336 citations · 493 across the 10 of their papers we have counts for
11 papers
Embodied, Situated, and Grounded Intelligence: Implications for AI
Tyler Millhouse, Melanie Moses, Melanie Mitchell
In April of 2022, the Santa Fe Institute hosted a workshop on embodied, situated, and grounded intelligence as part of the Institute's Foundations of Intelligence project. The work…
The Debate Over Understanding in AI's Large Language Models
Melanie Mitchell, David C. Krakauer
We survey a current, heated debate in the AI research community on whether large pre-trained language models can be said to "understand" language -- and the physical and social sit…
Evaluating Understanding on Conceptual Abstraction Benchmarks
Victor Vikram Odouard, Melanie Mitchell
A long-held objective in AI is to build systems that understand concepts in a humanlike way. Setting aside the difficulty of building such a system, even trying to evaluate one is…
Abstraction for Deep Reinforcement Learning
Murray Shanahan, Melanie Mitchell
We characterise the problem of abstraction in the context of deep reinforcement learning. Various well established approaches to analogical reasoning and associative memory might b…
Frontiers in Collective Intelligence: A Workshop Report
Tyler Millhouse, Melanie Moses, Melanie Mitchell
In August of 2021, the Santa Fe Institute hosted a workshop on collective intelligence as part of its Foundations of Intelligence project. This project seeks to advance the field o…
Frontiers in Evolutionary Computation: A Workshop Report
Tyler Millhouse, Melanie Moses, Melanie Mitchell
In July of 2021, the Santa Fe Institute hosted a workshop on evolutionary computation as part of its Foundations of Intelligence in Natural and Artificial Systems project. This pro…
Foundations of Intelligence in Natural and Artificial Systems: A Workshop Report
Tyler Millhouse, Melanie Moses, Melanie Mitchell
In March of 2021, the Santa Fe Institute hosted a workshop as part of its Foundations of Intelligence in Natural and Artificial Systems project. This project seeks to advance the f…
Why AI is Harder Than We Think
Melanie Mitchell
Since its beginning in the 1950s, the field of artificial intelligence has cycled several times between periods of optimistic predictions and massive investment ("AI spring") and p…
Adversarial Perturbations Are Not So Weird: Entanglement of Robust and Non-Robust Features in Neural Network Classifiers
Jacob M. Springer, Melanie Mitchell, Garrett T. Kenyon
Neural networks trained on visual data are well-known to be vulnerable to often imperceptible adversarial perturbations. The reasons for this vulnerability are still being debated…
Abstraction and Analogy-Making in Artificial Intelligence
Melanie Mitchell
Conceptual abstraction and analogy-making are key abilities underlying humans' abilities to learn, reason, and robustly adapt their knowledge to new domains. Despite of a long hist…
Next Wave Artificial Intelligence: Robust, Explainable, Adaptable, Ethical, and Accountable
Odest Chadwicke Jenkins, Daniel Lopresti, Melanie Mitchell
The history of AI has included several "waves" of ideas. The first wave, from the mid-1950s to the 1980s, focused on logic and symbolic hand-encoded representations of knowledge, t…