Researchers at the universities of Toulouse and Paris-Saclay in France found they could differentiate between human and computerized Go players by analyzing the statistical characteristics of thousands of games played by people and algorithms. The researchers built databases of 8,000 games played by amateur humans, 8,000 played by the software Gnugo, 8,000 played by the software Fuego, and 50 games played by the software AlphaGo. Their analysis found network-based software forms more “communities”–signs the algorithms are creating varied and diverse strategies–than humans. The team also found statistical differences between the computer- and human-generated networks are much larger than the variability within each network. They suggest these differences could form the basis of a new type of Turing test. “We think our work indicates a path towards a better characterization and understanding of the differences between human and computer decision-making processes, which could be applied in many different areas,” says Paris-Saclay’s Olivier Giraud.
More info here: Phys.org, Lisa Zyga