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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

02:08:40

Why Your GPUs are underutilised for AI - CentML CEO...

Machine Learning Street Talk (MLST) ·

2024/11/13

Prof. Gennady Pekhimenko (CEO of CentML, UofT) joins us in this *sponsored episode* to dive deep into AI system optimization and enterprise implementation. From NVIDIA's technical leadership model to the rise of open-source AI, Pekhimenko shares insights on bridging the gap between academic research and industrial applications. Learn about "dark silicon," GPU utilization challenges in ML workloads, and how modern enterprises can optimize their AI infrastructure. The conversation explores why some

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04:18:30

Eliezer Yudkowsky and Stephen Wolfram on AI X-risk

Machine Learning Street Talk (MLST) ·

2024/11/11

Eliezer Yudkowsky and Stephen Wolfram discuss artificial intelligence and its potential existen‑tial risks. They traversed fundamental questions about AI safety, consciousness, computational irreducibility, and the nature of intelligence. The discourse centered on Yudkowsky’s argument that advanced AI systems pose an existential threat to humanity, primarily due to the challenge of alignment and the potential for emergent goals that diverge from human values. Wolfram, while acknowledging potential

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02:42:54

Pattern Recognition vs True Intelligence - Francois...

Machine Learning Street Talk (MLST) ·

2024/11/06

Francois Chollet, a prominent AI expert and creator of ARC-AGI, discusses intelligence, consciousness, and artificial intelligence. Chollet explains that real intelligence isn't about memorizing information or having lots of knowledge - it's about being able to handle new situations effectively. This is why he believes current large language models (LLMs) have "near-zero intelligence" despite their impressive abilities. They're more like sophisticated memory and pattern-matching systems than truly

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01:53:11

The Elegant Math Behind Machine Learning - Anil...

Machine Learning Street Talk (MLST) ·

2024/11/04

Anil Ananthaswamy is an award-winning science writer and former staff writer and deputy news editor for the London-based New Scientist magazine. Machine learning systems are making life-altering decisions for us: approving mortgage loans, determining whether a tumor is cancerous, or deciding if someone gets bail. They now influence developments and discoveries in chemistry, biology, and physics—the study of genomes, extrasolar planets, even the intricacies of quantum systems. And all this before

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01:03:35

Michael Levin - Why Intelligence Isn't Limited To...

Machine Learning Street Talk (MLST) ·

2024/10/24

Professor Michael Levin explores the revolutionary concept of diverse intelligence, demonstrating how cognitive capabilities extend far beyond traditional brain-based intelligence. Drawing from his groundbreaking research, he explains how even simple biological systems like gene regulatory networks exhibit learning, memory, and problem-solving abilities. Levin introduces key concepts like "cognitive light cones" - the scope of goals a system can pursue - and shows how these ideas are transforming

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01:46:23

Speechmatics CTO - Next-Generation Speech Recognition

Machine Learning Street Talk (MLST) ·

2024/10/23

Will Williams is CTO of Speechmatics in Cambridge. In this sponsored episode - he shares deep technical insights into modern speech recognition technology and system architecture. The episode covers several key technical areas:* Speechmatics' hybrid approach to ASR, which focusses on unsupervised learning methods, achieving comparable results with 100x less data than fully supervised approaches. Williams explains why this is more efficient and generalizable than end-to-end models like Whisper. *

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02:45:32

Dr. Sanjeev Namjoshi - Active Inference

Machine Learning Street Talk (MLST) ·

2024/10/22

Dr. Sanjeev Namjoshi, a machine learning engineer who recently submitted a book on Active Inference to MIT Press, discusses the theoretical foundations and practical applications of Active Inference, the Free Energy Principle (FEP), and Bayesian mechanics. He explains how these frameworks describe how biological and artificial systems maintain stability by minimizing uncertainty about their environment.DO YOU WANT WORK ON ARC with the MindsAI team (current ARC winners)? MLST is sponsored by Tufa

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01:52:45

Joscha Bach - Why Your Thoughts Aren't Yours.

Machine Learning Street Talk (MLST) ·

2024/10/20

Dr. Joscha Bach discusses advanced AI, consciousness, and cognitive modeling. He presents consciousness as a virtual property emerging from self-organizing software patterns, challenging panpsychism and materialism. Bach introduces "Cyberanima," reinterpreting animism through information processing, viewing spirits as self-organizing software agents. He addresses limitations of current large language models and advocates for smaller, more efficient AI models capable of reasoning from first

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00:51:34

Decompiling Dreams: A New Approach to ARC? -...

Machine Learning Street Talk (MLST) ·

2024/10/19

Alessandro Palmarini is a post-baccalaureate researcher at the Santa Fe Institute working under the supervision of Melanie Mitchell. He completed his undergraduate degree in Artificial Intelligence and Computer Science at the University of Edinburgh. Palmarini's current research focuses on developing AI systems that can efficiently acquire new skills from limited data, inspired by François Chollet's work on measuring intelligence. His work builds upon the DreamCoder program synthesis system,

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00:46:21

It's Not About Scale, It's About Abstraction -...

