Google’s Hand-Fed AI Now Gives Answers, Not Just Search Results

Posted November 29th 2016


via Wired

Ask the Google search app “What is the fastest bird on Earth?,” and it will tell you.

“Peregrine falcon,” the phone says. “According to YouTube, the peregrine falcon has a maximum recorded airspeed of 389 kilometers per hour.”

That’s the right answer, but it doesn’t come from some master database inside Google. When you ask the question, Google’s search engine pinpoints a YouTube video describing the five fastest birds on the planet and then extracts just the information you’re looking for. It doesn’t mention those other four birds. And it responds in similar fashion if you ask, say, “How many days are there in Hanukkah?” or “How long is Totem?” The search engine knows that Totem is a Cirque de Soleil show, and that it lasts two-and-a-half hours, including a thirty-minute intermission.

Google answers these questions with the help from deep neural networks, a form of artificial intelligence rapidly remaking not just Google’s search engine but the entire company and, well, the other giants of the internet, from Facebook to Microsoft. Deep neutral nets are pattern recognition systems that can learn to perform specific tasks by analyzing vast amounts of data. In this case, they’ve learned to take a long sentence or paragraph from a relevant page on the web and extract the upshot—the information you’re looking for.

These new advancements demonstrate how deep learning is improving AI’s ability to understand natural human speech and respond in kind. Still, training these neural networks requires a great deal of human effort in the form of feeding in the right data. Pygmalion, Google’s team of 100 PhD linguists “not only demonstrate sentence compression, but actually label parts of speech in ways that help neural nets understand how human language works,” writes Wired‘s Cade Metz. As AI continues to move from this “supervised learning” to what is called “unsupervised learning,” where machines can absorb massive amounts of digital information on their own, there’s no telling how far or exactly where the technology will go.