Today's world is one big maze, connected by layers of concrete and asphalt that afford us the luxury of navigation by vehicle. For many of our road-related advancements -- GPS lets us fire fewer neurons thanks to map apps, cameras alert us to potentially costly scrapes and scratches, and electric autonomous cars have lower fuel costs -- our safety measures haven't quite caught up. We still rely on a steady diet of traffic signals, trust, and the steel surrounding us to safely get from point A to point B. To get ahead of the uncertainty inherent to crashes, scientists from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) and the Qatar Center for Artificial Intelligence developed a deep learning model that predicts very high-resolution crash risk maps. Fed on a combination of historical crash data, road maps, satellite imagery, and GPS traces, the risk maps describe the expected number of crashes over a period of time in the future, to identify high-risk areas and predict future crashes. Typically, these types of risk maps are captured at much lower resolutions that hover around hundreds of meters, which means glossing over crucial details since the roads become blurred together.

Today's world is one big maze, connected by layers of concrete and asphalt that afford us the luxury of navigation by vehicle. For many of our road-related advancements – GPS lets us fire fewer neurons thanks to map apps, cameras alert us to potentially costly scrapes and scratches, and electric autonomous cars have lower fuel costs – our safety measures haven't quite caught up. We still rely on a steady diet of traffic signals, trust, and the steel surrounding us to safely get from point A to point B. To get ahead of the uncertainty inherent to crashes, scientists from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) and the Qatar Center for Artificial Intelligence developed a deep learning model that predicts very high-resolution crash risk maps. Fed on a combination of historical crash data, road maps, satellite imagery, and GPS traces, the risk maps describe the expected number of crashes over a period of time in the future, to identify high-risk areas and predict future crashes. Typically, these types of risk maps are captured at much lower resolutions that hover around hundreds of meters, which means glossing over crucial details since the roads become blurred together.

The Qatar Computing Research Institute (QCRI) is one of three national research institutes established in 2010 by Qatar Foundation (QF) for education, science and community development. It operates under the umbrella of Hamad Bin Khalifa University and is steered operationally by the Research, Development, and Innovation (RDI) division, which was established within QF to oversee the three national research institutes' day-to-day operations. In this capacity, RDI provides high-level planning, coordination, and oversight to further the institutes' research priorities. QCRI was created with a mandate to support Qatar's transformation from a carbon economy to a knowledge-based economy. In doing so, it fulfills Qatar Foundation's overarching objectives of enabling national and regional change.

Qatar is a small peninsular nation on the northeastern coast of the Arabian Peninsula. Qatar is endowed with abundant hydrocarbon resources and is the world's largest producer of liquified natural gas (LNG), which accounts for over 80% of its export earnings. Like many of its wealthy neighbors, Qatar faces a unique dilemma with the onset of artificial intelligence (AI) technologies. Despite having one of the world's highest per-capita income and a highly educated local population, the majority of Qataris are under-employed and working in government white collar jobs where they are unable to fully realize the potential of their level of education. These are precisely the occupations that are likely to be made redundant by AI.1 The bulk of the workforce in Qatar consists of expatriates drawn primarily from South Asia and the Middle East and North Africa (MENA) region.

BigML and the Qatar Computing Research Institute (QCRI), part of Hamad Bin Khalifa University, bring the first edition of our Machine Learning School to Doha, the MLSD18. This event will be one of the first activities to be hosted by QCRI's new Qatar Center for Artificial Intelligence (QCAI). QCAI's mission is to help Qatar realize its vision of becoming a knowledge-based economy by developing and promoting cutting-edge AI innovations for the betterment of human society. We will hold a two-day crash course ideal for business leaders, industry practitioners, developers, graduate students, as well as advanced undergraduates, seeking a quick, practical, and hands-on introduction to Machine Learning to solve real-world problems. The MLSD18 will help attendees to understand how to work in this new wave of innovation that is changing the face of all sectors of the economy.

