Now we know what to call it, that vast, disturbing collection of worries about artificial intelligence and the myriad of threats we imagine, from machine bias to lost jobs to Terminator-like robots: "Machine behaviour." That's the term that researchers at the Massachusetts Institute of Technology's Media Lab have proposed for a new kind of interdisciplinary field of study to figure out how AI evolves, and what it means for humans. The stakes are high because there is lots of potential for human ability to be amplified by algorithms, but also lots of peril. Commentators and scholars, they write, "are raising the alarm about the broad, unintended consequences of AI agents that can exhibit behaviours and produce downstream societal effects -- both positive and negative -- that are unanticipated by their creators." There is "a fear of the potential loss of human oversight over intelligent machines," and the development of "autonomous weapons" means that "machines could determine who lives and who dies in armed conflicts."
Forward Vehicle Collision Warning (FCW) is one of the most important functions for autonomous vehicles. In this procedure, vehicle detection and distance measurement are core components, requiring accurate localization and estimation. In this paper, we propose a simple but efficient forward vehicle collision warning framework by aggregating monocular distance measurement and precise vehicle detection. In order to obtain forward vehicle distance, a quick camera calibration method which only needs three physical points to calibrate related camera parameters is utilized. As for the forward vehicle detection, a multi-scale detection algorithm that regards the result of calibration as distance priori is proposed to improve the precision. Intensive experiments are conducted in our established real scene dataset and the results have demonstrated the effectiveness of the proposed framework.
Over more than a decade, self-driving vehicles have logged millions of miles on roadways across the globe. Despite all that driving, researchers say, the machines are still unable to replicate the sophisticated problem-solving and spontaneity human drivers employ each time they get behind the wheel. In their ambitious attempt to create an autonomous car service, companies like Waymo run their software through millions of potential scenarios, create three-dimensional maps using lasers, and outfit their vehicles powerful sensors like LIDAR that can cost more than the cars they guide. The goal is to prepare the vehicle for anything it might encounter before it touches the road, creating a system of rules that predetermine behavior. Now, an upstart British company called Wayve claims to have created a self-driving car using technology that almost sounds Stone Age compared to the competition.
Hackers have demonstrated how they could trick a Tesla Model S to enter into the wrong lane by using a method called "adversarial attack," a way of manipulating a machine learning (ML) model. The Tesla Autopilot recognises lanes and assists control by identifying road traffic markings. The researchers from the Keen Security Lab of Chinese tech giant Tencent showed that by placing interference stickers on the road, the autopilot system could be fed information that would force it to make an abnormal judgement and make the vehicle enter a wrong lane. "In this demonstration, the researchers adjusted the physical environment (e.g. "This is not a real world concern given that a driver can easily override autopilot at any time by using the steering wheel or brakes and should be prepared to do so at all times," the spokesperson said.
Facial recognition systems developed for self-driving cars are better at identifying the faces of white people than those of darker skin tones, a study has revealed. Researchers say the inherent racism of these systems likely stems from a lack of dark-skinned individuals included in the training of the tech. The study found databases behind facial recognition technology being built for autonomous cars are up to 12 per cent worse at spotting people with darker skin. On average, the technology is 4.8 per cent more accurate at correctly spotting light-skinned individuals. A system was used with skin tones ranging from one to six, with a higher number linked to darker skin.
Finding the best way to get around a busy city is no easy task. At MWC 2019, Seat and IBM announced Mobility Advisor, which uses Watson artificial intelligence (AI) to work out the best way to reach your destination – whether it's a train, ride-hailing service or an electric scooter. The tool's suggestions will take into account traffic reports, weather forecasts, and any events happening in the city that day, so you won't get caught in the rain riding a hire bike, or reach a train station at the same time as a crowd of sports fans. Mobility Advisor is currently in development, and is intended to run as a mobile app on 4G and 5G networks. Over time, it will learn your preferences and make personalized recommendations based on the way you like to travel.
Advances in artificial intelligence (AI) software and hardware are giving rise to a multitude of smart devices that can recognize and react to sights, sounds, and other patterns--and do not require a persistent connection to the cloud. These smart devices, from robots to cameras to medical devices, could well unlock greater efficiency and effectiveness at organizations that adopt them. In some industries, smart machines may well help expand existing markets, threaten incumbents, and shift the way revenue and profits are apportioned among industry players. Rapid strides in technology and the growing investment in AI innovation signal how fast AI deployment is moving. Advances in software and hardware are propelling AI outside of the data center into devices and machines we use in our work and our everyday lives.
The mention of the words Artificial Intelligence (AI) conjures up science fiction-like images in the minds of many people, but it is becoming a very real part of day to day life without us even realising it. AI is and has been making a lasting impression on a number of key industries, not only streamlining otherwise tedious processes but also changing the way business is conducted on a much larger scale. Elnur Amikishiyev via 123RF 1. Education AI will likely be used predominantly to take the labour out of admin during the early stages of implementation, taking over things like grading assignments, recording marks, and any other computational tasks where machines could surpass people. The human element, however, will remain a constant in the form of teachers who will have greater freedom to focus on students' individual needs and finding ways to fill gaps in learning. Most notably AI is used to mark multiple-choice tests, but advancements in machine learning could soon enable it to evaluate and efficiently mark written responses.
Apple's secretive efforts to develop a self-driving car -- its so-called "Project Titan" -- have taken a hard turn in 2019 after it emerged that the iPhone-maker has reassigned 200 employees previously involved in its development. That's according to CNBC which, citing sources, reported that a portion of the 200 staff were moved to other projects inside Apple, while others -- and it isn't clear how many -- were let go altogether. The news was enough to prompt Apple to respond with a confirmation that included a rare mention of its automotive ambitions. "We have an incredibly talented team working on autonomous systems and associated technologies at Apple. As the team focuses their work on several key areas for 2019, some groups are being moved to projects in other parts of the company, where they will support machine learning and other initiatives, across all of Apple. We continue to believe there is a huge opportunity with autonomous systems, that Apple has unique capabilities to contribute, and that this is the most ambitious machine learning project ever," a spokesperson said.
Advancements in artificial intelligence continue to develop on industries like aviation, manufacturing and technology, and others. This is because, the offerings of AI, machine learning and deep learning can help companies to become more efficient. But one industry which is witnessing dramatic change is the automotive sector. AI is revolutionizing this industry and has entirely new ways for people to get around and will also impact the way traffic will be maintained in the cities. The attempts to create driverless cars are gaining promising with the availability of advanced technologies, notably AI.