New EU regulations on AI seek to ban mass and indiscriminate surveillance. For many, that is the good news. The'not so good' news is that the proposed prohibitions are considered by some as being too vague, with serious loopholes. Most recently, the European Data Protection Board (EDPB) and European Data Protection Supervisor (EDPS), called for a ban on the use of AI for the automated recognition of human features in "publicly accessible spaces" as well as other uses that might lead to "unfair discrimination". Broadly speaking, this reflects the response to the EU's attempt to set a standard on how tech is regulated around the world.
Across the world, mapping technology with Artificial Intelligence (AI) and machine learning allow users to have a variety of choices on their travels. Be it driving, flying, or walking, GPS systems are now a lifesaver in keeping users on track. Before this, most of us often used old maps or would buy travel maps whenever we wanted to move around. Today, map applications are not only available on GPS devices, but also on our mobile phones and are even built into our vehicles to provide better route directions. Despite this, there are still some challenges when it comes to mapping and location tagging.
We're Cruise, a self-driving service designed for the cities we love. We're building the world's most advanced, self-driving vehicles to safely connect people to the places, things, and experiences they care about. We believe self-driving vehicles will help save lives, reshape cities, give back time in transit, and restore freedom of movement for many. Cruisers have the opportunity to grow and develop while learning from leaders at the forefront of their fields. With a culture of internal mobility, there's an opportunity to thrive in a variety of disciplines.
Robotics today is not the same as assembly line Robots of the industrial age because AI is impacting many areas of Robotics. At the AI labs, we have been exploring a few of these areas using the Dobot Magician Robotic Arm in London. Our work was originally inspired by this post from Google which used the Dobot Magician( build your own machine learning powered robot arm using TensorFlow ...). In essence, the demo allows you use voice commands to enable the robotic arm to pick up specific objects (ex a red domino). This demo uses multiple AI technologies.
Simulation systems have become essential to the development and validation of autonomous driving (AD) technologies. The prevailing state-of-the-art approach for simulation uses game engines or high-fidelity computer graphics (CG) models to create driving scenarios. However, creating CG models and vehicle movements (the assets for simulation) remain manual tasks that can be costly and time consuming. In addition, CG images still lack the richness and authenticity of real-world images, and using CG images for training leads to degraded performance. Here, we present our augmented autonomous driving simulation (AADS). Our formulation augmented real-world pictures with a simulated traffic flow to create photorealistic simulation images and renderings. More specifically, we used LiDAR and cameras to scan street scenes. From the acquired trajectory data, we generated plausible traffic flows for cars and pedestrians and composed them into the background. The composite images could be resynthesized with different viewpoints and sensor models (camera or LiDAR). The resulting images are photorealistic, fully annotated, and ready for training and testing of AD systems from perception to planning. We explain our system design and validate our algorithms with a number of AD tasks from detection to segmentation and predictions. Compared with traditional approaches, our method offers scalability and realism. Scalability is particularly important for AD simulations, and we believe that real-world complexity and diversity cannot be realistically captured in a virtual environment. Our augmented approach combines the flexibility of a virtual environment (e.g., vehicle movements) with the richness of the real world to allow effective simulation.
With electric vehicles slowly gaining momentum toward becoming the dominant form of transportation in the U.S., two startups have struck up a partnership to help cities and utilities figure out where to put more car chargers. StreetLight Data, which sells transportation data to local governments, will offer Volta Charging's PredictEV tool to its customers. The tool uses AI to generate suggestions about where electric charging infrastructure would be most useful -- an urban planning consideration that is becoming more important as more electric vehicles hit the streets. Today, electric vehicles make up only around 2 percent of new vehicles sold in the U.S., but that number is rising rapidly. In 2020, Pew Research found that the number of EVs sold in the country had more than tripled since 2016.
It's no secret that global mobility ecosystems are changing rapidly. Like so many other industries, automakers are experiencing massive technology-driven shifts. The automobile itself drove radical societal changes in the 20th century, and current technological shifts are again quickly restructuring the way we think about transportation. The rapid progress in AI/ML has propelled the emergence of new mobility application scenarios that were unthinkable just a few years ago. These complex use cases require some rigorous MLOps planning.
In this decade, companies across the globe have embraced the potential of artificial intelligence for digital transformation and enhanced customer experience. One important application of AI is enabling companies to use the pools of data available with them for smart business use. BMW is one of the world's leading manufacturers of premium automobiles and mobility services. BMW uses artificial intelligence in critical areas like production, research and development, and customer service. BMW also runs a project dedicated to this technology called Project AI, for efficient use of artificial intelligence.
Artificial Intelligence is here to transform everything from our daily business lives. It is impacting both positive and negative manners. The experts, the tech-savvy, and even the common people are aware by now that AI is replacing human jobs. The questions and concerns have shifted to when and how, and which jobs will vanish first. If we look at the current situation, we are in the middle of the AI development phase.