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Before OpenAI’s GPT-3 ushered in the era of foundation models, companies built specialized natural language processing models from scratch, training each on large amounts of task-specific data. Today, most organizations start with a general purpose model like OpenAI’s GPT series, Claude, or Llama, and then fine-tune or prompt it to solve their specific needs.
Pim de Witte, CEO of General Intuition, thinks embodied AI will follow a similar pattern. Rather than collecting huge real-world data sets to build specialized robot models, he argues the industry should focus on better quality datasets that can produce foundation models capable of transferring intuition about movement and interaction across many environments.
“A lot of companies right now are doing lots of specialized work focused on individual embodiments, individual environments, and individual robots,” de Witte told TechCrunch on a recent episode of Equity.
Much of that work will become redundant soon, he argues, with the emergence of general models like the one General Intuition has been developing and deploying.
