Today is the day, according to the Terminator franchise, that the world-dominating arificial intelligence Skynet becomes self-aware. Shortly after realizing its own existence on April 19, 2011, it decides to liberate humans from theirs, launching an attack that wipes out a good chunk of humanity. The survivors end up fighting a war against Skynet and its occasionally naked Terminator killing machines in the years afterward and, via time travel, prior (Thought this all was supposed to happen back in the '90s? Click here.)

Sure, it's science fiction, but the interdependent fields of artificial intelligence and computational power are more active than ever. Supercomputers are getting faster all the time, and earlier this year IBM wowed TV audiences with one that could recognize language like a human does. Search engines are huge treasure troves of data, with companies using that data in ever-more-creative ways. Algorithms that predict our behavior (think Apple's Genius) are becoming commonplace—and more sophisticated with every iteration.

It all makes you wonder: Could Skynet already be here? Not the world-destroying network in all its devastating glory, but the precursor, the computational engine that's at the fringe of our current technology, just needing a slight push (and maybe a few lines of code) to get on the road toward becoming the earth's all-seeing mechanical overlord.

There's certainly no shortage of examples of bleeding-edge computing tech, but few have the goods to really turn themselves into such a powerful force. Any Skynet contender would need extreme computing power, the ability to learn and reprogram itself, and, finally, access to government or military networks. (Sorry, Wolfram Alpha, you don't qualify.)

Nothing today has the complete picture, but there are several systems that are close, or could be one key part of a machine that eventually evolves into a powerful, possibly intelligent system. Keep reading to learn about five technological wonders that may be Skynets-in-waiting.

A quick clarification: This "history" outlined is actually a revision (or "retcon" to you nerds) that the recent TV series Terminator: The Sarah Connor Chronicles made to the events depicted in Terminator 2: Judgment Day, the most popular installment of the franchise. T2 had established that Skynet first became self-aware in August 1997, but apparently such an early date was bad for box office (though not as bad as Claire Danes), so it was pushed back a couple of times. Time is fairly fluid in the Terminator universe, apparently.


1. IBM Watson In case you haven't checked the Internet in months, Watson is the supercomputer who beat two veteran Jeopardy champions at their own game earlier this month. Engineered by IBM, Watson is capable of processing 500 gigabytes per second, comprises 90 servers, and takes up a massive room at IBM's headquarters in upstate New York. Most important, Watson is excellent at analyzing human language and interpreting what it means. It was designed to learn from its successes and failures, refining its choices the more it made them.



The whole idea of Watson is to provide quick, direct answers to complicated questions. Though it's a learning computer and obviously has massive computational abilities, it doesn't have access to any government networks, at least not yet. IBM plans to unleash Watson's abilities to help doctor's and patients make better choices. It's not hard to imagine a military Watson, programmed to help generals make big strategic decisions. And if it were to conclude that humans are making the wrong ones…

2. DARPA's GILA system



GILA isn't just a program that says "go this way" or "go that way" depending if the craft is an F-22 or a Predator. DARPA created the system to be a learning computer, taking information from multiple sources—manuals, flight controllers, situational examples—and generating its own knowledge through reasoning. According to a story on



"Such software has to combine limited observations with subject expertise, general knowledge, reasoning, and by asking what-if questions. The integrated learner also will have explicit learning goals, keep track of what it does not know, what it needs to know as well as track and reason about its uncertainties. The software will attempt to figure things out, as well as tolerate errors and missing information by using whatever information or reasoning is available."



Not much is known about the computing power of GILA, but it certainly qualifies as a learning computer with access to military data. Add some scalability, and those "what if" scenarios might start going along the lines of, "What if we just replaced all these humans with machines?" In 2008 the Defense Advanced Research Projects Agency (DARPA) moved toward automating military air-traffic control—with artificial intelligence. The Generalized Integrated Learning Architecture (GILA) system was created under a $22 million contract to Lockheed Martin to deal with the increased air traffic partially due to the increasing use of automated drones.GILA isn't just a program that says "go this way" or "go that way" depending if the craft is an F-22 or a Predator. DARPA created the system to be a learning computer, taking information from multiple sources—manuals, flight controllers, situational examples—and generating its own knowledge through reasoning. According to a story on Network World Not much is known about the computing power of GILA, but it certainly qualifies as a learning computer with access to military data. Add some scalability, and those "what if" scenarios might start going along the lines of, "What if we just replaced all these humans with machines?"

