Machine Learning Street Talk (MLST)

AI Helps Ukraine - Charity Conference

A charity conference on AI to raise funds for medical and humanitarian aid for Ukraine

https://aihelpsukraine.cc/

YT version: https://youtu.be/LgwjcqhkOA4

Support us!

https://www.patreon.com/mlst 

Dr. Joscha Bach (born 1973 in Weimar, Germany) is a German artificial intelligence researcher and cognitive scientist focusing on cognitive architectures, mental representation, emotion, social modelling, and multi-agent systems. 

http://bach.ai/

https://twitter.com/plinz

TOC:

[00:00:00] Ukraine Charity Conference and NeurIPS 2022

[00:03:40] Theory of computation, Godel, Penrose

[00:11:44] Modelling physical reality

[00:15:19] Is our universe infinite?

[00:24:30] Large language models, and on DL / is Gary Marcus hitting a wall?

[00:45:17] Generative models / Codex / Language of thought

[00:58:46] Consciousness (with Friston references)

References:

Am I Self-Conscious? (Or Does Self-Organization Entail Self-Consciousness?) [Friston]

https://www.frontiersin.org/articles/10.3389/fpsyg.2018.00579/full

Impact of Pretraining Term Frequencies on Few-Shot Reasoning [Yasaman Razeghi]

https://arxiv.org/abs/2202.07206

Deep Learning Is Hitting a Wall [Gary Marcus]

https://nautil.us/deep-learning-is-hitting-a-wall-238440/

Turing machines

https://en.wikipedia.org/wiki/Turing_machine

Lambda Calculus

https://en.wikipedia.org/wiki/Lambda_calculus

Godel's incompletness theorem

https://en.wikipedia.org/wiki/G%C3%B6del%27s_incompleteness_theorems

Oracle machine

https://en.wikipedia.org/wiki/Oracle_machine

Support us (and please rate on podcast app)

https://www.patreon.com/mlst 

In this show tonight with Prof. Julian Togelius (NYU) and Prof. Ken Stanley we discuss open-endedness, AGI, game AI and reinforcement learning.  

[Prof Julian Togelius]

https://engineering.nyu.edu/faculty/julian-togelius

https://twitter.com/togelius

[Prof Ken Stanley]

https://www.cs.ucf.edu/~kstanley/

https://twitter.com/kenneth0stanley

TOC:

[00:00:00] Introduction

[00:01:07] AI and computer games

[00:12:23] Intelligence

[00:21:27] Intelligence Explosion

[00:25:37] What should we be aspiring towards?

[00:29:14] Should AI contribute to culture?

[00:32:12] On creativity and open-endedness

[00:36:11] RL overfitting

[00:44:02] Diversity preservation

[00:51:18] Empiricism vs rationalism , in gradient descent the data pushes you around

[00:55:49] Creativity and interestingness (does complexity / information increase)

[01:03:20] What does a population give us?

[01:05:58] Emergence / generalisation snobbery

References;

[Hutter/Legg] Universal Intelligence: A Definition of Machine Intelligence

https://arxiv.org/abs/0712.3329

https://en.wikipedia.org/wiki/Artificial_general_intelligence

https://en.wikipedia.org/wiki/I._J._Good

https://en.wikipedia.org/wiki/G%C3%B6del_machine

[Chollet] Impossibility of intelligence explosion

https://medium.com/@francois.chollet/the-impossibility-of-intelligence-explosion-5be4a9eda6ec

[Alex Irpan] - RL is hard

https://www.alexirpan.com/2018/02/14/rl-hard.html

https://nethackchallenge.com/

Map elites

https://arxiv.org/abs/1504.04909

Covariance Matrix Adaptation for the Rapid Illumination of Behavior Space

https://arxiv.org/abs/1912.02400

[Stanley] - Why greatness cannot be planned

https://www.amazon.com/Why-Greatness-Cannot-Planned-Objective/dp/3319155237

[Lehman/Stanley] Abandoning Objectives: Evolution through the Search for Novelty Alone

https://www.cs.swarthmore.edu/~meeden/DevelopmentalRobotics/lehman_ecj11.pdf

We had a conversation with Aidan Gomez, the CEO of language-based AI platform Cohere. Cohere is a startup which uses artificial intelligence to help users build the next generation of language-based applications. It's headquartered in Toronto. The company has raised $175 million in funding so far.

Language may well become a key new substrate for software building, both in its representation and how we build the software. It may democratise software building so that more people can build software, and we can build new types of software. Aidan and I discuss this in detail in this episode of MLST.

