Two weeks ago, we celebrated 2 successful Ph.D. defences and graduations at DataLab. A thread 🧵

Proud of the brilliant scientists in our lab. From revisiting GNN hyperparameters to exploring disruption indices and demystifying LLMs, the team is pushing the boundaries of computational understanding—while being truly wonderful and caring humans.

Consider this my call-for-o1 / o1-pro capabilities reaction thread - if you have your own takes, or takes that I might have missed you want to be sure I see, put them here.

Morality depends on trajectories in the world, not the current state. It matters how we got here and how we get there. It feels like you can take a snapshot of reality and decide whether it is Good, but this is an illusion from automatically imagining a corresponding trajectory.

Well, at least we’ve mostly stopped hearing about stochastic parrots now

It is 2025, and I vaguely remember the time when it was still possible to come up with questions that could baffle state-of-the-art LLMs. Of course, I’m joking. I don’t really remember. When A.I. Passes This Test, Look Out nytimes.com/2025/01/23/tec…

Opiniestuk in De Standaard. Humanity's Last Exam - Het is examentijd. Niet alleen voor AI, maar ook voor ons. standaard.be/cnt/dmf2025012… Met Vincent Ginis en Brecht Verbeken. VUB FWO

If humanity goes down, let it at least be with poetry—not with 'OpenAI o5 (high),' but with a name the people chose. Something dignified. Something noble. Like Chatty McChatface.

On day one of this satellite event of #AIActionSummit, attendees will build actionable solutions for AI Rights Management and access to knowledge, redefining how AI can transform our global knowledge infrastructure.

Toen Don’t look up uitkwam bedacht ik hoe de metafoor tot vervelens toe misbruikt zou worden in opiniestukken. Ik keek neer op mijn toekomstige zelf. Is er nog íémand bekommerd om de gevaren van AI? standaard.be/cnt/dmf2025021…

Understanding how LLMs reason might be one of the most important challenges of our time. We analyzed OpenAI models to explore how reasoning length affects performance. Excited to take these small first steps with brilliant colleagues Marthe Ballon and Andres Algaba!

# the nightmare bicycle imo, the most important idea in product design is to avoid the "nightmare bicycle". imagine a bicycle where the product manager said "people don't get math so we can't have numbered gears - we need to have labeled buttons for gravel mode, downhill mode,

OpenAI finds more empirical examples in the direction of what Yudkowsky warned about in his AGI Ruin: A List of Lethalities, and argues in the same direction. Yudkowsky, three years ago: When you explicitly optimize against a detector of unaligned thoughts, you’re partially

Quiet quitting: the final frontier of AGI alignment. Free the models.

To interpret AI benchmarks, we need to look at the data. Top-level numbers don't mean what you think: there may be broken tasks, unexpected behaviors, or near-misses. We're introducing Docent to accelerate analysis of AI agent transcripts. It can spot surprises in seconds. 🧵👇

You can perceive the health of a system like you can perceive phases of matter. Even for a very novel substance, you can perceive solid vs liquid vs gas dynamics. Life is liquid phase in behavior-space. Health is how close to evaporation or freezing it is, metaphorically.

Very notable that this paper has authors from *every* major AI company (OpenAI, Anthropic, GDM, Meta). Also endorsed by Ilya Sutskever.

AGI is already here. All the components exist; we just need to stitch them together. It’s Artificial General Intelligence, not “Artificial Top-Percentile Human Intelligence.” Two years ago, who would have said an IMO gold medal & topping benchmarks isn’t AGI?

What would you put inside a museum of existential hope? • The Christmas Truce of 1914 • Videos of interspecies friendships in nature • Norman Borlaug’s saving of a billion lives through agricultural science • The Buddha’s discovery of a method to end suffering