Publications
Our teams aspire to make discoveries that impact everyone, and core to our approach is sharing our research and tools to fuel progress in the field.
Our teams aspire to make discoveries that impact everyone, and core to our approach is sharing our research and tools to fuel progress in the field.
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1 - 15 of 10132 publications
DORSal: Diffusion for Object-centric Representations of Scenes et al.
Allan Jabri
Emiel Hoogeboom
Thomas Kipf
International Conference on Learning Representations (2024)
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Recent progress in 3D scene understanding enables scalable learning of representations across large datasets of diverse scenes. As a consequence, generalization to unseen scenes and objects, rendering novel views from just a single or a handful of input images, and controllable scene generation that supports editing, is now possible. However, training jointly on a large number of scenes typically compromises rendering quality when compared to single-scene optimized models such as NeRFs. In this paper, we leverage recent progress in diffusion models to equip 3D scene representation learning models with the ability to render high-fidelity novel views, while retaining benefits such as object-level scene editing to a large degree. In particular, we propose DORSal, which adapts a video diffusion architecture for 3D scene generation conditioned on frozen object-centric slot-based representations of scenes. On both complex synthetic multi-object scenes and on the real-world large-scale Street View dataset, we show that DORSal enables scalable neural rendering of 3D scenes with object-level editing and improves upon existing approaches.
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It’s no secret that generative artificial intelligence (GenAI) is rapidly changing the landscape of software development, with discussions about best practices for applying this transformative technology dominating the popular press [cite cite cite]. Perhaps nowhere on Earth have these discussions been more frequent and passionate than inside the organizations dedicated to making GenAI accessible and useful to developers, including at Google. During one such discussion between researchers on our DevOps Research and Assessment (DORA) and Engineering Productivity Research (EPR) teams, we were struck by a recurring finding common to development professionals both inside and outside of Google:
Using GenAI makes developers feel more productive, and developers who trust GenAI use it more.
On the surface, this finding may seem somewhat… obvious. But, for us, it highlighted the deep need to better understand the factors that impact developers’ trust in GenAI systems and ways to foster that trust, so that developers and development firms can yield the most benefit from their investment in GenAI development tools.
Here, we share findings from seven studies conducted at Google, regarding the productivity gains of GenAI use in development, the impacts of developers’ trust on GenAI use, and the factors we’ve observed which positively impact developers’ trust in GenAI. We conclude with five suggested strategies that organizations engaged in software development might employ to foster their developers’ trust in GenAI, thereby increasing their GenAI use and maximizing GenAI-related productivity gains.
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New regulations and increased awareness of data privacy have led to the deployment of new and more efficient differentially private mechanisms across public institutions and industries. Ensuring the correctness of these mechanisms is therefore crucial to ensure the proper protection of data. However, since differential privacy is a property of the mechanism itself, and not of an individual output, testing whether a mechanism is differentially private is not a trivial task. While ad hoc testing techniques exist under specific assumptions, no concerted effort has been made by the research community to develop a flexible and extendable tool for testing differentially private mechanisms. This paper introduces DP-Auditorium as a step advancing research in this direction. DP-Auditorium abstracts the problem of testing differential privacy into two steps: (1) measuring the distance between distributions, and (2) finding neighboring datasets where a mechanism generates output distributions maximizing such distance. From a technical point of view, we propose three new algorithms for evaluating the distance between distributions. While these algorithms are well-established in the statistics community, we provide new estimation guarantees that exploit the fact that we are only interested in verifying whether a mechanism is differentially private, and not in obtaining an exact estimate of the distance between two distributions. DP-Auditorium is easily extensible, as demonstrated in this paper by implementing a well-known approximate differential privacy testing algorithm into our library. We provide an extensive comparison to date of multiple testers across varying sample sizes and differential privacy parameters, demonstrating that there is no single tester that dominates all others, and that a combination of different techniques is required to ensure
proper testing of mechanisms.
