“No computer can yet pass the ‘Turing test’ and be taken as human,” writes Marcus du Sautoy, Simonyi professor for the public understanding of science and a professor of mathematics at the University of Oxford. “But the hunt for artificial intelligence is moving in a different, exciting direction that involves creativity, language – and even jazz.” [“AI robot: how machine intelligence is evolving,” The Observer, 31 March 2012] For those unfamiliar with the Turing Test, it comes from a 1950 paper by Alan Turing entitled “Computing Machinery and Intelligence.” It is a proposed test of a computer’s ability to demonstrate intelligence. Du Sautoy reports that Turing’s imagination was stimulated by book he read as a child that insisted the human body is machine — a marvelous machine, but nonetheless a machine. Turing then asked himself, “Can machines think?” That question, of course, leads to a number of other questions. Du Sautoy continues:
“If the body were a machine, Turing wondered: is it possible to artificially create such a contraption that could think like he did? This year is Turing’s centenary so would he be impressed or disappointed at the state of artificial intelligence? Do the extraordinary machines we’ve built since Turing’s paper get close to human intelligence? Can we bypass millions of years of evolution to create something to rival the power of the 1.5kg of grey matter contained between our ears? How do we actually quantify human intelligence to be able to say that we have succeeded in Turing’s dream? Or is the search to recreate ‘us’ a red herring? Should we instead be looking to create a new sort of machine intelligence different from our own?”
Those are excellent questions that will one day, hopefully, lead to excellent answers. In previous posts on the subject of artificial intelligence, I’ve noted that understanding how the human brain works remains a mystery. We’re learning more all the time but the human brain still holds secrets. George Dvorsky reports, “There’s an ongoing debate among neuroscientists, cognitive scientists, and even philosophers as to whether or not we could ever construct or reverse engineer the human brain. Some suggest it’s not possible, others argue about the best way to do it, and still others have already begun working on it.” [“How will we build an artificial human brain?” io9, 2 May 2012] The reason that scientists use the human brain as the standard for developing artificial intelligence is that they have come to the conclusion that lessons learned through millions of years of evolution should not be ignored.
Du Sautoy notes that IBM’s two famous computer vs. man victories (one in chess and the other in Jeopardy!) required two different approaches to problem solving. The human brain does both simultaneously. He writes:
“Playing chess requires a deep logical analysis of the possible moves that can be made next in the game. Winning at Jeopardy! is about understanding a question written in natural language and accessing quickly a huge database to select the most likely answer in as fast a time as possible. The two sorts of intelligence almost seem perpendicular to each other. The intelligence involved in playing chess feels like a vertical sort of intelligence, penetrating deeply into the logical consequences of the game, while Jeopardy! requires a horizontal thought process, thinking shallowly but expansively over a large data base.”
Because different problems require different approaches, Du Sautoy reports that “the AI community is beginning to question whether we should be so obsessed with recreating human intelligence.” He explains:
“[Human] intelligence is a product of millions of years of evolution and it is possible that it is something that will be very difficult to reverse engineer without going through a similar process. The emphasis is now shifting towards creating intelligence that is unique to the machine, intelligence that ultimately can be harnessed to amplify our very own unique intelligence.”
In other words, instead of trying to cram every possible problem-solving technique into a single “machine mind,” it may make more sense to target AI solutions. After all, a machine doesn’t need to be all things to all people. Du Sautoy points out that computers are already better at crunching numbers than humans. IBM’s “Blue Gene can perform 360 trillion operations a second, which compares with the 3 billion instructions per second that an average desktop computer can perform,” and far more than a human brain can achieve. He notes, “This extraordinary firepower is being used to simulate the behavior of molecules at an atomic level to explore how materials age, how turbulence develops in liquids, even the way proteins fold in the body.” He goes on to ask, however, “But isn’t this number-crunching rather than the emergence of a new intelligence?” He continues:
“The machine is just performing tasks that have been programmed by the human brain. It may be able to completely outperform my brain in any computational activity but when I’m doing mathematics my brain is doing so much more than just computation. It is working subconsciously, making intuitive leaps. I’m using my imagination to create new pathways which often involve an aesthetic sensibility to arrive at a new mathematical discovery. It is this kind of activity that many of us feel is unique to the human mind and not be reproducible by machines. For me, a test of whether intelligence is beginning to emerge is when you seem to be getting more out than you put in.”
To state it another way, du Sautoy believes that when machines get creative (i.e., when they begin “to surprise the creators”) then one can start debating about whether machines are actually thinking. Christopher Steiner puts it this way, “Creative types tend to think of themselves as doing work that is beyond the reach of automation. Computers can’t parse nuance, the thinking goes, or summon the imaginative powers that are required of writers, artists, technological innovators and policy-makers.” [“Automatons Get Creative,” Wall Street Journal, 17 August 2012] Steiner goes on to state, “As it turns out, however, this flattering assumption is mistaken. Computers can be creative after all.” Du Sautoy agrees. He reports, “Exciting new research is currently exploring how creative machines can be in music and art.”
Du Sautoy reports that “engineers at Sony’s Computer Science Laboratory in Paris are beginning to produce machines that create new and unique forms of musical composition.” Those machines have even been able to “do jazz improvisation live with human players.” He continues:
“Other projects have explored how creative machines can be at producing visual art. The Painting Fool is a computer program written by Simon Colton of Imperial College. Not everyone likes the art produced by the Painting Fool but it would be anemic art if they did. What’s extraordinary is that the programs in these machines are learning, and changing and evolving so that very soon the programmer no longer has a clear idea of how the results are being achieved and what it is likely to do next. It is this element of getting more out than you put in that represents something approaching emerging intelligence.”
Steiner believes that creativity can be treated like a separate problem set and that software can be developed to address the creativity problem set. He writes:
“The more we understand about creativity, the more we are able to distill it into the language of algorithms—the ‘brains’ behind computer programs. An algorithm takes a series of inputs and then, moving through its own decision tree, issues an output or an answer. The gears can be as simple as binary questions of yes/no—or they can be a series of complicated differential equations that draw on outside databases. The point, ultimately, is that algorithms are fast, repeatable and easy to use at massive scale. They are already determining some of the music that reaches our ears, movies that reach the big screen, decisions regarding national security and even the kind of people we often reach on the phone.”
In tomorrow’s post, we’ll look at few more ways that computers are getting creative and ask anew whether that really makes them intelligent.