111–120 of 415 posts
I am a little bitter that it is trained on stuff that I gave away for free and will be used by a billion dollar company to make more money. I contributed the majority of that code before it was even owned by Microsoft.
Can you elaborate and give some history? What code did you contribute, and how did it end up being used by Microsoft and then DeepMind?
Earlier quoted context omitted.
I'm already anticipating having the job title of "Query Engineer" sometime in the next 30 years, and I do NLP including large scale language model training. :(
One of the big venture capitalists predicted “prompt engineering” as a future high paid and high status position. Essentially handling large language models. Early prompt engineers will probably be drawn from “data science” communities and will be similarly high status, well but not as well paid, and require less mathematical knowledge. I’m personally expecting an “Alignment Engineer” role monitoring AI systems for u…
Calling it now: If current language models can solve competitive programming at an average human level, we’re only a decade or less off from competitive programming being as solved as Go or Chess. Deepmind or openAI will do it. If not them, it will be a Chinese research group on par with them. I’ll be considering a new career. It will still be in computer science but it won’t be writing a lot of code. There’ll be sev…
If we become mechanics of the software AI vehicles of the future, so be it.
This seems to have a narrower scope than GitHub Copilot. It generates more lines of code to a more holistic problem vs. GitHub Copilot that works as a "more advanced autocomplete" in code editors. Sure Copilot can synthesize full functions and classes but for me, it's the most useful when it suggests another test case's title or writes repetitive code like this.foo = foo; this.bar = bar etc... Having used Copilot I c…
Having used Copilot for a while, I am quite certain it will replace me as a programmer. It appears to me that when it comes to language models, intelligence = experience * context. Where experience is the amount what's encoded in the model, and context is the prompt. And the biggest limitation on Copilot currently is context. It behaves as an "advanced autocomplete" because it all is has to go on is what regular auto…
Calling it now: If current language models can solve competitive programming at an average human level, we’re only a decade or less off from competitive programming being as solved as Go or Chess. Deepmind or openAI will do it. If not them, it will be a Chinese research group on par with them. I’ll be considering a new career. It will still be in computer science but it won’t be writing a lot of code. There’ll be sev…
Yes, for very precise, comprehensive text descriptions of problems. It will take a far-far more advanced AI to write such descriptions for real-world problems. Writing requirements for a project is difficult work, and not for technical reasons, but for human reasons (people don't know what they want exactly, people have trouble imagining things they haven't seen yet, people are irrational, people might want something…
You need an agent with a large and coherent world model, in order to understand how your programs relate to the real world, in order to solve business tasks.
This isn’t something any program synthesis tech currently available can do, because none of it has a coherent world model.
GPT-3 comes closest to this, but isn’t able to engage in any kind of planning or abstract modeling, beyond semi coherent extrapolations from training data.
Maybe scaling up GPT by a few more orders of magnitude would work, by generating an emergent world model along the way.
I would stop programming if all we needed to write was unit tests :p
To compensate, lots of people would start programming if that happened though. Many scientists would be interested in solving their field's problems so easily - certainly maths would benefit from it.
This is extremely impressive, but I do think it’s worth noting that these two things were provided: - a very well defined problem. (One of the things I like about competitive programming and the like is just getting to implement a clearly articulated problem, not something I experience on most days.) - existing test data. This is definitely a great accomplishment, but I think those two features of competitive program…
100% agree. Someone (who?) had to take time and write the detailed requirements. In real jobs you rarely get good tickets with well defined expectations; it's one of most important developer's jobs to transform fuzzy requirement into a good ticket. (Side note: I find that many people skip this step, and go straight from fuzzy-requirement-only-discussed-on-zoom-with-Bob to code; open a pull request without much contex…
Fuzzy business requirements -> programmer specifies and writes tests -> AI codes
Earlier quoted context omitted.
The median rating is not descriptive of median ability, because a large number of Codeforces competitors only do one or a few competitions. A very small number of competitors hone their skills over multiple competitions. If we were to restrict our sample to competitors with more than 20 competitions, the median rating would be much higher than 1300. It's amazing that Alphacode achieved a 1300 rating, but compared to…
You can find the rating distribution filtered for >5 contests here: https://codeforces.com/blog/entry/71260 I am rated at 2100+ so I do agree that 1300 rating is low. But at the same time it solved https://codeforces.com/contest/1553/problem/D which is rated at 1500 which was actually non-trivial for me already. I had one wrong submit before getting that problem correct and I do estimate that 50% of the regular compe…
To be fair, it generated a set of (10) possible solutions, and at least one of them solved the problem.
Calling it now: If current language models can solve competitive programming at an average human level, we’re only a decade or less off from competitive programming being as solved as Go or Chess. Deepmind or openAI will do it. If not them, it will be a Chinese research group on par with them. I’ll be considering a new career. It will still be in computer science but it won’t be writing a lot of code. There’ll be sev…
This viewpoint seems to me to be very similar to the idea of 3rd generation languages replacing developers because programming will be so easy, it isn't about how easy it is to write code, I function as a limited mentat taking all the possible requirements, tradeoffs constraints, analyzing them and then building the model, then I write out the code, the code artifact is not the value I add. The artifact is how I communicate the value to the world.
This doesn't make programmers redundant anymore than Ruby, PHP, or Java made developers redundant because it freed them from having to manually remember and track memory usage and pointers, it is at most a tool to reduce the friction of getting what is in my head into the world.
I control the code and whoever controls the code controls the business. I posses the ability to make out the strands of flow control and see the future state of the application. For I am the Sr. Software engineer and I have seen where no Project Manager can see.
Apologies to Frank Herbet I just finished listening to Dune.
EDIT:
I got off track at the end but my point is that no matter how good the tools for developing the code are, they will never replace a software engineer anymore than electric drills and power saws replace home builders. It merely elevates our work.
Genuine question, what are the reasons to be a software engineer without much ML knowledge in 2022. Seems like a wake up call for developers
I hope you are right, but just to answer the question: all those other AI winters.