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I see it as more of a rationalization. When you already believe something, you look for intellectual reasons to justify that belief. Pascal's Wager feel like that to me. Its main power is hardening an existing belief rather than swaying an undecided mind.

Just don't look up any historical precedent.

I didn't save the original query so I asked again this morning and ended up getting the same response -- points for consistency, though it might have been more reassuring with the correct answer.

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.

I asked both gemini and chatgpt "do frontier models still have trouble counting letters?" and the first word of both responses was yes.

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.

My apologies, I got my info from an LLM. I guess they still have a ways to go in understanding current events.

Solving a bunch of math proofs is a far way from recursive self improvement. Don't worry, it's not like a tech tree in a video game where if you can prove a bunch of theorems then suddenly you unlock the next level of technology.

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.

Don't frontier models still have trouble counting letters? Or am I out of date? Either way, it doesn't seem to be the same amazing rate of progress we're seeing in other areas.

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.

It's a good talk and worth watching. The title doesn't really reflect what the talk is about, which is more about how to not lose those things while using AI tools. The title could maybe even be described as a kind of trojan horse because the answer (to the question of how to scale), if you read between the lines, seems to be basically: don't. Slow down and be intentional about every detail, focus on the user, and don't lower your standards.

Turns out there is an objective meaning to life, and it's trying to use AI to make money. Phew, I for one am glad to find that out finally. Think of all those poor people who didn't hold on to their brains, what sorry lives they must lead.

That feeling when you realize Idiocracy wasn't prophetic, it was optimistic.

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

I think you're getting at the heart of the sentiment behind the original quote better than the blog post.

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.

For me learning on my own, it was even illustrative to not use a near plane and see how things behind the camera would still appear in front of it.

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.

There's a reason puzzle solutions (like walkthroughs) are called spoilers. It's because they spoil the game.

Your point is correct, but let's not gloss over that most of those had 80% drawdowns or more. The nasdaq took 15 years to recover and it's only been 25 years total, meaning 60% of the time since then you'd have been living with a paper loss. Timing is difficult but powerful. (Just not as powerful as 25 years of waiting.)