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Websites Need Your Permission Too – User Sentiment and Decision Making on Web Permission Prompts in Desktop Chrome
Marian Harbach
CHI 2024, ACM
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The web utilizes permission prompts to moderate access to certain capabilities. We present the first investigation of user behavior and sentiment of this security and privacy measure on the web, using 28 days of telemetry data from more than 100M Chrome installations on desktop platforms and experience sampling responses from 25,706 Chrome users. Based on this data, we find that ignoring and dismissing permission prompts are most common for geolocation and notifications. Permission prompts are perceived as more annoying and interrupting when they are not allowed, and most respondents cite a rational reason for the decision they took. Our data also supports that the perceived availability of contextual information from the requesting website is associated with allowing access to a requested capability. More usable permission controls could facilitate adoption of best practices that address several of the identified challenges; and ultimately could lead to better user experiences and a safer web.
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Can Capacitive Touch Images Enhance Mobile Keyboard Decoding?
Billy Dou
Cedric Ho
Proceedings of UIST 2024 (2024)
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Capacitive touch sensors capture the two-dimensional spatial profile (referred to as a touch heatmap) of a finger's contact with a mobile touchscreen. However, the research and design of touchscreen mobile keyboards - one of the most speed- and accuracy-demanding touch interfaces - has focused on the location of the touch centroid derived from the touch image heatmap as the input, discarding the rest of the raw spatial signals. In this paper, we investigate whether touch heatmaps can be leveraged to further improve the tap decoding accuracy for mobile touchscreen keyboards. Specifically, we compared machine-learning models that decode user taps by using the centroids and/or the heatmaps as their input and studied the contribution due to the heatmap. The results show that adding the heatmap into the input feature set led to 21.4% relative reduction of character error rates on average, compared to using the centroid alone. Furthermore, we conducted online deployment testing of the heatmap-based decoder in a user study with 16 participants and observed lower error rate, faster typing speed, and higher self-reported satisfaction score based on the heatmap-based decoder than the centroid-based decoder. These findings underline the promise of utilizing touch heatmaps for improving typing experience in mobile keyboards.
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Given a training data-set $\mathcal{S}$, and a reference data-set $\mathcal{T}$, we design a simple and efficient algorithm to reweigh the loss function such that the limiting distribution of the neural network weights that result from training on $\mathcal{S}$ approaches the limiting distribution that would have resulted by training on $\mathcal{T}$. Such reweighing can be used to correct for Train-Test distribution shift, when we don't have access to the labels of $\mathcal{T}$. It can also be used to perform (soft) multi-criteria optimization on neural nets, when we have access to the labels of $\mathcal{T}$, but $\mathcal{S}$ and $\mathcal{T}$ have few common points.
As a motivating application, we train a graph neural net to recognize small molecule binders to MNK2 (a MAP Kinase, responsible for cell signaling) which are non-binders to MNK1 (a very similar protein), even in the absence of training data common to both data-sets. We are able to tune the reweighing parameters so that overall change in holdout loss is negligible, but the selectivity, i.e., the fraction of top 100 MNK2 binders that are MNK1 non-binders, increases from 54\% to 95\%, as a result of our reweighing.
We expect the algorithm to be applicable in other settings as well, since we prove that when the metric entropy of the input data-sets is bounded, our random sampling based greedy algorithm outputs a close to optimal reweighing, i.e., the two invariant distributions of network weights will be provably close in total variation distance.
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The article summarizes the unique challenges and strategies required for a successful GTM (Go to market) strategy in enterprise world. We cover how enterprise PM function is unique from regular PM, and why enterprise PMs must look at distribution as an inherent product process. We also share a framework for thinking about various components of GTM strategy. Key aspects include customer segmentation, account acquisition strategies, product packaging, positionining and marketing; and technical enablement and content distribution.
