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.
From Technology Review
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