Elo rating, widely used for skill assessment across diverse domains ranging from competitive games to large language models, is often understood as an incremental update algorithm for estimating a stationary Bradley-Terry (BT) model. However, our empirical analysis of practical matching datasets reveals two surprising findings: (1) Most games deviate significantly from the assumptions of the BT model and stationarity, raising questions on the reliability of Elo. (2) Despite these deviations, Elo frequently outperforms more complex rating systems, such as mElo and pairwise models, which are specifically designed to account for non-BT components in the data, particularly in terms of win rate prediction. This paper explains this unexpected phenomenon through three key perspectives: (a) We reinterpret Elo as an instance of online gradient descent, which provides no-regret guarantees even in misspecified and non-stationary settings. (b) Through extensive synthetic experiments on data generated from transitive but non-BT models, such as strongly or weakly stochastic transitive models, we show that the ''sparsity'' of practical matching data is a critical factor behind Elo's superior performance in prediction compared to more complex rating systems. (c) We observe a strong correlation between Elo's predictive accuracy and its ranking performance, further supporting its effectiveness in ranking.

The meteoric rise of online games has created a need for accurate skill rating systems for tracking improvement and fair matchmaking. Although many skill rating systems are deployed, with various theoretical foundations, less work has been done at analysing the real-world performance of these algorithms. In this paper, we perform an empirical analysis of Elo, Glicko2 and TrueSkill through the lens of surrogate modelling, where skill ratings influence future matchmaking with a configurable acquisition function. We look both at overall performance and data efficiency, and perform a sensitivity analysis based on a large dataset of Counter-Strike: Global Offensive matches.

A key challenge in professional fact-checking is its limited scalability in relation to the magnitude of false information. While many Natural Language Processing (NLP) tools have been proposed to enhance fact-checking efficiency and scalability, both academic research and fact-checking organizations report limited adoption of such tooling due to insufficient alignment with fact-checker practices, values, and needs. To address this gap, we investigate a co-design method, Matchmaking for AI, which facilitates fact-checkers, designers, and NLP researchers to collaboratively discover what fact-checker needs should be addressed by technology and how. Our co-design sessions with 22 professional fact-checkers yielded a set of 11 novel design ideas. They assist in information searching, processing, and writing tasks for efficient and personalized fact-checking; help fact-checkers proactively prepare for future misinformation; monitor their potential biases; and support internal organization collaboration. Our work offers implications for human-centered fact-checking research and practice and AI co-design research.

Artificial Intelligence and Romance are an inevitable match. Love is the basis of human experience, yet it remains one of the most challenging emotions to understand and define. Since the dawn of time, we have been searching for that special someone to share our lives with, and in recent years, technology has begun to play an increasingly important role in this quest. The advent of online dating has transformed the way we connect with potential partners, and the growth of social media has created new opportunities for building relationships. But as our interactions with technology become more and more intimate, what role will artificial intelligence (AI) play in our search for love? AI is disrupting virtually every other area of our lives, from how we work and communicate to how we shop and consume.

Matchmaking systems are vital for creating fair matches in online multiplayer games, which directly affects players' satisfactions and game experience. Most of the matchmaking systems largely rely on precise estimation of players' game skills to construct equitable games. However, the skill rating of a novice is usually inaccurate, as current matchmaking rating algorithms require considerable amount of games for learning the true skill of a new player. Using these unreliable skill scores at early stages for matchmaking usually leads to disparities in terms of team performance, which causes negative game experience. This is known as the ''cold-start'' problem for matchmaking rating algorithms. To overcome this conundrum, this paper proposes QuickSKill, a deep learning based novice skill estimation framework to quickly probe abilities of new players in online multiplayer games. QuickSKill extracts sequential performance features from initial few games of a player to predict his/her future skill rating with a dedicated neural network, thus delivering accurate skill estimation at the player's early game stage. By employing QuickSKill for matchmaking, game fairness can be dramatically improved in the initial cold-start period. We conduct experiments in a popular mobile multiplayer game in both offline and online scenarios. Results obtained with two real-world anonymized gaming datasets demonstrate that proposed QuickSKill delivers precise estimation of game skills for novices, leading to significantly lower team skill disparities and better player game experience. To the best of our knowledge, proposed QuickSKill is the first framework that tackles the cold-start problem for traditional skill rating algorithms.

Technical advancements make skill-based matchmaking techniques better every year, enticing average audiences to play more. But those same changes have also left a sour taste in some players' mouths who publishers have a vested interest in keeping happy -- their live streams help market games. Game companies have the seemingly impossible task of satisfying both sides; on one end, the massive player base of everyday gamers that define their bottom line and, on the other, the pros and content creators they use as for PR for those same audiences. But if these systems are indeed built to maximize players' enjoyment, it can sometimes seem like they're not working very well. Hate for skill-based matchmaking is hardly a phenomenon confined to top streamers or salty Call of Duty players. As awareness about these algorithms grows, communities in "Valorant," "Overwatch," "Apex Legends" and even more casual games like "FIFA" and "Dead by Daylight" have all, at one point or another, sharply criticized matchmaking for reducing their enjoyment of the game.

From smart infrastructure grids to bot-authored news reports, algorithms and artificial intelligence capabilities are routinely working behind the scenes in various aspects of our day-to-day lives. COVID-19 only accelerated the adoption of automation across industries and Gartner pegged "smarter, responsible [and] scalable AI" as one of its top 2021 data and analytics tech trends. In this roundup, we've highlighted some of the ways AI is transforming everything from animal conversation efforts to matchmaking in the digital age. The agtech company AppHarvest is using a number of transformative practices to reimagine farming in the 21st century, including AI. The company is tapping computer vision and AI to help its robo-harvester, Virgo, pick ripe produce right from the vine.

Competition is a primary driver of player satisfaction and engagement in multiplayer online games. Traditional matchmaking systems aim at creating matches involving teams of similar aggregated individual skill levels, such as Elo score or TrueSkill. However, team dynamics cannot be solely captured using such linear predictors. Recently, it has been shown that nonlinear predictors that target to learn probability of winning as a function of player and team features significantly outperforms these linear skill-based methods. In this paper, we show that using final score difference provides yet a better prediction metric for competitive balance. We also show that a linear model trained on a carefully selected set of team and individual features achieves almost the performance of the more powerful neural network model while offering two orders of magnitude inference speed improvement. This shows significant promise for implementation in online matchmaking systems.

Artificial Intelligence (AI) is probably one of the branches of Computer Science that is experiencing more growth nowadays. Even though it was born more than 70 years ago, it is in a historical period where it has generated more interest because of the revolution it has caused on the market today. Until very recently, there were limited computational capacities that made Artificial Intelligence produce very poor results on the problem being applied, which resulted in several periods of historical dissatisfaction in the industry and considerable reduction in both interests. In this case, discipline on the number of dedicated researchers. However, in recent years Artificial Intelligence has gained enormous momentum, able to solve problems with computers that were previously thought to be impossible, reaching levels that had never been reached before.

Millennials have become a growing force in society. Compared to their predecessors, the generation that grew with the Internet and electronic devices is considered more adept at adapting to new ideas and more open-minded regarding the unconventional. When it comes to Millennial relationships, online dating is a rapid-growing industry, with more than 1500 dating apps and websites operating around the world. According to Statista, online dating industry revenues reached US1.66 billion in 2019 and are expected to continue growing at an annual rate of 4.2 percent until 2023. Instead of having users simply swipe through headshots, many new dating apps and online platforms are leveraging artificial intelligence to introduce a variety of novel approaches to smart matchmaking.