Today's guest Mariana Mazzucato is one of our most requested. Mazzucato, a professor of economics at University College London and the founding director of its Institute for Innovation and Public Purpose, specializes in the political economy of technological development and public sector investment. In our conversation, recorded in Madrid while at the Bloomberg CityLab conference, she explains her concept of the "mission economy," her definition of state capacity, how to prevent top talent from fleeing to the private sector, and whether consultants or governments should be blamed for inefficiencies and civic failures. It's a wide-ranging interview, one that covers everything from the initial public financing of Silicon Valley algorithms to the history of moonshots.
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Your skin heals after a scratch. What if our roads, bridges and cities could self-repair after getting damaged, too? Scientist and engineer Mark Miodownik describes a new class of materials — animate matter — with the potential to sense damage, self-heal and even biodegrade when the job is done. Humanity's next great leap isn't making more stuff, he says — it's making stuff that doesn't fall apart.
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How could Karl Friston's Free Energy Principle become a blueprint for the future of AI?
In this episode of Eye on AI, host Craig Smith sits down with Karl Friston, the neuroscientist behind the Free Energy Principle and advisor to Verses AI, to explore how active inference and brain inspired generative models might move us beyond transformer based systems. They unpack how Axiom, Verses' new architecture, uses probabilistic beliefs and message passing to build agents that learn like brains instead of just predicting the next token.
We look at why transformers face scaling and reliability limits, how Free Energy unifies prediction, perception, and action, and what it means for an AI system to carry explicit uncertainty instead of overconfident guesses. Learn how active inference supports continual learning without catastrophic forgetting, how structure learning lets models grow and prune themselves, and why embodiment and interaction with the real world are essential for grounding language and meaning.
You will also hear how Axiom can sit beside or beneath large language models, how explicit uncertainty can reduce hallucinations in high stakes workflows, and where these ideas are already being tested in areas like logistics, robotics, and autonomous agents. By the end of the episode, you will have a clearer picture of how Karl Friston's Free Energy blueprint could reshape AI architectures, from enterprise planning systems to embodied agents that understand and act in the world.
Stay Updated: Craig Smith on X: https://x.com/craigss Eye on A.I. on X: https://x.com/EyeOn_AI
Nick Lane has some pretty wild ideas about the evolution of life.
He thinks early life was continuous with the spontaneous chemistry of undersea hydrothermal vents.
Nick’s story may be wrong, but I find it remarkable that with just that starting point, you can explain so much about why life is the way that it is — the things you’re supposed to just take as givens in biology class:
* Why are there two sexes? Why sex at all?
* Why are bacteria so simple despite being around for 4 billion years? Why is there so much shared structure between all eukaryotic cells despite the enormous morphological variety between animals, plants, fungi, and protists?
* Why did the endosymbiosis event that led to eukaryotes happen only once, and in the particular way that it did?
* Why is all life powered by proton gradients? Why does all life on Earth share not only the Krebs Cycle, but even the intermediate molecules like Acetyl-CoA?
His theory implies that early life is almost chemically inevitable (potentially blooming on hundreds of millions of planets in the Milky Way alone), and that the real bottleneck is the complex eukaryotic cell.
Watch on YouTube; listen on Apple Podcasts or Spotify.
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Timestamps
(00:00:00) – The singularity that unlocked complex life
(00:08:26) – Early life continuous with Earth's geochemistry
(00:23:36) – Eukaryotes are the great filter for intelligent life
(00:42:16) – Mitochondria are the reason we have sex
(01:08:12) – Are bioelectric fields linked to consciousness?
Ref: 868329
Get full access to Dwarkesh Podcast at www.dwarkesh.com/subscribe
In this episode, hosts Tim and Keith finally realize their long-held dream of sitting down with their hero, the brilliant neuroscientist Professor Karl Friston. The conversation is a fascinating and mind-bending journey into Professor Friston's life's work, the Free Energy Principle, and what it reveals about life, intelligence, and consciousness itself.
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***
They kick things off by looking back on the 20-year journey of the Free Energy Principle. Professor Friston explains it as a fundamental rule for survival: all living things, from a single cell to a human being, are constantly trying to make sense of the world and reduce unpredictability. It’s this drive to minimize surprise that allows things to exist and maintain their structure.
This leads to a bigger question: What does it truly mean to be "intelligent"? The group debates whether intelligence is everywhere, even in a virus or a plant, or if it requires a certain level of complexity.
Professor Friston introduces the idea of different "kinds" of things, suggesting that creatures like us, who can model themselves and think about the future, possess a unique and "strange" kind of agency that sets us apart.
From intelligence, the discussion naturally flows to the even trickier concept of consciousness. Is it the same as intelligence? Professor Friston argues they are different. He explains that consciousness might emerge from deep, layered self-awareness—not just acting, but understanding that you are the one causing your actions and thinking about your place in the world.
They also explore intelligence at different sizes. Is a corporation intelligent? What about the entire planet? Professor Friston suggests there might be a "Goldilocks zone" for intelligence. It doesn't seem to exist at the super-tiny atomic level or at the massive scale of planets and solar systems, but thrives in the complex middle-ground where we live.
Finally, they tackle one of the most pressing topics of our time: Can we build a truly conscious AI? Professor Friston shares his doubts about whether our current computers are capable of a feat like that. He suggests that genuine consciousness might require a different kind of "mortal" computation, where the machine's physical body and its "mind" are inseparable, much like in biological creatures.
