What did Google's AI Co-Scientist "Discover"? The Human Scientists' POV, from the Podovirus podcast
We're following up on our recent episode on Google's AI Co-Scientist with a special crossover episode from the Podovirus podcast. Hosts Dr Jessica Sacher and Dr Joe Campbell speak with José Penadés and Tiago Costa, scientists at Imperial College London who made a surprising discovery that Google's AI Co-Scientist later put forward as a hypothesis entirely on its own.
The episode explores the fascinating world of bacteriophages (viruses that infect bacteria) and phage-inducible chromosomal islands (PICIs) - DNA sequences that hijack virus reproduction to spread themselves as a bacterial defense mechanism. The key mystery was how capsid-forming PICIs, which only encode virus heads without tails, managed to spread across different bacteria. The surprising answer, which eluded human scientists for years but which Google's AI Co-Scientist discovered through literature analysis, is that these capsids evolved to connect with various virus tails in the environment.
This episode demonstrates how AI can now contribute to frontier scientific research beyond just grunt work - providing unbiased perspectives and key insights that accelerate discovery. It's a vivid example of science fiction becoming reality in our lifetimes.
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CHAPTERS:
(00:00) About the Episode
(04:37) Welcome to Podovirus Podcast
(04:58) Introducing the Special Guests and Topic
(06:30) Exploring Mobile Genetic Elements
(13:20) The Role of AI in Phage Research
(16:48) Mechanisms of Gene Transfer (Part 1)
(20:10) Sponsors: Oracle Cloud Infrastructure | NetSuite by Oracle
(22:43) Mechanisms of Gene Transfer (Part 2)
(23:36) Insights and Discoveries
(28:45) Future Directions and Applications (Part 1)
(32:35) Sponsors: Shopify
(34:32) Future Directions and Applications (Part 2)
(41:22) Unbiased Systems and Conjugation
(42:35) Google's Excitement and Experimental Evidence
(45:39) Benchmarking AI Systems
(49:29) Manuscript Revisions and Future Plans
(51:53) AI as a Collaborator in Scientific Research
(54:52) Challenges and Hypotheses in Phage Biology
(57:58) Future of AI in Scientific Research
(01:05:52) Concluding Thoughts and Future Collaborations
(01:12:07) Outro
Prof. Murray Shanahan - Machines Don't Think Like Us
Murray Shanahan is a professor of Cognitive Robotics at Imperial College London and a senior research scientist at DeepMind. He challenges our assumptions about AI consciousness and urges us to rethink how we talk about machine intelligence.
We explore the dangers of anthropomorphizing AI, the limitations of current language in describing AI capabilities, and the fascinating intersection of philosophy and artificial intelligence.
Show notes and full references: https://docs.google.com/document/d/1ICtBI574W-xGi8Z2ZtUNeKWiOiGZ_DRsp9EnyYAISws/edit?usp=sharing
Prof Murray Shanahan:
https://www.doc.ic.ac.uk/~mpsha/ (look at his selected publications)
https://scholar.google.co.uk/citations?user=00bnGpAAAAAJ&hl=en
https://en.wikipedia.org/wiki/Murray_Shanahan
https://x.com/mpshanahan
Interviewer: Dr. Tim Scarfe
Refs (links in the Google doc linked above):
Role play with large language models
Waluigi effect
"Conscious Exotica" - Paper by Murray Shanahan (2016)
"Simulators" - Article by Janis from LessWrong
"Embodiment and the Inner Life" - Book by Murray Shanahan (2010)
"The Technological Singularity" - Book by Murray Shanahan (2015)
"Simulacra as Conscious Exotica" - Paper by Murray Shanahan (newer paper of the original focussed on LLMs)
A recent paper by Anthropic on using autoencoders to find features in language models (referring to the "Scaling Monosemanticity" paper)
Work by Peter Godfrey-Smith on octopus consciousness
"Metaphors We Live By" - Book by George Lakoff (1980s)
Work by Aaron Sloman on the concept of "space of possible minds" (1984 article mentioned)
Wittgenstein's "Philosophical Investigations" (posthumously published)
Daniel Dennett's work on the "intentional stance"
Alan Turing's original paper on the Turing Test (1950)
Thomas Nagel's paper "What is it like to be a bat?" (1974)
John Searle's Chinese Room Argument (mentioned but not detailed)
Work by Richard Evans on tackling reasoning problems
Claude Shannon's quote on knowledge and control
"Are We Bodies or Souls?" - Book by Richard Swinburne
Reference to work by Ethan Perez and others at Anthropic on potential deceptive behavior in language models
Reference to a paper by Murray Shanahan and Antonia Creswell on the "selection inference framework"
Mention of work by Francois Chollet, particularly the ARC (Abstraction and Reasoning Corpus) challenge
Reference to Elizabeth Spelke's work on core knowledge in infants
Mention of Karl Friston's work on planning as inference (active inference)
The film "Ex Machina" - Murray Shanahan was the scientific advisor
"The Waluigi Effect"
Anthropic's constitutional AI approach
Loom system by Lara Reynolds and Kyle McDonald for visualizing conversation trees
DeepMind's AlphaGo (mentioned multiple times as an example)
Mention of the "Golden Gate Claude" experiment
Reference to an interview Tim Scarfe conducted with University of Toronto students about self-attention controllability theorem
Mention of an interview with Irina Rish
Reference to an interview Tim Scarfe conducted with Daniel Dennett
Reference to an interview with Maria Santa Caterina
Mention of an interview with Philip Goff
Nick Chater and Martin Christianson's book ("The Language Game: How Improvisation Created Language and Changed the World")
Peter Singer's work from 1975 on ascribing moral status to conscious beings
Demis Hassabis' discussion on the "ladder of creativity"
Reference to B.F. Skinner and behaviorism
MULTI AGENT LEARNING - LANCELOT DA COSTA
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https://discord.gg/aNPkGUQtc5
https://twitter.com/MLStreetTalk
Lance Da Costa aims to advance our understanding of intelligent systems by modelling cognitive systems and improving artificial systems.
