David Spivak, a mathematician known for his work in category theory, discusses a wide range of topics related to intelligence, creativity, and the nature of knowledge. He explains category theory in simple terms and explores how it relates to understanding complex systems and relationships.

MLST is sponsored by Brave:

The Brave Search API covers over 20 billion webpages, built from scratch without Big Tech biases or the recent extortionate price hikes on search API access. Perfect for AI model training and retrieval augmentated generation. Try it now - get 2,000 free queries monthly at http://brave.com/api.

We discuss abstract concepts like collective intelligence, the importance of embodiment in understanding the world, and how we acquire and process knowledge. Spivak shares his thoughts on creativity, discussing where it comes from and how it might be modeled mathematically.

A significant portion of the discussion focuses on the impact of artificial intelligence on human thinking and its potential role in the evolution of intelligence. Spivak also touches on the importance of language, particularly written language, in transmitting knowledge and shaping our understanding of the world.

David Spivak

http://www.dspivak.net/

TOC:

00:00:00 Introduction to category theory and functors

00:04:40 Collective intelligence and sense-making

00:09:54 Embodiment and physical concepts in knowledge acquisition

00:16:23 Creativity, open-endedness, and AI's impact on thinking

00:25:46 Modeling creativity and the evolution of intelligence

00:36:04 Evolution, optimization, and the significance of AI

00:44:14 Written language and its impact on knowledge transmission

REFS:

Mike Levin's work

https://scholar.google.com/citations?user=luouyakAAAAJ&hl=en

Eric Smith's videos on complexity and early life

https://www.youtube.com/watch?v=SpJZw-68QyE

Richard Dawkins' book "The Selfish Gene"

https://amzn.to/3X73X8w

Carl Sagan's statement about the cosmos knowing itself

https://amzn.to/3XhPruK

Herbert Simon's concept of "satisficing"

https://plato.stanford.edu/entries/bounded-rationality/

DeepMind paper on open-ended systems

https://arxiv.org/abs/2406.04268

Karl Friston's work on active inference

https://direct.mit.edu/books/oa-monograph/5299/Active-InferenceThe-Free-Energy-Principle-in-Mind

MIT category theory lectures by David Spivak (available on the Topos Institute channel)

https://www.youtube.com/watch?v=UusLtx9fIjs

Jürgen Schmidhuber, the father of generative AI shares his groundbreaking work in deep learning and artificial intelligence. In this exclusive interview, he discusses the history of AI, some of his contributions to the field, and his vision for the future of intelligent machines. Schmidhuber offers unique insights into the exponential growth of technology and the potential impact of AI on humanity and the universe.

YT version: https://youtu.be/DP454c1K_vQ

MLST is sponsored by Brave:

The Brave Search API covers over 20 billion webpages, built from scratch without Big Tech biases or the recent extortionate price hikes on search API access. Perfect for AI model training and retrieval augmentated generation. Try it now - get 2,000 free queries monthly at http://brave.com/api.

TOC

00:00:00 Intro

00:03:38 Reasoning

00:13:09 Potential AI Breakthroughs Reducing Computation Needs

00:20:39 Memorization vs. Generalization in AI

00:25:19 Approach to the ARC Challenge

00:29:10 Perceptions of Chat GPT and AGI

00:58:45 Abstract Principles of Jurgen's Approach

01:04:17 Analogical Reasoning and Compression

01:05:48 Breakthroughs in 1991: the P, the G, and the T in ChatGPT and Generative AI

01:15:50 Use of LSTM in Language Models by Tech Giants

01:21:08 Neural Network Aspect Ratio Theory

01:26:53 Reinforcement Learning Without Explicit Teachers

Refs:

★ "Annotated History of Modern AI and Deep Learning" (2022 survey by Schmidhuber):

★ Chain Rule For Backward Credit Assignment (Leibniz, 1676)

★ First Neural Net / Linear Regression / Shallow Learning (Gauss & Legendre, circa 1800)

★ First 20th Century Pioneer of Practical AI (Quevedo, 1914)

★ First Recurrent NN (RNN) Architecture (Lenz, Ising, 1920-1925)

★ AI Theory: Fundamental Limitations of Computation and Computation-Based AI (Gödel, 1931-34)

★ Unpublished ideas about evolving RNNs (Turing, 1948)

★ Multilayer Feedforward NN Without Deep Learning (Rosenblatt, 1958)

★ First Published Learning RNNs (Amari and others, ~1972)

★ First Deep Learning (Ivakhnenko & Lapa, 1965)

★ Deep Learning by Stochastic Gradient Descent (Amari, 1967-68)

★ ReLUs (Fukushima, 1969)

★ Backpropagation (Linnainmaa, 1970); precursor (Kelley, 1960)

★ Backpropagation for NNs (Werbos, 1982)

★ First Deep Convolutional NN (Fukushima, 1979); later combined with Backprop (Waibel 1987, Zhang 1988).

★ Metalearning or Learning to Learn (Schmidhuber, 1987)

★ Generative Adversarial Networks / Artificial Curiosity / NN Online Planners (Schmidhuber, Feb 1990; see the G in Generative AI and ChatGPT)

★ NNs Learn to Generate Subgoals and Work on Command (Schmidhuber, April 1990)

★ NNs Learn to Program NNs: Unnormalized Linear Transformer (Schmidhuber, March 1991; see the T in ChatGPT)

★ Deep Learning by Self-Supervised Pre-Training. Distilling NNs (Schmidhuber, April 1991; see the P in ChatGPT)

★ Experiments with Pre-Training; Analysis of Vanishing/Exploding Gradients, Roots of Long Short-Term Memory / Highway Nets / ResNets (Hochreiter, June 1991, further developed 1999-2015 with other students of Schmidhuber)

★ LSTM journal paper (1997, most cited AI paper of the 20th century)

★ xLSTM (Hochreiter, 2024)

★ Reinforcement Learning Prompt Engineer for Abstract Reasoning and Planning (Schmidhuber 2015)

★ Mindstorms in Natural Language-Based Societies of Mind (2023 paper by Schmidhuber's team)

https://arxiv.org/abs/2305.17066

★ Bremermann's physical limit of computation (1982)

EXTERNAL LINKS

CogX 2018 - Professor Juergen Schmidhuber

https://www.youtube.com/watch?v=17shdT9-wuA

Discovering Neural Nets with Low Kolmogorov Complexity and High Generalization Capability (Neural Networks, 1997)

https://sferics.idsia.ch/pub/juergen/loconet.pdf

The paradox at the heart of mathematics: Gödel's Incompleteness Theorem - Marcus du Sautoy

https://www.youtube.com/watch?v=I4pQbo5MQOs

(Refs truncated, full version on YT VD)

How seriously should governments take the threat of existential risk from AI, given the lack of consensus among researchers? On the one hand, existential risks (x-risks) are necessarily somewhat speculative: by the time there is concrete evidence, it may be too late. On the other hand, governments must prioritize — after all, they don’t worry too much about x-risk from alien invasions.

MLST is sponsored by Brave:

The Brave Search API covers over 20 billion webpages, built from scratch without Big Tech biases or the recent extortionate price hikes on search API access. Perfect for AI model training and retrieval augmentated generation. Try it now - get 2,000 free queries monthly at brave.com/api.

Sayash Kapoor is a computer science Ph.D. candidate at Princeton University's Center for Information Technology Policy. His research focuses on the societal impact of AI. Kapoor has previously worked on AI in both industry and academia, with experience at Facebook, Columbia University, and EPFL Switzerland. He is a recipient of a best paper award at ACM FAccT and an impact recognition award at ACM CSCW. Notably, Kapoor was included in TIME's inaugural list of the 100 most influential people in AI.

Sayash Kapoor

https://x.com/sayashk

https://www.cs.princeton.edu/~sayashk/

Arvind Narayanan (other half of the AI Snake Oil duo)

https://x.com/random_walker

AI existential risk probabilities are too unreliable to inform policy

https://www.aisnakeoil.com/p/ai-existential-risk-probabilities

Pre-order AI Snake Oil Book

https://amzn.to/4fq2HGb

AI Snake Oil blog

https://www.aisnakeoil.com/

AI Agents That Matter

https://arxiv.org/abs/2407.01502

Shortcut learning in deep neural networks

https://www.semanticscholar.org/paper/Shortcut-learning-in-deep-neural-networks-Geirhos-Jacobsen/1b04936c2599e59b120f743fbb30df2eed3fd782

77% Of Employees Report AI Has Increased Workloads And Hampered Productivity, Study Finds

https://www.forbes.com/sites/bryanrobinson/2024/07/23/employees-report-ai-increased-workload/

TOC:

00:00:00 Intro

00:01:57 How seriously should we take Xrisk threat?

00:02:55 Risk too unrealiable to inform policy

00:10:20 Overinflated risks

00:12:05 Perils of utility maximisation

00:13:55 Scaling vs airplane speeds

00:17:31 Shift to smaller models?

00:19:08 Commercial LLM ecosystem

00:22:10 Synthetic data

00:24:09 Is AI complexifying our jobs?

00:25:50 Does ChatGPT make us dumber or smarter?

00:26:55 Are AI Agents overhyped?

00:28:12 Simple vs complex baselines

00:30:00 Cost tradeoff in agent design

00:32:30 Model eval vs downastream perf

00:36:49 Shortcuts in metrics

00:40:09 Standardisation of agent evals

00:41:21 Humans in the loop

00:43:54 Levels of agent generality

00:47:25 ARC challenge

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

We have a chat with Alexander Mattick aka ZickZack from Yannic's Discord community. Alex is one of the leading voices in that community and has an impressive technical depth. Don't forget MLST has now started it's own Discord server too, come and join us! We are going to run regular events, our first big event on Wednesday 9th 1700-1900 UK time. 

Patreon: https://www.patreon.com/mlst

Discord: https://discord.gg/HNnAwSduud

YT version: https://youtu.be/rGOOLC8cIO4

[00:00:00] Introduction to Alex 

[00:02:16] Spline theory of NNs 

[00:05:19] Do NNs abstract? 

[00:08:27] Tim's exposition of spline theory of NNs

[00:11:11] Semantics in NNs 

[00:13:37] Continuous vs discrete 

[00:19:00] Open-ended Search

[00:22:54] Inductive logic programming

[00:25:00] Control to gain knowledge and knowledge to gain control

[00:30:22] Being a generalist with a breadth of knowledge and knowledge transfer

[00:36:29] Causality

[00:43:14] Discrete program synthesis + theorem solvers

Note: there are no politics discussed in this show and please do not interpret this show as any kind of a political statement from us.  We have decided not to discuss politics on MLST anymore due to its divisive nature. 

