AI Ethics

Research Report: 5 Questions That Cannot Be Avoided By Artificial Intelligence Ethics. Whether Biological Evolution Has A Direction Is The Key

Research Report: 5 Questions That Cannot Be Avoided By Artificial Intelligence Ethics. Whether Biological Evolution Has A Direction Is The Key

Summary: Explain 5 questions that need to be answered in formulating artificial intelligence ethics. This article is based on the author of Internet evolution and computer doctor Liu Feng, a computer science and technology strategy consulting institute of the Chinese Academy of Sciences and Tencent Institute jointly held the speech content of the

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Artificial Intelligence Raises Ethical Problems, And Tech Giants Set Up Ethical Committees To Meet Challenges

Artificial Intelligence Raises Ethical Problems, And Tech Giants Set Up Ethical Committees To Meet Challenges

This article is an exclusive selection of this artificial intelligence summit. With the development of artificial intelligence, machines are taking on more and more decision-making tasks , which has also triggered many new issues about social fairness and ethics.As the saying goes: the greater the power, the greater the responsibility. Artificial intelligence technology is becoming increasingly powerful, and those companies that first developed and deployed machine learning and artificial intelligence are now beginning to publicly discuss the challenges that the intelligent machines they create bring to ethics.At the MIT Technology Review summit, Eric, managing director of Microsoft Research, said: “We are at a turning point in artificial intelligence, which deserves to be bound and protected by human morality. ”。Hovetz carefully discussed similar issues with researchers from IBM and Google. Everyone is worried that the recent advances in artificial intelligence have made it more than humans in some ways, such as the healthcare industry, which may lose job opportunities for people in certain positions.Google researcher Maya Gupta called on the industry to work harder to propose reasonable development processes to ensure that the data used to train algorithms is fair, reasonable and impartial.

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