Machine Learning Street Talk (MLST)

Dan McQuillan, a creative computing lecturer at Goldsmiths University, discusses his book on resisting harmful AI trends. He explores necropolitics, positioning AI as a tool of neo-imperialism and critiquing societal norms around intelligence, likening it to historical eugenics. The conversation highlights the need for technology to support diversity and social equity. McQuillan also addresses the ethical implications of AI, advocating for an anti-fascist approach that challenges colonial structures, while emphasizing the importance of individual experiences in shaping societal values.

Dr. Joel Lehman, a machine learning research scientist, explores the intricate relationship between AI and human flourishing. He discusses how AI can embody principles of love and responsibility, drawing from psychology and philosophy. The conversation delves into the complexities of representing emotions in machine learning and the need for AI to align with human values. Lehman critiques traditional self-help approaches and highlights the importance of fostering creativity and personal growth amidst rapid technological change.

In this engaging discussion, Dr. Raphaël Millière, a Columbia University lecturer in philosophy, delves into the intersection of AI, linguistics, and cognition. He explores how deep learning challenges traditional notions of self-representation and consciousness. Millière tackles the complexities of mimicry in AI, uncovering biases it may perpetuate. He also analyzes the limitations of large language models, emphasizing the grounding problem and the intricacies of human-like understanding, raising thought-provoking questions about the future of AI and its ethical implications.

In this fascinating discussion, Professor Karl Friston, a renowned figure in neuroscience, dives into a groundbreaking vision of artificial intelligence that integrates humans and nature into a collective ecosystem. He explores the principles of active inference and shared narratives as tools for enhancing intelligence and ethical decision-making. Friston also challenges us to rethink our relationship with technology and each other, emphasizing the potential for AI to serve the common good while respecting human dignity. This thought-provoking conversation blurs the lines between consciousness, morality, and self-organization.

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.

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.

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.

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.

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.

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.