Electric drones booked through smartphones pick people up from office rooftops, shortening travel time by hours, reducing the need for parking and clearing smog from the air. This vision of the future is driving the government's "flying car" project. Major carrier All Nippon Airways, electronics company NEC Corp. and more than a dozen other companies and academic experts hope to have a road map for the plan ready by the year's end. "This is such a totally new sector Japan has a good chance for not falling behind," said Fumiaki Ebihara, the government official in charge of the project. For now, nobody believes people are going to be zipping around in flying cars any time soon.
Chinese companies are "aggressively investing" in artificial intelligence (AI) applications and show more thirst for talent, a joint study by Massachusetts Institute of Technology (MIT) and Boston Consulting Group (BCG) shows, at a time when the race for AI superiority is in the spotlight around the world. The conclusion – based on a survey of over 3,000 participants in 126 countries and 300 executives from China – shines a light on China's ambitions in AI, which is seen as a major driver of the new economy, and the perceived competitive threat the country poses to other big economies. "China's rapid rise in AI has been a wake-up call for nations, industries and corporate executives globally," says the report, which was released on Tuesday and titled Artificial Intelligence in Business Gets Real. "Indeed, many recent national programmes to advance the development of AI refer to China as a competitive threat." Betting big on the core technology behind an array of cutting-edge applications from autonomous driving to facial recognition, China's State Council last July laid out a three-step road map to AI supremacy.
The joint availability of computational power and huge datasets has considerably changed the landscape of Artificial Intelligence. In many fields, applications (self-driving cars, cybersecurity, e-health…) that seemed out of reach in the past are now closer to becoming a reality. Recent advances in Machine Learning, the key component of AI, show the growing maturity of algorithms that are now able to handle an increasing number of new tasks. However, simple adversarial attacks can still easily defeat a learning algorithm and the potentially massive deployment of AI tools in various environments raises many new concerns. Additionally to scalability and versatility of algorithms, awareness of drifting or fake data, privacy, interpretability, accountability are now all features that a learning and decision system should take into account.
Autonomous vehicles should be able to generate accurate probabilistic predictions for uncertain behavior of other road users. Moreover, reactive predictions are necessary in highly interactive driving scenarios to answer "what if I take this action in the future" for autonomous vehicles. There is no existing unified framework to homogenize the problem formulation, representation simplification, and evaluation metric for various prediction methods, such as probabilistic graphical models (PGM), neural networks (NN) and inverse reinforcement learning (IRL). In this paper, we formulate a probabilistic reaction prediction problem, and reveal the relationship between reaction and situation prediction problems. We employ prototype trajectories with designated motion patterns other than "intention" to homogenize the representation so that probabilities corresponding to each trajectory generated by different methods can be evaluated. We also discuss the reasons why "intention" is not suitable to serve as a motion indicator in highly interactive scenarios. We propose to use Brier score as the baseline metric for evaluation. In order to reveal the fatality of the consequences when the predictions are adopted by decision-making and planning, we propose a fatality-aware metric, which is a weighted Brier score based on the criticality of the trajectory pairs of the interacting entities. Conservatism and non-defensiveness are defined from the weighted Brier score to indicate the consequences caused by inaccurate predictions. Modified methods based on PGM, NN and IRL are provided to generate probabilistic reaction predictions in an exemplar scenario of nudging from a highway ramp. The results are evaluated by the baseline and proposed metrics to construct a mini benchmark. Analysis on the properties of each method is also provided by comparing the baseline and proposed metric scores.
Autonomous Vehicles(AV) are one of the brightest promises of the future which would help cut down fatalities and improve travel time while working in harmony. Autonomous vehicles will face with challenging situations and experiences not seen before. These experiences should be converted to knowledge and help the vehicle prepare better in the future. Online Transfer Learning will help transferring prior knowledge to a new task and also keep the knowledge updated as the task evolves. This paper presents the different methods of transfer learning, online transfer learning and organic computing that could be adapted to the domain of autonomous vehicles.
The benefits of autonomous vehicles (AVs) are widely acknowledged, but there are concerns about the extent of these benefits and AV risks and unintended consequences. In this article, we first examine AVs and different categories of the technological risks associated with them. We then explore strategies that can be adopted to address these risks, and explore emerging responses by governments for addressing AV risks. Our analyses reveal that, thus far, governments have in most instances avoided stringent measures in order to promote AV developments and the majority of responses are non-binding and focus on creating councils or working groups to better explore AV implications. The US has been active in introducing legislations to address issues related to privacy and cybersecurity. The UK and Germany, in particular, have enacted laws to address liability issues, other countries mostly acknowledge these issues, but have yet to implement specific strategies. To address privacy and cybersecurity risks strategies ranging from introduction or amendment of non-AV specific legislation to creating working groups have been adopted. Much less attention has been paid to issues such as environmental and employment risks, although a few governments have begun programmes to retrain workers who might be negatively affected.
In order to make educated decisions in this fast-moving field, all managers should have a basic understanding of AI. Here are four key facts that will give you an edge. AI systems learn from the data and feedback that they receive in response to their earlier decisions. Their predictions and actions are only as good as the data they have been trained on. This characteristic makes AI systems very different from traditional deduction- based programming.
Launching at Singapore port's Marina South Pier in quarter three 2018, Wilhelmsen Ships Service and Airbus will be piloting the delivery of spare parts, documents, water test kits and 3D printed consumables via Airbus' Skyways unmanned air system (UAS) to vessels at anchorage. With the signing of an MOU at maritime trade show Posidonia, the Maritime UAS project agreement covers a joint ambition to establish a framework for cooperation between the Parties, with the aim of investigating the potential deployment and commercialization of UAS for maritime deliveries use cases. Marking the very first time, the viability of autonomous drone delivery to vessels has been put to the test in hectic, real-world port conditions, Marius Johansen, VP Commercial, Ships Agency at Wilhelmsen Ships Service is confident with Airbus now onboard his agency team's long-term drone delivery aspirations will be fulfilled. "We are absolutely thrilled to be working with a forward thinking, industry leader like Airbus. When we announced last year that we were pursuing drone delivery, we were greeted with a fair amount of scepticism, but our collaboration with Airbus, shows we really do mean business".
Gaps in maps are a problem, particularly for systems being developed for self-driving cars. To address the issue, researchers from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) have created RoadTracer, an automated method to build road maps that's 45 percent more accurate than existing approaches. Using data from aerial images, the team says that RoadTracer is not just more accurate, but more cost-effective than current approaches. MIT professor Mohammad Alizadeh says that this work will be useful both for tech giants like Google and for smaller organizations without the resources to curate and correct large amounts of errors in maps. "RoadTracer is well-suited to map areas of the world where maps are frequently out of date, which includes both places with lower population and areas where there's frequent construction," says Alizadeh, one of the co-authors of a new paper about the system.
Map apps may have changed our world, but they still haven't mapped all of it yet. Specifically, mapping roads can be difficult and tedious: Even after taking aerial images, companies still have to spend many hours manually tracing out roads. As a result, even companies like Google haven't yet gotten around to mapping the vast majority of the more than 20 million miles of roads across the globe. Gaps in maps are a problem, particularly for systems being developed for self-driving cars. To address the issue, researchers from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) have created RoadTracer, an automated method to build road maps that's 45 percent more accurate than existing approaches.