151–160 of 263 posts
Why do we write code ? Code is not a goal. It's a tool. The tool can get better. But it will always be a tool and someone has to control the tool. Writing code is super easy. It's the easiest part of our job. The hard part of our jobs is to understand why we need to write code and right along that, what we write code for ? Maintenance Structuring Domain knowledge Refactoring Those are hard things to do. And they are…
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I mostly work with data mining on my personal projects(which is a couple of hours every day), and I'm pretty sure I didn't have to write a single regex since I've started using Copilot. It's hard for me to even imagine how I used to do it before, and how much time I've wasted on stupid mistakes and typos. Now I just write a comment of what I want and an example string. It does the job without me having to modify anyt…
How do you know the regular expressions are correct, without understanding them?
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>ventriloquist trick I don't understand your analogy here. It really is the machine talking; there is no human hiding behind the curtain.
I think better analogy is parrot speaking English. Certainly no ventriloquism there.
On the other hand parrots can act in the environment, LMs are isolated from the world and society. So a parrot has a chance to test its ideas out, but LMs don't.
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It does write its own tests, i.e. to the extent it checks the generated programs work on the data provided and discards the ones that don't. I imagine many of the coding challenges it's trained on come with a few tests as well.
I meant actually coming up with examples consisting of specific problems and their correct solutions (and maybe some counterexamples.) Ironically, I had just replaced 'test cases' with 'tests', because I thought that the former might seem too generic, and arguably satisfiable merely by rephrasing the problem statement as a test case to be satisfied.
It would be interesting to know how much of this improvement in the last 25 since he was a student comes from Moore's law, other hardware improvements, various new technologies not related to AI, amount of money being thrown at the problem... and how much of it are advancements in our understanding of AI.
It’s mostly Moores law and scaling. Language model progress will be the next Moores law until diminishing returns are reached.
That's 25x in one go. Maybe we have the chance to run GPT-4 ourselves and not need 20 GPU cards and a 1mil $ computer.
[1] https://deepmind.com/research/publications/2021/improving-la...
Which rate of progress are we ITT expecting from alphacode-like models? In another thread I predicted we’d see competitive programming “solved” in ten years or less. I didn’t rigorously explain what I meant by that in that thread, so I’ll clarify what I meant. I expect AI to beat humans at competitive programming at the same rate as AlphaGo beats human Go players. There could be diminshing returns soon but I don’t se…
I am willing take on your bet, as long you agree with my condition...you can't feed it with any of the millions of lines of code previously created by humans you aim to beat...;-)
Give the AI as much data as it wants, if it manages to solve something, it means it's a problem worth automating (is this a cat?)
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If a junior dev writes truly head-scratching code, you could ping that person and ask why they wrote this line a certain way, as opposed to a more straight-forward way. Correct me if I'm wrong but you can't ask an ML model to do that (yet).
True. But is it really important "why"? I think what's more important is whether we can correct AI's output in a way that makes it learn to avoid writing similar head-scratching code in the future.
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Compilers didn't replace any jobs, they created more. Similarly, this type of AI-assisted programming will allow more people to program and make existing programmers more productive.
In my view this type of system will only be usable by Real Computer Scientists and will completely kill off the workaday hacker. Think of all the people who bitterly complain that a C++ compiler does something unexpected under the banner of UB. That crowd cannot cope with a world in which you have to exactly describe your requirements to an AI. It is also analogous to TDD, so all the TDD haters, which is the overwhel…
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We're not seeing exponential rates of progress, in my opinion. We've been on the steep part of an S-curve.
When you're on that curve, it's indistinguishable until you hit the plateau. We're in an era where AI is continuing to improve and has already surpassed a level that many people doubted was achievable. Nobody knows when that progress will plateau. It's entirely possible that we plateau _after_ surpassing human-level intelligence.