Search Results for author: Gagan Bansal

Found 14 papers, 1 papers with code

Generation Probabilities Are Not Enough: Exploring the Effectiveness of Uncertainty Highlighting in AI-Powered Code Completions

no code implementations14 Feb 2023 Helena Vasconcelos, Gagan Bansal, Adam Fourney, Q. Vera Liao, Jennifer Wortman Vaughan

Through a mixed-methods study with 30 programmers, we compare three conditions: providing the AI system's code completion alone, highlighting tokens with the lowest likelihood of being generated by the underlying generative model, and highlighting tokens with the highest predicted likelihood of being edited by a programmer.

Code Completion

Aligning Offline Metrics and Human Judgments of Value of AI-Pair Programmers

no code implementations29 Oct 2022 Victor Dibia, Adam Fourney, Gagan Bansal, Forough Poursabzi-Sangdeh, Han Liu, Saleema Amershi

While functional correctness is clearly an important property of a code generation model, we argue that it may not fully capture what programmers value when collaborating with their AI pair programmers.

Association Code Generation

Reading Between the Lines: Modeling User Behavior and Costs in AI-Assisted Programming

1 code implementation25 Oct 2022 Hussein Mozannar, Gagan Bansal, Adam Fourney, Eric Horvitz

AI code-recommendation systems (CodeRec), such as Copilot, can assist programmers inside an IDE by suggesting and autocompleting arbitrary code; potentially improving their productivity.

Code Completion Recommendation Systems

Using Machine Translation to Localize Task Oriented NLG Output

no code implementations9 Jul 2021 Scott Roy, Cliff Brunk, Kyu-Young Kim, Justin Zhao, Markus Freitag, Mihir Kale, Gagan Bansal, Sidharth Mudgal, Chris Varano

One of the challenges in a task oriented natural language application like the Google Assistant, Siri, or Alexa is to localize the output to many languages.

Domain Adaptation Machine Translation +1

Human Evaluation of Spoken vs. Visual Explanations for Open-Domain QA

no code implementations30 Dec 2020 Ana Valeria Gonzalez, Gagan Bansal, Angela Fan, Robin Jia, Yashar Mehdad, Srinivasan Iyer

While research on explaining predictions of open-domain QA systems (ODQA) to users is gaining momentum, most works have failed to evaluate the extent to which explanations improve user trust.

Is the Most Accurate AI the Best Teammate? Optimizing AI for Teamwork

no code implementations27 Apr 2020 Gagan Bansal, Besmira Nushi, Ece Kamar, Eric Horvitz, Daniel S. Weld

To optimize the team performance for this setting we maximize the team's expected utility, expressed in terms of the quality of the final decision, cost of verifying, and individual accuracies of people and machines.

Decision Making

A Case for Backward Compatibility for Human-AI Teams

no code implementations4 Jun 2019 Gagan Bansal, Besmira Nushi, Ece Kamar, Dan Weld, Walter Lasecki, Eric Horvitz

We introduce the notion of the compatibility of an AI update with prior user experience and present methods for studying the role of compatibility in human-AI teams.

Decision Making

Gmail Smart Compose: Real-Time Assisted Writing

no code implementations17 May 2019 Mia Xu Chen, Benjamin N Lee, Gagan Bansal, Yuan Cao, Shuyuan Zhang, Justin Lu, Jackie Tsay, Yinan Wang, Andrew M. Dai, Zhifeng Chen, Timothy Sohn, Yonghui Wu

In this paper, we present Smart Compose, a novel system for generating interactive, real-time suggestions in Gmail that assists users in writing mails by reducing repetitive typing.

Language Modelling Model Selection

The Challenge of Crafting Intelligible Intelligence

no code implementations9 Mar 2018 Daniel S. Weld, Gagan Bansal

Since Artificial Intelligence (AI) software uses techniques like deep lookahead search and stochastic optimization of huge neural networks to fit mammoth datasets, it often results in complex behavior that is difficult for people to understand.

Stochastic Optimization

Revenue Forecasting for Enterprise Products

no code implementations21 Nov 2016 Amita Gajewar, Gagan Bansal

For any business, planning is a continuous process, and typically business-owners focus on making both long-term planning aligned with a particular strategy as well as short-term planning that accommodates the dynamic market situations.

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