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The last five years have witnessed a dramatic resurgence of excitement in the goal of creating intelligent machines. Technology companies are now investing billions of dollars in this field, new research laboratories are springing up around the globe, and competition for talent has become intense. In this Discourse Chris Bishop describes some of the recent technology breakthroughs which underpin this enthusiasm, and explores some of the many exciting opportunities which artificial intelligence offers.

Chris Bishop is the Laboratory Director at Microsoft Research Cambridge and is a professor of computer science at the University of Edinburgh. He has extensive expertise in artificial intelligence and machine learning.

This Discourse was filmed at the Royal Institution on 28 October 2016.

Large language models, like ChatGPT and Claude, have remarkably coherent communication skills. Yet, what this says about their “intelligence” isn’t clear. Is it possible that they could arrive at the same level of intelligence as humans without taking the same evolutionary or learning path to get there? Or, if they’re not on a path to human-level intelligence, where are they now and where will they end up? In this episode, with guests Tomer Ullman and Murray Shanahan, we look at how large language models function and examine differing views on how sophisticated they are and where they might be going.

Generative AI refers to a type of artificial intelligence that involves creating new and original data or content. Unlike traditional AI models that rely on large datasets and algorithms to classify or predict outcomes, generative AI models are designed to learn the underlying patterns and structure of the data and generate novel outputs that mimic human creativity.

ChatGPT is perhaps the most well-known example, but the field is far larger and more varied than text generation. Other applications of generative AI include image and video synthesis, speech generation, music composition, and virtual reality.

In this lecture, Professor Mirella Lapata will present an overview of this exciting—sometimes controversial—and rapidly evolving field.

Mirella Lapata is professor of natural language processing in the School of Informatics at the University of Edinburgh. Her research focuses on getting computers to understand, reason with, and generate natural language. She is the first recipient (2009) of the British Computer Society and Information Retrieval Specialist Group (BCS/IRSG) Karen Sparck Jones award and a Fellow of the Royal Society of Edinburgh, the ACL, and Academia Europaea.

This lecture is part of a series of events – How AI broke the internet – that explores the various angles of large-language models and generative AI in the public eye.

This series of Turing Lectures is organised in collaboration with The Royal Institution

Abstract: I will survey a current, heated debate in the AI research community on whether large pre-trained language models can be said to “understand” language—and the physical and social situations language encodes—in any important sense. I will describe arguments that have been made for and against such understanding, and, more generally, will discuss what methods can be used to fairly evaluate understanding and intelligence in AI systems. I will conclude with key questions for the broader sciences of intelligence that have arisen in light of these discussions.

Short Bio: Melanie Mitchell is Professor at the Santa Fe Institute. Her current research focuses on conceptual abstraction and analogy-making in artificial intelligence systems. Melanie is the author or editor of six books and numerous scholarly papers in the fields of artificial intelligence, cognitive science, and complex systems. Her 2009 book Complexity: A Guided Tour (Oxford University Press) won the 2010 Phi Beta Kappa Science Book Award, and her 2019 book Artificial Intelligence: A Guide for Thinking Humans (Farrar, Straus, and Giroux) was shortlisted for the 2023 Cosmos Prize for Scientific Writing.

https://www.youtube.com/watch?v=O5SLGAWSXMw

With their ability to generate human-like language and complete a variety of tasks, generative AI has the potential to revolutionise the way we communicate, learn and work. But what other doors will this technology open for us, and how can we harness it to make great leaps in technology innovation? Have we finally done it? Have we cracked AI?
Join Professor Michael Wooldridge for a fascinating discussion on the possibilities and challenges of generative AI models, and their potential impact on societies of the future.
Michael Wooldridge is Director of Foundational AI Research and Turing AI World-Leading Researcher Fellow at The Alan Turing Institute. His work focuses on multi-agent systems and developing techniques for understanding the dynamics of multi-agent systems. His research draws on ideas from game theory, logic, computational complexity, and agent-based modelling. He has been an AI researcher for more than 30 years and has published over 400 scientific articles on the subject.
This lecture is part of a series of events – How AI broke the internet – that explores the various angles of large-language models and generative AI in the public eye.
This series of Turing Lectures is organised in collaboration with The Royal Institution of Great Britain.

