Shyam Sankar: The rise of human-computer cooperation

62,527 views ・ 2012-09-06

TED


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Translator: Joseph Geni Reviewer: Morton Bast
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I'd like to tell you about two games of chess.
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The first happened in 1997, in which Garry Kasparov,
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a human, lost to Deep Blue, a machine.
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To many, this was the dawn of a new era,
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one where man would be dominated by machine.
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But here we are, 20 years on, and the greatest change
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in how we relate to computers is the iPad,
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not HAL.
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The second game was a freestyle chess tournament
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in 2005, in which man and machine could enter together
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as partners, rather than adversaries, if they so chose.
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At first, the results were predictable.
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Even a supercomputer was beaten by a grandmaster
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with a relatively weak laptop.
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The surprise came at the end. Who won?
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Not a grandmaster with a supercomputer,
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but actually two American amateurs
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using three relatively weak laptops.
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Their ability to coach and manipulate their computers
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to deeply explore specific positions
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effectively counteracted the superior chess knowledge
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of the grandmasters and the superior computational power
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of other adversaries.
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This is an astonishing result: average men,
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average machines beating the best man, the best machine.
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And anyways, isn't it supposed to be man versus machine?
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Instead, it's about cooperation, and the right type of cooperation.
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We've been paying a lot of attention to Marvin Minsky's
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vision for artificial intelligence over the last 50 years.
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It's a sexy vision, for sure. Many have embraced it.
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It's become the dominant school of thought in computer science.
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But as we enter the era of big data, of network systems,
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of open platforms, and embedded technology,
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I'd like to suggest it's time to reevaluate an alternative vision
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that was actually developed around the same time.
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I'm talking about J.C.R. Licklider's human-computer symbiosis,
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perhaps better termed "intelligence augmentation," I.A.
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Licklider was a computer science titan who had a profound
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effect on the development of technology and the Internet.
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His vision was to enable man and machine to cooperate
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in making decisions, controlling complex situations
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without the inflexible dependence
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on predetermined programs.
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Note that word "cooperate."
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Licklider encourages us not to take a toaster
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and make it Data from "Star Trek,"
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but to take a human and make her more capable.
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Humans are so amazing -- how we think,
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our non-linear approaches, our creativity,
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iterative hypotheses, all very difficult if possible at all
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for computers to do.
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Licklider intuitively realized this, contemplating humans
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setting the goals, formulating the hypotheses,
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determining the criteria, and performing the evaluation.
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Of course, in other ways, humans are so limited.
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We're terrible at scale, computation and volume.
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We require high-end talent management
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to keep the rock band together and playing.
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Licklider foresaw computers doing all the routinizable work
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that was required to prepare the way for insights and decision making.
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Silently, without much fanfare,
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this approach has been compiling victories beyond chess.
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Protein folding, a topic that shares the incredible expansiveness of chess —
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there are more ways of folding a protein than there are atoms in the universe.
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This is a world-changing problem with huge implications
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for our ability to understand and treat disease.
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And for this task, supercomputer field brute force simply isn't enough.
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Foldit, a game created by computer scientists,
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illustrates the value of the approach.
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Non-technical, non-biologist amateurs play a video game
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in which they visually rearrange the structure of the protein,
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allowing the computer to manage the atomic forces
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and interactions and identify structural issues.
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This approach beat supercomputers 50 percent of the time
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and tied 30 percent of the time.
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Foldit recently made a notable and major scientific discovery
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by deciphering the structure of the Mason-Pfizer monkey virus.
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A protease that had eluded determination for over 10 years
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was solved was by three players in a matter of days,
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perhaps the first major scientific advance
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to come from playing a video game.
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Last year, on the site of the Twin Towers,
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the 9/11 memorial opened.
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It displays the names of the thousands of victims
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using a beautiful concept called "meaningful adjacency."
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It places the names next to each other based on their
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relationships to one another: friends, families, coworkers.
