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When we think about artificial intelligence taking over human activities, this is the first thing that springs to mind. Artificial intelligence is now widely regarded as one of the most transformative technologies of our time. Technology offers immense potential for driving corporate growth, automating industrial processes, providing insightful results, and using targeted ads, among other things. Artificial intelligence's practical applications are no laughing matter. Many people have painted AI in a bad light because of the level of automation it causes.
Artificial intelligence (AI) is transforming every walk of life. Ever wondered about artificial intelligence examples that the common man is enjoying? Artificial Intelligence is a technology that has evolved so much in itself in the past few years. We can say that it has'truly' become intelligent. Artificial intelligence is a technology that makes a device smart and allows it to perform actions that simulate human beings.
John Deere is announcing the acquisition of a state-of-the-art algorithm package from artificial intelligence startup Light. For those of you wondering when driverless vehicles will truly begin to make their mark on society, the answer is: today. Up front: No, you won't be seeing green tractors rolling themselves down city streets anytime soon. But the timeline for fully autonomous farming is being massively accelerated. Today's purchase is all about John Deere's need for speed -- and accuracy, but first let's talk about rapid development.
As robots are becoming increasingly intelligent and autonomous, from self-driving cars to assistive robots for vulnerable populations, important ethical questions inevitably emerge wherever and whenever such robots interact with humans and thereby impact human well-being. Questions that must be answered include whether such robots should be deployed in human societies in fairly unconstrained environments and what kinds of provisions are needed in robotic control systems to ensure that autonomous machines will not cause humans harms or at least minimize harm when it cannot be avoided. The goal of this specialty is to provide the first interdisciplinary forum for philosophers, psychologists, legal experts, AI researchers and roboticists to disseminate their work specifically targeting the ethical aspects of autonomous intelligent robots. Note that the conjunction of "AI and robotics" here indicates the journal's intended focus is on the ethics of intelligent autonomous robots, not the ethics of AI in general or the ethics of non-intelligent, non-autonomous machines. Examples of questions that we seek to address in this journal are: -- computational architectures for moral machines -- algorithms for moral reasoning, planning, and decision-making -- formal representations of moral principles in robots -- computational frameworks for robot ethics -- human perceptions and the social impact of moral machines -- legal aspects of developing and disseminating moral machines -- algorithms for learning and applying moral principles -- implications of robotic embodiment/physical presence in social space -- variance of ethical challenges across different contexts of human -robot interaction
For years, Alphabet's Waymo and others leaders have promised autonomous vehicles are just around the bend. But that future has not arrived yet. "In one word, it's complexity," said James Peng, CEO and co-founder of Pony.ai, an autonomous vehicle company. "Every time there is a technical breakthrough, there are challenges. We have the AI, the fast computer chips, the sensors. Despite promises of life-saving, climate-change fighting, and cost-efficient driving, the reality is that "the autonomous vehicle nirvana is 10 years out," said Michael Dunne, CEO of autotech consultancy ZoZoGo. "While it's not impossible to get there, even the most advanced technologies are not there yet and used mainly in confined areas where things are predictable.
Humans may be one of the biggest roadblocks keeping fully autonomous vehicles off city streets. If a robot is going to navigate a vehicle safely through downtown Boston, it must be able to predict what nearby drivers, cyclists, and pedestrians are going to do next. Behavior prediction is a tough problem, however, and current artificial intelligence solutions are either too simplistic (they may assume pedestrians always walk in a straight line), too conservative (to avoid pedestrians, the robot just leaves the car in park), or can only forecast the next moves of one agent (roads typically carry many users at once.) MIT researchers have devised a deceptively simple solution to this complicated challenge. They break a multiagent behavior prediction problem into smaller pieces and tackle each one individually, so a computer can solve this complex task in real-time. Their behavior-prediction framework first guesses the relationships between two road users--which car, cyclist, or pedestrian has the right of way, and which agent will yield--and uses those relationships to predict future trajectories for multiple agents.
They drove the heavily instrumented ATV aggressively at speeds up to 30 miles an hour. They slid through turns, took it up and down hills, and even got it stuck in the mud -- all while gathering data such as video, the speed of each wheel and the amount of suspension shock travel from seven types of sensors. The resulting dataset, called TartanDrive, includes about 200,000 of these real-world interactions. The researchers believe the data is the largest real-world, multimodal, off-road driving dataset, both in terms of the number of interactions and types of sensors. The five hours of data could be useful for training a self-driving vehicle to navigate off road.
Drones are everywhere these days, filming dramatic reveals and awe-inspiring scenery for social media platforms. The problem is, they're not exactly approachable for beginners who have only ever used a smartphone. Last month, Snap debuted the $230 Pixy drone exactly for those people. It requires very little skill and acts like a personal robot photographer to help you produce nifty aerial shots. You don't need to pilot the Pixy.
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The global Automotive Cybersecurity Market size is projected to grow from USD 2.0 billion in 2021 to USD 5.3 billion by 2026, at a CAGR of 21.3%. Increasing incidents of cyber-attacks on vehicles and massive vehicles recalls by OEMs have increased awareness about automotive cybersecurity among OEMs globally. Moreover, increasing government mandates on incorporating several safety features, such as rear-view camera, automatic emergency braking, lane departure warning system, and electronic stability control, have further opened new opportunities for automotive cybersecurity service providers globally. As a result, there are various start-ups present in the automotive cybersecurity ecosystem. Government initiatives toward building an intelligent transport system have also further escalated the demand for cybersecurity solutions all over the world.