Developing a machine that can think and learn with the same sophistication as the human brain has long been a staple of science fiction. Is creating real artificial intelligence truly possible?

Creating an artificial mind that can understand complex ideas and learn from its experiences with the same facility as a human brain would have enormous economic, social and ethical implications. Although true artificial intelligence has traditionally belonged to the realm of science fiction, modern advances have brought many formerly impossible concepts into reality, so why should thinking machines be an exception?

Since the 1950s, computers have been regarded as “brain-like” in their operation due to a basic similarity with the human mind. Both computers and human brains process data through binary methods, with computers using ones and zeroes, while brains rely on neurons transmitting on/off signals known as action potentials. However, the similarity points to a central problem in creating artificial intelligence: how do neurons and action potentials give rise to complex processes such as memory, facial recognition and creativity — in a word, consciousness?

The lack of understanding about the mechanisms of consciousness has forced artificial intelligence researchers to shift strategies, focusing more on working backward from how the brain operates rather than adding increasing levels of complexity to a machine.

“Instead of trying to build thinking machines from the ground up, several major projects have recently turned to a new approach: replicating virtual brains through reverse-engineering,” science and business research blog Big Think explains. “By studying the neural networks in the brain, scientists have constructed computer-based models that mirror the brain’s complex biological networks. In turn, they can then run experiments on these brain-like computers in order to learn about how the brain thinks.”

Based in Switzerland, the members of the Blue Brain Project are pioneers in this new approach, collecting data from the neocortexes of rats for 15 years in order to build a 3-D model of the mammalian brain. Recently, the researchers successfully integrated their work by recreating the neocortical column of a two-week-old rat, which contains approximately 10,000 neurons. Although the human brain contains 100 billion neurons, the difference may be surmountable.

“It’s a question of scaling, and it’s a question of resolution. The next phase is going to be a massive expansion toward whole-brain models, and also toward very detailed molecular-level models,” Henry Markram, founder of the Brain Mind Institute and head of the Blue Brain Project, told Discover Magazine. “Technologically, in terms of computers and techniques to acquire data, it will be possible to build a model of the human brain within 10 years.”

Of course, there is a vast difference between replicating the biological structures of reasoning versus actual thinking. For one, creativity is a fundamental part of conscious intelligence, based on the ability to create new analogies and link multiple ideas that are not obviously related. Yet creative problem-solving depends not on concentration, but on allowing the mind to drift, and this sort of free-associative state is difficult to artificially induce.

“How do we invent new analogies? This is a major unsolved problem of cognitive science. Often, remembered and re-experienced emotions are the key to novel, unexpected analogies,” David Gelernter, a professor of computer science at Yale and chief scientist at Mirror Worlds Technologies, explains in an article for Edge. “No computer will be creative unless it can simulate all the nuances of human emotion.”

However, advances are already being made toward enabling computers to experience emotion and engage in free-thinking. Computer scientist Graham Mann, professor at Australia’s Murdoch University, recently unveiled a set of algorithms he developed to allow a computer to analyze simple stories and interpret the emotional response they evoke, according to Popular Science.

“His algorithm was based on Plutchick’s Wheel of Emotions, which illustrated emotions as a colour wheel and disallowed mutually exclusive states — like joy and sadness — from being experienced simultaneously,” iTnews reports. “The machine freely associated three stories: The Thirsty Pigeon; The Cat and the Cock; and The Wolf and the Crane. When queried on the association, the machine responded: ‘I felt sad for the bird.’”

While these advances are still far from achieving the level of sophistication needed to reach creative problem-solving, the limited degree of emotional response and free association achieved through Mann’s algorithms may be an important step toward creating an artificial consciousness. Of course, true artificial intelligence is unlikely to appear anytime soon, but when it does, it will signal a new — and possibly unsettling — era of technological development.

“In a way these possibilities are frightening, or at least thought-provoking,” Gelernter writes. “But after all, human intelligence is the most valuable stuff in the cosmos, and we are always running short. A computer-created increase in the worldwide intelligence supply would be welcome, to say the least.”

Earlier

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Bots with Brains: Future Robotic Overlords?

Resources

Can Computers Be Conscious?
by Max Miller
Big Think, Sept. 30, 2010

The Man Who Builds Brains
by David Kushner
Discover Magazine, December 2009

Dream-Logic, the Internet and Artificial Thought
by David Gelernter
Edge, July 8, 2010

Emo Computer Using New Algorithm to Finds Aesop’s Fables Strangely Moving
by Rebecca Boyle
Popular Science, Sept. 24, 2010

Researcher Builds Machines That Daydream
by Liz Tray
iTnews, Sept. 22, 2010