Babylon, a UK start-up, plans to "put an accessible and affordable health service in the hands of every person on earth" by putting artificial intelligence (AI) tools to work. Currently, the company has operations in the UK and Rwanda and hopes to expand to the Middle East, the United States, and China. The company's strategy is to combine the power of AI with the medical expertise of humans to deliver unparalleled access to healthcare. The Amazing Ways Babylon Health Is Using Artificial Intelligence To Make Healthcare Universally ... [ ] Accessible Babylon's engineers, doctors, and scientists developed an AI system that can receive data about the symptoms someone is suffering from, compare the information to a database of known conditions and illnesses to find possible matches, and then identify a course of action and related risk factors. People can use the "Ask Babylon" feature to inquire about their medical concerns to get an initial understanding of what they might be dealing with, but this service is not intended to replace the expertise of a doctor or be used in a medical emergency.

The recruitment process has begun for the world's very first university dedicated to artificial intelligence, here in the UAE. More than 3,000 people have started the process to attend The Mohamed bin Zayed University of Artificial Intelligence, which will open for students from September 2020. World's first artificial intelligence university to open in Abu Dhabi The university, based in Abu Dhabi's Masdar City, received the majority of applications from the UAE, Saudi Arabia, Algeria, Egypt, India and China. More than 230 eager students have completed applications to attend next year's course - less than two weeks after the launch of the university was announced. "It is gratifying that there has already been such a strong expression of interest so quickly after the announcement," said Prof Sir Michael Brady, interim president of MBZUAI.

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.

Artificial intelligence developer DeepMind has just announced its latest milestone: a bot called AlphaStar that plays the popular real-time strategy game StarCraft II at Grandmaster level. This isn't the first time a bot has outplayed humans in a strategy war game. In 1981, a program called Eurisko, developed by artificial intelligence (AI) pioneer Doug Lenat, won the US championship of Traveller, a highly complex strategy war game in which players design a fleet of 100 ships. Eurisko was consequently made an honorary Admiral in the Traveller navy. The following year, the tournament rules were overhauled in an attempt to thwart computers.

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.