Traffic accidents are a major unsolved problem worldwide. Yearly, it causes around 1.35 million deaths and 10 million people sustain nonfatal injuries9 in addition to having substantial negative economic and social effects. With approximately 90% of accidents being due to human errors, autonomous driving (AD) will play a vital role in saving human lives and substantial property damage. Moreover, it promises far greater mobility, energy saving, and less air pollution. Despite the recent advances to achieve such promising vision, enabling autonomous vehicles in complex environments is still decades away.6

The establishment of King Abdullah University of Science and Technology (KAUST) in 2009 was the fulfillment of a lifelong dream of its founder, the late King Abdullah of Saudi Arabia. His vision for the university was deeply rooted in the historical and cultural contexts of the Middle East. He intended the university to be seen as a revival of the old "house of wisdom," which was a premier institution of learning in Baghdad from the 9th century until the 13th century. Starting as a private library of the fabled Caliph Harun Al-Rasheed, it developed quickly into the 9th century equivalent of a research laboratory and a university. The house of wisdom was the birthplace of algebra and was a milieu where many developments took place in various fields of science and humanities.

Oil and gas (O&G) sources will still supply around 50% of the global energy demand by 2040.a In this article, we make the case for why the Arab region is well positioned for building world-class data science teams to fill the supply shortage of data professionals,5 especially in the O&G field critical to region's economy. This article presents challenges facing O&G industry players, such as governments, regulatory bodies, operators, and investors, and shows how Raisa Energy (with its Egypt-based data science team) is efficiently and effectively solving these challenges. Such challenges aim at assessing the economic viability of an O&G asset that depends on several factors (as shown in the accompanying figure) such as estimating well production, O&G prices, and risks associated with inputs uncertainty. It is worth emphasizing that the challenges presented here are global in nature and yet are tackled with a team fully formed from the region working at a world-class research and development level.

On December 2, 2010, Qatar was announced to host 2022 FIFA World Cup. That was time for celebrating the first-ever Middle Eastern country to organize the tournament. The 1.8M population of Qatar then (2.8M today) never imagined the journey their country was about to embarked. Indeed, in less than 10 years, the population grew by more than a half, pushing the available urban resources and services to their limit. At the same time, the country undertook an ambitious investment plan of $200B on various infra-structural projects including a brand new three-line metro network, six new stadiums, several new satellite cities, and an astonishing 4,300km of new roads, which tripled the size of the road network in only five years.3

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.

In the natural selection process, beneficial gene mutations are preserved from generation to generation until they become dominant in our genomes. The protection against pathogens drives the process. However, gene mutations that are protective against one pathogen could make people susceptible to new diseases whenever there is a change in the environment. Familial Mediterranean Fever (FMF) is one example of such disease. It is an autoimmune disease that has emerged over the past 20,000 years in southern Europe, the Middle East, and northern Africa.

Although the potential for artificial intelligence to transform healthcare in lower income countries has been much hyped, the technology is proving genuinely useful in helping Africa overcome difficulties in tackling diseases. Such technology can automate medical tasks and help doctors to do more with limited resources. It can even accelerate advances if certain barriers are overcome. The work of minoHealth AI Labs, the Ghana-based data science start-up that I founded, offers one example. By collecting medical images, we are seeking to automate radiology through the use of deep learning.

Actionable Cognitive Twins are the next generation Digital Twins enhanced with cognitive capabilities through a knowledge graph and artificial intelligence models that provide insights and decision-making options to the users. The knowledge graph describes the domain-specific knowledge regarding entities and interrelationships related to a manufacturing setting. It also contains information on possible decision-making options that can assist decision-makers, such as planners or logisticians. In this paper, we propose a knowledge graph modeling approach to construct actionable cognitive twins for capturing specific knowledge related to demand forecasting and production planning in a manufacturing plant. The knowledge graph provides semantic descriptions and contextualization of the production lines and processes, including data identification and simulation or artificial intelligence algorithms and forecasts used to support them. Such semantics provide ground for inferencing, relating different knowledge types: creative, deductive, definitional, and inductive. To develop the knowledge graph models for describing the use case completely, systems thinking approach is proposed to design and verify the ontology, develop a knowledge graph and build an actionable cognitive twin. Finally, we evaluate our approach in two use cases developed for a European original equipment manufacturer related to the automotive industry as part of the European Horizon 2020 project FACTLOG.

Researchers have used advanced AI and large sets of genomic data to unveil how humans have adapted to recent diseases. The method could also be applied to new pathogens such as the coronavirus that causes COVID-19, helping identify which gene mutations may be associated with more severe cases of the disease. The study, by researchers from Imperial College London, the Middle East Technical University, Turkey, and the Universita degli Studi di Bari Aldo Moro, Italy, is published today in a Special Issue of Molecular Ecology Resources, "Machine Learning techniques in Evolution and Ecology." Natural selection is the process by which beneficial gene mutations are preserved from generation to generation, until they become dominant in our genomes--the catalog of all our genes. One thing that can drive natural selection is protection against pathogens.

The digital revolution is here, but not everyone is benefiting equitably from it. And as Silicon Valley's ethos of "move fast and break things" spreads around the world, now is the time to pause and consider who is being left out and how we can better distribute the benefits of our new data economy. "Data is the main resource of a new digital economy," says Parminder Singh, executive director at nonprofit organization IT for Change. Global society will benefit because the economy will benefit, argues Singh, on decentralization of data and distributed digital models. Data commons--or open data sources--are vital to help build an equitable digital economy, but with that comes the challenge of data governance. "Not everybody is sharing data," says Singh. Big tech companies are holding onto the data, which stymies the growth of an open data economy, but also the growth of society, education, science, in other words, everything. According to Singh, "Data is a non-rival resource. It's not a material resource that if one uses it, other can't use it." Singh continues, "If all people can use the resource of data, obviously people can build value over it and the overall value available to the world, to a country, increases manifold because the same asset is available to everyone." One doesn't have to look very far to understand the value of non-personal data collected to help the public, consider GIS data from government satellites. Innovation plus the open access to geographic data helped not only create the Internet we know today, but those same tech companies.