Check out Cohere -- https://dashboard.cohere.ai/welcome/register?utm_source=influencer&utm_medium=social&utm_campaign=mlst

Support us!

https://www.patreon.com/mlst 

YT version: https://youtu.be/ooBt_di8DLs

TOC:

[00:00:00] Aidan Gomez intro

[00:02:12] What's it like being a CEO?

[00:02:52] Transformers

[00:09:33] Deepmind Chomsky Hierarchy

[00:14:58] Cohere roadmap

[00:18:18] Friction using LLMs for startups

[00:25:31] How different from OpenAI / GPT-3

[00:29:31] Engineering questions on Cohere

[00:35:13] Francois Chollet says that LLMs are like databases

[00:38:34] Next frontier of language models

[00:42:04] Different modes of understanding in LLMs

[00:47:04] LLMs are the new extended mind

[00:50:03] Is language the next interface, and why might that be bad?

References:

[Balestriero] Spine theory of NNs

https://proceedings.mlr.press/v80/balestriero18b/balestriero18b.pdf

[Delétang et al] Neural Networks and the Chomsky Hierarchy

https://arxiv.org/abs/2207.02098

[Fodor, Pylyshyn] Connectionism and Cognitive Architecture: A Critical Analysis

https://ruccs.rutgers.edu/images/personal-zenon-pylyshyn/docs/jaf.pdf

[Chalmers, Clark] The extended mind

https://icds.uoregon.edu/wp-content/uploads/2014/06/Clark-and-Chalmers-The-Extended-Mind.pdf

[Melanie Mitchell et al] The Debate Over Understanding in AI's Large Language Models

https://arxiv.org/abs/2210.13966

[Jay Alammar]

Illustrated stable diffusion

https://jalammar.github.io/illustrated-stable-diffusion/

Illustrated transformer

https://jalammar.github.io/illustrated-transformer/

https://www.youtube.com/channel/UCmOwsoHty5PrmE-3QhUBfPQ

[Sandra Kublik] (works at Cohere!)

https://www.youtube.com/channel/UCjG6QzmabZrBEeGh3vi-wDQ

This video is demonetised on music copyright so we would appreciate support on our Patreon! https://www.patreon.com/mlst 

We would also appreciate it if you rated us on your podcast platform. 

YT: https://youtu.be/_KVAzAzO5HU

Panel: Dr. Tim Scarfe, Dr. Keith Duggar

Guests: Prof. J. Mark Bishop, Francois Chollet, Prof. David Chalmers, Dr. Joscha Bach, Prof. Karl Friston, Alexander Mattick, Sam Roffey

The Chinese Room Argument was first proposed by philosopher John Searle in 1980. It is an argument against the possibility of artificial intelligence (AI) – that is, the idea that a machine could ever be truly intelligent, as opposed to just imitating intelligence.

The argument goes like this:

Imagine a room in which a person sits at a desk, with a book of rules in front of them. This person does not understand Chinese.

Someone outside the room passes a piece of paper through a slot in the door. On this paper is a Chinese character. The person in the room consults the book of rules and, following these rules, writes down another Chinese character and passes it back out through the slot.

To someone outside the room, it appears that the person in the room is engaging in a conversation in Chinese. In reality, they have no idea what they are doing – they are just following the rules in the book.

The Chinese Room Argument is an argument against the idea that a machine could ever be truly intelligent. It is based on the idea that intelligence requires understanding, and that following rules is not the same as understanding.

in this detailed investigation into the Chinese Room, Consciousness and Syntax vs Semantics, we interview luminaries J.Mark Bishop and Francois Chollet and use unreleased footage from our interviews with David Chalmers, Joscha Bach and Karl Friston. We also cover material from Walid Saba and interview Alex Mattick from Yannic's Discord. 

This is probably my favourite ever episode of MLST. I hope you enjoy it!  With Keith Duggar. 

Note that we are using clips from our unreleased interviews from David Chalmers and Joscha Bach -- we will release those shows properly in the coming weeks. We apologise for delay releasing our backlog, we have been busy building a startup company in the background.

