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Is it conceivable that ChatGPT could review itself and produce a superior revision to a human?
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By what metric? For most stuff 3.5 is better in my experience. Assuming you are trying to complete an actual task rather than have fun.
I've tried to make an assistant with ChatGPT, because it's cheaper, and it's abysmally worse than davinci-003 at following instructions and reasoning.
Is it conceivable that ChatGPT could review itself and produce a superior revision to a human?
Current GPT context is too low for this, I believe. We'll have to have something a little closer to AGI for this, and I'm unsure if a cyclic working memory (come up with an output, review the output, review that output, output) will get us to AGI itself. We still need selection functions that bind AI to the real world and not the dream state it lives in. Maybe just hooking it to compilers and allowing it to run test functions will get it a lot farther in self learning, but again, no one seems to know at this point.
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I’m not even kidding when I say I just did 6h or so of work in around 45 minutes
I'm worried it's going to decimate the already wobbly jr market - who are the sr devs of tomorrow to wrangle the LLM output after it replaces the jr's job of coding all day?
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There are already some cool projects that help LLM go beyond the context window limitation and work with even larger codebases like https://github.com/jerryjliu/llama_index and https://github.com/hwchase17/langchain .
The fundamental techniques that they use are highly lossey and are far inferior to ultra-long context length models where you can do it all in one prompt. Hate to break it to you and all the others.
I’m also curious what paper you are referencing that finds that more context vs more relevant context yields better results?
A good survey of the methods for “Augmented Language Models” (CoT, etc.) is here: https://arxiv.org/pdf/2302.07842.pdf
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I remember as a young gamer, asking my Dad whether Nvidia or Intel was a bigger company. It seemed obvious to me, since people always bragged about their GPUs online, but never the CPU, that Nvidia is the more important company. At that time, Nvidia was worth like 1% of Intel. Today, regarding online discussions, nothing has changed. People still brag about GPUs, never the CPU. But now Nvidia is worth 5 times as much…
I feel like this is comparing apples and oranges a bit, depending on when you were a kid. Even within the GPU market, Nvidia wasn't a huge player for a while. It's not just that GPUs are more important today, but that Nvidia went from being a small player in the GPU market to essentially being the GPU market. At the same time, Intel's presence in the CPU market has never been lower. Most "computers" are now smartphon…
My point was that CPU performance seemed to have stopped being the differentiating factor in computers a long time ago. That was evident even in the 2000s. It was impossible to make a CPU that ran much faster than competitors, and most of the innovation appears to be in power consumption, which isn't very exciting.
That's why gamers bragged about GPUs, because GPUs always made a massive difference. This also applies to AI today, GPUs make all the difference, CPUs are just a commodity. Intel become stuck in a commoditized business, while Nvidia was always bleeding edge.
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The only thing holding this back now is lack of enough context. That’s the big nut to crack. How do you hold enough information in memory at once?
GPT4 supports 32k tokens, which I guesstimate would be ~25k code tokens and perhaps ~2.5k lines of code. So already enough to work on a whole module of code at the same time. If you're using small microservices, it might already be good enough.
I have been playing with GPT4 and it's good. It's easy to imagine a bunch of use cases where even the maximal cost of a few dollars ($1.80 ish for the context alone) for a single query is worth it. If it's good enough to save you time, it's very easy to cross the threshold where it's cheaper than a person.
If you want to say what you think is important about an article, that's fine, but do it by adding a comment to the thread. Then your view will be on a level playing field with everyone else's: https://hn.algolia.com/?dateRange=all&page=0&prefix=false&so...
I'm more impressed that it's less than 3 times as expensive as Davinci GPT3. In usecases where most tokens are in the context even only 50% more expensive, while allowing 4x as much context. And you can pay a 100% surcharge to get another fourfold increase in context size. It only looks expensive when compared to the GPT-3.5-Turbo ChatGPT offering which is incredibly cheap (or alternatively Davinci is overpriced by n…
Can you please explain how GPT-4 is cheaper than davinci GPT3. Maybe I do not understand the pricing here…
Does anybody have their hands on it? Is it actually 6 times better at performing tasks, or are we paying more for improved corporate-friendly bumper bowling?
In my anecodotal experience, GPT-4 is at least a million times better than GPT-3. It's like night and day.