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Solving quality and maintenance problems with AI : combining machine learning, deep learning, and associative memory reasoning to improve operations /
Despite all the hype and money surrounding artificial intelligence ($1.2 trillion in 2017 alone), many companies aren't certain this "technology revolution" can help them solve current business needs. This report explores real-world business use cases that demonstrate how machine lear...
| Clasificación: | Libro Electrónico |
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| Autores principales: | , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Sebastopol, CA :
O'Reilly Media,
[2018]
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| Edición: | First edition. |
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
| Sumario: | Despite all the hype and money surrounding artificial intelligence ($1.2 trillion in 2017 alone), many companies aren't certain this "technology revolution" can help them solve current business needs. This report explores real-world business use cases that demonstrate how machine learning, deep learning, and associative memory reasoning together can provide lucrative returns today for companies across several industries in the area of predictive quality and maintenance (PQM). In this report, you'll learn how a combination of cutting-edge approaches--known as complimentary learning--can help your company increase uptime, reduce risk, stop over-maintenance of assets, and fix defects sooner. Interviews with companies spanning industries, including Accenture, Keystone, and Intel, demonstrate how these AI-powered PQM solutions impact businesses today. You'll explore: AI-based PQM solutions that make up a large and growing segment of the overall AI applications market How associative memory reasoning mimics the human's ability to learn, memorize and reason, surfacing the hidden connection in data How complementary learning combines several AI systems, including machine learning, deep learning, and cognitive computing How Accenture and Intel have used Intel Saffron technology to solve specific issues related to PQM |
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| Descripción Física: | 1 online resource (1 volume) : illustrations |
| ISBN: | 1491999551 9781491999554 |