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Gave me an outdated answer.
Gave me another outdated answer to a URL that doesn’t exist.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?
Then as you ask "provide sources.", it says simply "Source: Tech Review Websites". If this came from an actual person I would genuinely ask it "do you take me for gullible trash?".
It's still somewhat useful, due to Google Search crumbling away into nothingness, if you ask "link me five sites with info about [topic]".
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.
O no, you mean the AI hype is another bs tech bubble?!
The funniest part is that all the AI hype is focused on all the wrong things. There are absolutely great AI tools that get very little mention.
For example, I'm visually impaired and use AI tools A fair bit to help me get around the internet and such. Especially when it comes to using AI I to generate descriptions of images.
Not even a third of the way through... Holy crap.
A 100+ billion dollar valuation.
Absurd.
It's just as absurd as when WeWork got a 40 billion dollar valuation.
These VC's are insane.
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 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?
Then as you ask "provide sources.", it says simply "Source: Tech Review Websites". If this came from an actual person I would genuinely ask it "do you take me for gullible trash?".
It's still somewhat useful, due to Google Search crumbling away into nothingness, if you ask "link me five sites with info about [topic]".
"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.
O no, you mean the AI hype is another bs tech bubble?!
The funniest part is that all the AI hype is focused on all the wrong things. There are absolutely great AI tools that get very little mention.
For example, I'm visually impaired and use AI tools A fair bit to help me get around the internet and such. Especially when it comes to using AI I to generate descriptions of images.
Not even a third of the way through... Holy crap.
A 100+ billion dollar valuation.
Absurd.
It's just as absurd as when WeWork got a 40 billion dollar valuation.
These VC's are insane.
One thing I'd push back on in the article is:
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).