philosophy as it uses ideas around linguistics. 

1956 John McCarthy coins the term "artificial intelligence" at the first-ever AI conference at Dartmouth College. (McCarthy went on to invent the Lisp language.) Later that year, Allen Newell, J.C. Shaw and Herbert Simon create the Logic Theorist, the first-ever running AI computer program.

1967 Frank Rosenblatt builds the Mark 1 Perceptron, the first computer based on a neural network that "learned" through trial and error. Just a year later, Marvin Minsky and Seymour Papert publish a book titled Perceptrons, which becomes both the landmark work on neural networks and, at least for a while, an argument against future neural network research initiatives. 

1980 Neural networks, which use a backpropagation algorithm to train itself, became widely used in AI applications.

1995 Stuart Russell and Peter Norvig publish Artificial Intelligence: A Modern Approach (link resides outside ibm.com), which becomes one of the leading textbooks in the study of AI. In it, they delve into four potential goals or definitions of AI, which differentiates computer systems based on rationality and thinking versus acting. 

1997 IBM's Deep Blue beats then world chess champion Garry Kasparov, in a chess match (and rematch).

2004 John McCarthy writes a paper, What Is Artificial Intelligence? (link resides outside ibm.com), and proposes an often-cited definition of AI. By this time, the era of big data and cloud computing is underway, enabling organizations to manage ever-larger data estates, which will one day be used to train AI models. 

2011 IBM Watson® beats champions Ken Jennings and Brad Rutter at Jeopardy! Also, around this time, data science begins to emerge as a popular discipline.

2015 Baidu's Minwa supercomputer uses a special deep neural network called a convolutional neural network to identify and categorize images with a higher rate of accuracy than the average human. 

2016 DeepMind's AlphaGo program, powered by a deep neural network, beats Lee Sodol, the world champion Go player, in a five-game match. The victory is significant given the huge number of possible moves as the game progresses (over 14.5 trillion after just four moves). Later, Google purchased DeepMind for a reported USD 400 million.

2022 A rise in large language models  or LLMs, such as OpenAI’s ChatGPT, creates an enormous change in performance of AI and its potential to drive enterprise value. With these new generative AI practices, deep-learning models can be pretrained on large amounts of data.

2024 The latest AI trends point to a continuing AI renaissance. Multimodal models that can take multiple types of data as input are providing richer, more robust experiences. These models bring together computer vision image recognition and NLP speech recognition capabilities. Smaller models are also making strides in an age of diminishing returns with massive models with large parameter counts. 

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2024 stands to be a pivotal year for the future of AI, as researchers and enterprises seek to establish how this evolutionary leap in technology can be most practically integrated into our everyday lives.

The European Union’s AI Act entered into force on 1 August 2024. The EU AI Act is one of the first regulations of artificial intelligence to be adopted in one of the world’s largest markets. What is it going to change for businesses?

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August 21, 2024

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Researchers explore potential for AI in biomedical science

by Rutgers Cancer Institute

Generative artificial intelligence (AI) powered by human language has made remarkable progress and gained widespread use through tools such as ChatGPT. While it is mostly known for helping with reading and writing, scientists are starting to explore how this type of AI can be used in research.

In a recent study, Rutgers researchers, including from Rutgers Cancer Institute and RWJBarnabas Health, show that generative AI can model basic biological structures, like amino acids (the building blocks of proteins) and a loop-like structure commonly found in proteins. The study was published in Scientific Reports .

Researchers also found that generative AI can analyze the way a drug and its target protein interact. These capabilities are still in an early stage but are poised to evolve alongside the rapid advancement of generative AI technology, paving the way for potential applications in the biomedical sciences , including cancer research.

Wadih Arap, MD, Ph.D., director of Rutgers Cancer Institute at University Hospital and Renata Pasqualini, Ph.D., chief of the Division of Cancer Biology at Rutgers New Jersey Medical School and Rutgers Cancer Institute researcher are senior authors of the study. Other authors include Alexander M. Ille, Ph.D.; Christopher Markosian, MD/Ph.D. student, Stephen K. Burley, MD and Michael B. Mathews, Ph.D.

