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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

01:55:28

#75 - Emergence [Special Edition] with Dr. DANIELE...

Machine Learning Street Talk (MLST) ·

2022/04/29

An emergent behavior or emergent property can appear when a number of simpleentities 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

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01:05:38

#74 Dr. ANDREW LAMPINEN - Symbolic behaviour in AI...

Machine Learning Street Talk (MLST) ·

2022/04/14

Please note that in this interview Dr. Lampinen was expressing his personal opinions and they do not necessarily represent those of DeepMind. Patreon: https://www.patreon.com/mlst Discord: https://discord.gg/ESrGqhf5CB YT version: https://youtu.be/yPMtSXXn4OY Dr. Andrew Lampinen is a Senior Research Scientist at DeepMind, and he thinks that symbols are subjective in the relativistic sense. Dr. Lampinen completed his PhD in Cognitive Psychology at Stanford University. His background is in

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00:55:53

#73 - YASAMAN RAZEGHI & Prof. SAMEER SINGH - NLP...

Machine Learning Street Talk (MLST) ·

2022/04/07

Patreon: https://www.patreon.com/mlst Discord: https://discord.gg/ESrGqhf5CB YT version: https://youtu.be/RzGaI7vXrkk This week we speak with Yasaman Razeghi and Prof. Sameer Singh from UC Urvine. Yasaman recently published a paper called Impact of Pretraining Term Frequencies on Few-Shot Reasoning where she demonstrated comprehensively that large language models only perform well on reasoning tasks because they memorise the dataset. For the first time she showed the accuracy was linearly correlated

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01:24:48

#72 Prof. KEN STANLEY 2.0 - On Art and Subjectivity...

Machine Learning Street Talk (MLST) ·

2022/03/29

YT version: https://youtu.be/DxBZORM9F-8 Patreon: https://www.patreon.com/mlst Discord: https://discord.gg/ESrGqhf5CB Prof. Ken Stanley argued in his book that our world has become saturated with objectives. The process of setting an objective, attempting to achieve it, and measuring progress along the way has become the primary route to achievement in our culture. He’s not saying that objectives are bad per se, especially if they’re modest, but he thinks that when goals are ambitious then the

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01:02:35

#71 - ZAK JOST (Graph Neural Networks + Geometric DL)...

Machine Learning Street Talk (MLST) ·

2022/03/25

Special discount link for Zak's GNN course - https://bit.ly/3uqmYVq Patreon: https://www.patreon.com/mlst Discord: https://discord.gg/ESrGqhf5CB YT version: https://youtu.be/jAGIuobLp60 (there are lots of helper graphics there, recommended if poss) Want to sponsor MLST!? Let us know on Linkedin / Twitter. [00:00:00] Preamble [00:03:12] Geometric deep learning [00:10:04] Message passing [00:20:42] Top down vs bottom up [00:24:59] All NN architectures are different forms of information diffusion

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01:18:30

#70 - LETITIA PARCALABESCU - Symbolics, Linguistics...

Machine Learning Street Talk (MLST) ·

2022/03/19

Today we are having a discussion with Letitia Parcalabescu from the AI Coffee Break youtube channel! We discuss linguistics, symbolic AI and our respective Youtube channels. Make sure you subscribe to her channel! In the first 15 minutes Tim dissects the recent article from Gary Marcus "Deep learning has hit a wall". Patreon: https://www.patreon.com/mlst Discord: https://discord.gg/ESrGqhf5CB YT: https://youtu.be/p2D2duT-R2E [00:00:00] Comments on Gary Marcus Article / Symbolic AI [00:14:57]

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00:50:38

#69 DR. THOMAS LUX - Interpolation of Sparse...

Machine Learning Street Talk (MLST) ·

2022/03/12

Today we are speaking with Dr. Thomas Lux, a research scientist at Meta in Silicon Valley. In some sense, all of supervised machine learning can be framed through the lens of geometry. All training data exists as points in euclidean space, and we want to predict the value of a function at all those points. Neural networks appear to be the modus operandi these days for many domains of prediction. In that light; we might ask ourselves — what makes neural networks better than classical techniques like

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01:42:14

#68 DR. WALID SABA 2.0 - Natural Language...

Machine Learning Street Talk (MLST) ·

2022/03/07

Patreon: https://www.patreon.com/mlst Discord: https://discord.gg/HNnAwSduud YT version: https://youtu.be/pMtk-iUaEuQ Dr. Walid Saba is an old-school polymath. He has a background in cognitive psychology, linguistics, philosophy, computer science and logic and he’s is now a Senior Scientist at Sorcero. Walid is perhaps the most outspoken critic of BERTOLOGY, which is to say trying to solve the problem of natural language understanding with application of large statistical language models. Walid

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01:42:10

#67 Prof. KARL FRISTON 2.0

Machine Learning Street Talk (MLST) ·

2022/03/02

We engage in a bit of epistemic foraging with Prof. Karl Friston! In this show; we discuss the free energy principle in detail, also emergence, cognition, consciousness and Karl's burden of knowledge! YT: https://youtu.be/xKQ-F2-o8uM Patreon: https://www.patreon.com/mlst Discord: https://discord.gg/HNnAwSduud [00:00:00] Introduction to FEP/Friston [00:06:53] Cheers to Epistemic Foraging! [00:09:17] The Burden of Knowledge Across Disciplines [00:12:55] On-show introduction to Friston [00:14:23]

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00:50:31

#66 ALEXANDER MATTICK - [Unplugged / Community Edition]

