Machine Learning Street Talk (MLST) cover image

Machine Learning Street Talk (MLST)

Latest episodes

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Jul 28, 2024 • 50min

Sayash Kapoor - How seriously should we take AI X-risk? (ICML 1/13)

Sayash Kapoor, a Princeton Ph.D. candidate focused on AI's societal impacts, discusses critical issues surrounding AI's existential risks. He emphasizes the unreliability of risk probabilities in policymaking and critiques naive approaches like Pascal's Wager. The conversation also debunks myths regarding AI growth and investment, suggesting a more cautious, efficient model development. Additionally, Kapoor highlights the need for user-centric evaluations in AI and the distinct challenges posed by new benchmarks in assessing AI capabilities.
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Jul 18, 2024 • 1h 6min

Sara Hooker - Why US AI Act Compute Thresholds Are Misguided

Sara Hooker, VP of Research at Cohere, critiques compute thresholds in AI governance, highlighting limitations and proposing more inclusive AI models. She discusses the AI language gap, emphasizing the need for multilingual AI systems. The conversation delves into ethical considerations in AI development and the complexities of language diversity in AI capabilities.
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Jul 14, 2024 • 2h 15min

Prof. Murray Shanahan - Machines Don't Think Like Us

Prof. Murray Shanahan, from Imperial College London and DeepMind, challenges assumptions about AI consciousness. Topics include dangers of anthropomorphizing AI, limitations of describing AI capabilities, and the intersection of philosophy and artificial intelligence.
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Jul 8, 2024 • 1h 18min

David Chalmers - Reality+

World-renowned philosopher David Chalmers discusses the concept of virtual reality as genuine reality. He delves into the nature of reality, leading a good life, existence of God, and the possibility of living in a computer simulation. Chalmers provides new answers to age-old philosophical questions using cutting-edge technology, arguing that virtual worlds are not second-class worlds.
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Jul 6, 2024 • 2h 18min

Ryan Greenblatt - Solving ARC with GPT4o

Researcher Ryan Greenblatt from Redwood Research achieved state-of-the-art accuracy on Francois Chollet's ARC Challenge using GPT4o, discussing his unique approach, strengths and weaknesses of AI models, differences between AI and humans in learning and reasoning, combining techniques for smarter AI, risks and future advancements in AI, and the idea of agentic AI.
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Jun 29, 2024 • 1h

Aiden Gomez - CEO of Cohere (AI's 'Inner Monologue' – Crucial for Reasoning)

CEO of Cohere, Aidan Gomez, discusses tackling AI hallucinations, improving reasoning abilities, and the unique approach of Cohere in solving real-world business problems. They touch on the implications of AI for society, the startup scene, and their vision for driving productivity and innovation.
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Jun 18, 2024 • 2h 14min

New "50%" ARC result and current winners interviewed

Current winners of the ARC Challenge, Jack Cole and Mohammed Osman, along with collaborator Michael Hodel, share insights on their approach using language models and neural networks. They discuss fine-tuning techniques, creating new ARC-like tasks, and debating the true measure of intelligence in solving the ARC Challenge. The guests encourage exploration of ARC tasks and creative problem-solving among listeners.
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Jun 16, 2024 • 41min

Cohere co-founder Nick Frosst on building LLM apps for business

Nick Frosst, Co-founder of Cohere, discusses the future of LLMs and AGI, solving real business problems with AI models. He talks about Cohere's Command R* models, challenges with large language models, specialized vs. general-purpose models, retrieval augmented generation, and the evolving benchmarks in language model development.
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Jun 5, 2024 • 1h 17min

What’s the Magic Word? A Control Theory of LLM Prompting.

Aman Bhargava from Caltech and Cameron Witkowski from the University of Toronto discuss their groundbreaking paper on controlling language models. They explore how prompts can significantly influence model outputs, highlighting the importance of prompt engineering. Their work suggests that control theory concepts could lead to more reliable and capable language models.
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May 6, 2024 • 2h 32min

CAN MACHINES REPLACE US? (AI vs Humanity) - Maria Santacaterina

Maria Santacaterina delves into the limitations of AI, emphasizing the importance of human traits like empathy and creativity. She discusses the overreliance on AI in critical sectors, ethical considerations, and the concept of 'adaptive resilience'. The conversation highlights the need for strong governance and ethical frameworks for AI development.

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