Good. I’ve been aware of LLMs for about five years but I’ve been saying since the hype started that it can’t do what people are expecting/afraid it will do. It’s about time for sanity to prevail.
Same. I support AI completely as a tool to solve specific problems and that is about it. What is really cool is that AI libraries and such got a massive boost of needed development so plebs like me can code simple ANN apps in Python with little skill. Documentation has improved 100x and hardware support is fairly good.
LinkedIn seems to be an interesting indicator of where tech is in its hype cycle. I guess LinkedIn went from 100% AI-awesome-everything about 2 months ago to almost zero posts and ads about it. I suppose most of the vaporware AI products are imploding now…
Of course, algorithmic feeds are a thing, so your experience might be different.
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I end up having to play twenty questions with chatgpt. For example, I’ve been asking it for code examples for ffmpeg mpeg4 encoding with C++.
It will happily spit out completely non-working code, where the core part - feeding image frames to the encoder - works, but it doesn’t initialise or tidy up the encoding afterwards.
Until I say, “hey this code doesn’t seem to work and creates corrupted files”, and then it’s like, “oh yeah you also need to do a bunch of other stuff, just like this”. Repeat as it slowly adds more and more pieces until finally you end up with something that actually works.
Or it will happily dream up function names or mix python and C functions, or will refer to older APIs even when I’ve specifically said “use API version x.y” and so on and so forth.
If I didn’t know enough about the subject already, I’d never be able to tease out the answer. So in a sense it’s a mostly useful reference, but it can’t be relied on to actually and consistently provide a result because it’s all statistics and fuzzy text generation behind the scenes, not actual knowledge.
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To be honest, I was surprised it had any idea about FFMPEG. The biggest problem is that it sounds so authoritative.
If it said, “hey I don’t know a huge amount about X” then you could work with that. But it will blithely say “no problem” and spit out 6 pages of non working code that you then have to debug further, and if you don’t know the terms in the area you’re working in you end up blundering around trying to find the right trigger word to get what you want.
I remember I was using it and I was so frustrated with a piece of code because it was undocumented and so I decided to ask chat gpt. It said to use this python method that I didn’t know existed and I was so happy because it solved my exact problem. Of course it turned out that that method didn’t exist at all and I had just completely made it up. Which great probability says that there should be a method that does this, but they’re obviously isn’t. So why would you recommend it?
I’ve been tasked with some notion testing and research this week. I tried asking chat gpt some simple questions, and it kept feeding me this exact scenario over and over. I guess notion doesn’t like any kind of recursion. Or at least not without extensions being added.
I think that at the bottom of the trough people will stop caring about it altogether except for functionality that has been proven to be useful for
You’re describing the “plateau of productivity”, which is built into this theory:
From this trough of disillusionment the new technology then starts climbing the “slope of enlightenment” where people realistically reassess how the technology can be applied in useful ways, before it reaches the “plateau of productivity” where the new technology is incorporated into the market and and ideally starts to slowly pay off and grow.
and I agree with you! it’s clear that there are good use cases for AI (see this article i posted in Science), we just need to apply it correctly and not overpromise!
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When people constantly lie and grift, it’s normal and healthy to become disillusioned.
But I thought
blockchainAI was going to change everything!
