51–60 of 158 posts
Amazing! So the developer's role will shift to: 1) writing good enough descriptions of the code to be generated by the AI model 2) fixing any little issues in the generated code
To get this you can just grab any random 18 year old who knows js and have them hack something out. No one hires 18 year old js hackers though and there’s a reason for that.
Wow, this has the ability to be a total gamechanger. You have to be really observant about the bugs though, I would have totally missed the one with the price discount without executing it.
By lowering the barrier of entry of programming further, I wonder if we'll see more bugs (like the price discount) as a result of this?
It's much more difficult problem than automated driving though - for software, the space of intents of the user is orders of magnitude greater in size. It's the job of the model to determine the intent of the "programmer". Perhaps we could meet the model half way and come up with heavily-structured natural language to communicate intent.
But there is the thing, the natural description of a function is not always this unambiguous.
When you are telling a function to 'compute XYZ', what you are actually doing is 'check whether X.a exists, if so execute branch 1), else branch 2)'.
If the logic gets really complicated, then describing it accurately in human language isn't necessarily faster than doing it in code directly. Otherwise, we don't need invent programming languages like at all, we can just write compilers to interpret and execute human languages.
And I am interested, as whether the model itself is conditioned on the type constraint of class. It is neat that they pick Python in this case. But if it is Java or other static typed language, would this system condition its generation not only the natural text, but also the resulted type system? My bet, per my understanding of the language modeling approach they use is, they are not doing this, due to very high complexity and cost of the training, and domain adaptation.
Overall, this again is an interesting demo. But I think for code generation based on human language to be useful, we are really in a scenario, that you need to go 99% accurate for it to be remotely practical.
So are we all going to be out of a job?
If people are truly novice, with zero programming experience, how would they know the code is correct? If not, how to debug it?
I would say this is more promising for scenarios to generate more formularitive things like business report generation. But even that, it requires in depth understanding of what those data/tables really means, and how to handle exceptions, etc.
Mirror: https://twitter.com/i/broadcasts/1OyKAYWPRrWKb
Ok, we've changed to that from https://mybuild.microsoft.com/sessions/6c6ecd46-c39c-49d8-ba... . Thanks! If anyone knows a way to link to the start of the demo at 28m30s, or thereabouts, we can modify it again. (Edit: maybe https://blog.twitter.com/en_us/topics/product/2018/video-tim... can be used to make that work?)
It's not obvious that it works until you hit the play button and it starts at the right time. Seems like the only way to get it to play automatically is to embed it in a tweet like this: https://twitter.com/modeless/status/1263222139840167936
Hey, now we have a reason to write proper unambiguous code comments :-)
I had trouble accessing the relevant video snippet even after going through the conference registration, so here's a summary. You can view the demo at https://twitter.com/i/broadcasts/1OyKAYWPRrWKb starting around 29:00. It's Sam Altman demoing a massive Open AI model that was trained on GitHub OSS repos using a Microsoft supercomputer. It's not Intellicode, but the host says that they're working on compressing the m…
In all seriousness, the demo really looks amazing. I'm curious to see more elaborate, real world examples though.