A group of computer vision researchers from ETH Zurich want to do their bit to enhance AI development on smartphones. To wit: They've created a benchmark system for assessing the performance of several major neural network architectures used for common AI tasks. They're hoping it will be useful to other AI researchers but also to chipmakers (by helping them get competitive insights); Android developers (to see how fast their AI models will run on different devices); and, well, to phone nerds -- such as by showing whether or not a particular device contains the necessary drivers for AI accelerators. The app, called AI Benchmark, is available for download on Google Play and can run on any device with Android 4.1 or higher -- generating a score the researchers describe as a "final verdict" of the device's AI performance. AI tasks being assessed by their benchmark system include image classification, face recognition, image deblurring, image super-resolution, photo enhancement or segmentation.

Click heads to MIT's CSAIL to check out its weird and wonderful robotic research, and visits Wimbledon to see how AI is set to transform the tennis tournament. Find us online at www.bbc.com/click

Having visited more than 40 countries, 200 cities and lived in six countries in the last 10 years as well as working in the disruptive Internet of Things (IoT) space, the subject of how to make cities smarter and more citizen friendly is of particular interest to me for both professional and personal reasons. More than half of the world's population now live in cities - and the figure will rise to more than two thirds by 2050, according to a United Nations forecast. Growing numbers of city residents put pressure on energy and water resources, transport networks, environment, national healthcare budgets as well as many more aspects of the city. In the last few weeks I have been thinking about the most important problems that the most cities around the world may face, but they can be solved or reduced by Internet of Things and artificial intelligence (AI) enabled solutions. By IoT we simply mean when objects are connected to the Internet and exchange data.

Mechanical engineering is the backbone of Germany's industry. But will it stay that way? What does digital transformation mean for engineering companies, and what role does the service business play? We spoke with Bianca Illner, Managing Director of Management Services at VDMA. Peter Gaide is a freelance writer and the editor in chief of Transformation Beats.

Researchers have developed an AI-based computing model which can count cells from histopathological cancer tumour images. A team from the University of Jyväskylä state that they have taken the first step towards developing a digital service centre based on artificial intelligence where doctors and pathologists analyse tumour tissue samples visually with the help of software. The computing model is able to determine the T-cell count in cancer tissue based on nothing but a digital image and with an error margin of a few percent, according to tests. The researchers tested the model against five 523 images of intestinal cancer tumours where previously, the T-cell count of each image was determined by histopathologists. The team found that the model was successful in 90% of cases.

The other drivers wouldn't have noticed anything unusual as the two sleek limousines with German license plates joined the traffic on France's Autoroute 1. But what they were witnessing -- on that sunny, fall day in 1994 -- was something many of them would have dismissed as just plain crazy. It had taken a few phone calls from the German car lobby to get the French authorities to give the go-ahead. But here they were: two gray Mercedes 500 SELs, accelerating up to 130 kilometers per hour, changing lanes and reacting to other cars -- autonomously, with an onboard computer system controlling the steering wheel, the gas pedal and the brakes. Decades before Google, Tesla and Uber got into the self-driving car business, a team of German engineers led by a scientist named Ernst Dickmanns had developed a car that could navigate French commuter traffic on its own. The story of Dickmann's invention, and how it came to be all but forgotten, is a neat illustration how technology sometimes progresses: not in small steady steps, but in booms and busts, in unlikely advances and inevitable retreats --"one step forward and three steps back," as one AI researcher put it. It's also a warning of sorts, about the expectations we place on artificial intelligence and the limits of some of the data-driven approaches being used today.

Information Age discusses how artificial intelligence is transforming the NHS, and its potential in other sectors too with Charles A. Taylor, founder and chief technology officer at HeartFlow. The potential for AI in healthcare is tremendous as it increasingly becomes integrated into the healthcare ecosystem. AI is transforming the way doctors deliver cost-effective, high-quality diagnostic and treatment services to their patients. For example, the technology can identify patterns and anomalies in diagnostic data from medical scans at a speed and volume that humans are simply unable to replicate. The processing power of AI has applications far beyond providing simple diagnoses.

Heat demand prediction is a prominent research topic in the area of intelligent energy networks. It has been well recognized that periodicity is one of the important characteristics of heat demand. Seasonal-trend decomposition based on LOESS (STL) algorithm can analyze the periodicity of a heat demand series, and decompose the series into seasonal and trend components. Then, predicting the seasonal and trend components respectively, and combining their predictions together as the heat demand prediction is a possible way to predict heat demand. In this paper, STL-ENN-ARIMA (SEA), a combined model, was proposed based on the combination of the Elman neural network (ENN) and the autoregressive integrated moving average (ARIMA) model, which are commonly applied to heat demand prediction. ENN and ARIMA are used to predict seasonal and trend components, respectively. Experimental results demonstrate that the proposed SEA model has a promising performance.

It is now much easier than ever before to produce videos. While the ubiquitous video data is a great source for information discovery and extraction, the computational challenges are unparalleled. Automatically summarizing the videos has become a substantial need for browsing, searching, and indexing visual content. This paper is in the vein of supervised video summarization using sequential determinantal point process (SeqDPP), which models diversity by a probabilistic distribution. We improve this model in two folds. In terms of learning, we propose a large-margin algorithm to address the exposure bias problem in SeqDPP. In terms of modeling, we design a new probabilistic distribution such that, when it is integrated into SeqDPP, the resulting model accepts user input about the expected length of the summary. Moreover, we also significantly extend a popular video summarization dataset by 1) more egocentric videos, 2) dense user annotations, and 3) a refined evaluation scheme. We conduct extensive experiments on this dataset (about 60 hours of videos in total) and compare our approach to several competitive baselines.

The capability of making explainable inferences regarding physical processes has long been desired. One fundamental physical process is object motion. Inferring what causes the motion of a group of objects can even be a challenging task for experts, e.g., in forensics science. Most of the work in the literature relies on physics simulation to draw such infer- ences. The simulation requires a precise model of the under- lying domain to work well and is essentially a black-box from which one can hardly obtain any useful explanation. By contrast, qualitative reasoning methods have the advan- tage in making transparent inferences with ambiguous infor- mation, which makes it suitable for this task. However, there has been no suitable qualitative theory proposed for object motion in three-dimensional space. In this paper, we take this challenge and develop a qualitative theory for the motion of rigid objects. Based on this theory, we develop a reasoning method to solve a very interesting problem: Assuming there are several objects that were initially at rest and now have started to move. We want to infer what action causes the movement of these objects.