Patreon: https://www.patreon.com/mlst

Discord: https://discord.gg/HNnAwSduud

[00:00:00] Intro

[00:01:36] What we all need to understand about machine learning

[00:06:05] The Master Algorithm Target Audience

[00:09:50] Deeply Connected Algorithms seen from Divergent Frames of Reference

[00:12:49] There is a Master Algorithm; and it's mine!

[00:14:59] The Tribe of Evolution

[00:17:17] Biological Inspirations and Predictive Coding

[00:22:09] Shoe-Horning Gradient Descent

[00:27:12] Sparsity at Training Time vs Prediction Time

[00:30:00] World Models and Predictive Coding

[00:33:24] The Cartoons of System 1 and System 2

[00:40:37] AlphaGo Searching vs Learning

[00:45:56] Discriminative Models evolve into Generative Models

[00:50:36] Generative Models, Predictive Coding, GFlowNets

[00:55:50] Sympathy for a Thousand Brains

[00:59:05] A Spectrum of Tribes

[01:04:29] Causal Structure and Modelling

[01:09:39] Entropy and The Duality of Past vs Future, Knowledge vs Control

[01:16:14] A Discrete Universe?

[01:19:49] And yet continuous models work so well

[01:23:31] Finding a Discretised Theory of Everything

Patreon: https://www.patreon.com/mlst

Discord: https://discord.gg/HNnAwSduud

YT: https://www.youtube.com/watch?v=ZDY2nhkPZxw

We have a chat with Prof. Gary Marcus about everything which is currently top of mind for him, consciousness 

[00:00:00] Gary intro

[00:01:25] Slightly conscious

[00:24:59] Abstract, compositional models

[00:32:46] Spline theory of NNs

[00:36:17] Self driving cars / algebraic reasoning 

[00:39:43] Extrapolation

[00:44:15] Scaling laws

[00:49:50] Maximum likelihood estimation

References:

Grokking: Generalization Beyond Overfitting on Small Algorithmic Datasets

https://arxiv.org/abs/2201.02177

DEEP DOUBLE DESCENT: WHERE BIGGER MODELS AND MORE DATA HURT

https://arxiv.org/pdf/1912.02292.pdf

Bayesian Deep Learning and a Probabilistic Perspective of Generalization

https://arxiv.org/pdf/2002.08791.pdf

We are now sponsored by Weights and Biases! Please visit our sponsor link: http://wandb.me/MLST

Patreon: https://www.patreon.com/mlst

For Yoshua Bengio, GFlowNets are the most exciting thing on the horizon of Machine Learning today. He believes they can solve previously intractable problems and hold the key to unlocking machine abstract reasoning itself. This discussion explores the promise of GFlowNets and the personal journey Prof. Bengio traveled to reach them.

Panel:

Dr. Tim Scarfe

Dr. Keith Duggar

Dr. Yannic Kilcher

Our special thanks to: 

- Alexander Mattick (Zickzack)

References:

Yoshua Bengio @ MILA (https://mila.quebec/en/person/bengio-yoshua/)

GFlowNet Foundations (https://arxiv.org/pdf/2111.09266.pdf)

Flow Network based Generative Models for Non-Iterative Diverse Candidate Generation (https://arxiv.org/pdf/2106.04399.pdf)

Interpolation Consistency Training for Semi-Supervised Learning (https://arxiv.org/pdf/1903.03825.pdf)

Towards Causal Representation Learning (https://arxiv.org/pdf/2102.11107.pdf)

Causal inference using invariant prediction: identification and confidence intervals (https://arxiv.org/pdf/1501.01332.pdf)

Dr. Guy Emerson is a computational linguist and obtained his Ph.D from Cambridge university where he is now a research fellow and lecturer. On panel we also have myself, Dr. Tim Scarfe, as well as Dr. Keith Duggar and the veritable Dr. Walid Saba. We dive into distributional semantics, probability theory, fuzzy logic, grounding, vagueness and the grammar/cognition connection.

The aim of distributional semantics is to design computational techniques that can automatically learn the meanings of words from a body of text. The twin challenges are: how do we represent meaning, and how do we learn these representations? We want to learn the meanings of words from a corpus by exploiting the fact that the context of a word tells us something about its meaning. This is known as the distributional hypothesis. In his Ph.D thesis, Dr. Guy Emerson presented a distributional model which can learn truth-conditional semantics which are grounded by objects in the real world.

Hope you enjoy the show!

https://www.cai.cam.ac.uk/people/dr-guy-emerson

https://www.repository.cam.ac.uk/handle/1810/284882?show=full

https://www.semanticscholar.org/paper/Computational-linguistics-and-grammar-engineering-Bender-Emerson/bbd6f3b92a0f1ea8212f383cc4719bfe86b3588c

Patreon: https://www.patreon.com/mlst

We are now sponsored by Weights and Biases! Please visit our sponsor link: http://wandb.me/MLST

Patreon: https://www.patreon.com/mlst

Yann LeCun thinks that it's specious to say neural network models are interpolating because in high dimensions, everything is extrapolation. Recently Dr. Randall Balestriero, Dr. Jerome Pesente and prof. Yann LeCun released their paper learning in high dimensions always amounts to extrapolation. This discussion has completely changed how we think about neural networks and their behaviour.

[00:00:00] Pre-intro

[00:11:58] Intro Part 1: On linearisation in NNs

[00:28:17] Intro Part 2: On interpolation in NNs

[00:47:45] Intro Part 3: On the curse

[00:48:19] LeCun

[01:40:51] Randall B

YouTube version: https://youtu.be/86ib0sfdFtw

Patreon: https://www.patreon.com/mlst

The last decade has witnessed an experimental revolution in data science and machine learning, epitomised by deep learning methods. Many high-dimensional learning tasks previously thought to be beyond reach -- such as computer vision, playing Go, or protein folding -- are in fact tractable given enough computational horsepower. Remarkably, the essence of deep learning is built from two simple algorithmic principles: first, the notion of representation or feature learning and second, learning by local gradient-descent type methods, typically implemented as backpropagation.

While learning generic functions in high dimensions is a cursed estimation problem, most tasks of interest are not uniform and have strong repeating patterns as a result of the low-dimensionality and structure of the physical world.

Geometric Deep Learning unifies a broad class of ML problems from the perspectives of symmetry and invariance. These principles not only underlie the breakthrough performance of convolutional neural networks and the recent success of graph neural networks but also provide a principled way to construct new types of problem-specific inductive biases.

This week we spoke with Professor Michael Bronstein (head of graph ML at Twitter) and Dr.

Petar Veličković (Senior Research Scientist at DeepMind), and Dr. Taco Cohen and Prof. Joan Bruna about their new proto-book Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges.

See the table of contents for this (long) show at https://youtu.be/bIZB1hIJ4u8 

Patreon: https://www.patreon.com/mlst

The ultimate goal of neuroscience is to learn how the human brain gives rise to human intelligence and what it means to be intelligent. Understanding how the brain works is considered one of humanity’s greatest challenges. 

Jeff Hawkins thinks that the reality we perceive is a kind of simulation, a hallucination, a confabulation. He thinks that our brains are a model reality based on thousands of information streams originating from the sensors in our body.  Critically - Hawkins doesn’t think there is just one model but rather; thousands. 

Jeff has just released his new book, A thousand brains: a new theory of intelligence. It’s an inspiring and well-written book and I hope after watching this show; you will be inspired to read it too. 

https://numenta.com/a-thousand-brains-by-jeff-hawkins/

https://numenta.com/blog/2019/01/16/the-thousand-brains-theory-of-intelligence/

Panel:

Dr. Keith Duggar https://twitter.com/DoctorDuggar

Connor Leahy https://twitter.com/npcollapse

The field of Artificial Intelligence was founded in the mid 1950s with the aim of constructing “thinking machines” - that is to say, computer systems with human-like general intelligence. Think of humanoid robots that not only look but act and think with intelligence equal to and ultimately greater than that of human beings. But in the intervening years, the field has drifted far from its ambitious old-fashioned roots.

Dr. Ben Goertzel is an artificial intelligence researcher, CEO and founder of SingularityNET. A project combining artificial intelligence and blockchain to democratize access to artificial intelligence. Ben seeks to fulfil the original ambitions of the field.  Ben graduated with a PhD in Mathematics from Temple University in 1990. Ben’s approach to AGI over many decades now has been inspired by many disciplines, but in particular from human cognitive psychology and computer science perspective. To date Ben’s work has been mostly theoretically-driven. Ben thinks that most of the deep learning approaches to AGI today try to model the brain. They may have a loose analogy to human neuroscience but they have not tried to derive the details of an AGI architecture from an overall conception of what a mind is. Ben thinks that what matters for creating human-level (or greater) intelligence is having the right information processing architecture, not the underlying mechanics via which the architecture is implemented.

Ben thinks that there is a certain set of key cognitive processes and interactions that AGI systems must implement explicitly such as; working and long-term memory, deliberative and reactive processing, perc biological systems tend to be messy, complex and integrative; searching for a single “algorithm of general intelligence” is an inappropriate attempt to project the aesthetics of physics or theoretical computer science into a qualitatively different domain.

TOC is on the YT show description https://www.youtube.com/watch?v=sw8IE3MX1SY

Panel: Dr. Tim Scarfe, Dr. Yannic Kilcher, Dr. Keith Duggar

Artificial General Intelligence: Concept, State of the Art, and Future Prospects

https://sciendo.com/abstract/journals...

The General Theory of General Intelligence: A Pragmatic Patternist Perspective

https://arxiv.org/abs/2103.15100

Sara Hooker is VP of Research at Cohere and leader of Cohere for AI. We discuss her recent paper critiquing the use of compute thresholds, measured in FLOPs (floating point operations), as an AI governance strategy.

We explore why this approach, recently adopted in both US and EU AI policies, may be problematic and oversimplified. Sara explains the limitations of using raw computational power as a measure of AI capability or risk, and discusses the complex relationship between compute, data, and model architecture.

Equally important, we go into Sara's work on "The AI Language Gap." This research highlights the challenges and inequalities in developing AI systems that work across multiple languages. Sara discusses how current AI models, predominantly trained on English and a handful of high-resource languages, fail to serve the linguistic diversity of our global population. We explore the technical, ethical, and societal implications of this gap, and discuss potential solutions for creating more inclusive and representative AI systems.

We broadly discuss the relationship between language, culture, and AI capabilities, as well as the ethical considerations in AI development and deployment.

