Friday, 24 April 2009

Artificial Intelligence Cracks 4,000-Year-Old Mystery

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Artificial Intelligence Cracks 4,000-Year-Old Mystery

By Brandon Keim EmailApril 23, 2009 | 1:01:09 PMCategories: Archaeology

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An ancient script that's defied generations of archaeologists has yielded some of its secrets to artificially intelligent computers.

Computational analysis of symbols used 4,000 years ago by a long-lost Indus Valley civilization suggests they represent a spoken language. Some frustrated linguists thought the symbols were merely pretty pictures.

"The underlying grammatical structure seems similar to what's found in many languages," said University of Washington computer scientist Rajesh Rao.

The Indus script, used between 2,600 and 1,900 B.C. in what is now eastern Pakistan and northwest India, belonged to a civilization as sophisticated as its Mesopotamian and Egyptian contemporaries. However, it left fewer linguistic remains. Archaeologists have uncovered about 1,500 unique inscriptions from fragments of pottery, tablets and seals. The longest inscription is just 27 signs long.

In 1877, British archaeologist Alexander Cunningham hypothesized that the Indus script was a forerunner of modern-day Brahmic scripts, used from Central to Southeast Asia. Other researchers disagreed. Fueled by scores of competing and ultimately unsuccessful attempts to decipher the script, that contentious state of affairs has persisted to the present.

Among the languages linked to the mysterious script are Chinese Lolo, Sumerian, Egyptian, Dravidian, Indo-Aryan, Old Slavic, even Easter Island — and, finally, no language at all. In 2004, linguist Steve Farmer published a paper asserting that the Indus script was nothing more than political and religious symbols. It was a controversial notion, but not an unpopular one.

Rao, a machine learning specialist who read about the Indus script in high school and decided to apply his expertise to the script while on sabbatical in Inda, may have solved the language-versus-symbol question, if not the script itself.

"One of the main questions in machine learning is how to generalize rules from a limited amount of data," said Rao. "Even though we can't read it, we can look at the patterns and get the underlying grammatical structure."

Rao's team used pattern-analyzing software running what's known as a Markov model, a computational tool used to map system dynamics.

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