Cylance, a cybersecurity startup that leverages artificial intelligence and machine learning to combat online attacks, has raised $120 million in a series E round of funding led by Blackstone Tactical Opportunities, with participation from other unnamed investors. Founded in 2012 by Stuart McClure, an entrepreneur who sold an Internet security firm to McAfee for $86 million in 2004, Cylance is an endpoint protection platform designed to thwart malware, ransomware, and other forms of advanced threats using AI. Its suite of algorithm-based security protocols essentially inspect networks for weaknesses and shuts them down if any are detected. Cylance claims in excess of 4,000 customers, and said that it has revenues of $130 million for the 2018 fiscal year, representing a year-on-year growth of 90 percent. Prior to now, Cylance had raised around $177 million, including a $100 million tranche two years ago, and with another $120 million in the bank it said that it plans to double down on its global expansion efforts, with a particular focus on Europe, the Middle East, and Asia Pacific, and extend its product range.

Google plans to open an artificial-intelligence research center in Accra, Ghana, the latest in a string of investments the tech company has made in Africa. The research center will focus on using AI in areas such as healthcare, agriculture and education, Google said. "We're committed to collaborating with local universities and research centers, as well as working with policy makers on the potential uses of AI in Africa," t he company In a blog post on Wednesday. The new AI center in Ghana will open later this year and include machine learning researchers and engineers, Google said, thought it did provide details on the number of staff it will hire. Google CEO Sundar Pichai promised last year during a visit to Lagos that Google would continue raising its profile on the continent.

I found Semblance on the second floor of the Fuego Lounge, squeezed into a booth beside a dance floor and a small stage. It was early afternoon, and waitstaff were restocking the long, rectangular bar in the center of the room as game developers, press and PR handlers flitted from station to station. A cloth tent on the balcony offered psychedelic VR meditation; a geodesic dome on the roof showcased swirling galaxies. And all along the walls inside, indie games waited to be played. Semblance stood out among the row of screens for its energetic, purple-tinged visuals.

Countries in the Common Market for Eastern and Southern Africa (COMESA) are lagging behind with respect to robotics, artificial intelligence and technology infrastructure and skills acquisition. Jean Baptiste Mutabazi, the regional bloc's Director of Infrastructure, noted this during the COMESA Connect Industry Dialogue in Kigali themed "Smart Technologies for Sustainable Businesses." Held in Kigali, the meeting was organised by the COMESA Business Council and Rwanda's Private Sector Federation of Rwanda (PSF). While Egypt, Seychelles, Kenya and Mauritius lead in terms of internet penetration and mobile density and in trade in ICT services, Mutabazi said, the rest of the region largely lags behind in a number of ways, particularly with respect to robotics, artificial intelligence and technology infrastructure and skills acquisition. COMESA member states are; Burundi, the Comoros, the Democratic Republic of Congo, Djibouti, Egypt, Eritrea, Ethiopia, Kenya, Libya, Madagascar, Malawi, Mauritius, Rwanda, Sudan, Swaziland, Seychelles, Uganda, Zambia and Zimbabwe.

Russian scientists are developing an advanced automated submarine that will be powered by an external combustion engine, Igor Denisov, deputy director of the Foundation for Advanced Studies (FPI), revealed in an interview with Interfax, a Russian news agency. "We are planning to create an apparatus that will pass through the Northern Sea Route without floating up and without the use of nuclear power, including under the ice," Denisov said. "In order for this device to accomplish such a'feat,' its autonomy should be at least 90 days, which is already commensurate with the autonomy of modern submarines." The decision to forego the nuclear option to power the underwater vehicle was a conscious one, Denisov said, in order to make it increasingly safe. While a nuclear installation helps power submarines for uninterrupted movement throughout the world's oceans, it also puts its operational capabilities at risk.

SEOUL – Months before the first summit between the leaders of the two Koreas in 2000, South Korean tech giant Samsung Electronics Inc. invested $730,000 in Pyongyang's top computer lab. North Korean programmers there would develop online chess games and food recipes for Samsung to sell outside the North. Samsung quit the business as inter-Korea relations later deteriorated, and the lab -- Korea Computer Center -- was blacklisted last year for its alleged contribution to the North's weapons program. As companies from South Korea to Russia and China again look to cash in on easing tensions with Pyongyang, Samsung's now defunct businesses in Pyongyang and hundreds of similar failed joint ventures underline North Korea's status as one of the world's highest-risk investment destinations. Yet days before the historic meeting between U.S. President Donald Trump and North Korean leader Kim Jong Un in Singapore, a conference in Seoul to explore investment opportunities in North Korea drew about 600 attendees.

The optimization of expensive-to-evaluate black-box functions over combinatorial structures is an ubiquitous task in machine learning, engineering and the natural sciences. The combinatorial explosion of the search space and costly evaluations pose challenges for current techniques in discrete optimization and machine learning, and critically require new algorithmic ideas (NIPS BayesOpt 2017). This article proposes, to the best of our knowledge, the first algorithm to overcome these challenges, based on an adaptive, scalable model that identifies useful combinatorial structure even when data is scarce. Our acquisition function pioneers the use of semidefinite programming to achieve efficiency and scalability. Experimental evaluations demonstrate that this algorithm consistently outperforms other methods from combinatorial and Bayesian optimization.

Armed conflict has led to an unprecedented number of internally displaced persons (IDPs) - individuals who are forced out of their homes but remain within their country. IDPs often urgently require shelter, food, and healthcare, yet prediction of when large fluxes of IDPs will cross into an area remains a major challenge for aid delivery organizations. Accurate forecasting of IDP migration would empower humanitarian aid groups to more effectively allocate resources during conflicts. We show that monthly flow of IDPs from province to province in both Syria and Yemen can be accurately forecasted one month in advance, using publicly available data. We model monthly IDP flow using data on food price, fuel price, wage, geospatial, and news data. We find that machine learning approaches can more accurately forecast migration trends than baseline persistence models. Our findings thus potentially enable proactive aid allocation for IDPs in anticipation of forecasted arrivals.

This paper attempts to provide a state of the art in trend prediction using news headlines. We present the research done on predicting DJIA trends using Natural Language Processing. We will explain the different algorithms we have used as well as the various embedding techniques attempted. We rely on statistical and deep learning models in order to extract information from the corpuses.

Many datasets can be represented as graphs, being the graph explicitely embedded in data (e.g., the friendship relation of a social network) or built through some suitable similarity measure between data items (e.g., the number of papers coauthored by two researchers). Such graphs often exhibit a complex, multi-scale community structure where each node is invoved in many groups of nodes, so-called communities, of different sizes. One of the most popular graph clustering algorithm is known as Louvain in name of the university of its inventors [Blondel et al., 2008]. It is based on the greedy maximization of the modularity, a classical objective function introduced in [Newman and Girvan, 2004]. The Louvain algorithm is fast, memory-efficient, and provides meaningful clusters in practice. It does not enable an analysis of the graph at different scales, however [Fortunato and Barthelemy, 2007, Lancichinetti and Fortunato, 2011].