TRANSCRIPT:
https://app.rescript.info/public/share/FZkF8BO7HMt9aFfu2_q69WGT_ZbYZ1VVkC6RtU3eeOI
TOC:
00:00:00: Introduction & Retrospective on the Free Energy Principle
00:09:34: Strange Particles, Agency, and Consciousness
00:37:45: The Scale of Intelligence: From Viruses to the Biosphere
01:01:35: Modelling, Boundaries, and Practical Application
01:21:12: Conclusion
Laura Ruis, a PhD student at University College London and researcher at Cohere, explains her groundbreaking research into how large language models (LLMs) perform reasoning tasks, the fundamental mechanisms underlying LLM reasoning capabilities, and whether these models primarily rely on retrieval or develop procedural knowledge.
SPONSOR MESSAGES:
***
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Tufa AI Labs is a brand new research lab in Zurich started by Benjamin Crouzier focussed on o-series style reasoning and AGI. Are you interested in working on reasoning, or getting involved in their events?
Goto https://tufalabs.ai/
***
TOC
1. LLM Foundations and Learning
1.1 Scale and Learning in Language Models [00:00:00]
1.2 Procedural Knowledge vs Fact Retrieval [00:03:40]
1.3 Influence Functions and Model Analysis [00:07:40]
1.4 Role of Code in LLM Reasoning [00:11:10]
1.5 Semantic Understanding and Physical Grounding [00:19:30]
2. Reasoning Architectures and Measurement
2.1 Measuring Understanding and Reasoning in Language Models [00:23:10]
2.2 Formal vs Approximate Reasoning and Model Creativity [00:26:40]
2.3 Symbolic vs Subsymbolic Computation Debate [00:34:10]
2.4 Neural Network Architectures and Tensor Product Representations [00:40:50]
3. AI Agency and Risk Assessment
3.1 Agency and Goal-Directed Behavior in Language Models [00:45:10]
3.2 Defining and Measuring Agency in AI Systems [00:49:50]
3.3 Core Knowledge Systems and Agency Detection [00:54:40]
3.4 Language Models as Agent Models and Simulator Theory [01:03:20]
3.5 AI Safety and Societal Control Mechanisms [01:07:10]
3.6 Evolution of AI Capabilities and Emergent Risks [01:14:20]
REFS:
[00:01:10] Procedural Knowledge in Pretraining & LLM Reasoning
Ruis et al., 2024
https://arxiv.org/abs/2411.12580
[00:03:50] EK-FAC Influence Functions in Large LMs
Grosse et al., 2023
https://arxiv.org/abs/2308.03296
[00:13:05] Surfaces and Essences: Analogy as the Core of Cognition
Hofstadter & Sander
https://www.amazon.com/Surfaces-Essences-Analogy-Fuel-Thinking/dp/0465018475
[00:13:45] Wittgenstein on Language Games
https://plato.stanford.edu/entries/wittgenstein/
[00:14:30] Montague Semantics for Natural Language
https://plato.stanford.edu/entries/montague-semantics/
[00:19:35] The Chinese Room Argument
David Cole
https://plato.stanford.edu/entries/chinese-room/
[00:19:55] ARC: Abstraction and Reasoning Corpus
François Chollet
https://arxiv.org/abs/1911.01547
[00:24:20] Systematic Generalization in Neural Nets
Lake & Baroni, 2023
https://www.nature.com/articles/s41586-023-06668-3
[00:27:40] Open-Endedness & Creativity in AI
Tim Rocktäschel
https://arxiv.org/html/2406.04268v1
[00:30:50] Fodor & Pylyshyn on Connectionism
https://www.sciencedirect.com/science/article/abs/pii/0010027788900315
[00:31:30] Tensor Product Representations
Smolensky, 1990
https://www.sciencedirect.com/science/article/abs/pii/000437029090007M
[00:35:50] DreamCoder: Wake-Sleep Program Synthesis
Kevin Ellis et al.
https://courses.cs.washington.edu/courses/cse599j1/22sp/papers/dreamcoder.pdf
[00:36:30] Compositional Generalization Benchmarks
Ruis, Lake et al., 2022
https://arxiv.org/pdf/2202.10745
[00:40:30] RNNs & Tensor Products
McCoy et al., 2018
https://arxiv.org/abs/1812.08718
[00:46:10] Formal Causal Definition of Agency
Kenton et al.
https://arxiv.org/pdf/2208.08345v2
[00:48:40] Agency in Language Models
Sumers et al.
https://arxiv.org/abs/2309.02427
[00:55:20] Heider & Simmel’s Moving Shapes Experiment
https://www.nature.com/articles/s41598-024-65532-0
[01:00:40] Language Models as Agent Models
Jacob Andreas, 2022
https://arxiv.org/abs/2212.01681
[01:13:35] Pragmatic Understanding in LLMs
Ruis et al.
https://arxiv.org/abs/2210.14986
Today, we're joined by Tim Rocktäschel, senior staff research scientist at Google DeepMind, professor of Artificial Intelligence at University College London, and author of the recently published popular science book, “Artificial Intelligence: 10 Things You Should Know.” We dig into the attainability of artificial superintelligence and the path to achieving generalized superhuman capabilities across multiple domains. We discuss the importance of open-endedness in developing autonomous and self-improving systems, as well as the role of evolutionary approaches and algorithms. Additionally, we cover Tim’s recent research projects such as “Promptbreeder,” “Debating with More Persuasive LLMs Leads to More Truthful Answers,” and more.
The complete show notes for this episode can be found at https://twimlai.com/go/706.
Prof. Tim Rocktäschel, AI researcher at UCL and Google DeepMind, talks about open-ended AI systems. These systems aim to keep learning and improving on their own, like evolution does in nature.
