Explanatory Learning: Beyond Empiricism in Neural Networks

Abstract

We introduce Explanatory Learning (EL), a framework to let machines use existing knowledge buried in symbolic sequences -e.g. explanations written in hieroglyphic -by autonomously learning to interpret them. In EL, the burden of interpreting symbols is not left to humans or rigid human-coded compilers, as done in Program Synthesis. Rather, EL calls for a learned interpreter, built upon a limited collection of symbolic sequences paired with observations of several phenomena. This interpreter can be used to make predictions on a novel phenomenon given its explanation, and even to find that explanation using only a handful of observations, like human scientists do. We formulate the EL problem as a simple binary classification task, so that common end-to-end approaches aligned with the dominant empiricist view of machine learning could, in principle, solve it. To these models, we oppose Critical Rationalist Networks (CRNs), which instead embrace a rationalist view on the acquisition of knowledge. CRNs express several desired properties by construction, they are truly explainable, can adjust their processing at test-time for harder inferences, and can offer strong confidence guarantees on their predictions. As a final contribution, we introduce Odeen, a basic EL environment that simulates a small flatland-style universe full of phenomena to explain. Using Odeen as a testbed, we show how CRNs outperform empiricist end-to-end approaches of similar size and architecture (Transformers) in discovering explanations for novel phenomena. Explanation 1 : Explanation: Figure 1: The Odeen universe. A convenient environment to study and test the process of knowledge discovery in machines. Like the night sky was for humans. *Galileo did not sketch negative examples.

ASIF: Coupled Data Turns Unimodal Models to Multimodal Without Training

“…(ii) The recognition tag transitioned from being an output pulled out of the image by the neural stack (the label) to become an input that should be interpreted, and therefore processed by its own encoder (the free text caption). This corresponds to an epistemological perspective shift, as discussed by Norelli et al (2022).…”