Real-world competitive games, such as chess, go, or StarCraft II, rely on Elo models to measure the strength of their players. Since these games are not fully transitive, using Elo implicitly assumes they have a strong transitive component that can correctly be identified and extracted. In this study, we investigate the challenge of identifying the strength of the transitive component in games. First, we show that Elo models can fail to extract this transitive component, even in elementary transitive games. Then, based on this observation, we propose an extension of the Elo score: we end up with a disc ranking system that assigns each player two scores, which we refer to as skill and consistency. Finally, we propose an empirical validation on payoff matrices coming from real-world games played by bots and humans.

While the most sophisticated driverless cars on public roads can handle haboobs and rainstorms like champs, certain types of precipitation remain a challenge for them -- like snow. That's because snow covers cameras critical to those cars' self-awareness and tricks sensors into perceiving obstacles that aren't there, and because snow obscures road signs and other structures that normally serve as navigational landmarks. In an effort to spur on the development of cars capable of driving in wintry weather, startup Scale AI this week open-sourced Canadian Adverse Driving Conditions (CADC), a data set containing over 56,000 images in conditions including snow created with the University of Waterloo and the University of Toronto. While several corpora with snowy sensor samples have been released to date, including Linköping University's Automotive Multi-Sensor Dataset (AMUSE) and the Mapillary Vistas data set, Scale AI claims that CADC is the first to focus specifically on "real-world" driving in snowy weather. "Snow is hard to drive in -- as many drivers are well aware. But wintry conditions are especially hard for self-driving cars because of the way snow affects the critical hardware and AI algorithms that power them," wrote Scale AI CEO Alexandr Wang in a blog post.

In late 2018, Krzysztof Czarnecki, a professor at Canada's University of Waterloo, built a self-driving car and trained it to navigate surrounding neighborhoods with an annotated driving data set from researchers in Germany. The vehicle worked well enough to begin with, recognizing Canadian cars and pedestrians just as well as German ones. But then Czarnecki took the autonomous car for a spin in heavy Ontarian snow. It quickly became a calamity on wheels, with the safety driver forced to grab the wheel repeatedly to avert disaster. The incident highlights a gap in the development of self-driving cars: maneuvering in bad weather.