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 appearances from MIT Doctor of Philosophy Keith Duggar (https://www.linkedin.com/in/dr-keith-duggar/).
Episodes
Mentioned books
5 snips
Mar 4, 2023 • 1h 21min
#105 - Dr. MICHAEL OLIVER [CSO - Numerai]
Michael Oliver, Chief Scientist at Numerai, dives into the world of crowdsourced financial modeling. He discusses the unique approach Numerai takes in utilizing diverse models for stock predictions while addressing the limitations of AI like large language models. The conversation explores the intricacies of language and cognition, emphasizing how subjective experiences shape communication. Oliver also highlights the collaboration within the data science community and innovative methods that are breaking new ground in machine learning.
31 snips
Feb 22, 2023 • 1h 29min
#104 - Prof. CHRIS SUMMERFIELD - Natural General Intelligence [SPECIAL EDITION]
Chris Summerfield, Professor of Cognitive Neuroscience at Oxford and Research Scientist at DeepMind, dives into the fascinating world of intelligence in this engaging discussion. He unpacks key ideas from his new book, exploring how human knowledge shapes both decision-making and AI development. Topics include memory organization versus mere recall, the philosophical implications of AI understanding, and the future of AI that transcends biological limitations. Chris also addresses the complexities of AI creativity and the challenges of aligning AI with human-like intelligence.
15 snips
Feb 11, 2023 • 1h 2min
#103 - Prof. Edward Grefenstette - Language, Semantics, Philosophy
Edward Grefenstette, Head of Machine Learning at Cohere and Honorary Professor at UCL, delves into the fascinating intersection of language, semantics, and philosophy. He discusses the complexities of understanding semantics in AI, particularly in moral contexts, and highlights the significance of Reinforcement Learning from Human Feedback (RLHF) for enhancing model performance. Grefenstette also tackles deep learning's 'Swiss cheese problem' and explores philosophical insights on intelligence, agency, and the nature of creativity in relation to AI.
19 snips
Feb 11, 2023 • 55min
#102 - Prof. MICHAEL LEVIN, Prof. IRINA RISH - Emergence, Intelligence, Transhumanism
Prof. Michael Levin, a biologist at Tufts University, studies the emergence of complex life from simple beginnings. He explores how bioelectric dynamics can guide organism development. Joining him is Prof. Irina Rish from Université de Montréal, who delves into AI's evolution and its parallels with biological intelligence. Together, they discuss the nature of agency in living systems, the significance of communication among cells, and the philosophical implications of transhumanism, challenging traditional views on intelligence in both humans and machines.
13 snips
Feb 10, 2023 • 26min
#100 Dr. PATRICK LEWIS (co:here) - Retrieval Augmented Generation
Dr. Patrick Lewis, an AI and NLP Research Scientist at co:here, delves into the cutting-edge world of Retrieval-Augmented Language Models. He discusses the limitations of existing transformer models in handling large inputs, revealing the need for better techniques. The conversation highlights the importance of enhancing verifiability in language models by integrating credible sources. Patrick also explores the complexities of information retrieval in improving contextual relevance, using the innovative Atlas project as a prime example.
9 snips
Feb 5, 2023 • 1h 40min
#99 - CARLA CREMER & IGOR KRAWCZUK - X-Risk, Governance, Effective Altruism
Carla Cremer, a doctoral student at Oxford, and Igor Krawczuk, a researcher at EPFL, dive into the intricate world of AI risk and governance. They argue that AI risks are deeply rooted in traditional political issues, advocating for democratic approaches in risk assessment. Their discussion tackles the Effective Altruism movement's paradoxes, highlighting the need for institutional accountability. They emphasize the importance of transparency in AI tools and call for diverse perspectives in decision-making to navigate the complexities of governance and societal impact.
18 snips
Feb 3, 2023 • 1h 6min
[NO MUSIC] #98 - Prof. LUCIANO FLORIDI - ChatGPT, Singularitarians, Ethics, Philosophy of Information
Professor Luciano Floridi, a noted philosopher and expert in digital ethics from the University of Oxford, discusses the implications of living in an information-driven society. He highlights how the overwhelming data we create is eroding human agency and muddying the infosphere. Professor Floridi emphasizes the need for a robust philosophy of information to address ethical concerns, particularly regarding misinformation and AI's impact on reality. He also advocates for responsible AI governance to ensure technology serves humanity equitably.
5 snips
Feb 3, 2023 • 1h 7min
#98 - Prof. LUCIANO FLORIDI - ChatGPT, Superintelligence, Ethics, Philosophy of Information
Professor Luciano Floridi, a leading thinker in digital ethics from the University of Oxford, delves into the implications of the Information Revolution. He discusses the overwhelming data generation and the erosion of human agency. Floridi critiques the imbalance between tech growth and our understanding, emphasizing the need for ethical governance in AI. He also explores issues like misinformation and the transformation of societal engagement, advocating for collective responsibility and an information-centric worldview to navigate the complexities of our digital age.
Jan 28, 2023 • 25min
#97 SREEJAN KUMAR - Human Inductive Biases in Machines from Language
Sreejan Kumar, a fourth-year PhD student at Princeton Neuroscience Institute, dives into the fascinating world of human inductive biases in machines. He discusses his award-winning research on how humans learn and generalize quickly, and how to instill these biases in AI systems. The conversation explores the importance of using human language influences to enhance AI's understanding and capabilities. Sreejan emphasizes the potential of combining neural networks with program induction for a well-rounded intelligence, allowing for better collaboration between humans and machines.
36 snips
Dec 30, 2022 • 2h 49min
#96 Prof. PEDRO DOMINGOS - There are no infinities, utility functions, neurosymbolic
Pedro Domingos, Professor Emeritus at the University of Washington and author of "The Master Algorithm," dives deep into the intricate world of machine learning. He explores the concept of a master algorithm and debates its existence. The conversation branches into how utility functions shape AI behavior and the risks of misrepresenting truth in narratives. Domingos also discusses the relationship between human creativity and AI, emphasizing the importance of integrating different approaches, like neurosymbolic AI, to better understand intelligence.
