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Comment by ambrozk | Hacker News Reader

I see we've passed "first they ignore you" and moved onto the "next they laugh at you" phase of LLM denial. The next step is "you win."

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I don't think there's many people who doubt that LLMs could play an important role, substituting many human activities. The question is whether that'd be actually a good thing.

It's like proudly proclaiming that detractors of the food industry are mad that things like hot pockets, Doritos, fad breakfast cereal and wonderbread won over fresh food. Well, yes, they kind of won, and people are mad, what's there to be happy about?

If you look at more mainstream forums like Reddit, people are in really hard denial/look how stupid it is mode.

I think at least some people on HN are able to sort of see through its unformed intelligence into how it can be constrained to likely be hyper effective.

/r/programming would be the peer forum in Reddit, but they keep quiet about this subject for the most part, I'd assume that moderators don't want to handle the hot potato that threads on this would be.

/r/technology is where most of the discussion happens, it's full of angry and confused working class people who were already feeling squeezed by the US economic and political landscape, and lash out at Big Tech CEOs and "tech bros", it's not a place to have debate.

I have had some interesting conversations where I've tried to compare the 'word salad' of Pynchon with a relatively straightforward opening scene script generated by GPT. The writing forums really are almost wildly irrational about this -- when all I want to illustrate is that GPT can produce usable, mediocre value even if it's not high art. While at the same time Pynchon may ... in reality, have questionable value in his word salad of nonsense. Yet so called 'writers' will hold him on hallowed ground, while not even engaging with an AI script that generally accomplishes a task well.

I'm a former lit major and this sort of ignorance is common in the field. Failed writers constantly berate everything that works as crap, while bemoaning the loss of true art that no one cares about.

Functionality will trump art, 99/100. It's the way of the world. And AI is really #)($*ing functional.

Or maybe we've just moved onto the "next they laugh at you" part which will continue for a decade or so before any actually advancements in AI happen besides creation of larger matrixes of inputs to outputs, with equally poor top level performance. I don't think anybody is laughing about LLM's or their potential, but rather laughing and mocking the people who are AI doomers saying that everybody's job is kaput because LLMs can spit out empty copywriting and junior level code samples.

People's freakouts are completely reasonable. The pace of development in this field is unreal and shows few signs of slowing down. The primary blocker to adoption in many fields is just software integration, which will come within a few years at most. At that point we'll see what the limits of LLM capabilities are, and which professions are no longer economically viable.

My position here is certainly debatable but IMO the pace of development in AI as a whole is actually pretty lackluster, and instead appears monumental because of the massive influx of VC dollars (and development of computing hardware!) which allows for these more and more monolithic models. Training larger and larger models doesn't really represent "development" to me in the sense I usually think of it. The largest hurdle with AI, and one that LLM has not and likely will never solve, is that it doesn't truly understand anything at all, but is just instead (in simple terms) a large matrix of inputs to outputs. The areas where current AI falls flat on its face are areas where it will never be able to encroach as it works today, no matter how many TPU hours you waste on learning.

memory, internal iteration and scratchpads can and have been added. Foundation models don't include this yet, but it's not unexplored territory. Online learning is more difficult but I'd argue that various fine-tuning and control approaches are slowly working towards that.

So what is a brain other than a very complex non-linear function which takes inputs, iterates on them, integrates them with memory and eventually generates output?

> is that it doesn't truly understand anything at all, but is just instead (in simple terms) a large matrix of inputs to outputs

The first step to defeating the tiger is to realize that it cannot hurt you, for it is only made of simple atoms.

> So what is a brain other than a very complex non-linear function which takes inputs, iterates on them, integrates them with memory and eventually generates output?

A thing that processes and embodies sensory input, makes informed guesses about other people's intentions and values, grows tumors, worries about the dirty dishes.... How many other "complex non-linear functions" are made of meat?

>> is that it doesn't truly understand anything at all, but is just instead (in simple terms) a large matrix of inputs to outputs

> The first step to defeating the tiger is to realize that it cannot hurt you, for it is only made of simple atoms.

This is now my favorite rebuttal when people claim LLMs are simply some form of math.

When a person uses the phrase "No true understanding" I can tell they don't know what they are talking about at all.

It can engage, use, explain, transform and apply highly sophisticated concepts, philosophies, styles, and models of the world in writing. If that isn't understanding...

There's a distinct difference between asking somebody to write/copy/show a complex concept and understanding it. What I don't understand (get it?) is how people are still conflating LLM's behavior with true understanding of concepts when it's obviously not the same thing.

Please provide the definition of "true understanding." I think the only good argument here is that it lacks "lived experiences" and has synthetic understanding. That being said, have you been to a university? Profs seem to be entirely based on a synthetic understanding of the world anyways.

Indeed.

It can also write non-trivial code that works. There's a saying "if you can't code it, you don't know it". Conversely, if you can code it, you do understand it, at some functional level.

"No true understanding" is the new "no true Scotsman." If you're still repeating this line in mid-2023, you're not meaningfully engaging with the technological developments that are occurring in front of your eyes.