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When you put it all together, it's quite a computational
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challenge: 3,500 victims, 1,800 adjacency requests,
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the importance of the overall physical specifications
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and the final aesthetics.
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When first reported by the media, full credit for such a feat
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was given to an algorithm from the New York City
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design firm Local Projects. The truth is a bit more nuanced.
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While an algorithm was used to develop the underlying framework,
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humans used that framework to design the final result.
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So in this case, a computer had evaluated millions
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of possible layouts, managed a complex relational system,
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and kept track of a very large set of measurements
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and variables, allowing the humans to focus
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on design and compositional choices.
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So the more you look around you,
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the more you see Licklider's vision everywhere.
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Whether it's augmented reality in your iPhone or GPS in your car,
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human-computer symbiosis is making us more capable.
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So if you want to improve human-computer symbiosis,
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what can you do?
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You can start by designing the human into the process.
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Instead of thinking about what a computer will do to solve the problem,
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design the solution around what the human will do as well.
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When you do this, you'll quickly realize that you spent
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all of your time on the interface between man and machine,
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specifically on designing away the friction in the interaction.
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In fact, this friction is more important than the power
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of the man or the power of the machine
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in determining overall capability.
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That's why two amateurs with a few laptops
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handily beat a supercomputer and a grandmaster.
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What Kasparov calls process is a byproduct of friction.
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The better the process, the less the friction.
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And minimizing friction turns out to be the decisive variable.
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Or take another example: big data.
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Every interaction we have in the world is recorded
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by an ever growing array of sensors: your phone,
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your credit card, your computer. The result is big data,
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and it actually presents us with an opportunity
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to more deeply understand the human condition.
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The major emphasis of most approaches to big data
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focus on, "How do I store this data? How do I search
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this data? How do I process this data?"
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These are necessary but insufficient questions.
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The imperative is not to figure out how to compute,
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but what to compute. How do you impose human intuition
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on data at this scale?
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Again, we start by designing the human into the process.
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When PayPal was first starting as a business, their biggest
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challenge was not, "How do I send money back and forth online?"
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It was, "How do I do that without being defrauded by organized crime?"
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Why so challenging? Because while computers can learn
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to detect and identify fraud based on patterns,
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they can't learn to do that based on patterns
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they've never seen before, and organized crime
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has a lot in common with this audience: brilliant people,
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relentlessly resourceful, entrepreneurial spirit — (Laughter) —
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and one huge and important difference: purpose.
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And so while computers alone can catch all but the cleverest
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fraudsters, catching the cleverest is the difference
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between success and failure.
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There's a whole class of problems like this, ones with
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adaptive adversaries. They rarely if ever present with a
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repeatable pattern that's discernable to computers.
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Instead, there's some inherent component of innovation or disruption,
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and increasingly these problems are buried in big data.
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For example, terrorism. Terrorists are always adapting
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in minor and major ways to new circumstances, and despite
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what you might see on TV, these adaptations,
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and the detection of them, are fundamentally human.
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Computers don't detect novel patterns and new behaviors,
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but humans do. Humans, using technology, testing hypotheses,
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searching for insight by asking machines to do things for them.
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Osama bin Laden was not caught by artificial intelligence.
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He was caught by dedicated, resourceful, brilliant people
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in partnerships with various technologies.
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As appealing as it might sound, you cannot algorithmically
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data mine your way to the answer.
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There is no "Find Terrorist" button, and the more data
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we integrate from a vast variety of sources
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across a wide variety of data formats from very
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disparate systems, the less effective data mining can be.
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Instead, people will have to look at data
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and search for insight, and as Licklider foresaw long ago,
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the key to great results here is the right type of cooperation,
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and as Kasparov realized,
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that means minimizing friction at the interface.
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Now this approach makes possible things like combing
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through all available data from very different sources,
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identifying key relationships and putting them in one place,
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something that's been nearly impossible to do before.
