ISIS is quickly recruiting to supplant existing al-Shabab fighters in Somalia to declare a more entrenched presence in the horn of Africa. Three drone airstrikes on Sunday against the Al Qaeda-linked Islamic terrorist group Al-Shabab in Somalia killed four militants, according to the U.S. military. U.S. Africa Command officials said an initial assessment concluded that two airstrikes killed two militants and destroyed two vehicles in Qunyo Barrow, and the third airstrike killed two militants in Caliyoow Barrow. The precision airstrikes, which were in coordination with the Somali government, came a day after a truck bombing in Somalia's capital killed at least 78 people. People salvaging goods after a car bomb destroyed shops in Mogadishu, Somalia, on Saturday.

Prior work inspired by compression algorithms has described how the Burrows Wheeler Transform can be used to create a distance measure for bioinformatics problems. We describe issues with this approach that were not widely known, and introduce our new Burrows Wheeler Markov Distance (BWMD) as an alternative. The BWMD avoids the shortcomings of earlier efforts, and allows us to tackle problems in variable length DNA sequence clustering. BWMD is also more adaptable to other domains, which we demonstrate on malware classification tasks. Unlike other compression-based distance metrics known to us, BWMD works by embedding sequences into a fixed-length feature vector. This allows us to provide significantly improved clustering performance on larger malware corpora, a weakness of prior methods.

We investigate heterogenous employment effects of Flemish training programmes. Based on administrative individual data, we analyse programme effects at various aggregation levels using Modified Causal Forests (MCF), a causal machine learning estimator for multiple programmes. While all programmes have positive effects after the lock-in period, we find substantial heterogeneity across programmes and types of unemployed. Simulations show that assigning unemployed to programmes that maximise individual gains as identified in our estimation can considerably improve effectiveness. Simplified rules, such as one giving priority to unemployed with low employability, mostly recent migrants, lead to about half of the gains obtained by more sophisticated rules.

We consider a wide family of vehicle routing problem variants with many complex and practical constraints, known as rich vehicle routing problems, which are faced on a daily basis by C.H. Robinson (CHR). Since CHR has many customers, each with distinct requirements, various routing problems with different objectives and constraints should be solved. We propose a set partitioning framework with a number of route generation algorithms, which have shown to be effective in solving a variety of different problems. The proposed algorithms have outperformed the existing technologies at CHR on 10 benchmark instances and since, have been embedded into the company's transportation planning and execution technology platform.

The U.S. is facing a doctor shortage that is getting worse as an aging population of Baby Boomers lives longer, increasing the demand for medical professionals. But doctors are not the only ones feeling the pinch. Pathologists, scientists who study disease, have also been hit hard, with an overall decline in professionals from 2007 to 2017. "With many senior pathologists expected to retire in the coming years, a'pathologist gap' is likely to increase through 2030," according to a 2018 study by the National Center for Biotechnology Information. David West, co-founder and CEO of digital startup Proscia, said his company is hoping to help pathologists use their time more efficiently.

Automation Anywhere, a global leader in Robotic Process Automation (RPA), today announced the acqui-hire of product engineering start-up Cathyos Labs to support the company's expansion and product innovation. Cathyos Labs specializes in product development, automation, RPA and predictive analytics. Through the acqui-hire, Automation Anywhere will strengthen its engineering team to increase product development, support and delivery for customers in India and across the region. Cathyos Labs is the first talent led acquisition by Automation Anywhere. Throughout India, acqui-hires are becoming common as organizations purchase a company in order to acquire or gain access to its employees, but not necessarily the company's products and services.

Kenyan agri-tech startup Shamba Records has built a blockchain-based platform that uses artificial intelligence (AI) and big data to collect farmers' harvest records, process payments and issue credit. Founded in 2017 and already used by over 6,000 farmers, the Shamba Records platform provides users with features such as data collection and mapping, payment aggregation, smart contracts, and bulk SMS, making farms more efficient and collecting information that can allow them to access financial services. It was developed by a team with first-hand experience of farming in Africa. "Each founder has a story to tell of how inefficient the agriculture space in Africa is, for example, poor records, delayed payments, no credit alternatives, lack of communication tools, and so many other challenges," chief executive officer (CEO) George Maina told Disrupt Africa. These inefficiencies are major problems on a continent where over 600 million people depend on agriculture as their main source of income.

Increased market for cloud computing 3.5.1.2. Growth associated with artificial intelligence and cognitive computing 3.5.2.

Increased market for cloud computing 3.5.1.2. Growth associated with artificial intelligence and cognitive computing 3.5.2.

There is an analogy between machine learning systems and economic entities in that they are both adaptive, and their behaviour is specified in a more-or-less explicit way. It appears that the area of AI that is most analogous to the behaviour of economic entities is that of morally good decision-making, but it is an open question as to how precisely moral behaviour can be achieved in an AI system. This paper explores the analogy between these two complex systems, and we suggest that a clearer understanding of this apparent analogy may help us forward in both the socio-economic domain and the AI domain: known results in economics may help inform feasible solutions in AI safety, but also known results in AI may inform economic policy. If this claim is correct, then the recent successes of deep learning for AI suggest that more implicit specifications work better than explicit ones for solving such problems.