David Turturean, a recent MIT graduate and member of the technical staff at Physical Superintelligence (PSI), utilized early access to Anthropic’s Fable 5.2 model to resolve an eighteen-year-old puzzle regarding quantum computing.
“I’m totally accepting the idea that this might be the last paper I ever put out,” Turturean told TechCrunch, noting that he deferred his master’s at MIT to join PSI. “In a few months, like, why would you even put out a paper?”
The problem at hand, the Aaronson-Ambainis conjecture, addresses how quantum computers might outperform classical ones. Scott Aaronson, the problem's proposer, stated he doesn't fully grasp the proof but trusts its correctness because it was autoformalized in Lean and explained by top researchers PSI collaborated with.
This solution effectively closes a specific pathway for quantum computers to solve difficult problems quickly. “It’s a negative result that allows people to know, okay, we’re no longer looking there,” Turturean explained.
This achievement highlights the significant disruption AI is causing in the scientific community.
Aaronson, feeling a mix of satisfaction and fatigue, noted this was a central open problem. He was surprised the result wasn't included in OpenAI's “Mathocalypse” dump, questioning if it was truly harder than the problems they solved.
PSI’s CEO, Matt Pines, emphasized that an effective research system requires robust tools, memory systems, and simulation infrastructure to tackle problems with breadth and depth.
Currently, the company generates revenue by optimizing data centers to manage heat, power, and water. However, the long-term vision involves translating theoretical physics breakthroughs into tangible technologies. Pines recalled telling investors that theoretical physics was becoming a machine domain, a prediction he feels is becoming reality.
While AI has made strides in math, applying it to physics remains challenging due to the real-world variables nature introduces. Pines noted that physics is different from math because “nature gets a vote.”
The startup focuses on efficiently validating computational results with experiments. If a fact is true, it can be exploited to create new devices. PSI builds loops where AI suggests ideas that allow for world-changing applications.
As “neolabs” emerge, academic standards are still being defined. Jim Halverson, a Northeastern professor advising PSI, emphasized the importance of transparency and consulting experts. He noted that PSI waited for the model's release to share findings, allowing for thorough review.
Turturean reflected on the experience of reading an AI-generated solution, realizing the immense capacity of the technology. “And I think also it made me realize, oh my God, the weight of what AI can do,” he said.
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