MIT’s AI can train neural networks faster than ever before
It hopes this technique will “democratize AI.”
In an effort "to democratize AI," researchers at MIT have found a way to use artificial intelligence to train machine-learning systems much more efficiently. Their hope is that the new time- and cost-saving algorithm will allow resource-strapped researchers and companies to automate neural network design. In other words, by bringing the time and cost down, they could make this AI technique more accessible.
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. Now, MIT researchers have developed a NAS algorithm that automatically learns a convolution neural network in a fraction of the time -- just 200 GPU hours.
Speeding up the process in which AI designs neural networks could enable more people to use and experiment with NAS, and that could advance the adoption of AI. While this is certainly not uncomplicated, it could be a step toward putting AI and machine learning in the hands of more people and companies, freeing it from the towers of tech giants.
