We could not possibly know that such a model is aligned. If that is all it takes to get a generation ahead of Astra, and we are willing to move this fast, these pauses in training are not going to end up meaning very much, time is even shorter than we knew
But given the Chinese Communist Party’s power-seeking nature, it seems much more likely that China would agree to cooperate on AI safety if U.S. capabilities were comfortably ahead.
everybody on your team who is making architectural decisions, those people must know performance and they must make decisions that will allow the other people downstream of them to use an architecture which can be optimized later. If you don't do that, you're just rolling the dice.
And so basically everything that we can verify reasonably well with some feedback loop, the AIS are doing pretty well on. And that's sufficient to make AR and D go quite fast and to continue. But there's some parts of of developing uh aligned and safe AIs that are more subtle, hard to check, depend on, you know, detailed in the weeds things.
Structure and Interpretation of Computer Programs (SICP) by Gerald Jay Sussman and Hal Abelson. SICP is my all-time favourite programming book. It's also where I learned about the power of wishful thinking when coding. SICP uses wishful thinking as a design tool: write code as if the abstraction already existed. Pretend. Then, once the ideal abstraction has taken shape, you go ahead and implement those functions. This meta-level is what made SICP so valuable. Ultimately, it's a book that teaches how to think about code and problem solving. I'm tempted to even say that SICP is more a work of art than a pure coding book, but I won't go there. A beautiful book.
However, looking ahead, I still do not believe that future AI (say, in 15 years) will be based on the LLM stack. I believe it will necessarily have to move closer to its optimal, final form -- symbolic learning. Obviously this is a risky and contrarian belief -- the safe bet would be LRMs. But let's see.
Know where the money is coming from. Know where you plug into that. And like if you're just asking those questions, you're actually already like steps ahead of all of the other like fledgling perspective mathematicians.
And then I was like, okay, can you come up with an algorithm that is better than the algorithms that I came up with or that anybody else came up with and go ahead and like look at all the published work and synthesize that and then try to come up with something novel and it it's not able to do it. And I can give it a lot of time and it's it's still not able to do it.
It shouldn't It shouldn't be shelved next to How to Win Friends and Influence People because it's a manual not of how to gain power, but of how to keep power.
That's why I don't think accelerationist steamrolling will work. The only way this conflict resolves peacefully is some kind of grand bargain: a flagship policy plan that directly addresses the public's top fears about AI and meaningfully redistributes the gains.
For years, it was faster to mock up software than to ship it. Designers stayed "ahead" of engineering with prototypes. Now AI coding agents make development so much faster that the loop has flipped.
I actually think profitability happens when you underestimated the amount of demand you were going to get and loss happens when you overestimated the amount of demand you were going to get because you're buying the data centers ahead of time.
Write notes in your own words, connect them to existing notes, and let ideas emerge from the connections. I wrote a whole blog post about this, so go ahead and read it. Highly recommended.
Decades of grading data; standardized test scores; cross-sectional, longitudinal, observational, and experimental studies; along with many other types of ancillary and convergent evidence, ultimately tell the same story: education can raise the absolute performance of most students modestly, but it almost never meaningfully reshuffles the relative distribution of ability and achievement
I do not believe that analytical skills are the missing ingredient in thinking about the future. Rather, I believe that imagination about the future and a theory of change that helps to describe it clearly, are what is needed to look ahead in a more compelling way.
So Tesla hasn’t found a different, better way to bring driverless technology to market. Waymo is just so far ahead that it’s dealing with challenges Tesla hasn’t started thinking about.
Installed generators cost ~$800/kW, and the data center capital cost, including servers, is ~$40,000/kW, so adding 1% more makeup compute capacity is money ahead of purchasing generators.
I expect that the delta between 5 and 4 will be the same as between 4 and 3 and I think it is our job to live a few years in the future and remember that the tools we have now are going to kind of suck looking backwards at them and that’s how we make sure the future is better.
The Sprint 225 uses one TriplePower LED bulb. Its light pattern is wonderfully smooth, with no distracting rings; it seems almost perfectly optimized for night-running and night-hiking — it focuses most of the light ahead, but still manages to illuminate the periphery.
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