Many people with no skills are taking advantage of the LLM craze to artificially inflate their own value. I see it every day on LinkedIn.

People that previously have barely any experience in tech, now being hired in AI startups because they are good bullshitters.

> A human must verify everything an LLM presents as fact. Everything. If you don’t, we all pay the price. LLMs do not remove the onus of responsibility on the human being, if anything they amplify it because LLMs can generate lots more output more quickly that needs to be verified than humans can.

The sort of person that's going to offload their thinking to AI is the exact sort of person that is not going to verify anything because they've already offloaded their thinking to AI.

Unfortunately people sometimes get defensive against this take. But I think treating the LLM as you described can make you a better LLM user and help get better output. It helps understand the failure modes better, and moderate one’s reliance on them. Just like how we should do for every tool we work with.

> Now, we all pay the consequence, to the tune of hundreds of thousands if not millions of dollars of wasted productivity from teams that have to deal with the resulting fall-out of this usage of “AI”.

Brandolini's principle in action. It takes 10 times more energy to refute BS than to generate it. A related analogy to computing: it is easy to generate propositions, but hard to test if a given proposition is satisfiable or not, which curiously ties to P vs NP.

Apophatic intelligence? "We don't know what intelligence is, but LLMs with CoT are certainly not it despite being Turing-complete."

Watching for unexpected failure modes is surely worth it.

> LLMs are text-prediction engines. They are not Artificial Intelligence, and shouldn’t not be treated in any form or fashion as if they possess intelligence.

I agree that humans must verify LLM-produced facts, but strongly disagree with these kinds of "stochastic parrot therefore dumb" arguments.

Yes, an LLM is a "stochastic parrot". No, that doesn't imply that it is dumb. Enough to look at how Terence Tao asks ChatGPT to help him understand a solution that nobody had ever discussed before [1], or how a random guy asks ChatGPT in a handful of words to disprove a 30-year-old conjecture, with zero technical input [2].

If your parrot in a birdcage with internet access can finish the sentence, "The counterexample to the Dinitz–Garg–Goemans conjecture is...", then it's a pretty smart parrot, by all reasonable definitions of "smart". Just because someone bottled up the formula into matrix multiplications and added some random sampling to the outcome, that doesn't take away from the fact that the parrot said provably correct statements that the biggest experts in the field couldn't imagine.

And no, I'm not implying that the LLMs are correct all the time, or that their intelligence and reasoning works in any way like ours.

[1]: https://chatgpt.com/share/6a5fdc7a-d6f8-83e8-bbea-8deb42cfed... [2]: https://chatgpt.com/share/6a60b2eb-0b64-83ee-9c76-7931ca1de0...

Wow you just got us a complete nostalgia moment to the good old times when the computer who always beats us at chess became `not artificial intelligence`...

Every day I wake up and open HN.

“LLM has made legitimate mathematical discoveries” —> Wow the rate of progress is amazing. Highly upvoted.

“LLM does something not good” -> Does everyone else not realize LLMs are just dumb next token predictors? Highly upvoted.

So tired of this discourse and this site.

This is a conflation of issues, predicated on false understanding of what LLMs are.

This line of critique is pernicious because it is both technically correct, as description, and profoundly misleading.

Saying that outputs are a product of inputs is not interesting and to the point it is not explanatory.

What is interesting, is how they do what they do. What is the "statistically likely* next token? To answer that you can do exactly one thing, run the LLM. That's because what they are doing is interesting and not reducible.

What is more interesting is that in order to do what they do, given the architectures we apply and the training strategies we use and the harnesses we situate them in, LLM are recapitulating in their deep layers strategies observed in the animal brain. This is still suggestive, interpretibility is nascent: but it is also more than a little interesting. In some respects, for cognitive scientists interested in the manner in which mind merges from computational substrates, it is profoundly interesting.

One can incorporate this, and, still be viciously critical of bother the success and failure of LLM in the applications we have put them to, and of how we (as individuals and as institutions such as corporations) are integrating them into our work.

There is a lot to criticize!

But criticism can be taken more seriously when it is not obscured by misunderstanding or misrepresentation (intentional, or not) of what LLM are and why they are not remotely "parrots" in the pejorative sense.

The technology, as technology, at the scale we are architecting it, is doing things we did not imagine would be witnessed in our lifetime, if ever. Dismissing that and denying it because of the career, industry, society, and civilization challenges that technology brings are existential, is bad argumentation or bad faith.

Both can be true at once.

> We can chalk this up as another example of over-exhuberance by what folks believe LLMs can accomplish vs. what they actually are.

I see no credible corroboration. More likely its folks having no more care for what they are doing than the bots themselves.

