x

75% of people globally want unbiased news (Pew Research) but are concerned they're not getting that.

Get balanced coverage with Symby. See how stories are covered across diverse outlets and spot when one side covers a story the other skips.

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

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

en

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

en

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

en

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

en

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

en

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

en

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

en

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

en

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

en

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

en

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

en

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,

en

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

en

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

en

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

en

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

en

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

en

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

en

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

en

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

en

247 results

Similar Podcasts

Lead With AI

Tamara Nall

Linux in the Ham Shack

Black Sparrow Media

Data Engineering Podcast

Tobias Macey

Eye On A.I.

Craig S. Smith

Reflections

Livewire Labs