The intelligence community's (IC) stock in trade has always been knowing what nobody else knows. Now it's looking to tap into new technology to expand its ability to forecast geopolitical events in several ways, including finding out what everybody knows. The Intelligence Advanced Research Projects Activity (IARPA), the IC's lead research arm, is looking to bring artificial intelligence to bear in a number of intelligence-gathering areas. This includes two projects at opposite ends of the spy game: at one end, intercepting and interpreting classified communications, and at the other, finding a way to read the tea leaves in massive amounts of public information. IARPA recently launched a competition to support its Mercury program, which focuses on the more familiar (to the public anyway) element of spycraft known as signals intelligence.

Software development effort estimation is considered a fundamental task for software development life cycle as well as for managing project cost, time and quality. Therefore, accurate estimation is a substantial factor in projects success and reducing the risks. In recent years, software effort estimation has received a considerable amount of attention from researchers and became a challenge for software industry. In the last two decades, many researchers and practitioners proposed statistical and machine learning-based models for software effort estimation. In this work, Firefly Algorithm is proposed as a metaheuristic optimization method for optimizing the parameters of three COCOMO-based models. These models include the basic COCOMO model and other two models proposed in the literature as extensions of the basic COCOMO model. The developed estimation models are evaluated using different evaluation metrics. Experimental results show high accuracy and significant error minimization of Firefly Algorithm over other metaheuristic optimization algorithms including Genetic Algorithms and Particle Swarm Optimization.

In temporal planning, many different temporal network formalisms are used to model real world situations. Each of these formalisms has different features which affect how easy it is to determine whether the underlying network of temporal constraints is consistent. While many of the simpler models have been well-studied from a computational complexity perspective, the algorithms developed for advanced models which combine features have very loose complexity bounds. In this paper, we provide tight completeness bounds for strong, weak, and dynamic controllability checking of temporal networks that have conditions, disjunctions, and temporal uncertainty. Our work exposes some of the subtle differences between these different structures and, remarkably, establishes a guarantee that all of these problems are computable in PSPACE.

The hype around artificial intelligence is reaching a fevered pitch in the global economy. In international development, there are Nethope webinars, TechChange courses, and USAID guides to build our excitement around this new technology. Yet, the peak of the hype cycle is also when we should pause and take a hard look at what is real, and what is just aspiration. Technology Salons next month in Washington, DC and Johannesburg, SA will be taking a close look at artificial intelligence in international development. We want to dive into real examples, understand the risks and rewards, and publish a comprehensive guide to who is innovating, and what they are learning.

Dimensionality reduction is an essential technique for multi-way large-scale data, i.e., tensor. Tensor ring (TR) decomposition has become popular due to its high representation ability and flexibility. However, the traditional TR decomposition algorithms suffer from high computational cost when facing large-scale data. In this paper, taking advantages of the recently proposed tensor random projection method, we propose two TR decomposition algorithms. By employing random projection on every mode of the large-scale tensor, the TR decomposition can be processed at a much smaller scale. The simulation experiment shows that the proposed algorithms are $4-25$ times faster than traditional algorithms without loss of accuracy, and our algorithms show superior performance in deep learning dataset compression and hyperspectral image reconstruction experiments compared to other randomized algorithms.

Harnessing machine learning to improve health is a major ambition for both medical practitioners and the healthcare industry. If the two can join forces on a global scale in 2019, with the right investment and the right approach, AI could propel a revolution to democratise global health and to leapfrog access to health services in low- and middle-income countries. A chronic shortage of human resources is one of the major obstacles to better health and healthcare in many resource-poor settings. When it comes to global health, artificial intelligence offers huge opportunities to fill the gap left by critical healthcare worker shortages, particularly if combined with mobile phone technology. For example, social enterprises such as Peek Vision can use smart-phone based technology to enable healthcare providers to deliver cost-effective and targeted treatment to people with eyesight problems.

The post Intel AI Protects Animals with National Geographic Society, Leonardo DiCaprio Foundation appeared first on Intel Newsroom.

Artificial intelligence created by Intel is to be used in cameras to detect poachers entering wildlife reserves and alert park rangers before they can kill endangered animals. The technology firm has announced its software is to be used in TrailGuard AI cameras that are capable of object detection and image classification remotely, and which can alert rangers should a person or vehicle be detected. The cameras are to be distributed around wildlife reserves by non-profit organisation Resolve, and have been built in partnership with the National Geographic Society and the Leonardo DiCaprio Foundation. They will be deployed in African wildlife reserves and throughout Southeast Asia in early 2019, the technology firm said. The pencil-sized devices contain a long-life battery, which can last up to a year and a half without needing to be charged.

When the Apollo 11 spacecraft was orbiting the Moon prior to the first lunar landing, Nasa officials told the astronauts on board to look out for the'lovely girl with a big rabbit'. They were jokingly referring to a story from Chinese mythology in which the goddess Chang'e escapes Earth to live on the Moon with her pet, Jade Rabbit. This week, almost 50 years on from that'giant leap for mankind', the legend of Chang'e resurfaced -- and this time the joke is on the Americans as China announced it had became the first nation to land a spacecraft on the'dark side of the moon'. The robotic probe was named Chang'e 4, a product of China's £3.9 billion a year space exploration project. This week, almost 50 years on from that'giant leap for mankind', the legend of Chang'e resurfaced -- and this time the joke is on the Americans as China announced it had became the first nation to land a spacecraft on the'dark side of the moon' If ever there was a metaphor for the Communist super-power's obsessive secrecy and soaring global ambition, then this audacious secret mission provides it.

Elephants are proverbially hard to miss, but even these huge beasts can be swallowed up in the vast plains of Africa. This is a big problem for park rangers whose job is to protect the animals from poachers. In Serengeti National Park in Tanzania, for example, there are just 150 rangers responsible for safeguarding an area of land roughly the size of Belgium. A new solution to this proposed by conservation nonprofit Resolve is to use AI-equipped cameras to act as remote lookouts. Today, Resolve announced a new custom-made device called TrailGuard AI, which uses Intel-made vision chips to identify animals and humans that wander into view.