Artificial intelligence (AI) is being used both to monitor and prevent crimes in many countries. AI is used in such areas as bomb detection and deactivation, surveillance, prediction, social media scanning and interviewing suspects. However, for all the hype and hoopla around AI, there is scope for growth of its role in crime management. Currently, a few issues are proving problematic. AI is not uniformly engaged across countries in crime management. There is fierce debate on the ethical boundaries of AI, compelling law enforcement authorities to tread carefully.

Researchers from American University of Sharjah's (AUS) College of Engineering are benefitting from the university's new research agenda, with students and faculty working on research projects likely to have a global impact.

Google, while promising to "bring the benefits of AI to everyone," signed a deal with the Pentagon to use their AI technology to kill people. The outrage this caused among its employees, made Google cancel the contract. Though, at first glance, it seems like a victory for the non-violent use of AI, isn't it also quite naive? Technology firms working with the military-industrial complex is neither new or shocking. Amazon, Microsoft, and IBM are very involved without anybody paying much attention.

Since 2014, a series of pictures have emerged of converted Chinese armored vehicles, which had been modified into autonomous robots with cameras and LIDAR for navigation. China is also offering smaller machine gun equipped robots for export, and has expended significant resources in building legged robots. While the ZTZ-59 robotic tank is strictly noncombatant for now, it will only be a matter of time until the PLA, like every other major military, deploys greater numbers of robotic vehicles. Are Weaponized'Crab Walker' Robots The Future Of Chinese Land Wars?

If companies fail to make artificial intelligence (AI) a core competency within the next five years, they will face either stagnation or elimination, according to GlobalData, a leading data and analytics company. The company's latest Thematic Research report: 'Artificial Intelligence – Thematic Research', reveals that incumbents in virtually every industry are facing some kind of game-changing disruption from AI technologies, with some being better prepared than others for the challenges ahead. AI adoption is highest in the banking and financial services, automotive, technology and telecoms verticals while the construction, energy and education industries lag behind. The report also identifies the market leaders and notable disruptive start-ups across seven AI technology areas, namely; machine learning, data science, conversational platforms, computer vision, AI chips, smart robots and context-aware computing. Ed Thomas, Senior Analyst, Thematic Research Technology at GlobalData, commented, ''Nearly 70 years since Alan Turing posed the question'Can machines think?', AI is finally beginning to have an impact on the global economy.

A new NITI Aayog working paper outlines a comprehensive strategy for AI adoption in India. Given many countries, including China and France, have formalised strategies even as the US powers through with AI research, Indian policymakers will do well to pay heed to NITI's recommendations. AI adoption, NITI says citing Accenture data, could add $957 billion to the $6,397-bn gross value added projected for 2035--a 15% boost. It says the maximum benefits in terms of growth, development and inclusion could accrue from five focus areas--healthcare, agriculture, education, urban-/smart-city infrastructure, transportation & mobility. Some AI adoption is already happening--Andhra Pradesh is using this to predict school dropout while NITI is working on the launch of a concept developed by Tata Memorial Centre that involves algorithms matching patient slides with images of oncopathology slides from a detailed dataset of annotated images--this will improve early detection even though India has just about 500 expert oncopathologists, that too concentrated in the large cities.

Toyota Motor Corp. is considering to use self-driving vehicles as unmanned mobile convenience stores in cooperation with Seven-Eleven Japan Co., sources close to the matter said Friday. Under the plan, Toyota's autonomous electric vehicles -- which would carry Seven-Eleven products -- would offer self-service in the country's underpopulated areas, where it is less profitable for convenience store chains to operate outlets, the sources said. Toyota could also team up with Yamato Holdings Co. on door-to-door delivery services using driverless vehicles. The sources added Toyota is also looking to offer similar services, including mobile cafes for Starbucks coffee. In January, the automaker unveiled its e-Palette Concept Vehicle, announcing it will use the driverless electric platform to build an alliance with U.S. partners such as Amazon.com

The unmanned gates, which will debut in full Monday, will automate passport control for Japanese passengers. Images of passengers' faces will be taken in front of the gates and compared with passport data to confirm their identities. Thirty-one units will be installed at the airport's three terminals this month. The gates will also be introduced at Tokyo International Airport (Haneda), Chubu Centrair International Airport near Nagoya, Kansai International Airport in Osaka Prefecture and Fukuoka Airport. The gates have been tested at Haneda airport since October.

As the world's biggest militaries embrace artificial intelligence, Google says it will no longer be involved. The Silicon Valley tech giant announced AI principles Thursday, saying it wouldn't pursue weapons or other technologies whose principal purpose is to cause harm. It also said it won't pursue surveillance technologies that violate international norms. And it won't pursue AI technologies that violate human rights. Google has endured a recent employee backlash due to its involvement in a US Air Force project that used drones to automatically identify objects with the company's technology.

In an expanded edition published in 1988, they responded to claims that their 1969 conclusions significantly reduced funding for neural network research: "Our version is that progress had already come to a virtual halt because of the lack of adequate basic theories… by the mid-1960s there had been a great many experiments with perceptrons, but no one had been able to explain why they were able to recognize certain kinds of patterns and not others."