Category: Semantic technologies (Page 14 of 72)

Hitachi Solutions, Ltd. announced the release of “Business Efficiency Solution by Natural Language Processing (NLP) AI.” It combines Hitachi and Allganize’s expertise in Artificial Intelligence (AI) to provide business automation solutions, such as Robotic Process Automation (RPA), NLP and cognitive agents. Allganize’s AI technology is able to extract structured knowledge from millions of documents and other content sources. Hitachi’s “Katsubun Intellectual Information Mining” provides a set of tools and techniques to identify specific ways to improve business operations. The solution features:

• Katsubun Intellectual Information Mining analyzes clients’ operational processes and enables them to identify bottlenecks for process improvement.
• Alli AnswerBot streamlines customer service and automates business processes.
• Cognitive Search enriches and extracts structured knowledge from unstructured data sources to make content more searchable.
• Named Entity Recognition (NER) locates and classifies named entities such as brand names, people, product names, purchase order and account information from unstructured data sources.
• Review Analysis automatically converts opinions such as user feedback and reviews into measurable data for analysis.
• Text Classification assigns custom predefined categories to free-text for managing content.
• Sentiment Analysis, emotional AI that categorizes text by emotional state such as happy, angry or sad.

https://www.hitachi-solutions.co.jp/allganize/, https://allganize.ai

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Cambridge Quantum Computing (CQC) announced that it has built on earlier advances in “meaning- aware” Quantum Natural Language Processing (QNLP), establishing that QNLP is quantum-native with expected near-term advantages over classical computers. Natural language processing (NLP) is at the forefront of advances in contemporary artificial intelligence, and it is arguably one of the most challenging areas of the field. “Meaning-aware” NLP remains a distant aspiration using classical computers. The steady growth of quantum hardware and notable improvements in the implementation of quantum algorithms mean that we are approaching an era when quantum computers might perform tasks that cannot be done on classical computers with a reasonable amount of resources in a repeatable manner, and which are important and suitable for everyday use. In papers posted on arXiv – the scientific e-print repository, CQC’s scientists provide conceptual and mathematical foundations for near-term QNLP in quantum computer scientist-friendly terms. The paper is written in an expository style with tools that provide mathematical generality.

Aiming to canonically combine linguistic meanings with rich linguistic structure, most notably grammar, Professor Bob Coecke (Oxford University) and his team have proven that a quantum computer can achieve “meaning aware” NLP, thus establishing QNLP as quantum-native, on par with the simulation of quantum systems. Moreover, the leading Noisy Intermediate-Scale Quantum (NISQ) paradigm for encoding classical data on quantum hardware – variational quantum circuits – makes NISQ exceptionally QNLP-friendly.

https://cambridgequantum.com, https://arxiv.org/pdf/2012.03755.pdf

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Clarifai, an AI lifecycle platform provider for managing unstructured image, video, and text data, has joined the Automation Anywhere Technology Alliance Program (TAP) to drive AI search and discovery related use cases. Clarifai’s computer vision and natural language processing artificial intelligence (AI) models are now implemented with Automation Anywhere’s advanced robotic process automation (RPA) software platform. Clarfai uses deep learning to transform large amounts of unstructured image, video, and text data into structured data. These models can be customized to give customers the ability to moderate content, extract visual and textual metadata from content, detect logos within imagery, and more.

https://www.clarifai.com

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