YT Version: https://youtu.be/dBZp47999Ko

TOC:

[00:00:00] Intro

[00:02:12] FLOPS paper

[00:26:42] Hardware lottery

[00:30:22] The Language gap

[00:33:25] Safety

[00:38:31] Emergent

[00:41:23] Creativity

[00:43:40] Long tail

[00:44:26] LLMs and society

[00:45:36] Model bias

[00:48:51] Language and capabilities

[00:52:27] Ethical frameworks and RLHF

Sara Hooker

https://www.sarahooker.me/

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

https://scholar.google.com/citations?user=2xy6h3sAAAAJ&hl=en

https://x.com/sarahookr

Interviewer: Tim Scarfe

Refs

The AI Language gap

https://cohere.com/research/papers/the-AI-language-gap.pdf

On the Limitations of Compute Thresholds as a Governance Strategy.

https://arxiv.org/pdf/2407.05694v1

The Multilingual Alignment Prism: Aligning Global and Local Preferences to Reduce Harm

https://arxiv.org/pdf/2406.18682

Cohere Aya

https://cohere.com/research/aya

RLHF Can Speak Many Languages: Unlocking Multilingual Preference Optimization for LLMs

https://arxiv.org/pdf/2407.02552

Back to Basics: Revisiting REINFORCE Style Optimization for Learning from Human Feedback in LLMs

https://arxiv.org/pdf/2402.14740

Executive Order on the Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence

https://www.whitehouse.gov/briefing-room/presidential-actions/2023/10/30/executive-order-on-the-safe-secure-and-trustworthy-development-and-use-of-artificial-intelligence/

EU AI Act

https://www.europarl.europa.eu/doceo/document/TA-9-2024-0138_EN.pdf

The bitter lesson

http://www.incompleteideas.net/IncIdeas/BitterLesson.html

Neel Nanda interview

https://www.youtube.com/watch?v=_Ygf0GnlwmY

Scaling Monosemanticity: Extracting Interpretable Features from Claude 3 Sonnet

https://transformer-circuits.pub/2024/scaling-monosemanticity/

Chollet's ARC challenge

https://github.com/fchollet/ARC-AGI

Ryan Greenblatt on ARC

https://www.youtube.com/watch?v=z9j3wB1RRGA

Disclaimer: This is the third video from our Cohere partnership. We were not told what to say in the interview, and didn't edit anything out from the interview.

Since its beginning in the 1950s, the field of artificial intelligence has vacillated between periods of optimistic predictions and massive investment and periods of disappointment, loss of confidence, and reduced funding. Even with today’s seemingly fast pace of AI breakthroughs, the development of long-promised technologies such as self-driving cars, housekeeping robots, and conversational companions has turned out to be much harder than many people expected.  Professor Melanie Mitchell thinks one reason for these repeating cycles is our limited understanding of the nature and complexity of intelligence itself.

YT vid- https://www.youtube.com/watch?v=A8m1Oqz2HKc

Main show kick off [00:26:51]

Panel: Dr. Tim Scarfe, Dr. Keith Duggar, Letitia Parcalabescu (https://www.youtube.com/c/AICoffeeBreak/)

It has been over three decades since the statistical revolution overtook AI by a storm and over two  decades since deep learning (DL) helped usher the latest resurgence of artificial intelligence (AI). However, the disappointing progress in conversational agents, NLU, and self-driving cars, has made it clear that progress has not lived up to the promise of these empirical and data-driven methods. DARPA has suggested that it is time for a third wave in AI, one that would be characterized by hybrid models – models that combine knowledge-based approaches with data-driven machine learning techniques. 

Joining us on this panel discussion is polymath and linguist Walid Saba - Co-founder ONTOLOGIK.AI, Gadi Singer - VP & Director, Cognitive Computing Research, Intel Labs and J. Mark Bishop - Professor of Cognitive Computing (Emeritus), Goldsmiths, University of London and Scientific Adviser to FACT360.

Moderated by Dr. Keith Duggar and Dr. Tim Scarfe

https://www.linkedin.com/in/gadi-singer/

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

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

#machinelearning #artificialintelligence

Dr. Ishan Misra is a Research Scientist at Facebook AI Research where he works on Computer Vision and Machine Learning. His main research interest is reducing the need for human supervision, and indeed, human knowledge in visual learning systems. He finished his PhD at the Robotics Institute at Carnegie Mellon. He has done stints at Microsoft Research, INRIA and Yale. His bachelors is in computer science where he achieved the highest GPA in his cohort. 

Ishan is fast becoming a prolific scientist, already with more than 3000 citations under his belt and co-authoring with Yann LeCun; the godfather of deep learning.  Today though we will be focusing an exciting cluster of recent papers around unsupervised representation learning for computer vision released from FAIR. These are; DINO: Emerging Properties in Self-Supervised Vision Transformers, BARLOW TWINS: Self-Supervised Learning via Redundancy Reduction and PAWS: Semi-Supervised Learning of Visual Features by Non-Parametrically Predicting View Assignments with

Support Samples. All of these papers are hot off the press, just being officially released in the last month or so. Many of you will remember PIRL: Self-Supervised Learning of Pretext-Invariant Representations which Ishan was the primary author of in 2019.

References;

Shuffle and Learn - https://arxiv.org/abs/1603.08561

DepthContrast - https://arxiv.org/abs/2101.02691

DINO - https://arxiv.org/abs/2104.14294

Barlow Twins - https://arxiv.org/abs/2103.03230

SwAV - https://arxiv.org/abs/2006.09882

PIRL - https://arxiv.org/abs/1912.01991

AVID - https://arxiv.org/abs/2004.12943 (best paper candidate at CVPR'21 (just announced over the weekend) - http://cvpr2021.thecvf.com/node/290)

Alexei (Alyosha) Efros

http://people.eecs.berkeley.edu/~efros/

http://www.cs.cmu.edu/~tmalisie/projects/nips09/

Exemplar networks

https://arxiv.org/abs/1406.6909

The bitter lesson - Rich Sutton

http://www.incompleteideas.net/IncIdeas/BitterLesson.html

Machine Teaching: A New Paradigm for Building Machine Learning Systems

https://arxiv.org/abs/1707.06742

POET

https://arxiv.org/pdf/1901.01753.pdf

Professor Gary Marcus is a scientist, best-selling author, and entrepreneur. He is Founder and CEO of Robust.AI, and was Founder and CEO of Geometric Intelligence, a machine learning company acquired by Uber in 2016. Gary said in his recent next decade paper that — without us, or other creatures like us, the world would continue to exist, but it would not be described, distilled, or understood.  Human lives are filled with abstraction and causal description. This is so powerful. Francois Chollet the other week said that intelligence is literally sensitivity to abstract analogies, and that is all there is to it. It's almost as if one of the most important features of intelligence is to be able to abstract knowledge, this drives the generalisation which will allow you to mine previous experience to make sense of many future novel situations.   Also joining us today is Professor Luis Lamb — Secretary of Innovation for Science and Technology of the State of Rio Grande do Sul, Brazil. His Research Interests are Machine Learning and Reasoning, Neuro-Symbolic Computing, Logic in Computation and Artificial Intelligence, Cognitive and Neural Computation and also AI Ethics and Social Computing. Luis released his new paper Neurosymbolic AI: the third wave at the end of last year. It beautifully articulated the key ingredients needed in the next generation of AI systems, integrating type 1 and type 2 approaches to AI and it summarises all the of the achievements of the last 20 years of research.   We cover a lot of ground in today's show. Explaining the limitations of deep learning, Rich Sutton's the bitter lesson and "reward is enough", and the semantic foundation which is required for us to build robust AI.

Bob Coercke is a celebrated physicist, he's been a Physics and Quantum professor at Oxford University for the last 20 years. He is particularly interested in Structure which is to say, Logic, Order, and Category Theory. He is well known for work involving compositional distributional models of natural language meaning and he is also fascinated with understanding how our brains work. Bob was recently appointed as the Chief Scientist at Cambridge Quantum Computing.

Bob thinks that interactions between systems in Quantum Mechanics carries naturally over to how word meanings interact in natural language. Bob argues that this interaction embodies the phenomenon of quantum teleportation.

Bob invented ZX-calculus, a graphical calculus for revealing the compositional structure inside quantum circuits - to show entanglement states and protocols in a visually succinct but logically complete way. Von Neumann himself didn't even like his own original symbolic formalism of quantum theory, despite it being widely used!

We hope you enjoy this fascinating conversation which might give you a lot of insight into natural language processing. 

Tim Intro [00:00:00]

The topological brain (Post-record button skit) [00:13:22]

Show kick off [00:19:31]

Bob introduction [00:22:37]

Changing culture in universities [00:24:51]

Machine Learning is like electricity [00:31:50]

NLP -- what is Bob's Quantum conception? [00:34:50]

The missing text problem [00:52:59]

Can statistical induction be trusted? [00:59:49]

On pragmatism and hybrid systems [01:04:42]

Parlour tricks, parsing and information flows [01:07:43]

How much human input is required with Bob's method? [01:11:29]

Reality, meaning, structure and language [01:14:42]

Replacing complexity with quantum entanglement, emergent complexity [01:17:45]

Loading quantum data requires machine learning [01:19:49] 

QC is happy math coincidence for NLP [01:22:30]

The Theory of English (ToE) [01:28:23] 

... or can we learn the ToE? [01:29:56] 

How did diagrammatic quantum calculus come about? [01:31:04

The state of quantum computing today [01:37:49] 

NLP on QC might be doable even in the NISQ era [01:40:48] 

Hype and private investment are driving progress [01:48:34] 

Crypto discussion (moved to post-show) [01:50:38] 

Kilcher is in a startup (moved to post show) [01:53:40

Debrief [01:55:26] 

Performing reliably on unseen or shifting data distributions is a difficult challenge for modern vision systems, even slight corruptions or transformations of images are enough to slash the accuracy of state-of-the-art classifiers. When an adversary is allowed to modify an input image directly, models can be manipulated into predicting anything even when there is no perceptible change, this is known an adversarial example. The ideal definition of an adversarial example is when humans consistently say two pictures are the same but a machine disagrees. Hadi Salman, a Ph.D student at MIT (ex-Uber and Microsoft Research) started thinking about how adversarial robustness  could be leveraged beyond security.

He realised that the phenomenon of adversarial examples could actually be turned upside down to lead to more robust models instead of breaking them. Hadi actually utilized the brittleness of neural networks to design unadversarial examples or robust objects which_ are objects designed specifically to be robustly recognized by neural networks. 

