Machine Learning Street Talk (MLST)

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 

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