TOC: 

[00:00:00] Kick off

[00:00:46] Searle

[00:05:09] Bishop introduces CRA

[00:00:00] Stevan Hardad take on CRA 

[00:14:03] Francois Chollet dissects CRA

[00:34:16] Chalmers on consciousness

[00:36:27] Joscha Bach on consciousness

[00:42:01] Bishop introduction

[00:51:51] Karl Friston on consciousness

[00:55:19] Bishop on consciousness and comments on Chalmers 

[01:21:37] Private language games (including clip with Sam Roffey)

[01:27:27] Dr. Walid Saba on the chinese room (gofai/systematicity take)

[00:34:36] Bishop: on agency / teleology

[01:36:38] Bishop: back to CRA

[01:40:53] Noam Chomsky on mysteries 

[01:45:56] Eric Curiel on math does not represent

[01:48:14] Alexander Mattick on syntax vs semantics

Thanks to: Mark MC on Discord for stimulating conversation, Alexander Mattick, Dr. Keith Duggar, Sam Roffey. Sam's YouTube channel is https://www.youtube.com/channel/UCjRNMsglFYFwNsnOWIOgt1Q

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

Discord: https://discord.gg/ESrGqhf5CB

In this special edition episode, we have a conversation with Prof. Noam Chomsky, the father of modern linguistics and the most important intellectual of the 20th century. 

With a career spanning the better part of a century, we took the chance to ask Prof. Chomsky his thoughts not only on the progress of linguistics and cognitive science but also the deepest enduring mysteries of science and philosophy as a whole - exploring what may lie beyond our limits of understanding. We also discuss the rise of connectionism and large language models, our quest to discover an intelligible world, and the boundaries between silicon and biology.

We explore some of the profound misunderstandings of linguistics in general and Chomsky’s own work specifically which have persisted, at the highest levels of academia for over sixty years.  

We have produced a significant introduction section where we discuss in detail Yann LeCun’s recent position paper on AGI, a recent paper on emergence in LLMs, empiricism related to cognitive science, cognitive templates, “the ghost in the machine” and language. 

Panel: 

Dr. Tim Scarfe

Dr. Keith Duggar

Dr. Walid Saba 

YT version: https://youtu.be/-9I4SgkHpcA

00:00:00 Kick off

00:02:24 C1: LeCun's recent position paper on AI, JEPA, Schmidhuber, EBMs

00:48:38 C2: Emergent abilities in LLMs paper

00:51:32 C3: Empiricism

01:25:33 C4: Cognitive Templates

01:35:47 C5: The Ghost in the Machine

01:59:21 C6: Connectionism and Cognitive Architecture: A Critical Analysis by Fodor and Pylyshyn

02:19:25 C7: We deep-faked Chomsky

02:29:11 C8: Language

02:34:41 C9: Chomsky interview kick-off!

02:35:39 Large Language Models such as GPT-3

02:39:14 Connectionism and radical empiricism

02:44:44 Hybrid systems such as neurosymbolic

02:48:47 Computationalism silicon vs biological

02:53:28 Limits of human understanding

03:00:46 Semantics state-of-the-art

03:06:43 Universal grammar, I-Language, and language of thought

03:16:27 Profound and enduring misunderstandings

03:25:41 Greatest remaining mysteries science and philosophy

03:33:10 Debrief and 'Chuckles' from Chomsky

Vitaliy Chiley  is a Machine Learning Research Engineer at the next-generation computing hardware company Cerebras Systems. We spoke about how DL workloads including sparse workloads can run faster on Cerebras hardware.

[00:00:00] Housekeeping

[00:01:08] Preamble

[00:01:50] Vitaliy Chiley Introduction

[00:03:11] Cerebrus architecture

[00:08:12] Memory management and FLOP utilisation

[00:18:01] Centralised vs decentralised compute architecture

[00:21:12] Sparsity

[00:23:47] Does Sparse NN imply Heterogeneous compute?

[00:29:21] Cost of distributed memory stores?

[00:31:01] Activation vs weight sparsity

[00:37:52] What constitutes a dead weight to be pruned?

[00:39:02] Is it still a saving if we have to choose between weight and activation sparsity?

[00:41:02] Cerebras is a cool place to work

[00:44:05] What is sparsity? Why do we need to start dense? 

[00:46:36] Evolutionary algorithms on Cerebras?

[00:47:57] How can we start sparse? Google RIGL

[00:51:44] Inductive priors, why do we need them if we can start sparse?

[00:56:02] Why anthropomorphise inductive priors?

[01:02:13] Could Cerebras run a cyclic computational graph?

[01:03:16] Are NNs locality sensitive hashing tables?

References;

Rigging the Lottery: Making All Tickets Winners [RIGL]

https://arxiv.org/pdf/1911.11134.pdf

[D] DanNet, the CUDA CNN of Dan Ciresan in Jurgen Schmidhuber's team, won 4 image recognition challenges prior to AlexNet

https://www.reddit.com/r/MachineLearning/comments/dwnuwh/d_dannet_the_cuda_cnn_of_dan_ciresan_in_jurgen/ 

A Spline Theory of Deep Learning [Balestriero]

https://proceedings.mlr.press/v80/balestriero18b.html 

Check out Weights and Biases here! 

https://wandb.me/MLST

Lukas Biewald is an entrepreneur living in San Francisco. He was the founder and CEO of Figure Eight an Internet company that collects training data for machine learning.  In 2018, he founded Weights and Biases, a company that creates developer tools for machine learning. Recently WandB got a cash injection of 15 million dollars in its second funding round. 

