I'm deeply concerned about Europe's future on AI. One of my biggest worries is our erosion of agency, our ability to stay relevant and fight for our values in a future where AI becomes a civilisationally important technology. Myself, @DadaJudith , @bakkermichiel and others have written a scenario to outline a potential future we worry we are on track towards. europe2031.ai/ Every optimistic and realistic path I can see for Europe runs through a central node - one where Europe has more leverage, more importance and more say. One where Europe grows more, builds more where it matters, and takes ownership over its resilience. Europe 2031 is a five-year scenario of the continent's slide into irrelevance: how AI is driving it, and what can still be done. The co-authors are researchers, scientists and investors who have advised European leaders, co-authored national AI strategies, built and funded these systems from the inside. We have no interest in hype and we deeply care about this continent. Europe 2031 ends with five concrete recommendations: - drastically more compute on European soil - an AI middle-power coalition - labour-market reforms - a bold position in robotics and industrial AI - and a positive vision of what AI can do for society. Europe can still change course if it finds the political will and the courage to engage in the most ambitious political and economic agenda the continent has undertaken in peacetime. I encourage you to read it if you have the time:

I'm excited to share our position paper that has been accepted at ICML as a Spotlight paper. In this work we (@kleinric, @BenjaminRosman, Steven James and @stefsmlab) make a call to action for more focus on model collapse in the AI Fairness community arxiv.org/pdf/2605.04127

New research from the University of the Witwatersrand, South Africa, is shedding light on how language evolves, in both humans and artificial intelligence models. The study explores the role of culture and “iterated learning”, showing how language becomes more structured over generations in both human development and large-scale AI language models. 🔗 Read More: ow.ly/42Vr50Z4BjH #WitsForGood #WitsResearch #ResearchForGood

Why don’t neural networks learn all at once, but instead progress from simple to complex solutions? And what does “simple” even mean across different neural network architectures? Sharing our new paper @iclr_conf led by Yedi Zhang with Peter Latham arxiv.org/abs/2512.20607

📢 We’re now accepting applications for the 2026 School on Analytical Connectionism dedicated this year to Language Acquisition. 📍 Gothenburg, Sweden 🗓️ August 17–28, 2026 ☠️ Apply by April 17! 🔗 analytical-connectionism.net… 👇 Meet the experts joining us this summer!