There's outrage in the computer science community over a new feature rolled out by the ACM Digital Library that generates often inaccurate AI summaries. To make things worse, this is hidden behind a 'premier' paywall, so authors without access (for example, having graduated from University) can't even see what is being said. Why are these paper AI summaries harmful? The summaries themselves are deeply average.

I was not expecting to find a bunch of activist librarians at the lovely spires of King's College Chapel last week, but I was very glad that I did! I gave a talk to the Confederation of Open Access Repositories group that was having a meeting about "Turning scholarly publishing on its head". Luckily, I had my budding Four Ps for Collective Intelligence fresh on my brain, so I discussed it with the assembled librarians.

As part of the ARIA Engineering Ecosystem Resilience program, we've been convening a series of workshops here at the Cambridge Conservation Initiative to explore the potential of combining two very radically different approaches to modeling. Joe Millard wrote this to frame the discussion: We held two separate workshops to explore this;

Our neighbours France and the UK announced a Franco-British AI collaboration a few months ago dubbed the Entente CordIAle. Last week we held a couple of days of workshops with our Oxford and French buddies deep diving into details of what a partnership might actually involve; a particular pleasure with France given my group's long history of working with Inria on OCaml and other open source projects.

I've been building some big collective knowledge systems recently, both for scholarly literature or to power large-scale observational foundation models. While the modalities of knowledge in these systems are very different, they share a common set of design principles I've noticed while building individual pieces. A good computer architecture is one that can be re-used, and I've been mulling over what this exactly is for some time.

I've just released geotessera 0.7 to pypi for our TESSERA geospatial foundation model, following on from the first release earlier this year. To recap: With this new release, there's convenient documentation to show how you can freely access 150TB+ of CC-BY-licensed embeddings of the earth's surface.

There's a buzz forming around the upcoming AI Impact Summit next year in India, following up the AI Safety Summit here and the France Action Summit earlier this year. I headed down to a couple of events in London this week to help set the agenda, particularly around the importance of FAIR and ethical AI for sustainability being on the political agenda.

I got to shake Jensen Huang's hand as he received the 2025 Hawking Fellowship this evening at the Cambridge Union! He's a fitting winner for this award; he's not only tech's longest running CEO (33 years!), but also a founding engineer who deeply understands the technology stack. He also bucks the trend among bigtech and famously doesn't believe in firing people, preferring to "torture them into greatness" ^[1] instead.

I had an amazingly fun week at ICFP/SPLASH in Singapore; it was the first time that these two major programming languages conferences were held simultaneously. My submissions turned into a bit of a success disaster; I ended up chairing a workshop, giving several talks and a keynote, and organising a tutorial, and helping out a bunch of colleague and students.

This is part 5 of a series of posts ^[1] about ICFP 2025. In addition to giving a bunch of talks about Docker, post-POSIX and planetary computing, the greatest fun at a huge conference like ICFP and SPLASH is seeing talks given by my students (they grow up so fast!) and collaborators, and generally floating around random talks trying to deceipher ancient Greek lambdas floating on a projector.

This is part 4 of 5 of a series of posts ^[1] about ICFP 2025. After the excitement of presenting my Docker experience report, I went straight into giving a keynote talk at VMIL 2025.