The way we move about is changing -- and not just because, as the coronavirus pandemic recedes, we're able to actually move about again. Transportation is changing around the world, thanks to new breakthrough technologies that promise to revolutionize the way we travel. Whether it's planes, trains, or automobiles, here are some of the key trends shaping the present -- and future -- of transport as we know it. When you talk about the future of mobility, no piece of technology better sums up expectations than autonomous vehicles. Dismissed by experts as an impossibility less than two decades ago, self-driving cars have today driven tens of millions of miles, much of it on public roads.
I've known Jeff Nicholson for almost 20 years. Currently, he's the global leader for CRM at Pegasystems, and of course, he does a great job. But he retains that visionary picture of the world as it evolves. I've done several events jointly with him at varying live and virtual events that Pega sponsors -- mostly variations of PegaWorld -- and he is a true delight to work with on stage. Every year, despite how well I know him, he surprises me.
Delseny, Hervé, Gabreau, Christophe, Gauffriau, Adrien, Beaudouin, Bernard, Ponsolle, Ludovic, Alecu, Lucian, Bonnin, Hugues, Beltran, Brice, Duchel, Didier, Ginestet, Jean-Brice, Hervieu, Alexandre, Martinez, Ghilaine, Pasquet, Sylvain, Delmas, Kevin, Pagetti, Claire, Gabriel, Jean-Marc, Chapdelaine, Camille, Picard, Sylvaine, Damour, Mathieu, Cappi, Cyril, Gardès, Laurent, De Grancey, Florence, Jenn, Eric, Lefevre, Baptiste, Flandin, Gregory, Gerchinovitz, Sébastien, Mamalet, Franck, Albore, Alexandre
Machine Learning (ML) seems to be one of the most promising solution to automate partially or completely some of the complex tasks currently realized by humans, such as driving vehicles, recognizing voice, etc. It is also an opportunity to implement and embed new capabilities out of the reach of classical implementation techniques. However, ML techniques introduce new potential risks. Therefore, they have only been applied in systems where their benefits are considered worth the increase of risk. In practice, ML techniques raise multiple challenges that could prevent their use in systems submitted to certification constraints. But what are the actual challenges? Can they be overcome by selecting appropriate ML techniques, or by adopting new engineering or certification practices? These are some of the questions addressed by the ML Certification 3 Workgroup (WG) set-up by the Institut de Recherche Technologique Saint Exup\'ery de Toulouse (IRT), as part of the DEEL Project.
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.
Industrial Internet of Things (IIoT) lays a new paradigm for the concept of Industry 4.0 and paves an insight for new industrial era. Nowadays smart machines and smart factories use machine learning/deep learning based models for incurring intelligence. However, storing and communicating the data to the cloud and end device leads to issues in preserving privacy. In order to address this issue, federated learning (FL) technology is implemented in IIoT by the researchers nowadays to provide safe, accurate, robust and unbiased models. Integrating FL in IIoT ensures that no local sensitive data is exchanged, as the distribution of learning models over the edge devices has become more common with FL. Therefore, only the encrypted notifications and parameters are communicated to the central server. In this paper, we provide a thorough overview on integrating FL with IIoT in terms of privacy, resource and data management. The survey starts by articulating IIoT characteristics and fundamentals of distributive and FL. The motivation behind integrating IIoT and FL for achieving data privacy preservation and on-device learning are summarized. Then we discuss the potential of using machine learning, deep learning and blockchain techniques for FL in secure IIoT. Further we analyze and summarize the ways to handle the heterogeneous and huge data. Comprehensive background on data and resource management are then presented, followed by applications of IIoT with FL in healthcare and automobile industry. Finally, we shed light on challenges, some possible solutions and potential directions for future research.
As we make tremendous advances in machine learning and artificial intelligence technosciences, there is a renewed understanding in the AI community that we must ensure that humans being are at the center of our deliberations so that we don't end in technology-induced dystopias. As strongly argued by Green in his book Smart Enough City, the incorporation of technology in city environs does not automatically translate into prosperity, wellbeing, urban livability, or social justice. There is a great need to deliberate on the future of the cities worth living and designing. There are philosophical and ethical questions involved along with various challenges that relate to the security, safety, and interpretability of AI algorithms that will form the technological bedrock of future cities. Several research institutes on human centered AI have been established at top international universities. Globally there are calls for technology to be made more humane and human-compatible. For example, Stuart Russell has a book called Human Compatible AI. The Center for Humane Technology advocates for regulators and technology companies to avoid business models and product features that contribute to social problems such as extremism, polarization, misinformation, and Internet addiction. In this paper, we analyze and explore key challenges including security, robustness, interpretability, and ethical challenges to a successful deployment of AI or ML in human-centric applications, with a particular emphasis on the convergence of these challenges. We provide a detailed review of existing literature on these key challenges and analyze how one of these challenges may lead to others or help in solving other challenges. The paper also advises on the current limitations, pitfalls, and future directions of research in these domains, and how it can fill the current gaps and lead to better solutions.
General value functions (GVFs) in the reinforcement learning (RL) literature are long-term predictive summaries of the outcomes of agents following specific policies in the environment. Affordances as perceived valences of action possibilities may be cast into predicted policy-relative goodness and modelled as GVFs. A systematic explication of this connection shows that GVFs and especially their deep learning embodiments (1) realize affordance prediction as a form of direct perception, (2) illuminate the fundamental connection between action and perception in affordance, and (3) offer a scalable way to learn affordances using RL methods. Through a comprehensive review of existing literature on recent successes of GVF applications in robotics, rehabilitation, industrial automation, and autonomous driving, we demonstrate that GVFs provide the right framework for learning affordances in real-world applications. In addition, we highlight a few new avenues of research opened up by the perspective of "affordance as GVF", including using GVFs for orchestrating complex behaviors.
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.
Mashable's series Tech in 2025 explores how the challenges of today will dramatically change the near future. Where are the chilled out passengers on their phones, or napping, as an invisible "driver" navigates a crowded intersection? They're still mostly stuck in the backseat as a human driver shuttles them around. They're likely in a highly automated and autonomous-capable vehicle, but a human is still there monitoring the machine. The pandemic made us more comfortable with the idea of autonomous vehicles, but most industry experts still predict a slow transition to their widespread adoption in the U.S. When you're avoiding exposure to a deadly disease, perhaps a driverless robotaxi, like the Waymo One service in suburban Phoenix, looks more attractive.
You may even be using one to read this article. Wi-Fi has become essential to our personal and professional lives. The smartphone and the internet we use today wouldn't have been possible without wireless communication technologies such as Wi-Fi. In 1995 if you wanted to "surf" the internet at home, you had to chain yourself to a network cable like it was an extension cord. In 1997, Wi-Fi was invented and released for consumer use.