Comment by XCSme
4 years ago
But isn't it just predicting text patterns? It doesn't really know about Docker, just that after running commands X,Y you usually get output Z (of course with the stateful AI magic to make things more stable/consistent).
4 years ago
But isn't it just predicting text patterns? It doesn't really know about Docker, just that after running commands X,Y you usually get output Z (of course with the stateful AI magic to make things more stable/consistent).
I mean not to veer to far into the philosophical side of this, but what does it actually mean to know or understand something?
Did you see the demo the other day that was posted here of using stylographic analysis to identify alt accounts? Most of the comments were some form of "holy shit this is unbelievable", and the OP explained that he had used a very simple type of analysis to generate the matches.
We aren't quite as unique as we think was my takeaway from that. My takeawy from this, as well as the SD, DALL-E stuff is that we're all just basically taking what we heard from the past, modifying it a teeny bit, and spitting it back out.
Ok, sure.
…but people are getting the mistaken impression that this is an actual system, running actual commands.
I can also emulate a docker container. I’ll just write down the commands you send me and respond with some believable crap.
…but no one is going to run their web server on me, because that’s stupid. I can respond hundreds of times a second and maintain the internal state required for that.
Neither can this model.
It’s good, and interesting, but it’s not running code, it’s predicting sentences and when you’re running software it was to be accurate, fast, consistent and have a large internal data state.
Trying to run docker in gpt is fun. Trying to use docker in gpt to do work is stupid.
It’s never going to work as well as actually running docker.
It’s just for fun.
Models that write code and the execute that code will be in every way superior to models that try to memorise the cli api of applications.
It’s an almost pointless use of the technology.
Gpt may have “learnt” python; that’s actually interesting!
Docker is not interesting.
If I want to use the docker api, I can type `docker` on my computer and use it.
It's pretty sad that the thing that excites people the most about an amazing new language model is that it can do trivial command line actions, that you can do without the model.
Spending millions of dollars to produce a model that can do what you can already trivially do is very seriously not what openai just did.
> I can also emulate a docker container. I’ll just write down the commands you send me and respond with some believable crap.
Right. The thing that is impressive is that ChapGPT can do this effectively. This means that it has some "understanding" of how `pwd`, `ls`, `apt`, `docker`, etc all work. In some sense, this is an AI that knows how to read code like a human instead of like a machine.
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"It’s never going to work as well as actually running X. It’s just for fun." You must realize that X was also built by some kind of neural networks, i.e. humans, and the only reason we can't run an entire Linux kernel "in our heads" is mostly due to hardware, i.e. brains, limitations. Although, I do remember Brian Kernighan saying in an interview how he was able to run entire C programs "in his head" faster than the 1980s CPUs.
The point is that the future programming language will probably be the human language as an extremely high-level specification language, being able to hallucinate/invent/develop entire technological stacks (from protocols to operating systems to applications) on the fly.
Well, you don't have to convince me, I am pretty sure we are just deterministic machines, meerly responding to stimuli.
> what does it actually mean to know or understand something?
I think it means that you're able to apply the information to make predictions about the world. For example, you'll encounter something novel and be able to make accurate guesses about its behavior. Or, conversely, you will have high likelihood of inventing something novel yourself, based on the information you acquired (rather than through brute force).
do you mind linking to the demo you were talking about please? i didnt see it and it didnt come up in an algolia search
I think there is an element of it producing reasonable results because it is trained on largely seeing canned example output. In tutorials, the command that includes ‘hello world’ always outputs ‘hello world’, right? So it doesn’t take a genius to guess that <long blob of golfed code that includes the string ‘hello world’> should produce some output that includes ‘hello world’
Similarly in my explorations of this ‘pretend Linux’, it often produces whatever would be the most helpful output, rather than the correct output.