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Moving beyond employee surveillance in transportation

ZDNet

Greg Nichols covers robotics, AI, and AR/VR for ZDNet. A full-time journalist and author, he writes about tech, travel, crime, and the economy for global media outlets and reports from across the U. But when it comes to one sector, commercial driving, AI vision seems to be drastically reducing one of the most astounding examples of employee surveillance today. Literally, someone is always watching: That's the state of affairs in the commercial driving sector, where many advanced camera-based safety systems like video telematics and ADAS (Advanced Driver Assistance Systems) live stream 24-hour views into the vehicle. The predictable result is that professional drivers often have an unwelcome sense that someone is always watching.


Researchers use artificial intelligence to predict road user behavior - Actu IA

#artificialintelligence

For an autonomous car to drive safely, being able to predict the behavior of other road users is essential. A research team at the Massachusetts Institute of Technology's CSAIL, along with researchers at the Institute for Interdisciplinary Information Sciences (IIIS) at Tsinghua University in Beijing, have developed a new ML system that could one day help driverless cars predict in real time the upcoming movements of nearby drivers, cyclists and pedestrians. They titled their study, " M2I: From Factored Marginal Path Prediction to Interactive Prediction." Qiao Sun, Junru Gu, Hang Zhao are the IIIS members who participated in this study while Xin Huang and Brian Williams represented MIT. Humans are unpredictable, which makes predicting road user behavior in urban environments de facto very difficult.


Various Challenges for Applying Machine Learning in Healthcare

#artificialintelligence

Machine Learning is being used in many industries such as automobile, manufacturing, and retail industries. With the development of machine learning and deep learning algorithms, there are a huge number of useful predictions such as predicting the stock prices, house prices and loan default prediction. Furthermore, there is data available in different formats that could be used for machine learning predictions. As the data keeps growing, there is a lot of scope for development in the field of machine learning, and predictions are going to get better and better in the future. One of the interesting applications of machine learning is in the field of healthcare.


Nearest-neighbor missing visuals revealed

#artificialintelligence

The unsupervised K- Nearest Neighbour (KNN) algorithm is perhaps the most straightforward machine learning algorithm. However, a simple algorithm does not mean that analyzing the results is equally simple. As per my research, there are not many documented approaches to analyzing the results of the KNN algorithm. In this article, I will show you how to analyze and understand the results of the unsupervised KNN algorithm. I will be using a dataset on cars.


AI and machine learning has its own trolley problem debate

#artificialintelligence

Advances in robotics mean autonomous vehicles, industrial robots and medical robots will be more capable, independent and pervasive over the next 20 years. Eventually, these autonomous machines could make decision-making errors that lead to hundreds of thousands of deaths, which could be avoided if humans were in the loop. Such a future is reasonably frightening but more lives would be saved than lost if society adopts robotic technologies responsibly. Robots aren't "programmed" by humans to mimic human decision-making; they learn from large datasets to perform tasks like "recognize a red traffic light" using complex mathematical formulas induced from data. This machine learning process requires much more data than humans need. However, once trained, robots would outperform humans in any given task and AI and robotics have dramatically improved their performance over the past five years through machine learning.


Hyundai will invest $5 billion toward US manufacturing and innovation

Engadget

Hyundai will allocate an additional $5 billion toward investments in the US, the automaker announced on Sunday. The funds will support the company's work in electric vehicles, robotics, air taxis, self-driving cars and artificial intelligence. The announcement follows the recent news that Hyundai plans to build a $5.54 billion electric vehicle plant in Georgia. With that facility included, the automaker intends to invest $10 billion in the US by 2025. Some of the money will go toward supporting Boston Dynamics, which Hyundai acquired in 2021.


Could Brands Like Tesla Lure You Into Buying a Driverless Car?

#artificialintelligence

Tesla's founder Elon Musk said back in 2013: Self-driving cars are the natural extension of active safety and obviously something we think we should do. Fully-autonomous vehicles (AV) are no longer a technology of the future. Established and emerging manufacturers have embarked on a journey to produce the most reliable driverless cars to compete in a growing market. But people still don't trust AVs are safe, despite potential benefits of fuel efficiency, reduced emissions and improve mobility. We study the power of brands. Our research found companies can take advantage of their brand reputation to encourage consumers to adopt driverless cars.


BlackBerry, Tesla and Autonomous Car Safety

#artificialintelligence

At BlackBerry's analyst summit this week, a great deal of time was spent on the company's secure QNX operating system, its IVY platform for software management on cars, and other tools and utilities designed for the next generation of personal transportation. This conversation can't happen soon enough. A growing concern of mine is that automobile companies don't yet seem to fully understand the risk they are taking with platforms that aren't secure enough for products tied to human transportation and safety. Having someone hack your phone or PC is bad, but having someone hack your car could be deadly. So when the industry is talking about putting apps in cars, safety and security should be a far higher priority for many of the automotive OEMs than it seems to be.


Valeo says next-gen lidar can enable Level 4

#artificialintelligence

A recent Valeo test vehicle in Tokyo demonstrated how Scala lidars and a front camera, combined with vehicle-to-infrastructure communication, could autonomously steer through crowded boulevards, thread between lumbering trucks and zipping passenger vehicles, while navigating pedestrians. The system is Level 4-capable but operates in Level 2 mode during public testing, with a Valeo engineer always at the ready to take control. The self-driving system had its faltering moments, usually while negotiating scenarios that require bending traffic rules -- such as leaving a lane to go around idling trucks or bicyclists. And the steering and braking aren't always as smooth as would be done with a human touch. Valeo engineers say that coming versions will better address such borderline scenarios.


Apple car: design, self-driving technology and what we know so far

#artificialintelligence

Apple has arguably changed our lives more than any other company in the world during the past two decades or so. But aside from its digital devices such as iPhones, laptops, watches and operating systems, is there another direction it could go? The somewhat tentative answer to that has been transport, in the form of electric self-driving vehicles. Is Apple gearing up to challenge electric vehicle market leader Tesla, and what progress has been made so far? An Apple-branded car has been mooted for some years now, with sporadic reports of progress being made.