{"id":49130,"date":"2015-10-16T00:00:00","date_gmt":"2015-10-16T00:00:00","guid":{"rendered":"https:\/\/www.techopedia.com\/dont-look-back-here-they-come-the-advance-of-artificial-intelligence\/"},"modified":"2015-10-16T10:11:35","modified_gmt":"2015-10-16T10:11:35","slug":"dont-look-back-here-they-come-the-advance-of-artificial-intelligence","status":"publish","type":"post","link":"https:\/\/www.techopedia.com\/2\/31135\/technology-trends\/dont-look-back-here-they-come-the-advance-of-artificial-intelligence","title":{"rendered":"Don\u2019t Look Back, Here They Come! The Advance of Artificial Intelligence"},"content":{"rendered":"
Up until recently, the directors of corporations might bring laptops or tablets into board meetings (or, with larger firms, have assistants with those devices seated behind them) in order to be used as research tools if the need arose. The keyword here is “tools” \u2014 the devices were used to gather information so that the director might speak intelligently or vote on a particular topic \u2014 the computer system might even make a recommendation on actions to be taken, but the technology was always subservient to the director, who could choose to ignore the data gathered or the recommendation of the so-called “artificial intelligence<\/a>.”\n<\/p>\n Well, the game has just changed! As Rob Wile wrote in Business Insider back in 2014 in a piece titled “A Venture Capital Firm Just Named An Algorithm To Its Board Of Directors \u2014 Here’s What It Actually Does<\/a>,” a computer analysis system has been named as an equal, not a tool, to a Board of Directors. Wile writes, “Deep Knowledge Ventures, a firm that focuses on age-related disease drugs and regenerative medicine projects, says the program, called VITAL, can make investment recommendations about life sciences firms by poring over large amounts of data. \u2026 How does the algorithm<\/a> work? VITAL makes its decisions by scanning prospective companies’ financing, clinical trials, intellectual property and previous funding rounds.” The real kicker in the story is that VITAL is a voting member of the Board with equal voting weight as any other member.\n<\/p>\n Suffice to say that this is only the first of such news that will come down the pike.\n<\/p>\n Artificial intelligence has been scoring all kinds of wins. A self-taught computer made major news in January when it devised the “ultimate” strategy for winning at poker<\/a> after playing 2 trillion simulated hand. The reason that the story might not grab the attention of a lot of readers is that a computer has already won at chess (beating a Grand Master) and checkers (not to mention “Jeopardy”). This, however, is different. In those cases, the computer intelligence knew everything about the issue at hand and was able to scan, on-the-spot, millions of facts, moves, strategies, etc. to compete with an opponent. In this case, the AI does not know what cards the opponent has “in the hole” and is, therefore, dealing with incomplete knowledge. It also does not have a profile on its opponent to know when and how often she\/he “bluffs” and whether or not the opponent has any “tics” or expressions that give away bluffs (although it may learn them as the session goes on).\n<\/p>\n Michael Bowling, who led the project for the University of Alberta in Edmonton, Canada, explained the process for the Associated Press \u2014 the program considered 24 trillion simulated poker hands per second for two months, probably playing more poker than all humanity has ever experienced. The resulting strategy still won’t win every game because of bad luck in the cards. But over the long run \u2014 thousands of games \u2014 it won’t lose money. He commented, “We can go against the best (players) in the world and the humans are going to be the ones that lose money.”\n<\/p>\n The AP article gave further background on the project:\n<\/p>\n “The strategy applies specifically to a game called heads-up limit Texas Hold ’em. In the two-player game, each contestant creates a poker hand from two cards he is dealt face-down plus five other cards placed on the table face-up.<\/p>\n \t“Players place bets before the face-up cards are laid out, and then again as each card is revealed. The size of the wagers is fixed. While scientists have created poker-playing programs for years, Bowling’s result stands out because it comes so close to ‘solving’ its version of the game, which essentially means creating the optimal strategy. Poker is hard to solve because it involves imperfect information, where a player doesn’t know everything that has happened in the game he is playing \u2014 specifically, what cards the opponent has been dealt. Many real-world challenges like negotiations and auctions also include imperfect information, which is one reason why poker has long been a proving ground for the mathematical approach to decision-making called game theory.”\n<\/p><\/blockquote>\n The system, described in the journal Science, drew praise from other Artificial Intelligence researchers with Tuomas Sandholm of Carnegie Mellon University in Pittsburgh (who didn’t participate in the new work), calling Bowling’s results “a landmark” and saying, “it’s the first time that an imperfect-information game that is competitively played by people has been essentially solved.”\n<\/p>\n If this isn\u2019t enough to boggle your mind, how about the fact that a robot somewhere is sitting in front of a computer or TV screen and learning how to do things by watching, “Robot learns to use tools by ‘watching\u2019 YouTube videos<\/a>.” The story, found on the best place that I know of to keep up with new developments in AI technology, Kurzweil AI<\/a>, details how the system, developed by researchers at the University of Maryland and NICTA in Australia, is able to recognize shapes and learn methods of manipulating them.\n<\/p>\nAI as the Decision Makers<\/span><\/h2>\n
AI Outsmarting Humans?<\/span><\/h2>\n
AI Becoming More Intelligent<\/span><\/h2>\n
Robot Morality<\/span><\/h2>\n