Yoshua Bengio

Sam Harris talks with Scott Wiener, a California State Senator focused on AI legislation, and Yoshua Bengio, a pioneer in deep learning and AI safety. They dive into California's proposed Senate Bill 1047, addressing urgent discussions on the need for AI regulation amid rapid technological advancements. The trio debates the risks of an AI arms race and differing expert perspectives on safety protocols. They also explore the intricate challenges of balancing innovation with responsible oversight, highlighting the complexities of regulating emergent technologies.

Yoshua Bengio, a pioneering deep learning researcher and Turing Award winner, delves into the pressing issues of AI safety and design. He warns about the dangers of goal-seeking AIs and emphasizes the need for non-agentic AIs to mitigate existential threats. Bengio discusses reward tampering, the complexity of AI agency, and the importance of global governance. He envisions AI as a transformative tool for science and medicine, exploring how responsible development can harness its potential while maintaining safety.

Yoshua Bengio joins Host Pieter Abbeel to discuss large language models, higher level cognition, causality, responsible AI, and human creativity. SUBSCRIBE TO THE ROBOT BRAINS PODCAST TODAY | Visit therobotbrains.ai and follow us on YouTube at TheRobotBrainsPodcast, Twitter @therobotbrains, and Instagram @therobotbrains. Hosted on Acast. See acast.com/privacy for more information.

Yoshua Bengio, an award-winning computer scientist and pioneer in artificial neural networks, discusses the paramount importance of slowing down AI development despite its rapid adoption. He dives into the complexities of machine consciousness, the ethical dilemmas of AI manipulation, and the intriguing parallels between neural networks and human brain function. The conversation also touches on the alignment problem and the necessity for global cooperation to ensure responsible AI practices. Prepare for thought-provoking insights that challenge perceptions of technology’s role in society.

Yoshua Bengio, a leading AI safety researcher from Université de Montréal, joins the conversation to discuss the dire risks posed by advanced AI technologies. He highlights the potential for AI to manipulate, spread disinformation, and concentrate power, raising alarm over its impact on democracy. The discussion dives into the complexities of AI safety, agency, and the troubling distinction between mimicking emotion and true sentience. Bengio advocates for robust safety measures, regulatory frameworks, and an urgent need to align AI developments with human values.

Yoshua Bengio, along with Geoffrey Hinton and Yann Lecun, is considered one of the three people most responsible for the advancement of deep learning during the 1990s, 2000s, and now. Cited 139,000 times, he has been integral to some of the biggest breakthroughs in AI over the past 3 decades. Video version is available on YouTube. If you would like to get more information about this podcast go to https://lexfridman.com/ai or connect with @lexfridman on Twitter, LinkedIn, Facebook, or YouTube where you can watch the video versions of these conversations. 

Yoshua Bengio, a Turing Award-winning AI expert, shares insights alongside philosopher Hilary Lawson, physicist Sabine Hossenfelder, and evolutionary biochemist Nick Lane. They tackle the intriguing question of machine consciousness. The conversation critiques the Turing test and explores the complexities of replicating human thought in AI. They delve into the evolutionary significance of consciousness, the limitations of materialist views, and the moral implications of potentially conscious machines, ultimately questioning what it means to truly understand consciousness.

Happy episode 50! This week’s episode is being released on Monday to avoid Thanksgiving. Have suggestions for future podcast guests (or other feedback)? Let us know here!In episode 50 of The Gradient Podcast, Daniel Bashir speaks to Professor Yoshua Bengio. Professor Bengio is a Full Professor at the Université de Montréal as well as Founder and Scientific Director of the MILA-Quebec AI Institute and the IVADO institute. Best known for his work in pioneering deep learning, Bengio was one of three awardees of the 2018 A.M. Turing Award along with Geoffrey Hinton and Yann LeCun. He is also the awardee of the prestigious Killam prize and, as of this year, the computer scientist with the highest h-index in the world.Subscribe to The Gradient Podcast:  Apple Podcasts  | Spotify | Pocket Casts | RSSFollow The Gradient on TwitterOutline:* (00:00) Intro* (02:20) Journey into Deep Learning, PDP and Hinton* (06:45) “Inspired by biology”* (08:30) “Gradient Based Learning Applied to Document Recognition” and working with Yann LeCun* (10:00) What Bengio learned from LeCun (and Larry Jackel) about being a research advisor* (13:00) “Learning Long-Term Dependencies with Gradient Descent is Difficult,” why people don’t understand this paper well enough* (18:15) Bengio’s work on word embeddings and the curse of dimensionality, “A Neural Probabilistic Language Model”* (23:00) Adding more structure / inductive biases to LMs* (24:00) The rise of deep learning and Bengio’s experience, “you have to be careful with inductive biases”* (31:30) Bengio’s “Bayesian posture” in response to recent developments* (40:00) Higher level cognition, Global Workspace Theory* (45:00) Causality, actions as mediating distribution change* (49:30) GFlowNets and RL* (53:30) GFlowNets and actions that are not well-defined, combining with System II and modular, abstract ideas* (56:50) GFlowNets and evolutionary methods* (1:00:45) Bengio on Cartesian dualism* (1:09:30) “When you are famous, it is hard to work on hard problems” (Richard Hamming) and Bengio’s response* (1:11:10) Family background, art and its role in Bengio’s life* (1:14:20) OutroLinks:* Professor Bengio’s Homepage* Papers* Gradient-based learning applied to document recognition* Learning Long-Term Dependencies with Gradient Descent is Difficult* The Consciousness Prior* Flow Network based Generative Models for Non-Iterative Diverse Candidate Generation Get full access to The Gradient at thegradientpub.substack.com/subscribe

Canadian computer scientist Yoshua Bengio, one of the 'godfathers of AI,' discusses his shift towards preventing AI from harming humanity. He delves into policy changes, regulatory measures like the EU -AI Act, the need for legislation for AI safety, and the limitations of current AI systems.

The podcast explores the evolution of AI from early brain modeling to modern neural-inspired systems. It delves into the challenges of defining intelligence and the use of language models in machine learning. The progression of AI from perceptrons to deep neural networks is discussed, highlighting breakthroughs in the deep learning revolution.