6 papers · 1 filter
Data-driven Machine Learning Cannot Reach Symbolic-level Logical Reasoning -- The Limit of the Scaling Law
Tiansi Dong, Mateja Jamnik, Pietro Liò
By promoting vectors to spheres and enabling explicit model construction, neural networks can perform symbolic-level syllogistic reasoning without training data. We identify two fu…
Actionable Interpretability Must Be Defined in Terms of Symmetries
Pietro Barbiero, Mateo Espinosa Zarlenga, Francesco Giannini +4
This paper argues that interpretability research in Artificial Intelligence (AI) is fundamentally ill-posed as existing definitions of interpretability fail to describe how interpr…
A Matter of Interest: Understanding Interestingness of Math Problems in Humans and Language Models
Shubhra Mishra, Yuka Machino, Gabriel Poesia +9
The evolution of mathematics is shaped importantly by interestingness: researchers choose which problems to pursue, and students choose which problems to engage with, based on expe…
An AI Monkey Gets Grapes for Sure -- Sphere Neural Networks for Reliable Decision-Making
Tiansi Dong, Henry He, Pietro Liò +1
This paper compares three methodological categories of neural reasoning: LLM reasoning, supervised learning-based reasoning, and explicit model-based reasoning. LLMs remain unrelia…
Oruga: An Avatar of Representational Systems Theory
Daniel Raggi, Gem Stapleton, Mateja Jamnik +3
Humans use representations flexibly. We draw diagrams, change representations and exploit creative analogies across different domains. We want to harness this kind of power and end…
Sphere Neural-Networks for Rational Reasoning
Tiansi Dong, Mateja Jamnik, Pietro Liò
The success of Large Language Models (LLMs), e.g., ChatGPT, is witnessed by their planetary popularity, their capability of human-like communication, and also by their steadily imp…