Professor Melanie Mitchell gives the Margaret Boden Lecture for 2023 at the University of Cambridge. The Margaret Boden lectures are held annually by the Leverhulme Centre for the Future of Intelligence at Cambridge.

Abstract: While AI has made dramatic progress over the last decade in areas such as vision, language processing, and robotics, current AI systems still lack key aspects of human intelligence. In this lecture Professor Melanie Mitchell argues that the inability to form conceptual abstractions—and to make abstraction-driven analogies—is a primary source of brittleness and unreliability in state-of-the-art AI systems. She reflects on the role played by abstraction at all levels of intelligence, and on the prospects for developing AI systems with humanlike abilities for abstract reasoning and analogy.

https://www.youtube.com/watch?v=uEN_rOxKkag

Abstract: In this talk I’ll highlight several exciting trends in the field of AI and machine learning. Through a combination of improved algorithms and major efficiency improvements in ML-specialized hardware, we are now able to build much more capable, general purpose machine learning systems than ever before. As one example of this, I’ll give an overview of the Gemini family of multimodal models and their capabilities. These new models and approaches have dramatic implications for applying ML to many problems in the world, and I’ll highlight some of these applications in science, engineering, and health. This talk will present work done by many people at Google.

Bio: Jeff Dean joined Google in 1999 where he now serves as Google’s Chief Scientist, focusing on AI advances for Google DeepMind and Google Research. His areas of focus include machine learning and AI, and applications of AI to problems that help billions of people in societally beneficial ways. His work has been integral to many generations of Google’s search engine, its initial ad serving system, distributed computing infrastructure such as BigTable and MapReduce, the Tensorflow open-source machine learning system, as well as many libraries and developer tools.

Jeff received a Ph.D. in Computer Science from the University of Washington and a B.S. in Computer Science & Economics from the University of Minnesota. He is a member of the National Academy of Engineering and of the American Academy of Arts and Sciences, a Fellow of the Association for Computing Machinery (ACM) and of the American Association for the Advancement of Sciences (AAAS), and a winner of the 2012 ACM Prize in Computing and the 2021 IEEE John von Neumann medal.

Mar 15, 2024
UC Davis College of Engineering Dean’s Distinguished Speaker Melanie Mitchell, Professor at the Santa Fe Institute, presents “The Future of Artificial Intelligence”

Melanie Mitchell Santa Fe Institute AI is all around us recognizing our faces in photos, transcribing our speech, constructing our news feeds, navigating our driving routes, answering our search queries, and much more. But rapidly improving AI is poised to play a much bigger role in all of our lives. In this lecture, AI expert Melanie Mitchell will demystify how current-day AI works, how “intelligent” it really is, and what our expectations—and concerns—about its near-term and long-term prospects should be.

The United Nations Institute for Training and Research (UNITAR) delivers innovative training and conducts research on knowledge systems to increase the capacity of beneficiaries to respond to global and constantly evolving challenges.

https://www.youtube.com/watch?v=mDGsaRJ-eqA

Artificial Intelligence, the History and Future – with Chris Bishop

Nature of Intelligence, Episode 3: What kind of intelligence is an LLM?

The Turing Lectures: What is generative AI?

The Debate Over “Understanding” in AI’s Large Language Models

The Turing Lectures: The future of generative AI

Melanie Mitchell – Abstraction and Analogy: The Keys to Robust Artificial Intelligence

Jeff Dean (Google): Exciting Trends in Machine Learning

Melanie Mitchell Presents “The Future of Artificial Intelligence”

The Future of Artificial Intelligence

Artificial Intelligence and Diplomacy: Navigating the Intersection of Technology and Int. Relations

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