PubNub BLOCKS enables you to process your data mid-stream, to execute functions on your data in motion. This is huge, because you no longer need to spin up and manage new servers to run a simple function. It's all done in the network. That's why we say that PubNub is a programmable network. IBM Watson is a powerful technology that brings cognition to applications, with the ability to understand, reason, learn, and interact. I like to say it gives your application a brain, extending the capabilities of your application beyond simply interacting through a set of rules.

Like all hardware device makers eager to meet the newest market opportunity, Intel is placing multiple bets on the future of machine learning hardware. The chipmaker has already cast its Xeon Phi and future integrated Nervana Systems chips into the deep learning pool while touting regular Xeons to do the heavy lifting on the inference side. However, a recent conversation we had with Intel turned up a surprising new addition to the machine learning conversation--an emphasis on neuromorphic devices and what Intel is openly calling "cognitive computing" (a term used primarily--and heavily--for IBM's Watson-driven AI technologies). This is the first time to date we've heard the company make any definitive claims about where neuromorphic chips might fit into a strategy to capture machine learning, and marks a bold grab for the term "cognitive computing" which has been an umbrella term for Big Blue's AI business. Intel has been developing neuromorphic devices for some time, with one of the first prototypes that was well known in 2012.

In an expanded edition published in 1988, they responded to claims that their 1969 conclusions significantly reduced funding for neural network research: "Our version is that progress had already come to a virtual halt because of the lack of adequate basic theories… by the mid-1960s there had been a great many experiments with perceptrons, but no one had been able to explain why they were able to recognize certain kinds of patterns and not others."

The virtual world is built on physical infrastructure. Every search that gets submitted, email sent, page served, comment posted, and video loaded passes through data centers that can be larger than a football field. Those thousands of racks of humming servers use vast amounts of energy; together, all existing data centers use roughly 2% of the world's electricity, and if left unchecked, this energy demand could grow as rapidly as Internet use. So making data centers run as efficiently as possible is a very big deal. Thankfully, despite skyrocketing demand for computing, data center electricity use has flattened over the past few years, largely due to enormous opportunities to improve efficiency as these facilities scale up.1 But capturing these opportunities can be a very complicated process.

Fintech is having a huge impact on the finance industry, with artificial intelligence (AI), machine learning, data analytics and blockchain, all changing the way the industry works. The rapid pace of development and expected adoption of fintech technologies has led many industry experts to believe that the finance profession has peaked and that there will be the need for fewer finance professionals going forward. Although this is a very bold statement, there are varieties of reasons that point to this being the case. The foremost factor is blockchain technology (also known as Distributed Ledger Technology) and its potential seismic impact on financial transactions across the globe. Blockchain technology has started sweeping the different areas of transactional finance, such as clearing, settlement, payments and execution.

In a previous post, I introduced the Meta Meta-Model of Deep Learning. However, I did not introduce its details. A word of warning for the reader, the concepts in this section is in flux and in undergoing a lot of changes. Therefore, this article is just a reflection of my current understanding of the language of Deep Learning Meta Meta-Model. That's definitely a mouth full, so to make life simpler for everyone, I just call this the Deep Learning Canonical Patterns.

Keep those hands on the keyboard! We can do a lot on OSX and Linux without touching a mouse or GUI. Awesome command line tools for *N*X derivatives have been around since day one, and have expanded to include Python, Go, NodeJS, and hybrid tools. Even if you are not only running your pipeline through the command line, you can call most of these tools from Apache NiFi for processing. The book Data Science at the Command Line and GitHub offer an amazing set of quality tools to do a lot of pre- and post-processing and allow for a lot of transformations. I highly recommend looking at all of these amazing tools.

Our learning task: Given a training set S {(x1 , y1), (x2 , y2), . . . The simplest case: y { 1, 1} called binary classification problem If y is a real number it becomes a regression problem More general case, y can be a vector and each element is drawn from a finite set.

At RSNA 2016, the majority of significant new product announcements were modalities, not information technology. It almost seems that many radiology IT companies (or business segments) are planning to release new product introductions at HIMSS rather than at RSNA. While enterprise imaging remains the core radiology IT technology on display at RSNA, the big buzz this year was artificial intelligence and machine learning. As part of their Opening Session, Keith J. Dreyer, DO, PhD, and Robert M. Wachter, MD, discussed the good and the bad of the digital revolution in radiology. With artificial intelligence (AI) rapidly advancing thanks to events such as the ImageNet Large Scale Visual Recognition Challenge Competition, Dr. Dreyer believes AI will complement radiology and enable radiologists to become leaders in precision medicine; rather than becoming wary of AI, he said, radiology could work with AI to optimize the delivery of patient care.

As a Venture Capital firm for Artificial Intelligence we follow the growing AI market closely. For the German AI Landscape Map, we created a list of over 600 European AI startups based on internal research mainly deriving from our network and Crunchbase. Not every company that lists AI as a part of their product has AI in it. We have therefore taken the freedom to clean the raw data. We've ended up with 81 German Artificial Intelligence startups, which made it onto our map.