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  2. Document AI - Wikipedia

    en.wikipedia.org/wiki/Document_ai

    Document AI combines text data, which has a time dimension, with other types of data, such as the position of an address in a business letter, which is spatial. Historically in machine learning spatial data was analyzed using a convolutional neural network , and temporal data using a recurrent neural network .

  3. List of artificial intelligence projects - Wikipedia

    en.wikipedia.org/wiki/List_of_artificial...

    SNePS, simultaneously a logic-based, frame-based, and network-based knowledge representation, reasoning, and acting system. [41] Viv (software), a new AI by the creators of Siri. [42] Wolfram Alpha, an online service that answers queries by computing the answer from structured data. [43]

  4. Semantic analysis (machine learning) - Wikipedia

    en.wikipedia.org/wiki/Semantic_analysis_(machine...

    In machine learning, semantic analysis of a text corpus is the task of building structures that approximate concepts from a large set of documents. It generally does not involve prior semantic understanding of the documents. Semantic analysis strategies include: Metalanguages based on first-order logic, which can analyze the speech of humans.

  5. Category:Applications of artificial intelligence - Wikipedia

    en.wikipedia.org/wiki/Category:Applications_of...

    Examples of artificial intelligence algorithms applied to real-world problems. For technologies used in these applications see: Category:Artificial intelligence; Category:Classification algorithms; Category:Machine learning

  6. Applications of artificial intelligence - Wikipedia

    en.wikipedia.org/wiki/Applications_of_artificial...

    The use of AI in applications such as online trading and decision-making has changed major economic theories. [66] For example, AI-based buying and selling platforms estimate personalized demand and supply curves, thus enabling individualized pricing. AI systems reduce information asymmetry in the market and thus make markets more efficient. [67]

  7. Transformer (deep learning architecture) - Wikipedia

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

    document summarization; document generation; named entity recognition (NER) [113] writing computer code based on requirements expressed in natural language. speech-to-text; Beyond traditional NLP, the transformer architecture has had success in other applications, such as: biological sequence analysis; video understanding; protein folding (such ...

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