In many ways this year's CES looked a lot more like an autonomous-car show than a consumer electronics show. There were announcements aplenty from the likes of Ford, Baidu, Toyota, and others about self-driving vehicles, upcoming driving tests, and new partners. In a parking lot across from the Las Vegas Convention Center, several companies offered rides; you could even schedule a ride in a self-driving Lyft through the company's app and get dropped off at one of many casinos on the Strip. A couple of miles away in downtown Las Vegas, an eight-passenger autonomous shuttle bus ran in a loop around Fremont Street. It was part of an ongoing test between commuter transit company Keolis, autonomous-car maker Navya, and the city.

With a simple swipe of the band across sensors located throughout the park, the giant system knows where you are, what you're doing and what you need. The goal of the tech team who developed the MagicBands was to "root out all the friction within the Disney World experience." The goal would be to potentially offer customized guest experiences at those points. The more data Disney collects it can improve operational efficiency such as in the scheduling of 240,00 shifts for 80,000 employees each week, the better it is able to target marketing because the preferences and behaviors of past guests are used to create future packages and offers specific to them and Disney is even dabbling at making robotic versions of Mickey and Minnie and all of its characters that would move around among the guests and interact with them.

After spending nearly a decade quetly working on self-driving cars, Google finally wants to take people for a ride. Its autonomous spin-off, Waymo plans to add 500 Chrysler Pacifica minivans to the 100 already roaming the roads of Phoenix, and inviteds locals to sign up for free rides to school, work, soccer practice, or anywhere else they need to go. Waymo almost certainly possesses the most sophisticated autonomous technology in this increasingly crowded field. After three million miles on the road, its cars can drive thousands of miles without human intervention. But delivering the technology to consumers requires more than developing the right algorithms and hardware.

This paper presents a demonstration of how AI can be useful in the game design and development process of a modern board game. By using an artificial intelligence algorithm to play a substantial amount of matches of the Ticket to Ride board game and collecting data, we can analyze several features of the gameplay as well as of the game board. Results revealed loopholes in the game's rules and pointed towards trends in how the game is played. We are then led to the conclusion that large scale simulation utilizing artificial intelligence can offer valuable information regarding modern board games and their designs that would ordinarily be prohibitively expensive or time-consuming to discover manually.

Modeling the distribution of natural images is challenging, partly because of strong statistical dependencies which can extend over hundreds of pixels. Recurrent neural networks have been successful in capturing long-range dependencies in a number of problems but only recently have found their way into generative image models. We here introduce a recurrent image model based on multi-dimensional long short-term memory units which are particularly suited for image modeling due to their spatial structure. Our model scales to images of arbitrary size and its likelihood is computationally tractable. We find that it outperforms the state of the art in quantitative comparisons on several image datasets and produces promising results when used for texture synthesis and inpainting.