Earlier in this decade, when the hyperscalers and the academics that run with them were building machine learning frameworks to transpose all kinds of data from one format to another – speech to text, text to speech, image to text, video to text, and so on – they were doing so not just for scientific curiosity. …
Historically and traditionally, academic supercomputing centers have been very open about the architecture and setup of their HPC systems, and it makes sense if you think about it. …
The challenge with many of the complex modern technologies that are coming into datacenters is making them easy and cheap enough for enterprises to use at a their own scale, which is much more limited than that of hyperscalers and cloud builders, and employing their own skillsets, which are also more limited. …
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. …
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