{"id":5072,"date":"2017-04-05T16:40:52","date_gmt":"2017-04-05T20:40:52","guid":{"rendered":"https:\/\/digital.hbs.edu\/platform-digit\/submission\/google-alphago-how-will-a-recreational-program-change-the-world\/"},"modified":"2017-04-05T16:43:29","modified_gmt":"2017-04-05T20:43:29","slug":"google-alphago-how-a-recreational-program-will-change-the-world","status":"publish","type":"hck-submission","link":"https:\/\/d3.harvard.edu\/platform-digit\/submission\/google-alphago-how-a-recreational-program-will-change-the-world\/","title":{"rendered":"Google AlphaGo: How a recreational program will change the world"},"content":{"rendered":"

In March 2016, Google\u2019s AlphaGo program defeated the world champion of board game \u201cGo\u201d [1]. Go is popular in China and Korea and is 2500 years old [Go game rules\u00a0are here<\/a>]. This was a watershed moment in Artificial Intelligence as experts had not expected a computer to defeat professional Go players for many more decades.<\/p>\n

What is so special about Go compared to Chess?<\/strong><\/h2>\n

IBM\u2019s Deep Blue computer defeated Chess Grandmaster Kasparov in 1996 [2]. However, Go is more complicated than chess. There are 2 metric\u00a0that captures a game complexity:<\/p>\n

\"\"<\/a><\/p>\n

Source: Blogwriter analysis and [2]<\/em><\/p>\n

To give some perspective, the number of atoms in the universe = 10^80 [2]. Therefore, both chess and Go are almost impossible to beat\u00a0using brute force. Go is also 200 times more difficult to solve than chess.<\/p>\n

Data and algorithmic approach used by AlphaGo:<\/strong><\/h2>\n

AlphaGo achieved this using a combination of data and algorithms<\/p>\n

Large datasets:<\/u><\/h3>\n

The initial dataset for AlphaGo consisted of 30Mn board positions from 160,000 real-life games (Dataset A<\/span><\/strong>). This was divided into 2 parts \u2013 training and testing dataset. The training dataset was labelled (i.e. every board position corresponded to an eventual win or loss). AlphaGo then developed models to predict moves of a professional player. These models were tested on the testing dataset and the models correctly predicted the human move 57% of the time (far from perfect but prior\u00a0algorithms had achieved a success rate of only 44%) [3].<\/p>\n

AlphaGo also keeps playing against itself and generates even more data (Dataset B<\/strong><\/span>). It, thus, continues to generate and learn from more data and improves in performance.<\/p>\n

Algorithms:<\/span><\/h3>\n

\"\"<\/a><\/p>\n

A game like Go is essentially deterministic i.e. since all the moves must follow certain rules, a powerful computer can develop a game tree (like the one shown for tic tac toe on the right) for all possible moves and then work backwards to identify the move that has the highest probability of success. Unfortunately, Go\u2019s game tree is so large and branches out so much that it is \u00a0impossible for a computer to do this calculation. [4]<\/p>\n

AlphaGo identifies the best move by using 2 algorithms together. The first algorithm (Algorithm X<\/strong><\/span>) tries to reduce the breadth of the game while the second algorithm (Algorithm Y<\/strong><\/span>) reduces the depth of the game. Algorithm X comes up with possible moves for AlphaGo to play while algorithm Y attaches a value to each of these moves. This eliminates a number of moves that would be impractical (i.e. for which the probability of winning would be almost zero) and, thus, focuses the machine\u2019s computational power\u00a0on moves with higher winning probability<\/p>\n

Source:\u00a0https:\/\/commons.wikimedia.org\/wiki\/File:Tic-tac-toe-full-game-tree-x-rational.jpg<\/em><\/p>\n

 <\/p>\n

The following schematic explains how AlphaGo uses this combination of data and algorithms to win [Note: this schematic is very bare bones and gives a very high level overview of how AlphaGo works]<\/p>\n

