A Disruptive Research Playbook for Studying Disruptive Innovations
- URL: http://arxiv.org/abs/2402.13329v1
- Date: Tue, 20 Feb 2024 19:13:36 GMT
- Title: A Disruptive Research Playbook for Studying Disruptive Innovations
- Authors: Margaret-Anne Storey, Daniel Russo, Nicole Novielli, Takashi
Kobayashi, Dong Wang
- Abstract summary: We propose a research playbook with the goal of providing a guide to formulate compelling and socially relevant research questions.
We show it can be used to question the impact of two current disruptive technologies: AI and AR/VR.
- Score: 11.619658523864686
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: As researchers, we are now witnessing a fundamental change in our
technologically-enabled world due to the advent and diffusion of highly
disruptive technologies such as generative AI, Augmented Reality (AR) and
Virtual Reality (VR). In particular, software engineering has been profoundly
affected by the transformative power of disruptive innovations for decades,
with a significant impact of technical advancements on social dynamics due to
its the socio-technical nature. In this paper, we reflect on the importance of
formulating and addressing research in software engineering through a
socio-technical lens, thus ensuring a holistic understanding of the complex
phenomena in this field. We propose a research playbook with the goal of
providing a guide to formulate compelling and socially relevant research
questions and to identify the appropriate research strategies for empirical
investigations, with an eye on the long-term implications of technologies or
their use. We showcase how to apply the research playbook. Firstly, we show how
it can be used retrospectively to reflect on a prior disruptive technology,
Stack Overflow, and its impact on software development. Secondly, we show it
can be used to question the impact of two current disruptive technologies: AI
and AR/VR. Finally, we introduce a specialized GPT model to support the
researcher in framing future investigations. We conclude by discussing the
broader implications of adopting the playbook for both researchers and
practitioners in software engineering and beyond.
Related papers
- O1 Replication Journey: A Strategic Progress Report -- Part 1 [52.062216849476776]
This paper introduces a pioneering approach to artificial intelligence research, embodied in our O1 Replication Journey.
Our methodology addresses critical challenges in modern AI research, including the insularity of prolonged team-based projects.
We propose the journey learning paradigm, which encourages models to learn not just shortcuts, but the complete exploration process.
arXiv Detail & Related papers (2024-10-08T15:13:01Z) - The application of Augmented Reality (AR) in Remote Work and Education [9.275489976839754]
Augmented Reality (AR) technology is gradually transforming traditional work modes and teaching methods.
This paper delves into the application potential and actual effects of AR technology in remote work and education.
arXiv Detail & Related papers (2024-04-16T14:04:46Z) - On the Opportunities of Green Computing: A Survey [80.21955522431168]
Artificial Intelligence (AI) has achieved significant advancements in technology and research with the development over several decades.
The needs for high computing power brings higher carbon emission and undermines research fairness.
To tackle the challenges of computing resources and environmental impact of AI, Green Computing has become a hot research topic.
arXiv Detail & Related papers (2023-11-01T11:16:41Z) - Machine Unlearning: A Survey [56.79152190680552]
A special need has arisen where, due to privacy, usability, and/or the right to be forgotten, information about some specific samples needs to be removed from a model, called machine unlearning.
This emerging technology has drawn significant interest from both academics and industry due to its innovation and practicality.
No study has analyzed this complex topic or compared the feasibility of existing unlearning solutions in different kinds of scenarios.
The survey concludes by highlighting some of the outstanding issues with unlearning techniques, along with some feasible directions for new research opportunities.
arXiv Detail & Related papers (2023-06-06T10:18:36Z) - "That's important, but...": How Computer Science Researchers Anticipate
Unintended Consequences of Their Research Innovations [12.947525301829835]
We show that considering unintended consequences is generally seen as important but rarely practiced.
Principal barriers are a lack of formal process and strategy as well as the academic practice that prioritizes fast progress and publications.
We intend for our work to pave the way for routine explorations of the societal implications of technological innovations before, during, and after the research process.
arXiv Detail & Related papers (2023-03-27T18:21:29Z) - Selected Trends in Artificial Intelligence for Space Applications [69.3474006357492]
This chapter focuses on differentiable intelligence and on-board machine learning.
We discuss a few selected projects originating from the European Space Agency's (ESA) Advanced Concepts Team (ACT)
arXiv Detail & Related papers (2022-12-10T07:49:50Z) - Coordinated Science Laboratory 70th Anniversary Symposium: The Future of
Computing [80.72844751804166]
In 2021, the Coordinated Science Laboratory CSL hosted the Future of Computing Symposium to celebrate its 70th anniversary.
We summarize the major technological points, insights, and directions that speakers brought forward during the symposium.
Participants discussed topics related to new computing paradigms, technologies, algorithms, behaviors, and research challenges to be expected in the future.
arXiv Detail & Related papers (2022-10-04T17:32:27Z) - Contemporary Research Trends in Response Robotics [0.0]
This paper analyzes the technical content, statistics, and implications of the literature from bibliometric standpoints.
The aim is to study the global progress of response robotics research and identify the contemporary trends.
arXiv Detail & Related papers (2021-04-28T05:35:45Z) - Axes for Sociotechnical Inquiry in AI Research [3.0215443986383734]
We propose four directions for inquiry into new and evolving areas of technological development.
The paper provides a lexicon for sociotechnical inquiry and illustrates it through the example of consumer drone technology.
arXiv Detail & Related papers (2021-04-26T16:49:04Z) - Artificial Intelligence for IT Operations (AIOPS) Workshop White Paper [50.25428141435537]
Artificial Intelligence for IT Operations (AIOps) is an emerging interdisciplinary field arising in the intersection between machine learning, big data, streaming analytics, and the management of IT operations.
Main aim of the AIOPS workshop is to bring together researchers from both academia and industry to present their experiences, results, and work in progress in this field.
arXiv Detail & Related papers (2021-01-15T10:43:10Z) - Nose to Glass: Looking In to Get Beyond [0.0]
An increasing amount of research has been conducted under the banner of enhancing responsible artificial intelligence.
Research aims to address, alleviating, and eventually mitigating the harms brought on by the roll out of algorithmic systems.
However, implementation of such tools remains low.
arXiv Detail & Related papers (2020-11-26T06:51:45Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.