Lukas has a bachelors and masters in mathematics and computer science respectively from Stanford university.  He was a research student under the tutelage of the legendary Daphne Koller. 

Lukas Biewald

https://twitter.com/l2k

[00:00:00] Preamble

[00:01:27] Intro to Lukas

[00:02:46] How did Lukas build 2 sucessful startups?

[00:05:49] Rebalancing games with ML

[00:08:14] Elevator pitch for WandB

[00:10:38] Science vs Engineering divide in ML DevOps

[00:14:11] Too much focus on the minutiae?

[00:18:03] Vertical information sharing in large enterprises (metrics)

[00:20:37] Centralised vs Decentralised topology

[00:24:02] Generalisation vs specialisation

[00:28:59] Enhancing explainability

[00:33:14] Should we try and understand "the machine" or is testing / behaviourism enough?

[00:36:55] WandB roadmap

[00:39:06] WandB / ML Ops competitor space?

[00:44:10] How is WandB differentiated over Sagemaker / AzureML

[00:46:02] WandB Sponsorship of ML YT channels

[00:48:43] Alternatives to deep learning?

[00:53:47] How to build a business like WandB

Panel: Tim Scarfe Ph.D and Keith Duggar Ph.D

Note we didn't get paid by Weights and Biases to conduct this interview.

An emergent behavior or emergent property can appear when a number of simple entities operate in an environment, forming more complex behaviours as a collective. If emergence happens over disparate size scales, then the reason is usually a causal relation across different scales. Weak emergence describes new properties arising in systems as a result of the low-level interactions, these might be interactions between components of the system or the components and their environment. 

In our epic introduction we focus a lot on the concept of self-organisation, complex systems, cellular automata and strong vs weak emergence. In the main show we discuss this more in detail with Dr. Daniele Grattarola and cover his recent NeurIPS paper on learning graph cellular automata. 

YT version: https://youtu.be/MDt2e8XtUcA

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

Discord: https://discord.gg/ESrGqhf5CB

Featuring;

Dr. Daniele Grattarola

Dr. Tim Scarfe

Dr. Keith Duggar

Prof. David Chalmers

Prof. Ken Stanley

Prof. Julian Togelius

Dr. Joscha Bach

David Ha

Dr. Pei Wang

[00:00:00] Special Edition Intro: Emergence and Cellular Automata

[00:49:02] Intro to Daniele and CAs

[00:57:23] Numerical analysis link with CA (PDEs)

[00:59:50] The representational dichotomy of discrete and continuous at different scales

[01:05:21] Universal computation in CAs

[01:10:27] Computational irreducibility 

[01:16:33] Is the universe discrete?

[01:20:49] Emergence but with the same computational principle

[01:23:10] How do you formalise the emergent phenomenon 

[01:25:44] Growing cellular automata

[01:33:53] Openeded and unbounded computation is required for this kind of behaviour

[01:37:31] Graph cellula automata

[01:43:40] Connection to protein folding

[01:46:24] Are CAs the best tool for the job?

[01:49:37] Where to go to find more information

Please note that in this interview Dr. Lampinen was expressing his personal opinions and they do not necessarily represent those of DeepMind. 

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

Discord: https://discord.gg/ESrGqhf5CB

YT version: https://youtu.be/yPMtSXXn4OY

 Dr. Andrew Lampinen is a Senior Research Scientist at DeepMind, and he thinks that symbols are subjective in the relativistic sense. Dr. Lampinen completed his PhD in Cognitive Psychology at Stanford University. His background is in mathematics, physics, and machine learning. Andrew has said that his research interests are in cognitive flexibililty and generalization, and how these abilities are enabled by factors like language, memory, and embodiment.  Andrew with his coauthors has just released a paper called symbolic behaviour in artificial intelligence. Andrew lead in the paper by saying the human ability to use symbols has yet to be replicated in machines. He thinks that one of the key areas to bridge the gap here is considering how symbol meaning is established, and he strongly believes it is the symbol users themselves who agree upon the symbol meaning, And that the use of symbols entails behaviours which coalesce agreements about their meaning. Which in plain English means that symbols are defined by behaviours rather than their content.

