Situating AI on the road from data sharing to societal impact
release_yttxelvs7fdftlgaml4m4g5fvi
2020
Abstract
This talk is part of the AI Lunch series at the Jefferson Lab and was given remotely on 2 September 2020. Abstract By its very nature, Artificial Intelligence depends on the availability of data at scale. In this presentation - which shall be available at https://doi.org/10.5281/zenodo.3996019 when it starts - we will look at a range of factors that influence the nature and scale of data sharing, from open science to disasters, from research infrastructures to ethics, from cooperation to competition. We will then delve into how these factors affect the data life cycle and the research cycle and explore how data sharing (or the lack of it) translates into societal impact. On that journey, we will watch out for ways in which AI can and does contribute to or benefit from the sharing of data and associated resources (or not), which will then form the basis of our discussion. Further notes: https://github.com/Daniel-Mietchen/events/blob/master/JLab-AI-Lunch-Series.md
In text/plain
format
Archived Files and Locations
application/pdf 3.9 MB
file_sftke2wqo5e7jl77ojnsudijou
|
zenodo.org (repository) web.archive.org (webarchive) |
access all versions, variants, and formats of this works (eg, pre-prints)
Datacite Metadata (via API)
Worldcat
wikidata.org
CORE.ac.uk
Semantic Scholar
Google Scholar