AI Breakthrough: How Chess is Unmasking Hidden Flaws in AI
Since Covid-19 took over the world, computer scientist Tom Zahavy has rediscovered his love for chess. He dove into the world of chess youtube videos and Netflix’s The Queen’s Gambit, but he wasn’t just looking to improve his skills. Zahavy saw the game of chess as a canvas for testing new ideas in artificial intelligence. When posed with certain chess puzzles designed to be difficult for computers, Zahavy and his team set out to reveal the hidden limitations of chess programs.
Enter Penrose’s notorious puzzles, invented by mathematician Sir Roger Penrose in 2017. These puzzles flaw strong black pieces in awkward positions, presenting a challenge for even the most powerful computer chess programs. Zahavy, a research scientist at Google DeepMind, and his team developed a strategy to weave together multiple AI systems, creating a more agile system that excelled at dismantling Penrose’s puzzles. This new approach allows for a population of agents, making it more likely for one of them to solve the problem. These findings suggest that the key to tackling hard problems lies in finding more than one way to solve it.
This adventure into the flaws and limitations of AI goes beyond chess. Zahavy’s team recognized a recurring glitch in reinforcement learning systems — particularly with AlphaZero. These systems, while incredibly powerful, lack the understanding of failure and the creativity that comes with it. As Zahavy uncovered, AI systems simply don’t know how to fail or recognize failure, making them prone to repetitive behavior that doesn’t lead to solutions. Zahavy’s work at DeepMind is providing valuable insight into the limitations of AI and how they can be addressed.
So what are your thoughts on this groundbreaking revelation in the world of artificial intelligence? Is failure a critical component in the development of AI systems? Drop your comments below and let’s start a conversation about this fascinating breakthrough!
IntelliPrompt curated this article: Read the full story at the original source by clicking here