By Jonathan Vanian
May 14, 2019

Using artificial intelligence isn’t just about software. Companies are quickly realizing that the hardware it runs on and is trained on is also critical.

Take the example of Google, a leader in A.I. Last week, during its annual developer conference in Mountain View, Calif., the company debuted its new line of Internet-connected home products that are all linked to its voice-controlled Google Assistant.

Executives bragged on stage about how one of the new devices, the Nest Hub Max, can use its camera to immediately recognize individual family members. In that way, the technology can better respond to requests like playing music from someone’s song lists or showing photos that they are more likely to be interested in.

In the past, Google marketed its A.I. as equally compatible with any Android device. But increasingly, it’s tailoring its A.I. to its own hardware so that it operates more smoothly.

“The pendulum swings both ways,” Rishi Chandra, Google’s head of Home and Nest products told Fortune, explaining Google’s philosophical shift.

Facebook and Amazon are following a similar strategy to Google in their growing focus on customizing A.I to their hardware. They’re designing their own Internet-connected devices and data center chips tailored for their own machine-learning tasks.

What’s the lesson here for businesses that are trying to incorporate A.I. into their operations? Using A.I. isn’t as simple as feeding numbers into fancy software and waiting for a result that will lead to lots of profits. In fact, leading A.I. companies have deployed legions of employees to work on complex software while also fine tuning the hardware that runs and trains it. Some companies are also building their own data centers to handle some of the work, as Walmart recently did inside its futuristic store in Levittown, N.Y., rather than using a cloud service.

It’s all a huge and expensive undertaking. Don’t believe anyone who says differently.

Jonathan Vanian

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