Recently, Dr. France A. Córdova, director of the National Science Foundation, gave a presentation at the U.S. Council on Competitiveness meeting in Washington, D.C. She holds an extraordinary record of accomplishment and has made a tremendous impact on academia and the U.S.'s scientific community. Córdova is also the youngest person -- and first woman -- to serve as Chief Scientist at NASA. Her journey began with her love for STEM. In some ways, the future of work is largely linked to STEM.

Google is putting its supreme algorithm to work. In October, the company announced that they had reached quantum supremacy – the point at which a quantum computer can complete a task that no classical computer could practically achieve – with an algorithm that could verify that a given set of numbers was randomly distributed. Now, that algorithm is finding its first practical use as a random number generator.

Based on a joint work with Aryan Mokhtari, UT Austin, and Asu Ozdaglar, MIT. Imagine sitting in your autonomous car, going for a vacation. Your vehicle should follow the directions provided by the navigation app, and also use multiple sensors to monitor other vehicles, road signs, street light, etc. As a result, during the course of your journey, your car might need to take actions within a few seconds, such as turning or stopping. The question is how should your vehicle be programmed to be able to adapt to the new tasks within a short amount of time and limited data.

More than 13,000 artificial intelligence mavens flocked to Vancouver this week for the world's leading academic AI conference, NeurIPS. The venue included a maze of colorful corporate booths aiming to lure recruits for projects like software that plays doctor. Google handed out free luggage scales and socks depicting the colorful bikes employees ride on its campus, while IBM offered hats emblazoned with "I A ." Tuesday night, Google and Uber hosted well-lubricated, over-subscribed parties. At a bleary 8:30 the next morning, one of Google's top researchers gave a keynote with a sobering message about AI's future. Blaise Aguera y Arcas praised the revolutionary technique known as deep learning that has seen teams like his get phones to recognize faces and voices.

The application of artificial intelligence (AI) and machine learning to business and IT, from intelligent IT operations (AIOps) to service management to software testing, is keeping the data revolution moving at lightning speed. That's why data science remains a popular concentration for computer science students who have the talent for math and analytics. And it's why more organizations are clamoring for data scientists who can help make decisions faster and put their businesses ahead of competitors. To help you keep up, TechBeacon assembled this list of leading data scientists to follow on Twitter. Plus: Get the 2019 Forrester Wave for ESM.

Professor Hao Li used to think it could take two to three years for the perfection of deepfake videos to make copycats indistinguishable from reality. But now, the associate professor of computer science at the University of Southern California, says this technology could be perfected in as soon as six to 12 months. Deepfakes are realistic manipulated videos that can, for example, make it look a person said or did something they didn't. "The best possible algorithm will not be able to distinguish," he says of the difference between a perfect deepfake and real videos. Li says he's changed his mind because developments in computer graphics and artificial intelligence are accelerating the development of deepfake applications.

Some companies just don't mess around … They see the future and embrace the tools and resources to take them there. That's how the Storefriendly self-storage brand in Asia is doing it! The operator just released an innovative video that not only highlights its GObots unit-retrieval service but displays the power of artificial intelligence (AI). "Make Space for the Future" was developed by feeding 200 pieces of company-related information into an AI program and machine-learning system. The result is a flamboyant ad featuring Gary the GObot that illustrates the operator's services, technology features, customer appeal and more.

AI having applications in various sectors including agriculture has completely transformed the approaches of the agriculture market. AI in Agriculture helps the farmers in examining weather, soil, and field data to improve farming operations and crop productivity. AI in the agriculture market seems to be driven by the Internet of Things (IoT) due to its ability to revolutionize and transform current farming methods to a new level. Although, collecting accurate field data requires high initial investments which may hamper the growth of AI in the agriculture market. Some of the leading companies influencing the market are Ag Leader Technology, Trimble, Agribotix, Granular, SAP, Mavrx, PrecisionHawk, aWhere, IBM and Prospera Technologies.

With the rapid growth of knowledge, it shows a steady trend of knowledge fragmentization. Knowledge fragmentization manifests as that the knowledge related to a specific topic in a course is scattered in isolated and autonomous knowledge sources. We term the knowledge of a facet in a specific topic as a knowledge fragment. The problem of knowledge fragmentization brings two challenges: First, knowledge is scattered in various knowledge sources, which exerts users' considerable efforts to search for the knowledge of their interested topics, thereby leading to information overload. Second, learning dependencies which refer to the precedence relationships between topics in the learning process are concealed by the isolation and autonomy of knowledge sources, thus causing learning disorientation. To solve the knowledge fragmentization problem, we propose a novel knowledge organization model, knowledge forest, which consists of facet trees and learning dependencies. Facet trees can organize knowledge fragments with facet hyponymy to alleviate information overload. Learning dependencies can organize disordered topics to cope with learning disorientation. We conduct extensive experiments on three manually constructed datasets from the Data Structure, Data Mining, and Computer Network courses, and the experimental results show that knowledge forest can effectively organize knowledge fragments, and alleviate information overload and learning disorientation.

PERSONALIZA TION OF END-TO-END SPEECH RECOGNITION ON MOBILE DEVICES FOR NAMED ENTITIES Khe Chai Sim, Franc oise Beaufays, Arnaud Benard, Dhruv Guliani, Andreas Kabel, Nikhil Khare, T amar Lucassen, Petr Zadrazil, Harry Zhang, Leif Johnson, Giovanni Motta, Lillian Zhou Google, USA ABSTRACT We study the effectiveness of several techniques to personalize end-to-end speech models and improve the recognition of proper names relevant to the user. These techniques differ in the amounts of user effort required to provide supervision, and are evaluated on how they impact speech recognition performance. We propose using keyword-dependent precision and recall metrics to measure vocabulary acquisition performance. We evaluate the algorithms on a dataset that we designed to contain names of persons that are difficult to recognize. Therefore, the baseline recall rate for proper names in this dataset is very low: 2.4%. A data synthesis approach we developed brings it to 48.6%, with no need for speech input from the user. With speech input, if the user corrects only the names, the name recall rate improves to 64.4%. If the user corrects all the recognition errors, we achieve the best recall of 73.5%. To eliminate the need to upload user data and store personalized models on a server, we focus on performing the entire personalization workflow on a mobile device.