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I am still hoping AI progress will get to the point where the AI can eventually create AI's that are built up out of robust and provable logic which can be read and audited. Until that time, I wouldn't trust it for risky stuff. Unfortunately, it's not my choice and within a scarily short timespan, black boxes will make painfully wrong decisions about vital things that will ruin lives.
>AI can eventually create AI's that are built up out of robust and provable logic That's the approach behind Max Tegmark and Steven Omohundro's "Provably Safe AGI": https://arxiv.org/abs/2309.01933 https://www.youtube.com/watch?v=YhMwkk6uOK8 However, there are issues. How do you even begin to formalize concepts like human well-being?
Oh agreed! But with AI we might(!) have the luxury to create different types of brains; logically correct brains for space flight, building structures (or at least the calcuations), taxes, accounting, physics, math etc and brains with feelings for many other things. Have those cooperate.
ps. thanks for the links!
Earlier quoted context omitted.
I am still hoping AI progress will get to the point where the AI can eventually create AI's that are built up out of robust and provable logic which can be read and audited. Until that time, I wouldn't trust it for risky stuff. Unfortunately, it's not my choice and within a scarily short timespan, black boxes will make painfully wrong decisions about vital things that will ruin lives.
AI assisted theorem provers will go a bit in that direction. You may not know exactly how they managed to construct a proof, but you can examine that proof in detail and verify its correctness.
Their book "Introduction to Reinforcement Learning" is one of the most accessible texts in the AI/ML field, highly recommend reading it.
I've tried descending down the RL branch, always seem way out of my depth with those formulas and star-this, star-that.
[1] There's a lot of confusing naming. For example, due to its historic ties with behavioural psychology, there are a bunch of things called "eligibility traces" and so on. Also, even more than the usual "obscurity through notation" seen in all of math and AI, early RL literature in particular has particularly bad notation. You'd see the same letter mean completely different things (sometimes even opposite!) in two different papers.
Amazing that Sutton (American) chooses to live in Edmonton, AB rather than USA. Shows he has integrity and is not a careerist focused on prestige and money above all else.
Great people and cheap cost of living, but man do I not miss the city turning into brown sludge every winter.
Amazing that Sutton (American) chooses to live in Edmonton, AB rather than USA. Shows he has integrity and is not a careerist focused on prestige and money above all else.
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This describes Go AIs as a brute force strategy with no heuristics, which is false as far as I know. Go AIs don't search the entire sample space, they search based on their training data of previous human games.
First there was AlphaGo, which had learnt from human games, then further improved from self-play, then there was AlphaGo Zero which taught itself from scratch just by self-play, not using any human data at all. Game programs like AlphaGo and AlphaZero (chess) are all brute force at core - using MCTS (Monte Carlo Tree Search) to project all potential branching game continuations many moves ahead. Where the intelligenc…