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.

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.

Learning the causal-interaction network of multivariate Hawkes processes is a useful task in many applications. Maximum-likelihood estimation is the most common approach to solve the problem in the presence of long observation sequences. However, when only short sequences are available, the lack of data amplifies the risk of overfitting and regularization becomes critical. Due to the challenges of hyper-parameter tuning, state-of-the-art methods only parameterize regularizers by a single shared hyper-parameter, hence limiting the power of representation of the model. To solve both issues, we develop in this work an efficient algorithm based on variational expectation-maximization. Our approach is able to optimize over an extended set of hyper-parameters. It is also able to take into account the uncertainty in the model parameters by learning a posterior distribution over them. Experimental results on both synthetic and real datasets show that our approach significantly outperforms state-of-the-art methods under short observation sequences.

U.S. technology giant Microsoft has teamed up with a Chinese military university to develop artificial intelligence systems that could potentially enhance government surveillance and censorship capabilities. Two U.S. senators publicly condemned the partnership, but what the National Defense Technology University of China wants from Microsoft isn't the only concern. As my research shows, the advent of digital repression is profoundly affecting the relationship between citizen and state. New technologies are arming governments with unprecedented capabilities to monitor, track and surveil individual people. Even governments in democracies with strong traditions of rule of law find themselves tempted to abuse these new abilities.

The advanced technologies are impacting major cities in the Middle East that rising both economically and creating opportunities for business. UAE is the fastest growing city has a unique strategic plan, economic vision, and deep resources and attractive business concerns to disruption of AI in all areas. The artificial intelligence has the capability to solve various problems and greatest challenges that effect to improve resources in the Middle East countries. The Middle East countries like Jordan, Kuwait, Oman, Qatar, and Saudi Arabia are leading the direction to implement artificial intelligence services in their private and public domains. The advanced technology making a huge impact on human lives that will provide huge success in business and credible process to reduce risk in the operations by reducing deception methods.