The joke about someone using chatGPT to write a lengthy email that the recipient will summarize with ChatGPT is the perfect example of how pretend much work is.
Something went wrong once. Maybe not even in your organization, but it went wrong somewhere. Someone added a process to make sure that the problem didn't happen again, because that's what well-run organizations are supposed to do.
But too often, people don't think about the cost of the procedure. People are going to have to follow this procedure every time the situation happens for the next N years. How much does that cost in peoples' time? In money? How much did the mistake cost? How often did it happen? So was the procedure a net gain or a net loss? People don't ask that, but instead the procedure gets written and becomes "industry best practice".
(And for some industries, it is! Aviation, medical, defense... some of those have really tight regulation, and they require strict procedures. But not every organization is in those worlds...)
So now you have poor corporate drones that have to run through that maze of procedures, over and over. Well, if GPT can run the maze for you, that's really tempting. It can cut your boredom and tedium, cut out a ton of meaningless work, and make you far faster.
But on the other hand, if you are the person who wrote the procedure, you think that it matters that it be done correctly. The form has to be filled out accurately, not with random gibberish, not even with correct-sounding-but-not-actually-accurate data. So you cannot allow GPT to do the procedures.
The procedure-writers and procedure-doers live in different worlds and have different goals, and GPT doesn't fix that at all.
Even if you're married, you might not be married forever, could be in an open relationship, or a partner may cheat. None of which is the pharmacist's business.
It's not that they can't build a mental model, it's that they don't attempt to build one. LLMs jump straight from text to code with little to no time spent trying to architect the system.
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.
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.
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).
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”
I consider humans an "intelligent" species in the sense that a critical mass of us can organize to sustainably learn.
As individuals, without mentors, we would each die off very quickly. Even if we were fed and whatever until we were physically able to take care of ourselves, we wouldn't be able to keep ourselves out of trouble if we had to learn everything ourselves.
Contrast this with the octopus which develops from an egg without any mentorship, and within a year or so has a fantastically knowledgable and creative mind over its respective environment. And they thrive in every oceanic environment in the wet salty world, including coast lines, under permanent Arctic ice, to the deep sea.
To whatever degree they are "intelligent", it's an amazingly accelerated, fully independent, self-taught intelligence. Our species just can't compare on that dimension.
Fortunately, octopus only live a couple years and in an environment where technology is difficult (very hard to isolate and control conditions of all kinds in the ocean). Otherwise, the land octopus would have eaten all of us long ago.
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.
My least favorite type of argument on this site is when someone takes a word with a specific meaning and warps it well beyond the reasonable interpretation just so they can claim they’re making a good analogy. It seems to happen every day here with AI.
Brute force and typical scientific research are such dramatically different things, I have to wonder if bots are getting into HN I almost can’t believe someone would try and argue that’s weird to see the difference.
“Oh you’re using your limbs to move through water? How are you not a dolphin?” lol
Sorites paradox, but for bits of evidence in the Bayesian prior.
Just as a heap of sand stops being a heap when it's small enough, the difference between "science" (not just modern but everything from Newton and Galileo onwards) and "brute force" is the available evidence before whatever hypothesis we're testing.
Scientific research these days requires a lot of prior information, things humanity collectively has learned, as a foundation. We have a lot of weight on our Bayesian priors for whatever hypothesis we're testing.
Sorites even applies to your own attempt to mock it, as the difference between humans and dolphins is "just" a series of genetic changes. Absolutely they're different and it's obvious why you chose the example, but even then it's a series of distinct small changes that are each so small it's easy to blur them together than treat them as a continuum, like we do with water even though that's also discrete molecules.
Humanity massively predates the modern scientific method, it took millennia of mistakes to go from the Greeks being wrong about four elements to finding a bit less than the 91 natural elements, and from there to finding the nucleus (1911) and that it was made of protons and neutrons; and only then did we get to logical positivism (late 1920s), and it was only around WW2 (just before, Karl Popper 1934) that we switched to falsifiability.
Each grain on the heap. We know the fields of work, we know the space of possibilities within the paradigm, the shape of the research can be to constrain that space without finding the answer directly — a divide-and-conquer approach to reducing the space that needs to be then brute forced.
These days we can even automate much of the more obviously brute-force parts, which is why e.g. CERN throws away so much data from the detectors before it even reaches their "real" data processing system. And why SETI automatically processes out any signals that seem to be from in-system before the rest of the work.
Re: the Sorites paradox, I have a definition of a heap that I think is workable.
It's not a specific number; it's "a collection of items becomes a heap when some indeterminate number of items are obscured by other items on top of them, thus making the total number of items uncountable without disturbing the heap".
Therefore it depends on factors beyond just the number of grains of sand; if you have 1000 grains spread out on a surface so they are can all be distinctly counted, that's not a heap. But if you have 1000 grains gathered together, some on top of each other, then it becomes a heap.
Yes, that would be a ridiculous comparison. However, you’re suggesting that calling both a dolphin and a fish “aquatic” is a a false comparison because one is a mammal. Most normal people would call things that someone makes up and are eventually proven false a failed “guess”. Or at least they do when they aren’t busy trying to protect egos. Difference is, one wastes millions of dollars trying to prove every guess right.
But sure, well done, you really got me! Beep boop! I must be a bot because you don’t agree. ”lol”!
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.
A thing people miss is that there are many different right ways to solve a problem. A legacy system might need the compatibility or it might be a greenfield. If you leave a technical requirement out of the prompt you are letting the LLM decide. Maybe that will agree with your nuanced view of things, but maybe not.
We're not yet at a point where LLM coders will learn all your idiosyncrasies automatically, but those feedback loops are well within our technical ability. LLM's are roughly a knowledgeable but naïve junior dev; you must train them!
Hint: add that requirement to your system/app prompt and be done with it.
A big challenge is that programmers all have unique ever changing personal style and vision that they've never had to communicate before. As well they generally "bikeshed" and add undefined unrequested requirements, because you know someday we might need to support 10000x more users than we have. This is all well and good when the programmer implements something themselves but falls apart when it must be communicated to an LLM. Most projects/systems/orgs don't have the necessary level of detail in their documentation, documentation is fragmented across git/jira/confluence/etc/etc/etc., and it's a hodge podge of technologies without a semblance of consistency.
I think we'll find that over the next few years the first really big win will be AI tearing down the mountain of tech & documentation debt. Bringing efficiency to corporate knowledge is likely a key element to AI working within them.
Efficiency to corporate knowledge? Absolutely not, no way. My coworkers are beginning to use AI to write PR descriptions and git commits.
I notice, because the amount of text has been increased tenfold while the amount of information has stayed exactly the same.
This is a torrent of shit coming down on us, that we are all going to have to deal with it. The vibe coders will be gleefully putting up PRs with 12 paragraphs of "descriptive" text. Thanks no thanks!
