We study the problem of learning latent community structure from multiple correlated networks, focusing on edge-correlated stochastic block models with two balanced communities. Recent work of Gaudio, Rácz, and Sridhar (COLT 2022) determined the precise information-theoretic threshold for exact community recovery using two correlated graphs; in particular, this showcased the subtle interplay between community recovery and graph matching. Here we study the natural setting of more than two graphs. The main challenge lies in understanding how to aggregate information across several graphs when none of the pairwise latent vertex correspondences can be exactly recovered. Our main result derives the precise information-theoretic threshold for exact community recovery using any constant number of correlated graphs, answering a question of Gaudio, Rácz, and Sridhar (COLT 2022). In particular, for every $K \geq 3$ we uncover and characterize a region of the parameter space where exact community recovery is possible using $K$ correlated graphs, even though (1) this is information-theoretically impossible using any $K-1$ of them and (2) none of the latent matchings can be exactly recovered.

We study the problem of learning latent community structure from multiple correlated networks, focusing on edge-correlated stochastic block models with two balanced communities. Recent work of Gaudio, Rácz, and Sridhar (COLT 2022) determined the precise information-theoretic threshold for exact community recovery using two correlated graphs; in particular, this showcased the subtle interplay between community recovery and graph matching. Here we study the natural setting of more than two graphs. The main challenge lies in understanding how to aggregate information across several graphs when none of the pairwise latent vertex correspondences can be exactly recovered. Our main result derives the precise information-theoretic threshold for exact community recovery using any constant number of correlated graphs, answering a question of Gaudio, Rácz, and Sridhar (COLT 2022).

Regie.ai, a startup using OpenAI's GPT-3 text-generating system to create sales and marketing content for brands, today announced that it raised $10 million in Series A funding led by Scale Venture Partners with participation from Foundation Capital, South Park Commons, Day One Ventures and prominent angel investors. The fresh investment comes as VCs see a growing opportunity in AI-powered, copy-generating adtech companies, whose tech promise to save time while potentially increasing personalization. Previously a software engineer at Google and Meta, Sridhar is a data scientist by trade, having developed enterprise-scale AI systems that detect duplicate images and rank search results. Millen formerly was a VP at T-Mobile, leading the national sales teams (e.g., strategic accounts and public sector). With Regie, Sridhar says he and Millen aimed to create a way for companies to communicate with their customers via channels like email, social media, text, podcasts, online advertising and more.

Researchers at the University of Washington have figured out a way to use machine-learning algorithms to turn smart speakers into sensitive medical devices that can detect irregular heartbeats. The scientists use smart speakers like Amazon Echo or Google Home to send out an inaudible sound that bounces off a person's chest and returns to the device, reshaped in a way that reveals the heartbeat. An uneven cardiac rhythm can be associated with ailments including strokes or sleep apnea. The researchers employed a machine-learning algorithm to tease out the heartbeats from other sounds and signals such as breathing, which is easier to detect because it involves a much larger motion. The algorithm was also needed to zero in on erratic heart rhythms -- which from a health perspective are generally more important to identify than a steady "lub-dub."

Amazon's Echo and other smart speakers like the Google Home could be used to monitor the rhythm of a person's heart. Academics created an AI-powered device which monitors regular, and irregular, heartbeats using the same tools found in smart speakers. The prototype, which was built in a lab but could be incorporated into speakers in the future, was found to be almost as good as medical devices in hospitals. The search for heartbeats begins when a person sits within one to two feet of the smart speaker. Then the system plays an inaudible continuous sound, which bounces off the person and then returns to the speaker.

Have you ever been running late for work, your hand extended into your shower, cursing its name as the water slowly warms to a temperature that would allow you to enter? Well, you may be being unsympathetic to your hot water heater, because it's likely running all day and all night to keep between 40-80 gallons of water heated, so it can be ready at your command. As you ponder the inefficiency of such a system, imagine the hot water needs of a hotel or a high-rise apartment building, with hundreds of rooms and thousands of inhabitants. The founder in this week's Silicon Valley Insider, Sridhar Deivasigamani, estimates that at any point in time in the US, there could be as much as 6 billion gallons of water being kept hot for our consumption, one-sixth the size of Lake Tahoe. Intellihot, the Galesburg, IL company founded in 2009, designs and manufactures tankless water heaters, as well as monitoring devices and apps, for residential, commercial and industrial applications.

Gartner says more than 3 million workers across the world will have a'robo boss' by 2018. High time businesses reorient skill development programs to help mid-level managers stay relevant. In July, the Vodafone-Idea merger was approved by the Competition Commission of India (CCI). The mega deal will make the shareholders of both companies become part of the largest telecom company in India, and reward them in the future. It will also create a situation that can quickly escalate into a nightmare.

Successful analysis of video data requires an integration of techniques from KR, Computer Vision, and Machine Learning. Being able to detect and to track objects as well as extracting their changing spatial relations with other objects is one approach to describing and detecting events. Different kinds of spatial relations are important, including topology, direction, size, and distance between objects as well as changes of those relations over time. Typically these kinds of relations are treated separately, which makes it difficult to integrate all the extracted spatial information. We present a uniform and comprehensive spatial representation of moving objects that includes all the above spatial/temporal aspects, analyse different properties of this representation and demonstrate that it is suitable for video analysis.