Complexity rarely requires a lot of code but a lot of iterations. You don’t know the specifics yet in detail.
That’s actually one of the good parts of LLMs to quickly iterate on design and architecture in human language without having to write the code. After all we only want to decide left it right in the complex stage of development - doesn’t have to necessarily be a smooth ride to figure that out.
Only once the specifics are clear you have to get the good stuff.
That said: it’s still not yet there to really save time to the most of the seniors I’ve talked to -but it’s the lack of reliability and not prompt complexity that I’ve heard as criteria.
Fair line of thought, and it also seems quite a few of the ais we hear about as ‘successes’ (eg that train to drive a car virtually around a track, or play a game) seem to follow the approach you described.
However, by coming to a working solution by throwing billions of iterations (in the case of playing games) and having a human score the results for the next 10000 attempts, it seems to be a very laborious process, and hardly efficient. It ‘saves time’ by using the speed of the computer, yet consumes way more resources than simply having a skilled human. I’d even argue that paying a skilled human to do the task is orders of magnitude cheaper than assembling an array of GPU racks to attempt the task. It seems it can hardly be called ‘intelligent’, nor ‘learning’. It’s like the monkeys and the typewriters. Even if one did do a Shakespearean work, could the monkey be called a playwright?
Honest attempt to changing your mind :)
Complexity rarely requires a lot of code but a lot of iterations. You don’t know the specifics yet in detail.
That’s actually one of the good parts of LLMs to quickly iterate on design and architecture in human language without having to write the code. After all we only want to decide left it right in the complex stage of development - doesn’t have to necessarily be a smooth ride to figure that out.
Only once the specifics are clear you have to get the good stuff.
That said: it’s still not yet there to really save time to the most of the seniors I’ve talked to -but it’s the lack of reliability and not prompt complexity that I’ve heard as criteria.
Fair line of thought, and it also seems quite a few of the ais we hear about as ‘successes’ (eg that train to drive a car virtually around a track, or play a game) seem to follow the approach you described.
However, by coming to a working solution by throwing billions of iterations (in the case of playing games) and having a human score the results for the next 10000 attempts, it seems to be a very laborious process, and hardly efficient. It ‘saves time’ by using the speed of the computer, yet consumes way more resources than simply having a skilled human. I’d even argue that paying a skilled human to do the task is orders of magnitude cheaper than assembling an array of GPU racks to attempt the task. It seems it can hardly be called ‘intelligent’, nor ‘learning’. It’s like the monkeys and the typewriters. Even if one did do a Shakespearean work, could the monkey be called a playwright?
Oh I agree with that completely. The intelligence party is a marketing agent that works really well.
I only see (potential) use cases -I’ve yet to see them being efficient in the development context. :)