[fragmede]

Because a lot of smart people are spending a lot of time, money, and effort on this. It's as simple as that. We could go into all sorts of details, like how increase in GPU capabilities will improve training capabilities, both in size and speed, or how GPU(/TPU) capabilities will improve, or how better techniques will make training on the same data set result in better models, or where other improvements will make better use of existing models or make them better or where we're seeing additions to training data sets and how that will improve models using existing techniques. But it really all boils down to a lot of smart people, some with a lot of money, that are personally invested (with time and money) in making them better.

That doesn't mean there isn't possibly a plateau somewhere but it's somewhere way off in the distance.

[pinkgolem]

I mean a lot of highly payed/intelligent people worked in crypto/fusion/quantum computing, still all of those topics are evolving rather gradually.

[fragmede]

Crypto is actually picking up after FTX and Binance, though as it's a social problem of adoption rather than a technical issue, I'm not sure it's comparable.

The problem with fusion and quantum computing is that advances are being made, but because those advances aren't consumer facing, you don't see them. Eg December 2022, they managed to get more energy out of a fusion experiment than they put in. That's huge! I'm not going to see an effect on my power bill for another couple decades, if ever, but it's real actual solid progress. For quantum computing, they're moving past the singular q-bit tech demonstrations level and moving into actual practical applications like making chips that can talk to each other **. Again, doesn't remotely affect me or my laptop today, but we've moved past the 1998 Stanford/IBM 2 q-bit computer.

Meanwhile, I can adopt a new model getting dropped with an afternoon of work, and see the results in milliseconds, in the case of StableDiffusion-turbo.

* https://www.technologyreview.com/2023/11/16/1083491/whats-co... ** https://www.technologyreview.com/2023/01/06/1066317/whats-ne...

[quickthrower2]

We know why those are.

Cryptocurrency’s need to be fully decentralised is the thorn in it’s side. Be your own bank is a bit too much for most people used to cash or a bank account they can call up if there is a problem. It has fundamental social problems that there may be solutions to but probably not.

Fusion and quantum are massive physics and engineering challenges. With ML we are already building the chips to scale, so we know it is scalable and doable.

It is a 50 to 100 problem not 0 to 1.

[pinkgolem]

and we might know what problems those are for AI in a year or two, look back how crypto was viewed on by some...

I also was not arguing for ai to not improve by quit a bit more.. I actually think ai will make a few more big steps forward, but the garantee for this is not ankered in the inflowing capital/talent but instead in the relative clear path forward of the technologie and partly known ineffective architecture.

[fatherzine]

a more apt analogy is Moore's law