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To some, this has terrifying privacy and civil liberties
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implications. To others it foretells of an era of greater
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privacy and civil liberties protections,
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but privacy and civil liberties are of fundamental importance.
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That must be acknowledged, and they can't be swept aside,
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even with the best of intents.
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So let's explore, through a couple of examples, the impact
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that technologies built to drive human-computer symbiosis
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have had in recent time.
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In October, 2007, U.S. and coalition forces raided
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an al Qaeda safe house in the city of Sinjar
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on the Syrian border of Iraq.
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They found a treasure trove of documents:
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700 biographical sketches of foreign fighters.
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These foreign fighters had left their families in the Gulf,
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the Levant and North Africa to join al Qaeda in Iraq.
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These records were human resource forms.
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The foreign fighters filled them out as they joined the organization.
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It turns out that al Qaeda, too,
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is not without its bureaucracy. (Laughter)
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They answered questions like, "Who recruited you?"
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"What's your hometown?" "What occupation do you seek?"
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In that last question, a surprising insight was revealed.
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The vast majority of foreign fighters
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were seeking to become suicide bombers for martyrdom --
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hugely important, since between 2003 and 2007, Iraq
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had 1,382 suicide bombings, a major source of instability.
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Analyzing this data was hard. The originals were sheets
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of paper in Arabic that had to be scanned and translated.
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The friction in the process did not allow for meaningful
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results in an operational time frame using humans, PDFs
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and tenacity alone.
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The researchers had to lever up their human minds
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with technology to dive deeper, to explore non-obvious
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hypotheses, and in fact, insights emerged.
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Twenty percent of the foreign fighters were from Libya,
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50 percent of those from a single town in Libya,
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hugely important since prior statistics put that figure at
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three percent. It also helped to hone in on a figure
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of rising importance in al Qaeda, Abu Yahya al-Libi,
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a senior cleric in the Libyan Islamic fighting group.
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In March of 2007, he gave a speech, after which there was
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a surge in participation amongst Libyan foreign fighters.
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Perhaps most clever of all, though, and least obvious,
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by flipping the data on its head, the researchers were
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able to deeply explore the coordination networks in Syria
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that were ultimately responsible for receiving and
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transporting the foreign fighters to the border.
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These were networks of mercenaries, not ideologues,
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who were in the coordination business for profit.
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For example, they charged Saudi foreign fighters
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substantially more than Libyans, money that would have
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otherwise gone to al Qaeda.
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Perhaps the adversary would disrupt their own network
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if they knew they cheating would-be jihadists.
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In January, 2010, a devastating 7.0 earthquake struck Haiti,
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third deadliest earthquake of all time, left one million people,
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10 percent of the population, homeless.
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One seemingly small aspect of the overall relief effort
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became increasingly important as the delivery of food
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and water started rolling.
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January and February are the dry months in Haiti,
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yet many of the camps had developed standing water.
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The only institution with detailed knowledge of Haiti's
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floodplains had been leveled
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in the earthquake, leadership inside.
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So the question is, which camps are at risk,
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how many people are in these camps, what's the
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timeline for flooding, and given very limited resources
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and infrastructure, how do we prioritize the relocation?
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The data was incredibly disparate. The U.S. Army had
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detailed knowledge for only a small section of the country.
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There was data online from a 2006 environmental risk
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conference, other geospatial data, none of it integrated.
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The human goal here was to identify camps for relocation
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based on priority need.
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The computer had to integrate a vast amount of geospacial
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information, social media data and relief organization
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information to answer this question.
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By implementing a superior process, what was otherwise
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a task for 40 people over three months became
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a simple job for three people in 40 hours,
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all victories for human-computer symbiosis.
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We're more than 50 years into Licklider's vision
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for the future, and the data suggests that we should be
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quite excited about tackling this century's hardest problems,
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man and machine in cooperation together.
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Thank you. (Applause)
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(Applause)
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