\"\"<\/a><\/p>\n

Source: Blogwriter<\/em><\/p>\n

What value does a “recreational”\u00a0algorithm create?<\/strong><\/h2>\n

AlphaGo\u2019s value creation is beyond just its capability to solve a board game. Google acquired DeepMind (creator of AlphaGo) in 2014 for $500Mn [5] and, thus, clearly sees value in a seemingly recreational program. IBM\u2019s Watson is a great example of recreational programs becoming mainstream technologies. Watson started off as a computer to play jeopardy but now employs more than 10,000 employees [6]\u00a0and is being\u00a0used in healthcare, digital assistants etc.<\/p>\n

The advantage of AlphaGo is that it\u2019s algorithms are general purpose and not specific to Go [2]. It would be comparatively easy for Google to customize the algorithms to solve other AI challenges as well. The data that AlphaGo generates or has collected\u00a0is not useful for other application but the algorithms that power the machines are. Google is already using elements of AlphaGo for incremental improvements in its products like search, image recognition (automatic tagging of\u00a0images inside Google Photos), Google assistant [7].<\/p>\n

How will Google capture value from this?<\/strong><\/h2>\n
    \n
  1. Indirect value capture<\/u>: AlphaGo\u2019s algorithm is already improving Google\u2019s products (like 性视界, Photos and Assistant). Better Google products –> More engaged users –> More ad revenue for Google<\/li>\n
  2. Direct value capture<\/u>: In the future, Google can sell learning and computation service (built on top of AlphaGo\u2019s algorithm) to other firms. IBM Watson has already on-boarded more than 100 businesses onto its platform [8] and the Cognitive Inference division of IBM (that includes Watson) had revenue of $5Bn in Q4 2016 [9]. Google can, therefore, license this technology to products like Siri\u00a0in the future<\/li>\n<\/ol>\n

    Looking ahead<\/strong><\/h2>\n

    Google achieved the holy grail in Artificial Intelligence by developing AlphaGo. Its investment in these algorithms (and a seemingly worthless attempt to win at a board game) will pay rich dividends by improving its products like search, Photos, Assistant and self-driving cars as well as by solving other big problems in healthcare, manufacturing etc.<\/p>\n

    \"\"<\/a><\/p>\n

    Source:\u00a0https:\/\/xkcd.com\/1263\/<\/em><\/p>\n

    **<\/p>\n

    Sources<\/strong>:<\/p>\n

    [1]\u00a0https:\/\/www.theatlantic.com\/technology\/archive\/2016\/03\/the-invisible-opponent\/475611\/<\/p>\n

    [2]\u00a0https:\/\/www.scientificamerican.com\/article\/how-the-computer-beat-the-go-master\/<\/p>\n

    [3]\u00a0https:\/\/blog.google\/topics\/machine-learning\/alphago-machine-learning-game-go\/<\/p>\n

    [4]\u00a0https:\/\/www.tastehit.com\/blog\/google-deepmind-alphago-how-it-works\/<\/p>\n

    [5]\u00a0https:\/\/techcrunch.com\/2014\/01\/26\/google-deepmind\/<\/p>\n

    [6]\u00a0https:\/\/www.nytimes.com\/2016\/10\/17\/technology\/ibm-is-counting-on-its-bet-on-watson-and-paying-big-money-for-it.html<\/p>\n

    [7]\u00a0http:\/\/www.theverge.com\/2016\/3\/14\/11219258\/google-deepmind-alphago-go-challenge-ai-future<\/p>\n

    [8]\u00a0https:\/\/bits.blogs.nytimes.com\/2014\/10\/07\/ibms-watson-starts-a-parade\/<\/p>\n

    [9]\u00a0https:\/\/www.theregister.co.uk\/2017\/01\/20\/its_elementary_ibm_when_is_watson_going_to_make_some_money\/<\/p>\n

     <\/p>\n","protected":false},"excerpt":{"rendered":"

    AlphaGo program shocked the world by defeating Lee Sedol, the world champion in Go. 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