This 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.<p>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.<p>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.<p>Another was its existing 'semantic' (even illusory) knowledge of the Matrix rain.<p>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.<p>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.
Interesting 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.)<p>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.<p>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.
This - along with GPT - are great ways to create originality detectors, something desperately needed.<p>The generators get all the attention, but we should be finding ways to use these as discriminators, so that we can find innovative and original projects.<p>I would love to get a list of Github repos or Steam games ranked on originality/chronologically. Things that are innovative within their own time. There are people making fascinating things, but it takes days, weeks, months to comb through the wreckage to find them.<p>I have no faith that these models will ever write Slaves to Armok 1 or Finnegans Wake or Dead Stars or original works in their own time - but I think detecting them might be within reach, which is far more useful currently (or at least within my lifespan).<p>I also think that human programming languages look cool for a demo - but ultimately, there should be programming languages that neatly interface with NNs or whatever - rather than pure text manipulation. I'm sure a lot of resources get sucked up into that alone, modeling syntax, etc. There needs to be a programming language that AI would use, probably directly manipulating an AST of sorts (unless I misunderstood this model, and it's already doing that).
I'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]<p>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.<p>[1] <a href="https://en.wikipedia.org/wiki/Fast_inverse_square_root" rel="nofollow">https://en.wikipedia.org/wiki/Fast_inverse_square_root</a>
Cool article.<p>While I have been using GPT-3 via OpenAI’s APIs for about a half a year and I very much also appreciate using GitHub’s CoPilot because it saves me time, I wish for much more research into hybrid AI systems that are multi paradigm: deep learning, symbolic AI, new types of RL learning, breakthroughs in scaling conventional search, etc., etc.<p>There is so much work to get to the point where AI systems can effectively do counter factual reasoning, autonomously develop better models of the world, etc.<p>Symbolic AI as I learned it in the 1980s and deep learning in the last ten years are all great first steps, but we have a long way to go. Assuming parallel work in AI ethics, I don’t think there are any real limits on how much this technology can improve our lives.
I've been playing around with using gpt3 as a research assistant and it can work surprisingly well.<p>It's tricky to get the prompts right I think and you won't necessarily get novel insights, more like the distilled common wisdom of an area.<p>You can ask it to pretend to write the response to a subreddit. And you get an approximation of a subreddit filled with the type of experts you want, instantly answering your questions. Although they occasionally just spout non-sense.
Interesting.<p>Anyone have ideas as to why this works so well with JavaScript specifically? I tried to include similar commands in Python (i.e., use a prompt that implies Python based on commenting style) and it doesn't even write code, but instead keeps adding new comments.
Read the first page. Seem interesting but not interest in shooting game.<p>Given that game of life is … can it generate that abd sone if the patterns.<p>Also can it play go, chess, bridge … etc.<p>If not is it inherent or just this model.<p>Not a game developer and hence just question.
Spooky. Is there any existing tool that can do anything close to this at the moment?<p>Would have liked for the author to discuss a bit more the time spent optimizing the input, and his success rate.