Machine Learning Street Talk (MLST) ·

2024/10/12

François Chollet discusses the limitations of Large Language Models (LLMs) and proposes a new approach to advancing artificial intelligence. He argues that current AI systems excel at pattern recognition but struggle with logical reasoning and true generalization. This was Chollet's keynote talk at AGI-24, filmed in high-quality. We will be releasing a full interview with him shortly. A teaser clip from that is played in the intro! Chollet introduces the Abstraction and Reasoning Corpus (ARC) as

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00:47:17

Bold AI Predictions From Cohere Co-founder

Machine Learning Street Talk (MLST) ·

2024/10/10

Ivan Zhang, co-founder of Cohere, discusses the company's enterprise-focused AI solutions. He explains Cohere's early emphasis on embedding technology and training models for secure environments. Zhang highlights their implementation of Retrieval-Augmented Generation in healthcare, significantly reducing doctor preparation time. He explores the shift from monolithic AI models to heterogeneous systems and the importance of improving various AI system components. Zhang shares insights on using

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00:55:10

Open-Ended AI: The Key to Superhuman Intelligence? -...

Machine Learning Street Talk (MLST) ·

2024/10/04

Prof. Tim Rocktäschel, AI researcher at UCL and Google DeepMind, talks about open-ended AI systems. These systems aim to keep learning and improving on their own, like evolution does in nature. Ad: Are you a hardcore ML engineer who wants to work for Daniel Cahn at SlingshotAI building AI for mental health? Give him an email! - danielc@slingshot.xyz TOC: 00:00:00 Introduction to Open-Ended AI and Key Concepts 00:01:37 Tim Rocktäschel's Background and Research Focus 00:06:25 Defining

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01:37:18

Ben Goertzel on "Superintelligence"

Machine Learning Street Talk (MLST) ·

2024/10/01

Ben Goertzel discusses AGI development, transhumanism, and the potential societal impacts of superintelligent AI. He predicts human-level AGI by 2029 and argues that the transition to superintelligence could happen within a few years after. Goertzel explores the challenges of AI regulation, the limitations of current language models, and the need for neuro-symbolic approaches in AGI research. He also addresses concerns about resource allocation and cultural perspectives on

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01:56:55

Taming Silicon Valley - Prof. Gary Marcus

Machine Learning Street Talk (MLST) ·

2024/09/24

AI expert Prof. Gary Marcus doesn't mince words about today's artificial intelligence. He argues that despite the buzz, chatbots like ChatGPT aren't as smart as they seem and could cause real problems if we're not careful.Marcus is worried about tech companies putting profits before people. He thinks AI could make fake news and privacy issues even worse. He's also concerned that a few big tech companies have too much power. Looking ahead, Marcus believes the AI hype will die

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01:26:45

Prof. Mark Solms - The Hidden Spring

Machine Learning Street Talk (MLST) ·

2024/09/18

Prof. Mark Solms, a neuroscientist and psychoanalyst, discusses his groundbreaking work on consciousness, challenging conventional cortex-centric views and emphasizing the role of brainstem structures in generating consciousness and affect.MLST is sponsored by Brave: The Brave Search API covers over 20 billion webpages, built from scratch without Big Tech biases or the recent extortionate price hikes on search API access. Perfect for AI model training and retrieval augmentated generation. Try it

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01:13:46

Patrick Lewis (Cohere) - Retrieval Augmented Generation

Machine Learning Street Talk (MLST) ·

2024/09/16

Dr. Patrick Lewis, who coined the term RAG (Retrieval Augmented Generation) and now works at Cohere, discusses the evolution of language models, RAG systems, and challenges in AI evaluation. MLST is sponsored by Brave: The Brave Search API covers over 20 billion webpages, built from scratch without Big Tech biases or the recent extortionate price hikes on search API access. Perfect for AI model training and retrieval augmented generation. Try it now - get 2,000 free queries monthly at

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00:25:04

Ashley Edwards - Genie Paper (DeepMind/Runway)

Machine Learning Street Talk (MLST) ·

2024/09/13

Ashley Edwards, who was working at DeepMind when she co-authored the Genie paper and is now at Runway, covered several key aspects of the Genie AI system and its applications in video generation, robotics, and game creation. MLST is sponsored by Brave: The Brave Search API covers over 20 billion webpages, built from scratch without Big Tech biases or the recent extortionate price hikes on search API access. Perfect for AI model training and retrieval augmentated generation. Try it now - get 2,000

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01:30:25

Cohere's SVP Technology - Saurabh Baji

Machine Learning Street Talk (MLST) ·

2024/09/12

Saurabh Baji discusses Cohere's approach to developing and deploying large language models (LLMs) for enterprise use. * Cohere focuses on pragmatic, efficient models tailored for business applications rather than pursuing the largest possible models. * They offer flexible deployment options, from cloud services to on-premises installations, to meet diverse enterprise needs. * Retrieval-augmented generation (RAG) is highlighted as a critical capability, allowing models to leverage enterprise

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00:53:14

David Hanson's Vision for Sentient Robots

Machine Learning Street Talk (MLST) ·

2024/09/10

David Hanson, CEO of Hanson Robotics and creator of the humanoid robot Sofia, explores the intersection of artificial intelligence, ethics, and human potential. In this thought-provoking interview, Hanson discusses his vision for developing AI systems that embody the best aspects of humanity while pushing beyond our current limitations, aiming to achieve what he calls "super wisdom."YT version: https://youtu.be/LFCIEhlsozU MLST is sponsored by Brave: The Brave Search API covers over 20

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00:46:28

The Fabric of Knowledge - David Spivak

Machine Learning Street Talk (MLST) ·

2024/09/05

David Spivak, a mathematician known for his work in category theory, discusses a wide range of topics related to intelligence, creativity, and the nature of knowledge. He explains category theory in simple terms and explores how it relates to understanding complex systems and relationships.MLST is sponsored by Brave: The Brave Search API covers over 20 billion webpages, built from scratch without Big Tech biases or the recent extortionate price hikes on search API access. Perfect for AI model

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247 results

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