General Motors (GM) is taking its business to new heights by unveiling a flying self-driving taxi under its Cadillac brand at the Consumer Electronics Show (CES). The American carmaker shared a concept video showcasing a single-seater electric vertical takeoff and landing (eVTOL) aircraft that tops speeds of 56mph. Not only is GM's future taking to the skies, but the video also showed it is heading down the road with a new luxury autonomous shuttle that seats two passengers. The concept vehicles were revealed during the firm's morning remarks at the tech conference that is being held virtually for the first time due to the lingering coronavirus pandemic. General Motors (GM) shared a concept video of two futuristic vehicles under the Cadillac brand.
General Motors is almost ready to unwrap its revamped Bolt vehicles. Today, the automaker -- which just unveiled a new EV-inspired logo -- released a frustratingly short teaser and confirmed that two models will be formally revealed next month. One is a refreshed version of the classic Chevy Bolt, while the other is an "EUV," which apparently stands for Electric Utility Vehicle. We've known about the pair for some time: both were referenced as part of a GM event in March last year. The standard Bolt EV was supposed to come out in late 2020, but was pushed back due to the "current business situation," better known as the ongoing coronavirus pandemic.
It depends who you ask. Back in the 1950s, the fathers of the field Minsky and McCarthy, described artificial intelligence as any task performed by a program or a machine that, if it had been done by a human, would have to apply intelligence in order to accomplish it. That's obviously a fairly broad definition, which is why you will sometimes see arguments over whether something is truly AI or not. Modern definitions of what it means to create intelligence are slightly more specific. Francois Chollet, AI researcher at Google and creator of the machine-learning software library Keras, has said intelligence is tied to a system's ability to adapt and improvise in a new environment, to generalise its knowledge and apply it to unfamiliar scenarios. "Intelligence is the efficiency with which you acquire new skills at tasks you didn't previously prepare for," he said. "Intelligence is not skill itself, it's not what you can do, it's how well and how efficiently you can learn new things." It's a definition under which modern AI-powered systems, such as virtual assistants, would be characterised as having demonstrated'narrow AI'; the ability to generalise their training when carrying out a limited set of tasks, such as speech recognition or computer vision. Typically, AI systems demonstrate at least some of the following behaviours associated with human intelligence: planning, learning, reasoning, problem solving, knowledge representation, perception, motion, and manipulation and, to a lesser extent, social intelligence and creativity. This ebook, based on the latest ZDNet / TechRepublic special feature, advises CXOs on how to approach AI and ML initiatives, figure out where the data science team fits in, and what algorithms to buy versus build. AI is ubiquitous today, used to recommend what you should buy next online, to understanding what you say to virtual assistants, such as Amazon's Alexa and Apple's Siri, to recognise who and what is in a photo, to spot spam, or detect credit card fraud.
Eight technologies developed by MIT Lincoln Laboratory researchers, either wholly or in collaboration with researchers from other organizations, were among the winners of the 2020 R&D 100 Awards. Annually since 1963, these international R&D awards recognize 100 technologies that a panel of expert judges selects as the most revolutionary of the past year. Six of the laboratory's winning technologies are software systems, a number of which take advantage of artificial intelligence techniques. The software technologies are solutions to difficulties inherent in analyzing large volumes of data and to problems in maintaining cybersecurity. Another technology is a process designed to assure secure fabrication of integrated circuits, and the eighth winner is an optical communications technology that may enable future space missions to transmit error-free data to Earth at significantly higher rates than currently possible.
Hyundai Motor Group said Thursday it has scouted Tomaso Poggio and Daniela Rus, experts on artificial intelligence, to work together on various projects of AI technology development. Poggio and Rus are serving as technology consultants and have been giving advice on utilizing AI to build planning and technological strategies for new business models, to establish a global research organization and to set investment directions for research infrastructures. Poggio, who heads the Center for Brains, Minds and Machine at Massachusetts Institute of Technology, is considered as one of the founders of computational neuroscience. Rus, a renowned roboticist who is also from MIT, is director of the Computer Science and Artificial Intelligence Laboratory. She is a class of 2002 MacArthur fellow and has conducted research on robots and autonomous driving, according to the automaker.
Car manufacturers are catching on the new tech wave. After the pandemic hit and social-distancing became our new normal, a personal vehicle became one of the safest ways to travel -- with proper sanitation, of course. And as we're all moving to a touchless future, the automotive industry is no exception. According to the study by Voicebot.ai While analysts from Frost & Sullivan predict that the importance of digital voice assistants in automotive branding will increasingly grow. We believe that in the next few years, voice technology will become one of the key drivers transforming the automotive industry.
Deep learning is a group of exciting new technologies for neural networks. Through a combination of advanced training techniques and neural network architectural components, it is now possible to create neural networks that can handle tabular data, images, text, and audio as both input and output. Deep learning allows a neural network to learn hierarchies of information in a way that is like the function of the human brain. This course will introduce the student to classic neural network structures, Convolution Neural Networks (CNN), Long Short-Term Memory (LSTM), Gated Recurrent Neural Networks (GRU), General Adversarial Networks (GAN), and reinforcement learning. Application of these architectures to computer vision, time series, security, natural language processing (NLP), and data generation will be covered. High-Performance Computing (HPC) aspects will demonstrate how deep learning can be leveraged both on graphical processing units (GPUs), as well as grids. Focus is primarily upon the application of deep learning to problems, with some introduction to mathematical foundations. Readers will use the Python programming language to implement deep learning using Google TensorFlow and Keras. It is not necessary to know Python prior to this book; however, familiarity with at least one programming language is assumed.
I am behind the wheel of a Nissan Leaf, circling a parking lot, trying not to let the day's nagging worries and checklists distract me to the point of imperiling pedestrians. Like all drivers, I am unwittingly communicating my stress to this vehicle in countless subtle ways: the strength of my grip on the steering wheel, the slight expansion of my back against the seat as I breathe, the things I mutter to myself as I pilot around cars and distracted pedestrians checking their phones in the parking lot. "Hello, Corinne," a calm voice says from the audio system. The conversation that ensues offers a window into the ways in which artificial intelligence could transform our experience behind the wheel: not by driving the car for us, but by taking better care of us as we drive. Before coronavirus drastically altered our routines, three-quarters of U.S. workers--some 118 million people--commuted to the office alone in a car.
Detroit – Ford Motor Co. posted results Thursday that were not as grim as expected for its second quarter, during which its U.S. factories were shuttered for half the period to combat the spread of the novel coronavirus and car buyers sheltering in place. Ford reported a $1.12 billion (¥117.1 billion) second-quarter net profit, pushed into the black by a $3.5 billion gain on the value of its stake in the Argo AI autonomous vehicle operation. Without the one-time gain, the company lost $1.9 billion, or 35 cents per share. But that was far better than the $1.17 a share loss Wall Street had expected, according to FactSet. A year ago, Ford posted a $148 million net profit.
Nissan Chief Executive Makoto Uchida told shareholders Monday he is giving up half his pay after the automaker sank into the red amid plunging sales and plant closures in Spain and Indonesia. Uchida apologized for the poor results and promised a recovery by 2023, driven by cost cuts and new models showcasing electric cars and automated-driving technology. "We will tackle these challenges without compromise," he said at a live-streamed meeting. "I promise to bring Nissan back on a growth track." All the world's automakers have been hurt by nose-diving sales caused by the coronavirus pandemic.