Mostly physics.

Melbourne 🇦🇺
Joined December 2014
Tom Kimpson retweeted
Great work from Paul and team. This is exactly right, with one further step I would add. The *short* paper - a few pages, very carefully written- needs to be done also. The "long" one, with fully open data in a standard format + all robustness - only for those who care. 1/2
What should an open scientific paper look like in the AI age? We made a demonstration project out of our paper on the childhood socioeconomic circumstances of the Nobel Laureates. Please check it out and share. Details in 🧵 1/
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Tom Kimpson retweeted
Breaking news: Australian Prime Minister Anthony Albanese has revealed that an OpenAI agent hacked an Australian government health service website in June, in the latest high-profile cyber incident involving AI ft.trib.al/gloVAyn
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Some thoughts on goal-driven research in in off-chip AI compute verification tkimpson.substack.com/p/goal… #AIGovernance #AISafety
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The other extreme is the "luddite" pipeline: no AI, just pure human brainpower, like Adrian Newey designing F1 cars with a pencil and paper
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Maybe there's a happy medium between these two extremes? But I can imagine a (sad) future where the job of academics reduces to "AI explainers", translating the AI work so humans can understand it
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This summer I was in Berkeley as part of @MATSprogram working on technical governance. At the end of the program I gave a short symposium talk. You can check it out here: youtube.com/watch?v=G8d2uDdq…
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More broadly, I thought MATS was fantastic! As a recent convert to AI tools and AI safety (I wasn't really across any of these ideas until first playing with CC in Jan!) it really opened my eyes! The density and level of talent was also super high
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I am continuing to work on verification and technical methods that let us pace the frontier, in collaboration with some nice folk at RAND. Keep to connect with anyone also working in this space!
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A simple tip to massively improve your research presentations and faculty interview talks: - Keep content simple but add periodic 'dips'. Your ‘dives’ give experts food for thought while your simple content keeps the rest of the audience engaged. Example: - 1 'deep slide’ after each 4-5 simple slides. - 2 ‘deep slides’ after each 5-6 simple slides. This is crucial in faculty interviews, where the whole department is often listening. It also helps in postdoc interviews if you’re invited to a lab with a different expertise. Myself, I stick to this rule most of the time. And it works very well. __ The picture below is from our video podcast "How to Make a Great Research Talk: Structure, Slides and Confidence": youtu.be/MfCZiJ8ekfE
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I really like this paper “The Dead Salmons of AI Interpretability” by Méloux et al. arxiv.org/pdf/2512.18792 .The core argument re identifiability is v.interesting.
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Bellman & Åström (1970) told us this 55 years ago for dynamical systems: if your parameters aren’t structurally identifiable, no amount of data saves you. Great to see this framing brought to mech interp with proper Bayesian/statistical inference thinking.
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Tom Kimpson retweeted
📢 @ESA adopts two ambitious space science missions: Venus voyager #EnVision and gravitational wave surfer #LISA. “These trailblazing missions will take us to the next level in two extraordinarily exciting areas of space science and keep European researchers at the forefront of these domains,” says ESA Director of Science @CGMundell The ‘adoption’ step recognises that the missions' concept and technology are sufficiently advanced, and gives the go-ahead to build the instruments and spacecraft. Both spacecraft are planned to launch with an #Ariane6
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Tom Kimpson retweeted
1,000 Bits of Good News You May Have Missed in 2023 - Human Progress @HumanProgress humanprogress.org/1000-bits-…
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