The purpose of AI Magazine is to disseminate timely and informative articles that represent the current state of the art in AI and to keep its readers posted on AAAI-related matters. The articles are selected for appeal to readers engaged in research and

Sophie, the'digital human', greeted guests at the recent US launch of Air New Zealand's global marketing campaign A Better Way to Fly. Air New Zealand worked with Soul Machines to create'Sophie' as it explores how artificial intelligence and other disruptive technologies can be used to help travelers. Soul Machines says the technology behind Sophie uses neural networks and brain models to bring its digital humans to life from their cloud based human computing engine which sits on top of an artificial Intelligence platform powered by IBM Watson. Luxon says artificial intelligence is one of the areas they are focusing on to achieve this, starting with Oscar the Bot, and now, with Sophie.

For example, airlines have long tried to decipher travel patterns and passenger preferences, but recent advances in analytics, using machine learning algorithms, makes it possible to understand the nuances of whether passengers can or are willing to pay for additional ancillary services related to air travel – by making that transactional data visible and most importantly, actionable. Through the intelligence gained from advanced analytics, airlines can further hone their services based on passenger preferences – offering discounts on the types of food or retail that they know the passenger prefers – which ultimately leads to customer loyalty and retention, in addition to establishing new opportunities to generate revenue. The future of customer service in air travel will involve custom-built itineraries and curated add-on services, based on individual preferences, that provide real-time suggestions based on choices you've made before. Machine learning and predictive analytics is the next big wave in airline digitization that uses data, analytics and predictive algorithms to determine a traveler's propensity to spend, and presents airlines with a wealth of opportunities.

The purpose of AI Magazine is to disseminate timely and informative articles that represent the current state of the art in AI and to keep its readers posted on AAAI-related matters. The articles are selected for appeal to readers engaged in research and

Called the "Intel Loihi test chip," the processor is what Intel calls a "neuromorphic chip," meaning it's designed to learn from its environment. "The Intel Loihi research test chip includes digital circuits that mimic the brain's basic mechanics, making machine learning faster and more efficient while requiring lower compute power," Michael Mayberry, managing director of Intel Labs, wrote in a statement. This could help computers self-organize and make decisions ... "This could help computers self-organize and make decisions based on patterns and associations." But Intel's approach is different in that the Loihi test chip is designed to work and learn locally on whatever machine it's inside of.

This technology will make your device more energy efficient, enable and improve virtual and augmented reality experiences, provide for smarter camera functionalities, improve device security, and of course, allow for better audio connections. Mobile processors like Qualcomm's Snapdragon 835 leverage machine learning in an effort to extend and expand the boundaries of mobile performance. Yes, artificial intelligence and machine learning can aid and improve all sorts of functions and processes in minutes and specific ways, but as it concerns you, the user, your phone will simply do everything that you need it to – but faster, better, and with greater efficiency. Many devices already feature some form of machine learning (those with the Snapdragon 835 mobile processor, for example, like ODG's R-8 and R-9 smart glasses).

This technology will make your device more energy efficient, enable and improve virtual and augmented reality experiences, provide for smarter camera functionalities, improve device security, and of course, allow for better audio connections. Mobile processors like Qualcomm's Snapdragon 835 leverage machine learning in an effort to extend and expand the boundaries of mobile performance. Yes, artificial intelligence and machine learning can aid and improve all sorts of functions and processes in minutes and specific ways, but as it concerns you, the user, your phone will simply do everything that you need it to – but faster, better, and with greater efficiency. Many devices already feature some form of machine learning (those with the Snapdragon 835 mobile processor, for example, like ODG's R-8 and R-9 smart glasses).

Intel technology provides the compute power behind today's most important artificial intelligence (AI) initiatives. We're helping businesses of every kind accelerate solutions, automate operations, gather better insights--and make smarter decisions.

For example, natural language processing (NLP) supports language generation and understanding, whereas computer vision supports the understanding of image or videos. In business, audio and video are useful media for storytelling, brand awareness, business development, customer support, and education. By supporting tasks like data sourcing, data interpretation, data analytics, and narrative commentary, automated report generation tools can surface relevant metrics, tables, and charts, and generate multiple paragraphs of narrative. Companies like Narrative Science, Arria, Genpact, and many others are demonstrating various configurations of NLP, ML, and deep learning to deliver reports on everything from advertising performance and sales to employee satisfaction, competitive intelligence, and beyond.

The breakthrough came in 2006: Hinton led a published paper called A Fast Learning Algorithm for Deep Belief Nets, which first proposed the method of greedy layer-wise training for deep neural networks. In an competition run by ImageNet in 2012, Hinton's UofT team used convolutional neural networks (CNN) for image recognition application. Two of Hinton's earliest correspondents were Yoshua Bengio from the University of Montreal and his own postdoc student Yan Lecun, who joined Hinton's UofT lab in 1987 and now leads AI research at Facebook. This required patience, and although Bengio quipped at a Toronto machine learning event that "serious scientists don't give a timeline", the business world nonetheless felt tectonic shifts in real-time: machine learning has immediate applications in image recognition, voice recognition, translation, sentiment analysis and much more.