The Normal Computing Blog

Fast and exact Ornstein-Uhlenbeck processes with JAX

Fuji-Web is an intelligent AI partner that understands the user’s intent, navigates websites autonomously, and executes tasks on the user’s behalf while explaining each action step.

Online learning and hallucination detection are understood as frontier problems in AI and with LLMs. posteriors is a new open source Python library from Normal Computing that provides tools for uncertainty quantification and Bayesian computation using PyTorch and its functional API.

Thermodynamic computing offers a natural approach for fast, energy-efficient computations. We report on the first-ever experiment towards thermodynamic artificial intelligence: solving matrix inversion problems by allowing a system of coupled electrical oscillators to thermally equilibrate with its environment.

In this post we introduce Branches, our tool for prototyping and visualizing advanced LLM reasoning and planning algorithms. We apply Branches to the problem of generating Python code for HumanEval.

In this blog we discuss how the transformer architecture naturally extends over external memories, and share empirical results which leverage this capability. These methods are innate (don't require fine tuning) and outperform popular retrieval augmented generation methods.

We review key aspects of explainability for language models and introduce some Normal innovations.

In this blog, we introduce our method for regex-guided generation implemented in Outlines