Introduction [00:00:00]

DR KILCHER'S PHD HAT [00:11:18]

Main Introduction [00:11:38]

Hadi's Introduction [00:14:43]

More robust models == transfer better [00:46:41]

Features not bugs paper [00:49:13]

Manifolds [00:55:51]

Robustness and Transferability [00:58:00]

Do non-robust features generalize worse than robust? [00:59:52]

The unreasonable predicament of entangled features [01:01:57]

We can only find adversarial examples in the vicinity [01:09:30]

Certifiability of models for robustness [01:13:55]

Carlini is coming for you! And we are screwed [01:23:21]

Distribution shift and corruptions are a bigger problem than adversarial examples [01:25:34]

All roads lead to generalization [01:26:47]

Unadversarial examples [01:27:26]

In today's show we are joined by Francois Chollet, I have been inspired by Francois ever since I read his Deep Learning with Python book and started using the Keras library which he invented many, many years ago. Francois has a clarity of thought that I've never seen in any other human being! He has extremely interesting views on intelligence as generalisation, abstraction and an information conversation ratio. He wrote on the measure of intelligence at the end of 2019 and it had a huge impact on my thinking. He thinks that NNs can only model continuous problems, which have a smooth learnable manifold and that many "type 2" problems which involve reasoning and/or planning are not suitable for NNs. He thinks that many problems have type 1 and type 2 enmeshed together. He thinks that the future of AI must include program synthesis to allow us to generalise broadly from a few examples, but the search could be guided by neural networks because the search space is interpolative to some extent.

https://youtu.be/J0p_thJJnoo

Tim's Whimsical notes; https://whimsical.com/chollet-show-QQ2atZUoRR3yFDsxKVzCbj

Dr. Christian Szegedy from Google Research is a deep learning heavyweight. He invented adversarial examples, one of the first object detection algorithms, the inceptionnet architecture, and co-invented batchnorm. He thinks that if you bet on computers and software in 1990 you would have been as right as if you bet on AI now. But he thinks that we have been programming computers the same way since the 1950s and there has been a huge stagnation ever since. Mathematics is the process of taking a fuzzy thought and formalising it. But could we automate that? Could we create a system which will act like a super human mathematician but you can talk to it in natural language? This is what Christian calls autoformalisation. Christian thinks that automating many of the things we do in mathematics is the first step towards software synthesis and building human-level AGI. Mathematics ability is the litmus test for general reasoning ability. Christian has a fascinating take on transformers too. 

With Yannic Lightspeed Kilcher and Dr. Mathew Salvaris

Whimsical Canvas with Tim's Notes:

https://whimsical.com/mar-26th-christian-szegedy-CpgGhnEYDBrDMFoATU6XYC

YouTube version (with detailed table of contents) https://youtu.be/ehNGGYFO6ms

The race is on, we are on a collective mission to understand and create artificial general intelligence. Dr. Tom Zahavy, a Research Scientist at DeepMind thinks that reinforcement learning is the most general learning framework that we have today, and in his opinion it could lead to artificial general intelligence. He thinks there are no tasks which could not be solved by simply maximising a reward. 

Back in 2012 when Tom was an undergraduate, before the deep learning revolution he attended an online lecture on how CNNs automatically discover representations. This was an epiphany for Tom. He decided in that very moment that he was going to become an ML researcher.  Tom's view is that the ability to recognise patterns and discover structure is the most important aspect of intelligence. This has been his quest ever since. He is particularly focused on using diversity preservation and metagradients to discover this structure. 

In this discussion we dive deep into meta gradients in reinforcement learning. 

Video version and TOC @  https://www.youtube.com/watch?v=hfaZwgk_iS0

First episode in a series we are doing on ML DevOps. Starting with the thing which nobody seems to be talking about enough, security! We chat with cyber security expert Andy Smith about threat modelling and trust boundaries for an ML DevOps system. 

Intro [00:00:00]

ML DevOps - a security perspective [00:00:50]

Threat Modelling [00:03:03]

Adversarial examples? [00:11:27]

Nobody understands the whole stack [00:13:53]

On the size of the state space, the element of unpredictability [00:18:32]

Threat modelling in more detail [00:21:17]

Trust boundaries for an ML DevOps system [00:25:45]

Andy has a YouTube channel on cyber security! Check it out @ 

https://www.youtube.com/channel/UCywP24ly6h6NTusX88TQKTQ

https://www.linkedin.com/in/andysmith-uk/

Video version:

https://youtu.be/7Tz-3S4lypI

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

Christoph Molnar is one of the main people to know in the space of interpretable ML. In 2018 he released the first version of his incredible online book, interpretable machine learning. Interpretability is often a deciding factor when a machine learning (ML) model is used in a product, a decision process, or in research. Interpretability methods can be used to discover knowledge, to debug or justify the model and its predictions, and to control and improve the model, reason about potential bias in models as well as increase the social acceptance of models. But Interpretability methods can also be quite esoteric, add an additional layer of complexity and potential pitfalls and requires expert knowledge to understand. Is it even possible to understand complex models or even humans for that matter in any  meaningful way? 

Introduction to IML [00:00:00]

Show Kickoff [00:13:28]

What makes a good explanation? [00:15:51]

Quantification of how good an explanation is [00:19:59]

Knowledge of the pitfalls of IML [00:22:14]

Are linear models even interpretable? [00:24:26]

Complex Math models to explain Complex Math models? [00:27:04]

Saliency maps are glorified edge detectors [00:28:35]

Challenge on IML -- feature dependence [00:36:46]

Don't leap to using a complex model! Surrogate models can be too dumb [00:40:52]

On airplane pilots. Seeking to understand vs testing [00:44:09]

IML Could help us make better models or lead a better life [00:51:53]

Lack of statistical rigor and quantification of uncertainty [00:55:35]

On Causality [01:01:09]

Broadening out the discussion to the process or institutional level [01:08:53]

No focus on fairness / ethics? [01:11:44]

Is it possible to condition ML model training on IML metrics ? [01:15:27]

Where is IML going? Some of the esoterica of the IML methods [01:18:35]

You can't compress information without common knowledge, the latter becomes the bottleneck [01:23:25]

IML methods used non-interactively? Making IML an engineering discipline [01:31:10]

Tim Postscript -- on the lack of effective corporate operating models for IML, security, engineering and ethics [01:36:34]

Explanation in Artificial Intelligence: Insights from the Social Sciences (Tim Miller 2018)

https://arxiv.org/pdf/1706.07269.pdf

Seven Myths in Machine Learning Research (Chang 19) 

Myth 7: Saliency maps are robust ways to interpret neural networks

https://arxiv.org/pdf/1902.06789.pdf

Sanity Checks for Saliency Maps (Adebayo 2020)

https://arxiv.org/pdf/1810.03292.pdf

Interpretable Machine Learning: A Guide for Making Black Box Models Explainable.

https://christophm.github.io/interpretable-ml-book/

Christoph Molnar:

https://www.linkedin.com/in/christoph-molnar-63777189/

https://machine-master.blogspot.com/

https://twitter.com/ChristophMolnar

Please show your appreciation and buy Christoph's book here;

https://www.lulu.com/shop/christoph-molnar/interpretable-machine-learning/paperback/product-24449081.html?page=1&pageSize=4

Panel: 

Connor Tann https://www.linkedin.com/in/connor-tann-a92906a1/

Dr. Tim Scarfe 

Dr. Keith Duggar

Video version:

https://youtu.be/0LIACHcxpHU

Academics think of themselves as trailblazers, explorers — seekers of the truth.

Any fundamental discovery involves a significant degree of risk. If an idea is guaranteed to work then it moves from the realm of research to engineering. Unfortunately, this also means that most research careers will invariably be failures at least if failures are measured via “objective” metrics like citations. Today we discuss the recent article from Mark Saroufim called Machine Learning: the great stagnation. We discuss the rise of gentleman scientists, fake rigor, incentives in ML, SOTA-chasing, "graduate student descent", distribution of talent in ML and how to learn effectively.  

With special guest interviewer Mat Salvaris. 

Machine learning: the great stagnation [00:00:00]

Main show kick off [00:16:30]

Great stagnation article / Bad incentive systems in academia [00:18:24]

OpenAI is a media business [00:19:48]

Incentive structures in academia [00:22:13]

SOTA chasing [00:24:47]

F You Money [00:28:53]

Research grants and gentlemen scientists [00:29:13]

Following your own gradient of interest and making a contribution [00:33:27]

Marketing yourself to be successful [00:37:07]

Tech companies create the bad incentives [00:42:20]

GPT3 was sota chasing but it seemed really... "good"? Scaling laws? [00:51:09]

Dota / game AI [00:58:39]

Hard to go it alone? [01:02:08]

Reaching out to people [01:09:21]

Willingness to be wrong [01:13:14]

Distribution of talent / tech interviews [01:18:30]

What should you read online and how to learn? Sharing your stuff online and finding your niece [01:25:52]

Mark Saroufim:

https://marksaroufim.substack.com/

http://robotoverlordmanual.com/

https://twitter.com/marksaroufim

https://www.youtube.com/marksaroufim

Dr. Mathew Salvaris:

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

https://twitter.com/MSalvaris

Microsoft has an interesting strategy with their new “autonomous systems” technology also known as Project Bonsai. They want to create an interface to abstract away the complexity and esoterica of deep reinforcement learning. They want to fuse together expert knowledge and artificial intelligence all on one platform, so that complex problems can be decomposed into simpler ones. They want to take machine learning Ph.Ds out of the equation and make autonomous systems engineering look more like a traditional software engineering process. It is an ambitious undertaking, but interesting. Reinforcement learning is extremely difficult (as I cover in the video), and if you don’t have a team of RL Ph.Ds with tech industry experience, you shouldn’t even consider doing it yourself. This is our take on it!

There are 3 chapters in this video;

Chapter 1: Tim's intro and take on RL being hard, intro to Bonsai and machine teaching 

Chapter 2: Interview with Scott Stanfield [recorded Jan 2020] 00:56:41

Chapter 3: Traditional street talk episode [recorded Dec 2020] 01:38:13

This is *not* an official communication from Microsoft, all personal opinions. There is no MS-confidential information in this video. 

With:

Scott Stanfield

https://twitter.com/seesharp

Megan Bloemsma

https://twitter.com/BloemsmaMegan

Gurdeep Pall (he has not validated anything we have said in this video or been involved in the creation of it)

https://www.linkedin.com/in/gurdeep-pall-0aa639bb/

Panel: 

Dr. Keith Duggar

Dr. Tim Scarfe

Yannic Kilcher

Today we are going to talk about the *Data-efficient image Transformers paper or (DeiT) which Hugo is the primary author of. One of the recipes of success for vision models since the DL revolution began has been the availability of large training sets. CNNs have been optimized for almost a decade now, including through extensive architecture search which is prone to overfitting. Motivated by the success of transformers-based models

in Natural Language Processing there has been increasing attention in applying these approaches to vision models. Hugo and his collaborators used a different training strategy and a new distillation token to get a massive increase in sample efficiency with image transformers. 

