Artificial intelligence (AI) is not just a new technology that requires regulation. It is a powerful force that is reshaping daily practices, personal and professional interactions, and environments. For the well-being of humanity it is crucial that this power is used as a force of good. Ethics plays a key role in this process by ensuring that regulations of AI harness its potential while mitigating its risks. AI may be defined in many ways.

Google is ending a controversial practice in Europe where it requires smartphone makers seeking to pre-install Google's app store to also add other Google apps, such as search and Chrome. Instead, Google will allow device manufacturers to pre-install the Google Play Store on a stand-alone basis, and offer the option to pre-install Google's other proprietary apps for an extra, unspecified fee. The company's announcement Tuesday came ahead of an Oct. 29 deadline to comply with a European Union antitrust decision, which saw regulators slap the company with a $5 billion fine for bundling its apps in an allegedly anticompetitive manner. Google is fighting the order but is working to meet its terms, because not doing so by the deadline could risk further penalties. In making their decision, antitrust officials in Europe had said that Google's practice of tying the apps together could harm competition by giving Google a built-in advantage over new apps struggling to attract an audience.

AI promises considerable economic benefits, even as it disrupts the world of work. These three priorities will help achieve good outcomes. The time may have finally come for artificial intelligence (AI) after periods of hype followed by several "AI winters" over the past 60 years. AI now powers so many real-world applications, ranging from facial recognition to language translators and assistants like Siri and Alexa, that we barely notice it. Along with these consumer applications, companies across sectors are increasingly harnessing AI's power in their operations. Embracing AI promises considerable benefits for businesses and economies through its contributions to productivity growth and innovation. At the same time, AI's impact on work is likely to be profound. Some occupations as well as demand for some skills will decline, while others grow and many change as people work alongside ever-evolving and increasingly capable machines.

Every discussion about artificial intelligence seems to alternate between utopia and dystopia. Some believe that the productivity unleashed through automation will lift up all of society, creating a world of superabundance and more meaningful work, while others see robots taking our jobs and an acceleration of trends favoring capital over labor. In fact, in an article in Harvard Business Review, Accenture's Mark Knickrehm describes five distinct schools of thought, ranging from both extremes to various shades of gray in between. He suggests that leaders need to reinvent operating models, redefine jobs and include employees in the process of transformation. Yet that's easier said than done.

Huawei's new Mate 20 Pro has a massive screen, three cameras on the back and a fingerprint scanner embedded in the display. The new top-end phone from the Chinese firm aims to secure its place at the top of the market alongside Samsung, having recently beaten Apple to become the second-largest smartphone manufacturer in August. The Mate 20 Pro follows Huawei's tried and trusted format for its Mate series: a huge 6.39in QHD OLED screen, big 4,200mAh battery and powerful new Huawei Kirin 980 processor – Huawei's first to be produced at 7 nanometres, matching Apple's latest A12 chip in the 2018 iPhones. New for this year is an infrared 3D facial recognition system, similar to that used by Apple for its Face ID in the iPhone XS, and one of the first fingerprint scanners embedded in the screen that is widely available in the UK, removing the need for a fingerprint scanner on the back or a notch on the front. The Mate 20 Pro is water resistant to IP68 standards and has a sleek new design reminiscent of Samsung's S-series phones, with curved glass on the front and back.

SAN JOSE, Calif. and BARCELONA, Spain, Oct. 16, 2018 (GLOBE NEWSWIRE) -- (ARMTECHCON and IoT World Congress Barcelona) - Mathematical advances that are driving the historic growth of machine learning (ML) in the cloud are now within reach of edge node developers with NXP's eIQ edge intelligence software environment and customizable, system-level solutions for focused applications. The eIQ software environment includes the tools necessary to structure and optimize cloud-trained ML models to efficiently run in resource-constrained edge devices for a broad range of industrial, Internet-of-Things (IoT), and automotive applications. The turnkey, production-ready solutions are specifically targeted for voice, vision, and anomaly detection applications. By removing the heavy investment necessary to become ML experts, NXP enables tens of thousands of customers whose products need machine learning capability. "Having long recognized that processing at the edge node is really the driver for customer adoption of machine learning, we created scalable ML solutions and eIQ tools, to make transferring artificial intelligence capabilities from the cloud-to-the-edge even more accessible and easy to use," said Geoff Lees, senior vice president and general manager of microcontrollers.

Kevin Schawinski had a problem. In 2007 he was an astrophysicist at Oxford University and hard at work reviewing seven years' worth of photographs from the Sloan Digital Sky Survey--images of more than 900,000 galaxies. He spent his days looking at image after image, noting whether a galaxy looked spiral or elliptical, or logging which way it seemed to be spinning. Technological advancements had sped up scientists' ability to collect information, but scientists were still processing information at the same rate. After working on the task full time and barely making a dent, Schawinski and colleague Chris Lintott decided there had to be a better way to do this.

An essential part of an IT department's mission is to stay atop the latest technological trends. And that includes protecting corporate networks by leveraging the latest security solutions and processes. Artificial Intelligence (AI) in security, which heavily builds on Machine Learning (ML), has one key principle: to recognize patterns that emerge from past experiences and make predictions based on them. ML enables new-generation security solutions to react to new, unseen cyber-threats (i.e. AI and ML are also used with great success in fighting off sophisticated attacks such as advanced persistent threats (APTs), whose authors are particularly adept at flying under the radar for long periods.

What if you stopped learning after graduation? It sounds stultifying, but that is how most machine-learning systems are trained. They master a task once and then are deployed. But some computer scientists are now developing artificial intelligence that learns and adapts continuously, much like the human brain. Machine-learning algorithms often take the form of a neural network, a large set of simple computing elements, or neurons, that communicate via connections between them that vary in strength, or "weight."

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