Machine-learning is all the rage in fraud detection, with industry analysts, academics, businesses and technology media examining the advantages of algorithms and big data in the fight against e-commerce fraud. Especially for fraud analysts working in companies with small budgets, machine-learning tools are seen as a cost-effective way to tighten fraud controls while maintaining fast decision times, as Forrester noted in its 2015 cross-channel fraud report. There's no question that machine-learning tools can be an effective component of fraud reduction program, but relying on them to save staffing costs may not be cost-effective in the long run. That's because while machine learning is an invaluable tool in the fight against fraud, it relies on human input and insight to create a comprehensive solution that yields the best results. Algorithms are useful for identifying potential fraud quickly, but due to variability in consumer behavior – such as making online purchases while traveling abroad -- some transactions will be falsely flagged for decline.

From artificial intelligence to virtual reality, emerging technologies are rewriting the retail playbook at a rapid pace, suggests J. Walter Thompson Intelligence in a new report called Frontier(less) Retail. Launched in collaboration with WWD, the report explores the idea that brands and retailers are increasingly putting innovation at the core of their strategies. This relates to everything from digital integration through to the more future-looking technologies helping to shift their businesses forward. Rebecca Minkoff has boosted sales with smart mirrors in dressing rooms, it notes, while Kate Spade has had a hit with Everpurse, a smartphone-charging handbag. It also attributes the success of Under Armour in part to its positioning as a tech-forward brand, and references Topshop's new incubator program, Top Pitch, as a clever bid to achieve the same at a time when its young consumer base is more likely to spend on smartphones than splurge on streetwear. Within all this, however, it is keeping abreast of change that is proving one of the industry's biggest challenges.

This accessible, comprehensive book captures the essence of artificial intelligence -- solving the complex problems that arise wherever computer technology is applied. With his signature enthusiasm, George Luger demonstrates numerous techniques and strategies for addressing the many challenges facing computer scientists today. Diverse topics on this exciting and ever-evolving field range from perception and adaptation using neural networks and genetic algorithms, intelligent agents with ontologies, automated reasoning, natural language analysis, and stochastic approaches to machine learning. This book is ideal for a one - or two-semester university course on AI. "The style of writing and comprehensive treatment of the subject matter makes this a valuable addition to the AI literature." George Luger is currently a Professor of Computer Science, Linguistics, and Psychology at the University of New Mexico.

This is a guest post by Dana Van Aken, Andy Pavlo, and Geoff Gordon of Carnegie Mellon University. This project demonstrates how academic researchers can leverage our AWS Cloud Credits for Research Program to support their scientific breakthroughs. Database management systems (DBMSs) are the most important component of any data-intensive application. They can handle large amounts of data and complex workloads. But they're difficult to manage because they have hundreds of configuration "knobs" that control factors such as the amount of memory to use for caches and how often to write data to storage.

Anna, Senior Consultant at M-Brain Toronto, is responsible for new business development in California as well as managing and executing strategic analysis and advisory projects. Irida, Consultant at M-Brain Toronto, manages and executes strategic analysis and advisory projects. Research by M-Brain suggests that process optimization is the most popular application of machine learning among companies across verticals including financial services, high tech, retail, manufacturing, and healthcare. M-Brain identified and analyzed 60 machine learning application cases published by vendors of machine learning tools to understand the use and application of machine learning by industry. The analysis focused on five market verticals: Financial service, high tech, retail, manufacturing, and healthcare.

Apple and Amazon are fierce competitors, especially now that the iPhone maker has released its own home speaker. However, the companies seem to have been putting their differences aside lately. This year, Amazon and Apple introduced changes that goes beyond their rivalry, with the most recent move announced this week at the Worldwide Developers Conference. The Apple logo is displayed at a store in the central business district of Sydney, April 6, 2017. Apple announced the Amazon video app will finally come to the Apple TV worldwide this year.

Yes, artificial intelligence can be used to, say, garner award show accolades for Alicia Vikander in "Ex Machina" or hash out daddy issues with Tony Stark (a.k.a. But the hi-tech and seemingly futuristic system can also be used to enhance your online shopping experience, which is what luxury e-marketplace Orchard Mile is doing with My Mile, an e-boutique that's constantly being updated and personalized for each individual shopper. Behind the scenes, predictive and intuitive algorithms -- not scary robots -- use your shopping habits and preferences to essentially make your browsing and buying experience more focused (and less overwhelming), easier and hopefully, more fun. Orchard Mile -- named after two international luxury retail destinations Orchard Road in Singapore and the Magnificent Mile in Chicago -- was founded in late 2015 by Jennie Baik, former Head of Strategy for Burberry Americas and Julia LeClair, founder of personal shopper start-up MyDROBE. Orchard Mile either works directly with the brand or, in the case of designers who don't have full e-commerce build out, partners with Saks Fifth Avenue to drop ship the items.

As a Windows user interested in learning to use GPUs for deep learning I have been frustrated with the available software and books for a couple of years. Most books seem to have been written on Linux systems and then you are told that the code should work fine with Windows - which is not always so. Also several deep learning systems have not been set up for Windows yet. So when I first read about TensorFlow I decided to give it a try: I got this book and one other. Final evaluation first: I gave it 4 stars because the code worked for me (more accurately: much, not all of the code worked), but the situation was better than other books hence 4 stars.

According to analyst firm Gartner, the usage of chatbots will triple through 2019 as enterprises seek to increase customer satisfaction and reduce operating costs. But not all chatbots are equal. For businesses, chatbots (sometimes called "virtual agents" or "virtual customer assistants") need to be smart in order to be effective. Intelligent chatbots integrate with enterprise systems and the related rules; they can parse big data and use artificial intelligence to help customers resolve issues or perform transactions, such as paying a bill or extending a subscription. Some chatbots interact with customers to resolve issues, conduct transactions, and answer questions.

Attracted by the convenience of shopping anytime, anywhere, consumers are flocking to mobile commerce (m-commerce). We think the shift to browsing and buying via smartphones will sustain e-commerce growth, as the convenience of mobile encourages more frequent online shopping. We also expect the consumer-led shift to m-commerce to prompt a wave of retailer investments in artificial intelligence (AI) capabilities. Just over one-third of all US online retail sales will be made via mobile devices (including tablets) this year, according to Euromonitor International, which projects that that share will reach 43% by 2020. But m-commerce brings challenges for retailers.