The Covid-19 pandemic was devastating for many industries, but it only accelerated the use of artificial intelligence across the U.S. economy. Amid the crisis, companies scrambled to create new services for remote workers and students, beef up online shopping and dining options, make customer call centers more efficient and speed development of important new drugs. Even as applications of machine learning and perception platforms become commonplace, a thick layer of hype and fuzzy jargon clings to AI-enabled software.That makes it tough to identify the most compelling companies in the space--especially those finding new ways to use AI that create value by making humans more efficient, not redundant. With this in mind, Forbes has partnered with venture firms Sequoia Capital and Meritech Capital to create our third annual AI 50, a list of private, promising North American companies that are using artificial intelligence in ways that are fundamental to their operations. To be considered, businesses must be privately-held and utilizing machine learning (where systems learn from data to improve on tasks), natural language processing (which enables programs to "understand" written or spoken language) or computer vision (which relates to how machines "see"). AI companies incubated at, largely funded through or acquired by large tech, manufacturing or industrial firms aren't eligible for consideration. Our list was compiled through a submission process open to any AI company in the U.S. and Canada. The application asked companies to provide details on their technology, business model, customers and financials like funding, valuation and revenue history (companies had the option to submit information confidentially, to encourage greater transparency). Forbes received several hundred entries, of which nearly 400 qualified for consideration. From there, our data partners applied an algorithm to identify 100 companies with the highest quantitative scores--and that also made diversity a priority. Next, a panel of expert AI judges evaluated the finalists to find the 50 most compelling companies (they were precluded from judging companies in which they have a vested interest). Among trends this year are what Sequoia Capital's Konstantine Buhler calls AI workbench companies--building of platforms tailored to different enterprises, including Dataiku, DataRobot Domino Data and Databricks.

AI is being applied to whale research, especially to understand what whales are trying to communicate in the audible sounds they make to each other in the ocean. For example, marine biologist Shane Gero has worked to match clicks coming from whales around the Caribbean island nation of Dominica, to behavior he hopes will reveal the meanings of the sounds they make. Gero is a behavioral ecologist affiliated with the Marine Bioacoustics Lab at Aarhus University in Denmark, and the Department of Biology of Dalhousie University of Halifax, Nova Scotia. Gero works with a team from Project CETI, a nonprofit that aims to apply advanced machine learning and state-of-the-art robotics to listen to and translate the communication of whales. Project CETI has recently announced a five-year effort to build on Gero's work with a research project to try to decipher what sperm whales are saying to each other, according to a recent account in National Geographic.

Our mission is simple--we're changing the way we care for our parents so they can live safely at home as they age. But how we accomplish our mission is anything but simple. Every day, we're solving complex problems that don't come with a playbook. If you're someone who shares our core values--Own the Outcome, Solve with Empathy, and Act with Honor--let's talk. Founded in 2014, Honor is now one of the fastest-growing, non-medical home care companies in the U.S. Why?

Our mission is simple--we're changing the way we care for our parents so they can live safely at home as they age. But how we accomplish our mission is anything but simple. Every day, we're solving complex problems that don't come with a playbook. If you're someone who shares our core values--Own the Outcome, Solve with Empathy, and Act with Honor--let's talk. Founded in 2014, Honor is now one of the fastest-growing, non-medical home care companies in the U.S. Why?

Mike Pondsmith started playing Dungeons & Dragons in the late seventies, as an undergraduate at the University of California, Davis. The game, published just a few years before, popularized a newish form of entertainment: tabletop role-playing, in which players, typically using dice and a set of rule books, create characters who pursue open-ended quests within an established world. "The most stimulating part of the game is the fact that anything can happen," an early D&D review noted. Soon, other such games hit the market, including Traveller, a sci-fi game published in 1977, the year that "Star Wars" came out. Pondsmith, a tall Black man who grew up in multiple countries because his dad was in the Air Force, loved sci-fi, and fancied himself a bit like Lando Calrissian, the smooth-talking "Star Wars" rogue played by Billy Dee Williams.

