[starlust2]

> through brute force

The same is true of humanity in aggregate. We attribute discoveries to an individual or group of researchers but to claim humans are efficient at novel research is a form of survivorship bias. We ignore the numerous researchers who failed to achieve the same discoveries.

[suddenlybananas]

The fact some people don't succeed doesn't show that humans operate by brute force. To claim humans reason and invent by brute force is patently absurd.

[drdaeman]

Does “brute force” allow for heuristics and direction?

If it doesn’t (“brute” as opposite of “smart”, just dumb iteration to exhaustion) then you’re right, of course.

But if it does, then I’m not sure it’s patently absurd - novel ideas could be merely a matter of chance of having all the precursors together at the right time, a stochastic process. And it scales well, bearing at least some resemblance to brute force approaches - although the term is not entirely great (something around “stochastic”, “trial-and-error”, and “heuristic” is probably a better term).

[preciousoo]

It’s an absurd statement because you are human and are aware of how research works on an individual level.

Take yourself outside of that, and imagine you invented earth, added an ecosystem, and some humans. Wheels were invented ~6k years ago, and “humans” have existed for ~40-300k years. We can do the same for other technologies. As a group, we are incredibly inefficient, and an outside observer would see our efforts at building societies and failing to be “brute force”

[therealpygon]

You don’t consider thousands of scientists developing competing, and often incorrect, solutions for a single domain as a “brute force” attempt by humanity, but do when the same occurs with disparate solutions from parallel LLM attempts? That’s certainly an…opinion.

[tmaly]

What about Dyson and Alexander Graham Bell ?

[MITSardine]

I would argue that the ratio of work to breakthroughs is not a form of inefficiency, but something inevitable about the nature of breakthroughs.

In my opinion, a breakthrough is not the production of new knowledge, it is rather its adoption by the public (beginning with industry).

As such, the rate at which breakthroughs can emerge is bounded by factors external to the producers of breakthroughs. And these outside factors are possibly already limiting.

Another point I would make is that what constitutes a breakthrough is not conditioned by how significant it is, only that it is adopted as a change of processes or mental model. As such, more powerful tools can lead to larger leaps between breakthroughs, but not so much higher rate of breakthrough.

As tools become powerful enough to produce yesterday's year's worth of breakthroughs in a month, then the general public and industry will still wait a year before adopting new technology, only it will see larger progress from the previous iteration. This is in fact the case with LLMs. Even on an avant-garde forum as HN, a very common opinion is "I'm waiting out stagnation before I adopt".

As an over simplification, consider only breakthroughs those that come to have widespread commercial application. If we had an oracle for breakthroughs that could produce arbitrarily many today's-breakthroughs as fast as desired, we'd still be limited by our ability to put them in practice. Work must be allocated, carried out over time, and each new breakthrough requires changing processes and the people involved learning new things, which takes time and energy.

I think this human resistance to change is fundamentally what determines the achievable rate of breakthroughs. As the name implies, a breakthrough is a rupture. It is highly inefficient to be upending one's methods every month. It can even be outright impossible to keep up with all the theoretical advancements, before they have crystallized and been digested into accessible vulgarization, if that is not one's profession (i.e. all time devoted to it).

In my applied sciences field, industry is lagging behind some 20 years. And we ourselves are perhaps a century late to some theoretical advances (I can think of one off the top of my head). At the lowest level, there is resistance to change in that ideas take much longer to be carried to a working prototype, than it takes to have them. Hence, someone who constantly hops to new ideas is guaranteed not to make any progress. By necessity, some stubbornness is selected for. Once things are fleshed out (a multi year endeavour), you still have to convince the broader community (same sub field but not direct collaborators) that your idea has merits surpassing theirs, which is a problem best solved one retirement, and one past mentee hire, at a time. And ultimately convince industrial actors that they should dump millions industrializing these novel methods, when none of their competitors have been doing it (hence it is urgent to wait), the viability (robustness, scalability) of the idea remains to be seen, and the benefits weighed against the risk their practitioner user base won't be able to understand the full scope of the progress and see the need to invest time in learning new things and devising new processes (all of which takes time, money, and makes you dependent on this pioneering supplier). And, lastly, there are three other approaches claiming to be better alternatives.

I don't see a way around this pipeline, and more powerful tools can indeed accelerate some of the stages, but there will remain incompressible delays. Ideas need time to be diffused and understood, all the more if they were advancing at a rapid pace enabled by powerful AIs.