The Machine Age - NYPOST

The machine ageBy PETER NORVIG

Last Updated: 5:17 AM, February 13, 2011

Posted: 10:34 PM, February 12, 2011

Forty years ago this December, President Nixon decl ared a war on cancer,pledging a “total national commitment” to conquering the disease. Fifty yearsago this spring, President Kennedy declared a space race, promising to land aman safely on the moon before the end of the decade . And 54 years ago,Artificial Intelligence pioneer Herbert Simon decla red that “there are now in theworld machines that think” and predicted that a comp uter would be world chesschampion within 10 years.

How have these bold efforts fared? We all know that Neil Armstrong made the giantleap for mankind in 1969, on schedule, and by the mid-1970s the sight of astronautswalking and driving on the moon became so routine that people impatiently startedswitching their TVs back to baseball. But to the layman it seems that efforts in cancerand artificial intelligence have failed; there have been no breakthroughs giving us thecure or the answer.

But the truth is more complex. On the one hand, the Apollo program truly was aspectacular success, but it also marked the end of progress — no human has traveledmore than 400 miles away from Earth since 1975. Meanwhile, the war on cancer hasmade steady progress. In the last 20 years, death rates have decreased 21% in menand 12% in women, due to a combination of better diagnostic screening, reducedsmoking and specific treatments for dozens of different cancers, particularly lymphoma,leukemia and testicular cancer. Those hoping for a single “cure” were disappointedbecause cancer turned out to be not a single problem but a complex arrangement ofinter-related problems on which we continue to make incremental progress.

Artificial intelligence turned out to be more like cancer research than a moon shot. Wedon’t have HAL 9000, C-3PO, Commander Data, or the other androids imagined in themovies, but A.I. technology touches our lives many times every day, contributinghundreds of billions of dollars to the economy each year, and is improving steadily. Mostof this appears behind the scenes, in applications like these:

* Spam filtering programs using A.I. learning and classification techniques correctlyidentify over 99.9% of the 200 billion spam e-mails sent each day.

* Your Android smartphone can recognize your speech and transcribe it into words quite

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accurately, despite your “New Yawk” accent and the honking cabby passing by on thestreet behind you.

* A.I. chess programs play at the level of top human champions (defeating the worldchampion 40 years after Simon’s prediction, not 10). IBM’s Watson computer willeagerly take on “Jeopardy!” champs starting tomorrow. In checkers, an A.I. program hasachieved perfection — it can play flawlessly and it proved for the first time that checkersalways results in a draw if both sides play correctly.

* With Google’s machine translation system, you type (or speak) a sentence in any of 58languages and see (or hear) a translation into your desired language. The automatedlearning techniques in this project allow a new language to be added in two weeks ofwork rather than the two decades it previously took.

* Your Microsoft Kinect can recognize human motion and gestures well enough that youdon’t need a video game controller anymore.

* Google’s prototype self-driving cars cruised a continuous 1,000 miles of windinghighways and city streets without a single intervention from their human drivers. Butautonomous vehicles are nothing new — unmanned drone aircraft have been usedthroughout the last decade in war zones, and modern airline autopilots are perfectlycapable of flying a plane from liftoff to landing (but the human pilots usually prefer to dosome work themselves).

* A.I. systems approve credit card transactions, insurance applications and claims andloan applications, while detecting fraud and calculating risk; they route billions of phonecalls and Internet connections while relieving traffic jams and detecting suspiciousbehavior; they make stock trades better than humans (unfortunately we learned in 2008that that wasn’t a high enough standard).

On the way to these achievements, the A.I. community learned several surprisinglessons.

The things we thought were hard turned out to be easier. Early A.I. researchconcentrated on what seemed to be difficult intellectual tasks, such as playinggrandmaster level chess, or proving theorems in integral calculus. But it turned out thatthese examples of logical thinking are actually not so difficult for a computer toduplicate; all it takes is a few well-defined rules and a lot of computing power. Incontrast, tasks that we at first thought were easy turned out to be hard. A toddler (or adog) can distinguish hundreds of objects (ball, bottle, blanket, mother, etc.) just byglancing at them, but it turned out to be very difficult to build a computer vision systemthat performs at this level.