Machine Learning Street Talk (MLST) ·

2022/02/28

We have a chat with Alexander Mattick aka ZickZack from Yannic's Discord community. Alex is one of the leading voices in that community and has an impressive technical depth. Don't forget MLST has now started it's own Discord server too, come and join us! We are going to run regular events, our first big event on Wednesday 9th 1700-1900 UK time. Patreon: https://www.patreon.com/mlst Discord: https://discord.gg/HNnAwSduud YT version: https://youtu.be/rGOOLC8cIO4 [00:00:00] Introduction to

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01:28:27

#65 Prof. PEDRO DOMINGOS [Unplugged]

Machine Learning Street Talk (MLST) ·

2022/02/26

Note: there are no politics discussed in this show and please do not interpret this show as any kind of a political statement from us. We have decided not to discuss politics on MLST anymore due to its divisive nature. Patreon: https://www.patreon.com/mlst Discord: https://discord.gg/HNnAwSduud [00:00:00] Intro [00:01:36] What we all need to understand about machine learning [00:06:05] The Master Algorithm Target Audience [00:09:50] Deeply Connected Algorithms seen from Divergent Frames of

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00:51:47

#64 Prof. Gary Marcus 3.0

Machine Learning Street Talk (MLST) ·

2022/02/24

Patreon: https://www.patreon.com/mlst Discord: https://discord.gg/HNnAwSduud YT: https://www.youtube.com/watch?v=ZDY2nhkPZxw We have a chat with Prof. Gary Marcus about everything which is currently top of mind for him, consciousness [00:00:00] Gary intro [00:01:25] Slightly conscious [00:24:59] Abstract, compositional models [00:32:46] Spline theory of NNs [00:36:17] Self driving cars / algebraic reasoning [00:39:43] Extrapolation [00:44:15] Scaling laws [00:49:50] Maximum likelihood

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01:33:07

#063 - Prof. YOSHUA BENGIO - GFlowNets, Consciousness...

Machine Learning Street Talk (MLST) ·

2022/02/22

We 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

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01:29:50

#062 - Dr. Guy Emerson - Linguistics, Distributional...

Machine Learning Street Talk (MLST) ·

2022/02/03

Dr. Guy Emerson is a computational linguist and obtained his Ph.D from Cambridge university where he is now a research fellow and lecturer. On panel we also have myself, Dr. Tim Scarfe, as well as Dr. Keith Duggar and the veritable Dr. Walid Saba. We dive into distributional semantics, probability theory, fuzzy logic, grounding, vagueness and the grammar/cognition connection. The aim of distributional semantics is to design computational techniques that can automatically learn the meanings of words

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03:19:43

061: Interpolation, Extrapolation and Linearisation...

Machine Learning Street Talk (MLST) ·

2022/01/04

We are now sponsored by Weights and Biases! Please visit our sponsor link: http://wandb.me/MLST Patreon: https://www.patreon.com/mlst Yann LeCun thinks that it's specious to say neural network models are interpolating because in high dimensions, everything is extrapolation. Recently Dr. Randall Balestriero, Dr. Jerome Pesente and prof. Yann LeCun released their paper learning in high dimensions always amounts to extrapolation. This discussion has completely changed how we think about neural networks

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03:33:22

#60 Geometric Deep Learning Blueprint (Special Edition)

Machine Learning Street Talk (MLST) ·

2021/09/19

Patreon: https://www.patreon.com/mlst The last decade has witnessed an experimental revolution in data science and machine learning, epitomised by deep learning methods. Many high-dimensional learning tasks previously thought to be beyond reach -- such as computer vision, playing Go, or protein folding -- are in fact tractable given enough computational horsepower. Remarkably, the essence of deep learning is built from two simple algorithmic principles: first, the notion of representation or feature

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02:34:51

#59 - Jeff Hawkins (Thousand Brains Theory)

Machine Learning Street Talk (MLST) ·

2021/09/03

Patreon: https://www.patreon.com/mlst The ultimate goal of neuroscience is to learn how the human brain gives rise to human intelligence and what it means to be intelligent. Understanding how the brain works is considered one of humanity’s greatest challenges. Jeff Hawkins thinks that the reality we perceive is a kind of simulation, a hallucination, a confabulation. He thinks that our brains are a model reality based on thousands of information streams originating from the sensors in our body

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02:28:14

#58 Dr. Ben Goertzel - Artificial General Intelligence

Machine Learning Street Talk (MLST) ·

2021/08/11

The field of Artificial Intelligence was founded in the mid 1950s with the aim of constructing “thinking machines” - that is to say, computer systems with human-like general intelligence. Think of humanoid robots that not only look but act and think with intelligence equal to and ultimately greater than that of human beings. But in the intervening years, the field has drifted far from its ambitious old-fashioned roots. Dr. Ben Goertzel is an artificial intelligence researcher, CEO and founder of

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02:31:21

#57 - Prof. Melanie Mitchell - Why AI is harder than...

Machine Learning Street Talk (MLST) ·

2021/07/25

Since its beginning in the 1950s, the field of artificial intelligence has vacillated between periods of optimistic predictions and massive investment and periods of disappointment, loss of confidence, and reduced funding. Even with today’s seemingly fast pace of AI breakthroughs, the development of long-promised technologies such as self-driving cars, housekeeping robots, and conversational companions has turned out to be much harder than many people expected. Professor Melanie Mitchell thinks one

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01:11:17

#56 - Dr. Walid Saba, Gadi Singer, Prof. J. Mark...

Machine Learning Street Talk (MLST) ·

2021/07/08

It has been over three decades since the statistical revolution overtook AI by a storm and over two decades since deep learning (DL) helped usher the latest resurgence of artificial intelligence (AI). However, the disappointing progress in conversational agents, NLU, and self-driving cars, has made it clear that progress has not lived up to the promise of these empirical and data-driven methods. DARPA has suggested that it is time for a third wave in AI, one that would be characterized by hybrid

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