Category: convolution neural network

AI can help find more places to store captured CO2 underground

Post author By Randall
Post date March 1, 2020
Categories In Ai, Artificial Intelligence, co2, convolution neural network, earthquake, emissions, energy, environment, gadgetry, Gadgets, gear, green, machine learning, mit, Science, Tech

[ad_1] The team trained a convolutional neural network to find the “hidden correlations” in components of high-frequency data from simulated earthquakes. The AI learned how to find patterns that could be used to infer the ‘missing’ low frequencies, to put it another way. The result is an algorithmic system that, in the right circumstances, can […]

Adobe trained AI to detect facial manipulation in Photoshop

Post author By Randall
Post date June 14, 2019
Categories In adobe, Ai, convolution neural network, deepfake, detection, face away liquify, faces, facial expressions, gear, image manipulation, Internet, machine learning, photoshop, Research, Robots, Security, Tech, tomorrow, tool, uc berkeley

[ad_1] The team trained a convolutional neural network (CNN) to spot changes in images made with Photoshop’s Face Away Liquify feature, which was designed to change people’s eyes, mouth and other facial features. When put to the test, the neural network detected altered images up to 99 percent of the time. In comparison, people who […]

MIT’s AI can train neural networks faster than ever before

Post author By Randall
Post date March 22, 2019
Categories In Ai, algorithm, Artificial Intelligence, convolution neural network, Internet, machine learning, mit, neural architecture search, neural network, Robots, Tech, tomorrow
No Comments on MIT’s AI can train neural networks faster than ever before

[ad_1] Today, AI can design machine learning systems known as neural networks in a process called neural architecture search (NAS). But this technique requires a considerable amount of resources like time, processing power and money. Even for Google, producing a single convolution neural network — often used for image classification — takes 48,000 GPU hours. […]