The Rise of the AI Engineer

Either IP laws will catch up, or the countries with better IP laws will become more competitively successful.

Currently there's a lot intuition involved so it can come across as made up, but there are novel concepts which meaningfully affect the end quality of what you make, and it takes time to learn and/or discover them.

As time goes I expect our understanding of what underlies "good prompts" will start to bridge the gap from intuition to science much like how UX bridged into neuroscience and psychology. If you understand things like attention and logits that's already kind of happening: you can use that knowledge to identify gaps in the abilities of LLMs and start to bridge those gaps.

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People are convinced that future LLMs will obsolete prompt engineering. To me that'd be like people from the 90s thinking computers are going to obsolete UX because more powerful computers will be better at making user interfaces, and in turn anyone will be able to do it.

In some ways they'd be right: Today you don't need a UX expert at PARC to integrate a WYSIWYG interface into your product. Computers got so powerful that in milliseconds we can download libraries that implement the interface and render it across any form factor you can imagine. So now a WYSIWYG on your contact form is nothing.

But as computers got more powerful they could do new things, so UX advanced onto improving how we interface with those new things. Things like the Vision Pro will unlock new areas of UX based on novel capabilities they posses.

I think people are making a similar mistake with LLMs: they're focused on this idea that we'll just do the current things but better with more powerful models. But the more powerful models will be something we can "prompt engineer" into usecases we haven't even considered yet. (I also built notionsmith.ai and I'd argue it fits into that bucket a bit)

Of course, there was a big boom and bust cycle back then too, but just like then, this cycle is nowhere near its peak.

I __REALLY__ really wish this were true. But I'll be honest, I know quite a number of researchers at high level institutions (FAANG and top 10 unis) that don't understand things like probability distributions or the difference between likelihood and probability. There's a lot of "interpretability" left on the table simply through not understanding some basic mathematics, let along advanced (high dimensional statistics, differential geometry, set theory, etc). The AI engineering often "needs" less of an understanding.

But I don't think this is a good thing. I specifically have been vocal about how this is going to cause real world harm. Forget the AGI, just look at how people are using models today without any understanding. How people think you can synthesize new data without considering diversity of that data[0], can create "self healing code" that will generate high quality and good code[1,2], how people think LLMs understands causality[3,4], or just how fucking hard evaluation really is[5] (I really cannot stress this last one enough). There is a serious crisis in ML right now, and it is also the thing that made it explode in funding: hype. I don't think this is a bubble in the sense that AI will go away, but I think if we aren't careful with how we deal with this then it isn't unlikely to see heavy governmental restrictions placed on these things. Plus, a lot of us are pretty confident that just learning through data is not enough to get to AGI. It just isn't a high enough level of abstraction, besides being a pain (see the semantic deduplication comments about generation). But academia is even railroaded into SOTA chasing because that's what conferences like. NLP as an entire field right now is almost entirely composed of people just tuning big models instead of developing novel architectures (if you don't win, you struggle to get published despite differing factors). We let big labs spend massive amounts of compute to compare to little labs who can get similar performance with a hundredth, but don't publish those works. It is the curse of benchmarkism and it is maddening. Honestly, a lot of times I feel like a crazy person for bringing this up. Because when I say "ML needs a solid math basis and deep understanding of the science behind it" everyone agrees, but when the rubber hits the road and I suggest mathematical solutions to resolve these, I'm laughed at or told it is unnecessary.

[0] https://news.ycombinator.com/item?id=36509816

[1] https://news.ycombinator.com/item?id=36297867

[2] https://news.ycombinator.com/item?id=35806152

[3] https://news.ycombinator.com/item?id=36036859

[4] https://www.cs.helsinki.fi/u/ahyvarin/papers/NN99.pdf

[5] https://news.ycombinator.com/item?id=36116939