This was a question from a university researcher in the Pacific northwest. The good news is our Azure Machine Learning or Azure ML is a powerful native Platform as a Service (PaaS) offering which has been around since 2015. When you think ML many different things fall into this like AI, neural networks, predictive outcomes. Past data is often huge – the quantity of data is doubling about every 18 months and that's only increasing from here. Computers can consider far more variables than a human making the same decision.

That model is fairly slow. Essentially, that model is trying to pull out all stops to maximize tagger accuracy. Speed consequently suffers due to choices like using 4th order bidirectional tag conditioning. It's nearly as accurate (96.97% accuracy vs. 97.32% on the standard WSJ22-24 test set) and is an order of magnitude faster. Compared to MXPOST, the Stanford POS Tagger with this model is both more accurate and considerably faster. It all depends, but on a 2008 nothing-special Intel server, it tags about 15000 words per second.

A 25-year-old book about science has some explaining to do. Machines Who Think was conceived as a history of artificial intelligence, beginning with the first dreams of the classical Greek poets (and the nightmares of the Hebrew prophets), up through its realization as twentieth-century science. The interviews with AI's pioneer scientists took place when the field was young and generally unknown. They were nearly all in robust middle age, with a few decades of fertile research behind them, and luckily, more to come. Thus their explanations of what they thought they were doing were spontaneous, provisional, and often full of glorious fun.

If you have suggestions and comments please send email to Kevin Dowling, nivek@cmu.edu

Artificial Intelligence (AI) is an important and evolving concept that is having significant impact within the Customer Experience industry -- and it's a topic that is being talked about on a seemingly daily basis at this point. But is AI really ready for primetime in customer care? I spoke with Michael Johnston, Lead Inventive Scientist at Interactions, about frequently asked questions about AI and Machine Learning as they apply to customer care. Artificial Intelligence refers to the capability of a machine to imitate intelligent human behavior. Put another way, AI technologies are algorithms that attempt to mimic things that humans do.

Artificial Intelligence (AI) is an important and evolving concept that is having significant impact within the Customer Experience industry -- and it's a topic that is being talked about on a seemingly daily basis at this point. But is AI really ready for primetime in customer care? I spoke with Michael Johnston, Lead Inventive Scientist at Interactions, about frequently asked questions about AI and Machine Learning as they apply to customer care. How do you define Artificial Intelligence and Machine Learning? Artificial Intelligence refers to the capability of a machine to imitate intelligent human behavior.

Finding customer service help online can be a pain. Filtering through a knowledge base to find the right answer to your question can be an exercise in fighting with nested frequently asked questions documents. Microsoft is aiming to help by making it easier for companies to create intelligent bots that can answer common questions. The QnA Maker, launched in beta on Tuesday, will let users train an automated conversation partner on existing frequently-asked-questions content. After that information is fed in, the service will create a bot that will respond to customer questions with the content from the knowledge base.

A motherboard change can look like a new PC, so you might well have needed to talk to Microsoft even if you had been using a Microsoft account. Either way, you should recognise that Windows 10 is fundamentally different from Windows 7 in at least two important ways. Some major features – including Cortana, Notifications and apps – originated in the smartphone world. As with other mobile operating systems, everything is maintained from the cloud. Second, Windows 10 is just one part of Microsoft's cross-platform ecosystem, which includes smartphones, tablets, the Xbox One range of games consoles, cloud-based services such as OneDrive and Office 365, and dozens of Microsoft apps on Android and Apple iOS devices.

Our analytics engine Kairos processes unstructured data from millions of sites, blogs, and social platforms like Twitter and Tumblr. Billions of public posts are then analyzed and classified across 25,000 topics, emotions, and demographics--turning noisy social data into insights. In order to create predictions around the elections using our analytics platform Kairos, we built 4 metrics: Awareness, Positivity, Negativity and Intent, of which only Negativity and Intent proved to be valuable in predicting elections. Negativity and Intent are natural language processing classifiers which take advantage of sentence structure as well as keyword matching. Then we modeled the data against survey polls, primary results, and survey pools to obtain weights of influence for each of the social indices.