AI chatbots have advanced rapidly over the past few years, so much so that people are now using them as personal assistants, customer service representatives and even therapists. The large language models (LLMs) that power these chatbots were created using machine learning algorithms trained on the vast troves of text data found on the internet. And their success has many tech leaders, including Elon Musk and NVIDIA CEO Jensen Huang, claiming that a similar approach will yield humanoid robots capable of performing surgery, replacing factory workers or serving as in-home butlers within a few short years. But robotics experts disagree, says UC Berkeley roboticist Ken Goldberg. In two new papers published online today (Aug. 27) in the journal Science Robotics, Goldberg describes how what he calls the “100,000-year data gap” will prevent robots from gaining real-world skills as quickly as AI chatbots are gaining language fluency. In the second article, leading roboticists from MIT, Georgia Tech and ETH-Zurich summarize the heated debate among roboticists over whether the future of the field lies in collecting more data to train humanoid robots or relying on “good old-fashioned engineering” to program robots to complete real-world tasks.

Full report : In two new papers, UC Berkeley roboticist Ken Goldberg explains why robots are not gaining real-world skills as quickly as AI chatbots are gaining language fluency.