Tactical decision making is a critical feature for advanced driving systems, that incorporates several challenges such as complexity of the uncertain environment and reliability of the autonomous system. In this work, we develop a multi-modal architecture that includes the environmental modeling of ego surrounding and train a deep reinforcement learning (DRL) agent that yields consistent performance in stochastic highway driving scenarios. To this end, we feed the occupancy grid of the ego surrounding into the DRL agent and obtain the high-level sequential commands (i.e. lane change) to send them to lower-level controllers. We will show that dividing the autonomous driving problem into a multi-layer control architecture enables us to leverage the AI power to solve each layer separately and achieve an admissible reliability score. Comparing with end-to-end approaches, this architecture enables us to end up with a more reliable system which can be implemented in actual self-driving cars.

The Intensive Care Unit (ICU) is a hospital department where machine learning has the potential to provide valuable assistance in clinical decision making. Classical machine learning models usually only provide point-estimates and no uncertainty of predictions. In practice, uncertain predictions should be presented to doctors with extra care in order to prevent potentially catastrophic treatment decisions. In this work we show how Bayesian modelling and the predictive uncertainty that it provides can be used to mitigate risk of misguided prediction and to detect out-of-domain examples in a medical setting. We derive analytically a bound on the prediction loss with respect to predictive uncertainty. The bound shows that uncertainty can mitigate loss. Furthermore, we apply a Bayesian Neural Network to the MIMIC-III dataset, predicting risk of mortality of ICU patients. Our empirical results show that uncertainty can indeed prevent potential errors and reliably identifies out-of-domain patients. These results suggest that Bayesian predictive uncertainty can greatly improve trustworthiness of machine learning models in high-risk settings such as the ICU.

Look at this video of comedian Bill Hader impersonating former California governor Arnold Schwarzenegger. This video is a "deepfake." This is significant, not only because it reflects the fact that deepfakes are new, but that they're also easy. A big part of the danger of the technology is that, unlike older photo and video editing techniques, it will be more widely accessible to people without great technical skill. Now, the stakes are fairly low for the Hader video.

Algorithms are coming to the San Francisco District Attorney's Office. In a bid to combat implicit bias, artificial intelligence-backed software will redact race information from police incident reports before prosecutors make their initial charging decision.

Wheat Ridge, Colorado-based Lutheran Medical Center, which is part of Broomfield, Colorado-based SCL Health, wanted to improve its operations. "We determined a few years ago that for a hospital like ours that has a very challenging payer mix, … running an extremely cost-efficient operation was necessary for stability," said Lutheran Medical Center president and CEO Grant Wicklund in a phone interview. "One of the ways we identified we could become even more cost-efficient was to be absolutely world-class at having the appropriate length of stay." Noomi Hirsch, the medical center's vice president of operations, took the lead on the effort. In a phone interview, she explained that the organization was able to hit low-hanging fruit areas, but eventually started looking at options in the technology world to tackle the problem.

In a paper scheduled to be presented next week during the annual Conference on Computer Vision and Pattern Recognition (CVPR), scientists at IBM, Tel Aviv University, and Technion describe a novel AI model design -- Label-Set Operations (LaSO) networks -- designed to combine pairs of labeled image examples (e.g., a pic of a dog annotated "dog" and a sheep annotated "sheep") to create new examples that incorporate the seed images' labels (a single pic of a dog and sheep annotated "dog" and "sheep"). The coauthors believe that in the future, LaSO networks could be used to augment corpora that lack sufficient real-world data. "Our method is capable of producing a sample containing … labels present in two input samples," wrote the researchers. "The proposed approach might also prove useful for the interesting visual dialog use case, where the user can manipulate the returned query results by pointing out or showing visual examples of what she [or] he likes or doesn't like." LaSO networks learn to manipulate label sets of given samples and synthesize new ones corresponding to combined label sets, taking as input photos of different types and identifying common semantic content before implicitly removing concepts present in one sample from another sample.

FREMONT, CA – Industries and organizations across the globe have realized the potential of artificial intelligence (AI), and the oil and gas industry is one of them. Petroleum oil is one of the most prominent resources in the energy sector and is the basis for many products such as wax, lubricant, kerosene, petroleum jelly, and so on. Over the last few years, the crude oil reserves are steadily reaching their limits. Also, the rise of alternative fuel sources has resulted in the reduction of oil prices, which has raised concerns in the oil and gas sector. To alleviate the adverse effects plaguing the industry, many organizations are turning toward modern technologies to increase productivity as well as revenue.

UN expert calls for Saudi crown prince to be probed over journalist's death

SOME PEOPLE fear about robots taking work far from human beings, however there are a pair of jobs that even these sceptics admit most folk would no longer favor. One is cleansing up radioactive slay, in particular when it's miles internal a nuclear vitality self-discipline--and in particular if the vitality self-discipline in quiz has suffered a recent accident. These that gain contend with radioactive arena topic must first don protective suits that are inherently cumbersome and are additional encumbered by the air hoses wanted to allow the wearer to breathe. Even then their working hours are strictly restricted, in relate to resolve far from prolonged exposure to radiation and because operating in the suits is intelligent. Moreover, some forms of slay are too dangerous for even the besuited to intention safely.

SOME PEOPLE fear about robots taking work far from human beings, however there are a pair of jobs that even these sceptics admit most folk would no longer favor. One is cleansing up radioactive slay, in particular when it's miles internal a nuclear vitality self-discipline--and in particular if the vitality self-discipline in quiz has suffered a recent accident. These that gain contend with radioactive arena topic must first don protective suits that are inherently cumbersome and are additional encumbered by the air hoses wanted to allow the wearer to breathe. Even then their working hours are strictly restricted, in relate to resolve far from prolonged exposure to radiation and because operating in the suits is intelligent. Moreover, some forms of slay are too dangerous for even the besuited to intention safely.