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11

Good question! AlphaZero, though a major milestone, is most definitely not an AGI :) AlphaGo, though strong at the game of Go, is narrowly strong ("strong-narrow AI"), defined as strength in a single problem or type of problem (such as Go and other non-chance, perfect information games.) AGI, at minimum, must be about as strong as humans in all problems ...


4

Assuming you mean a mathematically perfect player, similar to what we can achieve trivially in Tic Tac Toe, then the answer is "maybe". The underlying reinforcement learning algorithms that it uses do have some convergence guarantees, but there are some caveats: Theories of convergence that apply to value and policy functions learned by RL assume ...


3

We cannot tell with certainty whether AlphaGo Zero would become perfect with enough training time. This is because none of the parts (Neural Network) that would benefit from infinite training time (= a nice approximation of "enough" training time) are guaranteed to ever converge to a perfect solution. The main limiting factor is that we do not know whether ...


3

To my understanding, this is basically a supervised learning problem, where from the self play we have games associated with their winners, and the network is being trained to map game states to likelihood of winning. Yes, although the data for this supervised learning problem was provided by self-play. As AlphaZero learned, the board evaluations of the ...


3

The technique used by AlphaGo is "Monte Carlo Tree Search", combined with a very well trained neural network. The network's job is to estimate the quality of different board states and moves. This estimation is deterministic. If you show AlphaGo the same board on two different occasions, it thinks it is exactly as good (or bad) on both occasions. Monte ...


2

Assumptions That May Be Incorrect There are two assumptions identifiable in the tone of the paper. All mental challenges can be reduced to a game with fixed rules. Machines better than humans are what humans really want or need. There is another two identifiable in the question. General intelligence exists in humans1 If it exists in humans, it is ...


2

Is it fair to compare AlphaGo with a Human player? Depends on the purpose of the comparison. If we are comparing ability to win a game of Go, then yes. If we are comparing learning ability, then maybe. It depends on the task. AlphaGo and systems like it are capable of learning only in well-described limited domains. There may be an analogy with sensory ...


2

The loss of the policy head here is really quite different from losses in, for instance, more "conventional" Supervised Learning approaches (where we typically expect/hope to see a relatively steady decrease in loss function). In this AlphaZero setup, the target that we're updating the policy head towards is itself changing during the training process. When ...


1

The paper that introduced AlphaGo, Mastering the game of Go with deep neural networks and tree search, motivates the use of MCTS Monte Carlo tree search (MCTS) uses Monte Carlo rollouts to estimate the value of each state in a search tree. As more simulations are executed, the search tree grows larger and the relevant values become more accurate. The ...


1

The algorithm in the 2012 survey article (your second link) is the most common / standard implementation. Whenever someone mentions using MCTS or UCT, without explicitly stating any other info, it's safe to assume that that pseudocode is what they're using. The paper by Kocsis and Szepesvári from 2006 (your first link) is (one of) the original publication(s)...


1

Yes AlphaGo Zero could become undeniably perfect. It has won 100:0 against AlphaGo Lee (which won 4:1 against 18-time world champion (human) Lee Sedol) and 89:11 against AlphaGo Master (which won 60 straight online games against human professional Go players from 29 December 2016 to 4 January 2017). From the official AlphaGo website: "AlphaGo's 4-1 ...


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