zlacker

AI today is in a weird position. What I can do today with AI was _inimaginable_ just 1 year ago. However, for a lot of people, the concept has worn out so fast, that they don't realize anymore what has happened... Some very intelligent people said some very silly things, such as that it was a bad search engine (it isn't a search engine) that it was a glorified word guesser (it doesn't exactly work as your telephone word suggestion). And so on and so forth. People always try to understand new technology through the lense of older technology. I do it, you certainly do it. This is how we grasp novelty. But AI is in a different dimension. I have been working in the domain for 30 years and I really didn't think we would reach this level in my life time. Talking to a computer to bring it to make some quite complicated task is INCREDIBLE... However, since communication is really ubiquitous for Humans, we tend to forget that it is an incredible achievement... In less than a year, we went for Scify ("Her") to reality, and in less than a year people have become blasé for something so fantastic... This is what consummerism did to people... They can't wonder more than a year...

>>claude+(OP)
It's a wonder. But if capex and opex are insane, and go up with every generation, the magic fades quickly.

Like, GitHub Copilot may be amazing, but if it looses money for every added user, if power users loose the company 4 or 8 times what they already pay, then maybe it's not an efficient use of compute resources.

>>ncruce+14
Yes, this. What made tech companies valued at higher revenue multiples than other industries is that new users could start using a product at near zero marginal cost once the tech was built. New revenue at zero marginal cost. AI is great but expensive to operate and the expense grows in direct proportion to usage. New users come in and you have to stand up a new data center full of H100s to serve it.

>>claude+(OP)
> Talking to a computer to bring it to make some quite complicated task is INCREDIBLE...

Are there any good examples of this? I struggle to use ChatGPT, maybe I'm using it not cleverly (or deeply) enough.

>>calf+8a
Recently I had to share a documentation written in Word, which I had to format in Markdown to put it on Github. I transformed my document into a text file for each chapter and then I asked chatGPT to transform each of these files into a Markdown page. And I also asked it to improve the English. Then I asked chatGPT to translate each of these files into different languages. The result is here: https://github.com/naver/tamgu/tree/master/documentations Basically, I did in a couple of hours, something that would have taken weeks of tedious work

>>claude+kl
OK then that's tedious work, not complex work.

I know that lots of people have personal stories of using ChatGPT but I was hoping something publicly reported on or like a showcase of truly impressive usages somewhere.

>>calf+6u
You are kidding right. Taking a raw text file and detecting every single header, sub-headers, keywords and pieces of code to add the right markdown tags is a simple task to you?

Have you ever tried to make a Python program to do exactly that?

I only used couple of sentences to build my prompt...

>>claude+5X
No, what is the computational complexity of the task? The computational complexity of the problem is not about how long your code is or how long you took to write it.

I don't know your CS background but perhaps I do not view the terms "complex" and "tedious" the way you assume. A tedious parser is certainly tedious to write, but it is not (necessarily) complex. And from an engineering standpoint it is questionable that you lost all the formatting information from Word, which would have already demarcated what things were headers, code, and so forth. So, you had to use a roundabout way—an LLM—to recover that information from the semantics.

If what you're really arguing is that ChatGPT works well for language translation tesks, in this case translating mixed prose, code, and foreign languages--sure I guess that's great at productivity and removing tedium, but it's not that surprising a usage given what LLMs are. They are language translators.

In other words you're saying it's complex but your argument reduces a task that is straightforward but tedious for humans, to the problem complexity of natural language processing.