arXiv subjects

Will Wang

Publications and source records attributed to Will Wang.

6 recordsLinked to original sources

arXiv2026

AI in the Enterprise: How People Use M365 Copilot Chat

M365 Copilot is used every week by millions of people across more than a million companies around the world as part of their workflows. Uniquely positioned in the AI landscape given its near-exclusive use for work purposes, M365 Copilot can offer a clear picture of how people use AI for work and where that usage may expand next. This paper characterizes that usage through direct classification of user interactions with M365 Copilot Chat. Based on an anonymized and privacy-preserving analysis of a sample of approximately 5.5 million sessions, we combine a learned classification of user intent with a classification of O*NET work activities done with M365 Copilot Chat. We find that M365 Copilot is emerging as an everyday assistant for knowledge work: writing dominates, but users also rely on it for information retrieval, analysis, decision making and strategizing, and evaluating and diagnosing programs and systems, among others. Information seeking tasks remain common, but time trends suggest a relative shift away from ``chat as search'' and toward content and communication-related work. Comparisons across occupational groupings and to work done in the labor market further show that usage is broad but uneven, where the relative share of work done with M365 Copilot Chat cuts across jobs in some cases and is occupation-specific in others. Areas of relative underrepresentation in the labor market suggest the next frontier for enterprise AI adoption.

cs.CY↗

arXiv2025

Working with AI: Measuring the Applicability of Generative AI to Occupations

With generative AI emerging as a general-purpose technology, understanding its economic effects is among society's most pressing questions. Existing studies of AI impact have largely relied on predictions of AI capabilities or focused narrowly on individual firms. Drawing instead on real-world AI usage, we analyze a dataset of 200k anonymized conversations with Microsoft Bing Copilot to measure AI applicability to occupations. We use an LLM-based pipeline to classify the O*NET work activities assisted or performed by AI in each conversation. We find that the most common and successful AI-assisted work activities involve information work--the creation, processing, and communication of information. At the occupation level, we find widespread AI applicability cutting across sectors, as most occupations have information work components. Our methodology also allows us to predict which occupations are more likely to delegate tasks to AI and which are more likely to use AI to assist existing workflows.

cs.AI↗

arXiv2025

WatchGuardian: Enabling User-Defined Personalized Just-in-Time Intervention on Smartwatch

While just-in-time interventions (JITIs) have effectively targeted common health behaviors, individuals often have unique needs to intervene in personal undesirable actions that can negatively affect physical, mental, and social well-being. We present WatchGuardian, a smartwatch-based JITI system that empowers users to define custom interventions for these personal actions with a small number of samples. For the model to detect new actions based on limited new data samples, we developed a few-shot learning pipeline that finetuned a pre-trained inertial measurement unit (IMU) model on public hand-gesture datasets. We then designed a data augmentation and synthesis process to train additional classification layers for customization. Our offline evaluation with 26 participants showed that with three, five, and ten examples, our approach achieved an average accuracy of 76.8%, 84.7%, and 87.7%, and an F1 score of 74.8%, 84.2%, and 87.2% We then conducted a four-hour intervention study to compare WatchGuardian against a rule-based intervention. Our results demonstrated that our system led to a significant reduction by 64.0 +- 22.6% in undesirable actions, substantially outperforming the baseline by 29.0%. Our findings underscore the effectiveness of a customizable, AI-driven JITI system for individuals in need of behavioral intervention in personal undesirable actions. We envision that our work can inspire broader applications of user-defined personalized intervention with advanced AI solutions.

cs.HC↗

arXiv2024

Slow quasiparticle dynamics and anyonic statistics in a fractional quantum Hall Fabry-P\'erot interferometer

Anyons are particles with fractional exchange statistics that emerge as elementary excitations of fractional quantum Hall phases. Experimentally, their exchange statistics can be measured in the edge-state Fabry-P\'erot interferometer. In these devices, the presence of $N_{qp}$ localized anyons in the bulk contributes a phase $N_{qp}\theta_a$ to the interference signal. Here we report the observation of large, hysteretic phase jumps in a monolayer graphene Fabry-P\'erot interferometer at $\nu=1/3$. When the filling factor is increased from $\nu<1/3$ towards the center of the plateau, we observe phase slips with magnitude $\Delta \theta \approx 2\pi/3$, consistent with the addition of individual quasiparticles to the interferometer bulk. In contrast to prior work, however, the phase slips occur as instantaneous jumps in the interference signal, indicative of quasiparticle equilibration times exceeding 20 minutes. We use this long timescale to investigate the effect of changes in interferometer area $A_I$ and $N_{QP}$ independently at fixed magnetic field, revealing a striking memory effect in the phase slip magnitude. In particular, as the $\nu=1/3$ plateau is approached from higher filling, we observed phase slips with $\Delta \theta$ significantly larger than $2\pi/3$ over the same range of gate voltage where quantized jumps are seen for increasing $\nu$. We discuss this asymmetry in terms of bulk-edge coupling of quasiparticles localized near the edge or in the bulk, and argue that this effect can be qualitatively reconciled with theoretical expectations for strongly interacting quasiparticles in the presence of weak disorder and strongly nonequilibrium charge dynamics. Our results highlight the key role played by charge dynamics on signatures of the anyon phase, and demonstrate that fractional quasiparticles can be indefinitely localized in nonequilibrium configurations.

cond-mat.mes-hall↗

arXiv2023

Automated Characterization of a Double Quantum Dot using the Hubbard Model

Semiconductor quantum dots are favorable candidates for quantum information processing due to their long coherence time and potential scalability. However, the calibration and characterization of interconnected quantum dot arrays have proven to be challenging tasks. One method to characterize the configuration of such an array involves using the Hubbard model. In this paper, we present an automated characterization algorithm that efficiently extracts the Hubbard model parameters, including tunnel coupling and capacitive coupling energy, from experimental stability diagrams. Leveraging the dual annealing optimizer, we determine the set of Hubbard parameters that best characterize the experimental data. We compare our method with an alternate, well-established measure of the tunnel coupling and find good agreement within the investigated regime. Our extracted tunnel couplings range from 69 to 517 $μ$eV, and we discuss the limiting factors of our method.

cond-mat.mes-hall↗

arXiv2021

Robotic Surgery With Lean Reinforcement Learning

As surgical robots become more common, automating away some of the burden of complex direct human operation becomes ever more feasible. Model-free reinforcement learning (RL) is a promising direction toward generalizable automated surgical performance, but progress has been slowed by the lack of efficient and realistic learning environments. In this paper, we describe adding reinforcement learning support to the da Vinci Skill Simulator, a training simulation used around the world to allow surgeons to learn and rehearse technical skills. We successfully teach an RL-based agent to perform sub-tasks in the simulator environment, using either image or state data. As far as we know, this is the first time an RL-based agent is taught from visual data in a surgical robotics environment. Additionally, we tackle the sample inefficiency of RL using a simple-to-implement system which we term hybrid-batch learning (HBL), effectively adding a second, long-term replay buffer to the Q-learning process. Additionally, this allows us to bootstrap learning from images from the data collected using the easier task of learning from state. We show that HBL decreases our learning times significantly.

cs.RO↗