The fact that open weights models are being discussed credibly at this level of capability should be a huge update for many. The implications of open models getting to frontier performance ensures that you can always have sovereign AI, have the ability to post train for your specific workflows, cost optimize for various workloads, and actually afford to do much more with AI (which opens up meaningfully different applications). Huge win for the applied AI layer.
在这样的能力水平上，open weights models 被认真地纳入讨论，这一事实本身对很多人来说都应当是一次巨大的认知更新。open models 达到 frontier performance（前沿性能）所带来的影响在于：你将始终可以拥有 sovereign AI（主权 AI），能够针对自己的特定工作流进行 post train（后训练），为不同工作负载做成本优化，并且真正负担得起用 AI 做更多事情（这会打开一些明显不同的新应用）。这对 applied AI layer（应用层 AI）来说是一次巨大胜利。
This is a good update for getting access to Fable. It also gives us a view into what the future is likely going to look like with AI regulation. The government will have frameworks that are used to determine future model releases past a certain threshold of capability or compute levels. Given all the constituents involved, and the economic and societal significance of AI, this was practically an inevitability. It may seem small but the implications are massive. It will mean that each model update will go through an extensive review, testing, and feedback process. And in that processes lots of groups will weigh in on the risk of the model, and there will be lots of subjectivity on what the actual risks are or practicalities of exploiting those risks. A positive potential future here would be we still get massive model progress but they just happen in bigger jumps at once, where the labs pack in major improvements since the cost and slow down of each review stacks up. On the other side, the risk is that past a certain threshold we may not get to see the rapid back and forth of model progress that we’ve gotten used to which can have negative compounding effects. Hoping for the former outcome.
这对获得 Fable 的访问权限来说是一个不错的进展。这也让我们得以一窥未来 AI 监管很可能会是什么样子。政府会建立一些 framework（框架），用来判定未来模型发布是否越过了某个 capability（能力）或 compute（算力）水平的阈值。考虑到其中牵涉的所有利益相关方，以及 AI 在经济和社会层面的重要性，这几乎是不可避免的。这件事看起来可能不大，但其影响极其深远。这意味着此后的每一次模型更新，都将经历广泛的审查、测试和反馈流程。而且在这个过程中，很多群体都会对模型的风险发表意见，对于实际风险究竟是什么、以及利用这些风险在实践中是否可行，也会存在大量主观判断。这里一种积极的可能未来是：我们仍然能看到模型取得巨大的进步，只不过这些进步会以更大的跳跃一次性出现，因为每次审查带来的成本和放缓会不断累积，于是实验室会把重大改进打包进去。另一方面，风险在于，一旦越过某个阈值，我们可能就看不到已经习以为常的那种模型快速往复迭代了，而这可能带来负面的复合效应。希望结果会是前一种。