Podcast
Machine Learning Street Talk (MLST)
Hosted by Machine Learning Street Talk (MLST) · EN
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 appearances from MIT Doctor of Philosophy Keith Duggar (https://www.linkedin.com/in/dr-keith-duggar/).
73episodes
Episodes
Newest firstAll episodes
Prof. Nick Chater - The Language Game (Part 1)
Mar 1, 2024Tap to summarizeNick Chater is Professor of Behavioural Science at Warwick Business School, who works on rationality and language using a range of theoretical and experimental approaches. We discuss his books The Mind is Flat, and the Language Game. Please support me on Patreon (this is now my main job!) - https://patreon.com/mlst - Access the private Discord, networking, and early access to content. MLST Discord: https://discord.gg/machine-learning-street-talk-mlst-937356144060530778 https://twitter.com/MLStreetTalk Buy The Language Game: https://amzn.to/3SRHjPm Buy The Mind is Flat: https://amzn.to/3P3BUUC YT version: https://youtu.be/5cBS6COzLN4 https://www.wbs.ac.uk/about/person/nick-chater/ https://twitter.com/nickjchater?lang=en
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Kenneth Stanley created a new social network based on serendipity and divergence
Feb 28, 2024Tap to summarizeSee what Sam Altman advised Kenneth when he left OpenAI! Professor Kenneth Stanley has just launched a brand new type of social network, which he calls a "Serendipity network". The idea is that you follow interests, NOT people. It's a social network without the popularity contest. We discuss the phgilosophy and technology behind the venture in great detail. The main ideas of which came from Kenneth's famous book "Why greatness cannot be planned". See what Sam Altman advised Kenneth when he left OpenAI! Professor Kenneth Stanley has just launched a brand new type of social network, which he calls a "Serendipity network".The idea is that you follow interests, NOT people. It's a social network without the popularity contest. YT version: https://www.youtube.com/watch?v=pWIrXN-yy8g Chapters should be baked into the MP3 file now MLST public Discord: https://discord.gg/machine-learning-street-talk-mlst-937356144060530778 Please support our work on Patreon - get access to interviews months early, private Patreon, networking, exclusive content and regular calls with Tim and Keith. https://patreon.com/mlst Get Maven here: https://www.heymaven.com/ Kenneth: https://twitter.com/kenneth0stanley https://www.kenstanley.net/home Host - Tim Scarfe: https://www.linkedin.com/in/ecsquizor/ https://www.mlst.ai/ Original MLST show with Kenneth: https://www.youtube.com/watch?v=lhYGXYeMq_E Tim explains the book more here: https://www.youtube.com/watch?v=wNhaz81OOqw
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$450M AI Startup In 3 Years | Chai AI
Jan 9, 2024Tap to summarizeChai AI is the leading platform for conversational chat artificial intelligence. Note: this is a sponsored episode of MLST. William Beauchamp is the founder of two $100M+ companies - Chai Research, an AI startup, and Seamless Capital, a hedge fund based in Cambridge, UK. Chaiverse is the Chai AI developer platform, where developers can train, submit and evaluate on millions of real users to win their share of $1,000,000. https://www.chai-research.com https://www.chaiverse.com https://twitter.com/chai_research https://facebook.com/chairesearch/ https://www.instagram.com/chairesearch/ Download the app on iOS and Android (https://onelink.to/kqzhy9 ) #chai #chai_ai #chai_research #chaiverse #generative_ai #LLMs
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#77 - Vitaliy Chiley (Cerebras)
Jun 16, 2022Tap to summarizeVitaliy 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
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#76 - LUKAS BIEWALD (Weights and Biases CEO)
Jun 9, 2022Tap to summarizeCheck 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.
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#75 - Emergence [Special Edition] with Dr. DANIELE GRATTAROLA
Apr 29, 2022Summary readyAn 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
Summary
#063 - Prof. YOSHUA BENGIO - GFlowNets, Consciousness & Causality
Feb 22, 2022Tap to summarizeWe are now sponsored by Weights and Biases! Please visit our sponsor link: http://wandb.me/MLST Patreon: https://www.patreon.com/mlst For Yoshua Bengio, GFlowNets are the most exciting thing on the horizon of Machine Learning today. He believes they can solve previously intractable problems and hold the key to unlocking machine abstract reasoning itself. This discussion explores the promise of GFlowNets and the personal journey Prof. Bengio traveled to reach them. Panel: Dr. Tim Scarfe Dr. Keith Duggar Dr. Yannic Kilcher Our special thanks to: - Alexander Mattick (Zickzack) References: Yoshua Bengio @ MILA (https://mila.quebec/en/person/bengio-yoshua/) GFlowNet Foundations (https://arxiv.org/pdf/2111.09266.pdf) Flow Network based Generative Models for Non-Iterative Diverse Candidate Generation (https://arxiv.org/pdf/2106.04399.pdf) Interpolation Consistency Training for Semi-Supervised Learning (https://arxiv.org/pdf/1903.03825.pdf) Towards Causal Representation Learning (https://arxiv.org/pdf/2102.11107.pdf) Causal inference using invariant prediction: identification and confidence intervals (https://arxiv.org/pdf/1501.01332.pdf)
Transcribe →#48 Machine Learning Security - Andy Smith
Mar 16, 2021Tap to summarizeFirst episode in a series we are doing on ML DevOps. Starting with the thing which nobody seems to be talking about enough, security! We chat with cyber security expert Andy Smith about threat modelling and trust boundaries for an ML DevOps system. Intro [00:00:00] ML DevOps - a security perspective [00:00:50] Threat Modelling [00:03:03] Adversarial examples? [00:11:27] Nobody understands the whole stack [00:13:53] On the size of the state space, the element of unpredictability [00:18:32] Threat modelling in more detail [00:21:17] Trust boundaries for an ML DevOps system [00:25:45] Andy has a YouTube channel on cyber security! Check it out @ https://www.youtube.com/channel/UCywP24ly6h6NTusX88TQKTQ https://www.linkedin.com/in/andysmith-uk/ Video version: https://youtu.be/7Tz-3S4lypI
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#031 WE GOT ACCESS TO GPT-3! (With Gary Marcus, Walid Saba and Connor Leahy)
Nov 28, 2020Tap to summarizeIn 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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Programming Languages, Software Engineering and Machine Learning
Sep 25, 2020Tap to summarizeThis 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 so we hope you enjoy the conversation! Spoiler alert, how many of you have read the Mythical Man-Month by Frederick P. Brooks?! 00:00:00 Introduction 00:06:37 Programming Languages 00:53:41 Applications of ML 01:55:59 What makes an exceptional SE and tech lead 01:22:08 Outro
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