The University of Southampton
University of Southampton Institutional Repository
Warning ePrints Soton is experiencing an issue with some file downloads not being available. We are working hard to fix this. Please bear with us.

AI3SD Video: AI for Science: Transforming Scientific Research

AI3SD Video: AI for Science: Transforming Scientific Research
AI3SD Video: AI for Science: Transforming Scientific Research
There is now broad recognition within the scientific community that the ongoing deluge of scientific data is fundamentally transforming academic research. Turing Award winner Jim Gray referred to this revolution as “The Fourth Paradigm: Data Intensive Scientific Discovery’. Researchers now need tools and technologies to manipulate, analyze, visualize, and manage vast amounts of research data. This talk will begin by reviewing the challenges posed by the explosive growth of experimental and observational data generated by large-scale facilities such as the Diamond Synchrotron and the CryoEM Facilities at the Rutherford Appleton Laboratory. Increasingly, scientists are beginning to use sophisticated machine learning and other AI technologies both to automate parts of the data pipeline and also to find new scientific discoveries in the deluge of experimental data. In particular, ‘Deep Learning’ neural networks have already transformed several areas of computer science and research scientists are now exploring their use in analyzing their ‘Big Scientific Data’. The talk concludes with a vision of how this ‘AI for Science’ agenda can be truly transformative for experimental scientific discovery.
AI3SD, AI3SD Event, Artificial Intelligence, Data Science, Deep Learning, Machine Intelligence, Machine Learning, ML, Research, Research Data Management, Responsible Research, Scientific Discovery
Hey, Tony
3a3daf85-9078-4d05-80b8-f23d79635351
Kanza, Samantha
b73bcf34-3ff8-4691-bd09-aa657dcff420
Frey, Jeremy G.
ba60c559-c4af-44f1-87e6-ce69819bf23f
Niranjan, Mahesan
5cbaeea8-7288-4b55-a89c-c43d212ddd4f
Hooper, Victoria
af1a99f1-7848-4d5c-a4b5-615888838d84
Hey, Tony
3a3daf85-9078-4d05-80b8-f23d79635351
Kanza, Samantha
b73bcf34-3ff8-4691-bd09-aa657dcff420
Frey, Jeremy G.
ba60c559-c4af-44f1-87e6-ce69819bf23f
Niranjan, Mahesan
5cbaeea8-7288-4b55-a89c-c43d212ddd4f
Hooper, Victoria
af1a99f1-7848-4d5c-a4b5-615888838d84

Hey, Tony (2020) AI3SD Video: AI for Science: Transforming Scientific Research. Kanza, Samantha, Frey, Jeremy G., Niranjan, Mahesan and Hooper, Victoria (eds.) AI3SD Summer Seminar Series 2020, Online, Southampton, United Kingdom. 01 Jul - 23 Sep 2020. (doi:10.5258/SOTON/P0053).

Record type: Conference or Workshop Item (Other)

Abstract

There is now broad recognition within the scientific community that the ongoing deluge of scientific data is fundamentally transforming academic research. Turing Award winner Jim Gray referred to this revolution as “The Fourth Paradigm: Data Intensive Scientific Discovery’. Researchers now need tools and technologies to manipulate, analyze, visualize, and manage vast amounts of research data. This talk will begin by reviewing the challenges posed by the explosive growth of experimental and observational data generated by large-scale facilities such as the Diamond Synchrotron and the CryoEM Facilities at the Rutherford Appleton Laboratory. Increasingly, scientists are beginning to use sophisticated machine learning and other AI technologies both to automate parts of the data pipeline and also to find new scientific discoveries in the deluge of experimental data. In particular, ‘Deep Learning’ neural networks have already transformed several areas of computer science and research scientists are now exploring their use in analyzing their ‘Big Scientific Data’. The talk concludes with a vision of how this ‘AI for Science’ agenda can be truly transformative for experimental scientific discovery.

Video
AI3SDOnlineSeminarSeries-14-TH-230920 - Version of Record
Available under License Creative Commons Attribution.
Download (754kB)

More information

Published date: 23 September 2020
Additional Information: Tony is the Chief Data Scientist at the Science and Technology Facilities Council. Tony’s original background was in Physics, completing his undergraduate degree and subsequent post-docs at the University of Oxford in the UK and then CalTech and CERN in the USA. He worked at the University of Southampton in the Physics Department originally before transferring to the Electronics and Computer Science Department where he created a leading research group in parallel computing. He was the director of the UK’s e-Science initiative (2001-2005) and then became the Vice President in Microsoft Research afterwards.
Venue - Dates: AI3SD Summer Seminar Series 2020, Online, Southampton, United Kingdom, 2020-07-01 - 2020-09-23
Keywords: AI3SD, AI3SD Event, Artificial Intelligence, Data Science, Deep Learning, Machine Intelligence, Machine Learning, ML, Research, Research Data Management, Responsible Research, Scientific Discovery

Identifiers

Local EPrints ID: 447159
URI: http://eprints.soton.ac.uk/id/eprint/447159
PURE UUID: a7b7017d-59f3-43ee-84da-028ea911589c
ORCID for Samantha Kanza: ORCID iD orcid.org/0000-0002-4831-9489
ORCID for Jeremy G. Frey: ORCID iD orcid.org/0000-0003-0842-4302

Catalogue record

Date deposited: 04 Mar 2021 17:38
Last modified: 11 Mar 2021 02:58

Export record

Altmetrics

Contributors

Author: Tony Hey
Editor: Samantha Kanza ORCID iD
Editor: Jeremy G. Frey ORCID iD
Editor: Mahesan Niranjan
Editor: Victoria Hooper

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×