Long-term investments in AI usage have sparked interest in various areas, including customer service, medical diagnostics, and self-driving cars. Because of the additional data accessible as a result of the study, better algorithms have been developed, allowing for more complicated AI systems to improve the user's experience using search engines and online translation tools. Cryptography and blockchain have made it simpler to develop these advancements since they can exchange data openly while keeping firm information private. Security is now high, but AI will raise it to a point where security breaches on online gaming websites and applications will be unheard of. Tesla isn't the only company focused on self-driving cars.
Instead of turning a traditional car into an autonomous one, the Amazon-owned self-driving car service Zoox has created its own type of autonomous vehicle without a steering wheel or front seat. Redesigning a car from the ground up also means redesigning car safety features. On Tuesday, the San Francisco-based company released its first (voluntary) safety report since revealing its electric robotaxi in December. The report highlights what the company considers more than 100 safety features not found in regular (human-driven, conventional) vehicles. Dr. Mark Rosekind, Zoox's chief safety innovation officer, broke down the key features into three categories in a recent call: driving control, redundancy (or back up in case of failure), and rider protection.
Getting your driving licence is a milestone moment for many people. You go through rigorous theory and practical tests, sometimes more than once, before you are given the privilege of being on the road. This, of course, is to ensure the safety of the driver, any passengers and other road users, writes Raina Victor of Birketts LLP. You are also aware of the consequences of driving going wrong, including that the liability for any accident falls (for the most part) on the driver. But what about car accidents that are not caused by the driver of the vehicle but the vehicle itself?
On February 25, the Shanghai Government announced its Implementation Plan for Accelerating the Development of the New Energy Automobile Industry (2021-2025). It proposes that by 2025, smart cars with conditional self-driving functionalities shall enter large-scale production, significant progress will be made to set up a standard system for testing, demonstrating smart cars. City officials noted that so far, Shanghai has opened 560 kilometers of test roads. A total of 152 vehicles from 22 companies have been issued with road test and demonstration qualifications, which make Shanghai the first amongst other Chinese cities. We know you don't want to miss any news or research breakthroughs. Subscribe to our popular newsletter Synced Global AI Weekly to get weekly AI updates.
Almost three-quarters (72%) of IT leaders are already using edge computing to provide innovative services, according to Intel. From ultra-connected autonomous cars to low-latency AR, VR and gaming: to remain competitive in the digital age, businesses will have little choice but to fully embrace the new opportunities that come with the deployment of edge computing, according to a new report published by Intel. Almost three-quarters (72%) of IT leaders are already using edge computing to provide innovative services, according to the chip giant, whether that is to create new products, open new revenue streams or boost efficiencies. "Businesses can no longer afford to ignore the edge," says the report, stressing the technology's potential to better access and understand the unprecedented amounts of data that are generated over networks every second. As the name suggests, edge technologies consist of moving the hosting of computer services to the edge of the network, so that the process happens as close as possible to the people that use the service, which significantly reduces latency.
Data science is one of the most buzzed about fields right now, and data scientists are in extreme demand. And with good reason -- data scientists are doing everything from creating self-driving cars to automatically captioning images. Given all the interesting applications, it makes sense that data science is a very sought-after career. Data science is applied in many field, including in developing self-driving cars. If you're reading this post, I'm assuming that you'd like to learn how to become a data scientist.
Every day across Australia, truckies are driving thousands of kilometres to get fresh produce from farms to markets. But what if the truck could do this job, without a driver? The NASDAQ-listed company TuSimple is celebrating a milestone, after transporting watermelons from Arizona to Oklahoma City using an autonomous truck. There were two humans in the truck during the trial -- and they did take control of the vehicle at the front and back end of the journey -- but for more than 1,500 kilometres, the truck was driving itself. "Our business case is to take the human driver out," TuSimple's Jim Mullen said.
If you're ready to take on a brand new challenge, and learn about AI techniques that you've never seen before in traditional supervised machine learning, unsupervised machine learning, or even deep learning, then this course is for you. Moreover, the course is packed with practical exercises that are based on real-life examples. So not only will you learn the theory, but you will also get some hands-on practice building your own models. There are five big projects on healthcare problems and one small project to practice.
The Self-Driving car might still be having difficulties understanding the difference between humans and garbage can, but that does not take anything away from the amazing progress state-of-the-art object detection models have made in the last decade. Combine that with the image processing abilities of libraries like OpenCV, it is much easier today to build a real-time object detection system prototype in hours. In this guide, I will try to show you how to develop sub-systems that go into a simple object detection application and how to put all of that together. I know some of you might be thinking why I am using Python, isn't it too slow for a real-time application, and you are right; to some extent. The most compute-heavy operations, like predictions or image processing, are being performed by PyTorch and OpenCV both of which use c behind the scene to implement these operations, therefore it won't make much difference if we use c or python for our use case here.