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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

01:36:21

#55 Self-Supervised Vision Models (Dr. Ishan Misra -...

Machine Learning Street Talk (MLST) ·

2021/06/21

Dr. Ishan Misra is a Research Scientist at Facebook AI Research where he works on Computer Vision and Machine Learning. His main research interest is reducing the need for human supervision, and indeed, human knowledge in visual learning systems. He finished his PhD at the Robotics Institute at Carnegie Mellon. He has done stints at Microsoft Research, INRIA and Yale. His bachelors is in computer science where he achieved the highest GPA in his cohort. Ishan is fast becoming a prolific scientist,

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02:24:12

#54 Gary Marcus and Luis Lamb - Neurosymbolic models

Machine Learning Street Talk (MLST) ·

2021/06/04

Professor Gary Marcus is a scientist, best-selling author, and entrepreneur. He is Founder and CEO ofRobust.AI, and was Founder and CEO of Geometric Intelligence, a machine learning company acquired by Uber in 2016. Gary said in his recent next decade paper that — without us, or other creatures like us, the world would continue to exist, but it would not be described, distilled, or understood. Human lives are filled with abstraction and causal description. This is so powerful. Francois Chollet the

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02:17:39

#53 Quantum Natural Language Processing - Prof. Bob...

Machine Learning Street Talk (MLST) ·

2021/05/19

Bob Coercke is a celebrated physicist, he's been a Physics and Quantum professor at Oxford University for the last 20 years. He is particularly interested in Structure which is to say, Logic, Order, and Category Theory. He is well known for work involving compositional distributional models of natural language meaning and he is also fascinated with understanding how our brains work. Bob was recently appointed as the Chief Scientist at Cambridge Quantum Computing. Bob thinks that interactions

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01:48:16

#52 - Unadversarial Examples (Hadi Salman, MIT)

Machine Learning Street Talk (MLST) ·

2021/05/01

Performing reliably on unseen or shifting data distributions is a difficult challenge for modern vision systems, even slight corruptions or transformations of images are enough to slash the accuracy of state-of-the-art classifiers. When an adversary is allowed to modify an input image directly, models can be manipulated into predicting anything even when there is no perceptible change, this is known an adversarial example. The ideal definition of an adversarial example is when humans consistently

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02:01:42

#51 Francois Chollet - Intelligence and Generalisation

Machine Learning Street Talk (MLST) ·

2021/04/16

In today's show we are joined by Francois Chollet, I have been inspired by Francois ever since I read his Deep Learning with Python book and started using the Keras library which he invented many, many years ago. Francois has a clarity of thought that I've never seen in any other human being! He has extremely interesting views on intelligence as generalisation, abstraction and an information conversation ratio. He wrote on the measure of intelligence at the end of 2019 and it had a huge impact on my

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01:33:22

#50 Christian Szegedy - Formal Reasoning, Program...

Machine Learning Street Talk (MLST) ·

2021/04/04

Dr. Christian Szegedy from Google Research is a deep learning heavyweight. He invented adversarial examples, one of the first object detection algorithms, the inceptionnet architecture, and co-invented batchnorm. He thinks that if you bet on computers and software in 1990 you would have been as right as if you bet on AI now. But he thinks that we have been programming computers the same way since the 1950s and there has been a huge stagnation ever since. Mathematics is the process of taking a fuzzy

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01:25:13

#49 - Meta-Gradients in RL - Dr. Tom Zahavy (DeepMind)

Machine Learning Street Talk (MLST) ·

2021/03/23

The race is on, we are on a collective mission to understand and create artificial general intelligence. Dr. Tom Zahavy, a Research Scientist at DeepMind thinks that reinforcement learning is the most general learning framework that we have today, and in his opinion it could lead to artificial general intelligence. He thinks there are no tasks which could not be solved by simply maximising a reward. Back in 2012 when Tom was an undergraduate, before the deep learning revolution he attended an

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00:37:27

#48 Machine Learning Security - Andy Smith

Machine Learning Street Talk (MLST) ·

2021/03/16

First 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

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01:40:12

047 Interpretable Machine Learning - Christoph Molnar

Machine Learning Street Talk (MLST) ·

2021/03/14

Christoph Molnar is one of the main people to know in the space of interpretable ML. In 2018 he released the first version of his incredible online book, interpretable machine learning. Interpretability is often a deciding factor when a machine learning (ML) model is used in a product, a decision process, or in research. Interpretability methods can be used to discover knowledge, to debug or justify the model and its predictions, and to control and improve the model, reason about potential bias in

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01:39:57

#046 The Great ML Stagnation (Mark Saroufim and Dr....

Machine Learning Street Talk (MLST) ·

2021/03/06

Academics think of themselves as trailblazers, explorers — seekers of the truth. Any fundamental discovery involves a significant degree of risk. If an idea is guaranteed to work then it moves from the realm of research to engineering. Unfortunately, this also means that most research careers will invariably be failures at least if failures are measured via “objective” metrics like citations. Today we discuss the recent article from Mark Saroufim called Machine Learning: the great stagnation. We

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02:30:17

#045 Microsoft's Platform for Reinforcement Learning...

