The "language models don't really understand anything" corner is getting smaller and smaller. In the last few months we've seen pretty definitive evidence that transformers can recombine concepts ([1], [2]) and do simple logical inference using contextual information ([3], "make the score font color visible"). I see no reason that this technology couldn't smoothly scale into human-level intelligence, yet lots of people seem to think it'll require a step change or is impossible.<p>That being said, robust systematic generalization is still a hard problem. But "achieve symbol grounding through tons of multimodal data" is looking more and more like the answer.<p>[1] <a href="https://openai.com/blog/dall-e/" rel="nofollow">https://openai.com/blog/dall-e/</a>
[2] <a href="https://distill.pub/2021/multimodal-neurons/" rel="nofollow">https://distill.pub/2021/multimodal-neurons/</a>
[3] <a href="https://openai.com/blog/openai-codex/" rel="nofollow">https://openai.com/blog/openai-codex/</a>
A warning to devs building on OpenAI APIs:
We spent months developing a chatbot using GPT3 for our game and released a video showcasing it: <a href="https://www.youtube.com/watch?v=nnuSQvoroJo&t=264s" rel="nofollow">https://www.youtube.com/watch?v=nnuSQvoroJo&t=264s</a><p>Afterwards OpenAI then added GPT3 chatbot guidelines disallowing basically anything like this.
We were in communication with them beforehand, but they decided later that any sort of free form chatbot was dangerous.<p>What they allow changes on a weekly basis, and is different for each customer. I don't understand how they expect companies to rely on them
I'm still surprised by the approach. I mean, great that it works this well -- but program synthesis is one of those rare domains where you can observe exactly what the outcome is after you generate something. You can see execution traces, variable values, what the JIT produced, etc. And all of this is relatively cheap -- often executing a code snippet should be far cheaper than an extra pass through a giant DNN right? So it's fascinating to me that they train entirely from dealing with code as text.<p>Imagine learning to develop recipes, not by ever cooking or eating or even seeing food, but only reading a giant library of cookbooks. Or learning to compose music but never hearing or playing anything -- only seeing scores.
They just finished a demo on twitch. Pretty crazy!<p><a href="https://www.twitch.tv/videos/1114111652" rel="nofollow">https://www.twitch.tv/videos/1114111652</a><p>Starts at 15:45.
They should have released this first instead of GitHub Copilot. The focus would then have been much more on "look at the cool stuff they can do" rather than "Microsoft is releasing a product that plagiarizes GPL code".<p>Once people had digested that and there had been a few other proof-of-concept business ideas around turning Codex into a SaaS (because some people will always queue to build their product on your API), announce the evil version. Not that I really think Copilot is evil, but the IP concerns are legitimate.
Will really be impressed when one could say: “here is this codebase, modify this function so that it would preduce [insert desired efect]” and also other functionality of project would not crash thumbling down…<p>Because writing code from scratch now is i think much rearer than improoving existing codebases. Aka bugfixing.
I thought OpenAI was originally supposed to be some kind of for-the-good, non-profit institution studying AI and its safe use in particular with an effort to make it more accessible and available to all through more open collaboration. This is cool research, sure; but what happened to making models available for use by others instead of just through some opaque APIs?<p>Maybe I'm just remembering wrong or conflating OpenAI with some other entity? Or maybe I bought too much of the marketing early on.
I feel that OpenAI Codex could become like Webflow for coding. It might sound ironic, but what tools like Webflow in the world of Web programming is to give the power of creators to build something fast that can long last (without the speciality of a decent web programmer).<p>If the same thing can happen in the world of programming, I guess evaluations like LeetCode and Whiteboarding can go away and bring in a new of logical thinking evaluation which could ultimately be a more realistic method of some programmer rising above the chain.
I really want to just play with this tech- it’s frightening but also the future, but I’m still waiting to be accepted on the GitHub copilot waitlist. I wonder how long this will take for people who don’t know someone who knows someone…
Can this read existing code and fix one missing piece? That will be cool.<p>Say I have a question I can't solve by searching through stackoverflow. If the AI can solve a problem like that, it will be great.
I think integrations like the MS Word example they show off at the end of the live demo have the potential to be even more impactful than just generating code for programmers.
In the demo video on <a href="https://openai.com/blog/openai-codex/#spacegame" rel="nofollow">https://openai.com/blog/openai-codex/#spacegame</a> it seems like it goes and does an image search for a picture of an asteroid, and them embeds without attribution a direct link to a image hosted on "d.newsweek.com". Not sure I'd call that a resounding example of generating good code...
If this actually worked, wouldn’t that be amazing? If you could break down a software idea into a blue print of concepts that need to be accomplished, and then dictate what should be done…<p>I doubt it works, but I wonder how many decades from now we will be able to walk through a finite number of simple requests and wrap them together as working software. Then people will be able to convert their blueprint into action!
I'm trying to extract some signal from this link...lots of upvotes, no comments, 30 min old, top 3 on HN...I'm worried this will be read as negative, but it's not, just learning, and enough time has passed I'm itching to jump in and ask:<p>- Is the significance here exactly what it says on the tin: the model behind GitHub's AI code completion will be shared with people on an invite basis? Or am I missing something?<p>- What is the practical import of the quote at the end of this comment?<p>"can now" makes me think its a new feature over Github's implementation, which would then indicate the "simple commands" could be general UI, or at least IDE UI, navigation.<p>If "can now" means "it is currently capable of, but will be capable of more", then I'd expect it to be the same as the current implementation on Github.<p>Quote: "Codex can now interpret simple commands in natural language and execute them on the user’s behalf—making it possible to build a natural language interface to existing applications."
I watched their 30 minute demo on Twitch this morning, really good!<p>I use their OpenAI beta APIs as a paying customer, I am still waiting for access to Codex.
I don't understand what is going on, why are people even spending time on this? I think this and copilot and etc are solving a non problem of "we will remove the boring part of programming" by generating a bunch of code, so now it's even more boring to read it and check if it actually does what you want.<p>In the same time zero of the developers I interviewed know how a linked list is laid out in memory, or what is the pro/con of continuous memory layouts, or even how a cpu works actually.<p>Maybe those things are not needed anymore, but I see their code... I think it will be better if they know them.