Technology

I don't think it's gonna go that way. In my experience the bigger the chunk of code you make it generate the more wrong it's gonna be, not just because it's a larger chunk of code, it's gonna be exponentially more wrong.

It's only good for generating small chunks of code at a time.

This is so true. I feel like my main job as a senior software engineer is to keep the bloat low and delete unused code. Its very easy to write code - maintaining it and focusing on the important bits is hard.

This will be one of the biggest and most challenging problems Computer Science will have to solve in the coming years and decades.

There's the other half of this problem, which is that the kind of code that LLMs are relatively good at pumping out with some degree of correctness are almost always the bits of code that aren't difficult to begin with. A sorting algorithm on command is nice, but if you're working on any kind of novel implementation then the hard bits are the business logic which in all likelihood has never been written before and is either sensitive information or just convoluted enough to make turning into a prompt difficult. You still have to have coders who understand architecture and converting requirements into raw logic to do that even with the LLMs.

Makes the Adeptus Mechanicus look like a realistic future. Really advanced tech, but no one knows how it works

uhhhh let me code here

CTO material right there.

Just like an average enterprise architect.

I've had some success with it if I'm giving it small tasks and describe in as much detail as possible. By design (from what I gather) it can only work on stuff it was able to use in training, which means the language needs to be documented extensively for it to work.

Stuff like Wordpress or MediaWiki code it does generally good at, actually helped me make the modules and templates I needed on mediawiki, but for both of those there's like a decade of forum posts, documentation, papers and other material that it could train with. Fun fact: in one specific problem (using a mediawiki template to display a different message whether you are logged in or not), it systematically gives me the same answer no matter how I ask. It's only after enough probing that GPT tells me because of cache issues, this is not possible lol. I figure someone must have asked about this same template somewhere and it's the only thing it can work off of from its training set to answer that question.

I also always double-check the code it gives me for any error or things that don't exist.

It actually doesn't have to be. For example the way I use Github Copilot is I give it a code snippet to generate and if it's wrong I just write a bit more code and the it usually gets it right after 2-3 iterations and it still saves me time.

The trick is you should be able to quickly determine if the code is what you want which means you need to have a bit of experience under your belt, so AI is pretty useless if not actively harmful for junior devs.

Overall it's a good tool if you can get your company to shell out $20 a month for it, not sure if I'd pay it out of my own pocket tho.

Whatever GitHub Copilot uses (the version with the chat feature), I don't find its code answers to be particularly accurate. Do we know which version that product uses?

Is GPT4 publicly available?

Use it as a boilerplate blaster, for shit you could write yourself

The fundamental problem is that at the end of the day it's just a glorified Markov chain. LLM doesn't have any actual understanding of what it produces in a human sense, it just knows that particular sets of tokens tend to go together in the data it's been trained on. GPT mechanic could very well be a useful building block for making learning systems, but a lot more work will need to be done before they can actually be said to understand anything in a meaningful way.

I suspect that to make a real AI we have to embody it in either a robot or a virtual avatar where it would learn to interact with its environment the way a child does. The AI has to build an internal representation of the physical world and its rules. Then we can teach it language using this common context where it would associate words with its understanding of the world. This kind of a shared context is essential for having AI understand things the way we do.