AI's Victories In Go Inspire Better Human Game Playing - Slashdot

Do you develop on GitHub? You can keep using GitHub but automatically sync your GitHub releases to SourceForge quickly and easily with this tool so your projects have a backup location, and get your project in front of SourceForge's nearly 30 million monthly users. It takes less than a minute. Get new users downloading your project releases today!

Sign up for the Slashdot newsletter! or check out the new Slashdot job board to browse remote jobs or jobs in your area.

Emily Willingham writes via Scientific American: In 2016 a computer named AlphaGo made headlines for defeating then world champion Lee Sedol at the ancient, popular strategy game Go. The "superhuman" artificial intelligence, developed by Google DeepMind, lost only one of the five rounds to Sedol, generating comparisons to Garry Kasparov's 1997 chess loss to IBM's Deep Blue. Go, which involves players facing off by moving black and white pieces called stones with the goal of occupying territory on the game board, had been viewed as a more intractable challenge to a machine opponent than chess. Much agonizing about the threat of AI to human ingenuity and livelihood followed AlphaGo's victory, not unlike what's happening right now with ChatGPT and its kin. In a 2016 news conference after the loss, though, a subdued Sedol offered a comment with a kernel of positivity. "Its style was different, and it was such an unusual experience that it took time for me to adjust," he said. "AlphaGo made me realize that I must study Go more."

At the time European Go champion Fan Hui, who'd also lost a private round of five games to AlphaGo months earlier, told Wired that the matches made him see the game "completely differently." This improved his play so much that his world ranking "skyrocketed," according to Wired. Formally tracking the messy process of human decision-making can be tough. But a decades-long record of professional Go player moves gave researchers a way to assess the human strategic response to an AI provocation. A new study now confirms that Fan Hui's improvements after facing the AlphaGo challenge weren't just a singular fluke. In 2017, after that humbling AI win in 2016, human Go players gained access to data detailing the moves made by the AI system and, in a very humanlike way, developed new strategies that led to better-quality decisions in their game play. A confirmation of the changes in human game play appear in findings published on March 13 in the Proceedings of the National Academy of Sciences USA.

The team found that before AI beat human Go champions, the level of human decision quality stayed pretty uniform for 66 years. After that fateful 2016-2017 period, decision quality scores began to climb. Humans were making better game play choices -- maybe not enough to consistently beat superhuman AIs but still better. Novelty scores also shot up after 2016-2017 from humans introducing new moves into games earlier during the game play sequence. And in their assessment of the link between novel moves and better-quality decisions, [the researchers] found that before AlphaGo succeeded against human players, humans' novel moves contributed less to good-quality decisions, on average, than nonnovel moves. After these landmark AI wins, the novel moves humans introduced into games contributed more on average than already known moves to better decision quality scores.