Gaps in maps are a problem, particularly for systems being developed for self-driving cars. To address the issue, researchers from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) have created RoadTracer, an automated method to build road maps that's 45 percent more accurate than existing approaches. Using data from aerial images, the team says that RoadTracer is not just more accurate, but more cost-effective than current approaches. MIT professor Mohammad Alizadeh says that this work will be useful both for tech giants like Google and for smaller organizations without the resources to curate and correct large amounts of errors in maps. "RoadTracer is well-suited to map areas of the world where maps are frequently out of date, which includes both places with lower population and areas where there's frequent construction," says Alizadeh, one of the co-authors of a new paper about the system.

MIT has created an artificial intelligence algorithm which can accurately tell you the recipe behind a dish after being shown no more than a picture. With the emergence of social media, it is not only the spread of information which has grown but also the popularity of image sharing. Everything from cat pictures to cupcakes bombards the internet every day, but there may now be a use for the latest delicious meal your friend has shared on their social network accounts -- as you may be able to cook it yourself just by having access to the picture. On Thursday, MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) said that a new artificial intelligence-based algorithm has been developed which can analyze still images of food in order to detect the likely ingredients and suggest a recipe to create the dish. The average recipe has nine ingredients and the most common ingredients found in today's dishes are salt, butter, sugar, olive oil, water, eggs, garlic cloves, milk, flour, and onion.

There are few things social media users love more than flooding their feeds with photos of food. Yet we seldom use these images for much more than a quick scroll on our cellphones. Researchers from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) believe that analyzing photos like these could help us learn recipes and better understand people's eating habits. In a new paper with the Qatar Computing Research Institute (QCRI), the team trained an artificial intelligence system called Pic2Recipe to look at a photo of food and be able to predict the ingredients and suggest similar recipes. "In computer vision, food is mostly neglected because we don't have the large-scale datasets needed to make predictions," says Yusuf Aytar, an MIT postdoc who co-wrote a paper about the system with MIT Professor Antonio Torralba.

Boeing Company has announced that it will partner again with the Qatar Computing Research Institute (QCRI), part of Hamad Bin Khalifa University, to host the fourth annual Machine Learning and Data Analytics Symposium (MLDAS) in Qatar. The event, to be held at the Qatar National Convention Centre on March 13 and 14, will feature top global experts discussing the latest solutions in the fields of machine learning, data mining, applied machine learning techniques and data analytics solutions. This year's event will focus on the growing use of deep learning and its potential impacts on the human condition, such as through autonomous vehicles and medical advances. Bernard Dunn, president of Boeing Middle East, North Africa and Turkey, said, "Boeing is proud of the partnership with QCRI as we are congregating expertise from around the world to help elevate the already high benchmark being set in Qatar in the field of data analytics and machine learning. "In an increasingly digitised and automated world, bringing together such expertise will only continue to inspire innovation and regional talent." Dr Ahmed Elmagarmid, executive director, QCRI, said the symposium had enjoyed an extraordinary response from the scientific community in the past three years and the research institute looked forward to working with Boeing to expand its success. "The event is a huge success for Qatar and the region and we are grateful for the outstanding role that Boeing has played.

Students, global experts and researchers recently gathered for the third annual Machine Learning and Data Analytics Symposium (MLDAS) in Doha. Organized by the Qatar Computing Research Institute (QCRI) and the Boeing Company, the event served as a platform to discuss happenings in machine learning and data analytics from applications to recent advances. "When we organized the first MLDAS three years ago, we wanted to drive conversations and innovations in the machine learning and data analytics field," Dragos Margineantu, MLDAS co-chair and technical fellow with Boeing Research & Technology, said. "Since that time, we have seen unprecedented growth and, though we're just one piece of a huge field, I'm proud that we've been involved and helped connect respected researchers with practitioners and students to further advance research in the fields of machine learning and data analytics." The two-day symposium included talks on how machine learning techniques can be applied to scientific discovery and aircraft health maintenance.