Machine learning enables knowledge management and knowledge bases to move beyond putting things in the right folders to the (re-)shaping and surfacing of knowledge, not only from published content but also from the ongoing work that people do. With this, a knowledge base is no…
Tasks are a fundamental unit of work in the daily lives of people, who are increasingly using digital means to keep track of, organize, triage and act on them. These digital tools – such as task management applications – provide a unique opportunity to study…
Unit testing represents the foundational basis of the software testing pyramid, beneath integration and end-to-end testing. Automated software testing researchers have proposed a variety of techniques to assist developers in this time-consuming task. In this paper we present an approach to support developers in writing…
Details of the designs and mechanisms in support of human-AI collaboration must be considered in the real-world fielding of AI technologies. A critical aspect of interaction design for AI-assisted human decision making are policies about the display and sequencing of AI inferences within larger decision-making…
The global food supply chain needs to evolve to meet a 50 percent increase in food demand by 2050. While food production grows every year, according to a report by the Food and Agriculture Organization (FAO), around 25 percent of roots and tubers, 20 percent…
Multilingual automatic speech recognition (ASR) models have shown great promise in recent years because of the simplified model training and deployment process. Conventional methods either train a universal multilingual model without taking any language information or with a 1-hot language ID (LID) vector to guide…
The identification of active binding drugs for target proteins (termed as drug-target interaction prediction) is the key challenge in virtual screening, which plays an essential role in drug discovery. Although recent deep learning-based approaches achieved better performance than molecular docking, existing models often neglect certain…
The past decade has produced a revolution in Artificial Intelligence (AI), after a half-century of AI repeatedly failing to meet expectations. What explains the dramatic change from 20th-century to 21st-century AI, and how can remaining limitations of current AI be overcome? Until now, the widely…
Autonomous systems that operate in the open world often use incomplete models oftheir environment. Model incompleteness is inevitable due to the practical limitations inprecise model specification and data collection about open-world environments. Due to thelimited fidelity of the model, agent actions may producenegative side effects(NSEs)…
Toxic language detection systems often falsely flag text that contains minority group mentions as toxic, as those groups are often the targets of online hate. Such over-reliance on spurious correlations also causes systems to struggle with detecting implicitly toxic language. To help mitigate these issues,…
Speech enhancement has recently achieved great success with various deep learning methods. However, most conventional speech enhancement systems are trained with supervised methods that impose two significant challenges. First, a majority of training datasets for speech enhancement systems are synthetic. When mixing clean speech and…
What explains the dramatic progress from 20th-century to 21st-century AI, and how can the remaining limitations of current AI be overcome? The widely accepted narrative attributes this progress to massive increases in the quantity of computational and data resources available to support statistical learning in…