00:00:00 Introduction

00:06:33 Data augmentation is all you need

00:09:53 Now the image patches are the convolutions though?

00:12:16 Where are those inductive biases hiding?

00:15:46 Distillation token

00:21:01 Why different resolutions on training

00:24:14 How data efficient can we get?

00:26:47 Out of domain generalisation

00:28:22 Why are transformers data efficient at all? Learning invariances

00:32:04 Is data augmentation cheating?

00:33:25 Distillation strategies - matching the intermediatae teacher representation as well as output

00:35:49 Do ML models learn the same thing for a problem?

00:39:01 How is it like at Facebook AI?

00:41:17 How long is the PhD programme?

00:42:03 Other interests outside of transformers?

00:43:18 Transformers for Vision and Language

00:47:40 Could we improve transformers models? (Hybrid models)

00:49:03 Biggest challenges in AI?

00:50:52 How far can we go with data driven approach?

Professor Mark Bishop does not think that computers can be conscious or have phenomenological states of consciousness unless we are willing to accept panpsychism which is idea that mentality is fundamental and ubiquitous in the natural world, or put simply, that your goldfish and everything else for that matter has a mind. Panpsychism postulates that distinctions between intelligences are largely arbitrary.

Mark’s work in the ‘philosophy of AI’ led to an influential critique of computational approaches to Artificial Intelligence through a thorough examination of John Searle's 'Chinese Room Argument'

Mark just published a paper called artificial intelligence is stupid and causal reasoning wont fix it. He makes it clear in this paper that in his opinion computers will never be able to compute everything, understand anything, or feel anything. 

00:00:00​ Tim Intro

00:15:04​ Intro 

00:18:49​ Introduction to Marks ideas 

00:25:49​ Some problems are not computable 

00:29:57​ the dancing was Pixies fallacy 

00:32:36​ The observer relative problem, and its all in the mapping 

00:43:03​ Conscious Experience 

00:53:30​ Intelligence without representation, consciousness is something that we do 

01:02:36​ Consciousness helps us to act autonomously 

01:05:13​ The Chinese room argument 

01:14:58​ Simulation argument and computation doesn't have phenomenal consciousness 

01:17:44​ Language informs our colour perception 

01:23:11​ We have our own distinct ontologies 

01:27:12​ Kurt Gödel, Turing and Penrose and the implications of their work 

Today we have professor Pedro Domingos and we are going to talk about activism in machine learning, cancel culture, AI ethics and kernels. In Pedro's book the master algorithm, he segmented the AI community into 5 distinct tribes with 5 unique identities (and before you ask, no the irony of an anti-identitarian doing do was not lost on us!). Pedro recently published an article in Quillette called Beating Back Cancel Culture: A Case Study from the Field of Artificial Intelligence. Domingos has railed against political activism in the machine learning community and cancel culture. Recently Pedro was involved in a controversy where he asserted the NeurIPS broader impact statements are an ideological filter mechanism.

Important Disclaimer: All views expressed are personal opinions.

00:00:00 Caveating

00:04:08 Main intro

00:07:44 Cancelling culture is a culture and intellectual weakness 

00:12:26 Is cancel culture a post-modern religion? 

00:24:46 Should we have gateways and gatekeepers? 

00:29:30 Does everything require broader impact statements? 

00:33:55 We are stifling diversity (of thought) not promoting it. 

00:39:09 What is fair and how to do fair?

00:45:11 Models can introduce biases by compressing away minority data 

00:48:36 Accurate but unequal soap dispensers 

00:53:55 Agendas are not even self-consistent 

00:56:42 Is vs Ought: all variables should be used for Is 

01:00:38 Fighting back cancellation with cancellation?

01:10:01 Intent and degree matter in right vs wrong. 

01:11:08 Limiting principles matter 

01:15:10 Gradient descent and kernels 

01:20:16 Training Journey matter more than Destination 

01:24:36 Can training paths teach us about symmetry?

01:28:37 What is the most promising path to AGI? 

01:31:29 Intelligence will lose its mystery

Dr. Simon Stringer. Obtained his Ph.D in mathematical state space control theory and has been a Senior Research Fellow at Oxford University for over 27 years. Simon is the director of the the Oxford Centre for Theoretical Neuroscience and Artificial Intelligence, which is based within the Oxford University Department of Experimental Psychology. His department covers vision, spatial processing, motor function, language and consciousness -- in particular -- how the primate visual system learns to make sense of complex natural scenes. Dr. Stringers laboratory houses a team of theoreticians, who are developing computer models of a range of different aspects of brain function. Simon's lab is investigating the neural and synaptic dynamics that underpin brain function. An important matter here is the The feature-binding problem which concerns how the visual system represents the hierarchical relationships between features. the visual system must represent hierarchical binding relations across the entire visual field at every spatial scale and level in the hierarchy of visual primitives.

We discuss the emergence of self-organised behaviour, complex information processing, invariant sensory representations and hierarchical feature binding which emerges when you build biologically plausible neural networks with temporal spiking dynamics. 

00:00:09 Tim Intro 

00:09:31 Show kickoff 

00:14:37 Hierarchical Feature binding and timing of action potentials 

00:30:16 Hebb to Spike-timing-dependent plasticity (STDP) 

00:35:27 Encoding of shape primitives 

00:38:50 Is imagination working in the same place in the brain 

00:41:12 Compare to supervised CNNs 

00:45:59 Speech recognition, motor system, learning mazes 

00:49:28 How practical are these spiking NNs 

00:50:19 Why simulate the human brain 

00:52:46 How much computational power do you gain from differential timings 

00:55:08 Adversarial inputs 

00:59:41 Generative / causal component needed? 

01:01:46 Modalities of processing i.e. language 

01:03:42 Understanding 

01:04:37 Human hardware 

01:06:19 Roadmap of NNs? 

01:10:36 Intepretability methods for these new models 

01:13:03 Won't GPT just scale and do this anyway? 

01:15:51 What about trace learning and transformation learning 

01:18:50 Categories of invariance 

01:19:47 Biological plausibility 

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

Adversarial examples have attracted significant attention in machine learning, but the reasons for their existence and pervasiveness remain unclear. there's good reason to believe neural networks look at very different features than we would have expected.  As articulated in the 2019 "features not bugs" paper Adversarial examples can be directly attributed to the presence of non-robust features: features derived from patterns in the data distribution that are highly predictive, yet brittle and incomprehensible to humans. 

Adversarial examples don't just affect deep learning models. A cottage industry has sprung up around Threat Modeling in AI and ML Systems and their dependencies. Joining us this evening are some of currently leading researchers in adversarial examples;

Florian Tramèr - A fifth year PhD student in Computer Science at Stanford University

https://floriantramer.com/​

https://twitter.com/florian_tramer​

Dr. Wieland Brendel - Machine Learning Researcher at the University of Tübingen & Co-Founder of layer7.ai

https://medium.com/@wielandbr​

https://twitter.com/wielandbr​

Dr. Nicholas Carlini - Research scientist at Google Brain working in that exciting space between machine learning and computer security. 

https://nicholas.carlini.com/​

We really hope you enjoy the conversation, remember to subscribe! 

Yannic Intro [00:00:00​]

Tim Intro [00:04:07​]

Threat Taxonomy [00:09:00​] 

Main show intro [00:11:30​]

Whats wrong with Neural Networks? [00:14:52​]

The role of memorization [00:19:51​]

Anthropomorphization of models [00:22:42​]

Whats the harm really though / focusing on actual ML security risks [00:27:03​]

Shortcut learning / OOD generalization [00:36:18​]

Human generalization [00:40:11​]

An existential problem in DL getting the models to learn what we want? [00:41:39​]

Defenses to adversarial examples [00:47:15​]

What if we had all the data and the labels? Still problems? [00:54:28​]

Defenses are easily broken [01:00:24​]

Self deception in academia [01:06:46​]

ML Security [01:28:15​]

https://www.youtube.com/watch?v=2PenK06tvE4

ena Voita is a Ph.D. student at the University of Edinburgh and University of Amsterdam. Previously, She was a research scientist at Yandex Research and worked closely with the Yandex Translate team. She still teaches NLP at the Yandex School of Data Analysis. She has created an exciting new NLP course on her website lena-voita.github.io which you folks need to check out! She has one of the most well presented blogs we have ever seen, where she discusses her research in an easily digestable manner. Lena has been investigating many fascinating topics in machine learning and NLP. Today we are going to talk about three of her papers and corresponding blog articles;

Source and Target Contributions to NMT Predictions -- Where she talks about the influential dichotomy between the source and the prefix of neural translation models.

https://arxiv.org/pdf/2010.10907.pdf

https://lena-voita.github.io/posts/source_target_contributions_to_nmt.html

Information-Theoretic Probing with MDL -- Where Lena proposes a technique of evaluating a model using the minimum description length or Kolmogorov complexity of labels given representations rather than something basic like accuracy

https://arxiv.org/pdf/2003.12298.pdf

https://lena-voita.github.io/posts/mdl_probes.html

Evolution of Representations in the Transformer - Lena investigates the evolution of representations of individual tokens in Transformers -- trained with different training objectives (MT, LM, MLM) 

https://arxiv.org/abs/1909.01380

https://lena-voita.github.io/posts/emnlp19_evolution.html

Panel Dr. Tim Scarfe, Yannic Kilcher, Sayak Paul

00:00:00 Kenneth Stanley / Greatness can not be planned house keeping

00:21:09 Kilcher intro

00:28:54 Hello Lena

00:29:21 Tim - Lenas NMT paper

00:35:26 Tim - Minimum Description Length / Probe paper

00:40:12 Tim - Evolution of representations

00:46:40 Lenas NLP course

00:49:18 The peppermint tea situation 

00:49:28 Main Show Kick Off 

00:50:22 Hallucination vs exposure bias 

00:53:04 Lenas focus on explaining the models not SOTA chasing

00:56:34 Probes paper and NLP intepretability

01:02:18 Why standard probing doesnt work

01:12:12 Evolutions of representations paper

01:23:53 BERTScore  and BERT Rediscovers the Classical NLP Pipeline paper

01:25:10 Is the shifting encoding context because of BERT bidirectionality

01:26:43 Objective defines which information we lose on input

01:27:59 How influential is the dataset?