Ad: Are you a hardcore ML engineer who wants to work for Daniel Cahn at SlingshotAI building AI for mental health? Give him an email! - danielc@slingshot.xyz
TOC:
00:00:00 Introduction to Open-Ended AI and Key Concepts
00:01:37 Tim Rocktäschel's Background and Research Focus
00:06:25 Defining Open-Endedness in AI Systems
00:10:39 Subjective Nature of Interestingness and Learnability
00:16:22 Open-Endedness in Practice: Examples and Limitations
00:17:50 Assessing Novelty in Open-ended AI Systems
00:20:05 Adversarial Attacks and AI Robustness
00:24:05 Rainbow Teaming and LLM Safety
00:25:48 Open-ended Research Approaches in AI
00:29:05 Balancing Long-term Vision and Exploration in AI Research
00:37:25 LLMs in Program Synthesis and Open-Ended Learning
00:37:55 Transition from Human-Based to Novel AI Strategies
00:39:00 Expanding Context Windows and Prompt Evolution
00:40:17 AI Intelligibility and Human-AI Interfaces
00:46:04 Self-Improvement and Evolution in AI Systems
Show notes (New!) https://www.dropbox.com/scl/fi/5avpsyz8jbn4j1az7kevs/TimR.pdf?rlkey=pqjlcqbtm3undp4udtgfmie8n&st=x50u1d1m&dl=0
REFS:
00:01:47 - UCL DARK Lab (Rocktäschel) - AI research lab focusing on RL and open-ended learning - https://ucldark.com/
00:02:31 - GENIE (Bruce) - Generative interactive environment from unlabelled videos - https://arxiv.org/abs/2402.15391
00:02:42 - Promptbreeder (Fernando) - Self-referential LLM prompt evolution - https://arxiv.org/abs/2309.16797
00:03:05 - Picbreeder (Secretan) - Collaborative online image evolution - https://dl.acm.org/doi/10.1145/1357054.1357328
00:03:14 - Why Greatness Cannot Be Planned (Stanley) - Book on open-ended exploration - https://www.amazon.com/Why-Greatness-Cannot-Planned-Objective/dp/3319155237
00:04:36 - NetHack Learning Environment (Küttler) - RL research in procedurally generated game - https://arxiv.org/abs/2006.13760
00:07:35 - Open-ended learning (Clune) - AI systems for continual learning and adaptation - https://arxiv.org/abs/1905.10985
00:07:35 - OMNI (Zhang) - LLMs modeling human interestingness for exploration - https://arxiv.org/abs/2306.01711
00:10:42 - Observer theory (Wolfram) - Computationally bounded observers in complex systems - https://writings.stephenwolfram.com/2023/12/observer-theory/
00:15:25 - Human-Timescale Adaptation (Rocktäschel) - RL agent adapting to novel 3D tasks - https://arxiv.org/abs/2301.07608
00:16:15 - Open-Endedness for AGI (Hughes) - Importance of open-ended learning for AGI - https://arxiv.org/abs/2406.04268
00:16:35 - POET algorithm (Wang) - Open-ended approach to generate and solve challenges - https://arxiv.org/abs/1901.01753
00:17:20 - AlphaGo (Silver) - AI mastering the game of Go - https://deepmind.google/technologies/alphago/
00:20:35 - Adversarial Go attacks (Dennis) - Exploiting weaknesses in Go AI systems - https://www.ifaamas.org/Proceedings/aamas2024/pdfs/p1630.pdf
00:22:00 - Levels of AGI (Morris) - Framework for categorizing AGI progress - https://arxiv.org/abs/2311.02462
00:24:30 - Rainbow Teaming (Samvelyan) - LLM-based adversarial prompt generation - https://arxiv.org/abs/2402.16822
00:25:50 - Why Greatness Cannot Be Planned (Stanley) - 'False compass' and 'stepping stone collection' concepts - https://www.amazon.com/Why-Greatness-Cannot-Planned-Objective/dp/3319155237
00:27:45 - AI Debate (Khan) - Improving LLM truthfulness through debate - https://proceedings.mlr.press/v235/khan24a.html
00:29:40 - Gemini (Google DeepMind) - Advanced multimodal AI model - https://deepmind.google/technologies/gemini/
00:30:15 - How to Take Smart Notes (Ahrens) - Effective note-taking methodology - https://www.amazon.com/How-Take-Smart-Notes-Nonfiction/dp/1542866502
(truncated, see shownotes)
Thomas Parr and his collaborators wrote a book titled "Active Inference: The Free Energy Principle in Mind, Brain and Behavior" which introduces Active Inference from both a high-level conceptual perspective and a low-level mechanistic, mathematical perspective.
Active inference, developed by the legendary neuroscientist Prof. Karl Friston - is a unifying mathematical framework which frames living systems as agents which minimize surprise and free energy in order to resist entropy and persist over time. It unifies various perspectives from physics, biology, statistics, and psychology - and allows us to explore deep questions about agency, biology, causality, modelling, and consciousness.
Buy Active Inference: The Free Energy Principle in Mind, Brain, and Behavior
https://amzn.to/4dj0iMj
YT version: https://youtu.be/lbb-Si5wa_o
Please support us on Patreon to get access to the private Discord server, bi-weekly calls, early access and ad-free listening.
https://patreon.com/mlst
Chapters should be embedded in the mp3, let me me know if issues
Watch behind the scenes, get early access and join the private Discord by supporting us on Patreon:
https://patreon.com/mlst (public discord)
https://discord.gg/aNPkGUQtc5
https://twitter.com/MLStreetTalk
DOES AI HAVE AGENCY? With Professor. Karl Friston and Riddhi J. Pitliya
Agency in the context of cognitive science, particularly when considering the free energy principle, extends beyond just human decision-making and autonomy. It encompasses a broader understanding of how all living systems, including non-human entities, interact with their environment to maintain their existence by minimising sensory surprise.
