But over the coming decades AI could dominate coding. I now believe in my lifetime it will be possible for an AI to win almost all coding competitions!
They feed you all these algorithms in college and your brain suggests new algorithms based on those patterns.
An AI agent can interact with an environment and learn from its environment by reinforcement learning. It is important to remember that pattern matching is different from higher forms of learning, like reinforcement learning.
To summarize, I think there are real limitations with this AI, but these limitations are solvable problems, and I anticipate significant future progress
Also Generative Adversarial Networks original implementation was to pit neural networks against each other to train them , they don't need human intervention.
Some come from the other end of the process.
I want to solve that problem -> Functionally, it’d mean this and that -> How would it work? -> What algorithms / patterns are there out there that could help.
Usually people with less formal education and more hands on experience, I’d wager.
More prone to end up reinventing the wheel and spend more time searching for solutions too.
Which fits the pattern matching described by the grandparent.
A few people I know, most of which haven’t been to college, or done much learning at all, but are used to work outside of what they know (that’s an important part), tend to solve problems with things they didn’t know at the time they set out to solve said problems.
Which doesn’t really fit the pattern matching mentioned by the grandparent. At least not in the way it was meant.
(Some call me heterodox, I prefer 'original thinker'.)
This exists: https://en.wikipedia.org/wiki/Automated_theorem_proving
It's like saying that calculators can solve complex math problems; it's true in a sense, but it's not not strictly true. We solve the complex math problems using calculators.
I would very much like GPT-f for something like SMT, then it could actually make Dafny efficient to check (and probably avoid needing to help it out when it gets stuck!)
This looks like a clever example of supervised learning. But supervised learning doesn't get you cause and effect, it is just pattern matching.
To get at cause and effect, you need reinforcement learning, like AlphaGo. You can imagine an AI writing code that is then scored for performing correctly. Overtime the AI will learn to write code that performs as intended. I think coding can be used as a "playground" for AI to rapidly improve itself, like how AlphaGo could play Go over and over again
AlphaGo learns a game with fixed, well-defined, measurable objectives, by trying it a bazillion times. In this autocomplete idiom the AI's objective is constantly shifting, and conveyed by extremely partial information.
But you could imagine a different arrangement, where the coder expresses the problem in a more structured way -- hopefully involving dependent types, probably involving tests. That deeper encoding would enable a deeper AI understanding (if I can responsibly use that word). The human-provided spec would have to be extremely good, because AlphaGo needs to run a bazillion times, so you can't go the autocomplete route of expecting the human to actually read the code and determine what works.
Then we shall be reaching singularity.