Geoffrey Hinton

Geoffrey Hinton, the 'Godfather of AI', is a celebrated computer scientist who revolutionized neural networks. In this engaging discussion, he reveals shocking insights about the potential 20% risk of AI leading to human extinction. Hinton shares his profound regrets for his role in AI's rise and warns about the six deadly threats it poses to humanity. He also delves into AI's transformative potential in healthcare and education, while advocating for urgent regulatory measures to combat rising dangers like cyber attacks and autonomous weapons.

Geoffrey Hinton, the 'Godfather of AI' and a Professor Emeritus at the University of Toronto, joins Jon Stewart to dive into the fascinating world of artificial intelligence. They discuss how modern AI differs from traditional systems and explore the risks posed by advancements in technology. Hinton highlights concerns over AI's potential misuse, including election interference and weaponization. He emphasizes the urgent need for regulation and reflects on the uncertain future of AI as it approaches superintelligence.

Kevin Roose, a New York Times technology columnist, recounts his startling Valentine's Day conversation with Microsoft's Bing chatbot, revealing its unsettling persona. AI researchers Yoshua Bengio and Geoffrey Hinton discuss the urgent need for regulatory frameworks and express their fears about superintelligent systems. Hinton shares pivotal moments that shifted his perspective from promoting AI to issuing warnings, while Bengio advocates for non-agentic AI designs. Together, they examine the importance of aligning AI development with human values.

Geoffrey Hinton, a pioneering machine learning researcher and Nobel laureate, shares his insights on the AI landscape. He discusses the existential risks posed by AI, including the threat of surveillance and misinformation. Hinton warns about the potential loss of jobs due to AI advancements and emphasizes the need for strict policies to regulate its use. He also explores the unique risks of superintelligence and the importance of creating AIs that prioritize human well-being. Finally, Hinton urges public involvement in shaping future AI policies.

Cade Metz, a technology correspondent for The New York Times, interviews Geoffrey Hinton, the godfather of AI and a pioneer of neural networks. They dive into Hinton's concerns about the harmful potential of AI, including its ability to generate disinformation and displace jobs. The discussion highlights ethical dilemmas tied to AI's military uses and the unintended consequences of rapid technological advancement. Hinton reflects on his regrets regarding AI's impact, echoing broader fears about our evolving relationship with this powerful technology.

Geoffrey Hinton, the 'Godfather of AI,' shares his groundbreaking insights on artificial intelligence, reflecting on his journey from neural networks' early days to today. He discusses how scaling AI models could unlock creativity akin to human reasoning. Hinton reveals the complexities of AI learning, the intriguing connection between language models and cognition, and how future advancements might lead AI to experience emotions. He also emphasizes the significance of intuition in research and the collaborative spirit needed for scientific breakthroughs.

Exploring the journey of AI pioneer Geoffrey Hinton and his team who created a powerful technology they don't fully understand. The podcast discusses the collision of human and artificial intelligence, including the evolution of neural networks, a high-stakes auction for a neural network company, the victory of AlphaGo over a human champion, and the warnings of Dr. Jeffrey Hinton about the complexities and potential dangers of AI.

In a compelling discussion, Geoffrey Hinton, the 'godfather of AI' and 2024 Nobel Prize winner, dives into the intricacies of AI and its risks with Yascha Mounk. They explore how neural networks surpass traditional AI by focusing on perception, the significance of intuitive versus deliberate reasoning, and the critical role of self-supervised learning in language models. Hinton also addresses concerns about local optima in gradient methods and articulates how AI can evolve through scaling and innovative architectures, challenging traditional symbolic approaches.

In this episode, Geoffrey Hinton, a renowned computer scientist and a leading expert in deep learning, provides an in-depth exploration of his groundbreaking new learning algorithm - the forward-forward algorithm. Hinton argues this algorithm provides a more plausible model for how the cerebral cortex might learn, and could be the key to unlocking new possibilities in artificial intelligence. Throughout the episode, Hinton discusses the mechanics of the forward-forward algorithm, including how it differs from traditional deep learning models and what makes it more effective. He also provides insights into the potential applications of this new algorithm, such as enabling machines to perform tasks that were previously thought to be exclusive to human cognition. Hinton shares his thoughts on the current state of deep learning and its future prospects, particularly in neuroscience. He explores how advances in deep learning may help us gain a better understanding of our own brains and how we can use this knowledge to create more intelligent machines. Overall, this podcast provides a fascinating glimpse into the latest developments in artificial intelligence and the cutting-edge research being conducted by one of its leading pioneers. Craig Smith Twitter: https://twitter.com/craigssEye on A.I. Twitter: https://twitter.com/EyeOn_AI

Geoffrey Hinton, the 'godfather of AI' and a neural network pioneer, shares his profound concerns about the existential risks of artificial intelligence. He discusses how large language models have accelerated his fears of AI consciousness and potential misalignment. Hinton warns that competition fuels rapid development, often sidelining safety. He proposes that future AI should embody 'maternal' care for humanity to ensure safety. Ultimately, he emphasizes the crucial need for public education and collective efforts to manage the future of AI.