According to the free energy principle, living organisms strive to minimize the difference between their predicted states and the actual sensory inputs they receive. This principle suggests that agency arises as a natural consequence of this process, particularly when organisms appear to plan ahead many steps in the future.
Riddhi J. Pitliya is based in the computational psychopathology lab doing her Ph.D at the University of Oxford and works with Professor Karl Friston at VERSES.
https://twitter.com/RiddhiJP
References:
THE FREE ENERGY PRINCIPLE—A PRECIS [Ramstead]
https://www.dialecticalsystems.eu/contributions/the-free-energy-principle-a-precis/
Active Inference: The Free Energy Principle in Mind, Brain, and Behavior [Thomas Parr, Giovanni Pezzulo, Karl J. Friston]
https://direct.mit.edu/books/oa-monograph/5299/Active-InferenceThe-Free-Energy-Principle-in-Mind
The beauty of collective intelligence, explained by a developmental biologist | Michael Levin
https://www.youtube.com/watch?v=U93x9AWeuOA
Growing Neural Cellular Automata
https://distill.pub/2020/growing-ca
Carcinisation
https://en.wikipedia.org/wiki/Carcinisation
Prof. KENNETH STANLEY - Why Greatness Cannot Be Planned
https://www.youtube.com/watch?v=lhYGXYeMq_E
On Defining Artificial Intelligence [Pei Wang]
https://sciendo.com/article/10.2478/jagi-2019-0002
Why? The Purpose of the Universe [Goff]
https://amzn.to/4aEqpfm
Umwelt
https://en.wikipedia.org/wiki/Umwelt
An Immense World: How Animal Senses Reveal the Hidden Realms [Yong]
https://amzn.to/3tzzTb7
What's it like to be a bat [Nagal]
https://www.sas.upenn.edu/~cavitch/pdf-library/Nagel_Bat.pdf
COUNTERFEIT PEOPLE. DANIEL DENNETT. (SPECIAL EDITION)
https://www.youtube.com/watch?v=axJtywd9Tbo
We live in the infosphere [FLORIDI]
https://www.youtube.com/watch?v=YLNGvvgq3eg
Mark Zuckerberg: First Interview in the Metaverse | Lex Fridman Podcast #398
https://www.youtube.com/watch?v=MVYrJJNdrEg
Black Mirror: Rachel, Jack and Ashley Too | Official Trailer | Netflix
https://www.youtube.com/watch?v=-qIlCo9yqpY
Please support us! https://www.patreon.com/mlst
https://discord.gg/aNPkGUQtc5
https://twitter.com/MLStreetTalk
YT version (with intro not found here) https://youtu.be/6iaT-0Dvhnc
This is the epic special edition show you have been waiting for! With two of the most brilliant scientists alive today.
Atoms, things, agents, ... observers. What even defines an "observer" and what properties must all observers share? How do objects persist in our universe given that their material composition changes over time? What does it mean for a thing to be a thing? And do things supervene on our lower-level physical reality? What does it mean for a thing to have agency? What's the difference between a complex dynamical system with and without agency? Could a rock or an AI catflap have agency? Can the universe be factorised into distinct agents, or is agency diffused? Have you ever pondered about these deep questions about reality?
Prof. Friston and Dr. Wolfram have spent their entire careers, some 40+ years each thinking long and hard about these very questions and have developed significant frameworks of reference on their respective journeys (the Wolfram Physics project and the Free Energy principle).
Panel: MIT Ph.D Keith Duggar
Production: Dr. Tim Scarfe
Refs:
TED Talk with Stephen:
https://www.ted.com/talks/stephen_wolfram_how_to_think_computationally_about_ai_the_universe_and_everything
https://writings.stephenwolfram.com/2023/10/how-to-think-computationally-about-ai-the-universe-and-everything/
TOC
00:00:00 - Show kickoff
00:02:38 - Wolfram gets to grips with FEP
00:27:08 - How much control does an agent/observer have
00:34:52 - Observer persistence, what universe seems like to us
00:40:31 - Black holes
00:45:07 - Inside vs outside
00:52:20 - Moving away from the predictable path
00:55:26 - What can observers do
01:06:50 - Self modelling gives agency
01:11:26 - How do you know a thing has agency?
01:22:48 - Deep link between dynamics, ruliad and AI
01:25:52 - Does agency entail free will? Defining Agency
01:32:57 - Where do I probe for agency?
01:39:13 - Why is the universe the way we see it?
01:42:50 - Alien intelligence
01:43:40 - The hard problem of Observers
01:46:20 - Summary thoughts from Wolfram
01:49:35 - Factorisability of FEP
01:57:05 - Patreon interview teaser
We explore connections between FEP and enactivism, including tensions raised in a paper critiquing FEP from an enactivist perspective.
Dr. Maxwell Ramstead provides background on enactivism emerging from autopoiesis, with a focus on embodied cognition and rejecting information processing/computational views of mind.
Chris shares his journey from robotics into FEP, starting as a skeptic but becoming convinced it's the right framework. He notes there are both "high road" and "low road" versions, ranging from embodied to more radically anti-representational stances. He doesn't see a definitive fork between dynamical systems and information theory as the source of conflict. Rather, the notion of operational closure in enactivism seems to be the main sticking point.
