Pathology, the study and diagnosis of disease, is a growth industry. As the global population ages and diseases such as cancer become more prevalent, demand for keen-eyed pathologists who can analyze medical images is on the rise. In the U.K. alone, about 300,000 tests are carried out daily by pathologists. In the U.S., there are only 5.7 pathologists for every 100,000 people. By 2030, this number is expected to drop to 3.7.

With the world gripped by the Coronavirus (COVID-19) epidemic, it's clear that the main ways to mitigate the impact are through personal hygiene (hand washing), increasing social distance (keeping away from people) and imposing quarantine for effected areas and self-quarantine. This is, however, after the event has taken hold and spread. What is really needed is an effective early warning system. This can then allow authorities to identify areas at risk, potential transmission routes and allow systems to be put in place to cope with a large-scale outbreak. Step forward Artificial Intelligence (AI) based systems able to process massive amounts of seemingly unrelated data and pick out trends.

You can't miss The Commodore. Dressed in black, from his hat to his shoes, he stands out in any crowd. But he is no fashion celebrity. His real name is Tokologo Phetla, and he is a rare breed in South Africa: an entrepreneur in the field of artificial intelligence (AI). He has developed an artificial intelligence software system, which he named Christopher, in honour of the machine developed by legendary computer scientist Alan Turing during the Second World War to crack the German encryption machine, Enigma.

We introduce sentenceMIM, a probabilistic auto-encoder for language modelling, trained with Mutual Information Machine (MIM) learning. Previous attempts to learn variational auto-encoders for language data have had mixed success, with empirical performance well below state-of-the-art auto-regressive models, a key barrier being the occurrence of posterior collapse with VAEs. The recently proposed MIM framework encourages high mutual information between observations and latent variables, and is more robust against posterior collapse. This paper formulates a MIM model for text data, along with a corresponding learning algorithm. We demonstrate excellent perplexity (PPL) results on several datasets, and show that the framework learns a rich latent space, allowing for interpolation between sentences of different lengths with a fixed-dimensional latent representation. We also demonstrate the versatility of sentenceMIM by utilizing a trained model for question-answering, a transfer learning task, without fine-tuning. To the best of our knowledge, this is the first latent variable model (LVM) for text modelling that achieves competitive performance with non-LVM models.

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.