We consider the problem where $n$ clients transmit $d$-dimensional real-valued vectors using $d(1+o(1))$ bits each, in a manner that allows the receiver to approximately reconstruct their mean. Such compression problems naturally arise in distributed and federated learning. We provide novel mathematical results and derive computationally efficient algorithms that are more accurate than previous compression techniques. We evaluate our methods on a collection of distributed and federated learning tasks, using a variety of datasets, and show a consistent improvement over the state of the art.

Google's Latest AI Ransomware Defense Only Goes So Far Google has launched a new AI-based protection in Drive for desktop that can shut down an attack before it spreads--but its benefits have their limits. Ransomware attacks have loomed for years as an urgent digital threat with no easy solution --especially as they have evolved to include data grab-and-leak attacks that may not even involve data-encrypting malware at all. Traditional ransomware that locks up files and systems is still rampant, though, and Google on Tuesday launched a new defense for its Google Drive for desktop apps that aims to quickly detect ransomware activity and halt cloud syncing before an infection can spread. While antivirus scanners monitor for signs of malware across a system, the new ransomware protections in Drive for desktop are meant to act as an additional line of defense. The detection capability is built on an AI model that Google trained using millions of real victims' files that had been encrypted with various strains of ransomware.

Artificial intelligence-powered systems are the battlefield's future.

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There is no denying that artificial intelligence (AI) plays a significant role in how we go about our daily lives. From predictive searches and automated translations to futuristic use cases like self-driven cars, AI has captured the imagination of managers, CXOs, tech workers and end users alike. That said, ask any two individuals what AI is, and one is very likely to get two conflicting answers. This is not just among ordinary people but also top-level decision-makers. Many business leaders want to understand how AI will impact their business.

For more than a decade, people have been trying to teach cars how to drive. In the not-too-distant future, this effort may come full circle, with cars teaching people how to drive; last week, General Motors applied for a patent on an autonomous vehicle equipped to "train drivers." Self-driving cars have taken a lot longer to come about than was predicted, with complications relating to technology, safety, and regulations all throwing wrenches in the spokes of progress. Google was one of the first companies to invest heavily in driverless vehicle development, launching its self-driving car project in early 2009 out of its X lab (also known as the Moonshot Factory). As recently as 2015, auto industry insiders predicted fully self-driving cars would be on the road by 2020. That wasn't the case, and two years later we're still waiting for the day we can kick back, put our feet up, and watch the scenery go by as autonomous cars deliver us to our destinations.

AI practitioners are all too familiar with statistics that over 80% of AI projects fail. A lot has been said about what organizations and data science teams can do to increase this low success rate. Nonetheless, even organizations with established machine learning (ML) practices and high-end AI teams struggle. Some AI initiatives become transformational for the business, while others show little return on investment or never even come to fruition. Of course, this isn't unique to AI projects, but since data science is a fairly new discipline, there's another factor impeding success: not everything can be solved with AI.

Last month, IEEE Spectrum went out to California to take a ride in one of Drive.ai's It's only been about a year since Drive.ai "This is in contrast to a traditional robotics approach," says Sameep Tandon, one of Drive.ai's "A lot of companies are just using deep learning for this component or that component, while we view it more holistically." Often, deep learning is used in perception, since there's so much variability inherent in how robots see the world.

Among all of the self-driving startups working towards Level 4 autonomy (a self-driving system that doesn't require human intervention in most scenarios), Mountain View, Calif.-based Drive.ai's Drive sees deep learning as the only viable way to make a truly useful autonomous car in the near term, says Sameep Tandon, cofounder and CEO. "If you look at the long-term possibilities of these algorithms and how people are going to build [self-driving cars] in the future, having a learning system just makes the most sense. There's so much complication in driving, there are so many things that are nuanced and hard, that if you have to do this in ways that aren't learned, then you're never going to get these cars out there." It's only been about a year since Drive went public, but already, the company has a fleet of four vehicles navigating (mostly) autonomously around the San Francisco Bay Area--even in situations (such as darkness, rain, or hail) that are notoriously difficult for self-driving cars.

Skydio says the X2D offers up to 35 minutes of flight time, can operate in both day or night, and features artificial intelligence tools that enable …