The group explores definitional issues around structure/organization, boundaries, and operational closure. Maxwell argues the generative model in FEP captures organizational dependencies akin to operational closure. The Markov blanket formalism models structural interfaces.
We discuss the concept of goals in cognitive systems - Chris advocates an intentional stance perspective - using notions of goals/intentions if they help explain system dynamics. Goals emerge from beliefs about dynamical trajectories. Prof Friston provides an elegant explanation of how goal-directed behavior naturally falls out of the FEP mathematics in a particular "goldilocks" regime of system scale/dynamics. The conversation explores the idea that many systems simply act "as if" they have goals or models, without necessarily possessing explicit representations. This helps resolve tensions between enactivist and computational perspectives.
Throughout the dialogue, Maxwell presses philosophical points about the FEP abolishing what he perceives as false dichotomies in cognitive science such as internalism/externalism. He is critical of enactivists' commitment to bright line divides between subject areas.
Prof. Karl Friston - Inventor of the free energy principle https://scholar.google.com/citations?user=q_4u0aoAAAAJ
Prof. Chris Buckley - Professor of Neural Computation at Sussex University https://scholar.google.co.uk/citations?user=nWuZ0XcAAAAJ&hl=en
Dr. Maxwell Ramstead - Director of Research at VERSES https://scholar.google.ca/citations?user=ILpGOMkAAAAJ&hl=fr
We address critique in this paper:
Laying down a forking path: Tensions between enaction and the free energy principle (Ezequiel A. Di Paolo, Evan Thompson, Randall D. Beere)
https://philosophymindscience.org/index.php/phimisci/article/download/9187/8975
Other refs:
Multiscale integration: beyond internalism and externalism (Maxwell J D Ramstead)
https://pubmed.ncbi.nlm.nih.gov/33627890/
MLST panel: Dr. Tim Scarfe and Dr. Keith Duggar
TOC (auto generated):
0:00 - Introduction
0:41 - Defining enactivism and its variants
6:58 - The source of the conflict between dynamical systems and information theory
8:56 - Operational closure in enactivism
10:03 - Goals and intentions
12:35 - The link between dynamical systems and information theory
15:02 - Path integrals and non-equilibrium dynamics
18:38 - Operational closure defined
21:52 - Structure vs. organization in enactivism
24:24 - Markov blankets as interfaces
28:48 - Operational closure in FEP
30:28 - Structure and organization again
31:08 - Dynamics vs. information theory
33:55 - Goals and intentions emerge in the FEP mathematics
36:58 - The Good Regulator Theorem
49:30 - enactivism and its relation to ecological psychology
52:00 - Goals, intentions and beliefs
55:21 - Boundaries and meaning
58:55 - Enactivism's rejection of information theory
1:02:08 - Beliefs vs goals
1:05:06 - Ecological psychology and FEP
1:08:41 - The Good Regulator Theorem
1:18:38 - How goal-directed behavior emerges
1:23:13 - Ontological vs metaphysical boundaries
1:25:20 - Boundaries as maps
1:31:08 - Connections to the maximum entropy principle
1:33:45 - Relations to quantum and relational physics
This show is sponsored by Numerai, please visit them here with our sponsor link (we would really appreciate it) http://numer.ai/mlst
Prof. Karl Friston recently proposed a vision of artificial intelligence that goes beyond machines and algorithms, and embraces humans and nature as part of a cyber-physical ecosystem of intelligence. This vision is based on the principle of active inference, which states that intelligent systems can learn from their observations and act on their environment to reduce uncertainty and achieve their goals. This leads to a formal account of collective intelligence that rests on shared narratives and goals.
To realize this vision, Friston suggests developing a shared hyper-spatial modelling language and transaction protocol, as well as novel methods for measuring and optimizing collective intelligence. This could harness the power of artificial intelligence for the common good, without compromising human dignity or autonomy. It also challenges us to rethink our relationship with technology, nature, and each other, and invites us to join a global community of sense-makers who are curious about the world and eager to improve it.
YT version: https://www.youtube.com/watch?v=V_VXOdf1NMw
Support us! https://www.patreon.com/mlst
MLST Discord: https://discord.gg/aNPkGUQtc5
TOC:
Intro [00:00:00]
Numerai (Sponsor segment) [00:07:10]
Designing Ecosystems of Intelligence from First Principles (Friston et al) [00:09:48]
Information / Infosphere and human agency [00:18:30]
Intelligence [00:31:38]
Reductionism [00:39:36]
Universalism [00:44:46]
Emergence [00:54:23]
Markov blankets [01:02:11]
Whole part relationships / structure learning [01:22:33]
Enactivism [01:29:23]
Knowledge and Language [01:43:53]
ChatGPT [01:50:56]
Ethics (is-ought) [02:07:55]
Can people be evil? [02:35:06]
Ethics in Al, subjectiveness [02:39:05]
Final thoughts [02:57:00]
References:
Designing Ecosystems of Intelligence from First Principles (Friston et al)
https://arxiv.org/abs/2212.01354
GLOM - How to represent part-whole hierarchies in a neural network (Hinton)
https://arxiv.org/pdf/2102.12627.pdf
Seven Brief Lessons on Physics (Carlo Rovelli)
https://www.amazon.co.uk/Seven-Brief-Lessons-Physics-Rovelli/dp/0141981725
How Emotions Are Made: The Secret Life of the Brain (Lisa Feldman Barrett)
https://www.amazon.co.uk/How-Emotions-Are-Made-Secret/dp/B01N3D4OON
Am I Self-Conscious? (Or Does Self-Organization Entail Self-Consciousness?) (Karl Friston)
https://www.frontiersin.org/articles/10.3389/fpsyg.2018.00579/full
Integrated information theory (Giulio Tononi)
https://en.wikipedia.org/wiki/Integrated_information_theory
In this NeurIPSs interview, we speak with Laura Ruis about her research on the ability of language models to interpret language in context. She has designed a simple task to evaluate the performance of widely used state-of-the-art language models and has found that they struggle to make pragmatic inferences (implicatures). Tune in to learn more about her findings and what they mean for the future of conversational AI.
