Howard Forman is a professor of radiology, economics, public health and management at Yale University. My clinical specialty of radiology revolves around image interpretation. We interpret X-rays, MRIs, PET scans, ultrasound, computed tomography and other diagnostic images. Though two people may appear similar, no two diagnostic images are ever identical. Every liver, brain, or gall bladder is a bit different.

Wimbledon's famous All England Lawn Tennis and Croquet Club, bedecked in the iconic purple and green and overflowing with Ivy, flowers and generations of tradition might, at first look, seem like a strange place to be contemplating how technology and sport have become such familiar bedfellows. And yet this most traditional of sporting environs is embracing technology on a whole host of levels, whilst maintaining the dignity and history as the home of tennis. Mick Desmond, the AELTC's commercial and media director explained that there was an "alchemy" in the balance of seeking what's new and innovative whilst understanding and enhancing what makes this most famous of tennis tournaments great. "We look at what's true in our brand in terms of the all-white dress code, the grass courts and the strawberries and cream, and as an event we do transcend the sport. We look at the things that make us special and we amplify them. "But in the digital realm particularly we can be very innovative and push the boundaries… whilst still enveloped in a Wimbledon way." This intent, according to former England cricketer and academic, Ed Smith, is not as new as many might think - insisting that embracing the latest technology has actually been at the heart of sport's development. "If you think about sport as we now play it, it's rested on a series of technological evolutions," he insists, taking us on a whirlwind tour of innovation, from lawnmowers to the iPhone via vulcanized rubber and, of course the impact of sport in the media. "The year 1923 brought the moment that changed sport forever - a radio station in New York saying we're going to put every second of the World Series on air.

"Really, we're building collision avoidance for industrial drones," said Alexander Harmsen, CEO and co-founder of Iris Automation. "We see this huge need for industrial drones for mining exploration, pipeline inspection, agricultural surveying, forestry, or even package delivery." Without a way to avoid mid-air collisions, drones risk crashing into a Cessna, a flock of geese or a 747. Worst case scenario: a drone gets sucked into a jet engine causing catastrophic engine failure as high-velocity bits of metal penetrate fuel tanks, hydraulic lines and the cabin. Iris Automation's solution is an AI computer that blends real-time images and 3D maps to track incoming objects.

Google has announced that it had acquired a visual recognition machine learning technology start-up, Moodstocks for an undisclosed amount. Incorporated in 2009, the French start-up originally introduced on-device image recognition in 2012, which enables smartphones to automatically recognize the content their camera detects. With the recent acquisition, Google had made a substantial investment in the machine learning technologies since accurate object recognition is one of the difficult problems for machine learning. For the past 3 years, the small team of researchers and engineers based in Google has taken advantage of deep learning to extend the reach of their object recognition algorithms, licensing machine-readable executable object code including the launch of a software development kit for OEMs to enable them to embed the code into their products. The Moodstocks API, meanwhile, enable users to integrate visual search into their applications in addition to analyzing the images related to color, shape, and texture.

Introduction This is the first in a series of posts exploring artificial neural network (ANN) implementations. The purpose of the article is to help the reader to gain an intuition of the basic concepts prior to moving on to the algorithmic implementations that will follow.

People on the streets of London, Düsseldorf, Bern, and other European cities will soon see robots rolling along the sidewalk to deliver prepared food, parcels, and retail goods to nearby customers. Starship Technologies, a robotics company launched two years ago by Skype cofounders Ahti Heinla and Janus Friis, says it plans to announce on Wednesday four industry partners that will be testing its delivery robots: Just Eat, a European food delivery company; Hermes, a German parcel delivery company (not to be confused with the similarly named French luxury goods maker); Metro Group, a major German retailer; and Pronto.co.uk, a food delivery service based in London. The four companies later this month will begin tests with real customers to assess the viability of automated product deliveries. A similar program is being developed for cities in the US. Starship has been conducting proof-of-concept tests without real customers for the past nine months.

Artificial intelligence that can understand and answer any work-related question it is asked has been made available in the UK for the first time. The computer software, called Starmind, uses machine learning to understand queries, then source answers from previous staff conversations on a subject or track down experts within the company who are able to help. Its creators refer to it as'brain technology', adding its aim is to become a central knowledge bank within any company, an instant database of information that can be accessed by anyone. AI software which understands and answers work-related questions has been made available in the UK. Starmind is an artificial intelligence software for the workplace, designed in Switzerland.

For more than a decade the company formerly known as Google, latterly rebranded Alphabet to illustrate the full breadth of its A to Z business ambitions, has engineered an annually increasing revenue generating empire which last year pulled in 75 billion. And it's done this mostly by mining user data for ad targeting intel. Slice it and dice it how you like but Google's business engine needs data like the human body needs oxygen. Most of its products are thus designed to remove friction to accessing more user data; whether it's free search, free email, free cloud storage, free document editing tools, free messaging apps, a fuzzy social network that no one loves but which is somehow still hanging around, free maps, a mobile OS platform that OEMs can load onto smartphone hardware without paying a license fee… Most of what Google builds it opens to all comers to keep the data pouring in. The bits and bytes must flow.

A Tesla Model S electric vehicle is shown in San Francisco, California, U.S., April 7, 2016. How much do we really know about what so-called self-driving vehicles can and cannot do? The fatal traffic accident involving a Tesla Motors car that crashed while using its Autopilot feature offers a stark reminder that such drivers are in uncharted territory--and of the steep cost of that uncertainty. The sensor systems that enable Tesla's hands-free driving are the result of decades of advances in computer vision and machine learning. Yet the failure of Autopilot -- built into 70,000 Tesla vehicles worldwide since October 2014 -- to help avoid the May 7 collision that killed the car's sole occupant demonstrates how far the technology has to go before fully autonomous vehicles can truly arrive.

Drowsy driving is notoriously tough to detect. There's no test to prove it, the way a breathalyzer can prove someone was driving drunk. But technology to detect drowsy driving is in the works. In commercial transport, one industry is leading the way: mining. The stakes are particularly high in this field since the enormous haul trucks used in mining are several times the height of a person.