LeCun Startup Challenges AGI Scaling Assumptions Boldly

If you ask Yann LeCun, Silicon Valley has a groupthink problem. Since leaving Meta in November, the researcher and AI luminary has taken aim at the orthodox view that large language models (LLMs) will get us to artificial general intelligence (AGI), the threshold where computers match or exceed human smarts. Everyone, he declared in a recent interview, has been "LLM-pilled." On January 21, San Francisco-based startup Logical Intelligence appointed LeCun to its board.

Building on a theory conceived by LeCun two decades prior, the startup claims to have developed a different form of AI, better equipped to learn, reason, and self-correct. Logical Intelligence has developed what's known as an energy-based reasoning model (EBM). Whereas LLMs effectively predict the most likely next word in a sequence, EBMs absorb a set of parameters--say, the rules to sudoku--and complete a task within those confines.

This method is supposed to eliminate mistakes and require far less compute, because there's less trial and error. The startup's debut model, Kona 1.0, can solve sudoku puzzles many times faster than the world's leading LLMs, despite the fact that it runs on just a single Nvidia H100 GPU, according to founder and CEO Eve Bodnia, in an interview with WIRED. (In this test, the LLMs are blocked from using coding capabilities that would allow them to "brute force" the puzzle.) Logical Intelligence claims to be the first company to have built a working EBM, until now just a flight of academic fancy.