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人工智能与机器学习:算法基础和哲学观点
Artificial Intelligence and Machine Learning: Algorithmic Foundations and Philosophical Perspectives
  
中文关键词:  人工智能;可计算性;智能度;语言识别;自动驾驶;工业4.0;区块链
英文关键词:artificial intelligence, computability, intelligence degrees, language recognition, autonomous car driving, industry 4.0, block chain
项目:中央高校基本科研业务费专项资金项目“我国转型期劳动者工资权及其法律保障完善研究”(15XNB009);人社部中国劳动科学研究院2016年科研合作项目“社会组织在协调劳动关系中的作用研究”(LKY 2017HZ 36);中国人民大学“杰出青年学者(A)岗”项目
作者单位
克劳斯·迈因策尔 慕尼黑工业大学 
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中文摘要:
      人工智能技术在最近几年的迅速发展,使得人们对人工智能在教育领域的应用充满了乐观情绪,不少人认为人工智能可以解决教育的所有问题。文章从教育的本质和人工智能的研究领域出发,指出人工智能是在机器上实现的教育,但其实际效果已经超越了教育本身。人工智能应用在教育上会对教育产生正面的促进作用,但是在破解教育难题方面还有大量工作要做。人工智能的突出成就和广泛应用会造成大量劳动者失业,其实质是高智识人群在不断剥夺低智识人群的工作机会和权利,会对作为教育产出的毕业生在求职市场上增加更加强大的竞争对手,进而对受教育者的学习动机和教育目的造成负面影响,引起社会发展失衡和不稳定。建议在大力通过人工智能技术促进教育发展和产业转型的同时,应该统筹规划、全面布局、均衡发展,尽量保障所有教育系统的产出——形态各异的毕业生都能找到合适的工作。
英文摘要:
      Since Alan M. Turing, Artificial Intelligence (AI) was reduced to symbolic AI with formal logic, automatic proving and computing. But, human intelligence has also been associated with language understanding. Instead of the Turing test, I suggest a working definition of intelligence degrees which can be realized in nature and technology. Natural intelligence emerged during the natural evolution of nervous systems and brains with different degrees of complexity. Artificial intelligence was developed in technical inventions depending on an exponential growth of computer power in tradition of the Turing machine. Brains, automata, and machines seem to be completely different, but they are mathematically equivalent with respect to language recognition. A hierarchy of automata and machines with different degrees of complexity can be distinguished. They recognize the same kinds of languages which are recognized by appropriate neural networks (and with that by corresponding biological brains with this degree of complexity). According to our working definition, we get degrees of intelligence in nature and technology. It is remarkable that analog neural networks can realize natural languages beyond Turing computability like human brains. Therefore, this paper argues for analog and digital intelligence which can be realized by neuromorphic computational architecture. But intelligence is by no means reduced to single brains and computers. Analog and digital aspects are also integrated in the global Internet of Things (IoT) to solve problems with different degrees of intelligence.
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