Will Generative AI Transform Robotics?

In an influential essay in 2019, entitled ‘The Bitter Lesson’, machine learning researcher Richard Sutton observed that the main driver of progress in artificial intelligence (AI) is the continued scaling up of computational power1. This view predicts that while manual approaches that embed human knowledge and understanding in AI agents lead to satisfying advances in the short term, in the long run they only stand in the way of developing more general, scalable methods. This provocative conclusion has led to heated debates about the role of human ingenuity, but the ‘bitter lesson’ paradigm has more or less played out in the area of natural language processing. By using scaled-up neural networks and as many text examples from the internet as possible as training data, researchers could solve previously complex problems of producing human language without syntactical errors. Further scaling has produced general-purpose and multimodal models with billions of parameters such as GPT-4, Claude, Gemini and Llama that have game-changing applications in science and society.