Laura Ruis
https://www.lauraruis.com/
https://twitter.com/LauraRuis
BLOOM
https://bigscience.huggingface.co/blog/bloom
Large language models are not zero-shot communicators [Laura Ruis, Akbir Khan, Stella Biderman, Sara Hooker, Tim Rocktäschel, Edward Grefenstette]
https://arxiv.org/abs/2210.14986
[Zhang et al] OPT: Open Pre-trained Transformer Language Models
https://arxiv.org/pdf/2205.01068.pdf
[Lampinen] Can language models handle recursively nested grammatical structures? A case study on comparing models and humans
https://arxiv.org/pdf/2210.15303.pdf
[Gary Marcus] Horse rides astronaut
https://garymarcus.substack.com/p/horse-rides-astronaut
[Gary Marcus] GPT-3, Bloviator: OpenAI’s language generator has no idea what it’s talking about
https://www.technologyreview.com/2020/08/22/1007539/gpt3-openai-language-generator-artificial-intelligence-ai-opinion/
[Bender et al] On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?
https://dl.acm.org/doi/10.1145/3442188.3445922
[janus] Simulators (Less Wrong)
https://www.lesswrong.com/posts/vJFdjigzmcXMhNTsx/simulators
This video is demonetised on music copyright so we would appreciate support on our Patreon! https://www.patreon.com/mlst
We would also appreciate it if you rated us on your podcast platform.
YT: https://youtu.be/_KVAzAzO5HU
Panel: Dr. Tim Scarfe, Dr. Keith Duggar
Guests: Prof. J. Mark Bishop, Francois Chollet, Prof. David Chalmers, Dr. Joscha Bach, Prof. Karl Friston, Alexander Mattick, Sam Roffey
The Chinese Room Argument was first proposed by philosopher John Searle in 1980. It is an argument against the possibility of artificial intelligence (AI) – that is, the idea that a machine could ever be truly intelligent, as opposed to just imitating intelligence.
The argument goes like this:
Imagine a room in which a person sits at a desk, with a book of rules in front of them. This person does not understand Chinese.
Someone outside the room passes a piece of paper through a slot in the door. On this paper is a Chinese character. The person in the room consults the book of rules and, following these rules, writes down another Chinese character and passes it back out through the slot.
To someone outside the room, it appears that the person in the room is engaging in a conversation in Chinese. In reality, they have no idea what they are doing – they are just following the rules in the book.
The Chinese Room Argument is an argument against the idea that a machine could ever be truly intelligent. It is based on the idea that intelligence requires understanding, and that following rules is not the same as understanding.
in this detailed investigation into the Chinese Room, Consciousness and Syntax vs Semantics, we interview luminaries J.Mark Bishop and Francois Chollet and use unreleased footage from our interviews with David Chalmers, Joscha Bach and Karl Friston. We also cover material from Walid Saba and interview Alex Mattick from Yannic's Discord.
This is probably my favourite ever episode of MLST. I hope you enjoy it! With Keith Duggar.
Note that we are using clips from our unreleased interviews from David Chalmers and Joscha Bach -- we will release those shows properly in the coming weeks. We apologise for delay releasing our backlog, we have been busy building a startup company in the background.
TOC:
[00:00:00] Kick off
[00:00:46] Searle
[00:05:09] Bishop introduces CRA
[00:00:00] Stevan Hardad take on CRA
[00:14:03] Francois Chollet dissects CRA
[00:34:16] Chalmers on consciousness
[00:36:27] Joscha Bach on consciousness
[00:42:01] Bishop introduction
[00:51:51] Karl Friston on consciousness
[00:55:19] Bishop on consciousness and comments on Chalmers
[01:21:37] Private language games (including clip with Sam Roffey)
[01:27:27] Dr. Walid Saba on the chinese room (gofai/systematicity take)
[00:34:36] Bishop: on agency / teleology
[01:36:38] Bishop: back to CRA
[01:40:53] Noam Chomsky on mysteries
[01:45:56] Eric Curiel on math does not represent
[01:48:14] Alexander Mattick on syntax vs semantics
Thanks to: Mark MC on Discord for stimulating conversation, Alexander Mattick, Dr. Keith Duggar, Sam Roffey. Sam's YouTube channel is https://www.youtube.com/channel/UCjRNMsglFYFwNsnOWIOgt1Q
Mariana Mazzucato, Professor in the Economics of Innovation and Public Value at University College London and author of several books including her latest, Mission Economy: a moonshot guide to changing capitalism, joins Scott to discuss the current state of capitalism, unions, and how to rethink the relationship between markets and governments. Follow Professor Mazzucato on Twitter, @MazzucatoM.
Scott opens with his thoughts on Pinterest's potential, the streaming space, and the at-home fitness market.
Algebra of Happiness: Be kind and evolve.
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Nick Lane is a biochemist at UCL and author of Transformer, The Vital Question, and many other amazing books on biology, chemistry, and life. Please support this podcast by checking out our sponsors:
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OUTLINE:
Here’s the timestamps for the episode. On some podcast players you should be able to click the timestamp to jump to that time.
(00:00) – Introduction
(05:45) – Origin of life
(19:31) – Panspermia
(25:05) – What is life?