> Now, we all pay the consequence, to the tune of hundreds of thousands if not millions of dollars of wasted productivity from teams that have to deal with the resulting fall-out of this usage of “AI”.

People said the same about email spam ... until they engaged spam filters. CVE report slop is simply spam. Complaints are better directed at the filters, not the filtered.

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This is the right conclusion for completely wrong reasons haha. I love hn. (aside from being entirely wrong as most models are now mixed modal so they are token prediction engines)

Each layer of attention can more through feature space “lit up weight clusters” in a way no other previous AI can. It can from that decode some rudimentary logic and world modeling and make deductions. Certainly better than any previous AI. Only a goof here would believe this wasn’t a serious advancement.

So don’t over sell it. But don’t sell it short with this “grrr in an engineer don’t threaten me with new tech” attitude.

This take is akin to teenage angsty takes and doesn’t really belong here.

Does a dog possess intelligence? Does a bird? Does a cricket? An amoeba?

I hate AI slop as much as the next guy but the amount of tribalism over AI is taking near-religious forms.

Nobody knows what intelligence is, therefore we don't know what does or does not possess it, therefore we don't know whether LLMs currently, or in the future, possess it.

Yes, LLMs can be stupid, guess what: so can I. That doesn't really change the argument at all.

I feel like I'm on a deja-vu from when DALL-E was released and everybody was fighting over whether AI can be creative yes or no. Same story, different words.

Intelligence, creativity: we have no idea what these words mean, and AI is helping us understand them better. That alone is an achievement of epic proportions. I am not joking here. Any computer scientist before 2015 would be absolutely blown away by what you can now do for 10 cents and an API call, yet somehow because of the tech-bro-iness of it all we get a tribal war over what is plainly visible in front of us:

LLMs are uncomfortably close to what we thought intelligent machines would look like

> A human must verify everything an LLM presents as fact. Everything.

I've thought about this for quite some time now.

No. A human doesn't need to verify everything. And the argument is really simple: stochastic.

Think of self-driving cars: We can show today - based on evidence and real data - that self-driving cars are safer than human drivers. That's a fact and the consequences are clear, more self-driving cars, less human-driven cars, less accidents, less hurt people, less dead people.

Are the cars 100% safe and NEVER make a mistake? No. But they don't need to. Nothing is ever 100% (in the real world).

Now back to AI for software creation. "Review is the bottleneck because EVERYTHING must be judged by a human." No. It doesn't. We just need to build AI review systems, that will do reviews better than (or at least as good as) humans. The human review quality bar is far below 100%. Far far far. If we can show (likely in the next 12-24 months I think) that AI review quality is consistently above the human review quality - again, based on evidence, based on real data - then that's it, then there's no good reason to have humans review the code.

Yes, there will be another layer in the system, another level of abstraction that will/must end at the human boundary.

You are right with the analysis, but wrong with the conclusions. Yes, LLM „thinking process“ is kinda non-deterministic in a sense that it does not follow logical reasoning and will not produce logically correct results in 100% cases. It has an error margin.

However, error margins are in the center of any engineering discipline. We cannot produce things measured with 100% accuracy. This is accepted fact. The focus is always not on eliminating errors, but on reducing them to acceptable minimum. With LLMs we should not expect an ideal logical thinker, but a process that may error sometimes, and we must design quality controls instead that push LLM outputs within acceptable margins. And it can work.

We can chalk this up as another example of over-exhuberance by what folks believe humans can accomplish vs. what they actually are.

Flesh-based “brain” is able to use its vast corpus of inputs and calculate the most statistically likely output in a given situation. It is probabilistic, and when you are dealing with probabilities in a situation where certainties, not probabilities, matter, you’re going to get dinged on credibility massively when your flesh-based brain gets the probabilities wrong at best, or in this case, claims a line of code generates a vulnerability when it is, in fact, a code comment.

Humans are prediction engines. They are not Pure Intelligence, and shouldn’t not be treated in any form or fashion as if they possess pure intelligence. What bothers me about this entire situation is that presumably the folks that have relied on the flesh-based “brains” to generate these vulnerabilities knew (or should have known) enough about their "tool" to know this would happen, but did not: To err is to be human.

Now, we all pay the consequence, to the tune of hundreds of thousands if not millions of dollars of wasted productivity from teams that have to deal with the resulting fall-out of this over reliance on fallible “brains".

A human must verify everything another human presents as fact. Everything. If you don’t, we all pay the price. Using a human does not remove the onus of responsibility on the human being in charge, if anything they amplify it because humans work for peanuts in some countries, and can generate lots more output more quickly that needs to be verified by the humans in charge.