Wednesday, December 06, 2017
In May 2017, researchers at Google Brain announced the creation of AutoML, an artificial intelligence (AI) that's capable of generating its own AIs. More recently, they decided to present AutoML with its biggest challenge to date, and the AI that can build AI created a "child" that outperformed all of its human-made counterparts. The Google researchers automated the design of machine learning models using an approach called reinforcement learning. AutoML acts as a controller neural network that develops a child AI network for a specific task. For this particular child AI, which the researchers called NASNet, the task was recognizing objects -- people, cars, traffic lights, handbags, backpacks, etc. -- in a video in real-time.
Machine learning is what gives many AI systems their ability to perform specific tasks. Although the concept behind it is fairly simple -- an algorithm learns by being fed a ton of data -- the process requires a huge amount of time and effort. By automating the process of creating accurate, efficient AI systems, an AI that can build AI takes on the brunt of that work. Ultimately, that means AutoML could open up the field of machine learning and AI to non-experts.
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