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Laboratory teams with Georgia Institute of Technology for AI energy-grid research

Agreement with ai4opt will drive research and training on ai problem-solving.

August 21, 2024

A new agreement between Los Alamos National Laboratory and the National Science Foundation’s Artificial Intelligence Institute for Advances in Optimization , or AI4OPT, at Georgia Institute of Technology will drive research in applied artificial intelligence and engage students and other professionals in the future of the burgeoning field.

“This collaboration will help develop new artificial intelligence technologies for the next generation of scientific discovery and the design of complex systems and the control of engineered systems,” said Russell Bent, scientist at Los Alamos. “At Los Alamos, we have a lot of interest in doing work related to the optimization of complex systems, and we see an opportunity with AI to make systems more resilient and efficient in the face of factors like climate change, extreme events and third-party actors.”

The agreement spells out a research and educational partnership that will focus especially on AI tools for advancing a next-generation power grid. Building, maintaining and optimizing the energy grid requires extensive computation, with the time and power that that computation entails. AI-informed approaches, including modeling, could efficiently and effectively address power-grid problems.

An AI approach to optimization and problem-solving

Optimization is the practice of finding solutions that can effectively and efficiently use resources. The new research partnership uses the expertise and resources at Georgia Tech to develop “trustworthy foundation models” — models that, incorporating AI, save on the vast computing needed by some models to solve complex problems.  

In a system like energy grids, optimization means quickly sorting through possibilities and resources and delivering immediate solutions for power distribution in a crisis. The research agreement will develop “optimization proxies” that build on current optimization methods by using broader parameters, including generator limits, line ratings, generator commitments and grid topologies. While artificial intelligence approaches have helped train optimization proxies for energy applications, doing so using broader parameters has so far been a significant research challenge.

The collaboration will also concentrate on solving problems posed by the wide range of missions and applications at the Laboratory. The team’s research will build upon pioneering efforts in graph-based, physics-informed machine learning to help address Laboratory mission problems.

Outreach and training opportunities

The Laboratory will also host a Grid Science Winter School and Conference in January 2025, with dozens of students and postdoctoral researchers among the participants in multiple days of lectures on methods and techniques applicable to the electrical grid by Lab scientists and its academic partners. This year, with Georgia Tech as a co-organizer and partner, artificial intelligence optimization for the energy grid will be front and center at the event.

The Laboratory began collaborating with Georgia Tech in 2020, with a focus on the energy grid. A number of industrial and academic organizations are part of AI4OPT, including Los Alamos. The institute’s mission is to facilitate “a paradigm shift in automated decision making at massive scales by fusing AI and Mathematical Optimization, to deliver breakthroughs that neither field can achieve independently.”

“The use-inspired research in AI4OPT seeks to address fundamental societal and technological challenges,” said Pascal Van Hentenryck, AI4OPT director. “The energy grid is of special importance as a complex system that is central to our everyday life. Working with Los Alamos, we have an opportunity to advance a research mission and educational vision that makes an impact for science and our society.”   

The three-year agreement runs through 2027 and is funded as a Laboratory Directed Research and Development program director’s initiative project on advancing Artificial Intelligence for Mission, or ArtIMis, which in turn supports the Laboratory’s signature institutional commitment in support of AI. Earl Lawrence is the project’s principal investigator, with Diane Oyen and Emily Casleton joining Bent as co-principal investigators.

Bent, Castleton, Lawrence and Oyen are also members of the AI Council at the Laboratory. The AI Council is charged with helping the Lab navigate the fast-evolving landscape around AI, build investment capacities, and forge industry and academic partnerships.

As emphasized in the announcement of the Department of Energy’s Frontiers in Artificial Intelligence for Science, Security and Technology (FASST) initiative , AI technologies will broadly transform the contributions of the laboratories to national missions. This AI4OPT partnership with Georgia Tech builds key strengths for that future.

LA-UR-24-28889

Brian Keenan (505) 412-8561 [email protected]

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