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Behind the scenes of Waymo's worst automated truck crash – TechCrunch - Channel969

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Probably the most critical crash thus far involving a self-driving truck may need resulted in solely average accidents, but it surely uncovered how unprepared native authorities and legislation enforcement are to take care of the brand new expertise. On Might 5, a Class 8 Waymo By way of truck working in autonomous mode with a human security operator behind the wheel was hauling a trailer northbound on Interstate 45 towards Dallas, Texas. At 3:11 p.m., simply outdoors Ennis, the modified Peterbilt was touring within the far proper lane when a passing truck and trailer combo entered its lane. The motive force of the Waymo By way of truck informed police that the opposite semi truck continued to maneuver into the lane, forcing Waymo's truck and trailer off the roadway. She was later taken to a hospital for accidents that Waymo described in its report back to the Nationwide Freeway Visitors Security Administration as "average."


Open-sourcing simulators for driverless cars

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"You put a car on the road which may be driving by the letter of the law, but compared to the surrounding road users, it's acting very conservatively. This can lead to situations where the autonomous car is a bit of a fish out of water," said Motional's Karl Iagnemma. Autonomous vehicles have control systems that learn how to emulate safe steering controls in a variety of situations based on real-world datasets of human driving trajectories. However, it is extremely hard to program the decision-making process given the infinite possible scenarios on real roads. Meanwhile, real-world data on "edge cases" (such as nearly crashing or being forced off the road) are hard to come by.


Why Self-Driving Cars Shouldn't Be Allowed

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Why Self-Driving Cars Shouldn't Be Allowed Why Self-Driving Cars Shouldn't Be Allowed Why should driverless cars be banned? Why are self-driving cars unsafe? Why are driverless cars safe?


Will AI Help or Hinder the Battle Against Climate Change?

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As the world fights climate change, will the increasingly widespread use of artificial intelligence (AI) be a help or a hindrance? In a paper published this week in Nature Climate Change, a team of experts in AI, climate change, and public policy present a framework for understanding the complex and multifaceted relationship of AI with greenhouse gas emissions, and suggest ways to better align AI with climate change goals. "AI affects the climate in many ways, both positive and negative, and most of these effects are poorly quantified," said David Rolnick, Assistant Professor of Computer Science at McGill University and a Core Academic Member of Mila – Quebec AI Institute, who co-authored the paper. "For example, AI is being used to track and reduce deforestation, but AI-based advertising systems are likely making climate change worse by increasing the amount that people buy." The paper divides the impacts of AI on greenhouse gas emissions into three categories: 1) Impacts from the computational energy and hardware used to develop, train, and run AI algorithms, 2) immediate impacts caused by the applications of AI - such as optimizing energy use in buildings (which decreases emissions) or accelerating fossil fuel exploration (which increases emissions), and 3) system-level impacts caused by the ways in which AI applications affect behaviour patterns and society more broadly, such as via advertising systems and self-driving cars.


3D Machine Learning 201 Guide: Point Cloud Semantic Segmentation

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Having the skills and the knowledge to attack every aspect of point cloud processing opens up many ideas and development doors. It is like a toolbox for 3D research creativity and development agility. And at the core, there is this incredible Artificial Intelligence space that targets 3D scene understanding. It is particularly relevant due to its importance for many applications, such as self-driving cars, autonomous robots, 3D mapping, virtual reality, and the Metaverse. And if you are an automation geek like me, it is hard to resist the temptation to have new paths to answer these challenges! This tutorial aims to give you what I consider the essential footing to do just that: the knowledge and code skills for developing 3D Point Cloud Semantic Segmentation systems. But actually, how can we apply semantic segmentation? And how challenging is 3D Machine Learning? Let me present a clear, in-depth 201 hands-on course focused on 3D Machine Learning.


Technology helps self-driving cars learn from own memories

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Researchers at Cornell Bowers CIS and the College of Engineering have produced three concurrent research papers on autonomous vehicles’ ability to create “memories” of previous experiences and use them in future navigation.


La veille de la cybersécurité

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A team of researchers at Cornell University has developed a new method enabling autonomous vehicles to create "memories" of previous experiences, which can then be used in future navigation. This will be especially useful when these self-driving cars can't rely on sensors in bad weather environments. Current self-driving cars that use artificial neural networks have no memory of the past, meaning they are constantly "seeing" things for the first time. And this is true regardless of how many times they've driven the exact same road. Killian Weinberger is senior author of the research and a professor of computer science.


New Method Helps Self-Driving Cars Create 'Memories'

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A team of researchers at Cornell University has developed a new method enabling autonomous vehicles to create "memories" of previous experiences, which can then be used in future navigation. This will be especially useful when these self-driving cars can't rely on sensors in bad weather environments. Current self-driving cars that use artificial neural networks have no memory of the past, meaning they are constantly "seeing" things for the first time. And this is true regardless of how many times they've driven the exact same road. Killian Weinberger is senior author of the research and a professor of computer science.


How to Use Artificial Intelligence in Talent Acquisition Process? - Wisestep

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Artificial Intelligence (AI) is the new buzzword, and we are constantly hearing or reading about Artificial Intelligence in the news, like the development of self-driving cars or driverless cars. Anyone interacting with a chatbot on any website is an AI tool. But did you ever wonder how exactly artificial intelligence in talent acquisition is used now-a-days? Before deep-diving into how AI plays a major role in the recruitment industry, let's learn about talent acquisition. Gartner defines Talent Acquisition is the process of identifying organizational staffing needs, recruiting qualified candidates, and selecting the candidates best suited for the available positions. The stakeholders include recruiters, HR managers, hiring managers, and top-level executives. The team's goal is to identify, acquire, assess, and hire candidates to fill open positions within the organization. For the majority of organizations, the talent acquisition team will be part of the HR team. In a few larger organizations, talent acquisition is a different team that collaborates with the HR team.


Dubai begins mapping streets for driverless car use

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Dubai Municipality vehicles such as this one will be used to digitally map the city's streets.