zlacker

>>kcorbi+(OP)
Interesting analysis. Suprised HN is so negative towards AI (and that the positive:negative ratio to AI is about the same as it was for Crypto a few years ago!)

The obvious difference is that AI has abundant use-cases, while Crypto only has tenuous ones.

Maybe there is added negativity considering it is a technology where there is clearly a potential threat to jobs on a personal level (e.g. lift operators were very negative towards automatic lifts).

>>Closi+g8
There is a lot of negativity about the way it is used I think.

Most people will agree that LLMs are pretty neat, but now instead of every startup being "like Uber but for ..." they are "like chatGPT but for ...".

Everyone is trying to chuck AI into their products and most of the time there is no need, or the product is just a thin fine-tune over an existing LLM model that adds essentially near-zero value. HN is fairly negative on that sort of thing I think (rightly so IMO)

>>mattlo+Ff
I think a major problem that is going to become more and more obvious is that AI is actually pretty expensive compared to good old deterministic computing. If there's a way to solve a problem without resorting to sending an inference request to a gpu cluster, we should do it that way. Otherwise you're wasting electricity.

>>__loam+cm
People said that about virtualized code, but then computers got 100x faster and now we're running 10 megabyte web apps in a 500 megabyte client to display a simple page of text, and it still loads acceptably fast.

The AI algos will get 100x faster through a combination of hardware and software optimizations. Then, deterministic vs AI will mean the unnoticeable difference between displaying some info to the user in 0.001s vs 0.1s. Then, AI will become the default.

>>everfr+IZ
I'm not sure if this actually correct. Performance increases were reliable and consistent for a long time but we're reaching the physical limitations of Moore's law. Unless you have new physics or new models of computation, we might reach an actual speed limit this decade when the transistors are limited but the size of atoms.

I also believe there will always be a need for determinism. There will absolutely be applications where the randomness of ai is unacceptable.

>>__loam+i41
New models of computation are a given, and improved application-specific circuits for the most widely-used models are also a given (I believe current models run mostly on enterprise GPUs). Together these could easily make AI models 100x more efficient even without any advancements in the underlying chipmaking processes.

> I also believe there will always be a need for determinism. There will absolutely be applications where the randomness of ai is unacceptable.

For high-assurance apps, I agree there will always be a need, sure. Of course, these high-assurance apps will be supervised by AI that can inspect it and raise alarm bells if anything unexpected happens.

For consumer apps though, an app might actually feel less "random" to the user if there's an AI that can intuit exactly what they are trying to accomplish when they perform certain actions in the app (much like a friendly tech-savvy teacher sitting down with you to help you accomplish something in the app).

>>everfr+9a1
You have a lot of faith in this ai stuff. It's not magic.