(38:20) – Photosynthesis
(41:55) – Prokaryotic vs eukaryotic cells
(51:56) – Sex
(59:39) – DNA
(1:06:51) – Violence
(1:17:25) – Human evolution
(1:23:21) – Neanderthals
(1:26:53) – Sensory inputs
(1:37:43) – Consciousness
(2:09:17) – AI and biology
(2:38:36) – Evolution
(2:59:07) – Fermi paradox
(3:12:27) – Cities
(3:20:14) – Depression
(3:22:50) – Writing
(3:30:49) – Advice for young people
(3:37:57) – Earth
We engage in a bit of epistemic foraging with Prof. Karl Friston! In this show; we discuss the free energy principle in detail, also emergence, cognition, consciousness and Karl's burden of knowledge!
YT: https://youtu.be/xKQ-F2-o8uM
Patreon: https://www.patreon.com/mlst
Discord: https://discord.gg/HNnAwSduud
[00:00:00] Introduction to FEP/Friston
[00:06:53] Cheers to Epistemic Foraging!
[00:09:17] The Burden of Knowledge Across Disciplines
[00:12:55] On-show introduction to Friston
[00:14:23] Simple does NOT mean Easy
[00:21:25] Searching for a Mathematics of Cognition
[00:26:44] The Low Road and The High Road to the Principle
[00:28:27] What's changed for the FEP in the last year
[00:39:36] FEP as stochastic systems with a pullback attractor
[00:44:03] An attracting set at multiple time scales and time infinity
[00:53:56] What about fuzzy Markov boundaries?
[00:59:17] Is reality densely or sparsely coupled?
[01:07:00] Is a Strong and Weak Emergence distinction useful?
[01:13:25] a Philosopher, a Zombie, and a Sentient Consciousness walk into a bar ...
[01:24:28] Can we recreate consciousness in silico? Will it have qualia?
[01:28:29] Subjectivity and building hypotheses
[01:34:17] Subject specific realizations to minimize free energy
[01:37:21] Free will in a deterministic Universe
The free energy principle made simpler but not too simple
https://arxiv.org/abs/2201.06387
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Today we’re joined by Tim Rocktäschel, a research scientist at Facebook AI Research and an associate professor at University College London (UCL).
Tim’s work focuses on training RL agents in simulated environments, with the goal of these agents being able to generalize to novel situations. Typically, this is done in environments like OpenAI Gym, MuJuCo, or even using Atari games, but these all come with constraints. In Tim’s approach, he utilizes a game called NetHack, which is much more rich and complex than the aforementioned environments.
In our conversation with Tim, we explore the ins and outs of using NetHack as a training environment, including how much control a user has when generating each individual game and the challenges he's faced when deploying the agents. We also discuss his work on MiniHack, an environment creation framework and suite of tasks that are based on NetHack, and future directions for this research.
The complete show notes for this episode can be found at twimlai.com/go/527.
Since reinforcement learning requires hefty compute resources, it can be tough to keep up without a serious budget of your own. Find out how the team at Facebook AI Research (FAIR) is looking to increase access and level the playing field with the help of NetHack, an archaic rogue-like video game from the late 80s.
Links discussed:
The NetHack Learning Environment:
https://ai.facebook.com/blog/nethack-learning-environment-to-advance-deep-reinforcement-learning/
Reinforcement learning, intrinsic motivation:
https://arxiv.org/abs/2002.12292
Knowledge transfer:
https://arxiv.org/abs/1910.08210
Tim Rocktäschel is a Research Scientist at Facebook AI Research (FAIR) London and a Lecturer in the Department of Computer Science at University College London (UCL). At UCL, he is a member of the UCL Centre for Artificial Intelligence and the UCL Natural Language Processing group. Prior to that, he was a Postdoctoral Researcher in the Whiteson Research Lab, a Stipendiary Lecturer in Computer Science at Hertford College, and a Junior Research Fellow in Computer Science at Jesus College, at the University of Oxford.
https://twitter.com/_rockt
Heinrich Kuttler is an AI and machine learning researcher at Facebook AI Research (FAIR) and before that was a research engineer and team lead at DeepMind.
https://twitter.com/HeinrichKuttler
https://www.linkedin.com/in/heinrich-kuttler/
Topics covered:
0:00 a lack of reproducibility in RL
1:05 What is NetHack and how did the idea come to be?
5:46 RL in Go vs NetHack
11:04 performance of vanilla agents, what do you optimize for
18:36 transferring domain knowledge, source diving
22:27 human vs machines intrinsic learning
28:19 ICLR paper - exploration and RL strategies
35:48 the future of reinforcement learning
43:18 going from supervised to reinforcement learning
45:07 reproducibility in RL
50:05 most underrated aspect of ML, biggest challenges?
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This week Dr. Tim Scarfe, Dr. Keith Duggar and Connor Leahy chat with Prof. Karl Friston. Professor Friston is a British neuroscientist at University College London and an authority on brain imaging. In 2016 he was ranked the most influential neuroscientist on Semantic Scholar. His main contribution to theoretical neurobiology is the variational Free energy principle, also known as active inference in the Bayesian brain. The FEP is a formal statement that the existential imperative for any system which survives in the changing world can be cast as an inference problem. Bayesian Brain Hypothesis states that the brain is confronted with ambiguous sensory evidence, which it interprets by making inferences about the hidden states which caused the sensory data. So is the brain an inference engine? The key concept separating Friston's idea from traditional stochastic reinforcement learning methods and even Bayesian reinforcement learning is moving away from goal-directed optimisation.
Remember to subscribe! Enjoy the show!
