AlphaGo Zero skips this step and learns to play simply by playing games against itself, starting from completely random play. In doing so, it quickly surpassed human level of play and defeated the previously published champion-defeating version of AlphaGo by 100 games to 0.

It also differs from previous versions in other notable ways.

• AlphaGo Zero only uses the black and white stones from the Go board as its input, whereas previous versions of AlphaGo included a small number of hand-engineered features.
• It uses one neural network rather than two. Earlier versions of AlphaGo used a “policy network” to select the next move to play and a ”value network” to predict the winner of the game from each position. These are combined in AlphaGo Zero, allowing it to be trained and evaluated more efficiently.
• AlphaGo Zero does not use “rollouts” - fast, random games used by other Go programs to predict which player will win from the current board position. Instead, it relies on its high quality neural networks to evaluate positions.

The full online version (only available with a Nature subscription) includes the first 100 moves of several games at various points in the learning process.

Fun to watch the progression among the self-play games.

So the "20 block" self-play games are from various stages of training, while the "40 block" folder come only from the strongest version?
That is confusing, I wish that they labeled the "in-training" games somehow, to be able to tell the strength.

A patriotic side note - I just learned that Julian Schrittwieser, one of the main authors of that paper, is from Austria and studied at the Technical University of Vienna. He has worked for Google since 2012 and switched to DeepMind when he heard Demis Hassabis talk about AlphaGo. His background is, naturally, in machine learning.

the best talents

AlphaGo versions We compare three distinct versions of AlphaGo:

1. AlphaGo Fan is the previously published program that played against Fan Hui in October
2015. This program was distributed over many machines using 176 GPUs.

2. AlphaGo Lee is the program that defeated Lee Sedol 4–1 in March, 2016. It was previously
unpublished but is similar in most regards to AlphaGo Fan . However, we highlight several
key differences to facilitate a fair comparison. First, the value network was trained from
the outcomes of fast games of self-play by AlphaGo, rather than games of self-play by the
policy network; this procedure was iterated several times – an initial step towards the tabula
rasa algorithm presented in this paper. Second, the policy and value networks were larger
than those described in the original paper – using 12 convolutional layers of 256 planes
respectively – and were trained for more iterations. This player was also distributed over
many machines using 48 TPUs, rather than GPUs, enabling it to evaluate neural networks
faster during search.

3. AlphaGo Master is the program that defeated top human players by 60–0 in January, 2017.
It was previously unpublished but uses the same neural network architecture, reinforcement
learning algorithm, and MCTS algorithm as described in this paper. However, it uses the
same handcrafted features and rollouts as AlphaGo Lee and training was initialised by
supervised learning from human data.

4. AlphaGo Zero is the program described in this paper. It learns from self-play reinforcement
learning, starting from random initial weights, without using rollouts, with no human supervision,
and using only the raw board history as input features. It uses just a single machine
in the Google Cloud with 4 TPUs (AlphaGo Zero could also be distributed but we chose to
use the simplest possible search algorithm).

This version is the one which played Ke Jie so not that strong cause Ke matched it equally during the first game and also Alpha go Master managed to win some games in the 20 games serie which was released.

If by "this" you mean AlphaGo Zero you are wrong, Ke Jie played a slightly stronger version of AlphaGo Master, not AlphaGo Zero (and he got stomped in the 1st game, the 0.5 score was a gift from AlphaGo to reduce the win margin, it was the 2nd game he played better and kept level for some time). But yes AG Master (which is much stronger than top humans) beat it 11 times out of 100, so if you want to call that "not that strong" you could do so, but others might find that an odd choice of words: I might say "not invincible" or "very strong but still beatable".

AlphaGo Master so rarely pincered approaches to its 4-4s that such opportunities rarely arose. However, AG Zero seems to like pincering a lot more now, often the 3-space low, or 2-space high as below.

Elsewhere, I read about a plan to learn "the" rules on its own. a) If this relies on an input of existing played games, it is possible. b) If there is nothing but the playing material, there cannot be a learning of _the_ rules - there can only be a learning of possible rulesets of possible games that might be played with the playing material.