“We don’t have robots that are nearly as good at understanding the physical world as a rat,” says Yann LeCun, one of the leading figures in the world of artificial intelligence.
He worked at Facebook-owner, Meta, for a decade, where he was chief AI scientist, but left in 2025 and founded Advanced Machine Intelligence Labs (AMI Labs).
His goal is to move AI beyond current systems like ChatGPT, Claude and Gemini. They have their uses, he says, but will never be able to tackle complicated situations in the real world, like getting a robot to do household chores.
“They’re not a path towards human level or human-like intelligence, or even animal-like intelligence, because they cannot deal with real world data, they just are not built for that,” he tells me on the sidelines of VivaTech, France’s leading technology conference.
So, Paris-based AMI Labs is busy developing a new type of artificial intelligence not based on the tech behind ChatGPT and its rivals.
Investors think it has potential. Earlier this year AMI Labs announced that it had raised more than $1bn (£760m), with investors including US computer chip giant Nvidia and the fund that manages the private wealth of Amazon-founder Jeff Bezos.
That so-called seed funding round – the earliest round of start-up fundraising – was one of the biggest of its kind in Europe.
Large Language Models (LLMs) like ChatGPT are extremely good at some things like coding, mathematical problems and generating text, LeCun says.
But he argues that these are well defined and predictable problems.
“They [LLMs] basically just accumulate knowledge… They can regurgitate something, you train them to regurgitate, but they’re not particularly smart. They don’t have an underlying understanding,” he says.
In the real world there is a bewildering array of outcomes to any action, which requires a more flexible type of artificial intelligence.
LeCun holds a pen upright on its tip. What happens when you let go, he asks? Even a toddler would know that the pen would topple over. But no human would bother to guess in which direction the pen might fall, there’s no way to tell.
But an LLM might try to generate a single prediction about the pen’s next move based on statistical patterns from its training data.
The prediction would almost certainly be wrong, because the system is not reasoning about the physical reality of the situation – it is generating what appears to be statistically plausible.
LeCun says the system his company is developing, called Joint Embedding Predictive Architecture (JEPA), is set up to deal with problems like that.
It creates abstractions of the real world that allow it to assess the outcomes of actions.
Creating these abstractions involves difficult maths, but essentially they filter out useless information, just leaving the AI with useful pictures of the world.
In the case of the pen, the AI would know that there’s no point in trying to predict which way the pen would fall.
