Artificial intelligence: Yann LeCun works on more flexible AI

AI is 'not smart' so what's next in artificial intelligence?

"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.

Building a more flexible artificial intelligence is a priority for the robotics industry.

But training them to safely perform household tasks like ironing or stacking the dishwasher is proving difficult and costly.

And, according to LeCun, current AI models are unlikely to ever be any good in that environment.

"LLMs are largely hopeless for robotics," he says.

"The claims that somehow by just scaling up LLMs, we're going to reach super human intelligence, that is simply not going to happen."

Many in the AI industry agree with LeCun.

Ingmar Posner is one of them. He is professor of Applied Artificial Intelligence at Oxford University and directs its Applied AI Lab. He is also an Amazon Scholar.

"My view is that the next decade will really be about systems that can explain... You need models that can answer questions like: What matters? What causes what? What would happen if I did something else - like if I took a different action?"

Posner and his team of around 10 researchers have been working for four years on an alternative form of AI, which falls into a loose category called World Models.

While World Models have conceptually been around for decades, one inspiration for this work was an influential paper published in 2018 by David Ha and Jurgen Schmidhuber.

Their insight was that, given advances in machine learning and compute power, an AI can learn how to do something purely from a learnt, "mental" simulation of what the world looks like.

Since 2018 that idea has catalysed a significant amount of research into world models, including the Dreamer World Model from Google. Last year a Dreamer variant worked out how to collect diamonds in the video game Minecraft, by imagining future scenarios to help it with decision making.

Posner hopes the AI system his team are working on will be another step forward. He calls it a "mechanistic world model", which will structure knowledge in a way the AI can use efficiently.

"You need systems that are able to compartmentalise and organise knowledge in such a way that it can be recalled, combined and modified when it matters," Posner says.

It's very difficult to say how long it will take to develop these new models, he adds.

"If you asked anyone in 2017 or 2018, how long it would be until you can have a ChatGPT sort of thing, they would go: 'Decades, decades of work'."

Other work on World Models is being done by DeepMind (part of Google-owner, Alphabet) with its Genie model and London-based Wayve has a system called Gaia.

Meanwhile, AI pioneer Fei-Fei Li founded World Labs in San Francisco in 2023 to develop a new AI model.

LeCun says that AMI Labs will spend the rest of this year refining their AI model and next year hopes that it will be put to use, at first in industrial settings.

If that's successful, then it will be time to think big.

"Eventually down the line we'll have sort of general generic intelligence systems that can be applied to just about anything in the world with minimal training or fine tuning."

What will happen to humans in a world where robots can operate independently?

"We're still going to need humans to figure out what questions to ask, what to build, what to create, which is really the properly human aspect," he says.

The AI will work for us he adds.

"Our interaction with future AI systems - even if they are smarter than us - is going to be like the interaction between a captain of industry or a political leader with their staff of assistants - many of whom are smarter than they are."