Antonio García Martínez has seen the future and it is terrifying. Which is why he is going to set up home ("this is the drone room right here") on a small island north of Seattle and live out the ravages of post-America, self-sufficiently, with a composting toilet and an AR-15 semi-automatic rifle. He is nervy and fast-talking, like a survivor who has seen unimaginably horrific things. And he has – he was once a product manager at Facebook. There is going to be a "violent revolt", he says.

Suspected money laundering enterprises are in the headlines again, ensnaring the state-controlled Industrial and Commercial Bank of China as well as . Yet for financial institutions that want to identify, track and stop money laundering and other financial crimes, the technological tools of banking's yesteryear no longer cut it. A new wave of tech tools can help banks and other financial firms discover dispersed and complex webs of illegal activity. Increasingly, financial firms are turning to artificial intelligence and sophisticated network analysis to detect such criminal enterprises. The legacy tools banks use to find suspicious dispersals of money often rely on logical "if-then" rules to spot criminal activity, as Wired reports.

An application that can, at the touch of a finger, draw on facial recognition technology to help a blind person "see" who or what is directly in front of him. A device that can gauge a person's risk of depression just by detecting his facial movements and tone of voice. A decade ago, such inventions that tap advanced systems like artificial intelligence (AI) and machine learning would have been impossible feats for most tertiary students. But the rise of cloud computing systems and mixed-reality products has powered the ability of students to add such capabilities to their applications, more than 50 of which were on show at the Microsoft Imagine Cup World Finals in Seattle, Washington, two weeks ago. Since 2003, technology giant Microsoft has organised the Imagine Cup, which brings together aspiring developers, entrepreneurs and technologists from all academic backgrounds. The contest spurs them to develop new technology applications, create a business plan and understand what is needed to bring a concept to market.

Artificial intelligence has jumped from sci-fi movie plots into mainstream news headlines in just a couple of years. And the headlines are often contradictory. AI is either a technological leap into greater prosperity or mass unemployment; it will either be our most valuable servant or terrifying master. But what is AI, how does it work, and what are the benefits and the concerns? AI is a computer system that can do tasks that humans need intelligence to do. "An intelligent computer system could be as simple as a program that plays chess or as complex as a driverless car," Mary-Anne Williams, professor of social robotics at the University of Technology, Sydney, said.

Honda Motor Co.'s go-it-alone strategy looks increasingly risky as an alliance-happy auto industry scales up to cope with the soaring investment needed for self-driving vehicles, electrified power trains, connected-car technologies and artificial intelligence. In the latest industry tieup, Toyota Motor Corp. and Mazda Motor Corp. on Friday announced plans to jointly invest in a $1.6 billion assembly plant in the U.S. with the capacity to produce 300,000 Toyota Corollas and a new Mazda crossover model starting 2021. Toyota will acquire about 5 percent in Mazda, which will hold a 0.25 percent in the bigger automaker. The duo will also combine engineering efforts on electric vehicle development and car-networking know-how. Honda President and CEO Takahiro Hachigo, like his predecessors, isn't a fan of big alliances that involve equity stakes.

Identifying 3 moss species by deep learning, using the "chopped picture" method Graduate School of Agriculture, Kyoto University, Japan * corresponding author: ise@kais.kyoto-u.ac.jp Abstract In general, object identification tends not to work well on ambiguous, amorphous objects such as vegetation. In this study, we developed a simple but effective approach to identify ambiguous objects and applied the method to several moss species. As a result, the model correctly classified test images with accuracy more than 90%. Using this approach will help progress in computer vision studies. Introduction Especially in recent years, deep learning has become a very effective tool for object identification (Krizhevsky et al. 2012, Szegedy et al. 2015).

In preparation for General Data Protection Regulation (GDPR) compliance, a global 100 financial services organization embarked on a journey to assess its core information processing environments with the objective of identifying opportunities to strengthen its data privacy protection programs. This article focuses on the technology challenges, approach, and lessons learned for the centralized testing environment. Situation Like many DevOps groups across the industry, this financial organization has adopted both continuous testing and quality testing regimes to deliver quality products leveraging agile methodology. The organization prefers to use production data to prepare the test data. While majority testing is primarily being done by an internal team, certain applications are tested by outsourced offshore teams.

Indium, rhodium, platinum, tellurium and gold: these are some of the rarest elements in the world. From smartphones (which contain a whopping 60 to 64 elements) to hybrid cars, wind turbines and medical equipment, much of the technology we depend upon contains a rich list of elemental ingredients. Meanwhile, demand for traditional metals such as copper and aluminium is rocketing, driven by the rapid growth of emerging economies in Asia and South America. If our voracious appetite for these minerals continues at the current rate then rare earth metals may be mined out in 15 to 20 years, and indium may only have another decade of supplies remaining. Even aluminium could run dry within the next century.

At first glance, you might mistake this enormous robot for a character from the latest Transformers blockbuster. But the 16 foot (five metre) tall machine is a robot called Eagle Prime that is ready to take on Japan in the Megabots Giant Robot Dual league. An incredible video has been released which shows the Eagle Prime in action ahead of the dual - which will be the world's first giant robot battle - taking place this month. At first glance, you might mistake this enormous robot for a character from the latest Transformers blockbuster. The Eagle Prime stands at 16 feet (five metres tall), weighs 12 tons, and has 430 horsepower.

I am currently pursuing Electronics & Communication Engineering from India and have currently developed a deep passion for Machine Learning & Artificial Intelligence. Recently I did a Machine Learning Research Intern and have come to the conclusion that I need to pursue MS. I have not given GRE exam and thus the US is kind of out of the option. My current GPA is 3.2/4.0 Can you telll me few universities that are having good programs?