Accelerate Science

Accelerate Science is a Schmidt Sciences sponsored project to ‘bring about a step change in Cambridge’s science capabilities through AI’.

The potential for machine learning in the sciences is now widely discussed, especially in the light of this year’s Nobel prizes. But the foundation of this work has much deeper roots. The biological sciences have undergone a quantitative transformation over the last two decades with the emergence of large scale biological sequencing and tools for transcriptomic analysis.

In 2019 when I moved from Amazon back to academia, I accepted a position at the University of Cambridge and a Senior AI Fellowship from the Alan Turing Institute. I was also asked if I would lead a new Schmidt Sciences funded initiative, “Accelerate Science”.

The aim was to bring about a step change in the sciences through AI. Our approach borrowed a lot from lessons from Data Science Africa, but also combined them with Cambridge’s history of supporting scientific innovation through computing.

Our first summit summarised the approaches when Ann Copestake (who as Head of Department had designed the programme and led the bid for funding) opened the meeting.

Interactive Map from the 2021 Accelerate Science Summit

Click on the relevant area of the image to find out more about a concept or unworkshop!

The purpose of Accelerate Science was already provided, a step change in Science at the University of Cambridge, alongside our summit and the unworkshops we built a programme of educational activities to enable that step change. Just like Data Science Africa we made sure that the community was built on people and their projects. The 2021’s summit showcased some projects, but Accelerate Science also funds projects at the interface of AI and science, organises ‘AI cafés’ and builds capabilities through workshops, courses and tutorials. Our focus on the interface is represented by the bridge shown in the image.

The spectrum of projects in AI for Science goes all the way from supporting a PhD or Masters student with our Machine Learning Clinics to the Nobel Prizes. We interviewed Pushmeet Kohli who leads the science team at DeepMind that had recently published AlphaFold that went on to win this year’s prize in chemistry. You can find the full video of the chat here.

Summary of Fireside Chat between Pushmeet and Neil.

Machine Commentary

NL: This is a graphical summary of the Accelerate Science Summit 2021.

Can you connect themes in the initiative to themes in the book? Don’t force the issue, only connect things where there’s a relationship.

Claude: Looking at the image and the book’s themes, there are several clear connections:

1. “ML & The Physical World” - Eagerness vs Reality This connects directly to Chapter 6 (“The Gremlin of Uncertainty”) where the book discusses the gap between theoretical models and practical reality, particularly in weather prediction and other physical systems. The book emphasizes how even perfect knowledge doesn’t guarantee perfect prediction.

2. “Too Much for Humans to Interpret” This aligns with the book’s recurring theme of human bandwidth limitations, introduced in Chapter 1 and developed throughout. The book uses Bauby’s “diving bell” analogy to explain how humans have severe communication and processing limitations compared to machines.

3. “Bringing Together Education & Research” This connects to the epilogue’s discussion of universities as “honest brokers” in AI development, particularly through initiatives like ai@cam at Cambridge. The book argues for the importance of combining different types of expertise.

4. “Usable Tech” and “Scientists Getting Proficient in ML” This relates to Chapter 11’s discussion of Human-Analogue Machines (HAMs) as interfaces between humans and complex systems, and the book’s broader argument that technology should augment rather than replace human intelligence.

5. Historical Context The timeline starting with EDSAC in 1949 connects directly to Chapter 5’s discussion of technological revolutions, drawing parallels between the printing press and computer revolutions in how they transform society.

6. “We Need Interactions” This connects to the book’s central thesis about the “atomic human” - that human intelligence is fundamentally social and interactive, a theme developed particularly in Chapters 7-9.

The initiative seems to share the book’s emphasis on bridging the gap between technical capability and human needs, while maintaining human agency in technological development.

NL: How does this initiative (depicted in the image and described in the post) connect to framework descrived in the “purpose, people, projects, principles and process” post?

Claude: Let me analyze how the Accelerate Science initiative connects to the five Ps framework:

1. Purpose:
   • Clearly stated as “bringing about a step change in Cambridge’s science capabilities through AI”
   • The image shows this through the bridge metaphor of “catalysing scientific research at Cambridge by improving links between Scientists and ML experts”
   • This shared purpose brings together computer scientists, domain scientists, and ML experts
2. People:
   • Shows key historical figures (Maurice Wilkes, Margaret Masterman, Karen Spärck Jones) creating a lineage of innovation
   • Features current leaders (Ann Copestake, Neil Lawrence)
   • Emphasizes diverse stakeholder involvement including both scientists and ML experts
   • Notably addresses the “who isn’t around the table” concern by actively working to bridge between different communities
3. Projects:
   • Several concrete “unworkshops” are highlighted:
     • ML and the Physical World
     • AI for Sustainable Design
     • Challenges for AI in Science and Mathematics
   • These represent actionable implementations of the broader purpose
   • Shows clear deliverables and focus areas
4. Principles: Several core principles are visible in the illustration:
   • “We need interactions” - emphasizing collaboration
   • “Scientists getting proficient in ML” - focus on capability building
   • “Education in the broadest sense” - commitment to learning
   • Balance between “eagerness to apply ML” and “hard to steer ML to most sensible place”
5. Process:
   • The historical timeline shows how institutional knowledge develops over time
   • The “unworkshops” format represents a specific process for bringing communities together
   • The bridge metaphor illustrates the process of connecting different communities
   • Shows evolution from EDSAC (1949) through to current AI applications, demonstrating how process develops organically

The initiative seems to particularly excel at balancing purpose (the high-level aim of accelerating science) with concrete projects (the unworkshops), while maintaining clear principles about the importance of interaction and education. This aligns well with the framework’s emphasis on moving from purpose to actionable projects while developing sustainable processes.

The image and description also reflect the framework’s warning about balancing process with purpose - while there are clear processes (like the unworkshops), they remain clearly connected to the core purpose rather than becoming bureaucratic exercises.

Click to see what the machine says about the initiative and the book