Machine Learning Street Talk (MLST)

Nick Chater is Professor of Behavioural Science at Warwick Business School, who works on rationality and language using a range of theoretical and experimental approaches. We discuss his books The Mind is Flat, and the Language Game.

Please support me on Patreon (this is now my main job!) - https://patreon.com/mlst - Access the private Discord, networking, and early access to content.

MLST Discord: https://discord.gg/machine-learning-street-talk-mlst-937356144060530778

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Buy The Language Game:

https://amzn.to/3SRHjPm

Buy The Mind is Flat:

https://amzn.to/3P3BUUC

YT version: https://youtu.be/5cBS6COzLN4

https://www.wbs.ac.uk/about/person/nick-chater/

https://twitter.com/nickjchater?lang=en

See what Sam Altman advised Kenneth when he left OpenAI! Professor Kenneth Stanley has just launched a brand new type of social network, which he calls a "Serendipity network". The idea is that you follow interests, NOT people. It's a social network without the popularity contest. We discuss the phgilosophy and technology behind the venture in great detail. The main ideas of which came from Kenneth's famous book "Why greatness cannot be planned".

See what Sam Altman advised Kenneth when he left OpenAI! Professor Kenneth Stanley has just launched a brand new type of social network, which he calls a "Serendipity network".The idea is that you follow interests, NOT people. It's a social network without the popularity contest.

YT version: https://www.youtube.com/watch?v=pWIrXN-yy8g

Chapters should be baked into the MP3 file now

Tim explains the book more here:

https://www.youtube.com/watch?v=wNhaz81OOqw

Brandon Rohrer who obtained his Ph.D from MIT is driven by understanding algorithms ALL the way down to their nuts and bolts, so he can make them accessible to everyone by first explaining them in the way HE himself would have wanted to learn!

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Brandon Rohrer is a seasoned data science leader and educator with a rich background in creating robust, efficient machine learning algorithms and tools. With a Ph.D. in Mechanical Engineering from MIT, his expertise encompasses a broad spectrum of AI applications — from computer vision and natural language processing to reinforcement learning and robotics. Brandon's career has seen him in Principle-level roles at Microsoft and Facebook. An educator at heart, he also shares his knowledge through detailed tutorials, courses, and his forthcoming book, "How to Train Your Robot."

YT version: https://www.youtube.com/watch?v=4Ps7ahonRCY

Brandon's links:

https://github.com/brohrer

https://www.youtube.com/channel/UCsBKTrp45lTfHa_p49I2AEQ

https://www.linkedin.com/in/brohrer/

How transformers work:

https://e2eml.school/transformers

Brandon's End-to-End Machine Learning school courses, posts, and tutorials

https://e2eml.school

Free course:

https://end-to-end-machine-learning.teachable.com/p/complete-course-library-full-end-to-end-machine-learning-catalog

Blog: https://e2eml.school/blog.html

Ziptie: Learning Useful Features [Brandon Rohrer]

https://www.brandonrohrer.com/ziptie

TOC should be baked into the MP3 file now

00:00:00 - Intro to Brandon

00:00:36 - RLHF

00:01:09 - Limitations of transformers

00:07:23 - Agency - we are all GPTs

00:09:07 - BPE / representation bias

00:12:00 - LLM true believers

00:16:42 - Brandon's style of teaching

00:19:50 - ML vs real world = Robotics

00:29:59 - Reward shaping

00:37:08 - No true Scotsman - when do we accept capabilities as real

00:38:50 - Externalism

00:43:03 - Building flexible robots

00:45:37 - Is reward enough

00:54:30 - Optimization curse

00:58:15 - Collective intelligence

01:01:51 - Intelligence + creativity

01:13:35 - ChatGPT + Creativity

01:25:19 - Transformers Tutorial

The world's second-most famous AI doomer Connor Leahy sits down with Beff Jezos, the founder of the e/acc movement debating technology, AI policy, and human values. As the two discuss technology, AI safety, civilization advancement, and the future of institutions, they clash on their opposing perspectives on how we steer humanity towards a more optimal path.

