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From DeepMind's research paper on arxiv.org:

In this paper, we apply a similar but fully generic algorithm, which we call AlphaZero, to the games of chess and shogi as well as Go, without any additional domain knowledge except the rules of the game, demonstrating that a general-purpose reinforcement learning algorithm can achieve, tabula rasa, superhuman performance across many challenging domains.

Does this mean AlphaZero is an example of AGI (Artificial General Intelligence)?

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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 worked on or solved by humans.

AGI is often associated with superintelligence, defined as intelligence that surpasses human levels.

AGI does not necessarily imply super-intelligence, in the sense that we'd consider an android that can perform all human activities with the same capability as humans as an Artificial General Intelligence.

But technically, AlphaGo is a narrow superintelligence in that it exceeds all human performance in a single problem.

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  • $\begingroup$ I do not disagree with you, but your answer seems to be about AlphaGo Zero rather than AlphaZero, which is still narrow, but not nearly as narrow as AlphaGo. $\endgroup$
    – David Hoelzer
    Dec 20, 2021 at 0:17
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    $\begingroup$ AlphaZero domain is very narrow. Can't read text. Doesn't do calculus. Can't do scrabble or jeopardy. AlphaTensor is starting in on linear algebra, but it takes a very particular (simplified gridworld-style ) game formulation. $\endgroup$
    – EngrStudent
    Oct 11, 2022 at 19:32

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