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Go Champ Scores Surprise Victory Over Supercomputer

A South Korean Go grandmaster scored his first win over a Google-developed supercomputer.

A South Korean Go grandmaster on Sunday scored his first win over a Google-developed supercomputer, in a surprise victory after three humiliating defeats in a high-profile showdown between man and machine.

Lee Se-Dol thrashed AlphaGo after a nail-biting match that lasted for nearly five hours - the fourth of the best-of-five series in which the computer clinched a 3-0 victory on Saturday.

Lee struggled in the early phase of the fourth match but gained a lead towards the end, eventually prompting AlphaGo to resign.

Google AI Beats Grandmaster in First 'Go' Showdown

The 33-year-old is one of the greatest players in modern history of the ancient board game, with 18 international titles to his name - the second most in the world.

"I couldn't be happier today...this victory is priceless. I wouldn't trade it for the world," a smiling Lee said after the match to cheers and applause from the audience.

"I can't say I wasn't hurt by the past three defeats...but I still enjoyed every moment of playing so it really didn't damage me greatly," he said.

'Ex Machina': Science Vs. Fiction

Lee earlier predicted a landslide victory over Artificial Intelligence (AI) but was later forced to concede that the AlphaGo was "too strong".

Lee had vowed to try his best to win at least one game after his second defeat.

Described as the "match of the century" by local media, the game was closely watched by tens of millions of Go fans mostly in East Asia as well as AI scientists.

A.I. Slam Dunks Impossible Game Of 'Go'

The most famous AI victory to date came in 1997, when the IBM-developed supercomputer Deep Blue beat the then-world class chess champion Garry Kasparov.

But Go, played for centuries mostly in Korea, Japan and China, had long remained the holy grail for AI developers due to its complexity and near-infinite number of potential configurations.

- ‘More creative than we imagined' -

Demis Hassabis, the head of the AlphaGo developer Google DeepMind, has described Go as the "Mount Everest" for AI scientists.

"Lee Se-Dol was an incredible player and was too strong for AlphaGo," Hassabis said after Sunday's match.

"It was doing well...but then, because of Lee's fantastic play, it was pressurised into some mistakes," he said, describing the loss as a "valuable" way to fix the problems with the supercomputer.

"Actually we are very happy because this is why we came here, to test AlphaGo and its limit and find out what its weaknesses were," he said.

Lee said those weaknesses included a difficulty in responding to certain unexpected plays by an opponent, which led to more mistakes.

Go involves two players alternately laying black and white stones on a chequerboard-like grid of 19 lines by 19 lines. The winner is the player who manages to seal off more territory.

On the 78th move, Lee placed a stone unexpectedly in the middle section of the board, stunning many experts and confusing the AlphaGo.

Hassabis later tweeted that the AlphaGo made a "mistake" on the following 79th move and only realized it several moves later.

AI Twitterbot Sounds A Lot Like Trump

AlphaGo uses two sets of "deep neural networks" that allow it to crunch data in a more human-like fashion - dumping millions of potential moves that human players would instinctively know were pointless.

It also employs algorithms that allow it to learn and improve from matchplay experience.

"I think AlphaGo is far more creative than we ever imagined. It makes us to rethink all the conventional rules and knowledge we learned in Go," said Lee Hyun-Wook, a TV commentator and professional Go player.

The last match is to be held on Tuesday in the Four Seasons Hotel in Seoul.

South Korean Go grandmaster Lee Se-Dol (right) scored his first win over a Google-developed supercomputer.

A few weeks back, researchers with Google's artificial neural networks team issued a

blog post

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

Hack Reactor

announced via Popular Science, that they were coming out with an app called

Dreamify

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.

See the Dreams of an Artificial Brain: Photos

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.

Photo First: Light Captured as Both Particle and Wave

Google has since published the

DeepDream

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.

Dreamscope

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?

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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 Painting Fool

. 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.

Did da Vinci Create a 3-D 'Mona Lisa'?

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

AARON

, 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

Lovelace 2.0

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.

How Real-Life A.I. Rivals 'Ex Machina'