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I don't think my point is really landing so I'll try once more and then give up.

Let's say frontier models today have no problem counting letters, I never really disputed that but only asked about it. It seems based on the other replies in this thread, it's a bit of a "who you ask" kind of thing, but let's grant that they have no issues with it now.

The first version of chatgpt was released 4 years ago next month. Which is not quite 5 years but close. In that time we've just barely managed to get spelling down. If we extrapolate that rate of progress forward 5 more years, are you still afraid for your job?

I think we're all more likely to lose our jobs from a downturn in the economy caused by the capex/debt bubble bursting than being made redundant by AI. (And the continual pricing reductions only seem to make this result more likely.) Hopefully neither happens and in 5 years we'll all still be gainfully employed.

https://chatgpt.com/share/6ac63b6a-481c-83e9-a8fa-a13ce7402d...

I used whatever the default free model and thinking time was. If progress was really as fast and continually cheaper as some worry it is, wouldn't we expect free models by now to know (or even perform) what frontier models were capable of as much as 2 year ago?

This deep in the "comparing logs" tangent we risk missing the point. It's not what exactly frontier models are capable of at this particular point in time. But that there's entire categories of problems that seem easy to us which LLMs really struggle with. We've stumbled on several just a few replies into casual conversation. (Can they count? Can they know if they can count? Can they reproduce results? How quickly do new capabilities filter into free models? And that's just what's come up naturally, if we wanted to pick adversarial examples there's more to choose from.)

So while there's a number of difficult problems that are easy for LLMs (like bulk generating lean proofs), there are plenty of things where progress is not so impressive.

If LLMs can struggle so much with such easy problems, what hard problems have we yet to discover that they'll struggle with? The fact that no one knows, 5 years in advance, what those problems will be does not mean the chance of them is zero.

So far progress on the things LLMs are good at is fast and easy. It's like fire in a room full of oxygen. But once the low hanging fruit is gone, and the oxygen is out of the room. How fast will the fire burn through steel walls?

In my opinion it's a mistake to look at only rate of progress on one type of problem (whether it be what LLMs are good at OR what they're bad at) and assume progress on all tasks will progress at that rate indefinitely. Isn't there a saying about exponential curves, in nature, all being sigmoids eventually?

I guess we'll just have to see. I wish you good luck with your wagers.

The fact that no one is taking you up on that bet I don't find to be particularly persuasive. I suspect there will be plenty of cognitive tasks LLMs struggle with in 5 years, maybe even 20. But I wouldn't hazard to guess which, I don't think anyone is capable of that level of foresight.

Machines are already far beyond human capability in plenty of ways. Including cognitive tasks like chess. We've already created the technology we need to destroy ourselves (nuclear weapons), and yet so far (knock on wood), we're still around.

We've even already had programs that can prove (brute force) theorems. As far as I can tell this isn't much different, except the space of theorems that computers can solve has expanded. How far? We can't really say yet.

Does solving more theorems than before suddenly mean computers are capable of anything? No.

It's easy to look at a fire burning through a forest and extrapolate that rate of progress across the whole world. But fire doesn't burn everything equally fast.

What other cognitive tasks will be a struggle to make progress on? I suspect there will be some, though which ones they are is anyone's guess.

(Omg I'm even talking like one now.)

The blog seems to be treating it as aspirational -- a goal. A puzzle to be understood and applied to make our lives richer. It's more of a perspective shift.

When you really get it, you won't think you're supposed to like bad sandwiches -- you'll realize what a gift any sandwich is. What a gift it is to sit in any crummy airport eating a soggy sandwich. You won't have to try to like it, you'll cry with gratitude at how lucky you are to even be there.

A lot of 3D graphics can be derived pretty easily just from knowing a few basics like divide by depth. I think knowing how to construct a transformation matrix from a coordinate system basis is another one -- that would remove the need to look up how to construct a perspective matrix, for example.

A few things like that will get you pretty far and you can kind of take the same journey of discovery as early 3D pioneers. That's one of the best ways to learn because you're much more likely to remember something you figured out compared to something you just read about.

It gets tricky with perspective-correct textures, but running into issues like that on your own (even if not solved on your own) is part of the fun of learning, I think.

I'm not sure what the equivalent would look like in the math field, but it probably involves a lot of mathematicians losing their jobs and the quality of human-produced math decreasing overall.

The quality of the math in general would be fine, since in this scenario cpus will keep producing it. The quality of cpu-cpu chess games is quite high, beyond human understanding in many cases.

Chess is a weird example because it doesn't really have any utility beyond itself. Even pure math sometimes ends up having use in the strangest places. Although if no one understands the frontier math (because no one is getting paid to), I'm not sure it even matters what the quality of the cpu math is?

It's a bit like a tree falling in a forest. If an LLM proves a theorem but no one understands it, did it make a sound?

I don't care if the claims are correct or incorrect. I just think the tone of the article is dishonest about its own impartiality and fairness, because it doesn't even attempt to interpret the claims in any way except the least favorable. If you want to be persuasive, you should refute a claim using the most favorable interpretation of that claim possible -- this does the opposite.

I'll quibble with the term rigor. I think the article contains the strictness that word implies, but not the thoroughness.

But this article is not nearly as impartial as it claims to be. It interprets all Zitron's claims in a narrow and overly-literal way. Missing the point and refuting a technicality.

For example, looking at the last 3 year's revenue/profit growth. This tells us nothing without looking at a larger context. Has revenue growth slowed down? Have profit margins compressed? What makes up the income and has that changed? Etc.

I think anyone being intellectually honest understands that these tables alone don't refute Zitron's claims that big tech are "dying and thrashing around" which is something that could take many years and easily hide under these kind of top line / bottom line numbers. (Maybe further analysis would refute the claim, but that analysis is not present here.)

Or Zitron's claims that AI capabilities are "reaching the upper limits" back in 2024. I don't think even Zitron would disagree that AI capabilities have grown since then, but that doesn't refute his point. How long the "reaching" takes and how wide the "upper limits" are is completely up to a subjective interpretation, which this post makes no attempt to even explore.

By all means dunk on futurists. But at least steelman their positions or you look just as biased as them. (Although maybe I am off base and this bias is meant to be clear from the start by admitting to pro-AI predictions all the way back in 2015.)

If you want to do something no one has done artistically, it helps if you're also doing something no one has done technically.

And novel designs stand out. It's no coincidence how many of those wildly successful games introduce totally new game designs, only made possible through their totally new game engines.

As with most art forms, the very best and most interesting examples treat rules like these as starting points, not end points.

Or better yet, call back to a certain classic and replace those words with "butt" and keep them in.

Then your front page will simply be filled with things like:

"Show HN: Look what I did with my butt."

"This new butt harness will revolutionize butt coding."

Before the recent floodgates cracked open, AI research made only slow incremental progress for decades. Why couldn't we already be back near that rate of progress?

Meta's decisions affect everyone, even non-users, because of their outsized impact on society. But they also have way more users than cigarettes, and deliberately prey on children and teens in ways that could affect them their whole lives (see recent lawsuit).

I would struggle to judge anyone working for either of these companies. I think the blame lies at the top, or is shared by all of us for failing to build a better society which prevents such exploitation.

Measuring damage to society, or the degree of moral bankruptcy in a company's leadership, is a very difficult thing to quantify.

So, I agree with you that these are personal choices, and everyone will draw the line differently based on who they're comfortable working for and what they're comfortable contributing to.