Noumenal Labs’s approach is to build models that are explicitly structured — in a manner that is consistent both with the means by which we intuitively understand and conceptualize the world, and the means by which our best scientific models describe it 7/7

Noumenal Labs is developing a new approach to macroscopic physics discovery, enabling machine intelligences to discover the different objects/concepts and object/concept types that generate time series data, as well as the type-specific rules that govern their behavior directly from data 6/7

Generically, these models lack the requisite structure to represent the world as we understand it, i.e., as composed of distinct objects and object types, as well as object-type-specific interactions, unless hand-specified by the user 5/7

This gap in performance is largely due to the relative simplicity of the generative models, underlying state space, and relationships typically used in the active inference literature 4/7

While theoretically well grounded, active inference has yet to yield state of the art performance in practice 3/7

Active inference is a form of Bayesian machine learning that replaces the objective function used in traditional machine learning — the cost or reward function — with an information theoretic surprise minimization objective, which is used for both inference and learning 2/7

If you have the skills and interest to join us, please apply on our website or directly here: jobs.gusto.com/postings/nou...
If you have any questions, please email: talent at noumenal dot ai 5/5

State space modeling
Maximum entropy inverse reinforcement learning
Structured 4D Gaussian splatting
Expert-level programming in Python and JAX 4/5

Energy-based models for inference and learning
Bayesian machine learning
Probabilistic programming languages
Neural partial differential equations
Graph neural networks 3/5

We are expanding our world-class research and development team to build the next generation of machine intelligence. We are looking for candidates with with expertise in the following domains: 2/5