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.
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.