No need to worry about comparisons with Microsoft’s Surface laptops or why Apple waited so long to do something so retro

Just looking at the board, there are hundreds of different places one could place down a stone — much more than is possible with a chessboard, which is smaller and whose pieces have more restrictions. There are about 10¹⁷⁰ possible games in Go, making it more than a googol (10¹⁰⁰) times more complex than chess. For this reason, Go is considered the most complex (popularized) board game.

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sults I’ve achieved, I plan to follow a predominantly LCHF diet forever. (Why wouldn’t I? It’s delicious!) I’ll monitor my weight and fast on occasion if it creeps up beyond five pounds of my target. And I’ll occasionally monitor my fasting blood glucose to assure it’s within the normal range. Having been a statistic, I’m damned determined not to go back.

No need to worry about comparisons with Microsoft’s Surface laptops or why Apple waited so long to do something so retroactively obvious. The Federighi introduction talk and demo will convincingly answer all questions.

Go is a two-player game played on a 19-by-19 board. Each player is given either black or white stones, and black goes first. Players place their stones at intersections and try to surround more territories around the board than their opponent (take up more than 50% of the board). A stone cannot be moved after it is placed. Stones can be captured if they are completely enclosed/surrounded by an opponent.

Before I start, I want to mention that the professional way to do it, is by not generating the avatar, but instead using static images for default avatars, because It’s quicker, simpler, less error-prone, more future-proof, and ultimately looks better than generating them programmatically.

“I thought AlphaGo was based on probability calculation and that it was merely a machine. But when I saw this move, I changed my mind. Surely, AlphaGo is creative.” - Lee Sedol, Winner of 18 World Go Titles

“It’s a very intuitive game. If you ask a great Go player how it is they decided on a move, they’ll often tell you it felt right. So these things are generally computers are not great at.” - Demis Hassabis, CEO of DeepMind

Note: This section describes the algorithm for AlphaGo Zero, the version of AlphaGo developed after the initial model beat the world champion. AlphaZero, the system upon which AlphaGo Zero operates, not only taught itself how to play Go but also shogi and chess, beating world champions in each. It provides the most use to applications beyond board games too.

At its core, AlphaGo Zero uses a Monte Carlo Tree Search guided by a neural network. For each board’s position s, the Monte Carlo Tree Search outputs probabilities of playing each move and the final move is the one with the highest output probability.

Almost two decades after then-world chess champion Garry Kasparov lost to IBM’s Deep Blue in a 1997 match, the most advanced Go programs could not beat a professional Go player without a handicap. Then, in May 2017 at the Future of Go Summit, AlphaGo — DeepMind’s AI for Go (DeepMind was later acquired by Google) — won a three-game match against Ke Jie, the world’s #1-ranked Go player.

Within the first two moves of chess, there are 400 possible next moves. Within the first two moves of Go, there are 130,000. The search space in Go is larger than the number of atoms in the universe, and for that reason, a Go-playing AI cannot use the brute-force methods that IBM’s Deep Blue used to beat Garry Kasparov (a brute-force method maps out the possible next moves and finds the most optimal one, such as Minimax).

It used a revolutionary new algorithm — one that relied not on previous brute-force algorithms like Minimax but one that sought to replicate the intuition of the masters with powerful reinforcement learning methods. AlphaGo variants became so powerful that the most skilled version yet, AlphaGo Zero, couldn’t train by playing against human Go champions like the first version of AlphaGo did because the skill inferiority of even the best human players would limit AlphaGo Zero’s capabilities. In the end, AlphaGo Zero’s only worthy match was itself… so it learned by playing against itself.

The idea for this article came to mind while I was working on a project where I have to set a default avatar for each user from the backend because I was dealing with a backend feeding two frontends, and maybe more in the future. But if you’re dealing with a single frontend, you would better consider doing it from the frontend, because it’s a lot easier.