Leopold Aschenbrenner

Leopold Aschenbrenner (born 2001 or 2002) is a German artificial intelligence (AI) researcher and investor. He was part of OpenAI's "Superalignment" team before he was fired in April 2024 over an alleged information leak, which Aschenbrenner disputes. He has published a popular essay called "Situational Awareness" about the emergence of artificial general intelligence and related security risks.He is the founder and CIO of Situational Awareness LP, a hedge fund investing in companies involved in the development of AI technology. Quotes Situational Awareness (2024) Leopold Aschenbrenner (June 2024). Situational Awareness. The AGI race has begun. We are building machines that can think and reason. By 2025/26, these machines will outpace many college graduates. By the end of the decade, they will be smarter than you or I; we will have superintelligence, in the true sense of the word. Along the way, national security forces not seen in half a century will be unleashed, and before long, The Project will be on. If we’re lucky, we’ll be in an all-out race with the CCP; if we’re unlucky, an all-out war. Before long, the world will wake up. But right now, there are perhaps a few hundred people, most of them in San Francisco and the AI labs, that have situational awareness. Through whatever peculiar forces of fate, I have found myself amongst them. Soon, the AIs will be running the world, but we’re in for one last rodeo. May their final stewardship bring honor to mankind. V. Parting Thoughts "Leopold Aschenbrenner Goes Over AI's Future" (2025) Werner John Leopold Aschenbrenner Goes Over AI's Future Forbes 2025-01-24 https://www.forbes.com/sites/johnwerner/2025/01/24/leopold-aschenbrenner-goes-over-ais-future/ It is strikingly plausible that by 2027, models will be able to do the work of an AI researcher/engineer That doesn’t require believing in sci-fi; it just requires believing in straight lines on a graph Imagine if when asked to solve a hard math problem, you had to instantly answer with the very first thing that came to mind,” he writes. “It seems obvious that you would have a hard time, except for the simplest problems. But until recently, that’s how we had LLMs solve math problems. Instead, most of us work through the problem step-by-step on a scratchpad, and are able to solve much more difficult problems that way. “Chain-of-thought” prompting unlocked that for LLMs. Despite excellent raw capabilities, they were much worse at math than they could be because they were hobbled in an obvious way, and it took a small algorithmic tweak to unlock much greater capabilities. GPT-4 has the raw smarts to do a decent chunk of many people’s jobs, but it’s sort of like a smart new hire that just showed up 5 minutes ago: it doesn’t have any relevant context, hasn’t read the company docs or Slack history or had conversations with members of the team, or spent any time understanding the company-internal codebase. A smart new hire isn’t that useful 5 minutes after arriving—but they are quite useful a month in! It seems like it should be possible, for example via very-long-context, to ‘onboard’ models like we would a new human coworker. This alone would be a huge unlock. Look. The models, they just want to learn. You have to understand this. The models, they just want to learn… External link Category: living people