VSAONLINE. SEASON 11. September 8, 20:00GMT. Eilene Tomkins-Flanagan
Evgeny Osipov
Dear all,
Welcome to the next talk of Season 11 on VSAONLINE. Eilene Tomkins-Flanagan and Mary Kelly will deliver an exciting talk
”Hey Pentti, We Did It!: A Fully Vector-Symbolic Lisp”
Date: August 25, 2025
Time: 20:00 GMT
Zoom: https://ltu-se.zoom.us/j/65564790287
Title: Hey Pentti, We Did It!: A Fully Vector-Symbolic Lisp.
Abstract: A key feature of symbolic computation is the productivity of formal grammars. Although vector-symbolic architectures (VSAs) allow the expression of composite symbolic sentences over a vector space, the VSA literature has left the study of formal grammars expressed in terms of VSAs under-explored. Until recently, it had not even been demonstrated that VSAs may express an unrestricted grammar (hence, form a Turing-complete system). However, the correspondence between grammar theory and automata theory permits us to study the computational properties of systems in which the terms of a given language are embedded. Thus, VSA-encoded languages supply a means to reason about computability and learning theory in the systems in which they are found. Accordingly, we present three languages: (1) a subset of Lisp 1.5 described over a generic VSA, demonstrating that any VSA may express an unrestricted grammar; (2) an extension of the prior language for Fourier-domain Holographic Reduced Representations that performs numeric computation efficiently; and (3) a type system described over a generic VSA that expresses only programs that halt in polynomial time. These preliminary contributions illustrate the expressive power of VSAs and make a fruitful basis for reasoning about recent progress in machine learning and challenges to the present transformer-led paradigm (notably in the case of the ARC-AGI task) as researchers push toward more "general" models. We find that the heuristic search approach augmenting transformers such that they become capable of addressing fluid reasoning tasks has long been known to be unsustainably inefficient, particularly compared to a human baseline, and that such inefficiencies may be rectified using the kinds of structured, productive representations we have demonstrated to be possible using VSAs. By imposing systematic restrictions on a search space via a formal language, we intend to build AI systems that fit a humanlike learning curve.
Evgeny Osipov
Dear all,
Reminder! See you all soon!