Artificial intelligence researchers at Facebook have figured out how to train their AI models for image recognition at eye-popping speeds. <\/p>\n
The company announced the results of the effort to speed up training time at the Data@Scale event in Seattle this morning. Using Facebooks custom GPU (graphics processing unit) hardware and some new algorithms, researchers were able to train their models on 40,000 images a second, making it possible to get through the ImageNet dataset in under an hour with no loss of accuracy, said Pieter Noordhuis, a software engineer at Facebook. <\/p>\n
You dont need a proper supercomputer to replicate these results, Noordhuis said. <\/p>\n
The system works to associate images with words, which is called supervised learning, he said. Thousands of images from a training set are assigned a description (say, a cat) and the system is shown all of the images with an associated classification. Then, researchers present the system with images of the same object (say, a cat) but without the description attached. If the system knows its looking at a cat, its learning how to associate imagery with descriptive words. <\/p>\n
The breakthrough allows Facebook AI researchers to start working on even bigger datasets; like, say, the billions of things posted to its website every day. Its also a display of Facebooks hardware expertise; the company made sure to note that its hardware is open-source, this means that for others to reap these benefits, theres no need for incredibly advanced TPUs, it said in a statement throwing some shade at Googles recent TPU announcement at Google I\/O. <\/p>\n
Facebook plans to release more details about its AI training work in a research paper published to its Facebook Research page. <\/p>\n
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Facebook's AI training models can now process 40000 images a second - GeekWire<\/a><\/p>\n","protected":false},"excerpt":{"rendered":" Artificial intelligence researchers at Facebook have figured out how to train their AI models for image recognition at eye-popping speeds. The company announced the results of the effort to speed up training time at the Data@Scale event in Seattle this morning. Using Facebooks custom GPU (graphics processing unit) hardware and some new algorithms, researchers were able to train their models on 40,000 images a second, making it possible to get through the ImageNet dataset in under an hour with no loss of accuracy, said Pieter Noordhuis, a software engineer at Facebook Continue reading