In fact this one is very special. Every now and then there comes a field of technology that strikes us as being especially exciting. With all the latest accomplishments in the field of artificial intelligence it's really hard not to get excited about AI. Companies such as Google, NVIDIA or Comma.ai are using neural networks to train cars that know how to drive themselves. Apps such as PRISMA are using AI to create artwork from photography that is inspired by real artists.

International Business Machines Corporation IBM has launched a new lineup of Linux-based servers that it built in collaboration with NVIDIA Corporation NVDA . The servers have been specifically designed to speed up artificial intelligence, advanced analytics and machine learning as well as to boost the efficiency of data centers. The new lineup of servers is powered by two IBM Power8 CPUs along with four Tesla P100 GPU accelerators manufactured by NVIDIA. The reason behind the high speed performance of these servers is that they also use NVIDIA's NVLink high-speed interface, which acts as a bridge between the CPU and GPU. This enables faster communication than a common PCIe bus found on a desktop computer.

Speaker: Richard E. Fairley, PhD, Principal Associate of Software and Systems Engineering Associates (S2EA) View bio (PDF, 228 KB) This full-day presentation will cover practical applications of systems engineering processes and methods as documented in SEBoK and 15288. SEBOK is the guide to the systems engineering body of knowledge; 15288 is the ISO/IEC/IEEE standard 15288:2015 for system engineering processes. Examples and case studies will be used to illustrate processes, methods, and techniques for developing purposefully engineered systems. In addition, the problems that arise when systems engineers and software engineers work together will be covered, as will approaches that can be used to mitigate those problems. This presentation is intended for those who are, or will be, involved in development or modification of a multidisciplinary engineered system and others who wish to learn more about systems engineering when software is a system element.

Futurist Gerd Leonhard has argued that we are at a crossroads. We have to decide what form our technology will take in the coming decades. Will we change it, or will it change us? If things continue in their current direction, it is our ever more powerful technology that will shape us – the machines will control their creators. Already we are seeing the psychological and physical effects of constant connection to the internet.

Neuroscience has focused on the detailed implementation of computation, studying neural codes, dynamics and circuits. In machine learning, however, artificial neural networks tend to eschew precisely designed codes, dynamics or circuits in favor of brute force optimization of a cost function, often using simple and relatively uniform initial architectures. Two recent developments have emerged within machine learning that create an opportunity to connect these seemingly divergent perspectives. First, structured architectures are used, including dedicated systems for attention, recursion and various forms of short- and long-term memory storage. Second, cost functions and training procedures have become more complex and are varied across layers and over time.

Public perception of A.I. and robots has changed often in the last 100 years. A.I. robots have been represented in pop culture as both friendly helpers like Wall-E, and sentient computer killers like HAL 9000. But now that actual homes and automobiles run on smart technology, it's no longer just pop culture. As robots are starting to look an awful lot like humans, science fiction is starting to look a lot less like fiction. If true A.I. (i.e. a machine/robot as smart and with behavior capabilities as skillful and flexible as ours) becomes a reality, is a world where humans have been replaced as dominant species nigh?

Nvidia Corp., a maker of graphics chips frozen out of Apple Inc. computers, has posted job listings that indicate a better relationship with the world's most valuable technology company. Current Mac computers feature graphics chips from Advanced Micro Devices Inc. Nvidia, which is the leading manufacturer of high-end graphics chips used in gaming machines, hasn't been an option for multiple generations of computer models from Cupertino, California-based Apple. Nvidia, in a job ad for a software engineer, said a successful applicant will "help produce the next revolutionary Apple products." The role would require "working in partnership with Apple" and writing code that will "define and shape the future"' of graphics-related software on Macs. There are three current job listings on Nvidia's database referencing Apple, with the latest one appearing last week.

History does not always make things easy for geniuses. When John McCarthy (1927-2011) was born in Boston on the eve of the Great Recession to a humble family of European immigrants, little seemed to presage that this child prodigy was to become a worthy successor to Alan Turing. The delicate health of John's little brother led the McCarthy family, who roamed the country in search of work opportunities, to settle in Los Angeles. It was there that John, a teenager already outstanding in mathematics, came into contact with the California Institute of Technology, Caltech, and taught himself college level mathematics after asking for their used textbooks. The future father of artificial intelligence tried to study while also working as a carpenter, fisherman and inventor (he devised a hydraulic orange-squeezer, among other things) to help his family. When he officially entered Caltech to study mathematics, he had already studied so much on his own that he was allowed to skip the first two courses.

Seven out of ten corporate executives say they are making significantly more investments in artificial intelligence (AI) than just two years ago, according to Accenture's recent Technology Vision survey. And more than half say they plan to use machine learning and embedded AI solutions extensively. The race toward a digital future has begun and within the next five years, mastering the impact of this technology on future strategy will be a critical task for every CEO. Computational speed, machine learning and natural user interfaces have all advanced to the point where computers can do jobs that, previously, only humans could do. Intelligent digital labor is set to spark a radical change in labor dynamics, with research from market analyst firm, Gartner, suggesting that by 2030, virtual talent spending will exceed 10 percent of human staff costs.