There’s never been more pressure to optimize the workplace. Over the last few years, turbulent economic conditions have left many businesses looking for ways...

Evolutionary Computation is under-appreciated. Objections include "Evolution takes millennia" and "I don't get the point".They are (or should be) very important to people in the AI community because they are a primitive precursor to intelligence. Understanding Evolutionary Computation (EC) will make understanding LLMs easier.In order to not have to define "AI" again, I will now define a new term SHAG which stands for "SuperHuman Answer Generator".A SHAG is a computer based system which can provide answers to questions that the client or programmer that is using the system cannot (or cannot be bothered to) compute an answer to themselves. Or shorter: A SHAG generates answers that humans cannot generate.Unsurprisingly, all LLMs are SHAGs. They can...

Traditional policy learning uses sampled trajectories from a replay buffer or behavior demonstrations to learn policies or trajectory models that map from state to action. This approach models a narrow behavior distribution. However, there is a challenge to guide high-dimensional output generation using low-dimensional demonstrations. Diffusion models have shown highly competitive performance on tasks like text-to-image synthesis. This success supports work toward policy network generation as a conditional denoising diffusion process. Refining noise into structured parameters consistently, the diffusion-based generator can discover various policies with superior performance and robust policy parameter space. Existing methods in this area include Parameter Generation and Learning to Learn...