We've been testing coolers for years, and our top picks are on sale for Amazon Prime Day. Every Prime Day, I find myself surprised by the random categories that seem to be prominently featured. You always expect a headphone deal or some other discounted tech . But you might not anticipate seeing heavily discounted coolers. I'm the lead tester for our guide to the Best Coolers, which means my entire apartment would be completely overrun if I didn't have my colleagues taking up space in their basements, garages, and camper vans to help me test them out.

Personal thermal comfort models aim to predict an individual's thermal comfort response, instead of the average response of a large group. Recently, machine learning algorithms have proven to be having enormous potential as a candidate for personal thermal comfort models. But, often within the normal settings of a building, personal thermal comfort data obtained via experiments are heavily class-imbalanced. There are a disproportionately high number of data samples for the "Prefer No Change" class, as compared with the "Prefer Warmer" and "Prefer Cooler" classes. Machine learning algorithms trained on such class-imbalanced data perform sub-optimally when deployed in the real world. To develop robust machine learning-based applications using the above class-imbalanced data, as well as for privacy-preserving data sharing, we propose to implement a state-of-the-art conditional synthetic data generator to generate synthetic data corresponding to the low-frequency classes. Via experiments, we show that the synthetic data generated has a distribution that mimics the real data distribution. The proposed method can be extended for use by other smart building datasets/use-cases.

Talk to a scientist in just about any discipline, and ask them the question -- based on their discipline -- "how does that stuff work?" You'll likely find that there are systems and networks that you have to consider before you can really understand how any given thing works: whether that's the human body, a food chain in an ecosystem, a chemical reaction, or a society as a whole. Without understanding the relationship between two animals in an ecosystem, two atoms in a molecule, or cells and tissues in our body, you just have a bunch of data: a list of cells, a readout of animals and what they eat, etc. Traditional machine learning models often take data this way: they take lists or tables of data and do some stuff (the details of which depend on the algorithm being used as well as a few other parameters) to make predictions about a thing. If this in-depth educational content is useful for you, subscribe to our AI research mailing list to be alerted when we release new material. Graphs are data structures that represent networks of, or relationships between the data they contain. Typically, they're represented as "nodes" and lines, or "edges".

RICHMOND, VIRGINIA – Robots about the size of a beer cooler could soon be rolling down Virginia sidewalks to deliver sandwiches, groceries or packages. Supporters say some pending legislation would make the state the first in the nation to regulate such devices. State lawmakers partnered with European company Starship Technologies on bills allowing Virginia cities to join two others in the U.S. and many across Europe where the company is testing its largely autonomous earthbound robots. Much like other tech companies' attempts at airborne drone deliveries, Starship aims to revolutionize the way people get their parcels. Representatives from the company visited Richmond on Wednesday for a demonstration.

Whether you're an avid Starbucks, coffee, or just a tech fan, this new upgraded Starbucks App will make your day. As one of the most loved coffee providers, there is, Starbucks just found another new and funky way to keep their customers both entertained and satisfied, and that's by adding a barista chatbot to their app. It's simply another excellent example of the good things that AI has to offer and how it will make our lives that little bit better. This new feature is aptly named My Starbucks Barista and is "an innovative conversational ordering system." There will be a limited beta version of the feature that's expected to be released in early 2017 that will enable customers to place their orders via a chatbot interface or simple voice command.

The next big thing in tech is going to be AI. That's what a lot of people in Silicon Valley has been saying lately. Add one more to the list: former Google CEO Eric Schmidt. "To me the biggest platforms will be the ones that will be driven by artificial intelligence," he told an audience gathered at Bell Labs, in Murray Hill, N.J., last month. Schmidt, now executive chairman of Google's parent company Alphabet, was a speaker at a conference celebrating the life and work of Claude Shannon, the legendary Bell Labs engineer whose ideas revolutionized modern communications.

People don't want cars, they want rides. And they're scrambling to do something about it. Just months after GM poured money into Lyft (the one with the pink mustasche), Toyota and Volkswagen both said today they were joining up with other ride-hailing rivals. Toyota, meanwhile, is partnering with Uber to, among other things, let people automatically deduct their car payments from the fares they make as Uber drivers. Clearly automakers have Silicon Valley envy.

What kind of person are you, really? What better way to find out than to ask someone who's not a person. As part of the NYCxDesign festival, IBM Watson painted me a portrait of who I really am deep down inside … on Twitter. IBM Watson is a computer program that parses natural language to analyze data. Different versions of Watson have gone on to become a doctor, a college teaching assistant, a world-class chef, as well as a Jeopardy!