He's a PhD candidate with Greg Pavliotis and Karl Friston jointly at Imperial College London and UCL, and a student in the Mathematics of Random Systems CDT run by Imperial College London and the University of Oxford. He completed an MRes in Brain Sciences at UCL with Karl Friston and Biswa Sengupta, an MASt in Pure Mathematics at the University of Cambridge with Oscar Randal-Williams, and a BSc in Mathematics at EPFL and the University of Toronto.
Summary:
Lance did pure math originally but became interested in the brain and AI. He started working with Karl Friston on the free energy principle, which claims all intelligent agents minimize free energy for perception, action, and decision-making. Lance has worked to provide mathematical foundations and proofs for why the free energy principle is true, starting from basic assumptions about agents interacting with their environment. This aims to justify the principle from first physics principles. Dr. Scarfe and Da Costa discuss different approaches to AI - the free energy/active inference approach focused on mimicking human intelligence vs approaches focused on maximizing capability like deep reinforcement learning. Lance argues active inference provides advantages for explainability and safety compared to black box AI systems. It provides a simple, sparse description of intelligence based on a generative model and free energy minimization. They discuss the need for structured learning and acquiring core knowledge to achieve more human-like intelligence. Lance highlights work from Josh Tenenbaum's lab that shows similar learning trajectories to humans in a simple Atari-like environment.
Incorporating core knowledge constraints the space of possible generative models the agent can use to represent the world, making learning more sample efficient. Lance argues active inference agents with core knowledge can match human learning capabilities.
They discuss how to make generative models interpretable, such as through factor graphs. The goal is to be able to understand the representations and message passing in the model that leads to decisions.
In summary, Lance argues active inference provides a principled approach to AI with advantages for explainability, safety, and human-like learning. Combining it with core knowledge and structural learning aims to achieve more human-like artificial intelligence.
https://www.lancelotdacosta.com/
https://twitter.com/lancelotdacosta
Interviewer: Dr. Tim Scarfe
TOC
00:00:00 - Start
00:09:27 - Intelligence
00:12:37 - Priors / structure learning
00:17:21 - Core knowledge
00:29:05 - Intelligence is specialised
00:33:21 - The magic of agents
00:39:30 - Intelligibility of structure learning
#artificialintelligence #activeinference
#93 Prof. MURRAY SHANAHAN - Consciousness, Embodiment, Language Models
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Professor Murray Shanahan is a renowned researcher on sophisticated cognition and its implications for artificial intelligence. His 2016 article ‘Conscious Exotica’ explores the Space of Possible Minds, a concept first proposed by philosopher Aaron Sloman in 1984, which includes all the different forms of minds from those of other animals to those of artificial intelligence. Shanahan rejects the idea of an impenetrable realm of subjective experience and argues that the majority of the space of possible minds may be occupied by non-natural variants, such as the ‘conscious exotica’ of which he speaks. In his paper ‘Talking About Large Language Models’, Shanahan discusses the capabilities and limitations of large language models (LLMs). He argues that prompt engineering is a key element for advanced AI systems, as it involves exploiting prompt prefixes to adjust LLMs to various tasks. However, Shanahan cautions against ascribing human-like characteristics to these systems, as they are fundamentally different and lack a shared comprehension with humans. Even though LLMs can be integrated into embodied systems, it does not mean that they possess human-like language abilities. Ultimately, Shanahan concludes that although LLMs are formidable and versatile, we must be wary of over-simplifying their capacities and limitations.