01:29:42 Where is the community going wrong?

01:31:55 Thoughts on GOFAI/Understanding in NLP?

01:36:38 Lena's NLP course 

01:47:40 How to foster better learning / understanding

01:52:17 Lena's toolset and languages

01:54:12 Mathematics is all you need

01:56:03 Programming languages

https://lena-voita.github.io/

https://www.linkedin.com/in/elena-voita/

https://scholar.google.com/citations?user=EcN9o7kAAAAJ&hl=ja

https://twitter.com/lena_voita

Professor Kenneth Stanley is currently a research science manager at OpenAI in San Fransisco. We've Been dreaming about getting Kenneth on the show since the very begininning of Machine Learning Street Talk. Some of you might recall that our first ever show was on the enhanced POET paper, of course Kenneth had his hands all over it. He's been cited over 16000 times, his most popular paper with over 3K citations was the NEAT algorithm. His interests are neuroevolution, open-endedness, NNs, artificial life, and AI. He invented the concept of novelty search with no clearly defined objective. His key idea is that there is a tyranny of objectives prevailing in every aspect of our lives, society and indeed our algorithms. Crucially, these objectives produce convergent behaviour and thinking and distract us from discovering stepping stones which will lead to greatness. He thinks that this monotonic objective obsession, this idea that we need to continue to improve benchmarks every year is dangerous. He wrote about this in detail in his recent book "greatness can not be planned" which will be the main topic of discussion in the show. We also cover his ideas on open endedness in machine learning. 

00:00:00 Intro to Kenneth 

00:01:16 Show structure disclaimer 

00:04:16 Passionate discussion 

00:06:26 WHy greatness cant be planned and the tyranny of objectives 

00:14:40 Chinese Finger Trap  

00:16:28 Perverse Incentives and feedback loops 

00:18:17 Deception 

00:23:29 Maze example 

00:24:44 How can we define curiosity or interestingness 

00:26:59 Open endedness 

00:33:01 ICML 2019 and Yannic, POET, first MSLST 

00:36:17 evolutionary algorithms++ 

00:43:18 POET, the first MLST  

00:45:39 A lesson to GOFAI people 

00:48:46 Machine Learning -- the great stagnation 

00:54:34 Actual scientific successes are usually luck, and against the odds -- Biontech 

00:56:21 Picbreeder and NEAT 

01:10:47 How Tim applies these ideas to his life and why he runs MLST 

01:14:58 Keith Skit about UCF 

01:15:13 Main show kick off 

01:18:02 Why does Kenneth value serindipitous exploration so much 

01:24:10 Scientific support for Keneths ideas in normal life 

01:27:12 We should drop objectives to achieve them. An oxymoron? 

01:33:13 Isnt this just resource allocation between exploration and exploitation? 

01:39:06 Are objectives merely a matter of degree? 

01:42:38 How do we allocate funds for treasure hunting in society 

01:47:34 A keen nose for what is interesting, and voting can be dangerous 

01:53:00 Committees are the antithesis of innovation 

01:56:21 Does Kenneth apply these ideas to his real life? 

01:59:48 Divergence vs interestingness vs novelty vs complexity 

02:08:13 Picbreeder 

02:12:39 Isnt everything novel in some sense? 

02:16:35 Imagine if there was no selection pressure? 

02:18:31 Is innovation == environment exploitation? 

02:20:37 Is it possible to take shortcuts if you already knew what the innovations were? 

02:21:11 Go Explore -- does the algorithm encode the stepping stones? 

02:24:41 What does it mean for things to be interestingly different? 

02:26:11 behavioral characterization / diversity measure to your broad interests 

02:30:54 Shaping objectives 

02:32:49 Why do all ambitious objectives have deception? Picbreeder analogy 

02:35:59 Exploration vs Exploitation, Science vs Engineering 

02:43:18 Schools of thought in ML and could search lead to AGI 

02:45:49 Official ending 

In the coming decades, the technology that enables virtual and augmented reality will improve beyond recognition. Within a century, world-renowned philosopher David J. Chalmers predicts, we will have virtual worlds that are impossible to distinguish from non-virtual worlds. But is virtual reality just escapism?

In a highly original work of 'technophilosophy', Chalmers argues categorically, no: virtual reality is genuine reality. Virtual worlds are not second-class worlds. We can live a meaningful life in virtual reality - and increasingly, we will.

What is reality, anyway? How can we lead a good life? Is there a god? How do we know there's an external world - and how do we know we're not living in a computer simulation? In Reality+, Chalmers conducts a grand tour of philosophy, using cutting-edge technology to provide invigorating new answers to age-old questions.

David J. Chalmers is an Australian philosopher and cognitive scientist specializing in the areas of philosophy of mind and philosophy of language. He is Professor of Philosophy and Neural Science at New York University, as well as co-director of NYU's Center for Mind, Brain, and Consciousness. Chalmers is best known for his work on consciousness, including his formulation of the "hard problem of consciousness."

Reality+: Virtual Worlds and the Problems of Philosophy

https://amzn.to/3RYyGD2

https://consc.net/

https://x.com/davidchalmers42

00:00:00 Reality+ Intro

00:12:02 GPT conscious? 10/10

00:14:19 The consciousness processor thought experiment (11/10)

00:20:34 Intelligence and Consciousness entangled? 10/10

00:22:44 Karl Friston / Meta Problem 10/10

00:29:05 Knowledge argument / subjective experience (6/10)

00:32:34 Emergence 11/10 (best chapter)

00:42:45 Working with Douglas Hofstadter 10/10

00:46:14 Intelligence is analogy making? 10/10

00:50:47 Intelligence explosion 8/10

00:58:44 Hypercomputation 10/10

01:09:44 Who designed the designer? (7/10)

01:13:57 Experience machine (7/10)

Connor Tan is a physicist and senior data scientist working for a multinational energy company where he co-founded and leads a data science team. He holds a first-class degree in experimental and theoretical physics from Cambridge university. With a master's in particle astrophysics. He specializes in the application of machine learning models and Bayesian methods. Today we explore the history, pratical utility, and unique capabilities of Bayesian methods. We also discuss the computational difficulties inherent in Bayesian methods along with modern methods for approximate solutions such as Markov Chain Monte Carlo. Finally, we discuss how Bayesian optimization in the context of automl may one day put Data Scientists like Connor out of work.

Panel: Dr. Keith Duggar, Alex Stenlake, Dr. Tim Scarfe

00:00:00 Duggars philisophical ramblings on Bayesianism

00:05:10 Introduction

00:07:30 small datasets and prior scientific knowledge

00:10:37 Bayesian methods are probability theory

00:14:00 Bayesian methods demand hard computations

00:15:46 uncertainty can matter more than estimators

00:19:29 updating or combining knowledge is a key feature

00:25:39 Frequency or Reasonable Expectation as the Primary Concept 

00:30:02 Gambling and coin flips

00:37:32 Rev. Thomas Bayes's pool table

00:40:37 ignorance priors are beautiful yet hard

00:43:49 connections between common distributions

00:49:13 A curious Universe, Benford's Law

00:55:17 choosing priors, a tale of two factories

01:02:19 integration, the computational Achilles heel

01:35:25 Bayesian social context in the ML community

01:10:24 frequentist methods as a first approximation

01:13:13 driven to Bayesian methods by small sample size

01:18:46 Bayesian optimization with automl, a job killer?

01:25:28 different approaches to hyper-parameter optimization

01:30:18 advice for aspiring Bayesians

01:33:59 who would connor interview next?

Connor Tann: https://www.linkedin.com/in/connor-tann-a92906a1/

https://twitter.com/connossor

Today we had a fantastic conversation with Professor Max Welling, VP of Technology, Qualcomm Technologies Netherlands B.V. 

Max is a strong believer in the power of data and computation and its relevance to artificial intelligence. There is a fundamental blank slate paradgm in machine learning, experience and data alone currently rule the roost. Max wants to build a house of domain knowledge on top of that blank slate. Max thinks there are no predictions without assumptions, no generalization without inductive bias. The bias-variance tradeoff tells us that we need to use additional human knowledge when data is insufficient.

Max Welling has pioneered many of the most sophistocated inductive priors in DL models developed in recent years, allowing us to use Deep Learning with non-euclidean data i.e. on graphs/topology (a field we now called "geometric deep learning") or allowing network architectures to recognise new symmetries in the data for example gauge or SE(3) equivariance. Max has also brought many other concepts from his physics playbook into ML, for example quantum and even Bayesian approaches. 

This is not an episode to miss, it might be our best yet! 

Panel: Dr. Tim Scarfe, Yannic Kilcher, Alex Stenlake

00:00:00 Show introduction 

00:04:37 Protein Fold from DeepMind -- did it use SE(3) transformer? 

00:09:58 How has machine learning progressed 

00:19:57 Quantum Deformed Neural Networks paper 

00:22:54 Probabilistic Numeric Convolutional Neural Networks paper

00:27:04 Ilia Karmanov from Qualcomm interview mini segment

00:32:04 Main Show Intro 

00:35:21 How is Max known in the community? 

00:36:35 How Max nurtures talent, freedom and relationship is key 

00:40:30 Selecting research directions and guidance 

00:43:42 Priors vs experience (bias/variance trade-off) 

00:48:47 Generative models and GPT-3 

00:51:57 Bias/variance trade off -- when do priors hurt us 

00:54:48 Capsule networks 

01:03:09 Which old ideas whould we revive 

01:04:36 Hardware lottery paper 

01:07:50 Greatness can't be planned (Kenneth Stanley reference) 

01:09:10 A new sort of peer review and originality 

01:11:57 Quantum Computing 

01:14:25 Quantum deformed neural networks paper 

01:21:57 Probabalistic numeric convolutional neural networks 

01:26:35 Matrix exponential 

01:28:44 Other ideas from physics i.e. chaos, holography, renormalisation 

01:34:25 Reddit 

01:37:19 Open review system in ML 

01:41:43 Outro 

Welcome to the Christmas special community edition of MLST! We discuss some recent and interesting papers from Pedro Domingos (are NNs kernel machines?), Deepmind (can NNs out-reason symbolic machines?), Anna Rodgers - When BERT Plays The Lottery, All Tickets Are Winning, Prof. Mark Bishop (even causal methods won't deliver understanding), We also cover our favourite bits from the recent Montreal AI event run by Prof. Gary Marcus (including Rich Sutton, Danny Kahneman and Christof Koch). We respond to a reader mail on Capsule networks. Then we do a deep dive into Type Theory and Lambda Calculus with community member Alex Mattick. In the final hour we discuss inductive priors and label information density with another one of our discord community members.  

