The cost of delays was estimated as 33 billion US dollars only in 2019 for the US National Airspace System, a peak value following a growth trend in past years. Aiming to address this huge inefficiency, we designed and developed a novel Data Analytics and Machine Learning system, which aims at reducing delays by proactively supporting re-routing decisions. Given a time interval up to a few days in the future, the system predicts if a reroute advisory for a certain Air Route Traffic Control Center or for a certain advisory identifier will be issued, which may impact the pertinent routes. To deliver such predictions, the system uses historical reroute data, collected from the System Wide Information Management (SWIM) data services provided by the FAA, and weather data, provided by the US National Centers for Environmental Prediction (NCEP). The data is huge in volume, and has many items streamed at high velocity, uncorrelated and noisy. The system continuously processes the incoming raw data and makes it available for the next step where an interim data store is created and adaptively maintained for efficient query processing. The resulting data is fed into an array of ML algorithms, which compete for higher accuracy. The best performing algorithm is used in the final prediction, generating the final results. Mean accuracy values higher than 90% were obtained in our experiments with this system. Our algorithm divides the area of interest in units of aggregation and uses temporal series of the aggregate measures of weather forecast parameters in each geographical unit, in order to detect correlations with reroutes and where they will most likely occur. Aiming at practical application, the system is formed by a number of microservices, which are deployed in the cloud, making the system distributed, scalable and highly available.

In this paper, we consider the problem of learning a policy by observing numerous non-expert agents. Our goal is to extract a policy that, with high-confidence, acts better than the average agents' performance. Such a setting is important for real-world problems where expert data is scarce but non-expert data can easily be obtained, e.g. by crowdsourcing. Our approach is to pose this problem as safe policy improvement in Reinforcement Learning. First, we evaluate an average behavior policy and approximate its value function. Then, we develop a stochastic policy improvement algorithm, termed Rerouted Behavior Improvement (RBI), that safely improves the average behavior. The primary advantages of RBI over current safe learning methods are its stability in the presence of value estimation errors and the elimination of a policy search process. We demonstrate these advantages in a Taxi grid-world domain and in four games from the Atari learning environment.

Today, we are just beginning to scratch the surface of what is possible with artificial intelligence (A.I.) and how individuals will interact with its various forms. Every single aspect of our society -- from cars to houses to products to services -- will be reimagined and redesigned to incorporate A.I. A child born in the year 2030 will not comprehend why his or her parents once had to manually turn on the lights in the living room. In the future, the smart home will seamlessly know the needs, wants, and habits of the individuals who live in the home prior to them taking an action. Before we arrive at this future, it is helpful to take a step back and reimagine how we design cars, houses, products, and services. We are just beginning to see glimpses of this future with the Amazon Echo and Google Home smart voice assistants.

This year, before the doors of the the Los Angeles Auto Show open to the public, the show held Automobility, an automotive tech showcase. As our cars become more like two-ton devices that we drive, auto shows are having to adjust their focus to include apps, AI, connected cars, and more. Here are a few of the most innovative tech stories from LA. Hyundai offered Blue Link, an app that allowed owners to unlock and start their cars via smartphone, in 2011. The second generation of Blue Link rolled out in 2014, adding smart watches to the app's repertoire. Now the service works with Amazon's Alexa in-home AI device.

Today, we are just beginning to scratch the surface of what is possible with artificial intelligence (A.I.) and how individuals will interact with its various forms. Every single aspect of our society -- from cars to houses to products to services -- will be reimagined and redesigned to incorporate A.I. A child born in the year 2030 will not comprehend why his or her parents once had to manually turn on the lights in the living room. In the future, the smart home will seamlessly know the needs, wants, and habits of the individuals who live in the home prior to them taking an action. Before we arrive at this future, it is helpful to take a step back and reimagine how we design cars, houses, products, and services. We are just beginning to see glimpses of this future with the Amazon Echo and Google Home smart voice assistants.