Dealing with uncertainty turned out to be more important than thinking with logicalprecision. We think of a clever argument or solution to a problem as one that contains aseries of irrefutable logical steps and are impressed when someone can come up with


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such a sequence. But this is exactly what computers do well. The hard part is dealingwith uncertainty, and choosing a good answer from among many possibilities. Thefundamental tools of A.I. shifted from Logic to Probability in the late 1980s, andfundamental progress in the theory of uncertain reasoning underlies many of the recentpractical advances.

Learning turned out to be more important than knowing. In the 1960s and 1970s, manyA.I. programs were known as “Expert Systems,” meaning that they were built byinterviewing experts in the field (for example, expert physicians for a medical A.I.system) and encoding their knowledge into logical rules that the computer could follow.This approach turned out to be fragile, for several reasons. First, the supply of expertsis sparse, and interviewing them is time-consuming. Second, sometimes they areexpert at their craft but not expert at explaining how they do it. Third, the resultingsystems were often unable to handle situations that went beyond what was anticipatedat the time of the interviews.

Current systems are more likely to be built from examples than from logical rules. Don’ttell the computer how an expert solved a problem. Rather, give it lots of examples ofwhat past problems have been like: Give it the features that describe the initial situation,a description of the solution used and a score indicating how well the solution worked.From that, the computer algorithm can learn what to do in similar situations (andsometimes in situations that are not so similar). This shift in focus is important becauseit is more robust, and because in today’s online world it is often much easier to gatherlots of examples than to interview an expert.

The focus shifted from replacing humans to augmenting them. Another implication ofthe phrase “Expert System” is that it replaces an expert with a computer system. Thatapproach made sense in the 1970s, when computers were rare and expensive andinvestment in computer technology focused on a limited number of high payoffapplications. But today computers are everywhere and it makes more sense to think ofaiding the performance of a human rather than replacing the human. That means thathuman and computer can each concentrate on what they do best. A good example isthe web search engine, which uses A.I. (and other technology) to sort through billions ofweb pages to give you the most relevant pages for your query. It does this far better andfaster than any human could manage. But the search engine still relies on the human tomake the final judgment: which link to click on, and how to interpret the resulting page.

The partnership between human and machine is stronger than either one alone. AsWerner von Braun said when he was asked what sort of computer should be putonboard in future space missions, “Man is the best computer we can put aboard aspacecraft, and the only one that can be mass produced with unskilled labor.” There isno need to replace humans; rather, we should think of what tools will make them moreproductive.

Let’s look at an example — automated translation from one language to another (forexample, English to French) — to see how these lessons have been learned. In the


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1960s, linguist Noam Chomsky correctly noted that there are an infinite number ofEnglish phrases; for example “big deal,” “really big deal,” “really, really big deal,” and soon. Therefore, the language could not be described by a finite list of examples, but onlyby a set of rules, called a grammar. The next two decades focused on trying to articulatethese rules, but the quest proved elusive. Language is too vague, context-dependent,and creative to be captured with a definitive set of rules that strictly delimit thegrammatical from the ungrammatical.

Around 1991, researchers at IBM resurrected an approach that had first beensuggested in 1949: to treat translation in the same way that cryptographers broke secretcodes in World War II, by counting the frequencies of letters and words and looking forpatterns. This approach obviously oversimplifies the grandeur of language, and isdoomed to be an incomplete theoretical model. But it turns out to be a good practicalmodel. With the primitive computers of 1949 it was infeasible, but with moderncomputing power and the wealth of language examples available on the web it hasproven to be the best technique available.

Here’s how it works.First collect examples of translated text. For example, a productbrochure on my desk has the phrase “Installation: Additional instructions andtroubleshooting assistance” under the heading “English” and the phrase “Installation:Instructions supplémentaires et assistance dépannage” under the heading “French.”Collect millions of phrase pairs like this. Then if anyone asks to translate exactly aphrase we have seen before, the computer can just look it up. With millions of knownphrases, this will happen a fair percentage of the time.

