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Word in technogenic multidimensional space 技术多维空间中的词
Pub Date : 2022-08-02 DOI: 10.17726/philit.2022.1.2
D. Bylieva
Today, artificial intelligence is actively mastering natural languages, becoming an interlocutor and partner of human in various aspects of activity. However, the symbolic approach, which implies the transfer of rules and logic, has failed, the number of rules and exceptions of the language does not allow its formalization, so modern «deep learning» of artificial neural networks involves an independent search for patterns in extensive databases. During training, artificial intelligence puts a word into a sentence so that the syntagmatic relationships are as close as possible to those of the target word in the base, taking into account both the semantic relationships of words and the relationships between words in the sequence of presentation. The «language» of information technologies is digital. During natural language processing, words are represented in vector form as a sequence of numbers. The idea of representing words mathematically is familiar to people and is usually associated with logical consistency. Visualization of the position of words in a multidimensional space created by artificial intelligence demonstrates a number of patterns, obvious semantic and syntactic relationships, but the essence of other relationships between words is not obvious. The mathematical representation of words, created by artificial intelligence, can allow you to look at the language from a new non-human point of view.
今天,人工智能正在积极地掌握自然语言,成为人类在各个活动方面的对话者和伙伴。然而,符号方法,这意味着规则和逻辑的转移,已经失败了,语言的规则和例外的数量不允许其形式化,所以人工神经网络的现代“深度学习”涉及在广泛的数据库中对模式的独立搜索。在训练过程中,人工智能将一个单词放入句子中,使其句法关系尽可能接近库中目标单词的句法关系,既考虑单词的语义关系,又考虑单词之间在呈现顺序上的关系。信息技术的“语言”是数字化的。在自然语言处理过程中,单词以数字序列的向量形式表示。用数学表示单词的想法对人们来说很熟悉,并且通常与逻辑一致性联系在一起。在人工智能创造的多维空间中,词语的位置可视化展示了许多模式,明显的语义和句法关系,但词语之间其他关系的本质并不明显。由人工智能创建的单词的数学表示可以让你从一个新的非人类的角度来看待语言。
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Philosophical Problems of IT & Cyberspace (PhilIT&C)
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