Nest Moves Towards More Home and Personal Integration

nestThe fully integrated home may still seem like decades away for most people but the Internet of Things is on the cusp of a full breakthrough in everyday society. Nest is one company on the forefront of this industry, best known for their internet-connected thermostats and smoke alarms that help to ensure your home is safe, of course, while you are at work or on vacation.

Well, Nest is at it again, now working on what they call an “operating system for the home,” in a new attempt attract consumers who may not yet be aware of the coming trend.

This is the first time, since Google’s $3.2 billion acquisition in 2014, that Nest has made a move to take more advantage of the technology company’s massively powerful cloud computing infrastructure.

“The key change for us is we are moving some intelligence to the cloud,” comments Matt Rogers, who you may know as the former Apple engineer who left the company to co-found Nest with chief executive Tony Fadell in 2010. “We needed to add some real learning to this. It can’t just be an algorithm that learns from the heuristics of sensors.”

Nest customers are quite familiar with the thermostat’s remarkable Home/Away arming feature but they are now looking to improve the device’s ability to detect if someone is home or away by taking better advantage of machine-learning techniques. This might include better using the thermostat’s motion sensors in combination with a smartphone’s GPS location.

Rogers goes on to say, “The groundwork we are laying — and this release is very much about groundwork — is the fundamental building blocks for what you need to build the platform for the home. You need to have family members, you need to know whether people are home or not . . . Most of what the team is working on these days are these kinds of building blocks.”

Secondly, Nest has announced the possibility of adding support for multiple family members with a multiple account option in the application.

Rogers also notes that sometimes the processing power for one job is too much for the system to do alone. He says, then, “To get to the next level of scale in machine learning, you have to have the cloud.”

 

 

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