Key post that gives a bit of insight into what the future of AI could look like. “The most interesting thing happening in AI isn't that one model is getting smarter. It's that intelligence is becoming increasingly customizable. The companies that win won't necessarily be the ones with the biggest models. They'll be the ones that turn intelligence into something uniquely their own.” The ability to combine your unique data, workflows, and a layer that can route intelligence to whatever model best performs the task is clearly the future.
这篇关键帖子让人稍微看清了 AI 的未来可能会是什么样子。“AI 里正在发生的最有趣的事情，并不是某一个 model 变得更聪明了，而是 intelligence（智能）正变得越来越可定制。最终胜出的公司，未必是那些拥有最大 model 的公司，而会是那些把 intelligence 变成自身独特能力的公司。” 能够把你独有的数据、workflows（工作流），以及一层可以把 intelligence 路由到最适合完成任务的 model 的系统结合起来，这显然就是未来。
It’s very easy to say “we need an FDA for AI” or some equivalent government agency. Well this is what that would look like. The capabilities of AI models have near infinite permutations. It’s going to be very hard have a purely objective set of metrics that can be universally applied before every model release, without extended back and forth, research, and debate between model labs, academics, and the government. Now, imagine this same process with every country that you’re doing business with, globally, for every single model release. Add in a backlog of dozens of AI model releases, and you can quickly see how in the limit this will dramatically slow down all AI progress. This is why we need to primarily focus on regulating the applied uses of AI, where the risk actually shows up.
说一句“我们需要一个 AI 版的 FDA”，或者某个类似的政府机构，是非常容易的。好，这就是那种东西实际会是什么样子。AI model 的能力几乎有无限多种排列组合。想要在每次 model 发布之前，都制定出一套纯粹客观、还能被普遍适用的指标，而不经历 model labs、学术界和政府之间反复拉扯、研究和辩论，这将会非常困难。现在，再想象一下：对于你在全球开展业务所涉及的每一个国家，在每一次 model 发布时，都要走一遍同样的流程。再加上积压的几十个 AI model 发布，你很快就能看出，如果走到极限，这会显著拖慢整个 AI 的进展。这也是为什么我们需要主要去监管 AI 的 applied uses（实际应用场景），因为风险真正出现的地方是在那里。
Open source going to win big
Open source 会大获全胜。