Accelerate-Spark information session
- Zoom housekeeping
- Microphone on mute when not speaking.
- “Raise Hand” or post in chat for questions.
A University Computing Laboratory
In 1932 John Lennard-Jones, Professor of Theoretical Physics at Bristol University, was appointed to the John Humphrey Plummer Chair of Theoretical Chemistry at Cambridge University, which had become available thanks to a munificent bequest to the University of £200,000. In his new position Lennard-Jones continued the research he had started at Bristol University on the application of quantum mechanics to problems associated with chemistry. He and his students identified a number of problems that gave rise to complex equations which could only be solved by numerical methods.
Cambridge Computing ((Cambridge Computing: The First 75 Years 2013), pg 21)
The Department of Computer Science and Technology was set up in the 1930s, when it was known as the Mathematical Laboratory. At that time ‘computer’ had a different meaning – a computer was a person employed to do numerical calculations by hand. But advances in mechanical systems looked promising.
The case for creating the Lab, which was made in a report to the University in 1936, argued that:
Mechanical devices for performing calculations were developing (and human ‘computers’ were becoming harder to find).
A range of scientists from across disciplines were looking to make use of machines for numerical work.
But, despite their promise, the machines available at the time were unsuitable for some complex problems – tackling these would require better machines to be developed.
In response to these needs, the Lab was set up with the aim of enabling researchers to make use of new mechanical computers and developing more sophisticated machines to tackle complex problems.
AI for Science
Recent years have brought a lot of excitement about artificial intelligence, and its potential to revolutionise research.
In some disciplines, machine learning is already supporting impressive advances. Just this week, for example, we’ve seen headlines about new work by DeepMind, using machine learning to predict patterns of protein folding – an advance that could unlock the development of a range of new drugs to treat different diseases.
In other areas, there is clear potential for machine learning to make a contribution, but current tools and techniques aren’t being used as widely as they could be.
One of biology’s biggest mysteries is how proteins fold to create exquisitely unique three-dimensional structures. Every living thing – from the smallest bacteria to plants, animals and humans – is defined and powered by the proteins that help it function at the molecular level.
So far, this mystery remained unsolved, and determining a single protein structure often required years of experimental effort. It’s tremendous to see the triumph of human curiosity, endeavour and intelligence in solving this problem. A better understanding of protein structures and the ability to predict them using a computer means a better understanding of life, evolution and, of course, human health and disease.
Professor Dame Janet Thornton, Director Emeritus of EMBL
As quoted in the CASP14 press release.
This week we received the wonderful news about the AlphaFold2’s breakthrough in protein folding prediction. I’ve already sent my congratulations to Demis and the team directly, but I’d also like to highlight how we fit into this landscape. Several colleagues have asked me how we can hope to contribute when we already have such tremendous strides being made by industrial labs. My perspective is that work like DeepMind’s provides beacons that demonstrate the possible. Those beacons inspire the community, but the success we’re looking for is achieved when such techniques have moved from the hands of the world’s leading AI company into the hands of the scientists. Our aim is to develop the portfolio of tools available to these scientists and the skills base to use those tools, empowering them to drive forward their discoveries. Cambridge University is in a strong position to lead on this agenda and I view the Accelerate Programme as being the route to that vision. With that in mind, I’d like to outline some of the thinking that Jess, Carl Henrik and I have been doing around strategy for Accelerate.
Core to our thinking is that the Programme should function as a ramp or a bridge. The bridge analogy addresses the intellectual isolation of machine learning techniques from the sciences, with disciplinary boundaries contributing to a situation where those students who are working on ML techniques within a scientific domain are often isolated from a wider ML community, lacking access to the expertise they need to avoid reinventing the wheel or chasing phantoms. If we can support these students then we can scale our activities across the University – providing opportunities and connections that build skills, ramp-up current activities, and deliver the step change we envision.
Supporting this community is at the core of what we’re doing. You’ve already seen the “Data for Science” course that we ran across the summer, our next edition will be in early February. Alongside this, we’re launching an Accelerate Machine Learning school, to be run also in February, which will take the first Cohort from the Data for Science course on the next step of their journey to creating their own machine learning tools. After initial introduction courses on ML, Challenger Mishra will deliver a day focusing on how AI techniques are used in science.
The Accelerate Programme
We’re now in a new phase of the development of computing, with rapid advances in machine learning. But we see some of the same issues – researchers across disciplines hope to make use of machine learning, but need access to skills and tools to do so, while the field machine learning itself will need to develop new methods to tackle some complex, ‘real world’ problems.
It is with these challenges in mind that the Computer Lab has started the Accelerate Programme for Scientific Discovery. This new Programme is seeking to support researchers across the University to develop the skills they need to be able to use machine learning and AI in their research.
To do this, the Programme is developing three areas of activity:
Research: we’re developing a research agenda that develops and applies cutting edge machine learning methods to scientific challenges, with four Accelerate Research fellows working directly on issues relating to computational biology, psychiatry, string theory and materials science. While we’re concentrating on STEM subjects for now, in the longer term our ambition is to build links with the social sciences and humanities.
Teaching and learning: building on the teaching activities already delivered through University courses, we’re creating a pipeline of learning opportunities to help PhD students and postdocs better understand how to use data science and machine learning in their work. Our programme with Spark is one element of this, and we’ll be announcing further activities soon.
Engagement: we hope that Accelerate will help build a community of researchers working across the University at the interface on machine learning and the sciences, helping to share best practice and new methods, and support each other in advancing their research. Over the coming years, we’ll be running a variety of events and activities in support of this, and would welcome your ideas about what might be most useful.
Accelerate-Spark data science for science residency
The next activity from the Accelerate Programme is our data for science residency with Cambridge Spark. This five-week programme will equip participants with tools and techniques in data science that they can apply in their work, with a focus on using the methods taught on the course to solve your research problems.
To give a bit more detail on the content of this course, I’ll hand over now to Raoul-Gabriel Urma, Cambridge Spark’s founder.
For more information on these subjects and more you might want to check the following resources.
- twitter: @lawrennd
- podcast: The Talking Machines
- newspaper: Guardian Profile Page
- blog: http://inverseprobability.com
Cambridge Computing: The First 75 Years. 2013. Third Millenium Publishing.