Peter Norvig continues to be correct, only more so —Two days ago, Twitter lit up with interesting and excellent demos and projects built on top of GPT-3.
The main difference between GPT-3 and its predecessor, GPT-2, is its size.In AI, size especially matters. This will not take your job. What about the filepath of the current file?Don't look at something like this as though watching your job be automated away. Current architectures have long known to be universal, capable of reproducing any computational structure (of finite depth for NNs, and Turing complete for RNNs); they have significant structural flexibility and in principle their learning can converge to "ideal processing structures" (which supposedly our brains also approach) given good enough training conditions (data, regimen, etc.). It’s On top of that, you can interact with GPT-3 via English. Would I need to train it again? How do you mean?A. An AI that really gets it?
grensley 79 days ago. ).> Language modelling and "AI", in its current form, uncovers only statistical correlations and barely scratches the surface of what "understanding" isBert is a language model. (it appears proper commenting is very important to the AI's ability to generate code)Is this a demo of their AI 'autocomplete' tech that they've built into Visual Studio and VS Code?It includes a segment with Sam Altman doing python code generation from nothing other than signatures and comment strings. It is neat that they pick Python in this case. Internal recurrent evaluations of logic and representations of language can all emerge.I wouldn't describe this process as simply statistical inference, since it has complex computational priors and structure involved. It will dot the i's and cross the t's.But it's unclear whether what it says is actually correct and logically coherent when used in the main part of the paper (and not just for the introduction of the paper) or just pleasing-sounding nonsense.My personal fear is that it will be very good at writing code that From bitter experience, though, I also know how unreliable these models can be. To me, these seemed to be obviously cherry picked examples, so you can't really evaluate the whole system based on them.> Fine tuning a vanilla language model on a giant corpus of code feels like a dead end for the field, long-term. Hence my comment above about lack of innovation etc. Is that not you doing the OpenAI demo around 29:00?Altman introduced the video at 29:00, but a different person is narrating the demo.Great, even more lag coming in the next version of visual studio.So that's basically program synthesis from natural language (ish) It requires only a minimal amount of specific knowledge. It might write formulas or produce graphs. specifications (i.e. There is no rigorous way to measure AI-generated I have thought about this before but I can see that logical errors are introduced which must be manually tested and reviewed anyway, so what if a more reliable approach could be achieved by training these data sets on test cases alongside passing code?Instead of Test Driven Development, Test Only Development? So it's one of the research directions that was cut short by the last AI winter. Like a super-powered version of SWIG.But that would require the condition on the type system, meaning the code-gen needs to understand the object's interface, which while not impossible in current techniques, but hard enough due computation complexity.Again I don't dispute this tool being interesting. Frameworks. It would be nice if they created a demo page where we could try this out. If it could generate its own billing and shipping storage to remember indefinitely after getting it from the user, then generate the relevant web scraping / web driving or API code for various websites, that'd be pretty sweet.Hey, now we have a reason to write proper unambiguous code comments :-)Donald Knuth would be proud! I'm curious to see more elaborate, real world examples though.However; I fear this moves software engineering closer to the role of something like plumbing.I've despaired at the state of most software I've used since as far back as I can remember, except when it comes to tools that have the maturity of something like linux, git, emacs, vim and the unix tools.For software to get good - it needs to be deeply understood by at least one person working on it. This is going to take your job, just like an assembly programmer from the 70s might consider Python to have basically taken their job. While it's true that some cannot even write fizzbuzz, some others can be extremely brilliant individuals with an excellent work ethic.I worked on project very much like this last summer, a transformer language model applied to code completion.Did you explore all of these things? It's trained to predict the next character in a sequence. But even that, it requires in depth understanding of what those data/tables really means, and how to handle exceptions, etc.Programming languages are automation tools. And here it is, in an early version, flawed surely but only set to improve.Edit to add: This subject while insanely interesting to me is well out of my wheelhouse. It’s the latest and greatest text-generating neural network. But claims it to be ground breaking or game changing is simply not right.Majority of programmers time, is not typing down the code. It does not have any capacity to "understand" programming, or anything at all. Checkout the mirror on Twitter shared by +corbins. But, if this thing was trained on all of GitHub, I could imagine that it come up with decent-looking code for a lot of examples; a beefy, smarter Google with some rudimentary contextual understanding, if you will. It kind of feels like working with offshore devs, but it's in real time.I've worked with developers from all around the globe. 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?
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