When Worlds Collide, the 1951 Sci Fi film, painted a rather gloomy vision of the future of mankind on earth. However, the prospects for blended reality, in which the physical and digital worlds collide, couldn't be more promising. By fusing the virtual and real worlds, the possibilities are endless. By eliminating filters and limitations, blended reality enables expression at the speed of thought to improve experiences and inspire new technology. Using healthcare as an example, blended reality has significant implications for more personalized care, prevention and treatment by converging human biology, real-world context and technology advancements.

The insideBIGDATA Guide to Deep Learning & Artificial Intelligence is a useful new resource directed toward enterprise thought leaders who wish to gain strategic insights into this exciting area of technology. In this guide, we take a high-level view of AI and deep learning in terms of how it's being used and what technological advances have made it possible. We also explain the difference between AI, machine learning and deep learning, and examine the intersection of AI and HPC. We also present the results of a recent insideBIGDATA survey to see how well these new technologies are being received. Finally, we take a look at a number of high-profile use case examples showing the effective use of AI in a variety of problem domains. With all the quickly evolving nomenclature in the industry today, it's important to be able to differentiate between AI, machine learning and deep learning.

Salesforce has announced Service Cloud Einstein to deliver artificial intelligence solutions to customer service agents. Salesforce pushed Einstein, its AI platform, heavily at Dreamforce last year. It has a huge development focus on the product. This effort has culminated in the release of Einstein for Service cloud. It delivers artificial intelligence, arguably machine learning, to the service industry.

If you're intrigued by IBM's Watson AI as a service, but reluctant to trust IBM with your data, Big Blue has a compromise. Now the bad news: It'll only be available to z System / z/OS mainframe users ... for now. It's a collection of popular frameworks -- in particular Apache SparkML, TensorFlow, and H2O -- packaged with bindings to common languages used in the trade (Python, Java, Scala), and with support for "any transactional data type." IBM is pushing it as a pipeline for building, managing, and running machine learning models through visual tools for each step of the process and RESTful APIs for deployment and management. Even as the number of frameworks for machine learning mushrooms, developers still have to perform a lot of heavy labor to create end-to-end production pipelines for training and working with models.

Last year, Huawei leapfrogged a couple of fellow Chinese hardware companies to secure its spot as the third largest smartphone maker worldwide. As the company takes aim at the number two spot, it's apparently working to differentiate itself even more from the competition by developing more proprietary tech, adding to a list of components that already includes its own in-house chips. According to a new report from Bloomberg, a smart assistant is the next step. The company is said to be hard at work on a Siri/Alexa/Google Assistant competitor, employing a team of more than 100 people tasked with its creation in Shenzhen. If true, the company would join Samsung in a growing list of Android handset makers opting not to go with Google's assistant – though the Galaxy maker sped up the process a bit with its recent acquisition of Viv Labs.

There are two options for controlling a robot inside of the human body: Either you try and build some sort of intricate and tiny robot submarine with self contained propulsion and navigation, which would be really really hard to do, or you just make the robot with a tiny bit of something that responds to magnetic fields, and control it externally with some big magnets. The latter approach is vastly less complicated, but it has one major drawback, which is that it's very hard to manage multiple robots. Here's the problem: Magnetic fields, being fields, aren't easily constrained to specific areas. Realistically, if you're using something like a clinical MRI scanner to create a magnetic field, whatever gradient you give the field will affect everything inside of the MRI, whether you've got one single microbot or a vast swarm of them. If you want two different robots to do two different things, you're out of luck.

"Recently acquired by Hewlett Packard Enterprise, SGI is a trusted leader in technical computing with a focus on helping customers solve their most demanding business and technology challenges." Dr. Eng Lim Goh joined SGI in 1989, becoming a chief engineer in 1998 and then chief technology officer in 2000. He oversees technical computing programs with the goal to develop the next generation computer architecture for the new many-core era. His current research interest is in the progression from data intensive computing to analytics, machine learning, artificial specific to general intelligence and autonomous systems. Since joining SGI, he has continued his studies in human perception for user interfaces and virtual and augmented reality.

Intrusion detection and analysis has received a lot of criticism and publicity over the last several years. The Gartner report took a shot saying Intrusion Detection Systems are dead, while others believe Intrusion Detection is just reaching its maturity. The problem that few want to admit is that the current public methods of intrusion detection, while they might be mature, based solely on the fact they have been around for a while, are not extremely sophisticated and do not work very well. While there is no such thing as 100% security, people always expect a technology to accomplish more than it currently does, and this is clearly the case with intrusion detection. It needs to be taken to the next level with more advanced analysis being done by the computer and less by the human. The current area of Intrusion Detection is begging for Machine Learning to be applied to it.

Right now, you can order a pizza, manage your to-do list and call an Uber on Amazon Echo and Google Home. The latest development from the smart speakers would give us yet another reason to leave our phones in our pocket. The Wall Street Journal reports that Amazon and Google are considering adding telephone functionality to their devices, but it won't be easy. Citing "people familiar with the matter," the Journal says Amazon and Google could introduce the ability to make and receive calls on their respective platforms later this year. The companies could make use of their existing communication platforms, since Amazon already has the business-focused videoconferencing tool Chime. Meanwhile, Google has Hangouts, Duo, and it recently recommitted to maintaining Google Voice.

Yahoo has announced TensorFlowOnSpark, its latest open source framework for distributed deep learning on big data clusters. Deep learning (DL) has evolved significantly in recent years. At Yahoo, we've found that in order to gain insight from massive amounts of data, we need to deploy distributed deep learning. Existing DL frameworks often require us to set up separate clusters for deep learning, forcing us to create multiple programs for a machine learning pipeline (see Figure 1 below). Having separate clusters requires us to transfer large datasets between them, introducing unwanted system complexity and end-to-end learning latency.