When most people think of Artificial Intelligence (AI) they probably think about their Amazon's Alexa, self-driving cars or Apple's Siri. However, AI can help be used for many other functions, including marketing and construction. Home builders and developers can incorporate AI in their marketing and construction efforts to impact growth in business and employee retention. The benefits of AI include improving efficiency in the workplace, solving complex problems, and even freeing up your time. Many businesses use AI-related machines or bots and other technologies today so they can use their time more wisely.
Sensor-based technologies are playing a key role in making artificial intelligence (AI) possible in various fields. LiDAR is one of the most promising sensor-based technology, used in autonomous vehicles or self-driving cars and became essential for such autonomous machines to get aware of its surroundings and drive properly without any collision risks. Autonomous vehicles already use various sensors and LiDAR is one of them that helps to detect the objects in-depth. So, right here we will discuss LiDAR technology, how it works, and why it is important for autonomous vehicles or self-driving cars. LIDAR stands for Light Detection and Ranging is a kind of remote sensing technology using the light in the form of a pulsed laser to measure ranges (variable distances) to the Earth.
The auto industry is currently experiencing a rapid shift to autonomous vehicles (AV). This evolution is spearheaded by new, innovative technology companies that are bringing cutting-edge automotive platforms to the market at an unprecedented pace. Currently, vehicles on the road are equipped with the ability to maneuver on their own on highways while in the presence of a human driver. The next logical step in the race to autonomy is self-driving capability in an urban setting -- first with a driver and eventually with humans acting solely as passengers. However, driving in cities is an exponentially more difficult problem to solve than maneuvering on highways.
Abstract: Deep Learning has enjoyed an impressive growth over the past few years in fields ranging from visual recognition to natural language processing. Improvements in these areas have been fundamental to the development of self-driving cars, machine translation, and healthcare applications. This progress has arguably been made possible by a combination of increases in computing power and clever heuristics, raising puzzling questions that lack full theoretical understanding. Here, we will discuss the relationship between the theory behind deep learning and its application. This panel discussion will be hosted remotely via Zoom.
Amazon Go is the first store where no checkout is required. Customer simply enter the store using the Amazon Go app to browse and take the required products or items they want and then leave. Customer being able to purchase, products without suing a counter or checkout. The following video shows how Self-driving Robot (Delivery Bot and named as YAPE) brings goods directly to you, it uses Facial Recognition to recognize the customer to deliver. It makes delivery fast and easy, bot easily navigates sidewalks. YAPE has a 70 kg loading capacity and can travel 80km on a single charge.
The future of mobility is electric, connected, autonomous and shared. With an estimated $100B invested in autonomous driving globally, autonomous driving technology was pitched as the biggest change in mobility since we stopped using horses. Yet in the midst of the COVID-19 global pandemic, where is this autonomous driving (AD) future we were promised? This episode features a radically open conversation between World Economic Forum's Head of Automotive and Autonomous Mobility Michelle Avary, Professor and Director of Duke University's Humans and Autonomy Laboratory Mary Missy Cummings, and Bryn Balcombe Founder of Roborace, as they discuss how COVID has altered our path to the future of mobility. The discussion twists and turns as Michelle, Missy and Bryn talk through the differences between how AI learns how to drive and how a human learns how to drive, cybersecurity in automated driving, why aftermarket AD tech is a no-go, and what a world with universal basic mobility for everyone could look like.
Artificial intelligence (AI) is no longer the stuff of science fiction. The technology is already disrupting multiple industries, many of which impact you on a daily basis. Own an iPhone X? Its facial recognition system is powered by AI. Ever been redirected by Google Maps because of an accident or construction ahead? And those are just a couple of small examples.
A version of this article originally appeared in Issues in Science and Technology. When Americans talk about automation, they tend to ask first how many jobs are at risk--or more broadly, how many jobs will there be, who will do them, and where will they be located. These are the wrong questions. They suggest a policy discussion that starts at the end, focused on mitigating negative impacts. This approach perpetuates a flawed view of how technology develops--one that plagues contemporary debates about the future of work--because it presents technological progress as a process of scientists and engineers applying knowledge and technique to the material world to find a single best way to perform some task.In short, this view of automation sees the consequences of technology for workers (job loss, lower wages, need for retraining, and the like) as largely inevitable.
To butcher a quote from the great science fiction writer William Gibson, "Autonomous vehicles are already here – they're just not very evenly distributed." In other words, while autonomous vehicles may not be in your city, they might be in the city next door. Autonomous vehicles are primed for exponential growth, and all indicators point to that growth beginning to happen sooner rather than later. By 2040, we can expect our highways to be bumper to bumper with over 33 million self-driving vehicles. By 2040, we can expect our highways to be bumper to bumper with over 33 million self-driving vehicles.
Human interaction with machines has experienced a great leap forward in recent years, largely driven by artificial intelligence (AI). From smart homes to self-driving cars, AI has become a seamless part of our daily lives. Voice interactions play a key role in many of these technological advances, most notably in language translation. Here, AI enables instant translation across a number of mediums: text, voice, images and even street signs. The technology works by recognizing individual words, then leveraging similarities in how various languages express the relationships between those words.