It's only a matter of time before TensorFlow reaches production Hadoop and Big Data clusters in Global 5000 industries. I have seen my example TensorFlow scripts running very consistently without crashing for thousands of uses. The one thing missing is Enterprise support for TensorFlow, especially containerized and managed. I think a number of Big Data vendors will start doing that soon. A good time to revisit and retry the Neural Image Caption Generator in TensorFlow, I will post my findings on how well it runs on a OSX 10.12 Intel 2.5 GHz i7 with 16 GB RAM.

Everyone's talking about how AI will revolutionize so many things. Most of today's discussions take place at the application level, although AI will impact software development too. Some vendors, ISVs and consultants anticipate such a shift, but they aren't able articulate the specifics yet. For years, organizations have been building software using certain types of knowledge. Now, they also have to consider the probable impacts of AI.

More than 90% of surveyed trusts say they still rely on hand-written reports - Photo credit: Flickr, ad.mak, CC BY 2.0 According to figures released following a Freedom of Information request by the communications solutions company Nuance, 93% of the 30 trusts that responded said they handwrite reports. The same proportion said they relied on traditional word processing methods to type up electronic patient records. However, the FoI also indicated an increasing interest in AI across the NHS, with 43% of trusts reporting that they were considering how to use AI as a way of allowing patients to "self-help" when accessing health services. In a statement published alongside the FoI responses, Nuance said that such technology included virtual assistants, speech recognition technology and chat-bots. Last year, Enfield Council became one of the first local authorities to use AI assistance, with the introduction of IPSoft's Amelia systems to help residents carry out online tasks.

Exciting changes are underway for technology in health care. In our post about what to expect in 2017 we touched on many key trends, but one particular emerging technology deserves a closer look: artificial intelligence (AI). "While the U.S. health industry lags behind other industries … in deploying emerging technologies such as artificial intelligence, drones, and virtual reality, 2017 is the year to prepare for the eventual arrival of these technologies and their impacts on business models, operations, workforce needs, and cybersecurity risks," stated consulting firm PWC's annual trend report. Here are three ways that artificial intelligence is being used to predict patient outcomes, potentially transforming the way doctors treat patients. In December 2016, Microsoft India, in collaboration with the country's L V Prasad Eye Institute, launched Microsoft Intelligent Network for Eyecare (MINE), a research group that will leverage artificial intelligence to deliver large-scale eye care.

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Facebook's search tool is about to get way more visual. Director of Applied Machine Learning Joaquin Candela published a blog post today (accompanying his presentation at the Machine Learning @Scale event in New York City) to share updates about Facebook's AI-based image-recognition tool. The improvements can hone in on photos to the "pixel level" and will let users search images based on their content -- whether or not they've been manually tagged. "Until recently, online search has always been a text-driven technology, even when searching through images," he writes. "Whether an image was discoverable was dependent on whether it was sufficiently tagged or had the right caption -- until now."

Most online shopping sites are great at suggesting items similar to the ones you've previously purchased. But until now, none have quite mastered how to use that data to actually predict what you will be interested in next. That's the premise of Propulse, a new Montreal-based startup that uses machine learning and image recognition software to attempt to understand a shopper's tastes and recommend new products accordingly. SEE ALSO: Amazon's getting serious about its delivery ambitions with a $1.4 billion hub The e-commerce platform, which officially launched Thursday, is billed as a sort of online retail analog to Netflix's video recommendation engine. Its algorithm analyzes nearly 40,000 attributes of product images or listings in order to suss out a customer's personal style and match it to known shopping patterns.

Credits to Jean-Nicholas Hould for his post that gives an intuitive approach to learn a basic Machine Learning algorithm and Sebastian Raschka's book on Machine Learning in Python. Machine Learning (ML) is playing a key role in a wide range of critical applications, such as Computer Vision, Data Mining, Natural Language Processing, Speech Recognition and others. ML provides potential solutions in all of the above mentioned domains and more, it's surely going to be the the driving force of our future digital civilization. ML can be a bit intimidating for a newcomer. The concept of ML might be quite abstract and the newcomer might be bombarding himself with multiple questions.

Prompted by an activation phrase, Dragon Drive recognizes a driver named Lior by his voice. Voice command in cars shows so much potential to help drivers keep their eyes on the road, but since its implementation, the technology largely resulted in frustration. Sure, placing a call to a specific contact usually works, but just try finding a destination in the navigation system. It becomes worse when the car doesn't show what commands it understands. Nuance, the company behind the majority of voice systems in cars, thinks it has the problem licked through the use of machine learning and the cloud, essentially equipping cars with a virtual assistant.