Sen. Joni Ernst, R-Iowa, and former under Secretary of Defense for policy Michèle Flournoy discuss possible actions to take if Russia invades Ukraine. This is a rush transcript of "Fox News Sunday" on December 5, 2021. This copy may not be in its final form and may be updated. President Biden and Russia's Vladimir Putin will hold a superpower phone JOE BIDEN, PRESIDENT OF THE UNITED STATES: I don't accept anybody's red We'll discuss the standoff with Senate Armed Services Committee member Joni Just how much of a threat is China? We'll talk about how to keep law and order in space with the vice chief of So, we need to be ready. U.S. faces around the world.

Narrative cartography is a discipline which studies the interwoven nature of stories and maps. However, conventional geovisualization techniques of narratives often encounter several prominent challenges, including the data acquisition & integration challenge and the semantic challenge. To tackle these challenges, in this paper, we propose the idea of narrative cartography with knowledge graphs (KGs). Firstly, to tackle the data acquisition & integration challenge, we develop a set of KG-based GeoEnrichment toolboxes to allow users to search and retrieve relevant data from integrated cross-domain knowledge graphs for narrative mapping from within a GISystem. With the help of this tool, the retrieved data from KGs are directly materialized in a GIS format which is ready for spatial analysis and mapping. Two use cases - Magellan's expedition and World War II - are presented to show the effectiveness of this approach. In the meantime, several limitations are identified from this approach, such as data incompleteness, semantic incompatibility, and the semantic challenge in geovisualization. For the later two limitations, we propose a modular ontology for narrative cartography, which formalizes both the map content (Map Content Module) and the geovisualization process (Cartography Module). We demonstrate that, by representing both the map content and the geovisualization process in KGs (an ontology), we can realize both data reusability and map reproducibility for narrative cartography.

In many scientific disciplines, coarse-grained causal models are used to explain and predict the dynamics of more fine-grained systems. Naturally, such models require appropriate macrovariables. Automated procedures to detect suitable variables would be useful to leverage increasingly available high-dimensional observational datasets. This work introduces a novel algorithmic approach that is inspired by a new characterisation of causal macrovariables as information bottlenecks between microstates. Its general form can be adapted to address individual needs of different scientific goals. After a further transformation step, the causal relationships between learned variables can be investigated through additive noise models. Experiments on both simulated data and on a real climate dataset are reported. In a synthetic dataset, the algorithm robustly detects the ground-truth variables and correctly infers the causal relationships between them. In a real climate dataset, the algorithm robustly detects two variables that correspond to the two known variations of the El Nino phenomenon.

In the wake of the woefully insufficient Pentagon report from June in which the U.S. government admitted it could not explain the vast majority of unidentified aerial phenomena, the Department of Defense is increasing its effort, creating an official group to study these events. The announcement, made late Tuesday, will see the establishment of the Airborne Object Identification and Management Synchronization Group (AOIMSG), succeeding the U.S. Navy's Unidentified Aerial Phenomena Task Force; it will be part of the office of Under Secretary of Defense for Intelligence & Security. The AOIMSG will work across the Department of Defense and the entire U.S. government'to detect, identify and attribute objects of interests in Special Use Airspace, and to assess and mitigate any associated threats to safety of flight and national security,' according to a press release issued by the DoD. The move to formally establish the office was made the Under Secretary of Defense for Intelligence & Security Ronald S. Moultrie, who was directed by Deputy Secretary of Defense Kathleen Hicks and Director of National Intelligence Avril Haines. The Pentagon is creating a group to study unidentified aerial phenomena.

While captioning models have obtained compelling results in describing natural images, they still do not cover the entire long-tail distribution of real-world concepts. In this paper, we address the task of generating human-like descriptions with in-the-wild concepts by training on web-scale automatically collected datasets. To this end, we propose a model which can exploit noisy image-caption pairs while maintaining the descriptive style of traditional human-annotated datasets like COCO. Our model separates content from style through the usage of keywords and stylistic tokens, employing a single objective of prompt language modeling and being simpler than other recent proposals. Experimentally, our model consistently outperforms existing methods in terms of caption quality and capability of describing long-tail concepts, also in zero-shot settings. According to the CIDEr metric, we obtain a new state of the art on both COCO and nocaps when using external data.