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2. Word2vec - Wikipedia

   en.wikipedia.org/wiki/Word2vec

   The word with embeddings most similar to the topic vector might be assigned as the topic's title, whereas far away word embeddings may be considered unrelated. As opposed to other topic models such as LDA , top2vec provides canonical ‘distance’ metrics between two topics, or between a topic and another embeddings (word, document, or otherwise).

3. Word embedding - Wikipedia

   en.wikipedia.org/wiki/Word_embedding

   In natural language processing, a word embedding is a representation of a word. The embedding is used in text analysis.Typically, the representation is a real-valued vector that encodes the meaning of the word in such a way that the words that are closer in the vector space are expected to be similar in meaning. [1]

4. Glossary of artificial intelligence - Wikipedia

   en.wikipedia.org/wiki/Glossary_of_artificial...

   Pronounced "A-star". A graph traversal and pathfinding algorithm which is used in many fields of computer science due to its completeness, optimality, and optimal efficiency. abductive logic programming (ALP) A high-level knowledge-representation framework that can be used to solve problems declaratively based on abductive reasoning. It extends normal logic programming by allowing some ...

5. Microsoft CEO Satya Nadella is an AI winner. He doesn ... - AOL

   www.aol.com/finance/microsoft-ceo-satya-nadella...

   For my forthcoming book on AI, I spoke to MIT economist David Autor and he essentially made this same point: Generative AI co-pilots might allow people who have been increasingly squeezed out of ...

6. Transformer (deep learning architecture) - Wikipedia

   en.wikipedia.org/wiki/Transformer_(deep_learning...

   Transformer architecture is now used in many generative models that contribute to the ongoing AI boom. In language modelling, ELMo (2018) was a bi-directional LSTM that produces contextualized word embeddings, improving upon the line of research from bag of words and word2vec. It was followed by BERT (2018), an encoder-only Transformer model. [35]

7. Bag-of-words model - Wikipedia

   en.wikipedia.org/wiki/Bag-of-words_model

   It disregards word order (and thus most of syntax or grammar) but captures multiplicity. The bag-of-words model is commonly used in methods of document classification where, for example, the (frequency of) occurrence of each word is used as a feature for training a classifier. [1] It has also been used for computer vision. [2]