Everything you need to know about Duchess of Sussex Meghan Markle and her new life as part of the British royal family. There's something about that "Markle sparkle" that has the world transfixed, seeing as Meghan Markle has now been named the world's "most powerful dresser" in a 2019 report from Lyst, a fashion search engine. It was a big year for the Duchess of Sussex, who stylishly seized the spotlight at dozens of public appearances and royal tours, and even when introducing the world to baby Archie -- and according to Lyst, shoppers took notice. There's something about that "Markle sparkle" that has the world transfixed, as Meghan Markle has been named the world's most powerful dresser of 2019. According to Lyst's annual Year in Fashion roundup, each of the Duchess' numerous fashion statements sparked a 216-percent average increase in search for similar items.

Cirta is a new machine-learning challenge for high-energy physics on Zindi, the Africa-based data-science challenge platform. Launched this autumn at the International Conference on High Energy and Astroparticle Physics (TIC-HEAP), Constantine, Algeria, Cirta challenges participants to provide machine-learning solutions for identifying particles in LHC experiment data. Cirta* is the first particle-physics challenge to specifically target computer scientists in Africa, and puts the public TrackML challenge dataset to new use. Created by ATLAS computer scientists Sabrina Amrouche and Dalila Salamani, the Cirta challenge aims to bring new blood into the growing field of machine learning for particle physics. "Zindi has a strong community of computer scientists based on the continent, and we're looking forward to reviewing their creative solutions to the challenge," says Salamani.

Artificial intelligence technology is advancing and bringing opportunities for society but also profound challenges for individual freedom. AI is a powerful enabler of surveillance technology, such as facial recognition, and many countries are grappling with appropriate rules for use, weighing the security benefits against privacy risks. Authoritarian regimes, however, lack strong institutional mechanisms to protect individual privacy--a free and independent press, civil society, an independent judiciary--and the result is the widespread use of AI for surveillance and repression. This dynamic is most acute in China, where the Chinese government is pioneering new uses of AI to monitor and control its population. China has already begun to export this technology along with laws and norms for illiberal uses to other nations. As AI-enabled surveillance technology spreads around the globe, how it is used poses profound challenges for the future of democracy, liberty, and individual freedom.

AI and machine learning algorithms require data. But the bulk of that data is of no use if it isn't first labeled by human annotators. This predicament has given rise to a cottage industry of startups, including Scale AI, which recently raised $100 million for its extensive suite of data labeling services. That's not to mention Mighty AI, Hive, Appen, and Alegion, which together occupy a data annotation tools segment that's anticipated to be worth $1.6 billion by 2025. CloudFactory is yet another vying for attention.

AI training data provider Samasource has raised a $14.8 million Series A funding round led by Ridge Ventures. The San Francisco headquartered company delivers Fortune 100 companies with the inputs they need for machine learning development in fields including autonomous transportation, e-commerce and robotics. And it does so with a global work-force of data-specialists, a large number of whom are located in East Africa. In addition to San Francisco, New York and the Hague, Samasource has offices and teams in Kenya and Uganda. The company has a global staff of 2900 and is the largest AI and data annotation employer in East Africa, according to CEO and founder Leila Janah.

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.