Uber AI (United States)

Data races are a prevalent class of concurrency bugs in shared-memory parallel programs, posing significant challenges to software reliability and reproducibility. While there is an extensive body of research on detecting data races and a wealth of practical detection tools across various programming languages, considerably less effort has been directed toward automatically fixing data races at an industrial scale. In large codebases, data races are continuously introduced and exhibit myriad patterns, making automated fixing particularly challenging. In this paper, we tackle the problem of automatically fixing data races at an industrial scale. We present Dr.Fix, a tool that combines large language models (LLMs) with program analysis to generate fixes for data races in real-world settings, effectively addressing a broad spectrum of racy patterns in complex code contexts. Implemented for Go—the programming language widely used in modern microservice architectures where concurrency is pervasive and data races are common—Dr.Fix seamlessly integrates into existing development workflows. We detail the design of Dr.Fix and examine how individual design choices influence the quality of the fixes produced. Over the past 18 months, Dr.Fix has been integrated into developer workflows at Uber demonstrating its practical utility. During this period, Dr.Fix produced patches for 224 (55%) from a corpus of 404 data races spanning various categories; 193 of these patches (86%) were accepted by more than a hundred developers via code reviews and integrated into the codebase.

Online platforms have sanctioned individuals and communities associated with ‘fringe’ movements linked to hate speech, violence, and terrorism — but can these sanctions contribute to the abandonment of these movements? Here, we investigate this question through the lens of r/exredpill, a recovery community on Reddit meant to help individuals leave movements within the Manosphere, a conglomerate of fringe Web-based movements focused on men’s issues. We conduct an observational study on the impact of sanctioning some of Reddit’s largest Manosphere communities on the activity levels and user influx of r/exredpill, the largest associated recovery subreddit. We find that banning a related radical community positively affects participation in r/exredpill in the period following the ban. Yet, quarantining the community, a softer moderation intervention, yields no such effects. We show that the effect induced by banning a radical community is stronger than for some of the widely discussed real-world events related to the Manosphere and that moderation actions against the Manosphere do not cause a spike in toxicity or malicious activity in r/exredpill. Overall, our findings suggest that content moderation acts as a deradicalization catalyst.

The Patent Act of 1946 banned infringer profit awards in utility patent cases by restricting all compensatory awards, including reasonable royalties, to the value of actual damages suffered and by eliminating infringer profits from reasonable royalty estimations. In 1964, the Supreme Court confirmed in Aro Manufacturing v. Convertible Top Replacement Co. that the 1946 Act limits reasonable royalty awards to the amount of the patentee’s actual damages, which courts must assess without regard to the value gained by the infringer. Subsequent courts, however, have ignored the 1946 Act and its correct interpretation in Aro, opting instead to permit profit disgorgement through reasonable royalty calculations. These extralegal calculations have fueled proliferation of patent assertion entities and expansion of the larger patent monetization market by enabling patent asserters to seek compensation based on infringer gains rather than patentee losses. This Article defends the 1946 Act and its interpretation in Aro as both good law and good policy. In support, this Article reviews over two-hundred years of patent damages law to identify the “series of historical accidents” that led to profit disgorgement and reasonable royalties in equity, explain how the 1946 Act restricted reasonable royalty calculations to damages only, and expose the second wave of accidents that caused modern courts to stray from the 1946 Act. Today, courts should enforce the Act’s restrictions on reasonable royalty awards by requiring plaintiffs to demonstrate actual injury-in-fact, limiting reasonable royalties to estimations of such injuries only, and disregarding evidence of infringer profits and other measures of infringer success when setting reasonable royalties.

Real-time communication (RTC) applications demand consistent low latency to ensure a smooth and interactive user experience. However, wireless networks, including WiFi and cellular, although they provide satisfactory median latency, often suffer from significant tail latency due to the highly variable network bandwidth. We observe that the control loop for managing the sending rate of RTC applications becomes inflated when congestion occurs at the wireless access point (AP), leading to untimely rate adaptation in response to wireless dynamics. Existing solutions fail to quickly adapt to bandwidth fluctuations due to the inflated control loop. In this paper, we propose Zhuge, a purely wireless AP-based solution that addresses these issues by separating congestion feedback from congested queues. Our approach involves the design of a Fortune Teller, which accurately estimates the wireless latency for each packet upon its arrival at the wireless AP. To ensure scalability, we also develop a Feedback Updater that translates the estimated latency into understandable feedback messages for various end-to-end protocols, delivering them back to the senders immediately for rate adaptation. Our evaluation, based on both trace-driven simulations and real-world scenarios, demonstrates that Zhuge significantly reduces the occurrence of large tail latency and alleviates RTC performance degradation by 22% to 95%.

We propose a multi-objective hierarchical recommender that solves the multi-objective recommendation problem in a hierarchical setting and was deployed on Uber Eats.

An important feature of modern query optimizers is the ability to produce a query plan that is optimal for the underlying data set. This requires the ability to estimate cardinalities and computational costs of intermediate query plan nodes, which is highly dependent on both the query shape and the underlying data distribution. Traditional methods include collecting statistics on base tables and implementing cardinality and computational cost derivation inside the optimizer, which is error-prone for complex query shapes. This paper presents Presto's novel history-based optimization framework (HBO), which collects execution histories and uses them to optimize similar queries in the future. The framework produces accurate estimates for complex query shapes in a lightweight, automated manner, and adapts automatically to changes in underlying data distributions. We present the design and implementation of the HBO framework and provide details on its use in various optimization rules, as well as details on implementing the statistics store on top of a Redis key-value store. We also present the results of running HBO in production in two large data infrastructure organizations (Meta and Uber).

Despite the importance of measuring racial-ethnic segregation and diversity in the United States, current measurements are largely based on the Census and, thus, only reflect segregation and diversity as understood through residential location. This leaves out the social contexts experienced throughout the course of the day during work, leisure, errands, and other activities. The National Experienced Racial-ethnic Diversity (NERD) dataset provides estimates of diversity for the entire United States at the census tract level based on the range of place and times when people have the opportunity to come into contact with one another. Using anonymized and opted-in mobile phone location data to determine co-locations of people and their demographic backgrounds, these measurements of diversity in potential social interactions are estimated at 38.2 m × 19.1 m scale and 15-minute timeframe for a representative year and aggregated to the Census tract level for purposes of data privacy. As well, we detail some of the characteristics and limitations of the data for potential use in national, comparative studies.