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I pooh-poohed ChatGPT when it first came out so I gave it another crack at a technical issue I’ve been avoiding.
Gave me an outdated answer.
Gave me another outdated answer to a URL that doesn’t exist.
Gave me the answer I told it won’t work in the initial prompt.
Scolded me for swearing at it.
This is what’s supposed to replace search engines?
I hope sonething better comes along because google ruined their search engine a decade ago. stract.com is probabky the closest to what google used to be.
As for chatgpt, it is not an index. It cannot refer you back to infornation it was trained on because it doesn’t build a massive indexed internet database.
It has some method of probable relations and conglomerarion of input. It is why it “hallucinates” information output, because it doesn’t “know” what is wrong or right info, it just fetches data based on probabilities of connections.
It is good at suggesting new music or movies based on your list of media you like, but it is terrible with actual factual info
Scolded me for swearing at it.
“You’ll fucking know when I’m swearing at you,” was my reply to that shit the last time I gave it a spin (after it regurgitated nonsense after many prompts specifically asking for not nonsense).
Your experience highlights what current iterations of LLMs are not well suited for, so I understand if that’s what you were hoping to achieve, why you were left wanting, or disillusioned.
There’s a lot of things that LLMs are really good at, or incredibly useful for, such as ingesting large bodies of text, and then analyzing them based on your ability to create well thought out prompts.
This can save you hours and hours, of reading time, and it’s something that you can verify the answer on relatively quickly, to double check the LLMs response accuracy.
They’re also good at doing something Google used to be good at, but sucks at now. Which enabling you to describe process, simple or complicated, short or long, that you either can’t recall the name of, or aren’t even sure where it’s called, and letting you know exactly what it is. Also, easily verifiable.
There’s plenty of other things too, but just remember that they are tools, not magic, or sentient intelligence.
The models are not real time, but there are tricks to figure out it’s most recent dates of ingestion, such as asking topical entertainment or news questions, but don’t go looking for a real-time information.
Also, I have yet to find a model that can provide an actual URL and specific source for anything it generates, which is why it’s a good practice to use them to do tasks, or get information, that would take you longer to do, or get, manually, but that can be easily verified once you receive it.
But if any research source cannot be used without verification, is it really useful? I agree, we should verfiy crucial information but when its wrong often, but confidently so, using natural language is a barrier not a benefit.
It’s not a peer-reviewed journal or academic level source, and shouldn’t be used as that.
But if I need to find some technical or scientific writings on a subject, but I don’t know the correct nomenclature or need a more narrow set of keywords, that is something I can describe to the LLM and get back.
The keywords in their response can help me then hunt down the journal article or papers that I need using traditional search engines. I’m not just brainstorming here, this is something I do often enough to find real utility in it.
Again, these are problems that can be solved with traditional search engines, but at the cost of time and frustration sifting though every potential result.
You can spit out a hundred more examples of what an LLM can’t do, but as I already said, they’re not magic, just tools.
Yes, but for the average user, if it confidently gives misinformation, then its worse than a search engine. It is removing the verification step of reading the source, seospam aside. The whole business model is on using it more, not selectively.
One thing the article leaves out is the costs of processing should go down over time. Hopefully, as power transitions,.it also becomes more sustainable. However, it starts to become a bit like uber and self driving cars. How long can they burn through other peoples money to undercut competitions until the actual plan becomes profitable.
I’m not advocating for openai, their business model, or the environmental and financial cost benefit of current LLM technology.
They suck, it’s dogshit, and it’s not worth cooking the planet for.
I also don’t disagree about the very real possibility that the average user may actually get dumber and more misinformed by relying on LLMs.
But we’re on Lemmy, and I’m just tired of all these comments incessantly complaining about about how LLM’s can’t do x,y, or z.
Imagine being on a carpentry forum, and every day people complained about how their new belt sander was dogshit at cutting 2x4’s or screwing in fasteners, so clearly the problem was with the concept of belt sander technology.
In your example, the thing missing is that the belt sander companies are selling their belt sanders as screw fastening, band saw multitools.
I always say about AI that it’s not the tool but who’s making it and why, and this is especially true for the average person. Your average person isn’t seeing the LLMs that are trained to identify anomalies in MRIs or iterate on chemical formulas to improve drugs in a simulation that takes milliseconds compared to the months of research it would take technicians to replicate the same experiments. So all they can talk about is the AI that is in their face all day, every day, as every company in the world tries to shoehorn it into their product somehow. And so they complain about the belt sanders that the company told them would fasten their screws and cut their 2x4’s.
The only way the complaining is going to stop is when the bubble bursts and these companies have to find a new way to chase the infinite profit pipedream.
Replace belt sander with CBD. A compound with very real and tangible benefits for specific use cases, but is marketed as a modern day snake oil cure all.
Imagine seeing people regularly complaining on bluelight, erowid, or whatever forums educated drug users frequent these days, bitching that CBD didn’t cure their asthma, or STDs, so therefore it has no medical value.
They know it’s a tool, yet they keep complaining about how the gas station CBD isn’t magic and failed to cure their gonorrhea, even though they already knew it was never going to be able to, no matter what the packaging said.
But my analogy wasn’t meant to be critically analyzed and dissected, it was a throwaway example to highlight the problem of people on Lemmy, who actually know better, but keep whinging about LLM’s providing bogus URLs for citations, etc.
Garbage in garbage out. You give a shit prompt, you generally get a shit answer.
If it doesn’t know how to answer a shitty question, it shouldn’t try to BS the answer.
No answer is better than a wrong answer delivered confidently.
One thing I’d push back on in the article is:
That cost-per-user doesn’t decrease as you add more customers. You need more servers. More GPUs.
This is assuming constant use, which is not the case. If I have a server handling LLM prompt requests, and for illustrative purposes each request uses 100% of the single discrete GPU in it, and I only have 1 customer, but that one customer only uses it 5% of the day (which would actually be pretty high in real terms), I can still add additional customers without needing to buy additional servers. The question is whether the given revenue of a single server outweighs its cost to run.
And when it comes to training, that is an upfront cost, that you could (if you get a model to where you want it) stop having to pay whenever you want. I’m pretty surprised they haven’t been really leaning into training models for medical diagnoses, because once you have a model that can e.g. spot a type of tumor with n% accuracy beyond a human, you don’t really have to refine it further if you don’t want to (after all, it’s not like the humans can choose to do it better themselves at that point, like they can with writing prompts).
I’d say they’ve probably long reached the point where they have enough customers around the world to hold the load on their servers fairly constant. The example with one user only taking 5% of a servers load only works for low customer counts, similar to how you can’t count on one wind turbine or solar plant to provide all of your energy but if you have enough of them you can provide a base line of fairly constant energy
