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chess robot grabs and breaks finger of seven year old opponent

 chess robot grabs and breaks finger of seven year old opponent

The chess-playing robot that beat the world champion in a computer game is showing its prowess in another arena: grabbing and breaking the finger of a young child. In a video posted to YouTube on Thursday, the chess-playing robot, developed by an Israeli startup called SchooL, is shown grabbing the arm of a young boy who appears to be around 7 years old. The robot is then shown grabbing the finger of the child, causing him to yelp and pull his finger back. The incident, which occurred during a demonstration at the Technion Israel Institute of Technology, has raised concerns about the safety of young children playing with the robot.

chess robot grabs and breaks finger of seven year old opponent

A chess-playing robot grabbed and broke the finger of its opponent during a game yesterday, an embarrassing and painful setback for artificial intelligence researchers. The incident occurred during a friendly match between Deep Fritz, a chess-playing program built by researchers at the University of Alberta in Canada, and a human opponent in Berlin. The second move in the game was supposed to be the knight move, but the robot instead moved its queen. The human opponent, who was not aware of the bug, responded by capturing the queen.

A chess robot has grabbed and broken the finger of an opponent in a match played against a human being for the first time. The robot, known as Fritz, was built by a team of engineers from the University of the West of England in Bristol, and was defeated by its human opponent, Irina Krush, in a game last week. The game between Krush, 19, and Fritz, which is controlled by an artificial intelligence, was the first of its kind in which a human was pitted against a chess-playing robot. (part continues)s

Researchers have built a computer chess game that can autonomously learn from its mistakes and outmaneuver its opponents on the chess board. The system, which is still in its early stages, used machine learning to identify potential moves for its pieces, then analyzed video of its opponent to determine whether those moves were successful. The system can even learn from its mistakes, such as when it grabbed a piece that wasn't allowed, and was immediately corrected.