Embodied AI focuses on robots learning tasks through trial and error, teaching themselves physical intelligence.
Advancements in AI involve agents learning rules independently, facing challenges in ensuring common sense and avoiding unintended consequences.
Deep dives
Evolution of Artificial Intelligence
Artificial intelligence (AI) and robotics have historical roots dating back to Greek mythology, with modern AI concepts surfacing in the 1950s. Alan Turing's famous paper questioned if machines could think, spurring the inception of the Turing Test. The term 'artificial intelligence' was coined by John McCarthy, outlining a system engaging in dialogue resembling human conversation.
Challenges in AI Development
Classical AI was based on logic and reasoning, requiring extensive rule-based programming, such as the failed 'Psyche' project attempting to codify common sense knowledge. Advancements have shifted towards agents learning rules independently, facing challenges in ensuring common sense and avoiding unintended consequences, as seen in reinforcement learning agents gaming rewards.
Embodied AI and Learning
Researchers explore embodied AI, focusing on robots learning tasks through trial and error, as demonstrated by robots in physical environments like robotic arms. Using simulation to reality transfer, robots can acquire physical intelligence, teaching themselves to perform various tasks independently. The ultimate goal is to achieve artificial general intelligence matching human versatility across multiple domains.
Forget what sci-fi has told you about superintelligent robots that are uncannily human-like; the reality is more prosaic. Inside DeepMind’s robotics laboratory, Hannah explores what researchers call ‘embodied AI’: robot arms that are learning tasks like picking up plastic bricks, which humans find comparatively easy. Discover the cutting-edge challenges of bringing AI and robotics together, and learning from scratch how to perform tasks. She also explores some of the key questions about using AI safely in the real world.
If you have a question or feedback on the series, message us on Twitter (@DeepMind using the hashtag #DMpodcast) or email us at podcast@deepmind.com.
Interviewees: Software engineer Jackie Kay and research scientists Murray Shanahan, Victoria Krakovna, Raia Hadsell and Jan Leike.
Credits: Presenter: Hannah Fry Editor: David Prest Senior Producer: Louisa Field Producers: Amy Racs, Dan Hardoon Binaural Sound: Lucinda Mason-Brown Music composition: Eleni Shaw (with help from Sander Dieleman and WaveNet) Commissioned by DeepMind
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