We are pretty certain that you must have come across multiple articles/blogs on artificial intelligence. It's plausibility of being able to ever evolve. But let's just take a moment here, and rather going forward take a step backwards and understand how and why exactly did artificial intelligence come into existence. The idea of machines being able to think, analyse, predict and understand like humans (even better and faster) has been a fantasy since centuries. So it would rather be tough to say where and how exactly did the concept of artificial intelligence came into being, but one of the first algorithm of machine learning (a sub-set of Artificial Intelligence) was designed in 1970, but it's true potential was unleashed in a famous paper in 1986 by David Rumelhart, Geoffrey Hinton and Ronald Williams (and is still vastly used as a basic model for machine learning) and the first ever breakthrough idea to be sprouted was in 1950.

I've never seen robots or AI as an evil job replacing or apocalypse causing fear monger. Rather robots or AI in this instance can help people solve grandiose problems. For instance, we have been using machine learning to play games, answer questions, perform data entry, optimize businesses, fly drones etc. Yet why haven't we used the same technology to solve a few very simple problems: keep robots standing up, improve robotic dexterity, and improve the dexterity of exoskeletal limbs? It isn't that humans aren't intelligent enough, it's just that machines are more precise, never get bored, or lose focus.

The strong interest in deep learning neural networks lies in the ability of neural networks to solve complex pattern recognition tasks – sometimes better than humans. Once trained, these machine learning solutions can run very quickly – even in real-time – and very efficiently on low-power mobile devices and in the datacenter. However training a machine learning algorithm to accurately solve complex problems requires large amounts of data that greatly increases the computational workload. Scalable distributed parallel computing using a high-performance communications fabric is an essential part of what makes the training of deep learning on large complex datasets tractable in both the data center and within the cloud. Very simply, the single node TF/s parallelism delivered by Intel Xeon processor and Intel Xeon Phi devices described in the previous article in this series is simply not enough for many complex machine learning training sets.

A recent article in The Commonwealth Fund blog, "Envisioning a Digital Health Advisor," raises the question of being able to use smartphone apps to get real-time, accurate and personalized guidance for health concerns. While one can envision the convenience, affordability and peace of mind that would result from their use, such services face a number of hurdles before they become reality. As a result, the "digital revolution" has not yet greatly affected most people's interactions with the health care system. These challenges fall into two main categories: fiscal/policy and technology. In a fee-for-service environment, the only way that healthcare practitioners get paid is to have face-to-face encounters with patients.

Training'complex multi-layer' neural networks is referred to as deep-learning as these multi-layer neural architectures interpose many neural processing layers between the input data and the predicted output results – hence the use of the word deep in the deep-learning catchphrase. While the training procedure is computationally expensive, evaluating the resulting trained neural network is not, which explains why trained networks can be extremely valuable as they have the ability to very quickly perform complex, real-world pattern recognition tasks on a variety of low-power devices including security cameras, mobile phones, wearable technology. These architectures can also be implemented on FPGAs to process information quickly and economically in the data center on low-power devices, or as an alternative architecture on high-power FPGA devices. The Intel Xeon Phi processor product family is but one part of Intel SSF that will bring machine-learning and HPC computing into the exascale era. Intel's vision is to help create systems that converge HPC, Big Data, machine learning, and visualization workloads within a common framework that can run in the data center – from smaller workgroup clusters to the world's largest supercomputers – or in the cloud.

Over the past few years, IBM has been devoting a great deal of corporate energy into developing Watson, the company's Jeopardy-beating supercomputing platform. Watson represents a larger focus at IBM that integrates machine learning and data analytics technologies to bring cognitive computing capabilities to its customers. To find out about how the company perceives its own invention, we asked IBM Fellow Dr. Alessandro Curioni to characterize Watson and how it has evolved into new application domains. Curioni, will be speaking on the subject at the upcoming ISC High Performance conference. He is an IBM Fellow, Vice President Europe and Director IBM Research – Zurich Research Laboratory, Switzerland.

It is no secret that much of the wisdom of the world lies in unstructured data, that is the kind that is not necessarily quantifiable and tidy. So it is in cybersecurity, and now IBM is putting Watson to work to make that knowledge more accessible. Towards that end, IBM Security has announced a new year-long research project through which it will collaborate with eight universities to help train its Watson artificial-intelligence system to tackle cybercrime. Knowledge about threats is often hidden in unstructured sources such as blogs, research reports and documentation, said Kevin Skapinetz, director of strategy for IBM Security. "Let's say tomorrow there's an article about a new type of malware, then a bunch of follow-up blogs," Skapinetz explained.

IBM Security announced Watson for Cyber Security on Tuesday, a cloud-based version of the company's cognitive technology that will focus on learning the language of cybersecurity. The project is working to improve security analysts' capabilities by automating the "connections between data, emerging threats and remediation strategies." IBM will collaborate with eight universities starting this fall to expand the collection of security data IBM has trained Watson with. With its Watson cybersecurity effort, IBM is working to automate threat intelligence, allowing a machine to make connections in data that humans are sometimes unable to find. As an added bonus, if the project proves successful, businesses could integrate Watson's cybersecurity into their security platforms, helping to bridge the cybersecurity skills gap. "Even if the industry was able to fill the estimated 1.5 million open cybersecurity jobs by 2020, we'd still have a skills crisis in security," said Marc van Zadelhoff, General Manager, IBM Security.

SAN FRANCISCO, CA--(Marketwired - May 9, 2016) - If you've been following the predictions made by UNU, a new "Swarm Intelligence" platform from Unanimous A.I., you might bet on the Kentucky Derby this weekend and won big, really BIG. That's because a day before the race, UNU's picks were published for the first four horses, in order. It's a bet called the Superfecta that paid 540 to 1 odds. And that's exactly how the horses came in. And this is not the first stunning pick UNU has made.

I'll introduce the Hierarchical Risk Parity (HRP) approach. HRP portfolios address three major concerns of quadratic optimizers in general and Markowitz's CLA in particular: instability, concentration and under-performance. HRP applies modern mathematics (graph theory and machine learning techniques) to build a diversified portfolio based on the information contained in the covariance matrix. However, unlike quadratic optimizers, HRP does not require the invertibility of the covariance matrix. In fact, HRP can compute a portfolio on an ill-degenerated or even a singular covariance matrix, an impossible feat for quadratic optimizers.