{"id":34526,"date":"2018-11-13T18:13:55","date_gmt":"2018-11-13T23:13:55","guid":{"rendered":"https:\/\/digital.hbs.edu\/platform-rctom\/submission\/deepmind-and-the-development-of-artificial-general-intelligence\/"},"modified":"2018-11-13T18:13:55","modified_gmt":"2018-11-13T23:13:55","slug":"deepmind-and-the-development-of-artificial-general-intelligence","status":"publish","type":"hck-submission","link":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/deepmind-and-the-development-of-artificial-general-intelligence\/","title":{"rendered":"DeepMind and the Development of Artificial General Intelligence"},"content":{"rendered":"

DeepMind is a leader in artificial intelligence research most famous for its AlphaGo program, an AI program that defeated the world\u2019s best player at Go[1]<\/a>. Its sole mission is to \u201cpush the boundaries of AI, developing programs that can learn to solve any complex problem without needing to be taught how\u201d[2]<\/a>. As a result, it aims to be at forefront of AI research, so that it can develop and apply artificial intelligence to a wide range of disciplines. It tackles real-world problems by collaborating with experts on long-term research projects centered on the application of AI to different fields.<\/p>\n

Despite recent advancements in AI, researchers at DeepMind recognize that there are constraints and areas of artificial intelligence that have yet to be explored in detail. For example, many of the problems tackled by AI have had limited action states, perfect information between participants, or have had one agent acting at a time. In the real world, most of these constraints don\u2019t exist and actions are more dynamic and varied. As a result, DeepMind has teamed up with Blizzard to use Starcraft II (a real-time strategy game) as a research learning environment, because gameplay in Starcraft II represents a \u201cmore difficult class of problems than considered in most prior [AI] work\u201d[3]<\/a> because it removes many of the constraints mentioned above. \u00a0In addition, Starcraft II brings to the forefront an issue that AI, and humans, have difficulty dealing with: uncertainty and lying. A key component of Starcraft gameplay is the idea of a \u201cfog of war\u201d. Each player is only able to see parts of the map where they currently have a unit[4]<\/a>. This lack of visibility increases the amount of uncertainty in a game and creates the opportunity for players to lie and provide false information[5]<\/a>. As a result, machines cannot simply \u201citerate\u201d and make the best move but must make a judgment based on the incomplete set of information that it has. This, in comparison to having perfect information, is much closer to reality for many of the problems that AI is trying to tackle.<\/p>\n

DeepMind\u2019s goal with Starcraft II is to build \u201cartificial general intelligence\u201d by better understanding what the \u201clearning paradigm\u201d is, so they can build an agent that can learn to \u201cplay any game without much prior knowledge\u201d[6]<\/a>. Unlike previous AIs like Deep Blue (chess) or IBM\u2019s Watson, newest AI by DeepMind is meant to \u201cremember, to strategize, and to learn\u201d[7]<\/a>. \u00a0Since DeepMind understands that their overall goal is lofty, they have broken the problem down into \u201csmaller, more manageable chunks\u201d[8]<\/a>, something that makes games a particularly attractive research environment. They\u2019ve partnered with Blizzard to gain access to a database of replays that the AI can learn from and have designed mini-games to break the overall gameplay into smaller problems[9]<\/a>. For the foreseeable future, DeepMind is trying to tackle the big picture problem by looking at the little pieces with the promise of general application in the future. They have opened the resources to the public to generate interest and gain insight from third parties. I believe DeepMind should also apply their AI in more practical ways in parallel with their work with Starcraft, so they can identify some transitional difficulties early on. They don\u2019t need to address any of the issues before the AI is more developed, but it could be helpful to see how the AI performs outside of the controlled environment if it is meant to have general applications.<\/p>\n

While I understand the pursuit of the overall big picture for building artificial general intelligence, does this grand quest make sense in a world with constrained resources? Will we, in the foreseeable future, use AI in a way where constraints will be minimal, if not nonexistent? Does it make more sense to tackle a problem with more constraints if that\u2019s the intermediate step anyway?<\/p>\n

Word Count: 800<\/p>\n

Photo Credit: DeepMind<\/p>\n

[1]<\/a> Elizabeth Gibney, \u201cWhat Google\u2019s winning Go algorithm will do next\u201d, Nature<\/em> (Mar 15, 2016).<\/p>\n

[2]<\/a> DeepMind website \u201cAbout Us\u201d, https:\/\/deepmind.com\/about\/<\/p>\n

[3]<\/a> Oriol Vinyals et al, \u201cStarcraft II: A New Challenge for Reinforcement Learning\u201d p. 1, Cornell University <\/em>(Aug 2017).<\/p>\n

[4]<\/a> Hochul Cho, \u201cInvestigation of the Effect of \u2018Fog of War\u2019 in the Prediction of StarCraft Strategy Using Machine Learning\u201d p. 2, Computers in Entertainment<\/em> (Jan 2017).<\/p>\n

[5]<\/a> Jonathan Cheng, \u201cHumans Still Rule in This Game \u2013 To win \u2018StarCraft,\u2019 machines need to learn to lie\u201d p. 2, Wall Street Journal<\/em> (Apr 23, 2016).<\/p>\n

[6]<\/a> Justin Groot, \u201cChecking in with the DeepMind Starcraft II Team: Interview with Oriol Vinyals\u201d, Blizzard News<\/em> (Mar 4, 2018).<\/p>\n

[7]<\/a> Christina Beck, \u201cNext AI Challenge: Computers take on StarCraft\u201d, The Christian Science Monitor<\/em> (Nov 05, 2016).<\/p>\n

[8]<\/a> \u201cShall we play a game?; Artificial intelligence\u201d, The Economist<\/em> (May 13, 2017).<\/p>\n

[9]<\/a> Oriol Vinyals et al, \u201cStarcraft II: A New Challenge for Reinforcement Learning\u201d p. 1, Cornell University <\/em>(Aug 2017).<\/p>\n","protected":false},"excerpt":{"rendered":"

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