YT version: https://youtu.be/BqkWpP3uMMU
Full references on the YT description.
[00:00:00] Introduction
[00:08:51] Consciousness and Consciousness Exotica
[00:34:59] Slightly Consciousness LLMs
[00:38:05] Embodiment
[00:51:32] Symbol Grounding
[00:54:13] Emergence
[00:57:09] Reasoning
[01:03:16] Intentional Stance
[01:07:06] Digression on Chomsky show and Andrew Lampinen
[01:10:31] Prompt Engineering
Find Murray online:
https://www.doc.ic.ac.uk/~mpsha/
https://twitter.com/mpshanahan?lang=en
https://scholar.google.co.uk/citations?user=00bnGpAAAAAJ&hl=en
MLST Discord: https://discord.gg/aNPkGUQtc5
Trends in Graph Machine Learning with Michael Bronstein - #446
Today we’re back with the final episode of AI Rewind joined by Michael Bronstein, a professor at Imperial College London and the Head of Graph Machine Learning at Twitter.
In our conversation with Michael, we touch on his thoughts about the year in Machine Learning overall, including GPT-3 and Implicit Neural Representations, but spend a major chunk of time on the sub-field of Graph Machine Learning.
We talk through the application of Graph ML across domains like physics and bioinformatics, and the tools to look out for. Finally, we discuss what Michael thinks is in store for 2021, including graph ml applied to molecule discovery and non-human communication translation.
Graph ML Research at Twitter with Michael Bronstein - #394
Today we’re excited to be joined by return guest Michael Bronstein, Head of Graph Machine Learning at Twitter. In our conversation, we discuss the evolution of the graph machine learning space, his new role at Twitter, and some of the research challenges he’s faced, including scalability and working with dynamic graphs. Michael also dives into his work on differential graph modules for graph CNNs, and the various applications of this work.
Philosophy of Intelligence with Matthew Crosby - TWiML Talk #91
This week on the podcast we’re featuring a series of conversations from the NIPs conference in Long Beach, California. I attended a bunch of talks and learned a ton, organized an impromptu roundtable on Building AI Products, and met a bunch of great people, including some former TWiML Talk guests.This time around i'm joined by Matthew Crosby, a researcher at Imperial College London, working on the Kinds of Intelligence Project. Matthew joined me after the NIPS Symposium of the same name, an event that brought researchers from a variety of disciplines together towards three aims: a broader perspective of the possible types of intelligence beyond human intelligence, better measurements of intelligence, and a more purposeful analysis of where progress should be made in AI to best benefit society. Matthew’s research explores intelligence from a philosophical perspective, exploring ideas like predictive processing and controlled hallucination, and how these theories of intelligence impact the way we approach creating artificial intelligence. This was a very interesting conversation, i'm sure you’ll enjoy.
Geometric Deep Learning with Joan Bruna & Michael Bronstein - TWiML Talk #90
This week on the podcast we’re featuring a series of conversations from the NIPs conference in Long Beach, California. I attended a bunch of talks and learned a ton, organized an impromptu roundtable on Building AI Products, and met a bunch of great people, including some former TWiML Talk guests. This time around I'm joined by Joan Bruna, Assistant Professor at the Courant Institute of Mathematical Sciences and the Center for Data Science at NYU, and Michael Bronstein, associate professor at Università della Svizzera italiana (Switzerland) and Tel Aviv University. Joan and Michael join me after their tutorial on Geometric Deep Learning on Graphs and Manifolds. In our conversation we dig pretty deeply into the ideas behind geometric deep learning and how we can use it in applications like 3D vision, sensor networks, drug design, biomedicine, and recommendation systems. This is definitely a Nerd Alert show, and one that will get your multi-dimensional neurons firing. Enjoy!
a16z Podcast: Artificial Intelligence and the 'Space of Possible Minds'
What is A.I. or artificial intelligence but the 'space of possible minds', argues Murray Shanahan, scientific advisor on the movie Ex Machina and Professor of Cognitive Robotics at Imperial College London.
In this special episode of the a16z Podcast brought to you on the ground from London, Shanahan -- along with journalist-turned-entrepreneur Azeem Azhar (who also curates The Exponential View newsletter on AI and more) and The Economist Deputy Editor Tom Standage (the author of several tech history books) -- we discuss the past, present, and future of A.I. ... as well as how it fits (or doesn't fit) with machine learning and deep learning.
But where are we now in the A.I. evolution? What players do we think will lead, if not win, the current race? And how should we think about issues such as ethics and automation of jobs without descending into obvious extremes? All this and more, including a surprise easter egg in Ex Machina shared by Shanahan, whose work influenced the movie.
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