Watch behind the scenes, get early access and join the private Discord by supporting us on Patreon. We have some amazing content going up there with Max Bennett and Kenneth Stanley this week!

Post-interview with Beff and Connor: https://www.patreon.com/posts/97905213

Pre-interview with Connor and his colleague Dan Clothiaux: https://www.patreon.com/posts/connor-leahy-and-97631416

Leahy, known for his critical perspectives on AI and technology, challenges Jezos on a variety of assertions related to the accelerationist movement, market dynamics, and the need for regulation in the face of rapid technological advancements. Jezos, on the other hand, provides insights into the e/acc movement's core philosophies, emphasizing growth, adaptability, and the dangers of over-legislation and centralized control in current institutions.

Throughout the discussion, both speakers explore the concept of entropy, the role of competition in fostering innovation, and the balance needed to mediate order and chaos to ensure the prosperity and survival of civilization. They weigh up the risks and rewards of AI, the importance of maintaining a power equilibrium in society, and the significance of cultural and institutional dynamism.

Beff Jezos (Guillaume Verdon):

YT: https://www.youtube.com/watch?v=0zxi0xSBOaQ

TOC:

00:00:00 - Intro

00:03:05 - Society library reference

00:03:35 - Debate starts

00:05:08 - Should any tech be banned?

00:20:39 - Leaded Gasoline

00:28:57 - False vacuum collapse method?

00:34:56 - What if there are dangerous aliens?

00:36:56 - Risk tolerances

00:39:26 - Optimizing for growth vs value

00:52:38 - Is vs ought

01:02:29 - AI discussion

01:07:38 - War / global competition

01:11:02 - Open source F16 designs

01:20:37 - Offense vs defense

01:28:49 - Morality / value

01:43:34 - What would Conor do

01:50:36 - Institutions/regulation

02:26:41 - Competition vs. Regulation Dilemma

02:32:50 - Existential Risks and Future Planning

02:41:46 - Conclusion and Reflection

Note from Tim: I baked the chapter metadata into the mp3 file this time, does that help the chapters show up in your app? Let me know. Also I accidentally exported a few minutes of dead audio at the end of the file - sorry about that just skip on when the episode finishes.

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

YT version: https://youtu.be/n8G50ynU0Vg

In this interview on MLST, Dr. Tim Scarfe interviews Mahault Albarracin, who is the director of product for R&D at VERSES and also a PhD student in cognitive computing at the University of Quebec in Montreal. They discuss a range of topics related to consciousness, cognition, and machine learning.

Throughout the conversation, they touch upon various philosophical and computational concepts such as panpsychism, computationalism, and materiality. They consider the "hard problem" of consciousness, which is the question of how and why we have subjective experiences.

Albarracin shares her views on the controversial Integrated Information Theory and the open letter of opposition it received from the scientific community. She reflects on the nature of scientific critique and rivalry, advising caution in declaring entire fields of study as pseudoscientific.

A substantial part of the discussion is dedicated to the topic of science itself, where Albarracin talks about thresholds between legitimate science and pseudoscience, the role of evidence, and the importance of validating scientific methods and claims.

They touch upon language models, discussing whether they can be considered as having a "theory of mind" and the implications of assigning such properties to AI systems. Albarracin challenges the idea that there is a pure form of intelligence independent of material constraints and emphasizes the role of sociality in the development of our cognitive abilities.

Albarracin offers her thoughts on scientific endeavors, the predictability of systems, the nature of intelligence, and the processes of learning and adaptation. She gives insights into the concept of using degeneracy as a way to increase resilience within systems and the role of maintaining a degree of redundancy or extra capacity as a buffer against unforeseen events.

