Ars Technica: “The chief technology officer of a robotics startup told me earlier this year, “We thought we’d have to do a lot of work to build ‘ChatGPT for robotics.’ Instead, it turns out that, in a lot of cases, ChatGPT is ChatGPT for robotics.” Until recently, AI models were specialized tools. Using AI in a particular area, like robotics, meant spending time and money creating AI models specifically and only for that area. For example, Google’s AlphaFold, an AI model for predicting protein folding, was trained using protein structure data and is only useful for working with protein structures.So this founder thought that to benefit from generative AI, the robotics company would need to create its own specialized generative AI models for robotics. Instead, the team discovered that for many cases, they could use off-the-shelf ChatGPT for controlling their robots without the AI having ever been specifically trained for it. I’ve heard similar things from technologists working on everything from health insurance to semiconductor design. To create ChatGPT, a chatbot that lets humans use generative AI by simply having a conversation, OpenAI needed to change large language models (LLMs) like GPT3 to become more responsive to human interaction. But perhaps inadvertently, these same changes let the successors to GPT3, like GPT3.5 and GPT4, be used as powerful, general-purpose information-processing tools—tools that aren’t dependent on the knowledge the AI model was originally trained on or the applications the model was trained for. This requires using the AI models in a completely different way—programming instead of chatting, new data instead of training. But it’s opening the way for AI to become general purpose rather than specialized, more of an “anything tool.”