Not at all. Being able to give an example of something or spit something out that resembles a concept is not the same thing as understanding it, at least in my opinion. Keyword being true understanding, although I'll concede that it might have arguably basic understanding of such concepts.

You need to define "true understanding" in some concrete, measurable way, or accept that you're just using it to avoid thinking clearly about AI capabilities.

The difficulty with that is that trying to define "true understanding" crosses deeply into the philosophical realm. Moreover, a lack of ability to define a term does not invalidate my position. That being said, I'll instead present you with a question intended to display what I believe is the difference between the two. If I write an example of an integral, it clearly shows that I know what an integral is, but does that mean that I understand integrals? Even if I could concisely answer any question you asked about integrals (i.e. an all-knowing AI), that is not the same as my definition of "truly understanding" integrals. To pose another philosophical question which highlights what I believe it is to "truly understand" something: Does the library of babel understand? I would say that despite it having the answer to any question you ask, it does not "truly understand" anything. I wish that I could put the difference between these two into concise wording, but alas I've been unable to quite put my finger on it despite having spent some good part of the day giving thought to it.

Some of these concepts are explained/explored much better than I could do on the wikipedia[0] page for understanding, particularly the "Assessment" section.

[0]https://en.wikipedia.org/wiki/Understanding

What about the AP-Calc student that has "merely" practiced hundreds of integrals and is therefore equipped, not just to pass the examination, but to use integrals as part of more complex theories down the line?

If they know only enough to apply the concept successfully without knowing its fundamentals, have they "understood" it?

One reason I'm sympathetic to this view is how far behind pre-AI automation is. Whenever I work with small-businesses and governments (and bigco's too but in different ways) I am amazed by how much make work there actually is. Somebody is doing data entry, but considers it valuable because some fraction of that work is identifying accuracy issues early, or translating between systems and domains. Then somebody is applying basically rote business logic to make a decision and route things to somebody else to do work. All of that could be automated. As an industry we've been automating that kind of thing for 50+ years, but still there are mountains of inefficient processes that could be automated, that are basically just shuffling paper. But the overhead cost of hiring someone to analyze your processes and adopt it to some kind of system, the costs of maintaining that system, to costs of getting it wrong when the requirement didn't account for various edge-cases that weren't in the requirements because of the tacit knowledge embedded in employees, often makes the project not worth it or a failure.

If LLM capabilities just help provide better glue (or even just political/hype motivation) to enable easier integration/automation of basic white-collar workflows there could be significant disruption. There is a lot of low-hanging fruit out there ... every spreadsheet is a new potential SaaS business.

Yes, people keep saying LLM tech is laughable because it can't solve for cold-fusion or some nonsense in a single prompt.

In reality, all it has to do is replace a crap ton of white collar "lgtm and pass it on" jobs that exist with minimal transformation or logic applied. This is a crap ton of people

A decade can still be quite short considering the types of problems that may need to be solved, so perhaps that is the timescale that some of the people you're referring to are thinking in?

You are missing the final key phase: everything gets crappier and we just accept it. There is no denying that LLMs are the future. But, much like the SEO optimized Internet and offshored goods, everything just sucks a little more.

Or hyper processed foods, and city planning that encourages sedentarism. Yes, congratulations to Kraft and Ford on their victory, now 38% of the adult population of the US has diabetes.

People said this about Bitcoin. They said it about Google Glass. They said it about VR headsets. They never seemed to notice the same logic applied to Microsoft Bob.

Never heard of Microsoft Bob but after glancing at its Wikipedia article [0], it sounds super useful for beginners! It's Clippy's dad.

[0] https://en.wikipedia.org/wiki/Microsoft_Bob

The immediate uptake and application of this technology to everyday lives is what different here. Bitcoin was NEVER hyped outside of Silicon Valley to much of a degree that people here think it was.

> Bitcoin was NEVER hyped outside of Silicon Valley to much of a degree that people here think it was.

I directly experienced Bitcoin maximalists selling Bitcoin-backed retirements to seniors far from the Valley.

The hype was so bad, the subsequent price-drop was a pop-culture event that got mocked by mainstream comedians to mainstream audiences.

> Bitcoin was NEVER hyped outside of Silicon Valley

You opened up IE/Edge and the "MSN/News" showed "Get rich with Crypto!!11". Today you open up Edge and the "News" show "Get rich with AI!!11" (With an AI generated Musk face).

Yes, there is a hype cycle that afflicts every new piece of tech. The existence of hype isn't evidence either way. What makes smart people think that this time is different is a straightforward comparison of the capabilities of LLMs and the skills needed to perform various human jobs, including (ominously) high-paying, scarce jobs.

> including (ominously) high-paying, scarce jobs

You mean like being a CEO and getting a bunch of money where the only credential is having previously been a CEO regardless of success in previous role? I think LLMs or even chat bots could handle a lot of those positions quite easily.

What is "winning" here? How do you "win" art? Human writers don't exist anymore? Seems like a really odd way of viewing writing.

I am pretty sure the next step is "they fight you", before you win.

True! And that will come very soon.

You only progress to the next step if you’re actually good. It’s possible you stay mediocre, and your only function was to amuse the gatekeepers.

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