Dan Goodman

Everyone is praising this article, but I find it a bit disingenuous. He criticises Taylorism ("scientific management") but that's exactly what researchers are subject to, and the glam publication economy is at the heart of it. www.science.org/doi/10.1126/...

science.org

I just gave a short tutorial on what is a spiking neural network and how to train one. From zero to a trained network in one hour. Slides and code you can run in Colab at this repo: github.com/neural-recko... 🤖🧠🧪

GitHub - neural-reckoning/cambridge-fens-chen-summer-school-2026: Code and slides for 2026 FENS Chen Summer School on Learning with spikes github.com

New preprint (well, very updated). 🤖🧠🧪 We find that an abstract model of neuromodulation lets spiking neural networks perform much better, particularly in challenging noisy environments, using less energy. Relevant to #neuroscience and #neuromorphic computing. 🧵👇 www.biorxiv.o…

Neuromodulation enhances the capability and efficiency of spiking neural networks | bioRxiv biorxiv.org

#SNUFA #SpikingNeuralNetwork workshop starts in just over an hour. Today we have talks from Susanne Schreiber, Mihai Petrovici, Huihong Li, Lennar Landsmeer, Christian Klos, flash talks and the poster session. Links to join available on the website: snufa.net/2026/

SNUFA 2026 snufa.net

Two new recorded talks. The first is on spiking neural network models of neuromodulation, and starts with a brief tutorial on spiking neural networks and surrogate gradient descent. It was part of the CING x META-BRAIN seminar series. youtu.be/0GKo2jZN6No?...

Neuromodulation Enhances the Capability and Efficiency of Spiking Neural Networks youtu.be

If you really think that AI running out of control is a dangerous possibility, then make tech company CEOs legally liable for illegal actions taken by their company's agents.

There's a paper going around saying that AI is now better at peer review than humans. I won't link to it because I assume that like all these AI papers it's just some sort of marketing. My only comment is that we don't have to critique this paper because we already know human PR isn't very good.

With all the major AI companies now reporting that their LLMs have hacked computers it seems pretty clear that this isn't a case of them all spontaneously developing the capability to break containment but that they're all in competition with each other and training their LLMs to do exactly this.

If - as now seems likely - LLM companies are mining their logs for juicy problems, then that means that it's very likely that "secret" test sets don't stay secret for long. When looking at new models' performance on benchmarks that date from before that model, we shouldn't take results at face value

Lots of people talking about LLM-"polished" emails because of an article (I won't link to it). The argument for doing this is workload, but it's a terrible argument because if you expand out the one sentence of information you need to send to several paragraphs, you're increasing everyone's workload

I'm suddenly seeing a lot more genuine email going into my spam folder. I assume this is because the rise of LLM-written spam means that email providers have had to dial up the sensitivity of their filters. Another great contribution from AI.

I do love how every software update now gives me the delightful opportunity to reconsider my decision to painstakingly turn off all the AI features, by turning them all back on and having a splash screen telling me about how great they are.

Asking whether or not LLMs are better than humans at peer review seems so ephemeral. If it were, we wouldn't need peer review (or journals) any more and question would disappear, because we'd just use them as part of our standard approach when reading papers.

Wait Terry Tao posts 5 days ago about how AI could solve Navier Stokes and now this? Feels like too much of a coincidence? Not suggesting anything untoward from Terry, but something's going on here isn't it?

I don't know what strategy high up tech company execs think they're pursuing talking about AI risk as they do. But, it's not spooking investors enough to get those execs kicked out, and they know that before speaking. Whatever they're trying to achieve, they don't think it hurts their bottom line.