00:00:00 Show teaser intro
00:16:24 Main formalism for FEP
00:28:29 Path Integral
00:30:52 How did we feel talking to friston?
00:34:06 Skit - on cultures (checked, but maybe make shorter)
00:36:02 Friston joins
00:36:33 Main show introduction
00:40:51 Is prediction all it takes for intelligence?
00:48:21 balancing accuracy with flexibility
00:57:36 belief-free vs belief-based; beliefs are crucial
01:04:53 Fuzzy Markov Blankets and Wandering Sets
01:12:37 The Free Energy Principle conforms to itself
01:14:50 useful false beliefs
01:19:14 complexity minimization is the heart of free energy [01:19:14 ]Keith:
01:23:25 An Alpha to tip the scales? Absoute not! Absolutely yes!
01:28:47 FEP applied to brain anatomy
01:36:28 Are there multiple non-FEP forms in the brain?
01:43:11 a positive conneciton to backpropagation
01:47:12 The FEP does not explain the origin of FEP systems
01:49:32 Post-show banter
https://www.fil.ion.ucl.ac.uk/~karl/
#machinelearning
Karl Friston is one of the greatest neuroscientists in history, cited over 245,000 times, known for many influential ideas in brain imaging, neuroscience, and theoretical neurobiology, including the fascinating idea of the free-energy principle for action and perception.
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This conversation is part of the Artificial Intelligence podcast. If you would like to get more information about this podcast go to https://lexfridman.com/ai or connect with @lexfridman on Twitter, LinkedIn, Facebook, Medium, or YouTube where you can watch the video versions of these conversations. If you enjoy the podcast, please rate it 5 stars on Apple Podcasts, follow on Spotify, or support it on Patreon.
Here’s the outline of the episode. On some podcast players you should be able to click the timestamp to jump to that time.
OUTLINE:
00:00 – Introduction
01:50 – How much of the human brain do we understand?
05:53 – Most beautiful characteristic of the human brain
10:43 – Brain imaging
20:38 – Deep structure
21:23 – History of brain imaging
32:31 – Neuralink and brain-computer interfaces
43:05 – Free energy principle
1:24:29 – Meaning of life
My guest today is Vaughn Tan, who studies quality, innovation, and organizational behavior. His resume is bonkers. He’s a PhD from Harvard, Was an infantry signals logistician in the Republic of Singapore Army, then worked at Google on advertising, Earth, Maps, spaceflight, and Fusion Tables. He’s also been a wood sculptor. But the topic of our conversation is how to foster quality and innovation in ourselves and inside of companies—lessons he learned in part by studying inside some of the world’s best restaurants. If you enjoy this conversation, I recommend you also check out his new book, The Uncertainty Mindset Innovation Insights from the Frontiers of Food. Please enjoy my conversation with Vaughn Tan.
For more episodes go to InvestorFieldGuide.com/podcast.
Sign up for the book club, where you’ll get a full investor curriculum and then 3-4 suggestions every month at InvestorFieldGuide.com/bookclub.
Follow Patrick on Twitter at @patrick_oshag
Show Notes
1:33 - (First Question) – Interesting ways to identify high quality
5:06 – The current problem with the way we think about the world
8:56 – How people think about their careers and college
11:21 – Uncertainty vs risk, and productive discomfort
19:08 – Cultivation of discomfort for an individual
24:05 – Successful innovation cultures
32:25 – Analyzing quality and restaurant bread
37:43 – The Slug idea
40:43 – His research project where he observed restaurants
45:44 – How do people mandate their own structure in the face of uncertainty
53:46 – How employees should approach this rent-to-buy hiring structure
57:17 – Example of someone who took advantage of uncertainty time
1:00:05 – Playful adults
1:00:07 – Jerry Neumann Podcast Episode
1:03:10 – Other changes companies can make to their culture to be more innovative
1:08:19 – The difference between simplicity and complexity
1:11:12 – How he applies his thinking into several different ideas, like Cannabis
1:16:17 – Asking the right question
1:19:05 – Andy Rachleff Podcast Episode
1:20:19 – Kindest thing anyone has done for Vaughn
Learn More
For more episodes go to InvestorFieldGuide.com/podcast.
Sign up for the book club, where you’ll get a full investor curriculum and then 3-4 suggestions every month at InvestorFieldGuide.com/bookclub
Follow Patrick on twitter at @patrick_oshag
In this episode, I speak with Arthur Gretton, Wittawat Jitkrittum, Zoltan Szabo and Kenji Fukumizu, who, alongside Wenkai Xu authored the 2017 NIPS Best Paper Award winner “A Linear-Time Kernel Goodness-of-Fit Test.” In our discussion, we cover what exactly a “goodness of fit” test is, and how it can be used to determine how well a statistical model applies to a given real-world scenario. The group and I the discuss this particular test, the applications of this work, as well as how this work fits in with other research the group has recently published. Enjoy! In our discussion, we cover what exactly a “goodness of fit” test is, and how it can be used to determine how well a statistical model applies to a given real-world scenario. The group and I the discuss this particular test, the applications of this work, as well as how this work fits in with other research the group has recently published. Enjoy! This is your last chance to register for the RE•WORK Deep Learning and AI Assistant Summits in San Francisco, which are this Thursday and Friday, January 25th and 26th. These events feature leading researchers and technologists like the ones you heard in our Deep Learning Summit series last week. The San Francisco will event is headlined by Ian Goodfellow of Google Brain, Daphne Koller of Calico Labs, and more! Definitely check it out and use the code TWIMLAI for 20% off of registration. The notes for this show can be found at twimlai.com/talk/100.
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