7 papers
AI and the Research-Education Environment of Physics
Savannah Thais, Koji Hashimoto, David S. Berman +6
In the current era of AI transforming the research-education environment of physics, variety of issues and concerns arise. The KITP program "Generative AI for High and Low Energy P…
Towards Worst-Case Guarantees with Scale-Aware Interpretability
Lauren Greenspan, David Berman, Aryeh Brill +9
Neural networks organize information according to the hierarchical, multi-scale structure of natural data. Methods to interpret model internals should be similarly scale-aware, exp…
A path to natural language through tokenisation and transformers
David S. Berman, Alexander G. Stapleton
Natural languages exhibit striking regularities in their statistical structure, including notably the emergence of Zipf's and Heaps' laws. Despite this, it remains broadly unclear…
Modelling financial time series with quantum field theory
Dimitrios Bachtis, David S. Berman, Arabella Schelpe
We use a quantum field theory with inhomogeneous couplings and explicit symmetry-breaking to model an ensemble of financial time series from the SP 500 index. The cont…
Teaming LLMs to Detect and Mitigate Hallucinations
Demian Till, John Smeaton, Peter Haubrick +3
Recent work has demonstrated state-of-the-art results in large language model (LLM) hallucination detection and mitigation through consistency-based approaches which involve aggreg…
NCoder -- A Quantum Field Theory approach to encoding data
David S. Berman, Marc S. Klinger, Alexander G. Stapleton
In this paper we present a novel approach to interpretable AI inspired by Quantum Field Theory (QFT) which we call the NCoder. The NCoder is a modified autoencoder neural network w…