Military drones or unmanned aerial vehicles have been around for a long time and their first tactical use with reconnaissance cameras was tested by Israeli Intelligence in the late 1960s. Israel continued to develop the technology, successfully using it to neutralize Syrian air defences at the start of the 1982 Lebaon War. The U.S. military also adopted it, successfully using the Israeli-developed Pioneer UAV for real-time intelligence over Iraq, Bosnia and Kosovo during the 1990s. It was only a matter of time until weapons were first deployed on U.S. drones and this occurred immediately after 9/11 when Osama bin Laden was observed from an unarmed Predator. They, along with their larget successor the Reaper, were subsequently equipped with Hellfire missiles, attacking a host of targets across Afghanistan, Pakistan, Iraq, Somalia, Yemen and Libya.

It is a particularly well-suited approach to better understand the underlying structure of data when scientific understanding of the data is at an early stage. BN modelling is designed to sort out directly from indirectly related variables and offers a far richer modelling framework than classical approaches in epidemiology like, e.g., regression techniques or extensions thereof. In contrast to structural equation modelling (Hair, Black, Babin, Anderson, Tatham et al. 1998), which requires expert knowledge to design the model, the Additive Bayesian Network (ABN) method is a data-driven approach (Lewis and Ward 2013; Kratzer, Pittavino, Lewis, and Furrer 2019b). It does not rely on expert knowledge, but it can possiarXiv:1911.09006v1

European satellite missions Sentinel-1 (S1) and Sentinel-2 (S2) provide at highspatial resolution and high revisit time, respectively, radar and optical imagesthat support a wide range of Earth surface monitoring tasks such as LandUse/Land Cover mapping. A long-standing challenge in the remote sensingcommunity is about how to efficiently exploit multiple sources of information and leverage their complementary. In this particular case, get the most out ofradar and optical satellite image time series (SITS). Here, we propose to dealwith land cover mapping through a deep learning framework especially tailoredto leverage the multi-source complementarity provided by radar and opticalSITS. The proposed architecture is based on an extension of Recurrent NeuralNetwork (RNN) enriched via a customized attention mechanism capable to fitthe specificity of SITS data. In addition, we propose a new pretraining strategythat exploits domain expert knowledge to guide the model parameter initial-ization. Thorough experimental evaluations involving several machine learningcompetitors, on two contrasted study sites, have demonstrated the suitabilityof our new attention mechanism combined with the extend RNN model as wellas the benefit/limit to inject domain expert knowledge in the neural networktraining process.

Omar Costilla Reyes reels off all the ways that artificial intelligence might benefit his native Mexico. It could raise living standards, he says, lower health care costs, improve literacy and promote greater transparency and accountability in government. But Mexico, like many of its Latin American neighbors, has failed to invest as heavily in AI as other developing countries. That worries Costilla Reyes, a postdoc at MIT's Department of Brain and Cognitive Sciences. To give the region a nudge, Costilla Reyes and three other MIT graduate students -- Guillermo Bernal, Emilia Simison and Pedro Colon-Hernandez -- have spent the last six months putting together a three-day event that will bring together policymakers and AI researchers in Latin America with AI researchers in the United States. The AI Latin American sumMIT will take place in January at the MIT Media Lab.

With the rise of robots, machine learning (ML) and artificial intelligence (AI), the employees of today are in panic mode about the state of their future career prospects. Will they have a job in 20 years' time… 10 years' time… or even next year? The spectre of an apocalyptic, dwindling future workforce is terrifying for most people, especially in Africa, which is traditionally manpower-centric. But, the reality is that these super-intelligent machines and robots might well be doing humankind a massive favour. "Machine learning will enable technology to replace the work of hands and the workplace of the future will probably include much more head-work," says Deseré Orrill, chairman of data-led marketing company Ole!Connect.

Geographically, the global artificial intelligence in retail market is segmented into five major regions, namely, North America, Europe, Asia Pacific, Latin America, and the Middle East and Africa. The global AI in retail market is analyzed methodically with respect to major countries in each of the regions with the help of bottom-up approach to arrive at the most precise market estimation. At present, North America holds a dominating position in the global AI in retail market. The region has high technology adoption rate, presence of key players & start-ups, and high penetration of internet. Consequently, North America is expected to retain its dominance throughout the forecast period.

For the second year in a row, Canada has refused visas to dozens of researchers - most of them from Africa - who were hoping to attend an artificial intelligence (AI) conference in Vancouver. The hassles have caused at least one other AI conference to choose a different country for their next event. The Neural Information Processing Systems conference (NeurIPS), which brings together thousands of experts and researchers from all over the world, will be held in Vancouver next month. Last week, NeurIPS began hearing that several attendees had had their visas denied. It was the second year in a row the conference has had visa troubles.

"AI is the broader concept of machines being able to carry out tasks in a way that is considered smart. The smart processes include machines being able to function automatically, reason and learn by themselves," explains Claudia Ayin, an independent ICT consultant. Machine learning is the aspect of AI that allows computers to learn by themselves. "Machine learning is therefore a branch of AI that is able to process large data sets and let machines learn for themselves without having been explicitly programmed," she adds. According to MarketsandMarkets, an Indian research company, in 2018 the worldwide AI in agriculture market was valued at €545 million and, by 2025, is expected to reach €2.4 billion as more and more smallholder farmers adopt new, data-driven technologies.

By 1433, the Chinese admiral Zheng He had already sailed from China to India, Indonesia, and even Africa on caravels twice as large as those Christopher Columbus used 59 years later for his fateful journey. China could have been the country to discover America. Instead, its government surprisingly decided to put an end to its naval activities and burn its entire fleet of ships, indirectly allowing Spain to conquer America and bring prosperity to Europe. It took more than 5 centuries for China to recover from this political decision. What could make such an advanced country deliberately turn away from its future?

Adverse drug-drug interactions (DDIs) remain a leading cause of morbidity and mortality. Identifying potential DDIs during the drug design process is critical for patients and society. Although several computational models have been proposed for DDI prediction, there are still limitations: (1) specialized design of drug representation for DDI predictions is lacking; (2) predictions are based on limited labelled data and do not generalize well to unseen drugs or DDIs; and (3) models are characterized by a large number of parameters, thus are hard to interpret. In this work, we develop a C hemicA l S ubstrucT urE R epresentation ( CASTER) framework that predicts DDIs given chemical structures of drugs. CASTER aims to mitigate these limitations via (1) a sequential pattern mining module rooted in the DDI mechanism to efficiently characterize functional substructures of drugs; (2) an auto-encoding module that leverages both labelled and unlabelled chemical structure data to improve predictive accuracy and generalizability; and (3) a dictionary learning module that explains the prediction via a small set of coefficients which measure the relevance of each input substructures to the DDI outcome. We evaluated CASTER on two real-world DDI datasets and showed that it performed better than state-of-the-art baselines and provided interpretable predictions. 1 Introduction Adverse drug-drug interactions (DDIs) are caused by pharmacological interactions of drugs. They result in a large number of morbidity and mortality, and incur huge medical costs (Giacomini et al. 2007; Onakpoya, Heneghan, and Aronson 2016).