Poachers have a new enemy: artificial intelligence. Scientists have developed an AI system that can continuously learn and adapt to illegal activities and help rangers move swiftly to protect endangered animals in the wild. Researchers are using artificial intelligence (AI) to solve increasing poaching activities, applying computer science and game theory to lead the innovation to outwit poachers in the wild. A team of scientists have successfully created an AI system that "learns" information and uses data to map out ranger patrols that are most effective in protecting endangered animals living in the wild. These ranger patrols are still the most direct wildlife protection methods against poachers.

According to Weisensee et al., Data warehouse architecture follows following principles: ETL process is the foundation of BI. Success and failure of BI projects depends upon ETL process. It plays a vital role to integrate and enhance the worth of data. After the extraction, cleansing and arrangement of data, it will be loaded into data warehouse. In short, ETL is the transferring process of data from data source to the target data warehouse.

Justin Sullivan/GettyEatsa is San Francisco's fully automated fast food restaurant where orders appear in a cubby. At San Francisco's first fully automated restaurant, meals appear in little glass cubbies, just 90 seconds after customers order and pay on wall-mounted iPads. It's a human-less experience – no waitstaff, no cashier, no one to get your order wrong and no one to tip. The moment before the meal appears, the see-through display screen that fronts the cubbies goes black for the few seconds when you might catch sight of the hand that feeds you. Eatsa has not yet achieved total automation.

Unlike Consumer IoT, Industrial IoT (IIoT) is applied to manufacturing and supply chain environment, the under development segment in IoT segment. IIoT is expected to create vivid impact across the ecosystem players and industry verticals like governments, city infrastructure, aviation, power generation, transportation and many more. Without any doubt IIoT is going to create real market opportunity for IoT. The inherent journey began during the era of Industry 1.0 when mechanization of production system revolutionizes the world and real race of industrialization starts. Since then industries went through several innovations and upgradation such as mass production, assembly line, division of labor, electricity, automation of production process, and integration with IT system throughout the journey of Industry 2.0 and Industry 3.0. The emergence of robust internet connectivity & network, IoT technologies, industrial robotics, cloud based technologies, and intelligent machines pushed the whole concept of industry into a new shape called Industry 4.0, the fourth industrial revolution.

Despite plans to invest in machines and artificial intelligence as part of their strategy to boost productivity, many automotive and industrial equipment companies are failing to implement the measures needed to harness these capabilities, according to a new report from Accenture. The report, "Machine dreams: Making the Most of the Connected Industrial Workforce," is based on interviews with more than 500 business executives in Asia, Europe and the United States involved in setting their company's strategy for the connected industrial workforce. According to the report, manufacturing and production are undergoing rapid change as machines and AI are becoming closely integrated with personnel, creating the connected industrial workforce. By combining mobile, safety and tracking technologies with analytics, companies are enhancing the activities of an industrial worker. The report concludes that the creation of a connected industrial workforce is already part of the business strategy of the majority of automotive and industrial equipment producers, cited by 94 percent of respondents.

Mainland Chinese start-up Horizon Robotics, founded by the former head of online search giant Baidu's Institute of Deep Learning, claims it is on pace to bring chips with built-in artificial intelligence (AI) technology to market. "General processors are too slow for AI functions. A dedicated chip will dramatically increase the speed of these functions," Yu Kai, the founder and chief executive of Horizon Robotics told the South China Morning Post. Founded in Beijing in July, Horizon Robotics is developing chips and software that attempt to mimic how the human brain solves abstract tasks, such as voice and image recognition, that are difficult for regular computer programmes. It also makes sensors for smart devices.

It's a notion marketers will drool over: Imagine if their overloaded job responsibilities could be wiped clean by the use of robots to do tedious marketing tasks for them. Just as we already use programmatic buying and other data-driven marketing tools to simplify difficult and time-consuming processes, artificial intelligence (AI) is viewed by both robotics experts and marketing professionals as a tool to expedite menial content creation in the future. Efforts to bring AI to the mainstream are underway. IBM's Watson AI is already making appearances in ads holding conversations with celebrities. Robotic writing solutions have been used for simple writing tasks, such as recapping sporting events.

Human patrols serve as the most direct form of protection of endangered animals, especially in large national parks. However, protection agencies have limited resources for patrols. With support from the National Science Foundation (NSF) and the Army Research Office, researchers are using artificial intelligence (AI) and game theory to solve poaching, illegal logging and other problems worldwide, in collaboration with researchers and conservationists in the U.S., Singapore, Netherlands and Malaysia. "In most parks, ranger patrols are poorly planned, reactive rather than pro-active, and habitual," according to Fei Fang, a Ph.D. candidate in the computer science department at the University of Southern California (USC). Fang is part of an NSF-funded team at USC led by Milind Tambe, professor of computer science and industrial and systems engineering and director of the Teamcore Research Group on Agents and Multiagent Systems.

How we explain reality to ourselves is a construction with many parts. We gather knowledge and generate meaning through our experiences and traditions; from what we learn in school, at work and at home; from how we witness others explaining reality for themselves (on TV, via social media, etc). This narrative that we tell ourselves everyday throughout our entire lives largely defines who we are and how we approach the world. The first time I ran (in Mexico) an adaptation of Stanford's workshop "Makers in Residence" (an intensive 80 hour program for high schoolers on digital fabrication and design thinking which was designed by the Transformative Learning Technology Lab) I was shocked by the comments of participants regarding their place in relation to technology. Most participants were impressed that they were "smarter" than the computers they programmed; when I asked them more about it I started understanding the new narrative that a generation of kids growing up surrounded by digital technology are developing in their heads.

One key buzzword these days is "algorithm," which technically means any computational formula but which has come to mean a formula that predicts our behavior. Amazon and Netflix have algorithms that predict what books a user is likely to want to read or what movies and TV shows he or she is likely to want to watch. Facebook has an algorithm that predicts the news a user is likely to want. Dating sites like Match.com and OkCupid use algorithms to predict with whom we would fall in love. Google, with the most famous algorithm of all, predicts what we want when we type a search term.