Machine Learning Street Talk (MLST) ·

2021/02/28

Microsoft has an interesting strategy with their new “autonomous systems” technology also known as Project Bonsai. They want to create an interface to abstract away the complexity and esoterica of deep reinforcement learning. They want to fuse together expert knowledge and artificial intelligence all on one platform, so that complex problems can be decomposed into simpler ones. They want to take machine learning Ph.Ds out of the equation and make autonomous systems engineering look more like a

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00:52:22

#044 - Data-efficient Image Transformers (Hugo Touvron)

Machine Learning Street Talk (MLST) ·

2021/02/25

Today we are going to talk about the *Data-efficient image Transformers paper or (DeiT) which Hugo is the primary author of. One of the recipes of success for vision models since the DL revolution began has been the availability of large training sets. CNNs have been optimized for almost a decade now, including through extensive architecture search which is prone to overfitting. Motivated by the success of transformers-based models in Natural Language Processing there has been increasing attention

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01:35:14

#043 Prof J. Mark Bishop - Artificial Intelligence Is...

Machine Learning Street Talk (MLST) ·

2021/02/19

Professor Mark Bishop does not think that computers can be conscious or have phenomenological states of consciousness unless we are willing to accept panpsychism which is idea that mentality is fundamental and ubiquitous in the natural world, or put simply, that your goldfish and everything else for that matter has a mind. Panpsychism postulates that distinctions between intelligences are largely arbitrary. Mark’s work in the ‘philosophy of AI’ led to an influential critique of computational

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01:33:59

#042 - Pedro Domingos - Ethics and Cancel Culture

Machine Learning Street Talk (MLST) ·

2021/02/11

Today we have professor Pedro Domingos and we are going to talk about activism in machine learning, cancel culture, AI ethics and kernels. In Pedro's book the master algorithm, he segmented the AI community into 5 distinct tribes with 5 unique identities (and before you ask, no the irony of an anti-identitarian doing do was not lost on us!). Pedro recently published an article in Quillette called Beating Back Cancel Culture: A Case Study from the Field of Artificial Intelligence. Domingos has railed

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01:26:56

#041 - Biologically Plausible Neural Networks - Dr....

Machine Learning Street Talk (MLST) ·

2021/02/03

Dr. Simon Stringer. Obtained his Ph.D in mathematical state space control theory and has been a Senior Research Fellow at Oxford University for over 27 years. Simon is the director of the the Oxford Centre for Theoretical Neuroscience and Artificial Intelligence, which is based within the Oxford University Department of Experimental Psychology. His department covers vision, spatial processing, motor function, language and consciousness -- in particular -- how the primate visual system learns to make

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01:36:15

#040 - Adversarial Examples (Dr. Nicholas Carlini,...

Machine Learning Street Talk (MLST) ·

2021/01/31

Adversarial examples have attracted significant attention in machine learning, but the reasons for their existence and pervasiveness remain unclear. there's good reason to believe neural networks look at very different features than we would have expected. As articulated in the 2019 "features not bugs" paper Adversarial examples can be directly attributed to the presence of non-robust features: features derived from patterns in the data distribution that are highly predictive, yet brittle and

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01:58:21

#039 - Lena Voita - NLP

Machine Learning Street Talk (MLST) ·

2021/01/23

ena Voita is a Ph.D. student at the University of Edinburgh and University of Amsterdam. Previously, She was a research scientist at Yandex Research and worked closely with the Yandex Translate team. She still teaches NLP at the Yandex School of Data Analysis. She has created an exciting new NLP course on her website lena-voita.github.io which you folks need to check out! She has one of the most well presented blogs we have ever seen, where she discusses her research in an easily digestable manner

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02:46:26

#038 - Professor Kenneth Stanley - Why Greatness...

Machine Learning Street Talk (MLST) ·

2021/01/20

Professor Kenneth Stanley is currently a research science manager at OpenAI in San Fransisco. We've Been dreaming about getting Kenneth on the show since the very begininning of Machine Learning Street Talk. Some of you might recall that our first ever show was on the enhanced POET paper, of course Kenneth had his hands all over it. He's been cited over 16000 times, his most popular paper with over 3K citations was the NEAT algorithm. His interests are neuroevolution, open-endedness, NNs, artificial

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01:35:25

#037 - Tour De Bayesian with Connor Tann

Machine Learning Street Talk (MLST) ·

2021/01/11

Connor Tan is a physicist and senior data scientist working for a multinational energy company where he co-founded and leads a data science team. He holds a first-class degree in experimental and theoretical physics from Cambridge university. With a master's in particle astrophysics. He specializes in the application of machine learning models and Bayesian methods. Today we explore the history, pratical utility, and unique capabilities of Bayesian methods. We also discuss the computational

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01:42:31

#036 - Max Welling: Quantum, Manifolds & Symmetries...

Machine Learning Street Talk (MLST) ·

2021/01/03

Today we had a fantastic conversation with Professor Max Welling, VP of Technology, Qualcomm Technologies Netherlands B.V. Max is a strong believer in the power of data and computation and its relevance to artificial intelligence. There is a fundamental blank slate paradgm in machine learning, experience and data alone currently rule the roost. Max wants to build a house of domain knowledge on top of that blank slate. Max thinks there are no predictions without assumptions, no generalization

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