Artificial intelligence (AI) is changing society as profoundly as the steam engine and electricity have done. But unlike past technological revolutions, the AI revolution offers a unique chance to improve lives without opening up and exacerbating global inequalities. That will require widening of the locations where AI is done. The vast majority of experts are in North America, Europe and Asia. Africa, in particular, is barely represented.

On Friday, Google released two papers that were published in the journals Archives of Pathology and Laboratory Medicine and The American Journal of Surgical Pathology. The first paper set out to show that the algorithm could be used to pick up cancer cells on the tissue images it was presented. In addition to looking at the slides from the Netherlands, the algorithm also had to look at 108 slides from another laboratory.

Computer scientists at TU Wien (Vienna) are improving artificial intelligence by drawing inspiration from biology. The new approaches achieve amazing results with surprisingly little effort. A naturally grown brain works quite differently than an ordinary computer program. It does not use code consisting of clear logical instructions, it is a network of cells that communicate with each other. Simulating such networks on a computer can help to solve problems which are difficult to break down into logical operations.

Last Wednesday, Oct. 17, traders on the floor of the New York Stock Exchange (NYSE) were treated to a first-ever experience: A collaborative robot (cobot) rang the day's closing bell. The cobot was Universal Robots' UR5e with a two-finger gripper from Robotiq. This particular ceremony celebrated the five-year anniversary of ROBO Global, the first-ever robotics, automation, and AI index. Having launched in October 2013, it currently invests into more than 80 companies across the globe, including 12 subsectors ranging from manufacturing to healthcare. Despite the sudden departure of Rethink Robotics, which was one of the leaders in the cobot market, the cobot industry remains the fastest-growing sector of the industrial automation market.

In 2017, over half of senior artificial intelligence (AI) professionals stated that a lack of qualified personnel in their field is the single biggest barrier to AI implementation across businesses. As more and more companies choose to explore AI, deep learning and machine learning (ML), this knowledge gap begins to cause some serious problems. The solution may be in de-humanising ML functions by using automatic (also known as augmented or assisted) ML techniques. AI experts are costly, with a reported average annual salary of $314,000. But before you can even worry about affording an AI expert, you have to find one.

This example of programmable architecture uses lightweight materials and drones to help it adapt to environmental changes. Digital fabrication and automation is changing the way we build, allowing for cutting-edge concepts to take form through computer-aided design tools and integrating robotics into building techniques. Three graduate students over at University of Stuttgart's Institute for Computational Design and Construction (ICD) and Institute of Building Structures and Structural Design (ITKE) recently unveiled a modular architectural canopy that can be reconfigured in real-time, using drones. Dubbed Cyber Physical Macro Material, the 2.5-metre (8.2-foot) high canopy is designed as a "new dynamic (and intelligent) agile architecture for public spaces," which can respond to weather conditions. Built with lightweight carbon fibre filament, magnets and a variety of sensors and processors, the canopy demonstrates the possibility of'live' construction processes, facilitated by unmanned aerial vehicles (UAVs).

BURNHAM, United Kingdom--(BUSINESS WIRE)--Oct 22, 2018--Large-scale organisations are becoming increasingly open about the implementation of artificial intelligence (AI), robotic and automation technologies, according to research announced today by Redwood Software. Analysing the 2017/2018 annual reports of all 100 publically listed companies, 39 per cent mentioned the use of automation across the business, 34 per cent cited AI and 21 per cent of the reports nodded to the use of robotics. In comparison to last year's results, mentions of robotics were up 162 per cent, while references to AI more than tripled, highlighting a 386 per cent increase year-on-year. While all three technologies were raised across the reports for a variety of reasons, insurance company Aviva said it was looking to both AI and robotic automation to increasingly transform the efficiency of operations, as well as transform the customer experience. Meanwhile, online British supermarket Ocado mentioned AI, robotics and automation are helping drive innovation and create a sustainable technological advantage in an increasingly competitive market.

Unmanned Aerial Vehicles (UAVs) have recently rapidly grown to facilitate a wide range of innovative applications that can fundamentally change the way cyber-physical systems (CPSs) are designed. CPSs are a modern generation of systems with synergic cooperation between computational and physical potentials that can interact with humans through several new mechanisms. The main advantages of using UAVs in CPS application is their exceptional features, including their mobility, dynamism, effortless deployment, adaptive altitude, agility, adjustability, and effective appraisal of real-world functions anytime and anywhere. Furthermore, from the technology perspective, UAVs are predicted to be a vital element of the development of advanced CPSs. Therefore, in this survey, we aim to pinpoint the most fundamental and important design challenges of multi-UAV systems for CPS applications. We highlight key and versatile aspects that span the coverage and tracking of targets and infrastructure objects, energy-efficient navigation, and image analysis using machine learning for fine-grained CPS applications. Key prototypes and testbeds are also investigated to show how these practical technologies can facilitate CPS applications. We present and propose state-of-the-art algorithms to address design challenges with both quantitative and qualitative methods and map these challenges with important CPS applications to draw insightful conclusions on the challenges of each application. Finally, we summarize potential new directions and ideas that could shape future research in these areas.

Ordinal Data are those where a natural order exist between the labels. The classification and pre-processing of this type of data is attracting more and more interest in the area of machine learning, due to its presence in many common problems. Traditionally, ordinal classification problems have been approached as nominal problems. However, that implies not taking into account their natural order constraints. In this paper, an innovative R package named ocapis (Ordinal Classification and Preprocessing In Scala) is introduced. Implemented mainly in Scala and available through Github, this library includes four learners and two pre-processing algorithms for ordinal and monotonic data. Main features of the package and examples of installation and use are explained throughout this manuscript.

We reconsider the training objective of Generative Adversarial Networks (GANs) from the mixed Nash Equilibria (NE) perspective. Inspired by the classical prox methods, we develop a novel algorithmic framework for GANs via an infinite-dimensional two-player game and prove rigorous convergence rates to the mixed NE, resolving the longstanding problem that no provably convergent algorithm exists for general GANs. We then propose a principled procedure to reduce our novel prox methods to simple sampling routines, leading to practically efficient algorithms. Finally, we provide experimental evidence that our approach outperforms methods that seek pure strategy equilibria, such as SGD, Adam, and RMSProp, both in speed and quality.