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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

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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01:34:33

UK Algoshambles, Neuralink, GPT-3 and Intelligence

Machine Learning Street Talk (MLST) ·

2020/09/07

This week Dr. Tim Scarfe, Dr. Keith Duggar and Yannic "Lightspeed" Kilcher respond to the "Algoshambles" exam fiasco in the UK where the government were forced to step in to standardise the grades which were grossly inflated by the schools. The schools and teachers are all paid on metrics related to the grades received by students, what could possibly go wrong?! The result is that we end up with grades which have lost all their value and students are coached for the exams and don't actually learn

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01:36:16

Sayak Paul

Machine Learning Street Talk (MLST) ·

2020/07/17

This week we spoke with Sayak Paul, who is extremely active in the machine learning community. We discussed the AI landscape in India, unsupervised representation learning, data augmentation and contrastive learning, explainability, abstract scene representations and finally pruning and the recent super positions paper. I really enjoyed this conversation and I hope you folks do too! 00:00:00 Intro to Sayak 00:17:50 AI landscape in India 00:24:20 Unsupervised representation learning 00:26:11 DATA

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01:45:41

Robert Lange on NN Pruning and Collective Intelligence

Machine Learning Street Talk (MLST) ·

2020/07/08

We speak with Robert Lange! Robert is a PhD student at the Technical University Berlin. His research combines Deep Multi-Agent Reinforcement Learning and Cognitive Science to study the learning dynamics of large collectives. He has a brilliant blog where he distils and explains cutting edge ML research. We spoke about his story, economics, multi-agent RL, intelligence and AGI, and his recent article summarising the state of the art in neural network pruning. Robert's article on pruning in NNs

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01:58:02

WelcomeAIOverlords (Zak Jost)

Machine Learning Street Talk (MLST) ·

2020/06/30

We welcome Zak Jost from the WelcomeAIOverlords channel. Zak is an ML research scientist at Amazon. He has a great blog at http://blog.zakjost.com and also a Discord channel at https://discord.gg/xh2chKX WelcomeAIOverlords: https://www.youtube.com/channel/UCxw9_WYmLqlj5PyXu2AWU_g 00:00:00 INTRO START 00:01:07 MAIN SHOW START 00:01:59 ZAK'S STORY 00:05:06 YOUTUBE DISCUSSION 00:24:12 UNDERSTANDING PAPERS 00:29:53 CONTRASTIVE LEARNING INTRO 00:33:00 BRING YOUR OWN LATENT PAPER 01:03:13 GRAPHS IN ML AND

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01:02:33

Facebook Research - Unsupervised Translation of...

Machine Learning Street Talk (MLST) ·

2020/06/24

In this episode of Machine Learning Street Talk Dr. Tim Scarfe, Yannic Kilcher and Connor Shorten spoke with Marie-Anne Lachaux, Baptiste Roziere and Dr. Guillaume Lample from Facebook Research (FAIR) in Paris. They recently released the paper "Unsupervised Translation of Programming Languages" which was an exciting new approach to learned translation of programming languages (learned transcoder) using an unsupervised encoder trained on individual monolingual corpora i.e. no parallel language data

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02:33:31

Francois Chollet - On the Measure of Intelligence

Machine Learning Street Talk (MLST) ·

2020/06/19

We cover Francois Chollet's recent paper. Abstract; To make deliberate progress towards more intelligent and more human-like artificial systems, we need to be following an appropriate feedback signal: we need to be able to define and evaluate intelligence in a way that enables comparisons between two systems, as well as comparisons with humans. Over the past hundred years, there has been an abundance of attempts to define and measure intelligence, across both the fields of psychology and AI. We

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01:51:37

OpenAI GPT-3: Language Models are Few-Shot Learners

Machine Learning Street Talk (MLST) ·

2020/06/06

In this episode of Machine Learning Street Talk, Tim Scarfe, Yannic Kilcher and Connor Shorten discuss their takeaways from OpenAI’s GPT-3 language model. With the help of Microsoft’s ZeRO-2 / DeepSpeed optimiser, OpenAI trained an 175 BILLION parameter autoregressive language model. The paper demonstrates how self-supervised language modelling at this scale can perform many downstream tasks without fine-tuning. 00:00:00 Intro 00:00:54 ZeRO1+2 (model + Data parallelism) (Connor) 00:03:17 Recent

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01:12:45

Jordan Edwards: ML Engineering and DevOps on AzureML

Machine Learning Street Talk (MLST) ·

2020/06/03

This week we had a super insightful conversation with Jordan Edwards, Principal Program Manager for the AzureML team! Jordan is on the coalface of turning machine learning software engineering into a reality for some of Microsoft's largest customers. ML DevOps is all about increasing the velocity of- and orchastrating the non-interactive phase of- software deployments for ML. We cover ML DevOps and Microsoft Azure ML. We discuss model governance, testing, intepretability, tooling. We cover the

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02:28:30

One Shot and Metric Learning - Quadruplet Loss...

Machine Learning Street Talk (MLST) ·

2020/06/02

*Note this is an episode from Tim's Machine Learning Dojo YouTube channel. Join Eric Craeymeersch on a wonderful discussion all about ML engineering, computer vision, siamese networks, contrastive loss, one shot learning and metric learning. 00:00:00 Introduction 00:11:47 ML Engineering Discussion 00:35:59 Intro to the main topic 00:42:13 Siamese Networks 00:48:36 Mining strategies 00:51:15 Contrastive Loss 00:57:44 Trip loss paper 01:09:35 Quad loss paper 01:25:49 Eric's Quadloss Medium

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