A Google-developed supercomputer stunned South Korean Go grandmaster Lee Se-Dol by taking the first game of a five-match showdown between man and machine in Seoul on Wednesday.
After about 3-1/2 hours of play, Lee, one of the greatest players of the ancient board game in the modern era, resigned when it became clear the AlphaGo computer had taken an unassailable lead.
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"I was shocked by the result," Lee acknowledged afterwards.
"AlphaGo made some moves that no human would ever make. It really surprised me," he said, adding that the computer had shut out the game "in a perfect manner."
Despite the shock loss, Lee said he had no regrets and was looking forward to the remaining four matches.
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"I had some failures in the early stages today so if I improve on this, I think I still have some chance to win," he said.
Although the computer had whitewashed European champion Fan Hui 5-0 last October, it had been expected to struggle against 33-year-old Lee, who has topped the world rankings for most of the past decade.
But its creators had been bullish going into the match at the Four Seasons hotel in the South Korean capital, saying the computer, which employs algorithms that allow it to learn and improve from matchplay experience, was even stronger than when it took on Fan.
"We are very, very excited by this historic moment and very, very pleased with how AlphaGo performed," Demis Hassabis, the CEO of AlphaGo developer DeepMind, said after the victory.
"We think that Lee will come up with new strategies and... try some different things tomorrow. We'll have to see how AlphaGo will deal with it," Hassabis said.
The match-up sparked enough interest to warrant an Internet live-stream as well as live TV broadcasts in South Korea, China and Japan.
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"I was shocked. Everyone was," said Kim Seong-Ryong, a Korean Go commentator and professional player.
"Something none of us thought would happen has just happened."
The five-day battle for supremacy between man and machine has been seen as a major test of what scientists and engineers have achieved in the sphere of Artificial Intelligence over the past 10 years or so.
The most famous A.I. victory to date came in 1997, when the IBM-developed supercomputer Deep Blue beat the then-world class chess champion Garry Kasparov.
But experts say Go presents an entirely different challenge as the complexity of the game and almost incalculable number of move options mean that the computer must be capable of human-like "intuition" to prevail.
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"Go really is our Mount Everest," said Hassabis, adding that the public response to the clash with Lee had been "far bigger than we expected."
When Lee first accepted the A.I. challenge, he had confidently predicted a clear-cut win, saying that AlphaGo's performance against Fan had been nowhere near good enough to defeat him.
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But the grandmaster had confessed to some pre-match nerves on Tuesday.
Technical Breakthrough Go involves two players alternately laying black and white stones on a checkerboard-like grid of 19 lines by 19 lines. The winner is the player who manages to seal off more territory.
The game reputedly has more possible board configurations than there are atoms in the Universe, and mastery by a computer was thought to be at least a decade away until the victory over Fan last year.
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Creating "general" or multi-purpose, rather than "narrow," task-specific intelligence, is the ultimate goal in A.I. -- something resembling human reasoning based on a variety of inputs, and self-learning from experience In the case of Go, Google developers realized a more "human-like" approach would win over brute computing power.
AlphaGo uses two sets of "deep neural networks" containing millions of connections similar to neurons in the brain.
It is able to predict a winner from each move, thus reducing the search base to manageable levels -- something co-creator David Silver has described as "more akin to imagination."
South Korean Go grandmaster Lee Se-Dol lost the first game of a five-match showdown between man and machine.
A few weeks back, researchers with Google's artificial neural networks team issued a
about its A.I. system, Deep Dream, that could see pictures in clouds and even (arguably) create original art. Yesterday, a team of four engineering students at
announced via Popular Science, that they were coming out with an app called
that used Deep Dream's source code to create psychedelic art out of ordinary images. It gets technical, and a little existential, but the basic gist is that by running an image recognition processes in reverse, an image recognition system was able to generate original images rather than just identify them. After training the system with thousands of images of a particular object -- a starfish, say -- the team discovered that the neural network would identify "starfishy" elements in other, unrelated images. The results are trippy, to say the least. But Deep Dream is not the first computer to generate art. We take a look at it here, along with some other examples of machine-generated art.
