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11/02/2024 03:28:47 pm

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Chinese Robot to Compete in 2020 College Entrance Exams

Chinese Robot

(Photo : GETTY IMAGES / Ni Qin) Chinese child pretending to be a robot.

Chinese tech firm iFly Tek is building an artificial intelligence (AI) robot that will compete in the national college entrance examinations by 2020.

IT firm iFly Tek aims to make the AI robot participate in college entrance exams in 2020 with the goal of beating 80 percent of Chinese students. During a product launch held in Beijing, the company's CEO Liu Qingfeng said their goal is a difficult one, according to China Daily.

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"It is easy for AI to pass an exam, because the machine has much stronger memory ability than humans," Liu said in a statement. "But to beat 80 percent of students --- which means it can enroll at a key university in China --- is difficult."

Liu added that the AI robot's participation in the national entrance exam, which is also called the gaokao, is the best evidence of its ability to learn new things and to reason. About 9 million students take the gaokao every year and it is among the most competitive test in the country.

A few months ago, it was reported that iFly Tek will collaborate with Japan's National Institute of Informatics (NII) on the AI projects, which run on a US$24.6 million budget. The project was first initiated four years ago in Japan but the Torobo-kun robot failed in the entrance exam for the University of Tokyo.

Recently, the AI robot, which is called iFly Hyperbrain, demonstrated good performance in politics and essay writing tests. In addition, Japan's Todai robot is slated to participate in next year's National Center Test for University Admissions, the report adds.

A U.S.-based institute has also created an AI system that can pass SAT college geometry tests and beat the average 11th-grade student. The Allen Institute for Artificial Intelligence announced in September the breakthrough in its AI system.

Hy Yu, iFly Tek's senior deputy president, said the concept of AI was first brought up six decades ago, but scientists had no opportunity to truly develop it. Nowadays, machines have a treasure trove of materials to learn from.

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