Chatbots have rapidly advanced in recent years, and so have the large language models, or LLMs, powering them. LLMs use machine learning algorithms trained on vast amounts of text data. Many technology leaders, including Tesla CEO Elon Musk and NVIDIA CEO Jensen Huang, believe a similar approach will make humanoid robots capable of performing surgery, replacing factory workers, or serving as in-home butlers within a few short years. Other robotics experts disagree, according to UC Berkeley roboticist Ken Goldberg. In two new papers published online in the journal Science Robotics, Goldberg described how the “100,000-year data gap” will prevent robots from gaining real-world skills as quickly as artificial intelligence chatbots have gained language fluency. In the second article, leading roboticists from MIT, Georgia Tech, and ETH-Zurich summarized 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 opinion : Why humanoid robots aren’t advancing as fast as AI chatbots.