2 citations · 4 across the 4 of their papers we have counts for
6 papers
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 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…
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…