The conversation concludes with her discussing the potential benefits of collective intelligence, likening the adaptability and resilience of interconnected agent systems to those found in natural ecosystems.

https://www.linkedin.com/in/mahault-albarracin-1742bb153/

00:00:00 - Intro / IIT scandal

00:05:54 - Gaydar paper / What makes good science

00:10:51 - Language

00:18:16 - Intelligence

00:29:06 - X-risk

00:40:49 - Self modelling

00:43:56 - Anthropomorphisation

00:46:41 - Mediation and subjectivity

00:51:03 - Understanding

00:56:33 - Resiliency

Technical topics:

1. Integrated Information Theory (IIT) - Giulio Tononi

2. The "hard problem" of consciousness - David Chalmers

3. Panpsychism and Computationalism in philosophy of mind

4. Active Inference Framework - Karl Friston

5. Theory of Mind and its computation in AI systems

6. Noam Chomsky's views on language models and linguistics

7. Daniel Dennett's Intentional Stance theory

8. Collective intelligence and system resilience

9. Redundancy and degeneracy in complex systems

10. Michael Levin's research on bioelectricity and pattern formation

11. The role of phenomenology in cognitive science

Chai AI is the leading platform for conversational chat artificial intelligence.

Note: this is a sponsored episode of MLST.

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

Watch behind the scenes, get early access and join private Discord by supporting us on Patreon: https://patreon.com/mlst

https://discord.gg/aNPkGUQtc5

https://twitter.com/MLStreetTalk

In this comprehensive exploration of the field of deep learning with Professor Simon Prince who has just authored an entire text book on Deep Learning, we investigate the technical underpinnings that contribute to the field's unexpected success and confront the enduring conundrums that still perplex AI researchers.

Key points discussed include the surprising efficiency of deep learning models, where high-dimensional loss functions are optimized in ways which defy traditional statistical expectations. Professor Prince provides an exposition on the choice of activation functions, architecture design considerations, and overparameterization. We scrutinize the generalization capabilities of neural networks, addressing the seeming paradox of well-performing overparameterized models. Professor Prince challenges popular misconceptions, shedding light on the manifold hypothesis and the role of data geometry in informing the training process. Professor Prince speaks about how layers within neural networks collaborate, recursively reconfiguring instance representations that contribute to both the stability of learning and the emergence of hierarchical feature representations. In addition to the primary discussion on technical elements and learning dynamics, the conversation briefly diverts to audit the implications of AI advancements with ethical concerns.

Follow Prof. Prince:

https://twitter.com/SimonPrinceAI

https://www.linkedin.com/in/simon-prince-615bb9165/

Get the book now!

https://mitpress.mit.edu/9780262048644/understanding-deep-learning/

https://udlbook.github.io/udlbook/

Panel: Dr. Tim Scarfe -

https://www.linkedin.com/in/ecsquizor/

https://twitter.com/ecsquendor

TOC:

[00:00:00] Introduction

[00:11:03] General Book Discussion

[00:15:30] The Neural Metaphor

[00:17:56] Back to Book Discussion

[00:18:33] Emergence and the Mind

[00:29:10] Computation in Transformers

[00:31:12] Studio Interview with Prof. Simon Prince

[00:31:46] Why Deep Neural Networks Work: Spline Theory

[00:40:29] Overparameterization in Deep Learning

[00:43:42] Inductive Priors and the Manifold Hypothesis

[00:49:31] Universal Function Approximation and Deep Networks

[00:59:25] Training vs Inference: Model Bias

[01:03:43] Model Generalization Challenges

[01:11:47] Purple Segment: Unknown Topic

[01:12:45] Visualizations in Deep Learning

[01:18:03] Deep Learning Theories Overview

[01:24:29] Tricks in Neural Networks

[01:30:37] Critiques of ChatGPT

[01:42:45] Ethical Considerations in AI

References on YT version VD: https://youtu.be/sJXn4Cl4oww

Watch behind the scenes with Bert on Patreon: https://www.patreon.com/posts/bert-de-vries-93230722

Note, there is some mild background music on chapter 1 (Least Action), 3 (Friston) and 5 (Variational Methods) - please skip ahead if annoying. It's a tiny fraction of the overall podcast.

YT version: https://youtu.be/2wnJ6E6rQsU

Please support us https://www.patreon.com/mlst

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