1.6k citations · 12.4k across the 394 of their papers we have counts for
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From Observation to Insight: Mechanistic World Models and the Quest for Autonomous Discovery
Ingmar Posner, Anson Lei, Bernhard Schölkopf
Recent advances in foundation models have transformed AI for Science, enabling remarkably accurate predictive performance across domains ranging from protein folding to weather for…
Trustworthy AI Suffers from Invariance Conflicts and Causality is The Solution
Ruta Binkyte, Ivaxi Sheth, Zhijing Jin +3
As artificial intelligence (AI), including machine learning (ML) models and foundation models (FMs), are increasingly deployed in high-stakes domains, ensuring their trustworthines…
Imagining and building wise machines: The centrality of AI metacognition
Samuel G. B. Johnson, Amir-Hossein Karimi, Yoshua Bengio +8
Although AI has become increasingly smart, its wisdom has not kept pace. In this article, we examine what is known about human wisdom and sketch a vision of its AI counterpart. We…
Language Models Can Reduce Asymmetry in Information Markets
Nasim Rahaman, Martin Weiss, Manuel Wüthrich +4
This work addresses the buyer's inspection paradox for information markets. The paradox is that buyers need to access information to determine its value, while sellers need to limi…
Deep Backtracking Counterfactuals for Causally Compliant Explanations
Klaus-Rudolf Kladny, Julius von Kügelgen, Bernhard Schölkopf +1
Counterfactuals answer questions of what would have been observed under altered circumstances and can therefore offer valuable insights. Whereas the classical interventional interp…
A Measure-Theoretic Axiomatisation of Causality
Junhyung Park, Simon Buchholz, Bernhard Schölkopf +1
Causality is a central concept in a wide range of research areas, yet there is still no universally agreed axiomatisation of causality. We view causality both as an extension of pr…