After the MRI, the images get uploaded onto a secure cloud, and EIRL begins its analysis looking for abnormalities. Each scan is then checked by a radiologist followed by a neurosurgeon. The final report, with the images, is produced within 10 days and accessible through a secure portal. The EIRL for brain aneurysms algorithm was approved by the Japanese Pharmaceutical and Medical Devices Agency (PMDA) in the category of software as a medical device in Japan in September. The algorithm is based entirely on Japanese patients, but it could be generalized to other populations, says Takahashi, though she notes that their group is looking into studies showing that the Japanese anatomy of brain vessels may vary slightly from other ethnic groups and whether the algorithm would therefore need to be validated in other populations.

This article represents my personal views and not those of Digital Catapult, NHS Digital or any other organisation. In a few years time there'll be lots of AI apps to help with radiology. I mean apps deployed and approved and working [1]. This is great news: the demand for radiology is growing and there aren't enough radiologists [2]. Any tool that can prioritise the urgent cases, quickly eliminate the normal scans, highlight abnormal areas in images, help with a second opinion or assist a more junior clinician will be of great value.

A Chinese wildlife park has sparked outcry after making visitors submit to facial recognition scanning, with one law professor taking it to court. Professor Guo Bing is taking action against Hangzhou safari park, after it replaced its existing fingerprinting system with the new technology. "I [filed this case] because I feel that not only my [privacy] rights are being infringed upon but those of many others," Guo, from Zhejiang University of Sci-Tech, said according to an audio recording of an interview posted by state-run Beijing News. Guo is attempting to force the park to return the money he paid for an annual pass and highlight its misuse of data gathered by the software. A court in Fuyang has accepted his case.

Z Advanced Computing, Inc. (ZAC), an AI (Artificial Intelligence) software startup, is developing its Smart Home product line through a paid-pilot for smart appliances for BSH Home Appliances, the largest manufacturer of home appliances in Europe and one of the largest in the world. BSH Home Appliances Corporation is a subsidiary of the Bosch Group, originally a joint venture between Robert Bosch GmbH and Siemens AG. ZAC Smart Home product line uses ZAC Explainable-AI Image Recognition. ZAC is the first to apply Explainable-AI in Machine Learning. "You cannot do this with other techniques, such as Deep Convolutional Neural Networks," said Dr. Saied Tadayon, CTO of ZAC.

Human beings have always built machines, and they have always feared their own creations. And so it is with Artificial Intelligence. The current excitement mingled with fear surrounding it happened once before, in the 1960s, shortly after its invention. At the time, experts of all sorts predicted that a revolution of machine intelligence was fast approaching. People lost interest in that technology as the expectations foundered.

The evolution of autonomous vehicles illustrates to what extent many people, including any number of experts, do not understand technological development cycles, and get things seriously wrong. Click on this link, read the story and watch the video. It is from December 2018, a little less than a year ago, and chronicles the first trip in Waymo One, the autonomous vehicle transportation service created by the Alphabet subsidiary in Phoenix, Arizona, written by Andrew J. Hawkins, a journalist from The Verge. Journalists tend to cover these kinds of events better than the companies' press releases, which tend to be a bit too idealistic and perfect. Now click on this link, and again you will find a story and a video.

It has long been the stuff of science fiction but now mind-reading machines may actually be here and they may not be invasive. Researchers from the Russian corporation Neurobotics and the Moscow Institute of Physics and Technology have found a way to visualize a person's brain activity as actual images without the use of invasive brain implants. The work has the potential to enable new non-invasive post-stroke rehabilitation devices controlled by brain signals as well as novel cognitive disorder treatments. In order to do achieve such applications, neurobiologists need to understand how the brain encodes information by studying it in real-time such as when a person is watching a video. This is where the new brain-computer interface developed by the researchers comes in. Using artificial neural networks and electroencephalography, or EEG, a technique for recording brain waves via electrodes placed noninvasively on the scalp, the team was able to visualize what test subjects were looking at in videos in real-time.

There's no existing classification for what makes a company an "AI company." Unfortunately for some, AI is just a buzzword that gets tacked onto marketing materials to attract funding. Startups are leveraging AI, and while this has brought some truly innovative solutions to the market, it's also been the spawning ground of a lot of fake AI startups. One of the most telling surveys of 2019 was conducted by London-based MMC Ventures, where it was revealed that 40% of Europe's AI startups are not using any AI at all. This might seem to be a controversial finding, but for many of us who work with AI on a daily basis, it's not particularly surprising.

A test car operated by a robot crashes into a soft-sided electric vehicle. Robot-operated cars that smash into soft-sided sitting ducks, glass capillaries that help detect leaks and mountable gizmos for capturing data. Those are among the myriad testing methods on display at the recent Automotive Testing Expo in Novi, Mich. Testing for all sorts of things has always been important in the auto industry. But the advent of autonomous vehicles and advanced driver assist systems, or ADAS, has added both a new urgency and new challenges for companies who make their money conducting such tests, analyzing test data or creating testing systems.

A team of researchers at the University of Luxembourg and the University of Ontario Institute of Technology have recently proposed a new approach to design trajectories for energy-efficient unmanned aerial vehicle (UAV)-enabled wireless communications. Their paper, prepublished on arXiv, specifically focuses on cases in which an UAV acts as a flying base station (BS) to serve ground users (GSs) within some predetermined latency constraints. "Our goal is to design the UAV trajectory to minimize the total energy consumption while satisfying the RT requirement and energy budget, which is accomplished via jointly optimizing the trajectory and UAV's velocities along subsequent hops," the researchers wrote in their paper. Optimizing a UAV's trajectory and its velocities together can be somewhat difficult to achieve. To do so, the researchers developed an approach that carries out two consecutive steps. Their approach entails the use of two distinct algorithms, a heuristic search and a dynamic programming (DP) algorithm.