Machine Learning Street Talk (MLST)

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/

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

This week join Dr. Tim Scarfe, Yannic Kilcher, and Keith Duggar have a conversation with Dr. Rebecca Roache in the last of our 3-part series on the social dilemma Netflix film. Rebecca is a senior lecturer in philosophy at Royal Holloway, university of London and has written extensively about the future of friendship. 

People claim that friendships are not what they used to be. People are always staring at their phones, even when in public  Social media has turned us into narcissists who are always managing our own PR rather than being present with each other. Anxiety about the negative effects of technology are as old as the written word. Is technology bad for friendships? Can you have friends through screens? Does social media cause polarization? And is that a bad thing? Does it promote quantity over quality? Rebecca thinks that social media and echo chambers are less ominous to friendship on closer inspection. 

00:00:32 Teaser clip from Rebecca and her new manuscript on friendship

00:02:52 Introduction 

00:04:56 Memorisation vs reasoning / is technology enhancing friendships 

00:09:29 Word of warcraft / gaming communities / echo chambers / polarisation 

00:12:34 Horizontal vs Vertical social attributes 

00:17:18 Exclusion of others opinions 

00:20:36 The power to silence others / truth verification 

00:23:58 Misinformation 

00:27:28 Norms / memes / political terms and co-opting / bullying 

00:31:57 Redefinition of political terms i.e. racism 

00:36:13 Virtue signalling 

00:38:57 How many friends can you have / spread thin / Dunbars 150 

00:42:54 Is it morally objectionable to believe or contemplate objectionable ideas, punishment 

00:50:52 Is speaking the same thing as acting  

00:52:24 Punishment - deterrence vs retribution / historical 

00:53:59 Yannic: contemplating is a form of speaking 

00:57:32 silencing/blocking is intellectual laziness - what ideas are we allowed to talk about 

01:04:53 Corporate AI ethics frameworks 

01:09:14 Autonomous Vehicles 

01:10:51 the eternal Facebook world / online vs offline friendships 

01:14:05 How do we get the best out of our online friendships