• Behavioral Machine Learning generalization LLM neurips persona prompt stanford “Behavioral Machine Learning? Behind this question lies a whole stream of research aimed at studying the behavior of AI models when we ask them to imitate, closely or remotely, a human being. However, this approach is fraught with pitfalls, and…
• Causality and AI? Between reasoning fantasies and scientific reality Causality Concept Extraction Graph LLM Diving into causality in AI means navigating between the dream of a reasoning machine capable of identifying cause and effect, and scientific reality, which highlights the limits of our statistical models. This dossier traces the rise of “causal representation learning”,…
• Tame your LLM architectures hallucinations limits LLM robustness safety LLMs are gaining ground everywhere, with incredible promises of new high-performance and, brace yourself, “intelligent” tools. Research is progressing more slowly than these promises, and regularly gives us a clearer and more precise view of things. Here, we outline the…
• Let's dance the Mamba DinoV2 Efficiency LLM mamba Sequence Mamba announces a new, efficient and versatile architecture family that is making its mark on the artificial intelligence landscape. Bonus: a better understanding of image embeddings from DinoV2, and a new way to bypass Large Language Models.
• AlphaGeometry: what lessons can we learn from DeepMind's latest exploit? Deepmind Exploring the solution space IA Symbolic LLM synthetic data Deepmind has caused quite a stir with this AI that can solve complex problems in geometry. This approach offers us a number of theoretical and practical lessons for addressing other problems with Deep Learning: how to combat hallucinations, the value…