Panel: Dr. Tim Scarfe, Yannic Kilcher, Alex Stenlake, Dr. Keith Duggar

Enjoy the show and don't forget to subscribe!

00:00:00 Welcome to Christmas Special! 

00:00:44 SoTa meme 

00:01:30 Happy Christmas! 

00:03:11 Paper -- DeepMind - Outperforming neuro-symbolic models with NNs (Ding et al)

00:08:57 What does it mean to understand? 

00:17:37 Paper - Prof. Mark Bishop Artificial Intelligence is stupid and causal reasoning

wont fix it

00:25:39 Paper -- Pedro Domingos -  Every Model Learned by Gradient Descent Is Approximately a Kernel Machine

00:31:07 Paper - Bengio - Inductive Biases for Deep Learning of Higher-Level Cognition

00:32:54 Anna Rodgers - When BERT Plays The Lottery, All Tickets Are Winning

00:37:16 Montreal AI event - Gary Marcus on reasoning 

00:40:37 Montreal AI event -- Rich Sutton on universal theory of AI

00:49:45 Montreal AI event -- Danny Kahneman, System 1 vs 2 and Generative Models ala free energy principle

01:02:57 Montreal AI event -- Christof Koch - Neuroscience is hard

01:10:55 Markus Carr -- reader letter on capsule networks

01:13:21 Alex response to Marcus Carr 

01:22:06 Type theory segment --  with Alex Mattick from Discord

01:24:45 Type theory segment -- What is Type Theory 

01:28:12 Type theory segment -- Difference between functional and OOP languages 

01:29:03 Type theory segment -- Lambda calculus 

01:30:46 Type theory segment -- Closures 

01:35:05 Type theory segment -- Term rewriting (confluency and termination) 

01:42:02 MType theory segment -- eta term rewritig system - Lambda Calculus  

01:54:44 Type theory segment -- Types / semantics 

02:06:26 Type theory segment -- Calculus of constructions 

02:09:27 Type theory segment -- Homotopy type theory 

02:11:02 Type theory segment -- Deep learning link 

02:17:27 Jan from Discord segment -- Chrome MRU skit 

02:18:56 Jan from Discord segment -- Inductive priors (with XMaster96/Jan from Discord) 

02:37:59 Jan from Discord segment -- Label information density (with XMaster96/Jan from Discord) 

02:55:13 Outro

Dr. Eray Ozkural is an AGI researcher from Turkey, he is the founder of Celestial Intellect Cybernetics. Eray is extremely critical of Max Tegmark, Nick Bostrom and MIRI founder Elizier Yodokovsky and their views on AI safety. Eray thinks that these views represent a form of neoludditism and they are capturing valuable research budgets with doomsday fear-mongering and effectively want to prevent AI from being developed by those they don't agree with. Eray is also sceptical of the intelligence explosion hypothesis and the argument from simulation.

Panel -- Dr. Keith Duggar, Dr. Tim Scarfe, Yannic Kilcher

00:00:00 Show teaser intro with added nuggets and commentary

00:48:39 Main Show Introduction 

00:53:14 Doomsaying to Control  

00:56:39 Fear the Basilisk!  

01:08:00 Intelligence Explosion Ethics  

01:09:45 Fear the Automous Drone! ... or spam  

01:11:25 Infinity Point Hypothesis  

01:15:26 Meat Level Intelligence 

01:21:25 Defining Intelligence ... Yet Again  

01:27:34 We'll make brains and then shoot them 

01:31:00 The Universe likes deep learning 

01:33:16 NNs are glorified hash tables 

01:38:44 Radical behaviorists  

01:41:29 Omega Architecture, possible AGI?  

01:53:33 Simulation hypothesis 

02:09:44 No one cometh unto Simulation, but by Jesus Christ  

02:16:47 Agendas, Motivations, and Mind Projections  

02:23:38 A computable Universe of Bulk Automata 

02:30:31 Self-Organized Post-Show Coda 

02:31:29 Investigating Intelligent Agency is Science 

02:36:56 Goodbye and cheers!  

https://www.youtube.com/watch?v=pZsHZDA9TJU

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

This week Dr. Tim Scarfe, Sayak Paul and Yannic Kilcher speak with Dr. Simon Kornblith from Google Brain (Ph.D from MIT). Simon is trying to understand how neural nets do what they do. Simon was the second author on the seminal Google AI SimCLR paper. We also cover "Do Wide and Deep Networks learn the same things?", "Whats in a Loss function for Image Classification?",  and "Big Self-supervised models are strong semi-supervised learners". Simon used to be a neuroscientist and also gives us the story of his unique journey into ML.

00:00:00 Show Teaser / or "short version"

00:18:34 Show intro

00:22:11 Relationship between neuroscience and machine learning

00:29:28 Similarity analysis and evolution of representations in Neural Networks

00:39:55 Expressability of NNs

00:42:33 Whats in a loss function for image classification

00:46:52 Loss function implications for transfer learning

00:50:44 SimCLR paper 

01:00:19 Contrast SimCLR to BYOL

01:01:43 Data augmentation

01:06:35 Universality of image representations

01:09:25 Universality of augmentations

01:23:04 GPT-3

01:25:09 GANs for data augmentation??

01:26:50 Julia language

@skornblith

https://www.linkedin.com/in/simon-kornblith-54b2033a/

https://arxiv.org/abs/2010.15327

Do Wide and Deep Networks Learn the Same Things? Uncovering How Neural Network Representations Vary with Width and Depth

https://arxiv.org/abs/2010.16402

What's in a Loss Function for Image Classification?

https://arxiv.org/abs/2002.05709

A Simple Framework for Contrastive Learning of Visual Representations

https://arxiv.org/abs/2006.10029

Big Self-Supervised Models are Strong Semi-Supervised Learners

In this special edition, Dr. Tim Scarfe, Yannic Kilcher and Keith Duggar speak with Gary Marcus and Connor Leahy about GPT-3. We have all had a significant amount of time to experiment with GPT-3 and show you demos of it in use and the considerations.

Note that this podcast version is significantly truncated, watch the youtube version for the TOC and experiments with GPT-3 https://www.youtube.com/watch?v=iccd86vOz3w

This week Dr. Tim Scarfe, Dr. Keith Duggar and Yannic Kilcher discuss multi-arm bandits and pure exploration with Dr. Wouter M. Koolen, Senior Researcher, Machine Learning group, Centrum Wiskunde & Informatica.

Wouter specialises in machine learning theory, game theory, information theory, statistics and optimisation. Wouter is currently interested in pure exploration in multi-armed bandit models, game tree search, and accelerated learning in sequential decision problems. His research has been cited 1000 times, and he has been published in NeurIPS, the number 1 ML conference 14 times as well as lots of other exciting publications.

Today we are going to talk about two of the most studied settings in control, decision theory, and learning in unknown environment which are the multi-armed bandit (MAB) and reinforcement learning (RL) approaches

- when can an agent stop learning and start exploiting using the knowledge it obtained

- which strategy leads to minimal learning time

00:00:00 What are multi-arm bandits/show trailer

00:12:55 Show introduction

00:15:50 Bandits 

00:18:58 Taxonomy of decision framework approaches 

00:25:46 Exploration vs Exploitation 

00:31:43 the sharp divide between modes 

00:34:12 bandit measures of success 

00:36:44 connections to reinforcement learning 

00:44:00 when to apply pure exploration in games 

00:45:54 bandit lower bounds, a pure exploration renaissance 

00:50:21 pure exploration compiler dreams 

00:51:56 what would the PX-compiler DSL look like 

00:57:13 the long arms of the bandit 

01:00:21 causal models behind the curtain of arms 

01:02:43 adversarial bandits, arms trying to beat you 

01:05:12 bandits as an optimization problem 

01:11:39 asymptotic optimality vs practical performance 

01:15:38 pitfalls hiding under asymptotic cover 

01:18:50 adding features to bandits 

01:27:24 moderate confidence regimes  

01:30:33 algorithms choice is highly sensitive to bounds 

01:46:09 Post script: Keith interesting piece on n quantum 

http://wouterkoolen.info

https://www.cwi.nl/research-groups/ma...

#machinelearning

This week Dr. Tim Scarfe, Dr. Keith Duggar, Yannic Kilcher and Connor Leahy cover a broad range of topics, ranging from academia, GPT-3 and whether prompt engineering could be the next in-demand skill, markets and economics including trading and whether you can predict the stock market, AI alignment, utilitarian philosophy, randomness and intelligence and even whether the universe is infinite! 

00:00:00 Show Introduction 

00:12:49 Academia and doing a Ph.D 

00:15:49 From academia to wall street 

00:17:08 Quants -- smoke and mirrors? Tail Risk 

00:19:46 Previous results dont indicate future success in markets 

00:23:23 Making money from social media signals? 

00:24:41 Predicting the stock market 

00:27:20 Things which are and are not predictable 

00:31:40 Tim postscript comment on predicting markets 

00:32:37 Connor take on markets 

00:35:16 As market become more efficient.. 

00:36:38 Snake oil in ML 

00:39:20 GPT-3, we have changed our minds 

00:52:34 Prompt engineering a new form of software development? 

01:06:07 GPT-3 and prompt engineering 

01:12:33 Emergent intelligence with increasingly weird abstractions 

01:27:29 Wireheading and the economy 

01:28:54 Free markets, dragon story and price vs value 

01:33:59 Utilitarian philosophy and what does good look like? 

01:41:39 Randomness and intelligence 

01:44:55 Different schools of thought in ML 

01:46:09 Is the universe infinite? 

Thanks a lot for Connor Leahy for being a guest on today's show. https://twitter.com/NPCollapse -- you can join his EleutherAI community discord here: https://discord.com/invite/vtRgjbM

#machinelearning

This week Dr. Tim Scarfe, Dr. Keith Duggar and Yannic Kilcher speak with veteran NLU expert Dr. Walid Saba. 

Walid is an old-school AI expert. He is a polymath, a neuroscientist, psychologist, linguist,  philosopher, statistician, and logician. He thinks the missing information problem and lack of a typed ontology is the key issue with NLU, not sample efficiency or generalisation. He is a big critic of the deep learning movement and BERTology. We also cover GPT-3 in some detail in today's session, covering Luciano Floridi's recent article "GPT‑3: Its Nature, Scope, Limits, and Consequences" and a commentary on the incredible power of GPT-3 to perform tasks with just a few examples including the Yann LeCun commentary on Facebook and Hackernews. 