But there will always be novel sentences that we have not seen before. For those we willhave to assemble pieces that we have seen as parts of different phrases.

For example, when asked to translate “I need additional instructions” into French, thecomputer should recognize that we’ve seen “additional instructions” before and that itcorresponds to “instructions supplémentaires.” It should also recognize that it has seen“I need” in other phrase pairs, and that it translates sometimes to “J’ai besoin d’,”sometimes to “J’ai besoin de,” and so on. Furthermore, it should realize that in all theFrench examples we have seen, “d’instructions” is more common than “de instructions.”Putting all these pieces together, we come up with the translation “J’ai besoind’instructions supplémentaires.”

The whole process relies on statistics: on the counts of the number of examples wehave seen. There are no explicit grammatical rules, but the system still implements thegrammatical judgment that was known to the native speakers who created all theexample phrases.

This approach may seem simplistic — all it is doing is counting up how often variouscombinations of words have been seen before. Of course, in a real system there arequite a few embellishments on this simple scheme, but the fact remains that itfundamentally relies on example. The key is that at some point the sheer number of


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examples passes a threshold and the system goes from performing terribly to beingquite competent. This is related to the way in which a child learns a language. In bothcases learning is involved, but the human brain is different from computer memory, inways that we do not yet fully understand, so the analogy can only be taken so far. Still, itis striking that the team of Google engineers were able to build a translation system thathandles 58 languages, even though some of the languages are spoken by no one on theteam.

This approach of relying on examples — on massive amounts of data — rather than oncleverly composed rules, is a pervasive theme in modern A.I. work. It has been appliedto closely related problems like speech recognition and to very different problems likerobot navigation. IBM’s Watson system also relies on massive amounts of data, spreadover hundreds of computers, as well as a sophisticated mechanism for combiningevidence from multiple sources.

The current decade is a very exciting time for A.I. development because the economicsof computer hardware has just recently made it possible to address many problems thatwould have been prohibitively expensive in the past. In addition, the development ofwireless and cellular data networks means that these exciting new applications are nolonger locked up in research labs, they are more likely to be available to everyone asservices on the web.

You can now use your phone to translate speech in real time, having a conversation thatwould not have been possible in the past. We’ll keep seeing ever more powerful andmore intelligent applications of computer power becoming available to the generalpublic.

Where is this all going? Will we reach a time when computers actually think?

Many computer scientists would say that the question is ill-posed, and that any answerto it wouldn’t mean much. After all, speakers of English have come to agree that“airplanes can fly,” using a verb that formerly was reserved only for birds, not formachines, and we generally agree that “submarines do not swim.” But these are factsabout linguistic usage, not about aeronautical or underwater engineering. (In fact,speakers of Russian would say that submarines do swim.)

So computers will increase in their capabilities, and we will find ways to relate to them:as tools, advisors, workers, pets, companions,and other roles. But the words we use todescribe what machines do, including whether we use the word “think,” has more to dowith how we view ourselves than it does with the actual capabilities of the machines.

Peter Norvig is director of research for Google and the author of several books aboutartificial intelligence.

MAN vs. MACHINE


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Watson will compete against “Jeopardy!” champions Ken Jennings and Brad Ruttertomorrow through Wednesday (7 p.m. on WABC, Channel 7). In a 15-question practiceround in January, Watson won — but only barely. It was tied with Jennings until the finalquestion.

Watson, which has been under development by IBM for more than five years, is notconnected to the Internet. Rather, the software uses 3,000 core computer processorstaking up the space of about eight refrigerators. Its hard drives are loaded withterabytes of books and information, which Watson searches with thousands ofalgorithms simultaneously. One of the biggest issues is determining what the question isreally asking, translating a “natural language” query into something Watson canunderstand and find the appropriate answer.

IBM hopes to sell Watson to corporations — especially if it can impress Alex Trebek.

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