In a globally connected marketplace, new technologies ensure customer transactions won't get lost in translation. The world is becoming increasingly connected, and companies in search of worldwide markets need to be able to communicate with customers in their native tongues. They're depending on sophisticated new machine translation technologies to break down language barriers. "When you first enter a market, the early adopters for any new product -- whether it's a personal care product or a tech product -- tend to be internationally focused and English friendly, so you might think you're doing well," said Ben Sargent, content globalization strategist at the consulting firm Common Sense Advisory. To reach 80 percent of the world's total online population, businesses need to communicate in at least 12 languages, and to reach 98 percent, they need to translate across 48 languages.

Adrian is an industry analyst and recovering academic, providing research and advisory services for buyers, sellers, and investors in emerging technology markets. His coverage areas include cognitive computing, big data / analytics, the Internet of things, and cloud computing. Adrian co-authored Cognitive Computing and Big Data Analytics (Wiley, 2015) and is currently writing a book on the business and societal impact of these emerging technologies. He has held executive positions at several consulting and analyst firms. Adrian also held academic appointments in computer science at Drexel University and SUNY-Bingamton, and adjunct faculty positions in the business schools at NYU and Boston College.

I still remember when I trained my first recurrent network for Image Captioning. Within a few dozen minutes of training my first baby model (with rather arbitrarily-chosen hyperparameters) started to generate very nice looking descriptions of images that were on the edge of making sense. Sometimes the ratio of how simple your model is to the quality of the results you get out of it blows past your expectations, and this was one of those times. What made this result so shocking at the time was that the common wisdom was that RNNs were supposed to be difficult to train (with more experience I've in fact reached the opposite conclusion). Fast forward about a year: I'm training RNNs all the time and I've witnessed their power and robustness many times, and yet their magical outputs still find ways of amusing me. This post is about sharing some of that magic with you. We'll train RNNs to generate text character by character and ponder the question "how is that even possible?" By the way, together with this post I am also releasing code on Github that allows you to train character-level language models based on multi-layer LSTMs. You give it a large chunk of text and it will learn to generate text like it one character at a time. You can also use it to reproduce my experiments below.

AI has impacted our day-to-day lives for years, whether that's automated voice calls or virtual personal assistants - like Siri - or even self-driving cars. The next step is to implement AI technology into personal and cyber security systems. Currently, one or two guards will monitor a bank of security screens, and it is a successful method of security, but it is not full proof. Eliminating human error is a key driver behind bringing Artificial Intelligence to security through intelligent video analytics. Humans can easily get distracted, generally have short attention spans, and often find it difficult to focus on multiple things at once - a bank of security screens.

Will Content Marketing Die To Robotic AI? Stellaris 1.2.5 hotfix patch released, with AI fixes

Stellaris 1.2.5 hotfix patch released, with AI fixes

Stellaris 1.2.5 hotfix patch released, with AI fixes

Stellaris 1.2.5 hotfix patch released, with AI fixes

Will Content Marketing Die To Robotic AI?

Ford Motor Co. intends to have a fully driverless vehicle -- no steering wheel, no pedals -- on the road within five years. The car will initially be used for commercial ride-hailing or ride-sharing services; sales to consumers will come later. "This is a transformational moment in our industry and it is a transformational moment for our company," said CEO Mark Fields, as he announced the plan at Ford's Silicon Valley campus in Palo Alto, California. Ford's approach to the autonomous car breaks from many other companies, like Mercedes-Benz and Tesla Motors, which plan to gradually add self-driving capability to traditional cars. Just last month, BMW AG, Intel Corp. and the automotive camera maker Mobileye announced a plan to put an autonomous vehicle with a steering wheel on the road by 2021.