With the current data science boom, many companies and organizations are stepping outside of their traditional business models to scope work that applies rigorous quantitative methodology and machine learning – areas of analysis previously in the realm of HPC organizations. …
Over the last couple of years, we have focused extensively on the hardware required for training deep neural networks and other machine learning algorithms. …
Over the last two years, we have highlighted deep learning use cases in enterprise areas including genomics, large-scale business analytics, and beyond, but there are still many market areas that are still building a profile for where such approaches fit into existing workflows. …
While there are some sectors of the tech-driven economy that thrive on rapid adoption on new innovations, other areas become rooted in traditional approaches due to regulatory and other constraints. …
Machine learning is a rising star in the compute constellation, and for good reason. …
Over the last year in particular, we have documented the merger between high performance computing and deep learning and its various shared hardware and software ties. …
There is little doubt that 2017 will be a dense year for deep learning. …
In the course of this three-part series on the challenges and opportunities for enterprise machine learning, we have worked to define the landscape and ecosystem for these workloads in large-scale business settings and have taken an in-depth look at some of the roadblocks on the path to more mainstream machine learning applications. …
In part one of this series last week, we discussed the emerging ecosystem of machine learning applications and what promise those portend. …
Earlier this month, Samsung acquired Viv, the AI platform built by the creators of Siri that seeks to “open up the world of AI assistants to all developers. …
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