Is AI Hiding Its Full Power? With Geoffrey Hinton

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How did we go from digital computers to AI seemingly everywhere? Neil deGrasse Tyson, Chuck Nice, & Gary O’Reilly dive into the mechanics of thinking, how AI got its start, and what deep learning really means with cognitive and computer scientist, Nobel Laureate, and one of the architects of AI, Geoffrey Hinton.

Hinton explains the fundamental shift from logic-based rule programming to the biological approach: building systems that learn the way a brain does. Learn about the history of computer science that led to the breakthroughs we have today. We break down the structure of artificial neural networks and the meaning of AI buzzwords like “deep learning” really mean. You’ll learn about the layering of data processing and how the first layer of neurons might detect a simple edge, the second a beak, and the third a bird’s head.

Hinton explains the light bulb moment of backpropagation, the mathematical breakthrough using calculus to send force backward to strengthen previous connections. Is this a process we share with neural nets? We discuss whether AI thinks like us and explore why AlphaGo succeeded in beating us at our own game by generating its own data and whether LLMs will hit a ceiling as they run out of human-written text. Can AI reason? What does it mean for something or someone to think?

As the science turns toward the future, we tackle heavy questions regarding the massive energy demands of data centers and whether AI can reinvent solar technology to save itself. Hinton discusses the “Volkswagen Effect,” where a model might strategically underperform to avoid being unplugged. We dive into consciousness to ask if subjective experience is just a byproduct of complex perception and if chatbots already possess it. What are the upsides as well as the downsides? The singularity isn’t imminent yet… but the “yet” is doing a lot of heavy lifting.

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Timestamps:
00:00 – Introduction: Geoffrey Hinton
03:00 – Approaches to Make and Intelligent System
07:19 – How Artificial Neural Nets Work
14:12 – Making a Neural Net By Hand
24:41 – Backpropigation Breakthrough
33:44 – Why AI Seemed to Arrive So Fast
34:39 – What is Thinking?
37:50 – Is AI Better at Learning?
47:41 – Can We Humanize AI?
50:58 – Setting Up Guardrails
54:04 – Is AI Lying to Us?
58:47 – Will AI Be the End of Us All?
01:00:51 – Does AI Hallucinate?
01:04:36 – The Upside
01:08:13 – Will AI Create More AI?
01:10:17 – AI Nuclear Winter: Will We Unite?
01:15:10 – 2024 Nobel Prize in Physics
01:16:44 – The Price of Replacing All the Jobs
01:22:50 – Achieving Consciousness
01:28:37 – The Work to Be Done Before the Singularity