If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
The racing industry is on the fast track to driverless racecars, thanks to AI. At the center of this evolution is Roborace, the world's first autonomous racing competition. Conceived by renowned car designer Daniel Simon -- a former Bugatti designer who's gone on to create various cars for Hollywood -- the "Robocar" is designed, developed, and built by the Roborace organization. Teams compete by writing the software and developing deep neural networks that consume the sensor data to see, think, and act. The cars -- which are 4.8-meters-long -- can reach speeds of over 300 kilometers per hour.
With automobiles becoming increasingly reliant on sensors to perform various driving tasks, it is important to encode the relevant CAN bus sensor data in a way that captures the general state of the vehicle in a compact form. In this paper, we develop a deep learning-based method, called Drive2Vec, for embedding such sensor data in a low-dimensional yet actionable form. Our method is based on stacked gated recurrent units (GRUs). It accepts a short interval of automobile sensor data as input and computes a low-dimensional representation of that data, which can then be used to accurately solve a range of tasks. With this representation, we (1) predict the exact values of the sensors in the short term (up to three seconds in the future), (2) forecast the long-term average values of these same sensors, (3) infer additional contextual information that is not encoded in the data, including the identity of the driver behind the wheel, and (4) build a knowledge base that can be used to auto-label data and identify risky states. We evaluate our approach on a dataset collected by Audi, which equipped a fleet of test vehicles with data loggers to store all sensor readings on 2,098 hours of driving on real roads. We show in several experiments that our method outperforms other baselines by up to 90%, and we further demonstrate how these embeddings of sensor data can be used to solve a variety of real-world automotive applications.
Accidents and attacks that involve chemical, biological, radiological/nuclear or explosive (CBRNE) substances are rare, but can be of high consequence. Since the investigation of such events is not anybody's routine work, a range of AI techniques can reduce investigators' cognitive load and support decision-making, including: planning the assessment of the scene; ongoing evaluation and updating of risks; control of autonomous vehicles for collecting images and sensor data; reviewing images/videos for items of interest; identification of anomalies; and retrieval of relevant documentation. Because of the rare and high-risk nature of these events, realistic simulations can support the development and evaluation of AI-based tools. We have developed realistic models of CBRNE scenarios and implemented an initial set of tools.
In medicine, false positives are expensive, scary, and even painful. Yes, the doctor eventually tells you that the follow-up biopsy after that bloop on the mammogram puts you in the clear. But the intervening weeks are excruciating. A false negative is no better: "Go home, you're fine, those headaches are nothing to worry about." The problem with avoiding both false positives and negatives, though, is that the more you do to get away from one, the closer you get to the other.
The next time your car bottoms out on a nasty pothole, grit your teeth and try to spare a thought for the people trying to end that problem and smooth out your journey. Yotta helps local authorities and utility companies understand their infrastructure - like roads and streetlights - better by surveying and analysing the environment. ZDNet talked to Manish Jethwa, Yotta's chief product and technology officer, to find out more. ZDNet: How would you describe Yotta and the business you are in? Jethwa: We're a technology business that has been around for some 25 years in the highways arena.
Now you see it, now you don't! Five minutes into a harrowing cross-state winter drive, I received a warning on the dashboard. All my front-facing sensors and cameras were obscured with ice, rendering the vehicle's numerous active safety systems disabled. Winter can make it extremely hard for sensors to do their jobs, but Waymo has a trick that my car didn't -- machine learning! At this week's Google I/O conference, Waymo discussed how it uses sister company Google's developments in machine learning to help its self-driving vehicles navigate snowy climates.
To operate effectively in tomorrow's smart cities, autonomous vehicles (AVs) must rely on intra-vehicle sensors such as camera and radar as well as inter-vehicle communication. Such dependence on sensors and communication links exposes AVs to cyber-physical (CP) attacks by adversaries that seek to take control of the AVs by manipulating their data. Thus, to ensure safe and optimal AV dynamics control, the data processing functions at AVs must be robust to such CP attacks. To this end, in this paper, the state estimation process for monitoring AV dynamics, in presence of CP attacks, is analyzed and a novel adversarial deep reinforcement learning (RL) algorithm is proposed to maximize the robustness of AV dynamics control to CP attacks. The attacker's action and the AV's reaction to CP attacks are studied in a game-theoretic framework. In the formulated game, the attacker seeks to inject faulty data to AV sensor readings so as to manipulate the inter-vehicle optimal safe spacing and potentially increase the risk of AV accidents or reduce the vehicle flow on the roads. Meanwhile, the AV, acting as a defender, seeks to minimize the deviations of spacing so as to ensure robustness to the attacker's actions. Since the AV has no information about the attacker's action and due to the infinite possibilities for data value manipulations, the outcome of the players' past interactions are fed to long-short term memory (LSTM) blocks. Each player's LSTM block learns the expected spacing deviation resulting from its own action and feeds it to its RL algorithm. Then, the the attacker's RL algorithm chooses the action which maximizes the spacing deviation, while the AV's RL algorithm tries to find the optimal action that minimizes such deviation.
The autonomous car technology promises to replace human drivers with safer driving systems. But although autonomous cars can become safer than human drivers this is a long process that is going to be refined over time. Before these vehicles are deployed on urban roads a minimum safety level must be assured. Since the autonomous car technology is still under development there is no standard methodology to evaluate such systems. It is important to completely understand the technology that is being developed to design efficient means to evaluate it. In this paper we assume safety-critical systems reliability as a safety measure. We model an autonomous road vehicle as an intelligent agent and we approach its evaluation from an artificial intelligence perspective. Our focus is the evaluation of perception and decision making systems and also to propose a systematic method to evaluate their integration in the vehicle. We identify critical aspects of the data dependency from the artificial intelligence state of the art models and we also propose procedures to reproduce them.
With massive breakthroughs in smart technologies being reported every month, it won't be long until our transport industries are dominated by AI. Here are just some of the ways artificial intelligence is changing the face of transport, and what we can expect in the near future. Autonomous cars have quickly moved from the realm of sci-fi into reality. Though still in the early stages, these AI-driven vehicles could drastically change how we get from A to B in the near future. From plowing snow to collecting garbage, self-driving trucks could soon be taking over a lot of our dirty work.
Everyone working in the autonomous vehicle space said it was inevitable. In America--and in the rest of the world--cars kill people, around 40,000 in the US and 1.25 million in the globe each year. Self-driving cars would be better. But no one promised perfection. Still, the death of Elaine Herzberg, struck by a self-driving Uber in Tempe, Arizona, two weeks ago, felt like a shock.