It’s sad to see it spit out text from the training set without the actual knowledge of date and time. Like it would be more awesome if it could call time.Now(), but it 'll be a different story.
if you ask it today’s date, it actually does that.
It just doesn’t have any actual knowledge of what it’s saying. I asked it a programming question as well, and each time it would make up a class that doesn’t exist, I’d tell it it doesn’t exist, and it would go “You are correct, that class was deprecated in {old version}”. It wasn’t. I checked. It knows what the excuses look like in the training data, and just apes them.
It spouts convincing sounding bullshit and hopes you don’t call it out. It’s actually surprisingly human in that regard.
I haven’t used GPT-4 for that, but it’s all dependent on the data fed into it. Like if you ask a question about Javascript, there’s loads of that out there for it to look at. But ask it about Delphi, and it’ll be less accurate.
And they’ll both suffer from the same issue, which is when they reach the edge of their “knowledge”, they don’t realise it and output data anyway. They don’t know what they don’t know.
These LLMs generally and GPT-4 in particular really shine if you supply enough and the right context. Give it some code to refactor, to turn hastily slapped together code into idiomatic and well written code, align a code snippet to a different design pattern etc. Platforms like https://phind.com pull in web search results as you interact with them to give you more correct and current information etc.
LLMs are by no means a panacea and have serious limitations, but they are also magic for certain tasks and something I would be very, very sad to miss in my day to day.
It’s pretty hit or miss though… I’ve had lots of good calculations with the odd wrong one sprinkled in, making it unreliable for doing maths.
Mostly because it presents the result with absolute certainty.
They are mostly large language models , I have trained few smaller models myself, they generally splurt out next word depending on the last word , another thing they are incapable of, is spontaneous generation, they heavily depend on the question , or a preceding string ! But most companies are portraying it as AGI , already !
It’s sad to see it spit out text from the training set without the actual knowledge of date and time. Like it would be more awesome if it could call
time.Now(), but it 'll be a different story.if you ask it today’s date, it actually does that.
It just doesn’t have any actual knowledge of what it’s saying. I asked it a programming question as well, and each time it would make up a class that doesn’t exist, I’d tell it it doesn’t exist, and it would go “You are correct, that class was deprecated in {old version}”. It wasn’t. I checked. It knows what the excuses look like in the training data, and just apes them.
It spouts convincing sounding bullshit and hopes you don’t call it out. It’s actually surprisingly human in that regard.
Oh great, Silicon Valley’s AI is just an overconfident intern!
Oh great, Silicon Valley’s AI is just a major tech executive!
It’s super weird that it would attempt to give a time duration at all, and then get it wrong.
Bard is kind of trash though. GPT-4 tends to so much better in my experience.
I haven’t used GPT-4 for that, but it’s all dependent on the data fed into it. Like if you ask a question about Javascript, there’s loads of that out there for it to look at. But ask it about Delphi, and it’ll be less accurate.
And they’ll both suffer from the same issue, which is when they reach the edge of their “knowledge”, they don’t realise it and output data anyway. They don’t know what they don’t know.
These LLMs generally and GPT-4 in particular really shine if you supply enough and the right context. Give it some code to refactor, to turn hastily slapped together code into idiomatic and well written code, align a code snippet to a different design pattern etc. Platforms like https://phind.com pull in web search results as you interact with them to give you more correct and current information etc.
LLMs are by no means a panacea and have serious limitations, but they are also magic for certain tasks and something I would be very, very sad to miss in my day to day.
they are both shit at adding and subtracting numbers, dates and whatnot… they both cant do basic math unfortunately
It’s a language model, I don’t know why you would expect math. Tell it to output code to perform the math, that’ll work just fine.
Then it should say so instead of attempting and failing at the one thing computers are supposed to be better than us at
Well, if I try to use Photoshop to calculate a polynomial it’s not gonna work all that well either, right tool for the job and all.
The fact that LLMs are terrible at knowing what they don’t know should be well known by now (ironically).
And if Photoshop had a way to ask it for such, it’d be a mistake.
Gpt thinking it knows something and hallucinating is ultimatelya bug, not a feature, no matter what the apologists say
I know. It’s still baffling how much it messes up when adding two numbers.
I just asked GPT-4:
Its reply:
It’s pretty hit or miss though… I’ve had lots of good calculations with the odd wrong one sprinkled in, making it unreliable for doing maths. Mostly because it presents the result with absolute certainty.
They are mostly large language models , I have trained few smaller models myself, they generally splurt out next word depending on the last word , another thing they are incapable of, is spontaneous generation, they heavily depend on the question , or a preceding string ! But most companies are portraying it as AGI , already !