Facebook Flow Is An AI Factory Of The Future
We have been convinced for many years that machine learning, the kind of artificial intelligence that actually works in practice, not in theory, would be a key element of the next platform. …
We have been convinced for many years that machine learning, the kind of artificial intelligence that actually works in practice, not in theory, would be a key element of the next platform. …
Supercomputer maker Cray might not roll out machines for deep learning anytime in 2016, but like other system vendors with deep roots in high performance computing, which leverages many of the same hardware elements (strong interconnect and GPU acceleration, among others), they are seeing how to loop their expertise into a future where machine learning rules. …
Training ‘complex multi-layer’ neural networks is referred to as deep-learning as these multi-layer neural architectures interpose many neural processing layers between the input data and the predicted output results – hence the use of the word deep in the deep-learning catchphrase. …
Whether in the brain or in code, neural networks are shaping up to be one of the most critical areas of research in both neuroscience and computer science. …
There has been a resounding uptick in attention around machine learning, but with relatively few large-scale systems in production (and even fewer public stories about progress and roadblocks), the wider story is all about the potential and dramatically less about the possibilities for problems. …
If the last year of stories here from research labs at the forefront of deep learning hasn’t made it clear, the accelerator of choice for training the models that will feed the next generation of speech and image recognition (not to mention a wealth of other application areas) is certainly GPUs. …
The high end of the computing industry has always captivated us, and we still find the forces at work in the upper echelons of the datacenters of the world, and the hardware and software that is created to run the largest and most complex workloads found there, fascinating. …
There is a simple test to figure out just how seriously social network Facebook is taking machine learning, and it has nothing to do with research papers or counting cat pictures automagically with neural networks. …
Stanford University PhD candidate, Song Han, who works under advisor and networking pioneer, Dr. …
For Google, Baidu, and a handful of other hyperscale companies that have been working with deep neural networks and advanced applications for machine learning well ahead of the rest of the world, building clusters for both the training and inference portions of such workloads is kept, for the most part, a well-guarded secret. …
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