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Comment by keeganryan | Hacker News Reader

This builds on the Kickmix quantum language from Google. Essentially, Kickmix is an assembly language for classically simulatable quantum circuits, and finding more efficient circuits is basically a game of code golf with the assembly instructions. That means LLMs benefit from the closed loop of verification, and can rapidly test and discard ideas. It's not too surprising that previous academics didn't try to optimize to this level, and a good task to automate. See ecdsa.fail for another example of circuit optimization using the Kickmix language.

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It's a nice illustration of the Envelope Principle: AI is good at filling in the blanks, where the knowledge is 'within the envelope' of things already discovered but the area itself is still not fully exhausted. Real progress happens by expanding the area of the envelope, not by coloring in the blanks.

People (most people?) have spent their entire decades long careers coloring in the blanks within the envelope of human knowledge. I think it’s kind of shitty to suggest that they weren’t making “real” progress with their lives, just because AI can now do the same thing.

Of course it is progress. But there are orders here: filling in the blanks is super useful and for sure you can - or should I say could - make a living with that. But there is another level above that which is where the total area is increased. That's not meant as a sleight against people that were ensuring all the ground was covered, it's just establishing the fact that there are multiple levels to knowledge.

If you want to make it seem as if I'm pissing on people that were doing useful science that's your privilege, but there is a reason we use terms like 'groundbreaking' about discoveries that are different.

As for lost careers: I've been schlepping bits my whole life, and probably an RTX 6000 could re-run that in under a year, and no doubt what I did was of less significance than that done by the lowliest of scientists. Still, I don't derive my sense of self value from what future technology can do any more than I think that a day laborer should compare themselves with a tractor or a combine.

I'm not sure of your background, but FWIW everyone in my PhD program in my elite CS program had varying levels of anguish and angst when realizing that their efforts were mostly suited for "coloring in the blanks." The PhD Grind [1] felt like a poignant capture of those feelings.

[1]: https://dokumen.pub/the-phd-grind-a-phd-student-memoir-3.htm...

The link got slashdotted.

this is what research was for the majority of people, even those talented enough to do it. there's a reason we remember the names of people who invent whole ass new fields, like einstein, grothendieck, turing, riemann, etc.

the hope might be that AI will fill in the remaining envelopes and let us focus on building new theories. but that's like trying to build a castle in the sky without being grounded and understanding how castles on earth are built. Imo, it won't work, but maybe I'm wrong. It's not like people need to know how to write assembly these days.

> Real progress happens by expanding the area of the envelope, not by coloring in the blanks.

Respectfully disagree. What one person may classify as expanding the envelope, another one with broader knowledge may classify as coloring in the blanks. Much knowledge in one field comes from cross-pollination from other fields.

Let me give you an example I just learned about. The database community spent decades trying (to some extent) to solve the implementation of materialized views to arbitrary complex SQL queries. Then, a few years ago, someone with experience in digital signal processing brought their expertise to the DB field and completed the puzzle. [1]

Someone with DB expertise but no signal processing expertise might say that is expanding the envelope, but another person with experience in both may say it is coloring in the blanks.

AI work now may seem like coloring in the blanks, but given it's vast expertise in multiple fields, my bet is that for most people it will look like pushing the envelope very soon.

[1] https://arxiv.org/abs/2203.16684

That is a very interesting example but it serves very much to illustrate my point: this is latent knowledge, we effectively already have it but we just haven't put 2 and 2 together. Unfortunately AI excels at such problems because it oversees a much larger slice of our knowledge than most individual humans. I've always said that cross domain application of knowledge is the easiest way to make progress and I haven't really seen anything conflicting with that to date.

So while you start off with 'disagreement' in fact it appears we agree far more than we disagree. Yes, it is important that this work is done. But you clearly illustrate that the envelope did not in fact expand. But all the time scientists around the world do in fact expand the envelope of our knowledge and one of my fears is that by undercutting a lot of such knowledge work by putting this digital combine to work on it that we will lose the infrastructure to do the rest. We'll see a lot of progress in a very short time and then we'll find out that because we took away that class of problems that it has become almost impossible to keep the infrastructure, incentive structure and educational paths in place to be able to do the next round.

You see a similar thing happening in software development. All of a sudden it has become a lot harder to start as a career. And lots of people are not even going to try this direction if there is no first rung on the ladder.

At this point, it’s better to ponder a question: Are there fundamental reasons why a search trajectory during reinforcement learning can’t leave the envelope? And: what is the envelope, anyway? A metaphorical surface in a metaphorical space of ideas with no defined procedures to make it into an actual metric space?

Reinforcement learning can definitely (in principle) leave this envelope, but it depends crucially on whether the feedback generates new information from reality, rather than just repackaging what humans have already discovered in the form of RL environments and densified rewards. So the question is not about RL per se, but what is the information source, what is the bandwidth of information it supplies, and then only finally the effectiveness of RL in distilling information that into models.

Great point. Perhaps when we have AI robots interacting in the real world doing experiments we'll have that.

That is right now. [1]

[1] https://www.reuters.com/world/anthropic-quietly-sets-up-biol...

> Unfortunately AI excels at such problems because it oversees a much larger slice of our knowledge than most individual humans. I've always said that cross domain application of knowledge is the easiest way to make progress and I haven't really seen anything conflicting with that to date.

What is unfortunate about this? I suspect we concur in not buying into the AGI/ASI/RSI/<jargon of the day> hype, but this isn't this kind of breakthrough extremely valuable regardless?

Maybe it's not expanding the convex hull of what is possible, but the fact is that human progress is extremely jagged even within that convex hull -- for myriad reasons limitations related to funding, cross-domain expertise, number of scientists, etc.

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If you're worried about what it takes for a novice setting out in the field, it might be useful to see how specific "mature" subfields have evolved in their training structures. Eg. I have a background in theoretical physics and it is common for grad students to do courses for a couple of more years and take a few more years to reach the same productivity compared to grad students in (say) CS/AI.

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