😅😂 9th edition of taco bell 50k ultra marathon is happening this Saturday October 3rd in Denver Colorado. This is one of the weirdest marathons you will see. Runners have to stop at all 10 taco bells. They must eat a menu item from 9 of 10 taco bells. By 4th stop everyone must eat a chulapa supreme or crunch wrap supreme. By 8th stop everyone must eat a burrito supreme or nacho bell grande. And they have to finish it under 11 hours. Race starts at 7:30 am #food #Marathon #colorado

Spent 5 days at 7,000 ft in Colorado and Wyoming: > sleep down 15% > nervous system down 17% > resting heart rate up 16% Very happy to be back at sea level today.

OpenAI and Anthropic oversold AI security breaches to pressure feds into protecting turf: insiders trib.al/Jo6xMk5

People clowning on him don’t understand what he’s saying. All the wealth of humanity to date supports perhaps 250k living math phds. Roughly the population of St. Louis, Missouri. The training pipeline for that group has been irreparably shattered in the last month. A phd is supposed to make an original contribution to their field to graduate. That’s just…. not possible anymore. 938 years after the founding of the first university in Bologna… Do universities now reward… teaching ? comprehension of something discovered by a machine? application ? do mathematicians become quotidian (gasp of disgust) engineers? Tao is upset because he knows none of those outside the field care about its future. He is a horrified gardener watching humanity gorge on its seed corn. It is irreparable of course. The old way is dead dead. We live in the short interregnum before the new king is born: a Lean crawler that spawns a billion copies exploring every corner of math latenspace. So much math to understand that even if 8 billion humans had the ability of the 250k mathematicians alive today, it would still take a million years to comprehend. It is ironic and sad.. because Tao himself is a pioneer of collaborative math: math that is understood by a combination of minds rather than an pindividual. The tools that Tao began exploring a few years ago, solving problems through blog posts and using Lean to guarantee each mind’s contribution stood on its own when assembled into the greater truth, have been turned against him. Math’s path to utilize multiple minds didn’t restrict access to human minds, and now the machines have blitzkrieged themselves into the heart of the matter. The agents use rudimentary message boards, working 10,000 to a task, tirelessly, using Lean to verify the correctness of each contribution. It was good while it lasted… and now it’s gone.

I’m starting to wonder whether clean code and architecture was just something designed for humans. Does clean code matter to agents? It’s all chinese to them. I’ve yet to hit a wall where they’re like “sorry, can’t make sense of this codebase anymore.”

Last month I wrote about how we can build a positive and safe future for everyone: meta.com/thefutureisforevery… Every lab has the responsibility and incentive to move at the pace required to train its models safely, and the ability to take its own actions to ensure that happens. The reality is: - People won't want to use agents that are misaligned with them and that don't do what they ask, so labs have a strong natural incentive to make their models more aligned. There is a lot of debate about slowing progress on capabilities until alignment catches up. My view is that trust and alignment are quickly becoming the most important capabilities that will differentiate agents and models. Any lab that doesn't focus on alignment will fall behind. - Labs face significant liability if their models cause harm, so they have a strong incentive to prevent this as well. Meta delayed shipping Muse for several months to focus on safety and security. We didn't call for everyone else to do this before we would. We just did it as part of our day-to-day work because it was clearly the right thing for people and for us. I'm proud of the security foundations we've built. - Engaging independent evaluators and advisors is industry best practice. MSL already does this today in several areas because it helps produce better work. Other labs can just do this too. In general, it would be helpful for there to be a larger and more diverse ecosystem of evaluators. - Committing the significant majority of compute towards serving people rather than racing towards recursive self-improvement is one of the best ways to ensure we develop this technology safely. Meta has made this commitment and other labs can do this as well. I believe the key to building a positive future for everyone is maintaining the right balance of power. This is within our power to do.

这个充分说明：支撑着很多研究者前行的，其实是高人一等的优越感而并非对世界的探索欲和解决问题的好奇心，甚至有太多学术界的人的人生意义建立在这种优越感上。而人工智能把这层优越感给戳破了。

We Must Pace the Frontier: I’ve written a new essay on why the AI industry should slow down, with a three-part plan for doing so. Anthropic is unilaterally committing to the first of these steps. We’ll provide third-party evaluators with permanent, employee-level access to our systems, so that they can verify adherence to our safety measures, report on incidents, and assess models’ alignment during training. You can read the full post here: darioamodei.com/post/we-must…