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PODCAST

Machine Learning Street Talk (MLST)

Machine Learning Street Talk (MLST)

Welcome! We engage in fascinating discussions with pre-eminent figures in the AI field. Our flagship show covers current affairs in AI, cognitive science, neuroscience and philosophy of mind with in-depth analysis. Our approach is unrivalled in terms of scope and rigour – we believe in intellectual diversity in AI, and we touch on all of the main ideas in the field with the hype surgically removed. MLST is run by Tim Scarfe, Ph.D (https://www.linkedin.com/in/ecsquizor/) and features regular

technology

All Episodes

02:56:03

#035 Christmas Community Edition!

Machine Learning Street Talk (MLST) ·

2020/12/27

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

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02:39:09

#034 Eray Özkural- AGI, Simulations & Safety

Machine Learning Street Talk (MLST) ·

2020/12/20

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

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01:51:24

#033 Prof. Karl Friston - The Free Energy Principle

Machine Learning Street Talk (MLST) ·

2020/12/13

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

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01:30:29

#032- Simon Kornblith / GoogleAI - SimCLR and Paper...

Machine Learning Street Talk (MLST) ·

2020/12/06

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

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02:44:06

#031 WE GOT ACCESS TO GPT-3! (With Gary Marcus, Walid...

Machine Learning Street Talk (MLST) ·

2020/11/28

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

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01:48:08

#030 Multi-Armed Bandits and Pure-Exploration (Wouter...

Machine Learning Street Talk (MLST) ·

2020/11/20

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

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01:50:32

#029 GPT-3, Prompt Engineering, Trading, AI...

Machine Learning Street Talk (MLST) ·

2020/11/08

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

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02:20:32

NLP is not NLU and GPT-3 - Walid Saba

Machine Learning Street Talk (MLST) ·

2020/11/04

#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

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02:04:35

AI Alignment & AGI Fire Alarm - Connor Leahy

Machine Learning Street Talk (MLST) ·

2020/11/01

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

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01:26:59

Kaggle, ML Community / Engineering (Sanyam Bhutani)

Machine Learning Street Talk (MLST) ·

2020/10/28

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

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01:30:35

Sara Hooker - The Hardware Lottery, Sparsity and...

Machine Learning Street Talk (MLST) ·

2020/10/20

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

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01:16:18

The Social Dilemma Part 3 - Dr. Rebecca Roache

Machine Learning Street Talk (MLST) ·

2020/10/11

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

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01:45:36

The Social Dilemma - Part 2

Machine Learning Street Talk (MLST) ·

2020/10/06

This week on Machine Learning Street Talk, Dr. Tim Scarfe, Dr. Keith Duggar, Alex Stenlake and Yannic Kilcher have a conversation with the founder and principal researcher at the Montreal AI Ethics institute -- Abhishek Gupta. We cover several topics from the Social Dilemma film and AI Ethics in general. 00:00:00 Introduction 00:03:57 Overcome our weaknesses 00:14:30 threat landscape blind spots 00:18:35 differential reality vs universal shaping 00:24:21 shared reality incentives and tools

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01:07:19

The Social Dilemma - Part 1

Machine Learning Street Talk (MLST) ·

2020/10/03

In this first part of our three part series on the Social Dilemma Netflix film, Dr. Tim Scarfe, Yannic "Lightspeed" Kilcher and Zak Jost gang up with Cybersecurity expert Andy Smith. We give you our take on the film. We are super excited to get your feedback on this one! Hope you enjoy. 00:00:00 Introduction 00:06:11 Moral hypocrisy 00:12:38 Road to hell is paved with good intentions, attention economy 00:15:04 They know everything about you 00:18:02 Addiction 00:21:22 Differential

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01:24:08

Capsule Networks and Education Targets

Machine Learning Street Talk (MLST) ·

2020/09/29

In today's episode, Dr. Keith Duggar, Alex Stenlake and Dr. Tim Scarfe chat about the education chapter in Kenneth Stanley's "Greatness cannot be planned" book, and we relate it to our Algoshambes conversation a few weeks ago. We debate whether objectives in education are a good thing and whether they cause perverse incentives and stifle creativity and innovation. Next up we dissect capsule networks from the top down! We finish off talking about fast algorithms and quantum computing. 00:00:00

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01:23:33

Programming Languages, Software Engineering and...

Machine Learning Street Talk (MLST) ·

2020/09/25

This week Dr. Tim Scarfe, Dr. Keith Duggar, Yannic "Lightspeed" Kilcher have a conversation with Microsoft Senior Software Engineer Sachin Kundu. We speak about programming languages including which our favourites are and functional programming vs OOP. Next we speak about software engineering and the intersection of software engineering and machine learning. We also talk about applications of ML and finally what makes an exceptional software engineer and tech lead. Sachin is an expert in this field

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01:13:40

Computation, Bayesian Model Selection, Interactive...

Machine Learning Street Talk (MLST) ·

2020/09/22

This week Dr. Keith Duggar, Alex Stenlake and Dr. Tim Scarfe discuss the theory of computation, intelligence, Bayesian model selection, the intelligence explosion and the the phenomenon of "interactive articles". 00:00:00 Intro 00:01:27 Kernels and context-free grammars 00:06:04 Theory of computation 00:18:41 Intelligence 00:22:03 Bayesian model selection 00:44:05 AI-IQ Measure / Intelligence explosion 00:52:09 Interactive articles 01:12:32 Outro

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01:37:29

Kernels!

Machine Learning Street Talk (MLST) ·

2020/09/18

Today Yannic Lightspeed Kilcher and I spoke with Alex Stenlake about Kernel Methods. What is a kernel? Do you remember those weird kernel things which everyone obsessed about before deep learning? What about Representer theorem and reproducible kernel hilbert spaces? SVMs and kernel ridge regression? Remember them?! Hope you enjoy the conversation! 00:00:00 Tim Intro 00:01:35 Yannic clever insight from this discussion 00:03:25 Street talk and Alex intro 00:05:06 How kernels are taught 00:09:20

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01:25:52

Explainability, Reasoning, Priors and GPT-3

Machine Learning Street Talk (MLST) ·

2020/09/16

This week Dr. Tim Scarfe and Dr. Keith Duggar discuss Explainability, Reasoning, Priors and GPT-3. We check out Christoph Molnar's book on intepretability, talk about priors vs experience in NNs, whether NNs are reasoning and also cover articles by Gary Marcus and Walid Saba critiquing deep learning. We finish with a brief discussion of Chollet's ARC challenge and intelligence paper. 00:00:00 Intro 00:01:17 Explainability and Christoph Molnars book on Intepretability 00:26:45 Explainability -

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01:27:36

SWaV: Unsupervised Learning of Visual Features by...

Machine Learning Street Talk (MLST) ·

2020/09/14

This week Dr. Tim Scarfe, Yannic Lightspeed Kicher, Sayak Paul and Ayush Takur interview Mathilde Caron from Facebook Research (FAIR). We discuss Mathilde's paper which she wrote with her collaborators "SWaV: Unsupervised Learning of Visual Features by Contrasting Cluster Assignments" @ https://arxiv.org/pdf/2006.09882.pdf This paper is the latest unsupervised contrastive visual representations algorithm and has a new data augmentation strategy and also a new online clustering strategy. Note; Other

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