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jeudi 26 janvier 2017
AI Software Learns to Make AI Software
Technology Review (01/18/17) Tom Simonite
Several research organizations, including Google Brain and DeepMind, are working to create artificial intelligences (AI) that can in turn develop machine-learning software. In many cases, the results coming from machines programming other machines match or exceed work done by humans. If self-programming AI techniques become practical, they could increase the pace at which machine learning is adopted throughout the economy without requiring more machine-learning experts, who already are in short supply. One set of experiments from DeepMind suggests self-teaching methods could alleviate the problem of AI software needing to consume massive amounts of data on a specific task. Researchers challenged their software to create machine-learning systems for collections of multiple, related problems. The software produced designs that demonstrated an ability to generalize and adopt new tasks with less training. A team at the Massachusetts Institute of Technology (MIT) plans to open source the software behind their experiments, in which an AI designed deep-learning systems that matched systems made by humans on standard tests for object recognition. However, these techniques require extreme computer power and are not yet viable replacements for machine-learning experts. MIT Media Lab's Otkrist Gupta believes companies will be motivated to find ways to make automated machine learning practical.
Several research organizations, including Google Brain and DeepMind, are working to create artificial intelligences (AI) that can in turn develop machine-learning software. In many cases, the results coming from machines programming other machines match or exceed work done by humans. If self-programming AI techniques become practical, they could increase the pace at which machine learning is adopted throughout the economy without requiring more machine-learning experts, who already are in short supply. One set of experiments from DeepMind suggests self-teaching methods could alleviate the problem of AI software needing to consume massive amounts of data on a specific task. Researchers challenged their software to create machine-learning systems for collections of multiple, related problems. The software produced designs that demonstrated an ability to generalize and adopt new tasks with less training. A team at the Massachusetts Institute of Technology (MIT) plans to open source the software behind their experiments, in which an AI designed deep-learning systems that matched systems made by humans on standard tests for object recognition. However, these techniques require extreme computer power and are not yet viable replacements for machine-learning experts. MIT Media Lab's Otkrist Gupta believes companies will be motivated to find ways to make automated machine learning practical.
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