Recursive Feature Machine (RFM), and welcome new members

Welcome new CG members!

 Please do introduce yourself to the list if you like, with a brief pointer
to any relevant
background and interest in the subject of AI KR,

Many join to learn about AI KR, so feel free to just  browse the list
archives or check the background
In a nutsheel  this CG is about understanding current challenges and
developments n AI.ML from a symbolic KR perspective

Notes *not complete, and not always up to date:
https://www.w3.org/community/aikr/wiki/Main_Page
https://www.w3.org/community/aikr/wiki/How_to_Contribute_to_the_CG

Today's topic
https://www.w3.org/community/aikr/wiki/Recursive_Feature_Machines

A polite reminder that KR in this CG is about* natural language * ( *NOT
about mathematics)

Today's topic * Recursive Feature Machine *as a universal method for
concept extraction transforms our approach to AI interpretability and
control

 https://arxiv.org/abs/2502.03708

GitHub code (related neural controllers):
https://github.com/dmbeaglehole/neural_controllers

   -

This builds on earlier RFM foundations like Radhakrishnan et al. (2022) but
focuses on interpretability applications.
Recursive Feature Machine as a universal method for concept extraction
transforms our approach to AI interpretability and control
*...method that unveils these hidden internal representations, offering new
avenues for monitoring and steering AI behavior with unprecedented
precision.....concept representations are not static artifacts tied to a
single language or task domain. Instead, they show remarkable
transferability across different linguistic frameworks. This implies that
fundamental semantic structures learned by AI models can be reliably mapped
and manipulated regardless of the language context, a feature that holds
enormous potential for multilingual applications and universal AI
interpretability. Moreover, the technique allows for the combination of
multiple concept representations, enabling multi-concept steering where
several streams of thought or ideas can be concurrently navigated within a
model’s reasoning process.*