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> Imagine if real programmer needed to write a solution a hundred times To be fair, a lot of creative work requires plenty of trial and error. And since no pro

by Vetch 5y ago

> Imagine if real programmer needed to write a solution a hundred times To be fair, a lot of creative work requires plenty of trial and error. And since no problems are solved from scratch, all things considered, the most immediate contributors to your result and you might have iterated through tens of dozens of possibilities. My advantage as a human is I can often tell you why I am eliminating this branch of the search space. The catch is my reasoning can be flawed. But we do ok. > just copying previous solutions with slight adjustments. It's not just doing that, Copilot can do a workable job providing suggestions for an invented DSL. A better analogy than autocomplete is inpainting missing or corrupted details based on a surrounding context. Except instead of a painting we are probabilistically filling in patterns common in solutions to leetcode style problems. Novelty beyond slight adjustments comes in when constraints are insufficient to pin down a problem to a known combination of concepts. The intelligence of the model is then how appropriate its best guesses are. The limitations to GPT3 codex and AlphaCode seems to be they're relatively weak at selection and that they require problem spaces with enough data to distill a sketch of and how to inpaint well in them. Leetcode style puzzles are constructed to be soluble in a reasonable number of lines, are not open ended and have a trick to them. One can complain that while we're closer to real world utility, we're still restricted to the closed worlds of verbose apis, games and puzzles. While lots of commenters seem concerned about jobs, I look forward to having the dataset oliphaunt and ship computer from Fire Upon Deep someday soon.

briga 5y ago
>The limitations to GPT3 codex and AlphaCode seems to be they're relatively weak at selection This really does seem like the key here--the knowledge apparently is all in the language model, we just haven't found the best ways to extract that knowledge in a consistent and coherent manner. Right now it's just: generate a bunch of examples and cherry pick the good ones.
- hnfong 5y ago
  The way you put it sounds so like the P?=NP problem: If it's easy to tell whether a solution is valid, is it also easy to generate it?
noduerme 5y ago
>> While lots of commenters seem concerned about jobs, I look forward to having the dataset oliphaunt and ship computer from Fire Upon Deep someday soon. I think this is more worthy of debate than anything about DSL models or current limits to problem spaces. I'm not concerned about my job, but I am concerned about a world where corporate money starts shifting toward managing AIs as beasts rather than coding clever solutions. I'm concerned about it because (1) It has always been possible in theory to invent an infinite number of solutions and narrow them down, if you have the processing power, to those that "work", but, this leaves us in a position where we don't understand the code we're running (as a society) or how to fix it (as individuals). And (2) because learning to manage an elephant, as a beast, is utterly different from learning to build an elephant, and it will lead to a dumbing-down of people entering the trade. In turn, they'll become more reliant on things just working the way they're expected to work. This is a very negative cycle for humanity as a whole. Given the thing you're looking forward to, it's only about 30 years before no one can write code at all; worse, no one will know how to fix a broken machine. I don't think that's the thing we should advocate for.
- hnfong 5y ago
  "Understanding the code" might not be that big of a deal as you might think -- we have this problem today already. A talented coder might leave the company and the employer may not be able to hire a replacement who's as good. Now they have to deal with some magic in the codebase. I don't hear people giving advice not to hire smart people. At least with AI, you can (presumably) replicate the results if you re-run everything from the same state. There's also a very interesting paragraph in the paper (I'm in no position to judge whether it's valid or not) that touches on this subject, but with a positive twist : Interpretability. One major advantage of code generation models is that code itself is relatively interpretable. Understanding the behavior of neural networks is challenging, but the code that code generation models output is human readable and can be analysed by traditional methods (and is therefore easier to trust). Proving a sorting algorithm is correct is usually easier than proving a network will sort numbers correctly in all cases. Interpretability makes code generation safer for real-world environments and for fairer machine learning. We can examine code written by a human-readable code generation system for bias, and understand the decisions it makes.

by Vetch 5y ago

briga 5y ago

>The limitations to GPT3 codex and AlphaCode seems to be they're relatively weak at selection This really does seem like the key here--the knowledge apparently is all in the language model, we just haven't found the best ways to extract that knowledge in a consistent and coherent manner. Right now it's just: generate a bunch of examples and cherry pick the good ones.

hnfong 5y ago
The way you put it sounds so like the P?=NP problem: If it's easy to tell whether a solution is valid, is it also easy to generate it?

noduerme 5y ago

>> While lots of commenters seem concerned about jobs, I look forward to having the dataset oliphaunt and ship computer from Fire Upon Deep someday soon. I think this is more worthy of debate than anything about DSL models or current limits to problem spaces. I'm not concerned about my job, but I am concerned about a world where corporate money starts shifting toward managing AIs as beasts rather than coding clever solutions. I'm concerned about it because (1) It has always been possible in theory to invent an infinite number of solutions and narrow them down, if you have the processing power, to those that "work", but, this leaves us in a position where we don't understand the code we're running (as a society) or how to fix it (as individuals). And (2) because learning to manage an elephant, as a beast, is utterly different from learning to build an elephant, and it will lead to a dumbing-down of people entering the trade. In turn, they'll become more reliant on things just working the way they're expected to work. This is a very negative cycle for humanity as a whole. Given the thing you're looking forward to, it's only about 30 years before no one can write code at all; worse, no one will know how to fix a broken machine. I don't think that's the thing we should advocate for.

hnfong 5y ago
"Understanding the code" might not be that big of a deal as you might think -- we have this problem today already. A talented coder might leave the company and the employer may not be able to hire a replacement who's as good. Now they have to deal with some magic in the codebase. I don't hear people giving advice not to hire smart people. At least with AI, you can (presumably) replicate the results if you re-run everything from the same state. There's also a very interesting paragraph in the paper (I'm in no position to judge whether it's valid or not) that touches on this subject, but with a positive twist : Interpretability. One major advantage of code generation models is that code itself is relatively interpretable. Understanding the behavior of neural networks is challenging, but the code that code generation models output is human readable and can be analysed by traditional methods (and is therefore easier to trust). Proving a sorting algorithm is correct is usually easier than proving a network will sort numbers correctly in all cases. Interpretability makes code generation safer for real-world environments and for fairer machine learning. We can examine code written by a human-readable code generation system for bias, and understand the decisions it makes.