A popular concept in science fiction is the singularity, a moment of explosive accelerating growth in technology and artificial intelligence that rewrites the world. One of the better explanations for how this could happen is described by the Scottish sci-fi author Charles Stross as “a hard take-off singularity in which a human-equivalent AI rapidly bootstraps itself to de-facto god-hood.”
To translate: If an AI is capable of improving (“boostrapping”) itself, or of building another, smarter AI, then that next version can do the same, and soon you have exponential growth. In theory this could lead to a system rapidly surpassing human intelligence, and, if you’re in a Stross novel, probably a computer that’s going to start eating people’s brains.
The singularity still seems to be a long ways off (until we crack Moore’s Law), but at Google I/O, we got a glimpse of our future robot overlords from Google CEO Sundar Pichai.
The new technology is called AutoML, and it uses a machine learning system (ML) to make other machine learning systems faster or more efficient. Essentially, it’s a program that teaches other programs how to learn, without actually teaching them any specific skills (it’s the liberal arts college of algorithms).
AutoML comes from the Google Brain division (not to be confused with DeepMind, the other Google AI project). Whereas DeepMind is more focused on general-purpose AI that can adapt to new tasks and situations, Google Brain is focused on deep learning, which is all about specializing and excelling in narrowly defined tasks.
According to Google, AutoML has already been used to design neural networks for speech and image recognition. (Fun fact: The networks to accomplish these two tasks are usually nearly identical. Images are typically analyzed by looking at repeating patterns in pixels, and speech is analyzed by turning sound into a graph of frequency over time that’s analyzed the same way). Designed by AutoML, the image recognition algorithms were as good as those designed by humans, and the speech recognition algorithms were, as of February 2017, “0.09 percent better and 1.05x faster than the previous state-of-the-art model.”
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