Explained: How the AI that drives ChatGPT will move into physical world | World News

Cade Metz

Companies like OpenAI and Midjourney build chatbots, image generators and other artificial intelligence tools that operate in the digital world.

Now, a start-up founded by three former OpenAI researchers is using the technology development methods behind chatbots to build AI technology that can navigate the physical world.

 

Covariant, a robotics company headquartered in Emeryville, California, is creating ways for robots to pick up, move and sort items as they are shuttled through warehouses and distribution centres. Its goal is to help robots gain an understanding of what is going on around them and decide what they should do next. The technology also gives robots a broad understanding of the English language, letting people chat with them as if they were chatting with ChatGPT. The technology, still under development, is not perfect. But it is a clear sign that the artificial intelligence systems that drive online chatbots and image generators will also power machines in warehouses, on roadways and in homes.

Like chatbots and image generators, this robotics technology learns its skills by analysing enormous amounts of digital data. That means engineers can improve the technology by feeding it more and more data.

 

Covariant, backed by $222 million in funding, does not build robots. It builds the software that powers robots. The company aims to deploy its new technology with warehouse robots, providing a road map for others to do much the same in manufacturing plants and perhaps even on roadways with driverless cars.

 

The AI systems that drive chatbots and image generators are called neural networks, named for the web of neurons in the brain. By pinpointing patterns in vast amounts of data, these systems can learn to recognise words, sounds and images. This is how OpenAI built ChatGPT, giving it the power to instantly answer questions, write term papers and generate computer programs. It learned these skills from text culled from across the internet. (Several media outlets, including The New York Times, have sued OpenAI for copyright infringement.)

 

Companies are now building systems that can learn from different kinds of data at the same time. By analysing both a collection of photos and the captions that describe those photos, for example, a system can grasp the relationships between the two. It can learn that the word “banana” describes a curved yellow fruit.

OpenAI employed that system to build Sora, its new video generator. By analysing thousands of captioned videos, the system learned to generate videos when given a short description of a scene, like “a gorgeously rendered papercraft world of a coral reef, rife with colourful fish and sea creatures.”

 

Covariant, founded by Pieter Abbeel, a professor at the University of California, Berkeley, and three of his former students, Peter Chen, Rocky Duan and Tianhao Zhang, used similar techniques in building a system that drives warehouse robots.

The company helps operate sorting robots in warehouses across the globe. It has spent years gathering data — from cameras and other sensors — that shows how these robots operate.

 

“It ingests all kinds of data that matter to robots — that can help them understand the physical world and interact with it,” Chen said.

 

By combining that data with the huge amounts of text used to train chatbots like ChatGPT, the company has built AI technology that gives its robots a much broader understanding of the world around it. After identifying patterns in this stew of images, sensory data and text, the technology gives a robot the power to handle unexpected situations in the physical world. The robot knows how to pick up a banana, even if it has never seen a banana before.

 

It can also respond to plain English, much like a chatbot. If you tell it to “pick up a banana,” it knows what that means. If you tell it to “pick up a yellow fruit,” it understands that, too.

 

The technology, called RFM., for robotics foundational model, makes mistakes, much like chatbots do. Though it often understands what people ask of it, there is always a chance that it will not. It drops objects from time to time.

 

Gary Marcus, an AI entrepreneur and an emeritus professor of psychology and neural science at New York University, said the technology could be useful in warehouses and other situations where mistakes are acceptable. But he said it would be more difficult and riskier to deploy in manufacturing plants and other potentially dangerous situations.

 

“It comes down to the cost of error,” he said. “If you have a 150-pound robot that can do something harmful, that cost can be high.”

 

By combining that data with the huge amounts of text used to train chatbots it has built AI technology that gives its robots a broader understanding of the world

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