Building games through natural language using OpenAI’s code-DaVinci model
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Building games through natural language using OpenAI’s code-DaVinci model
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Re: Building games through natural language using OpenAI’s code-DaVinci model
#2is there anyway I can try this for myself?
that is take the instructions and get the in quotes compiler to output the game
Re: Building games through natural language using OpenAI’s code-DaVinci model
#3[deleted]
Re: Building games through natural language using OpenAI’s code-DaVinci model
#4Interesting how you still need to have some intuitive sense of what's going on under the hood. (You can't say "make Zelda," you have to ask for an array of symbols and manipulate them.)
In that sense it feels like this is still programming, but at a higher level of abstraction with a weird fuzzy compiler. Now we can go from natural language -> JavaScript -> assembly etc. rather than just the last two.
Mediocre programmers use APIs, while good programmers know what's behind the curtain and can debug them. I suspect this will stay the same, no matter how many layers of abstraction we add.
Re: Building games through natural language using OpenAI’s code-DaVinci model
#5This is one of those things that's so incredible and mind-blowing I really want to share it with friends or family, but WHY it is so impressive is locked behind a high enough sophistication that it would mostly be lost on them.
Having written a script a decade ago about a future in which software issues would be solved not by debugging or programming, but by finding the right way to communicate concepts to AIs, it's wild to see those nuances emerge.
One of the most interesting details in the post is the bit about asking for a function to create an array rather than the array itself.
Another was its existing 'semantic' (even illusory) knowledge of the Matrix rain.
It's going to be wild seeing this develop over the next few years. I'm sure we'll soon be seeing: specialized discriminators acting as code linters (even for human produced code), efforts at having GPT-3 write more modular instructions for Codex from generalized statements, and a recursive refinement as Codex plus the selection process of humans supervising it re-enters the open source dataset which will go on to train future iterations.
The thing it seems so many evaluating the tech right now overlook when predicting its future is the compounding rate of improvement as opposed to the more linear rates common across past technological parallels which relied on limited human resources.
Re: Building games through natural language using OpenAI’s code-DaVinci model
#6I'd be curious to see what the upper limit of this is. Could it for example, be trained to optimize video games? I think of the magic fast inverse square root optimization in Quake that dramatically reduced the cost of calculating angles.[1]
I bet there's all sorts of non-intuitive optimizations one could do in modern video games that are otherwise too tedious for most programmers to perform.
Re: Building games through natural language using OpenAI’s code-DaVinci model
#7Interesting how you still need to have some intuitive sense of what's going on under the hood. (You can't say "make Zelda," you have to ask for an array of symbols and manipulate them.) In that sense it feels like this is still programming, but at a higher level of abstraction with a weird fuzzy compiler. Now we can go from natural language -> JavaScript -> assembly etc. rather than just the last two. Mediocre progra…
> Mediocre programmers use APIs, while good programmers know what's behind the curtain and can debug them. I suspect this will stay the same, no matter how many layers of abstraction we add.
The skill of both such categories (API developers and developers who use API's) is defined by the ability to know the _least_ amount of complexity needed for a given set of requirements. You may be appealing to some "deeper" sense of what it means to be a programmer, but in terms of what companies are willing to pay - if you get the same job done in a way that is easier to do in the future, you should be rewarded for that, because it saves your own time and the time of anyone who will need to work on that program in the future.
I think this is (only mildly) lacking in nuance. The ability to use AI for this task is surely limited at the moment - and people who know more about programming are certainly more capable of using these systems. As we go forward though, it's important to be able to admit that if an AI can produce a solution faster (and you have easy access to said AI, not a given), then you may be wasting time trying to "roll your own" in pursuit of being a good programmer.
On the other hand, until this AI-assisted experience is democratized, you're correct that it is a good idea to have engineers around who know this stuff from first principles. For now, I'm not terribly concerned that those folks will go away.
Re: Building games through natural language using OpenAI’s code-DaVinci model
#8I'd be curious to see what the upper limit of this is. Could it for example, be trained to optimize video games? I think of the magic fast inverse square root optimization in Quake that dramatically reduced the cost of calculating angles.[1] I bet there's all sorts of non-intuitive optimizations one could do in modern video games that are otherwise too tedious for most programmers to perform. [1] https://en.wikipedia…
It can't do that from scratch yet; these kinds of optimizations require nontrivial mathematical understanding and informed judgement of trade-offs.
But it is capable of knowing your function is an inverse square root and inserting a known optimized version.
Re: Building games through natural language using OpenAI’s code-DaVinci model
#9Spooky. Is there any existing tool that can do anything close to this at the moment?
Would have liked for the author to discuss a bit more the time spent optimizing the input, and his success rate.
Re: Building games through natural language using OpenAI’s code-DaVinci model
#10Spooky. Is there any existing tool that can do anything close to this at the moment? Would have liked for the author to discuss a bit more the time spent optimizing the input, and his success rate.
Yea this. I remember doing some demos recently at work using OpenAI Codex and showcasing how easy it was to write SQL and Python given some natural language requirements.
The bit I didn't really say (I was working a particular angle!) was that I spent a fair bit of time on the prompt design. Changing a word here or there could lead to a drastically different outcome. Over time I got better at learning how to engineer the prompts so the code fulfilled my intention but it was a learning process for sure.