We consider the task of learning latent community structure from multiple correlated networks. First, we study the problem of learning the latent vertex correspondence between two edge-correlated stochastic block models, focusing on the regime where the average degree is logarithmic in the number of vertices. We derive the precise information-theoretic threshold for exact recovery: above the threshold there exists an estimator that outputs the true correspondence with probability close to 1, while below it no estimator can recover the true correspondence with probability bounded away from 0. As an application of our results, we show how one can exactly recover the latent communities using \emph{multiple} correlated graphs in parameter regimes where it is information-theoretically impossible to do so using just a single graph.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. FBI investigators identified Robert Jacob Hoopes as a suspect in the injury of an ICE officer during protests in Portland, Ore., using facial recognition software, according to a criminal complaint from the case. In the criminal complaint, an unidentified FBI special agent said that a photo shared on OregonLive.com -- the online version of The Oregonian -- was put into "commercially available facial recognition software." The software allegedly provided 30 possible comparison photos from public databases. FBI Portland reviewed the photos and found one from a Reed College SmugMug page called "Canyon Day April '23," in which a tattoo on the suspect's forearm is visible.

While the field of AI has always been prone to exaggerated claims, never has the hype, confusion, and sometimes even genuine dishonesty surrounding AI been higher. With the exception of what New Sapience has created, everything you read about AI is about "Machine Learning" (ML) – a technology that never should have been called AI in the first place. The correct term is Data Science. If people would stop calling it AI, perhaps there would not be a rush to use ML in automated decision making. Data science has many useful applications in pattern recognition.

Airbnb is a mission-driven company dedicated to helping create a world where anyone can belong anywhere. It takes a unified team committed to our core values to achieve this goal. Airbnb's various functions embody the company's innovative spirit and our fast-moving team is committed to leading as a 21st century company. In the Geos team, we empower emerging businesses and regional operational teams to achieve their goals. Specifically, the community team is driving acquisition and helping out the business in many ways (Clubs leaders communication, Meetups facilitators, etc.).

Synthetic data is a promising technology and is in its early adoption phase. To bridge to mainstream adoption, the research community needs a place where they can learn about it, discuss the latest innovations and experiment. Synthetic data is computer-generated image data that models the real world. In the visual domain, synthetic data has shown promise in creating more capable and ethical AI models. By creating a centralized hub for datasets, papers, code, and resources, we aim to bring together researchers from industry and academia to advance state-of-the-art synthetic data.

After a couple of troublesome years, many businesses are looking forward to 2022 with cautious optimism. The worldwide global pandemic has accelerated the digital ambitions of many enterprises, creating a fertile ground for the development of AI and related technologies. Ahead of RE•WORK's upcoming Deep Learning Hybrid Summit on February 17-18, we asked some of our expert community what they predict for AI and Deep Learning in 2022. Which trend associated with Deep Learning and AI are you most interested in/passionate about? Why do you think this is so relevant today?

Originally published on Towards AI the World's Leading AI and Technology News and Media Company. If you are building an AI-related product or service, we invite you to consider becoming an AI sponsor. At Towards AI, we help scale AI and technology startups. Let us help you unleash your technology to the masses. According to Alation's State of Data Culture Report, 87% of employees attribute poor data quality to why most organizations fail to adopt AI meaningfully.

This new conference series promotes multidisciplinary research on tools and methodologies for efficiently capturing knowledge from a variety of sources and creating representations that can be (or eventually can be) useful for reasoning. The conference attracted researchers from diverse areas of AI, including knowledge representation, knowledge acquisition, intelligent user interfaces, problem solving and reasoning, planning, agents, text extraction, and machine learning. Knowledge acquisition has been a challenging area of research in AI, with its roots in early work to develop expert systems. Driven by the modern internet culture and knowledge-based industries, the study of knowledge capture has a renewed importance. Although there has been considerable work over the years in the area, activities have been distributed across several distinct research communities.

The workshop focused on possible solutions to both technical and organizational challenges to mobility and manipulation research. This article presents the highlights of that discussion along with the content of the accompanying exhibits. Fortunately, these applications can be successful through simple repetitive behaviors or remote human operation. However, useful autonomy needed for operation in general situations requires advanced mobility and manipulation. Opening doors, retrieving specific items, and maneuvering in cluttered environments are required for useful deployment in anything but the most controlled environment. The mobile manipulation skills necessary to perform tasks in arbitrary environments may not result from current approaches to robotics and AI. Moving toward true robot autonomy may require new paradigms, hardware, and ways of thinking. The goal of the AAAI 2008 Workshop on Mobility and Manipulation was not only to demonstrate current research successes to the AAAI community but also to road-map future mobility and manipulation challenges that create synergies between artificial intelligence and robotics. The half-day workshop included both a session on the exhibits and a panel discussion. The panel consisted of five prominent researchers who led a discussion of future directions for mobility and manipulation research.

Developing agents that could perceive the world, reason about what they perceive in relation to their own goals and acts, has been the Holy Grail of AI. Early attempts at such holistic intelligence (for example, SRI International's AI researchers turned their attention to component technologies for structuring a single agent, such as planning, knowledge representation, diagnosis, and learning. Although most of AI research was focused on single-agent issues, a small number of AI researchers gathered at the Massachusetts Institute of Technology Endicott House in 1980 for the First Workshop on Distributed AI. The main scientific goal of distributed AI (DAI) is to understand the principles underlying the behavior of multiple entities in the world, called agents and their interactions. The discipline is concerned with how agent interactions produce overall multiagent system (MAS) behavior.