Viral particles are colorized purple in this color-enhanced transmission electron micrograph from a COVID-19 patient in the United States. Computer modeling can help epidemiologists predict how and where the illness will move next. Viral particles are colorized purple in this color-enhanced transmission electron micrograph from a COVID-19 patient in the United States. Computer modeling can help epidemiologists predict how and where the illness will move next. Scientists who use math and computers to simulate the course of epidemics are taking on the new coronavirus to try to predict how this global outbreak might evolve and how best to tackle it.

ABU DHABI, 4th March, 2020 (WAM) -- A senior delegation from the Mohamed bin Zayed University of Artificial Intelligence, MBZUAI, the world's first graduate-level, research-based artificial intelligence university, recently discussed potential collaboration opportunities with Egypt's educational institutions during a recent visit to the country. The visit - organised by Egypt's Ministry of Higher Education and Scientific Research and the UAE Embassy in Cairo - touched upon the importance of the exchange of students and knowledge in the field of artificial intelligence, AI, to provide reciprocal benefits for the UAE and Egypt. Led by Professor Ling Shao, Executive Vice President and Provost, Assistant Professor Dr. Hang Dai, and Reem Al Orfali, Director of Student Affairs, the MBZUAI delegation met with representatives from the Supreme Council of Universities to demonstrate the breadth of the University's education and research facilities. The meeting emphasised the value of enabling both countries' plans to develop AI capacity for economic and societal empowerment. Discussions with the Supreme Council of Universities, as well as University deans, head of departments, and faculty members during the visit included joint research projects that would further the use of AI in healthcare and Arabic language processing amongst other fields, creating joint AI labs and a collaborative AI competition, exchanging professors, co-advising students, and the potential for summer and winter schools in AI, as well as exploring the scope for offering dual or joint degrees.

Caspar.AI honored for achievements in AI Technology for Real Estate CB Insights today named Caspar.AI to the fourth annual AI 100 ranking, showcasing the 100 most promising private artificial intelligence companies in the world. Featured in CB Insights real-estate AI category, Caspar is reshaping the real estate industry to allow for smart, sustainable design that both improves the residents' living experience and reduces overall costs for property management. Real estate developers partner with Caspar to build differentiated smart properties, drive additional revenue, save costs, and enhance resident experience. "We are delighted to be awarded as the top AI company for real estate," said Dr. Ashutosh Saxena, Founder & CEO of Caspar.AI & Former Faculty in the Department of Computer Science at Cornell University. "People spend two-thirds of their time at home. There is a massive opportunity for AI to reimagine how people live in their homes. Our Caspar Sense and Caspar Adapt technology, understand the resident activities and automatically adapts home to their preferences. "It's been remarkable to see the success of the companies named to the Artificial Intelligence 100 over the last four years.

In the service of business, of society at large, artificial intelligence (AI) can be effective. Can it also be ethical? The wisdom of crowds, gleaned from social media, can paint a gestalt picture of how a government agency's, bank's or retailer's efforts are being received on the ground, so to speak. And it can also (perhaps), fed through models and analytics, can bolster decision-making for the greater, common good. Public opinion matters, after all, but across the social media platforms, the chatrooms -- the chatbots, even -- making sense of qualitative data is a challenge for most enterprises.

We propose DefogGAN, a generative approach to the problem of inferring state information hidden in the fog of war for real-time strategy (RTS) games. Given a partially observed state, DefogGAN generates defogged images of a game as predictive information. Such information can lead to create a strategic agent for the game. DefogGAN is a conditional GAN variant featuring pyramidal reconstruction loss to optimize on multiple feature resolution scales. We have validated DefogGAN empirically using a large dataset of professional StarCraft replays. Our results indicate that DefogGAN can predict the enemy buildings and combat units as accurately as professional players do and achieves a superior performance among state-of-the-art defoggers. Figure 1: Comparison of DefogGAN prediction to ground truth.

The main source of information regarding ancient Mesopotamian history and culture are clay cuneiform tablets. Despite being an invaluable resource, many tablets are fragmented leading to missing information. Currently these missing parts are manually completed by experts. In this work we investigate the possibility of assisting scholars and even automatically completing the breaks in ancient Akkadian texts from Achaemenid period Babylonia by modelling the language using recurrent neural networks.

Recent empirical and theoretical studies have shown that many learning algorithms -- from linear regression to neural networks -- can have test performance that is non-monotonic in quantities such the sample size and model size. This striking phenomenon, often referred to as "double descent", has raised questions of if we need to re-think our current understanding of generalization. In this work, we study whether the double-descent phenomenon can be avoided by using optimal regularization. Theoretically, we prove that for certain linear regression models with isotropic data distribution, optimally-tuned $\ell_2$ regularization achieves monotonic test performance as we grow either the sample size or the model size. We also demonstrate empirically that optimally-tuned $\ell_2$ regularization can mitigate double descent for more general models, including neural networks. Our results suggest that it may also be informative to study the test risk scalings of various algorithms in the context of appropriately tuned regularization.

In this paper we provide a comprehensive introduction to knowledge graphs, which have recently garnered significant attention from both industry and academia in scenarios that require exploiting diverse, dynamic, large-scale collections of data. After a general introduction, we motivate and contrast various graph-based data models and query languages that are used for knowledge graphs. We discuss the roles of schema, identity, and context in knowledge graphs. We explain how knowledge can be represented and extracted using a combination of deductive and inductive techniques. We summarise methods for the creation, enrichment, quality assessment, refinement, and publication of knowledge graphs. We provide an overview of prominent open knowledge graphs and enterprise knowledge graphs, their applications, and how they use the aforementioned techniques. We conclude with high-level future research directions for knowledge graphs.

How have publishers evolved and what do they see ahead? Amid rising fears that artificial intelligence (AI) will threaten journalists' jobs and take over the newsroom, the Journalism AI report – a project by Polis in collaboration with Google News Initiative – sought to find out how exactly AI technologies are being applied to journalism. However, AI is a'significant part of journalism already but it is unevenly distributed' and news organizations are already applying aspects of intelligent technology in their operations, to help them work more efficiently and improve monetization. "One of the key aspects of AI and journalism is that it allows the whole journalism model to become more holistic, with a feedback loop between the different parts of the production and dissemination process" Artificial intelligence systems can be useful in helping newsrooms to categorize content or information at scale for different news gathering purposes. For example, since 2015 The Associated Press have been using a management tool, SAM, which algorithmically sifts through social media platforms to alert the newsroom on likely breaking news events.

Global Artificial Intelligence and Machine Learning Market 2020, presents a professional and in-depth study on the current state of the industry globally, providing basic overview of Artificial Intelligence and Machine Learning market including definitions, classifications, applications and industry chain structure. The report compares this data with the current state of the Artificial Intelligence and Machine Learning market and thus discuss upon the upcoming trends that have brought the Artificial Intelligence and Machine Learning market transformation. Industry predictions along with the statistical implication presented in the report delivers an accurate scenario of the Artificial Intelligence and Machine Learning market. The market forces determining the shaping of the worldwide Artificial Intelligence and Machine Learning market have been evaluated in detail. In addition to this, the supervisory outlook of the Artificial Intelligence and Machine Learning market has been covered in the report from both the Global and local perspective.