5 ms·
“Its biggest issue is in the productization.” That’s not true at all. The biggest issue is that it doesn’t work. You can’t actually trust ai systems and that’s
by CooCooCaCha 2y ago
“Its biggest issue is in the productization.”
That’s not true at all. The biggest issue is that it doesn’t work. You can’t actually trust ai systems and that’s not a product issue.
- startupsfail 2y agoIlya is one of the Founders of the original nonprofit. This is also an issue. It does look like he was not the Founder or in any control of the for profit venture.
- senseiV 2y agoif the ai is the product, and the product isnt trustable, isnt that a product issue??
- shwaj 2y agoIt’s a core technology issue. The AI isn’t the product, e.g. the ChatGPT interface is the main product that is layered above the core AI tech. The issue is trustworthiness isn’t solvable by applying standard product management techniques on a predictable schedule. It requires scientific research.
- tivert 2y ago> That’s not true at all. The biggest issue is that it doesn’t work. You can’t actually trust ai systems and that’s not a product issue. I don't know about that, it seems to work just fine at creating spam and clone websites.
- tbrownaw 2y agoIt works fine for some things. You just need a clearly defined task where LLM + human reviewer is on average faster (ie cheaper) than a human doing the same task themselves without that assistance.
- still_grokking 2y agoGiven the fact that you need to review, research, and usually correct every detail of AI output, how can that be faster than just doing it right yourself in the first place? Do you have some examples of such tasks?
- tbrownaw 2y agoYes. There's the one that $employer built a POC app for and found did in fact save time. There's also github copilot which apparently a large chunk of people find saves time for them (and which $employer is reviewing to figure out if they can identify which people / job functions benefit precisely enough to pay for group licensing).
- anonymousab 2y ago> You can’t actually trust ai systems For a lot of (very profitable) use cases, hallucinations and 80/20 are actually more than good enough. Especially when they are replacing solutions that are even worse.
- player1234 2y agoWhat use cases? This kind of thing is stated all the time, never any examples.
- roguas 2y agoAll the usecases we see. Take a look at perplexity optimising short internet research. If I get this mostly right its fine enough, saved my 30 minutes of mindless clicking and reading - even if some errors are there.
- CooCooCaCha 2y agoYou make it sound like LLMs just make a few small mistakes when in reality they can hallucinate on a large scale.
- jedberg 2y agoAny use case where you treat the output like the work of a junior person and check it. Coding, law, writing. Pretty much anywhere that you can replace a junior employee with an LLM. Google or Meta (don't remember which) just put out a report about how many human-hours they saved last year using transformers for coding.
- still_grokking 2y agoWhat are examples of these (very profitable) use cases? Producing spam has some margin on it, but is it really very profitable? And else?