Digital Fabrication Tools at Work: Probing Professionals' Current Needs and Desired Futures

Abstract: Digital fabrication tools have transformed how people work in micro- and small-scale manufacturing settings. While increasing efficiency and precision, these tools raise concerns around user agency and control. This paper describes an exploratory study investigating the felt work experience and desired futures of professionals who use fabrication tools. We conducted co-design workshops with 23 professionals who use 3D printers, laser cutters, and CNC routers. We probed about current practices; machine awareness and autonomy; and user agency. Our findings reveal that current tools are not very professional. They are unreliable and untrustworthy. Participants desired smarter tools that can actively prevent errors and perform self-calibration and self-maintenance. They had few concerns that more intelligence would impact agency. They desired tools that could negotiate trade-offs between time, cost, and quality; and that can operate as super-human shop assistants. We discuss the implications of these findings as opportunities for research that can improve professionals’ work experience.

19/10/2020

future of work, india, vocational technicians, ai, automation, policy, algorithmic fairness, skills, hci4d

15:12

29/06/2020

Interpretable and Personalized Apprenticeship Scheduling: Learning Interpretable Scheduling Policies from Heterogeneous User Demonstrations

Andras Gabor Kupcsik, Markus Spies, Alexander Klein and
Marco Todescato, Nicolai Waniek, Philipp Schillinger, Mathias Bürger

Nathalie Rauschmayr, Vikas Kumar, Rahul Huilgol and
Andrea Olgiati, Satadal Bhattacharjee, Nihal Harish, Vandana Kannan, Amol Lele, Anirudh Acharya, Jared Nielsen, Lakshmi Ramakrishnan, Ishan Bhatt, Kohen Chia, Neelesh Dodda, Zhihan Li, Jiacheng Gu, Miyoung Choi, Balajee Nagarajan Nagarajan, Jeffrey Geevarghese, Denis Davydenko, Sifei Li, Lu Huang, Edward Kim, Tyler Hill, Krishnaram Kenthapadi

Keywords Paper

19:27

05/04/2021

Amazon SageMaker Debugger: A System for Real-Time Insights into Machine Learning Model Training

Nathalie Rauschmayr, Vikas Kumar, Rahul Huilgol and
Andrea Olgiati, Satadal Bhattacharjee, Nihal Harish, Vandana Kannan, Amol Lele, Anirudh Acharya, Jared Nielsen, Lakshmi Ramakrishnan, Ishan Bhatt, Kohen Chia, Neelesh Dodda, Zhihan Li, Jiacheng Gu, Miyoung Choi, Balajee Nagarajan Nagarajan, Jeffrey Geevarghese, Denis Davydenko, Sifei Li, Lu Huang, Edward Kim, Tyler Hill, Krishnaram Kenthapadi

Manuel Wuthrich, Felix Widmaier, Felix Grimminger and
Shruti Joshi, Vaibhav Agrawal, Bilal Hammoud, Majid Khadiv, Miroslav Bogdanovic, Vincent Berenz, Julian Viereck, Maximilien Naveau, Ludovic Righetti, Bernhard Schölkopf, Stefan Bauer

fairness, shift scheduling, healthcare, shift work, interview, nurse scheduling problem, hospital, conflict resolution, organizational justice, allocation norms, work-life balance, equality, need, equity, roster

12:13

28/07/2020

Roland Hafner, Tim Hertweck, Philipp Kloeppner and
Michael Bloesch, Michael Neunert, Markus Wulfmeier, Saran Tunyasuvunakool, Nicolas Heess, Martin Riedmiller

policy learning, zero-order optimization, reinforcement learning, model predictive control, robotics, model-based learning

5:09

03/05/2021