Deep Dream can generate surprisingly compelling images, depending on what parameters are established when it first begins to process a picture. Each layer in a neural network builds on the ones beneath it, so running an image through lower layers tends to generate lines and simple patterns. In the higher-level layers, however, the network is looking for more sophisticated features and will tend to generate complex images and entire objects. When the Google team had Deep Dream process an image of a cloudy sky, it began creating images of fantastic hybrid animals like the "pig-snail" and the "camel-bird." Google's name for the process? "Inceptionism." In the image above, a neural network programmed to distinguish architectural and animal elements was cut loose on a landscape. The resulting output is therefore not based on any sample image -- it's purely a result of the A.I.'s "thoughts" on the issue.
Google has since published the
source code, putting A.I. artistry in the hands of the people. Almost immediately after the code was made public, enterprising engineers and hobbyists began creating tools to explore the possibilities of Google's neural network.
is one of several Web apps that has popped up in recent days, and it looks like Instagram on powerful alkaloids. While Dreamscope doesn't give access to the full spectrum of Deep Dream's abilities, it does make the process quick and easy. Just upload an image, select one of the 19 provided filters, and you'll get your own A.I. art show within about 15 seconds. (The first wave of "user-friendly" Deep Dream tools took hours or even days to process an image.) Above is one of the world's most famous public domain images -- the 1970 meeting between Richard Nixon and Elvis Presley -- as run through Dreamscope's "demonic" filter. Captures the moment nicely, doesn't it?
The imagery Deep Dream produces is unique in terms of how it's produced, but machine-generated art -- sometimes called digital art or generative art -- has actually been around for quite a while. Probably the most familiar example is fractal art, in which dedicated software turns algorithmic equations into still images and animations. Fractals are natural phenomena which occur both in mathematics and biology. In a fractal, recursive patterns repeat at different scales -- so that a tiny sliver of a fern leaf will look much the same as the larger fern leaf itself. These repeating geometric patterns can be plotted mathematically, in two or three dimensions, then converted into lines, shapes and colors. The resulting images are virtually infinite in variety and complexity, depending on how you tweak each iteration of a fractal.
Machine-generated art has been exhibited in galleries all over the world since at least the 1960s. But artists and historians have historically disagreed over whether such exhibits are truly created by computers, or whether computers are simply another tool used by the human artist. Another open question: Can you even term a machine-generated image or object as "Art"? British computer scientist Simon Colton has been exploring these questions with his A.I. project known as
. The A.I. system, adapted for exhibition in galleries, takes a digital picture of each visitor then selects from thousands of abstract templates and image filters. The Painting Fool makes its choices depending upon processes that govern the machine's "mood" -- for instance, scanning text from a newspaper. If its mood is dark enough, it might not paint at all. The Painting Fool also learns from its mistakes and Colton is continually adjusting the A.I.'s algorithms to meet his seven criteria for true creativity: skill, appreciation, imagination, learning, intentionality, reflection and invention. The program has recently branched out to start producing sculptures, animations and poetry.
But there's still that sticky question about Art, with a capital A. Even if a machine does generate original images -- or objects or manuscripts -- do these creations truly constitute artistic expression? The software system known as
, for instance, has been creating original artistic images since 1973. Developed by painter and computer scientist Harold Cohen, the program has gone through different stylistic periods in which it has created both highly abstract and highly representative images. AARON's drawings are created though a system of custom printing machines and have been exhibited at the Tate Gallery in London. But while Cohen describes AARON as an A.I., he has officially left the issue of Art as an open question. In an effort to resolve the issue, computer researcher Mark Riedl recently proposed a new variation on the Turing Test, designed to identify true artificial intelligence. His
test would require that an A.I. produce a range of creative work -- paintings, poems, designs -- that expert observers would find indistinguishable from the work of a human artist. Riedl's contention: If a machine can create art that is indistinguishable from human art, then the A.I. has achieved human-level intelligence.