A self-driving Uber car that struck and killed an Arizona woman wasn't able to recognize that pedestrians jaywalk, federal safety investigators revealed in documents released earlier this week. Elaine Herzberg, 49, died after she was hit in March 2018 by a Volvo SUV, which had an operator in the driver's seat and was traveling at about 40 mph in autonomous mode at night in Tempe. The fatal accident came as a result of the automated Uber's not having "the capability to classify an object as a pedestrian unless that object was near a crosswalk," said one of the documents released by the National Traffic Safety Board, or NTSB. Because the car couldn't recognize Herzberg as a pedestrian or a person -- instead alternating between classifications of "vehicle, bicycle, and an other" -- it couldn't correctly predict her path and concluded that it needed to brake just 1.3 seconds before it struck her as she wheeled her bicycle across the street a little before 10 p.m. Uber told the NTSB that it "has since modified its programming to include jaywalkers among its recognized objects," but other concerns were also expressed in NTSB's report. Uber had disabled the emergency braking system, relying on the driver to stop in this situation, but the system wasn't designed to alert the operator, who "intervened less than a second before impact by engaging the steering wheel," the documents said.
Ecopia is creating the first HD Map of Waterloo Region. Today, drivers use maps for way-finding and to generally orientate themselves with their surroundings, but as the task of driving shifts from the in-car driver to in-vehicle automation, the role of digital maps shifts significantly. These next generation maps for machines come in the form of a highly accurate and realistic representation of the road, generally referred to as high-definition (HD) maps. The base layers of the Waterloo Region HDMap, created by Ecopia's Global Feature Extraction services, offers a highly accurate and highly attributed representation of the road, including attributes such as lane model, traffic signs, road furniture and lane geometry, as autonomous vehicles need very different maps from those that are currently used in today's navigation systems. HDMaps of Waterloo Region will be available to SMEs and academia on a platform hosted and developed by Ecopia.
Is there anything today that can't possibly be done by Artificial Intelligence? From self-driving cars, 3D printing, sex robots that can breathe, and many other AI innovations, AI can do just about everything. To that end, researchers from Pennsylvania healthcare provider, Geisinger have trained an AI to predict which patients are at risk of dying within a course of a year, reports New Scientist. SEE ALSO: Chrome's New Feature Uses AI To Describe Images For Blind And Low-Vision Users Artificial Intelligence can reportedly determine when a person will die based on their heart test results, even if these results look normal to doctors. Dr. Brandon Fornwalt at the healthcare provider, Geisinger, trained the AI with examining 1.77 million electrocardiogram (ECG) results from almost 400,000 people to predict patterns that signal towards future cardiac issues.
Try zeroing in on an orange. While the human brain may correctly identify the images, a machine might mistake them for a missile or jaguar, says Chaz Firestone, assistant professor in the Department of Psychological and Brain Sciences. Those mistakes might seem comical at face value, but could prove deadly if a self-driving car doesn't recognize a person in its path, for example. Or when we begin relying more on automated radiology to screen for anomalies like tumors or tissue damage. "Most of the time, research in our field [of artificial intelligence] is about getting computers to think like people," says Firestone.
"There is but one truly serious question in philosophy, and that is suicide," wrote Albert Camus in The Myth of Sisyphus. This is equally true for a human navigating an absurd existence and an artificial intelligence navigating a morally insoluble situation. As AI-powered vehicles take the road, questions about their behavior are inevitable -- and the escalation to matters of life or death equally so. This curiosity often takes the form of asking whom the car should steer for should it have no choice but to hit one of a variety of innocent bystanders. There are a number of reasons this question is a silly one, yet at the same time a deeply important one.
Machine learning & Artificial Intelligence Top Training center in Bangalore ML & AI Coaching Center in Bangalore Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI. The field of Artificial Intelligence (ai systems) encompasses computer science, natural language processing, math, psychology, neuroscience, data science, machine learning and many other disciplines.
Artificial Intelligence and Machine Learning have empowered our lives to a large extent. The number of advancements made in this space has revolutionized our society and continue making society a better place to live in. In terms of perception, both Artificial Intelligence and Machine Learning are often used in the same context which leads to confusion. AI is the concept in which machine makes smart decisions whereas Machine Learning is a sub-field of AI which makes decisions while learning patterns from the input data. In this blog, we would dissect each term and understand how Artificial Intelligence and Machine Learning are related to each other.
Recently, students on the Peiyangyuan Campus of Tianjin University (TJU) caught their first glimpse of a little blue and white self-driving car moving leisurely around the campus. If students blocked its way, it would automatically slow down and brake. Actually, the driverless vehicle is being used for express delivery and was recently put into use in mid-October. Despite its budding appearance, it is the latest product from Alibaba's Cainiao E.T. Logistics Laboratory, which holds the leading edge in international autopilot capability. The Cainiao unmanned vehicle is moving on the Peiyangyuan Campus of Tianjin University.
We have now entered the era of artificial intelligence. In just a few years, the number of applications using AI has grown tremendously, from self-driving cars to recommendations from your favourite streaming provider. Almost every major research field is now using AI. Behind all this, there is one constant: the reliance, in one way or another, on deep learning. Thanks to its power and flexibility, this new subset of AI approach is now everywhere, even in ecology we show in'Applications for deep learning in ecology'.
Here's a look at industry specific companies that utilise various forms of artificial intelligence to solve some really interesting and particular problems for different markets. If you want to be included in any of the list don't forget to comment below. If you use Apple News or similar simple visit the site on a web browser to make comments. Imagia -- helps detect changes in cancer early Kuznech -- computer vision products range Lunit Inc. -- a range of medical imaging software Zebra Medical Vision -- medical imaging to help physicians and practitioners Aerial Achron -- automated UAV operations Airware -- drones for industrial purposes Alive.ai Developers, Studios and Consultants (only a few listed) Aitia Amplify Applied AI Blindspot Solutions Cogent Crossing Minds DSP Expert Systems Explosion Minds.ai