The speaker reflects on the awe-inspiring moment when they discovered that a machine (chess computer Deep Blue) could perform what they had always thought of as a uniquely human skill - intelligence. It challenged their previous understanding of memory, patterns, intuition, guts, and instinct all working together, and wonders if the human mind is not simply a set of algorithms.
The gradual shift towards a digital world may lead to a decrease in innovation in the physical space, while simultaneously increasing the importance of physical contact in our lives.
In this episode, the guest discusses the idea of using raw data to extract hierarchies of abstractions and the challenges in developing learning systems that can form those hierarchies. They delve into the Wolfram language and Wolfram Alpha as representing the dream of what AI is meant to be, a full-scale computational language.
The podcast discusses how current AI builders and neural networks function, how they learn from human interactions, and the potential for advancements to improve lifetime experiences and human interactions online.
Monte Carlo tree search is a vital component for AI systems like AlphaGo to make better decisions and beat top humans at games like Go.
Biologists now talk about the intelligence of not just human brain, but also any kind of brain. Computer simulation has proven to be useful in simulating aspects of human intelligence.