vouaobrasil

One thing I've seen with LLMs that I've not seen with any other emergent technology is the level of backlash against it. I think we're seeing a point of polarization in society where there will be two truly opposing sides that are against each other and no reconciliation will be possible between them (pro and anti-AI), and that it has a good chance of fundamentally destabilizing society.

> People still play chess and go and StarCraft. People do what people want to do.

Poor analogy. Because math is one of those job-hobby type hybrids where the job may be enjoyable but you still need the academic infrastructure to do it on a modern level, both because you need funding and you need others to motivate you to keep to a certain standard.

Job-hobby hybrids like this are not the same as running and Starcraft where you can do it by yourself and still reap a lot of benefit from it in the same way. Hobbyists might still do math but if it were just up to the hobbyists, we wouldn't have the level of discovery we have today.

Keep in mind this guy is a prof teaching computer science and is likely to leave his sons a decent inheritance. Not knocking on that but the situation is likely to be a lot different if you don't have that security, and the advice you give to your children could be a lot different too if you can't leave them a lot.

> You cannot prompt what you don’t know exists. And there’s a second part to that: You cannot properly review what you don’t understand.

Seems to me there's an awful lot of moving goalposts when it comes to AI these days. Who's to say that in ten years AI won't become darn good at guessing what people want and just making it?

A horrifying future in my opinion...

It is because in fact MOST mathematicians (and researchers) don't really just want "advances" in their field. They also want to have some significance and play a role in it. What's the point of doing research, which takes a lot of work for much less pay than industry, except to have some of the glory?

I didn't go into math so I could read the results of others all day. I went into it to contribute meaningfully, and I certainly don't consider interpreting the results of a machine to be a meaningful contribution.

Your perspective is just the perspective of a consumer of things. In that case, it doesn't matter where they come from.

Definitely true....but what has also happened is there is also a proliferation of shit code that people actually use and experience. It's not as if people eventually admit that software engineering is hard and then actually make something good. We just get more shit. Like how every second site nowadays has that nasty fade-in scrolling effect, that the cursor is overridden, etc. Or Wordpress plugins...

In my opinion, not a good direction. Maybe just good for the stockholders and investors and someone wanting to make a quick buck.

Pro photographer here. And I've counted at least ten of these photo editing apps that have appeared in the last year, many of them in the last few months. All of them LLM-assisted or completely vibe coded.

They definitely work but...

Unlike Lightroom or Darktable (an open source Raw editor that took years to get to a sophisticated level), they are all rough around the edges. Maybe in the future they'll get better in terms of being really refined to handle advanced photography, but right now they're hobby applications.

To me this highlights the absurdity of AI, because enormous amounts of energy is wasted on duplicating stuff and making something new.

I won't try and argue that nothing nice can come out of it, but it seems absurd to me to waste so much energy as a species this way. I wouldn't be surprised if there were a hundred more of these apps in another year.

There have been at least 10 (probably more) photo editing suites. It seems that now everyone who doesn't want to learn the existing suites just can make their own with AI.

The scene-referred workflow is vastly superior because it makes all the adjustments before the full Raw data is converted to the display space.

You don't even need to know what that means for it to be useful. In practice, it just means less artifacts, less weird stuff that happens with color near clipping highlights, etc. You use the software in exactly the same way you'd use the display-referred workflow, except that under the hood, the modules are working with the linear Raw data.

> It’s easy to say you’ll go it alone, but staying and improving it for not just yourself but for everyone else is more valuable.

There's almost no way to easily improve society now. Almost all practical pathways for people are just furthering technology past the point of diminishing returns...

I agree there's still some value in helping people but now it's mostly just helping people adapt to this dystopia of furthering our destructive influence, and it's still depressing.

The problem is that in the future, LLMs will change society so much that we won't even want to go after these things any more because the world will live and die on cheap prototypes and trends.

It's like all the food being replaced by MacDonalds food that is served from a replicator (from Star Trek). Sure, we can survive on it, sort of, but if all we ever know is this food and we're too busy to cook (or no one knows how to manufacture a stove) then we'll never know anything else. Maybe someone will learn how to tweak the recipe a little but our minds will be conditioned to context-switch so much that we'll never even think of learning to cook great meals.

That's what LLMs do. They're shit.

That is true, but perhaps making it so that almost nobody ever feels special is a bad thing for society....

> This does not affect people building new theories.

Actually, the problem is, somehow the skill of building new theories in math is directly tied to slaving hard over a problem. It's the very experience of slaving away that actually somehow causes ideas to form. Pretty much all mathematicians understand this. Yes, senior mathematicians now can form some new theories, but what about junior ones who will have very little experience in working hard on a problem by hand?

Of course, they could work on the problem by hand anyway, but they won't because no one will pay them when a machine can do it.

Mathematics is less about the end product and having a healthy community of people to actually understand the proofs and eventually apply them.

That community won't exist as many people simply won't even enter the field because it's reprehensible and contemptible, not to mention boring, just to read machine-generated proofs and verify them.

Collaborating on, or at least working on unsolved problems is what motivates most people.

AI is like a cheat code in a video game. You get to the end faster but fewer people want to play if the cheat code is always on. You can't turn it off either because the very challenge is to do something unique.

Not exactly, because we would have cool people to inspire us and hang out with us.

But your argument is nonsensical because even if Gauss and von Neumann appeared, they wouldn't go into random fields and just prove things mechanically. They'd have to attend seminars, teach others, collaborate with others, and generally inspire others with their brilliance. It's the precise lack of this activity that makes AI in math so reprehensible.

Your argument encapsulates a contradiction because human mathematicians wouldn't be dropping proofs arbitrarily like AI is doing. They would do something completely different. Even the best of them.