Now there's a better way to cheat at crossword puzzles--with artificial intelligence. Researchers from three universities designed a system that uses a type of AI known as deep learning to help computers understand language more quickly and effectively. As a bonus, the researchers built a web-based tool that's handy when a particularly baffling crossword clue comes around. The crossword assistant is just a demo of how deep learning is improving machines' ability to understand language, said Felix Hill, a Ph.D. candidate at the University of Cambridge and lead author of a paper describing the research. Learning language is challenging for computers because it's difficult to recreate the rich and diverse information sources available to humans when they learn to speak and read, he said. Researchers focused on helping computers understand phrases.

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The manufacturing hub for the electronics industry, Kunshan, in Jiangsu province, is seeking a drastic reduction in labour costs as it undergoes a makeover after an industrial explosion killed 146 people in 2014. The county, one-seventh the size of neighbouring Shanghai and the mainland's first county to achieve US 4,000 per capita income, was adjudged the best county for its economic performance by Forbes for seven years in a row. However, the blaze, blamed on poor safety standards and haphazard industrialisation, dented Kunshan's pride. More than a year on, the county, which attracts much of its investment from Taiwan, is trying to reinvent its growth strategy. It is accelerating growth by replacing humans with robots and encouraging start-ups.

Deemed by many as the ultimate technology for a smart IoT world, artificial intelligence (AI) is intelligence exhibited in software that allows machines, devices, services and other'things' to learn and behave like a human. The difference from other systems that do the same is the fact that the AI software can do this without any coding done by a person or any other manual input. AI's main goal is to find solutions to problems. This is done through reasoning, planning, natural language processing, perception, and others. AI itself splits into several different branches.

Technology has been fundamentally reshaping every aspect of our life. From the onset of 21st-century, technology has not left a single prospect of our life untouched. From industries to medical technology, applied science has picked pace towards an era of automation. The next big step in mankind is the evolution of "Internet of Things". Internet of things is built on the intelligence of multiple sensors and connected devices that collect data and make crucial decisions.

The card payments industry is in an ongoing and eternal battle with fraudsters. As financial institutions take steps to close off one avenue to the swindlers, new techniques to cheat the system are uncovered – and so the game of cat-and-mouse goes on. The fraudsters get smarter and arm themselves with increasingly advanced tools while the banks play endless catch-up. In the digital age, computer processing power cuts both ways: it assists banks in their analysis and detection of fraud, but it also opens up new avenues to cheat and steal. The big difference between the warring parties is that the fraudsters move and evolve in real time, whereas fraud mitigation and prevention is typically response-driven.

At its I/O Developers Conference this week, Google announced a new product that had been an open secret for several months. The Tensor Processing Unit (TPU) is named after the TensorFlow software it uses for its machine learning programs. In announcing the chip, Google referred to the TPU as an accelerator chip, able to speed up a given task, like data analysis or voice translation. When he introduced the TPU at the I/O conference, Google CEO Sundar Pichai said it provides an order of magnitude better performance per watt than existing chips for machine learning tasks. While it's unlikely to usurp CPUs and GPUs already in use in the machine learning world, the TPU could potentially speed the machine learning process without using much more energy.

The Image and Signal Processing (ISP) Group at the IPL develops data analysis techniques and vision algorithms. We focus on methods able to extract knowledge from empirical data drawn by sensory (mostly imaging) systems. These measurements depend on the properties of the scenes and the physics of the imaging process. Our approach to signal, image and vision processing combines machine learning theory with the understanding of the underlying physics and biological vision. Applications mainly focus on computational visual neuroscience and remote sensing data analysis.

Most autonomous vehicle control software is deliberately designed for well-constrained driving that's nice, calm, and under control. Not only is this a little bit boring, it's also potentially less safe: If your car autonomous vehicle has no experience driving aggressively, it won't know how to manage itself if something goes wrong. At Georgia Tech, researchers are developing control algorithms that allow small-scale autonomous cars to power around dirt tracks at ludicrous speeds. They presented some this week at the 2016 IEEE International Conference on Robotics and Automation in Stockholm, Sweden. Using real-time onboard sensing and processing, the little cars maximize their speed while keeping themselves stable and under control.