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Best Dataset Paper Award at the Learning at Scale Conference

Best Paper Award at the User Interface Software and Technology (UIST) Conference

President’s Medal, Stevens Institute of Technology

Alan Perlis Award for Imagination in Computer Science, Carnegie Mellon University

10-Year Outstanding Research Contributions Award, Brain Informatics Conference

Generative AI Task Force : Chair

Spring: Studying papers and reasoning to play games

Read and reap the rewards: Learning to play atari with the help of instruction manuals

Combining computational controls with natural text reveals aspects of meaning composition

Mind Matters: A Tribute to Allen Newell (Carnegie Mellon Symposia on Cognition Series)

Recent Advances in Robot Learning: Machine Learning (The Springer International Series in Engineering and Computer Science, 368)

Machine Learning: An Artificial Intelligence Approach (Volume I)

Tom Mitchell is interested in many areas of computer science, but especially in how to construct computers that learn from experience. At the heart of the problem of machine learning is the question of how to automatically formulate general hypotheses given a collection of very specific training examples. His research has addressed a number of approaches to this question, including statistical approaches that find regularities over large numbers of training examples, and analytical approaches that generalize from very few examples and rely instead on prior knowledge and reasoning.

Machine Learning

Artificial Intelligence

Computational Neuroscience

Carnegie Mellon University Profile 

Personal Website

Google Scholar Citations

How AI is infiltrating labor union contracts, in Pittsburgh and beyond

As artificial intelligence surges across daily life, so do concerns AI

Why a YouTube Chat About Chess Got Flagged for Hate Speech

Founders University Professor

Artificial intelligence is already revolutionizing almost every aspect of society.  But what additional advancements will we see in the near future?  And what ethical issues should we address along the way?








In this, the second of a two-part episode – and our Season One finale – we explore what’s next for AI in areas such as disaster response, robotics, creativity, health care and transportation. And we discuss the ethical considerations surrounding its use and the importance of regulation to ensure safe and beneficial outcomes for society.








Sharing their insights on this episode are AI experts from Carnegie Mellon University and various organizations and companies including: Rachel Mandelbaum, Aarti Singh, Robin Murphy, Tom Mitchell, Dan Green, Deva Ramanan, Jim Garrett, Zach Lipton, Carmel Majidi, Assaf Gad, Martial Hebert, Raj Reddy, Ramayya Krishnan, Chris Mueller, and Drew Thurlow.








We’ll see you for Season Two of Where What If Becomes What’s Next from Carnegie Mellon University.  Stay tuned!

Mind to Machine Part 2: What’s Next for AI

On February 5, 2024, Tom Mitchell from Carnegie Mellon visited the Lab to talk about "Four Trends in Generative AI." 

Abstract
Generative AI systems such as ChatGPT represent an inflection point in technical progress in AI. They exhibit new abilities that were unpredicted just a few years ago, from passing law school entrance exams, to writing mathematical proofs in the form of poetry. Where is this technology headed? Although nobody can predict the future, we should still try. This talk will present four fundamental trends that will help shape the future of generative AI over the coming years.

Tom Mitchell: Four Trends in Generative AI

ABSTRACT:
 
If there is ever going to be a decade in which AI revolutionizes education, it should be this decade.  We are in a unique position historically because of the confluence of two key trends: (1) we are currently seeing dramatic progress in AI with systems such as ChatGPT, and (2) several online education platforms now routinely teach millions of students, providing plenty of training data from which we should be able to learn to teach better.  This talk will explore the current state of the art, and where we might be headed.
 
BIO: 
Tom M. Mitchell is the Founders University Professor at Carnegie Mellon University, where he founded the world's first Machine Learning Department.   His research interests include machine learning, artificial intelligence, cognitive neuroscience, and the impact of AI on society.   A current focus is on AI and education, where he has worked closely with organizations that field computer learning environments to millions of students, including Squirrel AI in China, and CK12.org in the U.S.   He has testified to a variety of U.S. congressional committees regarding potential uses and impacts of artificial intelligence, and is currently co-chairing a U.S. National Academies study on AI and the future of work, as well as a task force studying Large Language Models for the Special Competitive Studies Project.  Mitchell is an elected member of the U.S. National Academy of Engineering, and the American Academy of Arts and Sciences, and a Fellow and Past President of the Association for the Advancement of Artificial Intelligence (AAAI).   
 


website: www.cs.cmu.edu/Thi~tom
Hi-resolution photo: www.cs.cmu.edu/~tom/TomWEF.jpg
Longer CV: http://www.cs.cmu.edu/~tom/resume.pdf

Where Can AI Take Education by 2030? With Dr. Tom Mitchell

This presentation originally premiered at AI at Wharton's inaugural AI and the Future of Work Conference, held on campus at the Wharton School on May 22-23, 2024. 

Tom M. Mitchell
Founders University Professor, Machine Learning Department,
Carnegie Mellon University

To learn more, visit: https://ai.wharton.upenn.edu/events/ai-and-the-future-of-work-conference/

Keynote Presentation: Tom Mitchell – Wharton AI &amp; the Future of Work Conference 2024

In this thought-provoking YouTube video, join us as we delve into the rapidly evolving landscape of artificial intelligence. Tom Mitchell, Carnegie Mellon University takes us on a journey through the latest advancements and emerging trends that are shaping the future of AI technology.
Throughout the talk, Tom Mitchell provides insightful commentary on the remarkable developments in generative AI systems, drawing from decades of experience in the field. From the groundbreaking capabilities of systems like GPT-4 to the innovative integration of AI in various domains, this presentation offers a comprehensive overview of where we stand today.
Highlights include demonstrations of AI's prowess, such as generating rhyming proofs of mathematical theorems and providing personalized assistance based on email content. Additionally, Tom Mitchell explores the implications of personal large language models and the rise of multimodal foundational models, offering intriguing insights into the potential applications of AI in our daily lives.
Moreover, Tom Mitchell discusses the paradigm shift towards open-source AI models, challenging the conventional wisdom surrounding AI development and democratizing access to cutting-edge technology.
Whether you're a seasoned AI enthusiast or a curious observer, this video promises to spark your imagination and ignite your curiosity about the future of artificial intelligence. Join us as we unravel the possibilities and embrace the exciting prospects that lie ahead in the world of AI.
Don't miss out on this captivating discussion—watch now and embark on a journey into the future of AI!
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