If you have tapped into IoT's connectivity alone, you haven't explored even half of the technology's potential. The real power lies in data-centric IoT. The IoT is a giant network of connected things and people – all of which collect and share data about the way they are used and about the environment around them. That includes an extraordinary number of objects of all shapes and sizes – from smart microwaves, which automatically cook your food for the right length of time, to self-driving cars, whose complex sensors detect objects in their path, to wearable fitness devices that measure your heart rate and the number of steps you've taken that day, then use that information to suggest exercise plans tailored to you. There are even connected footballs that can track how far and fast they are thrown and record those statistics via an app for future training purposes.

Conferences and vendor marketing materials are full of trite and banal sayings. Say something that seems to be profound, and perhaps they'll think that everything else you have to say is just as profound. One of the common refrains you might hear at many an AI and data-focused event is the pithy statement that "data is the new oil" as if that's supposed to mean something profound. The first time I heard this expression (about a decade ago, I should add), it was an interesting point to make about how "important" and "strategic" data is. But every time I've heard it since, it's bandied about to imply something more than it is.

With every place we go and every action we take, it feels like we're constantly being asked to submit personal data. Download a free app, and you're asked for your email address. To redeem that discount offer at the pet store, you must supply your phone number. Play a video game, and it wants to connect to your social media. AI, the artificial intelligence displayed by machines, can be misunderstood if we define it only by what's seen in media and entertainment – the cute droid sidekicks from a blockbuster movie or the facial-recognition software used by television detectives.

Technological innovations expand the possibilities of our world, but they can also shake-up society in a disorienting manner. Periods of major technological advancement are often marked by alienation. While our generation has seen the boon of the Internet, the path to a new world may be paved with Artificial Intelligence. Artificial intelligence is defined as the development of computer systems to perform tasks that normally require human intelligence, including speech recognition, visual perception, and decision-making. As recently as a decade ago, artificial intelligence evoked the image of robots, but AI is software not hardware.

This week, more than 2,000 Google employees walked out of work to protest President Trump's immigration ban. Far from disciplining them for leaving their desks, CEO Sundar Pichai and co-founder Sergey Brin treated workers to impassioned speeches of support. "Proud, moved, and touched to be at a company that boldly stands for its people," Googler Sam Tse tweeted. While Pichai and Brin were no doubt speaking from personal conviction--Brin's family fled the former Soviet Union when he was a boy--they also had little choice but to back their employees. Trump's directive cut to the heart of Silicon Valley's treasured values of globalism and openness, values widely embraced by the workers themselves.

Products using artificial intelligence (AI) are creeping into our lives: in the home, online, at work, in the marketplace, in the doctor's office. What if AI gets carried away, if it hasn't already? Plenty of movies and books that contemplate this. While those scenarios may be easy to dismiss, the consequences of what could happen are not. Unless it's fully grasped for its benefits, companies that use AI are putting their brands at risk if society doesn't adequately understand how it benefits from the technology.

Electricity disaggregation focuses on classification ofindividual appliances by monitoring aggregate electricalsignals. In this paper we present a novel algorithmto automatically correct labels, discard contaminatedtraining samples, and boost signal to noise ratio throughhigh frequency noise reduction. We also propose amethod for prioritized classification which classifies applianceswith the most intense signals first. When testedon four houses in Kaggles Belkin dataset, these methodsautomatically relabel over 77% of all training samplesand decrease error rate by an average of 45% in bothreal power and high frequency noise classification.