Project Adam is a new deep-learning system modeled after the human brain that has greater image classification accuracy and is 50 times faster than other systems in the industry. Project Adam is an initiative by Microsoft researchers and engineers that aims to demonstrate that large-scale, commodity distributed systems can train huge deep neural networks effectively. For proof, the researchers created the world’s best photograph classifier, using 14 million images from ImageNet, an image database divided into 22,000 categories. Included in the vast array of categories are some that pertain to dogs. Project Adam knows dogs. It can identify dogs in images. It can identify kinds of dogs. It can even identify particular breeds, such as whether a corgi is a Pembroke or a Cardigan.
If any of this sounds familiar, a couple of years ago Google used a network of 16,000 computers to train an algorithm to identify images of cats. That is a difficult task for computers, and it was an impressive achievement. Project Adam is 50 times faster—and more than twice as accurate, as outlined in a paper currently under academic review. In addition, it is efficient, using 30 times fewer machines, and scalable, areas in which the Google effort fell short.
Deep learning techniques using neural networks are receiving a lot of attention these days. Is this the future of machine learning? Here is a short video produced by Microsoft Research highlighting Project Adam.
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