Continuum Analytics, H2O.ai, and MapD recently announced the formation of the GPU Open Analytics Initiative (GOAI) to create common data frameworks enabling developers and statistical researchers to accelerate data science on GPUs.
Late last year, the NVIDIA Inception Program hosted a “Cool Demo Contest” for GPU-accelerated startups that are applying deep learning to their innovations.
To make Siri great, Apple employed several artificial intelligence experts three years ago to apply deep learning to their intelligent mobile smart assistant. The team began training a neural net to replace the original Siri.
Leo Meyerovich, CEO of Graphistry Inc., shares how GPUs and machine learning are protecting the largest companies and organizations in the world by visually alerting them of attacks and big outages.
Hugo Latapie, principal engineer at Cisco shares how they are using NVIDIA GPUs and deep learning in their products for a variety of applications such as encrypted network traffic classification, video compression and state-of-the-art crowd analytics
Joshua Patterson, principal data scientist of Accenture Labs shares how his team is using NVIDIA GPUs and GPU-accelerated libraries to quickly detect security threats by analyzing anomalies in large-scale network graphs.
We love seeing all of the GPU-related tweets – here’s some that we came across this week: #HPEAspire great session on how @nvidia @hpe deep level machine learning can solve more of the world's problems together #HPC — Heath Muchmore (@MuchmoreIT) May
Adam McLaughlin, PhD student at Georgia Tech shares how he is using NVIDIA Tesla GPUs for his research on Betweenness Centrality – a graph analytics algorithm that tracks the most important vertices within a network.
