Yann LeCun's new venture is a contrarian bet against large language models In an exclusive interview, the AI pioneer shares his plans for his new Paris-based company, AMI Labs. Yann LeCun is a Turing Award recipient and a top AI researcher, but he has long been a contrarian figure in the tech world. He believes that the industry's current obsession with large language models is wrong-headed and will ultimately fail to solve many pressing problems. Instead, he thinks we should be betting on world models--a different type of AI that accurately reflects the dynamics of the real world. He is also a staunch advocate for open-source AI and criticizes the closed approach of frontier labs like OpenAI and Anthropic. Perhaps it's no surprise, then, that he recently left Meta, where he had served as chief scientist for FAIR (Fundamental AI Research), the company's influential research lab that he founded. Meta has struggled to gain much traction with its open-source AI model Llama and has seen internal shake-ups, including the controversial acquisition of ScaleAI. LeCun sat down with in an exclusive online interview from his Paris apartment to discuss his new venture, life after Meta, the future of artificial intelligence, and why he thinks the industry is chasing the wrong ideas.

Eggie, Neo, Isaac and Memo are domestic robots. But would you let them load your dishwasher? The idea of having a friendly robot butler that can do all the dull duties of running a home has existed for decades. But now, thanks to AI, it's genuinely happening and this year the first truly multi-purpose domestic bots will start to enter homes. In Silicon Valley, they're being trained at speed to fold laundry, load the dishwasher, and clean up after us.

A dozen or so young men and women, eyes obscured by VR headsets, shuffle around a faux kitchen inside a tech company's Silicon Valley headquarters. Their arms are bent at the elbows, palms facing down. One pilot stops to pick up a bottle of hot sauce from a counter, hinging at the waist, making sure to keep her hands in view of the camera on her headset at all times. Meters away, two humanoid robots, with bulbous joints and expressionless plastic domes for faces, stand at a desk. In front of each is a crumpled towel; to its right, a basket. More often than not, the towel catches on the edge of the basket and the robot freezes. Then an engineer steps in and returns the towel to a crumpled heap, and the sequence begins again. This was the scene inside the Silicon Valley headquarters of Figure AI on an August morning this year. The three-year-old startup was in a sprint ahead of the October announcement of its next robot, the Figure 03, which was undergoing top-secret training when TIME visited.

According to a survey of 65 AI experts in the UK and Japan, robots could perform around 39% of household tasks within the next decade, with grocery shopping being the most likely to be automated. However, caring for the young and old is less likely to be affected. The increased use of AI in homes could also result in privacy concerns. One of the report's authors, Ekaterina Hertog, suggested that society needs to have a public discussion about privacy in the age of smart technology, as per a report from the Guardian. Despite this, she believes that greater automation could lead to improved gender equality by reducing the burden of unpaid work, which is mainly borne by women.

There is a knowledge gap regarding which types of failures robots undergo in domestic settings and how these failures influence customer experience. We classified 10,072 customer reviews of small utilitarian domestic robots on Amazon by the robotic failures described in them, grouping failures into twelve types and three categories (Technical, Interaction, and Service). We identified sources and types of failures previously overlooked in the literature, combining them into an updated failure taxonomy. We analyzed their frequencies and relations to customer star ratings. Results indicate that for utilitarian domestic robots, Technical failures were more detrimental to customer experience than Interaction or Service failures. Issues with Task Completion and Robustness & Resilience were commonly reported and had the most significant negative impact. Future failure-prevention and response strategies should address the technical ability of the robot to meet functional goals, operate and maintain structural integrity over time. Usability and interaction design were less detrimental to customer experience, indicating that customers may be more forgiving of failures that impact these aspects for the robots and practical uses examined. Further, we developed a Natural Language Processing model capable of predicting whether a customer review contains content that describes a failure and the type of failure it describes. With this knowledge, designers and researchers of robotic systems can prioritize design and development efforts towards essential issues.

This is the first post in an intended series on what is the current state of Artificial Intelligence capabilities, and what we can expect in the relative short term. I will be at odds with the more outlandish claims that are circulating in the press, and amongst what I consider an alarmist group that includes people in the AI field and outside of it. In this post I start to introduce some of the key components of my future arguments, as well as show how different any AI system might be from us humans. Some may recognize the title of this post as an homage to the 1974 paper by Thomas Nagel, "What Is It Like to Be a Bat?". Two more recent books, one from 2009 by Alexandra Horowitz on dogs, and one from 2016 by Peter Godfrey-Smith on octopuses also pay homage to Nagel's paper each with a section of a chapter titled "What it is like", and "What It's Like", respectively, giving affirmative responses to their own questions about what is it like to be a dog, or an octopus.

"We are finally seeing an inflection point in the industry", says Whipsaw CEO and Principal Designer, Dan Harden as he talks about how robots are slowly entering our households. Back at the beginning of the 2000s, the only robots you could find around the house were probably either toys (RC cars, RoboSapiens), or domestic cleaning robots like the vacuum cleaner or the lawn-mower. Today, home service robots are increasingly becoming an emerging trend, creating a unique new opportunity for designers to establish the identity, personality, form, function, and usability factors of these soon-to-emerge home service robots. "It is one of the most exciting design frontiers since the very founding of our profession", Harden tells Yanko Design. The west has been rather slow in adopting robots in domestic settings (something I often attribute to films like Terminator, iRobot, or Transformers, which haven't really made robots look too friendly), while countries in the east like Japan and China (who haven't been inherently exposed to'evil robots') have traditionally been much more accepting robots in their domestic lives.

Robotics is the field of engineering that is focused on developing machines – usually by combining knowledge from both the field of mechanics and electronics – that are able to either fully or partly take over tasks that would normally be carried out by humans. As technology advanced, components such as processors, electric motors and different kind of sensors have become increasingly more compact and precise, stimulating the use of robotics in other fields than only that of manufacturing. Robotics, fueled by artificial Intelligence (AI) is rapidly changing the way we work and interact with technology. Technological advances have drastically changed the capabilities of modern robotics, allowing them to be operational and effective in a wide range of industries. New technologies – such as artificial intelligence, machine learning, and advanced computer vision – form the basis of the further development of robotics, allowing them to be used in processes which were, up until today, deemed to only be executable by humans.

GARY MARCUS: The dominant vision in the field right now is, collect a lot of data, run a lot of statistics, and intelligence will emerge. And I think that's wrong. I think that having a lot of data is important, and collecting a lot of statistics is important. But I think what we also need is deep understanding, not just so-called "deep learning." So deep learning finds what's typically correlated, but we all know that correlation is not the same thing as causation.

GARY MARCUS: The dominant vision in the field right now is, collect a lot of data, run a lot of statistics, and intelligence will emerge. And I think that's wrong. I think that having a lot of data is important, and collecting a lot of statistics is important. But I think what we also need is deep understanding, not just so-called "deep learning." So deep learning finds what's typically correlated, but we all know that correlation is not the same thing as causation.