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Thesios: Synthesizing Accurate Counterfactual I/O Traces from I/O Samples
Mangpo Phothilimthana
Soroush Ghodrati
Selene Moon
ASPLOS 2024, Association for Computing Machinery
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Representative modeling of I/O activity is crucial when designing large-scale distributed storage systems. Particularly important use cases are counterfactual “what-if” analyses that assess the impact of anticipated or hypothetical new storage policies or hardware prior to deployment. We propose Thesios, a methodology to accurately synthesize such hypothetical full-resolution I/O traces by carefully combining down-sampled I/O traces collected from multiple disks attached to multiple storage servers. Applying this approach to real-world traces that a real ready routinely sampled at Google, we show that our synthesized traces achieve 95–99.5% accuracy in read/write request numbers, 90–97% accuracy in utilization, and 80–99.8% accuracy in read latency compared to metrics collected from actual disks. We demonstrate how The-sios enables diverse counterfactual I/O trace synthesis and analyses of hypothetical policy, hardware, and server changes through four case studies: (1) studying the effects of changing disk’s utilization, fullness, and capacity, (2) evaluating new data placement policy, (3) analyzing the impact on power and performance of deploying disks with reduced rotations-per-minute (RPM), and (4) understanding the impact of increased buffer cache size on a storage server. Without Thesios, such counterfactual analyses would require costly and potentially risky A/B experiments in production.
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Artificial Intelligence (AI) holds the promise of transforming healthcare by improving patient outcomes, increasing accessibility and efficiency, and decreasing the cost of care. Realizing this vision of a healthier world for everyone everywhere requires partnerships and trust between healthcare systems, clinicians, payers, technology companies, pharmaceutical companies, and governments to drive innovations in machine learning and artificial intelligence to patients. Google is one example of a technology company that is partnering with healthcare systems, clinicians, and researchers to develop technology solutions that will directly improve the lives of patients. In this chapter we share landmark trials of the use of AI in healthcare. We also describe the application of our novel system of organizing information to unify data in electronic health records (EHRs) and bring an integrated view of patient records to clinicians. We discuss our consumer focused innovation in dermatology to help guide search journeys for personalized information about skin conditions. Finally, we share a perspective on how to embed ethics and a concern for all patients into the development of AI.
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SEMQA: Semi-Extractive Multi-Source Question Answering
Haitian Sun
NAACL (2024) (to appear)
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Recently proposed long-form question answering (QA) systems, supported by large language models (LLMs), have shown promising capabilities. Yet, attributing and verifying their generated abstractive answers can be difficult, and automatically evaluating their accuracy remains an ongoing challenge.
In this paper, we introduce a new QA task for answering multi-answer questions by summarizing multiple diverse sources in a semi-extractive fashion. Specifically, Semi-extractive Multi-source QA (SEMQA) requires models to output a comprehensive answer while mixing between factual quoted spans---copied verbatim from given input sources---and non-factual free-text connectors that glue these spans together into a single cohesive passage. This setting bridges the gap between the outputs of well-grounded but constrained extractive QA systems and more fluent but harder to attribute fully abstractive answers. Particularly, it enables a new mode for language models that leverages their advanced language generation capabilities, while also producing fine in-line attributions by-design that are easy to verify, interpret, and evaluate. To study this task, we create the first dataset of this kind with human-written semi-extractive answers to natural and generated questions, and define text-based evaluation metrics. Experimenting with several LLMs in various settings, we find this task to be surprisingly challenging, demonstrating the importance of our work for developing and studying such consolidation capabilities.
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Hardware-Assisted Fault Isolation: Going Beyond the Limits of Software-Based Sandboxing
Anjo Vahldiek-Oberwagner
Tal Garfinkel
Deian Stefan
Michael LeMay
Evan Johnson
Mohammadkazem Taram
Chris Fallin
Ravi Sahita
Joey Rudek
Shravan Narayan
Dean Tullsen
IEEE Micro (2024)
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Hardware-assisted Fault Isolation (HFI) is a minimal extension to current processors that supports secure, flexible, and efficient in-process isolation. HFI addresses the limitations of software-based isolation (SFI) systems including: runtime overheads, limited scalability, vulnerability to Spectre attacks, and limited compatibility with existing code. HFI can be seamlessly integrated into exisiting SFI systems (e.g. WebAssembly), or directly sandbox unmodified native binaries. To ease adoption, HFI proposes incremental changes to existing high-performance processors.