[00:00:00] Intro to Andrew and Symbolic Behaviour paper

[00:07:01] Semantics underpins the unreasonable effectiveness of symbols

[00:12:56] The Depth of Subjectivity

[00:21:03] Walid Saba - universal cognitive templates

[00:27:47] Insufficiently Darwinian 

[00:30:52] Discovered vs invented

[00:34:19] Does language have primacy

[00:35:59] Research directions

[00:39:43] Comparison to BenG OpenCog and human compatible AI

[00:42:53] Aligning AI with our culture

[00:47:55] Do we need to model the worst aspects of human behaviour? 

[00:50:57] Fairness

[00:54:24] Memorisatation on LLMs

[01:00:38] Wason selection task

[01:03:45] Would an Andrew hashtable robot be intelligent?

Dr. Andrew Lampinen

https://lampinen.github.io/

https://twitter.com/AndrewLampinen

Symbolic Behaviour in Artificial Intelligence

https://arxiv.org/abs/2102.03406

Imitating Interactive Intelligence

https://arxiv.org/abs/2012.05672

https://www.deepmind.com/publications/imitating-interactive-intelligence

Impact of Pretraining Term Frequencies on Few-Shot Reasoning [Yasaman Razeghi]

https://arxiv.org/abs/2202.07206

Big bench dataset

https://github.com/google/BIG-bench

Teaching Autoregressive Language Models Complex Tasks By Demonstration [Recchia]

https://arxiv.org/pdf/2109.02102.pdf

Wason selection task

https://en.wikipedia.org/wiki/Wason_selection_task

Gary Lupyan

https://psych.wisc.edu/staff/lupyan-gary/

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

Discord: https://discord.gg/ESrGqhf5CB

YT version: https://youtu.be/RzGaI7vXrkk

This week we speak with Yasaman Razeghi and Prof. Sameer Singh from UC Urvine. Yasaman recently published a paper called Impact of Pretraining Term Frequencies on Few-Shot Reasoning where she demonstrated comprehensively that large language models only perform well on reasoning tasks because they memorise the dataset. For the first time she showed the accuracy was linearly correlated to the occurance rate in the training corpus, something which OpenAI should have done in the first place! 

We also speak with Sameer who has been a pioneering force in the area of machine learning interpretability for many years now, he created LIME with Marco Riberio and also had his hands all over the famous Checklist paper and many others. 

We also get into the metric obsession in the NLP world and whether metrics are one of the principle reasons why we are failing to make any progress in NLU. 

[00:00:00] Impact of Pretraining Term Frequencies on Few-Shot Reasoning

[00:14:59] Metrics

[00:18:55] Definition of reasoning

[00:25:12] Metrics (again)

[00:28:52] On true believers 

[00:33:04] Sameers work on model explainability / LIME 

[00:36:58] Computational irreducability 

[00:41:07] ML DevOps and Checklist

[00:45:58] Future of ML devops

[00:49:34] Thinking about future

Prof. Sameer Singh

https://sameersingh.org/

Yasaman Razeghi

https://yasamanrazeghi.com/

References;

Impact of Pretraining Term Frequencies on Few-Shot Reasoning [Razeghi et al with Singh]

https://arxiv.org/pdf/2202.07206.pdf

Beyond Accuracy: Behavioral Testing of NLP Models with CheckList [Riberio et al with Singh]

https://arxiv.org/pdf/2005.04118.pdf

“Why Should I Trust You?” Explaining the Predictions of Any Classifier (LIME) [Riberio et al with Singh]

https://arxiv.org/abs/1602.04938

Tim interviewing LIME Creator Marco Ribeiro in 2019

https://www.youtube.com/watch?v=6aUU-Ob4a8I

Tim video on LIME/SHAP on his other channel

https://www.youtube.com/watch?v=jhopjN08lTM

Our interview with Christoph Molar

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

Interpretable Machine Learning book @ChristophMolnar

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

Machine Teaching: A New Paradigm for Building Machine Learning Systems [Simard]

https://arxiv.org/abs/1707.06742

Whimsical notes on machine teaching

https://whimsical.com/machine-teaching-Ntke9EHHSR25yHnsypHnth

Gopher paper (Deepmind)

https://www.deepmind.com/blog/language-modelling-at-scale-gopher-ethical-considerations-and-retrieval

https://arxiv.org/pdf/2112.11446.pdf

EleutherAI

https://www.eleuther.ai/

https://github.com/kingoflolz/mesh-transformer-jax/

https://pile.eleuther.ai/

A Theory of Universal Artificial Intelligence based on Algorithmic Complexity [Hutter]

https://arxiv.org/pdf/cs/0004001.pdf