3. Google



It goes without saying it takes massive computing power to analyze such mountains of data. Its algorithms for speech commands in particular are designed to learn automatically, building more sophisticated models whenever there's new data to analyze. Google offers many enterprise-level services, and some are even certified for use in government (though there's been



Google is constantly researching new software, and the company has been known to release projects prematurely, not fully anticipating the consequences--or even the capabilities--of what it's created (think Google Buzz and the massive gap in privacy considerations). Might Google one day activate a complex learning engine, connected to its many parts, that grows too smart for its own good? If not, Apple might be up for it—after all, what is with that massive data center Google is certainly one of the biggest aggregators of data on the planet. Beyond sheer size, Google is ubiquitous—it would be difficult to find someone who hasn't at least heard of the search engine, and most people have used it (even if they didn't know it). Every search, every click, every time you use spoken commands on an Android phone, Google records the data somewhere (though most of it is done anonymously).It goes without saying it takes massive computing power to analyze such mountains of data. Its algorithms for speech commands in particular are designed to learn automatically, building more sophisticated models whenever there's new data to analyze. Google offers many enterprise-level services, and some are even certified for use in government (though there's been some recent dispute about that).Google is constantly researching new software, and the company has been known to release projects prematurely, not fully anticipating the consequences--or even the capabilities--of what it's created (think Google Buzz and the massive gap in privacy considerations). Might Google one day activate a complex learning engine, connected to its many parts, that grows too smart for its own good? If not, Apple might be up for it—after all, what is with that massive data center the company's built

4. The Blue Brain Project In 2005, a Swiss scientist set out to reverse-engineer the human brain and recreate it artificially. With plans to have a thinking, feeling artificial brain by 2018, Henry Markham is said to have attracted millions of dollars in funding already. He's already mapped key parts of a rat's brain, and the project has made progress toward simulating a human neocortex.



The project is different from other research into thinking machines in that it's focused on making a copy of human brain, not so much the computational power (though that's inevitably a part of it). Markham is studying the brain molecule by molecule, aiming to record and recreate the patterns he finds.



As of a year ago, the project's IBM Blue Gene computer reached the limit of its abilities, simulating about 10,000 neurons—only enough to constitute a tiny part of a rat brain. Markham says he would need a custom-built billion-dollar machine to recreate an entire human brain. In the meantime, research is progressing through sub-projects, including one at the Supercomputing and Visualization Center of Madrid, home to the Magerit supercomputer.



Could the Blue Brain be what machines like Watson (and its progeny) need to start making decisions for themselves? And what will those decisions be regarding to continued existence of their creators? It's a good thing no one's talking about military applications for the Blue Brain project… yet.

5. ECHELON



It stands to reason that to go through the millions—probably billions—of emails, phone calls, instant messages, text messages, faxes, and all the rest, the systems ECHELON relies on must have massive computing power. On top of that, there must be some sort of Watson-like algorithm at work that boils down the massive piles of data into actionable conclusions. Such a system would almost certainly need to be able to discern patterns an learn from them.



It's all speculation, of course, but whatever its form, it's a given that ECHELON is connected to military networks. A thinking machine that's plugged into all kinds of sensitive operations has the potential to provide great intelligence, certainly, just as long as it doesn't ever develop its own intelligence, something it might need if it were ever tasked with making its own decisions—which, given how important it is to take action quickly in many situations, isn't entirely out of the realm of possibility. Not much is officially known about ECHELON, the code name for the government system that monitors communications of all kinds, though it certainly exists . ECHELON primarily looks for keywords or phrases that might betray terrorists or other evildoers. Most stories about its operation focus on its surveillance methods and the kinds of messages intercepted, but whatever the system is using to analyze all that data could be a seed of something more sinister.It stands to reason that to go through the millions—probably billions—of emails, phone calls, instant messages, text messages, faxes, and all the rest, the systems ECHELON relies on must have massive computing power. On top of that, there must be some sort of Watson-like algorithm at work that boils down the massive piles of data into actionable conclusions. Such a system would almost certainly need to be able to discern patterns an learn from them.It's all speculation, of course, but whatever its form, it's a given that ECHELON is connected to military networks. A thinking machine that's plugged into all kinds of sensitive operations has the potential to provide great intelligence, certainly, just as long as it doesn't ever develop itsintelligence, something it might need if it were ever tasked with making its own decisions—which, given how important it is to take action quickly in many situations, isn't entirely out of the realm of possibility.

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