WATCH NOW | Annual STAG Public Lecture 2023: Artificial Intelligence & Scientific Discovery: The Deep Learning Revolution
Virtual
Recording now available online!
This talk reviews the challenges posed by the explosive growth of data generated by facilities such as the Diamond Synchrotron and the CryoEM Facilities at the Rutherford Appleton Laboratory. Scientists are beginning to use machine learning and AI tech to automate parts of the data pipeline and to find new scientific discoveries. The ‘Deep Learning’ neural networks discovered in 2012 have already transformed many areas of computer science. Most recently these Deep Learning networks have created powerful ‘chatbots’ such as ChatGPT. The talk concludes with a vision of how this ‘AI for Science’ agenda can be truly transformative for many areas of scientific discovery. Click on "View Details" to access the recording.
Artificial Intelligence & Scientific Discovery: The Deep Learning Revolution
By Professor Tony Hey Chief Data Scientist, STFC
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, analyse, visualise, 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 vast quantities of experimental data. In particular, the ‘Deep Learning’ neural networks discovered in 2012 have already transformed many areas of computer science and research scientists are now exploring their use in analysing their ‘Big Scientific Data’. Most recently these Deep Learning networks have been used to create impressively powerful ‘chatbots’ such as ChatGPT that have the potential to transform many areas of science and computer programming. The talk concludes with a vision of how this ‘AI for Science’ agenda can be truly transformative for many areas of scientific discovery.
Tony Hey began his career as a theoretical physicist with a doctorate in particle physics from the University of Oxford. After a career in particle physics that included research positions at Caltech, MIT and CERN, and a professorship at the University of Southampton, he became interested in computing and computer science. Tony left Southampton in 2001 to lead the UK’s ground-breaking ‘eScience’ initiative. In 2005 Tony joined Microsoft as a Vice-President in Microsoft Research in the US and returned to the UK in 2015 as Chief Data Scientist at the Rutherford Appleton Laboratory. Here he founded a new ‘Scientific Machine Learning’ group applying machine learning technologies to the ‘Big Scientific Data’ generated by several of the UK’s major facilities (Diamond Synchrotron, the ISIS neutron and muon source, the Electron Microscopy facility and the Central Laser Facility). Tony is a prolific author and science communicator having co-authored three popular books on science and computing – ‘The New Quantum Universe’, ‘Einstein’s Mirror’ and ‘The Computing Universe – Journey Through a Revolution’ – as well as the best-selling graduate text ‘Gauge Theories in Particle Physics’ with Ian Aitchison. His latest book is a collection of articles on ‘AI for Science’. In 2005, he was awarded a CBE for his services to science.
