A Rwandan firm, Shaka AI Ltd, has debuted on the global Artificial Intelligence scene, providing services to an American firm on a Knowledge Process Outsourcing model. Knowledge Process Outsourcing (KPO) is the allocation of relatively high-level tasks, to an outside organisation or a different group usually in a different geographic location. Shaka AI is a joint venture between two Canadian firms and a Rwandan start-up. It is registered in Rwanda. The Rwandan start-up; SOLVIT Africa, specializes in providing practical internship/apprenticeship opportunities.

Incidents of conflict and protest, along with many other structural variables, are fed into constituent models. Input variables would include things like population density, GDP growth, travel time to the nearest city, proportion of barren land, years since independence, and type of government. Several different models, each of which uses a different method, compute a probability of conflict. Constituent models could be a conflict history regression model, natural resources model, and an aggregate machine learning model. The results from the constituent models get combined to produce a final risk score.

In simple terms, it is the language of computers.

In the late '90s, wildlife conservationists Zoe Jewell and Sky Alibhai were grappling with a troubling realization. The pair had been studying black rhino populations in Zimbabwe, and they spent a good deal of their time shooting the animals with tranquilizer darts and affixing radio collars around their necks. But after years of work, the researchers realized there was a major problem: Their technique, commonly used by all manner of wildlife scientists, seemed to be causing female rhinos to have fewer offspring. The researchers published their findings in 2001, igniting a controversy in the conservation world. The problem, says Duke University professor of conservation ecology Stuart Pimm, is that being "collared" is extremely stressful for animals.

Deep learning architectures (DLA) have shown impressive performance in computer vision, natural language processing and so on. Many DLA make use of cloud computing to achieve classification due to the high computation and memory requirements. Privacy and latency concerns resulting from cloud computing has inspired the deployment of DLA on embedded hardware accelerators. To achieve short time-to-market and have access to global experts, state-of-the-art techniques of DLA deployment on hardware accelerators are outsourced to untrusted third parties. This outsourcing raises security concerns as hardware Trojans can be inserted into the hardware design of the mapped DLA of the hardware accelerator. We argue that existing hardware Trojan attacks highlighted in literature have no qualitative means how definite they are of the triggering of the Trojan. Also, most inserted Trojans show a obvious spike in the number of hardware resources utilized on the accelerator at the time of triggering the Trojan or when the payload is active. In this paper, we propose a hardware Trojan attack called Input Interception Attack (IIA). In this attack we make use of the statistical properties of layer-by-layer output to make sure that asides from being stealthy, our IIA is able to trigger with some measure of definiteness. This IIA attack is tested on DLA used to classify MNIST and Cifar-10 data sets. The attacked design utilizes approximately up to 2% more LUTs respectively compared to the un-compromised designs. This paper also discusses potential defensive mechanisms that could be used to combat such hardware Trojans based attack in hardware accelerators for DLA.

From elephants and rhinos to sea turtles and lemurs, poaching is quickly driving many endangered species to the brink of extinction. Often, governments and activists struggle to effectively monitor vast expanses of land for handfuls of poachers who travel at night. So what if artificial intelligence did it for them? In South Africa, conservationists were making no headway on preventing rampant rhino poaching. Hluhluwe–iMfolozi Park, the "birthplace of rhinos," was a particular hotspot, logging hundreds of dead rhinos in a single year.

Major IT Tech Company, Tech Mahindra, has set up its first development center in Timisoara, Romania. The development center will leverage 5G technology for network transformation for their Europe based clients, says the announcement. Tech Mahindra has 100 members team at the development center. The team manages different core, transmission network, and Radio Access Network operations. Tech Mahindra, in the announcement, also noted that the company plans to use Artificial Intelligence, Machine Learning (AI/ML) technologies to further automate ad transform network operations.

After decades of rapid progress, advances in medical technologies have slowed in recent years. But there are reasons to be hopeful, according to the Global Innovation Index 2019, with artificial intelligence, genomics, and mobile health applications all poised to transform global healthcare. GENEVA, 31 October 2019 - In the context of the joint technical symposium by the World Health Organization (WHO), the World Intellectual Property Organization (WIPO) and the World Trade Organization (WTO), GII Co-Editor Sacha Wunsch-Vincent presented the results of the GII 2019 Theme Chapter on Creating Healthy Lives -- The Future of Medical Innovation, answering the question: "Are we about to enter a new "golden age" of medical innovation?" Over the last century, improvements in healthcare have doubled life expectancy in developed and developing countries, resulting in an expanded workforce, greater economic growth and improved quality of life. Driving those improvements has been healthcare innovation, both in technologies, such as chemotherapy and joint replacements, and in processes, such as better hygiene and enhanced public health planning.

While many chatbots didn't live up to the hype, industries like fintech, healthcare, and retail are quietly adopting the technology to free up busy professionals' time and offer guided, personalized experiences to consumers. In 2016, chatbots were all the rage. That year, Facebook made the Messenger bot platform the centerpiece of its F8 developer conference. Microsoft's Satya Nadella referred to chat as the "third run-time" -- an indispensable piece of operating a platform, second only to the operating system and the web browser. Mentions of chatbots in earnings calls and press releases skyrocketed, and for many, it seemed that chatbots might be the next big disruptive technology. Thousands of companies commissioned their own chatbots in anticipation. In the end, though, the expected paradigm shift didn't happen. There are many reasons why chat didn't take off in 2016. For one, consumers found that many of the tasks the first chatbots were built to perform -- like relaying the news or finding a recipe -- took more time when a bot was involved. Another problem was that bots regularly needed human assistance to understand commands. Even Facebook's much-hyped personal assistant, M, closed down shortly after it was revealed that human handlers were responsible for some 70% of the bot's responses. But while many chatbots didn't meet users' high expectations, they haven't entirely fallen short. Today, the bots are still being used across industries like fintech, healthcare, sales and CRM, retail, and even law -- and they're having important, though quiet, effects. The important chatbots of 2019 aren't all-knowing virtual butlers; they're highly targeted applications of conversational technology. While they may seem less flashy, these bots are advancing their technology and making a demonstrable impact on their industries.

Technology is transforming our food chain, with some of the most important innovation occurring in the rise of precision farming. Together, artificial intelligence (AI) and automation are revamping the agriculture industry, helping farmers operate efficiently and in new ways. Today's farm is powered by data, along with a variety of devices and technologies, including sensors, GPS satellites, drones, and robots. This combination of automation and farming may mean to less friction and fewer obstacles for farmers, both for crop-based decisions and interacting with the USDA for permits. These exciting advancements, however, are not possible without dependable and scalable data centers.