{"id":8716,"date":"2018-09-24T07:00:26","date_gmt":"2018-09-24T15:00:26","guid":{"rendered":"http:\/\/formtek.com\/blog\/?p=8716"},"modified":"2018-07-30T20:05:29","modified_gmt":"2018-07-31T04:05:29","slug":"artificial-intelligence-using-light-and-optics-to-advance-state-of-the-art-ai","status":"publish","type":"post","link":"https:\/\/formtek.com\/blog\/artificial-intelligence-using-light-and-optics-to-advance-state-of-the-art-ai\/","title":{"rendered":"Artificial Intelligence: Using Light and Optics to Advance State-of-the-Art AI"},"content":{"rendered":"

Machine Learning and Artificial Intelligence algorithms currently require training, and lot’s of it. Lots and lots of data, and lots and lots of computer cycle crunching. Often days or weeks of time are needed.<\/p>\n

Some researchers are investigating better ways to perform training.\u00a0 Researchers at Stanford<\/a>, for example, are using light and optical chips to train artificial neural networks. The new optical technique can potential be less expensive, faster and more energy efficient than the standard electronic-based computing.<\/p>\n

Shanhui Fan<\/a>, research team leader at Stanford, said that<\/a> “using an optical chip to perform neural network computations more efficiently than is possible with digital computers could allow more complex problems to be solved\u201d.<\/p>\n

Yichen Shen<\/a>, MIT physics post-doc , said that<\/a> “deep learning is mainly matrix multiplications, so it works very well with the nature of light. With light you can make deep learning computing much faster and thousands of times more energy-efficient.\u201d<\/p>\n

Tyler Hughes<\/a>, Standord PhD student and researcher, said that<\/a> “using a physical device rather than a computer model for training makes the process more accurate. Also, because the training step is a very computationally expensive part of the implementation of the neural network, performing this step optically is key to improving the computational efficiency, speed and power consumption of artificial networks.”<\/p>\n

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Machine Learning and Artificial Intelligence algorithms currently require training, and lot’s of it. Lots and lots of data, and lots and lots of computer cycle crunching. Often days or weeks of time are needed. Some researchers are investigating better ways…<\/span><\/p>\n

Read more ›<\/a><\/div>\n

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