论文标题
非常简单的统计证据，表明Alphago在玩GO游戏中超出了人类的限制
Very simple statistical evidence that AlphaGo has exceeded human limits in playing GO game
论文作者
论文摘要
深度学习技术在解决人工智能的挑战性问题方面取得了长足的进步，因此，基于人工神经网络的机器学习再次引起了人们的关注。在某些领域，基于深度学习的人工智能超出了人类的能力。对于机器来说，在GO游戏中击败人似乎非常困难，但Alphago已证明在游戏中击败了专业玩家。通过查看连续铺设的距离的距离的统计分布，我们发现了一个清晰的痕迹，即Alphago超过了人类的能力。比普通球员比专业球员和专业球员的alphago表明远处的石头铺设更加频繁。此外，Alphago的差异比普通球员和专业球员的差异要明显得多。
Deep learning technology is making great progress in solving the challenging problems of artificial intelligence, hence machine learning based on artificial neural networks is in the spotlight again. In some areas, artificial intelligence based on deep learning is beyond human capabilities. It seemed extremely difficult for a machine to beat a human in a Go game, but AlphaGo has shown to beat a professional player in the game. By looking at the statistical distribution of the distance in which the Go stones are laid in succession, we find a clear trace that Alphago has surpassed human abilities. The AlphaGo than professional players and professional players than ordinary players shows the laying of stones in the distance becomes more frequent. In addition, AlphaGo shows a much more pronounced difference than that of ordinary players and professional players.
