Competitive Programming with AlphaCode

And next year they can filter out 99.99%. And the year after that, 99.9999%. So literally, an exponentially greater number of monkey/typewriting units. (An AI produced Shakespeare play coming soon).

>> we have no clue at all what that actually is and how these model learn

This is why I'm super cool-to-cold about the AI/deep learning classes being sold to young people who would otherwise be learning fundamental programming skills. It appears to me like trying to teach someone to ride a horse before they understand what skin, bones, muscles, animals, and horses are.

>>get anywhere close to human (expert) capability in any sufficiently complex domain

You can get close enough to scalp a lot of billionaires, but at the end of the day it's always going to be human coders banging our heads against management, where they ask for shit they can't visualize and it's our job to visualize how their employees/customers will use it. Yes it involves domain specific knowledge, but it also requires, er, having eyeballs and fingers, and understanding how a biological organism uses a silicon-based device. That's kind of the ultimate DS knowledge, after all. Now, lots of coders just copy-pasta a front end, but after all the hooplah here I'd be extremely surprised if in ten years an AI has caught up to your basic web mill in Indonesia when it comes to building a decent website.

Lower-level coding could become more and more automated, raising the values and wages of complementary skills such as requirements elicitation and understanding of business impact from technological decisions. [1]

Some of these, however, can be done by businesspeople who know how to think and express their ideas precisely, such that a neural model can turn them into a decent draft of code. (These days, many more youths learn to code before going into other fields. They have training for thinking precisely.) There can be fewer job opportunities for some groups of developers.

Thus, a hedge against possible job loss is still required. Owning substantial equity in a company/startup and other assets would be one good strategy.

[1] https://en.wikipedia.org/wiki/Complementary_good

Also, these points are not to be taken separately. They're part of a broader argument and should be treated as a unit.

1. Programming competitions are deliberately scoped down. Actual day-to-day work consists of meeting with stakeholders, conducting research, synthesizing that research with prior knowledge to form a plan, then executing. This work skips to the plan synthesis, relying on pattern-matching for the research component.

2. This current work, even if refined, would be insufficient to conduct daily programming work. This is just an extension of point 1; I acknowledge that you're talking about the future and a hypothetical better system.

3. The components required for your hypothetical programming bot are the components not covered by this work.

4. Context-aware/deep search tools are still very incomplete. There are some hints that better user-intent models are around the corner (i.e. companies like TikTok have built models that can adroitly assess users' intents/interests). I've seen no work on bringing those models to bear on something more nebulous like interpreting business needs. (But I also haven't been actively searching for them) Also, Google, who dumps a large amount of money into search every year, is among the best we have and it's definitely far from what we'd need for business-aware programming bots.

5. Conducting the research step in the programming process automatically will require better tools.

6. Conversational AI is still very incomplete. See Tay bot from Microsoft for examples of what goes wrong at scale. People, in general, are also not very aware of themselves during discussions and even very intelligent people get locked in a particular mindset that precludes further conversation. If a user tries fighting the bot by insisting that what they said should be sufficient (as they definitely do to other humans) that could pollute the bot's data and result in worse behavior.

7. Meeting with stakeholders part of the programming process automatically will also require better tools.

8. By points 5 & 7, critical domains still require more research. There is ongoing research in fields like Q&A, even some commercial attempts, but they're focused on mostly low-level problems ("construct an answer given this question and some small input")[0].

9. Advanced logical reasoning is advanced pattern matching + the ability to generate new reasoning objects on the fly.

10. Current systems are limited in the number of symbols they can manage effectively, or otherwise use lossy continuous approximations of meaning to side-step the symbol issue (it's a rough approximation of the truth, I think). See [1] for an up-to-date summary on this problem. Key phrase: binding problem neural networks

11. Current "reasoning" systems do not actually perform higher level reasoning. By points 9+10.

12. Given the rich history and high investment over time these fields (points 4, 6, and 11), it is unlikely that there will be a sufficiently advanced solution within the next 15-40 years. These fields have been actively worked for decades; the current influx of cash has accelerated only certain types of work: work that generates profit. Work on core problems has kept going at largely the same pace as usual because the core problems are hard-- extra large models can only take you so far, and they're not very useful without obnoxious amounts of compute that aren't easily replicated.

13. Given the long horizon in point 12, programmers will likely be required to continue to massage business inputs into a machine-usable format.

The horizon estimate in point 11 was a gut estimate and assumes that we continue working in parallel on all of the required subproblems, which is not guaranteed. The market is fickle and might lay off researchers in industry labs if they can't produce novel work quickly enough. With the erosion of tenure-track positions taking place in higher education (at least in the US) it's possible that progress might regress to below what it was before this recent AI boom period.

[0]: https://research.facebook.com/downloads/babi/ [1]: https://arxiv.org/pdf/2012.05208.pdf

Where is that quote from? Why are you quoting it? Am I supposed to reply to it?

>> You are the one making a claim with certainty based off of guesswork, not me.

I'm not talking about you. I'm talking about the paper, the team behind it and work on large language models trained on online data, in general.

The paper indeed makes a vague claim of "guarding" against data leakage by a "strict temporal split" which means they ensured that the validation and test data used for fine-tuning was not available to the model. That of course doesn't mean much. What matters is if the data on which the model was trained included programs like the ones the model was asked to generate. Clearly, it did, otherwise the model would not have been able to generate any programs that could be used as solutions to the test problems.

And I think you have the rule on the burden of proof a bit wrong. I don't have to prove anything that is already well-known. For instance, if I said that gravity makes things fall down, I wouldn't bear any burden of proof. Accordingly, there is no doubt that neural nets can only represent what is in their training set. That's how neural nets work: they model their training data. They can't model data that is not in their training set. It wouldn't even be fair to expect a neural net to learn to represent data that it wasn't trained on, and just to be clear, I'm not saying that there should be such an expectation, or that it is even desirable. This modelling ability of neural nets is useful. In fact, this is the real strength of neural nets, they are extremely good at modelling. I mean, duh! Why are we even discussing this?

But this is something that the deep learning community is trying to deny, to itself primarily, it seems. Which is exceedingly strange. Work like the one linked above prefers to make bizarre claims about reasoning abilty that we are, presumably, expected to believe arises magickally just by training on lots of data, as if there's a threshold of volume above which data is miraculously transsubstantiated into an element with quite different propeties, from which reasoning or "critical thinking" (dear god) emerges even in the complete absence of anything remotely like a reasoning mechanism. This is nonsense. Why not admit that in order for a large language model to be able to generate code, it must see code "like" the one it's asked to generate? Then we can talk about what "like" means, which is the interesting question. All this attempt to pussyfoot around what those systems are really doing is so counter-productive.

Again, this is not about anything you specifically say, but a criticism of deep learning reserach in general. I don't presume you're a deep learning researcher.

>> It's a weak objection anyway; just the task of translating english prose into the general algorithm you want to apply is already an impressive feat.

Not as impressive as you think. The problem descriptions used on CodeForces etc are not arbitrary English prose. They don't ask participants to write a poem about Spring (and I don't mean the old Java library). So it's not "prose" but very precise specifications. They could be represented as a Controlled Natural Language. So something much easier to model than arbitrary English.

And, yet again, the performance of the model is crap.