Time stamps on the YouTube version

0:00:00 Walid intro 

00:05:03 Knowledge acquisition bottleneck 

00:06:11 Language is ambiguous 

00:07:41 Language is not learned 

00:08:32 Language is a formal language 

00:08:55 Learning from data doesn’t work  

00:14:01 Intelligence 

00:15:07 Lack of domain knowledge these days 

00:16:37 Yannic Kilcher thuglife comment 

00:17:57 Deep learning assault 

00:20:07 The way we evaluate language models is flawed 

00:20:47 Humans do type checking 

00:23:02 Ontologic 

00:25:48 Comments On GPT3 

00:30:54 Yann lecun and reddit 

00:33:57 Minds and machines - Luciano 

00:35:55 Main show introduction 

00:39:02 Walid introduces himself 

00:40:20 science advances one funeral at a time 

00:44:58 Deep learning obsession syndrome and inception 

00:46:14 BERTology / empirical methods are not NLU 

00:49:55 Pattern recognition vs domain reasoning, is the knowledge in the data 

00:56:04 Natural language understanding is about decoding and not compression, it's not learnable. 

01:01:46 Intelligence is about not needing infinite amounts of time 

01:04:23 We need an explicit ontological structure to understand anything 

01:06:40 Ontological concepts 

01:09:38 Word embeddings 

01:12:20 There is power in structure 

01:15:16 Language models are not trained on pronoun disambiguation and resolving scopes 

01:17:33 The information is not in the data 

01:19:03 Can we generate these rules on the fly? Rules or data? 

01:20:39 The missing data problem is key 

01:21:19 Problem with empirical methods and lecunn reference 

01:22:45 Comparison with meatspace (brains) 

01:28:16 The knowledge graph game, is knowledge constructed or discovered 

01:29:41 How small can this ontology of the world be? 

01:33:08 Walids taxonomy of understanding 

01:38:49 The trend seems to be, less rules is better not the othe way around? 

01:40:30 Testing the latest NLP models with entailment 

01:42:25 Problems with the way we evaluate NLP 

01:44:10 Winograd Schema challenge 

01:45:56 All you need to know now is how to build neural networks, lack of rigour in ML research 

01:50:47 Is everything learnable 

01:53:02  How should we elevate language systems? 

01:54:04 10 big problems in language (missing information) 

01:55:59 Multiple inheritance is wrong 

01:58:19 Language is ambiguous 

02:01:14 How big would our world ontology need to be? 

02:05:49 How to learn more about NLU 

02:09:10 AlphaGo 

Walid's blog: https://medium.com/@ontologik

LinkedIn: https://www.linkedin.com/in/walidsaba/

Ryan Greenblatt from Redwood Research recently published "Getting 50% on ARC-AGI with GPT-4.0," where he used GPT4o to reach a state-of-the-art accuracy on Francois Chollet's ARC Challenge by generating many Python programs.

Sponsor:

Sign up to Kalshi here https://kalshi.onelink.me/1r91/mlst -- the first 500 traders who deposit $100 will get a free $20 credit! Important disclaimer - In case it's not obvious - this is basically gambling and a *high risk* activity - only trade what you can afford to lose.

We discuss:

- Ryan's unique approach to solving the ARC Challenge and achieving impressive results.

- The strengths and weaknesses of current AI models.

- How AI and humans differ in learning and reasoning.

- Combining various techniques to create smarter AI systems.

- The potential risks and future advancements in AI, including the idea of agentic AI.

https://x.com/RyanPGreenblatt

https://www.redwoodresearch.org/

Refs:

Getting 50% (SoTA) on ARC-AGI with GPT-4o [Ryan Greenblatt]

https://redwoodresearch.substack.com/p/getting-50-sota-on-arc-agi-with-gpt

On the Measure of Intelligence [Chollet]

https://arxiv.org/abs/1911.01547

Connectionism and Cognitive Architecture: A Critical Analysis [Jerry A. Fodor and Zenon W. Pylyshyn]

https://ruccs.rutgers.edu/images/personal-zenon-pylyshyn/proseminars/Proseminar13/ConnectionistArchitecture.pdf

Software 2.0 [Andrej Karpathy]

https://karpathy.medium.com/software-2-0-a64152b37c35

Why Greatness Cannot Be Planned: The Myth of the Objective [Kenneth Stanley]

https://amzn.to/3Wfy2E0

Biographical account of Terence Tao’s mathematical development. [M.A.(KEN) CLEMENTS]

https://gwern.net/doc/iq/high/smpy/1984-clements.pdf

Model Evaluation and Threat Research (METR)

https://metr.org/

Why Tool AIs Want to Be Agent AIs

https://gwern.net/tool-ai

Simulators - Janus

https://www.lesswrong.com/posts/vJFdjigzmcXMhNTsx/simulators

AI Control: Improving Safety Despite Intentional Subversion

https://www.lesswrong.com/posts/d9FJHawgkiMSPjagR/ai-control-improving-safety-despite-intentional-subversion

https://arxiv.org/abs/2312.06942

What a Compute-Centric Framework Says About Takeoff Speeds

https://www.openphilanthropy.org/research/what-a-compute-centric-framework-says-about-takeoff-speeds/

Global GDP over the long run

https://ourworldindata.org/grapher/global-gdp-over-the-long-run?yScale=log

Safety Cases: How to Justify the Safety of Advanced AI Systems

https://arxiv.org/abs/2403.10462

The Danger of a “Safety Case"

http://sunnyday.mit.edu/The-Danger-of-a-Safety-Case.pdf

The Future Of Work Looks Like A UPS Truck (~02:15:50)

https://www.npr.org/sections/money/2014/05/02/308640135/episode-536-the-future-of-work-looks-like-a-ups-truck

SWE-bench

https://www.swebench.com/

Using DeepSpeed and Megatron to Train Megatron-Turing NLG

530B, A Large-Scale Generative Language Model

https://arxiv.org/pdf/2201.11990

Algorithmic Progress in Language Models

https://epochai.org/blog/algorithmic-progress-in-language-models

This week Dr. Tim Scarfe, Alex Stenlake and Yannic Kilcher speak with AGI and AI alignment specialist Connor Leahy a machine learning engineer from Aleph Alpha and founder of EleutherAI.

Connor believes that AI alignment is philosophy with a deadline and that we are on the precipice, the stakes are astronomical. AI is important, and it will go wrong by default. Connor thinks that the singularity or intelligence explosion is near. Connor says that AGI is like climate change but worse, even harder problems, even shorter deadline and even worse consequences for the future. These problems are hard, and nobody knows what to do about them.

00:00:00 Introduction to AI alignment and AGI fire alarm 

00:15:16 Main Show Intro 

00:18:38 Different schools of thought on AI safety 

00:24:03 What is intelligence? 

00:25:48 AI Alignment 

00:27:39 Humans dont have a coherent utility function 

00:28:13 Newcomb's paradox and advanced decision problems 

00:34:01 Incentives and behavioural economics 

00:37:19 Prisoner's dilemma 

00:40:24 Ayn Rand and game theory in politics and business 

00:44:04 Instrumental convergence and orthogonality thesis 

00:46:14 Utility functions and the Stop button problem 

00:55:24 AI corrigibality - self alignment 

00:56:16 Decision theory and stability / wireheading / robust delegation 

00:59:30 Stop button problem 

01:00:40 Making the world a better place 

01:03:43 Is intelligence a search problem? 

01:04:39 Mesa optimisation / humans are misaligned AI 

01:06:04 Inner vs outer alignment / faulty reward functions 

01:07:31 Large corporations are intelligent and have no stop function 

01:10:21 Dutch booking / what is rationality / decision theory 

01:16:32 Understanding very powerful AIs 

01:18:03 Kolmogorov complexity 

01:19:52 GPT-3 - is it intelligent, are humans even intelligent? 

01:28:40 Scaling hypothesis 

01:29:30 Connor thought DL was dead in 2017 

01:37:54 Why is GPT-3 as intelligent as a human 

01:44:43 Jeff Hawkins on intelligence as compression and the great lookup table 

01:50:28 AI ethics related to AI alignment? 

01:53:26 Interpretability 

01:56:27 Regulation 

01:57:54 Intelligence explosion 

Discord: https://discord.com/invite/vtRgjbM

EleutherAI: https://www.eleuther.ai

Twitter: https://twitter.com/npcollapse

LinkedIn: https://www.linkedin.com/in/connor-j-leahy/

Join Dr Tim Scarfe, Sayak Paul, Yannic Kilcher, and Alex Stenlake have a conversation with Mr. Chai Time Data Science; Sanyam Bhutani!

00:00:00 Introduction 

00:03:42 Show kick off 

00:06:34 How did Sanyam get started into ML 

00:07:46 Being a content creator 

00:09:01 Can you be self taught without a formal education in ML? 

00:22:54 Kaggle 

00:33:41 H20 product / job 

00:40:58 Intepretability / bias / engineering skills 

00:43:22 Get that first job in DS 

00:46:29 AWS ML Ops architecture / ml engineering 

01:14:19 Patterns 

01:18:09 Testability 

01:20:54 Adversarial examples 

Sanyam's blog -- https://sanyambhutani.com/tag/chaitimedatascience/

Chai Time Data Science -- https://www.youtube.com/c/ChaiTimeDataScience

Dr. Tim Scarfe, Yannic Kilcher and Sayak Paul chat with Sara Hooker from the Google Brain team! We discuss her recent hardware lottery paper, pruning / sparsity, bias mitigation and intepretability. 

The hardware lottery -- what causes inertia or friction in the marketplace of ideas? Is there a meritocracy of ideas or do the previous decisions we have made enslave us? Sara Hooker calls this a lottery because she feels that machine learning progress is entirely beholdant to the hardware and software landscape. Ideas succeed if they are compatible with the hardware and software at the time and also the existing inventions. The machine learning community is exceptional because the pace of innovation is fast and we operate largely in the open, this is largely because we don't build anything physical which is expensive, slow and the cost of being scooped is high. We get stuck in basins of attraction based on our technology decisions and it's expensive to jump outside of these basins. So is this story unique to hardware and AI algorithms or is it really just the story of all innovation? Every great innovation must wait for the right stepping stone to be in place before it can really happen. We are excited to bring you Sara Hooker to give her take. 

YouTube version (including TOC): https://youtu.be/sQFxbQ7ade0

Show notes; https://drive.google.com/file/d/1S_rHnhaoVX4Nzx_8e3ESQq4uSswASNo7/view?usp=sharing

Sara Hooker page; https://www.sarahooker.me