IBM is expanding its flash storage lineup to power cloud data centers that carry out so-called cognitive computing. The company's newest FlashSystem arrays, introduced Wednesday, combine its fast and relatively affordable FlashCore technology with a scale-out architecture designed to be easy to expand. Cognitive computing, which IBM defines as real-time data analysis for immediate, automated decision-making, is at the heart of much of IBM's current technology push for enterprises and service providers. Its Watson technology is the star of the show but only the most visible part of what the company is doing in this space. An example of cognitive computing is a mobile operator analyzing information about phone call quality to make decisions on the fly about changes in the network, said Andy Walls, an IBM Fellow and CTO for flash systems. Large-scale, real-time computing needs flash, IDC analyst Eric Burgener said.

Machine learning is in the midst a renaissance. Technical advancements in machine learning are enabling a broad assortment of new possibilities for designers to better engage with and understand users. Yet, these technologies also bring new challenges and require new ways of thinking about the design of user interfaces and interactions. Using intuitive analogies and tangible examples, Patrick Hebron offers an introduction to contemporary machine-learning systems and explores how these emerging capabilities will transform the next generation of computing interfaces, such as search engines, intelligent assistants, connected homes, and open-world video games. This webcast is intended for user interface and user experience designers as well as anyone interested in how recent advancements in machine learning can radically enhance software capabilities through natural language processing, image recognition, content personalization, and behavior prediction.

Three weeks ago we reported an amusing anecdote out of China in which robot waiters in a Guangzhou restaurant had been "fired" because whencustomers flocked to the Heweilai Restaurant chain in the southern Chinese city, they found they were not all they are cracked up to be. "A staff member said the robots couldn't effectively handle soup dishes, often malfunctioned, and had to follow a fixed route that sometimes resulted in clashes. A customer also said the robots were unable to do tasks such as topping up water or placing a dish on the table." "The robots weren't able to carry soup or other food steady and they would frequently break down. The boss has decided never to use them again," said one employee. We joked in the summary saying that "for now, it appears, China's minimum wage workers, and it has a few hundred million of those, will not be phased out just yet." According to a report released by the MIT Technology Review, where some saw failure in China's "novelty" worker robots, the Chinese government saw nothing less than the opportunity to perfect what will soon put million of Chinese workers out of a job: an army of worker robots. Because while there is certanly humor to be found in the anecdote about a robot "termination", the Chinese government is keen to change this.

Machine learning is one of the fastest-growing and most exciting fields out there, and deep learning represents its true bleeding edge. In this course, you'll develop a clear understanding of the motivation for deep learning, and design intelligent systems that learn from complex and/or large-scale datasets. We'll show you how to train and optimize basic neural networks, convolutional neural networks, and long short term memory networks. Complete learning systems in TensorFlow will be introduced via projects and assignments. You will learn to solve new classes of problems that were once thought prohibitively challenging, and come to better appreciate the complex nature of human intelligence as you solve these same problems effortlessly using deep learning methods.

The title speaks much for itself. We are entering an era in computational technology, that is making what seemed like the wildest fantasies 10-15 years ago, actually becoming reality. More and more of the tedious and hard manual labor work of checking, analyzing and confirming numbers in computations and systems is being taken over by computers and machines, that can do this at an efficiency surpassing that of humans by tenfolds. But what does this mean for the future of analysis and work done by machines? No matter how quick, precise and superior computers and machines become over humans, there are still certain tasks that humans will be able to do better, due to our ability to improvise and be creative. For now, those are attributes that a computer or machine cannot learn to the same extent as a human.

NIPS 2015 was bigger than ever, literally: at circa 3700 attendees this was roughly twice as many attendees as last year, which in turn was roughly twice as many as the previous year. This is clearly unsustainable, but given the frenzied level of vendor and recruiting activities, perhaps there is room to grow. The main conference is single track, however, and already 3 days long: so even more action is moving to the poster sessions, which along with the workshops creates the feel of a diverse collection of smaller conferences. Obviously, my view of the action will be highly incomplete and biased towards my own interests. Reinforcement learning continues to ascend, extending the enthusiasm and energy from ICML.

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Comet Labs has assembled a coalition of investors and corporate partners to help accelerate product and customer development cycles for fast growing robotics and machine learning companies. Startups are paired with experienced mentors and are given access to industry partners and platform technology. The fund's partners include the world's largest global manufacturing, agriculture, and healthcare companies. About Comet Labs Comet Labs is an early-stage venture capital firm associated with a 300M fund, Legend Star. They work with startups developing solutions in specific industry verticals, as well as those building enabling technologies.

The technologies included within the HfS Intelligent Automation Continuum are evolving at a rapid rate today as are the market positionings of the various sofwtare vendors. To account for these changes, HfS has evolved the Continuum to reflect the increased confluence of Robotic Process Automation and Cognitive Computing and the role that Service Orchestration platforms can play in building comprehensive solutions for the automation of IT and business processes. This POV outlines the new Intelligent Automation Continuum and explains the reasons for the changes that have been made.

Sentient's mission is to transform how businesses tackle their most complex, mission critical problems by empowering them to make the right decisions faster. Sentient's technology has patented evolutionary and perceptual capabilities that will provide customers with highly sophisticated solutions, powered by the largest compute grid dedicated to distributed artificial intelligence. For more information, visit www.sentient.ai Utilizing evolutionary computation and deep learning – designed to continuously evolve and improve – Sentient aims to create the world's most powerful intelligent system. This system enables researchers, innovators and companies to solve mission- critical, high value, problems.