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

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Why This Matters

Yann LeCun emphasizes that current AI systems like ChatGPT are limited in understanding and interacting with the physical world, highlighting the need for more advanced, flexible AI that can handle real-world complexities. This shift could significantly impact the development of robots and AI applications capable of performing tasks in unpredictable environments, pushing the industry toward more human-like intelligence. The focus on next-generation AI technologies signals a major evolution in how machines will interact with and understand the physical world, benefiting consumers and industries alike.

Key Takeaways

"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.

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