人工智能与炼金术
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摘要
机器学习带来的新一轮技术创新浪潮,已经深刻地改变了社会和人们的生活,正在成为工业创新的重要力量,并引发了科技领域的新一轮淘金热。像任何新技术一样,人工智能和机器学习既有积极的影响,也有消极的影响。当这场席卷全球的新科技风暴来临时,我们该如何去拥抱它?就在人们为人工智能已经取得的巨大成功惊叹不已之时,一位著名的机器学习和人工智能学者发出了不同凡响的警告,他认为人工智能的研究已经偏离了科学和工程的界限,进入了"炼金术"领域。机器学习疾风暴雨式的快速的进展导致其丧失了理论的严格性和精确性。这种看法有多准确?机器学习应该如何将其快速创新与坚实的科学和工程基础相结合?本文就机器学习领域及其主要技术挑战做了简短的讨论。
Machine learning has taken the world by storm and is expected to be a significant force in industrial innovation going forward. It has triggered a new gold rush in the tech sector. Like any new technology, artificial intelligence and machine learning will have positive, as well as negative effects. How can we face the new wave of technological innovation in the whole world? Just as people marvel at the tremendous success of AI, a famous machine learning and AI scholar has issued an extraordinary warning. He believes that the study of artificial intelligence has deviated from the boundaries of science and engineering and entered the field of alchemy. The rapid progress of machine learning has led to loss of rigor and precision. How accurate is this perception? And what should the field do to combine rapid innovation with solid science and engineering? This paper gives a brief discussion about the field of machine learning and its major technical challenges.
Machine learning has taken the world by storm and is expected to be a significant force in industrial innovation going forward. It has triggered a new gold rush in the tech sector. Like any new technology, artificial intelligence and machine learning will have positive, as well as negative effects. How can we face the new wave of technological innovation in the whole world? Just as people marvel at the tremendous success of AI, a famous machine learning and AI scholar has issued an extraordinary warning. He believes that the study of artificial intelligence has deviated from the boundaries of science and engineering and entered the field of alchemy. The rapid progress of machine learning has led to loss of rigor and precision. How accurate is this perception? And what should the field do to combine rapid innovation with solid science and engineering? This paper gives a brief discussion about the field of machine learning and its major technical challenges.
引文
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[7]王磊,王晓东.机器学习算法导论.北京:清华大学出版社,2019
[2]Aurelien Geron.Hands-on Machine Learning with Scikit-Learn&TensorFlow,O’Reilly,California,2017
[3]Ian Goodfelow,Yoshua Bengio,Aaron Courville.Deep Learning,MITPress,Cambridge,MA,2016
[4]Matthew Hutson.AI researchers allege that machine learning is alchemy,Science,May 2018,https://www.sciencemag.org/news/2018/05/ai-researchers-allege-machi ne-learning-alchemy.
[5]Shai Shalev-Shwartz,Shai Ben-David.Understanding Machine Learning:From Theory To Algorithms,Cambridge Press,Cambridge,UK,2014
[6]王晓东.算法设计与分析.第4版.北京:清华大学出版社,2018
[7]王磊,王晓东.机器学习算法导论.北京:清华大学出版社,2019