Peter Chen

It’s no secret that foundation models have transformed AI in the digital world. Large language models (LLMs) like ChatGPT, LLaMA, and Bard revolutionized AI for language. While OpenAI’s GPT models aren’t the only large language model available, they have achieved the most mainstream recognition for taking text and image inputs and delivering human-like responses — even with some tasks requiring complex problem-solving and advanced reasoning.

ChatGPT’s viral and widespread adoption has largely shaped how society understands this new moment for artificial intelligence.

The next advancement that will define AI for generations is robotics. Building AI-powered robots that can learn how to interact with the physical world will enhance all forms of repetitive work in sectors ranging from logistics, transportation, and manufacturing to retail, agriculture, and even healthcare. It will also unlock as many efficiencies in the physical world as we’ve seen in the digital world over the past few decades.

While there is a unique set of problems to solve within robotics compared to language, there are similarities across the core foundational concepts. And some of the brightest minds in AI have made significant progress in building the “GPT for robotics.”

What enables the success of GPT?

To understand how to build the “GPT for robotics,” first look at the core pillars that have enabled the success of LLMs such as GPT.

Foundation model approach

GPT is an AI model trained on a vast, diverse dataset. Engineers previously collected data and trained specific AI for a specific problem. Then they would need to collect new data to solve another. Another problem? New data yet again. Now, with a foundation model approach, the exact opposite is happening.

Instead of building niche AIs for every use case, one can be universally used. And that one very general model is more successful than every specialized model. The AI in a foundation model performs better on one specific task. It can leverage learnings from other tasks and generalize to new tasks better because it has learned additional skills from having to perform well across a diverse set of tasks.