Additionally AlphaGo has the advantage that it started with a database of human play, so it has some ideas what kinds of positions humans miscalculate.
As for your tank vs horseback analogy, that's flawed at the moment. AlphaGo is probably reasonably close in strength to the human facing him. Improved human knowledge could tip the balance.
However in the future it will become an apt analogy. Computers are going to become so good that knowing the relative weaknesses in their style of play may reduce the handicap you need against them, but won't give you a chance of becoming even with them. That happened close to 20 years ago in chess, and is now only a question of time in Go.
Yes. A representation of ladders is among the input features of its neural networks.
https://gogameguru.com/i/2016/03/deepmind-mastering-go.pdf
Stone colour 3 Player stone / opponent stone / empty
Ones 1 A constant plane filled with 1
Turns since 8 How many turns since a move was played
Liberties 8 Number of liberties (empty adjacent points)
Capture size 8 How many opponent stones would be captured
Self-atari size 8 How many of own stones would be captured
Liberties after move 8 Number of liberties after this move is played
Ladder capture 1 Whether a move at this point is a successful ladder capture
Ladder escape 1 Whether a move at this point is a successful ladder escape
Sensibleness 1 Whether a move is legal and does not fill its own eyes
Zeros 1 A constant plane filled with 0
Player color 1 Whether current player is black
(The number is how many 19x19 planes the feature consists of.)
Weaknesses are only relative to capabilities of the opponent to exploit them. If a tank has a weak spot that rockets can hit, but it's being opposed by humans on horseback, is it really a weakness in that context?