Candidate data structures and algorithms for representing ground-able compositional knowledge in computers

Some of the most interesting ways to represent knowledge in computers using Mathematical structures. Graphs and graph neural networks multi-spacial grid cell representations grid representations probabilistic programming holograms Voronoi tesselations – they allow distance calculations between any concepts https://towardsdatascience.com/the-geometry-of-thought-700047775956 word2vec – its such as simple model, its worth thinking about how to scale this up.

Variables in grounded language learning

I keep reading grounded language learning papers trying to figure out what is the minimal ingredient to achieve a grounded representation in computers. One theme I keep seeing and coming back to is that while everything is grounded, compositionality may need more abstract thinking. It seems like there definitely must be some recursive computation or… Continue reading Variables in grounded language learning

My notes on ”A Benchmark for Systemic Generalization in Grounded Language Understanding”

I meant to read this paper for a long time and finally got around to it. Paper link: https://arxiv.org/abs/2003.05161v2 Laura Ruis is the main author, I hope she goes on to do more work in this “general direction” 🙂 Jacob Andreas of NTM fame is a co-author, awesome! Brenden Lake who does tons of interesting… Continue reading My notes on ”A Benchmark for Systemic Generalization in Grounded Language Understanding”

What is grounded language learning

“An unknown, but potentially large, fraction of animal and human intelligence is a direct consequence of the perceptual and physical richness of our environment, and is unlikely to arise without it”. – John Locke (1632-1704) Powerful state of the art machine learning systems such as word2vec and GPT-3 are powerful machine learning models that seem to… Continue reading What is grounded language learning

Notes on “A language of thought for the mental representation of geometric shapes”

I’m going to try to write notes on the papers I read in this blog as well. Here is my first one. Link to paper: https://psyarxiv.com/28mg4 Authors: Mathias Sablé-Meyer, Kevin Ellis, Joshua Tenenbaum, Stanislas Dehaene Both Joshua Tenenbaum and Stanislas Dehaene are 2 of my favorite researchers, so I had to read this joint paper… Continue reading Notes on “A language of thought for the mental representation of geometric shapes”