Although much of the attention around deep learning for voice has focused on speech recognition, developments in artificial speech synthesis (text to speech) based on neural network approaches have been just as swift. …
Today’s enterprises need deep learning, but most don’t know how to get started. …
Intel’s multi-year effort to expand its reach beyond its PC and server processor roots has taken the chip maker down multiple paths, some of which have ended in dead ends. …
For the past five and a half years, which is not quite an eternity in the IT business but is something akin to a half of a generation or so, IBM’s revenues have been declining, quarter in and quarter out. …
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In this webcast, we learn from Nick Curcuru, vice president of the big data practice at MasterCard, about what needs to be in place both technically and in terms of management models and processes so that the benefits can be fully achieved. …
After a long, long wait and years of anticipation, it looks like IBM is finally getting ready to ship commercial versions of its Power9 chips, and as expected, its first salvo of processors aimed at the datacenter will be aimed at HPC, data analytics, and machine learning workloads. …
Today, most machine learning is done on processors. Some would say that acceleration of learning has to be done on GPUs, but for most users that is not good advice for several reasons. …
Governments like to spread the money around their indigenous IT companies when they can, and so it is with the AI Bridging Cloud Infrastructure, or ABCI, supercomputer that is being commissioned by the National Institute of Advanced Industrial Science and Technology (AIST) in Japan. …
IBM has spent the past several years putting a laser focus on what it calls cognitive computing, using its Watson platform as the foundation for its efforts in such emerging fields as artificial intelligence (AI) and is successful spinoff, deep learning. …
One of the reasons we have written so much about Chinese search and social web giant, Baidu, in the last few years is because they have openly described both the hardware and software steps to making deep learning efficient and high performance at scale. …
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