New intelligence can be added to mobile devices like the iPhone, Android devices, and low-power computers like Raspberry Pi with Facebook's new open-source Caffe2 deep-learning framework. Caffe2 can be used to program artificial intelligence features into smartphones and tablets, allowing them to recognize images, video, text, and speech and be more situationally aware. It's important to note that Caffe2 is not an AI program, but a tool allowing AI to be programmed into smartphones. It takes just a few lines of code to write learning models, which can then be bundled into apps. The release of Caffe2 is significant.

Training and deploying AI models is often associated with massive data centers or super computers, with good reason. The ability to continually process, create, and improve models from all kinds of information: images, video, text, and voice, at massive scale, is no small computing feat. Deploying these models on mobile devices so they're fast and lightweight can be equally daunting. Overcoming these challenges requires a robust, flexible, and portable deep learning framework. Facebook has been working with others in the open source community to build such a framework.

This is the most comprehensive book available on the deep learning and available as free html book for reading at http://www.deeplearningbook.org/ Written by three experts in the field, Deep Learning is the only comprehensive book on the subject." This is not available as PDF download. Printing seems to work best printing directly from the browser, using Chrome. Other browsers do not work as well.

In the past 10 years, the best-performing artificial-intelligence systems -- such as the speech recognizers on smartphones or Google's latest automatic translator -- have resulted from a technique called "deep learning." Deep learning is, in fact, a new name for an approach to artificial intelligence called neural networks, which have been going in and out of fashion for more than 70 years. Neural networks were first proposed in 1944 by Warren McCullough and Walter Pitts, two University of Chicago researchers who moved to MIT in 1952 as founding members of what's sometimes called the first cognitive science department. Neural nets were a major area of research in both neuroscience and computer science until 1969, when, according to computer science lore, they were killed off by the MIT mathematicians Marvin Minsky and Seymour Papert, who a year later would become co-directors of the new MIT Artificial Intelligence Laboratory. The technique then enjoyed a resurgence in the 1980s, fell into eclipse again in the first decade of the new century, and has returned like gangbusters in the second, fueled largely by the increased processing power of graphics chips. "There's this idea that ideas in science are a bit like epidemics of viruses," says Tomas Poggio, the Eugene McDermott Professor of Brain and Cognitive Sciences at MIT, an investigator at MIT's McGovern Institute for Brain Research, and director of MIT's Center for Brains, Minds, and Machines.

No one doubts that artificial intelligence has unimaginable potential. Within the next couple of years, it will revolutionize every area of our life, including medicine. Although many have their fears and doubts about AI taking over the world, Stephen Hawking even said that the development of full artificial intelligence could spell the end of the human race. However, I am fully convinced if humanity prepares appropriately for the AI-age, artificial intelligence will prove to be the next successful area of cooperation between humans and machines. Concerning healthcare, artificial intelligence will redesign it completely – and for the better.

In a soft-furnished studio space behind a warehouse in west Berlin, a group of international scientists are debating our robot future. An engineer from a major European carmaker is just finishing a cautiously optimistic progress report on self-driving vehicles. Increasingly, he explains, robot cars are learning to differentiate cars from more vulnerable moving objects such as pedestrians or cyclists. Some are already better than humans at telling apart different breeds of dog. "But of course," he says, "these are small steps."

Artificial intelligence (AI) and machine learning are surging in popularity as these technologies become the foundation for making networks smarter, faster, and more intuitive. Today machine learning and AI are being touted as key elements to making the Internet of Things (IoT) and 5G a success. In fact at the recent Mobile World Congress conference in Barcelona, Spain, carriers like SK Telecom and Reliance talked about using machine learning by feeding it with analytics from network monitoring. Plus, big name companies like IBM are incorporating AI into well-known projects like Watson, which is being used for everything from security to IoT to the cloud. They are based upon deep learning neural networks, a technology first conceived more than 70 years ago.

Patrick Winston is one of the greatest teachers at M.I.T., and for 27 years was Director of the Artificial Intelligence Laboratory (which later became part of CSAIL). Patrick teaches 6.034, the undergraduate introduction to AI at M.I.T. and a recent set of his lectures is available as videos. I want to point people to lectures 12a and 12b (linked individually below). In these two lectures he goes from zero to a full explanation of deep learning, how it works, how nets are trained, what are the interesting problems, what are the limitations, and what were the key breakthrough ideas that took 25 years of hard thinking by the inventors of deep learning to discover. The only prerequisite is understanding differential calculus.

IBM said Anaconda will also integrate with the PowerAI software distribution for machine learning and deep learning so enterprises can take advantage of PowerAI performance and GPU (graphics processing unit) optimization for data intensive cognitive workloads. The agreement gives IBM another leg up in the fight to win business from data scientists and developers working on deep learning applications. Anaconda said it already has more than 16 million downloads. Offering the platform through IBM's Cognitive Systems unit will allow clients to quickly scale up the deep learning applications they develop using Anaconda. The advent of big data has been a bonanza for IT providers aiming to provide enterprises and other organizations with the tools to identify patterns in large data sets to convert information to actionable intelligence.

Financial Technology - or FinTech - Is Now Part of the @CloudExpo Program! Accordingly, attendees at the upcoming 20th Cloud Expo at the Javits Center in New York, June 6-8, 2017, will find fresh new content in a new track called FinTech, which will incorporate machine learning, artificial intelligence, deep learning, and blockchain into one track. Financial enterprises in New York City, London, Singapore, and other world financial capitals are embracing a new generation of smart, automated FinTech that eliminates many cumbersome, slow, and expensive intermediate processes from their businesses. FinTech brings efficiency as well as the ability to deliver new services and a much improved customer experience throughout the global financial services industry. FinTech is a natural fit with cloud computing, as new services are quickly developed, deployed, and scaled on public, private, and hybrid clouds.