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Misgendering refers to the act of incorrectly identifying or addressing someone's gender.
While misgendering is both a factual inaccuracy and a toxic act of identity erasure, research on fact-checking and toxicity detection does not address it.
We are the first to bridge this gap by introducing a dataset, \dataset, to assist in developing interventions for misgendering.
The misgendering interventions task can be divided into two sub-tasks: (i) detecting misgendering, followed by (ii) editing misgendering where misgendering is present, in domains where editing is appropriate.
We introduce a dataset containing a total of 3806 instances of tweets, YouTube comments, and LLM-generated text about 30 non-cisgender individuals annotated for whether they contain misgendering or not.
LLM-generated text is also annotated for edits required to fix misgendering.
Using this dataset, we set initial benchmarks by evaluating existing NLP systems and highlight challenges for future models to address.
Additionally, we conducted a survey of non-cisgender individuals in the US to understand opinions about automated interventions for text-based misgendering.
We find interest for interventions along with concerns for potential harm.
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AI-Enhanced API Design: A New Paradigm in Usability and Efficiency
Mak Ahmad
David R Karger
Kwan-Liu Ma
CHI EA '24: Extended Abstracts of the 2024 CHI Conference on Human Factors in Computing Systems (2024)
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This study uses mixed methods to evaluate API design methods, focusing on design and consumption phases. Our goal was to understand the impact of API governance approaches on productivity and usability. A controlled developer experiment (n=34) demonstrated
a 10% increased requirement fulfillment using API Improvement Proposals (AIPs) and linter versus no protocols. Meanwhile, 73% of 33 surveyed API consumers preferred AIP-aligned designs for enhanced usability and comprehensibility. Complementing this, a
custom large language model called the API Architect received average expert ratings of just 5/10 for specification quality, revealing gaps versus manual design. The quantitative performance metrics combined with qualitative user feedback provide evidence from
multiple angles that strategically integrating industry best practices with maturing AI capabilities can meaningfully improve API design outcomes. This research offers empirical insights from developer and consumer perspectives to advance scholarly discourse
and industry practice regarding optimal API design workflows.
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Meta-Manager: A Tool for Collecting and Exploring Meta Information about Code
Amber Horvath
Brad A. Myers
CHI '24: Proceedings of the CHI Conference on Human Factors in Computing Systems (2024)
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Modern software engineering is in a state of flux. With more development utilizing AI code generation tools and the continued reliance on online programming resources, understanding code and the original intent behind it is becoming more important than it ever has been. To this end, we have developed the “Meta-Manager”, a Visual Studio Code extension, with a supplementary browser extension, that automatically collects and organizes changes made to code while keeping track of the provenance of each part of the code, including code that has been copy-pasted from popular programming resources online. These sources and subsequent changes are represented in the editor and may be explored using searching and filtering mechanisms to help developers answer historically hard-to-answer questions about code, its provenance, and its design rationale. In our evaluation of Meta-Manager, we found developers
were successfully able to use it to answer otherwise unanswerable questions about an unfamiliar code base.
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Hierarchical Text Spotter for Joint Text Spotting and Layout Analysis
Winter Conference on Applications of Computer Vision 2024 (2024) (to appear)
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We propose Hierarchical Text Spotter (HTS), the first method for the joint task of word-level text spotting and geometric layout analysis.
HTS can annotate text in images with a hierarchical representation of 4 levels: character, word, line, and paragraph.
The proposed HTS is characterized by two novel components:
(1) a Unified-Detector-Polygon (UDP) that produces Bezier Curve polygons of text lines and an affinity matrix for paragraph grouping between detected lines;
(2) a Line-to-Character-to-Word (L2C2W) recognizer that splits lines into characters and further merges them back into words.
HTS achieves state-of-the-art results on multiple word-level text spotting benchmark datasets as well as geometric layout analysis tasks.
Code will be released upon acceptance.
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