TEXTGRAD - Revolutionizing LLMs with Automatic Differentiation via Text

Artificial Intelligence (AI) is constantly evolving, and one of the latest breakthroughs is the introduction of TEXTGRAD, a novel framework that utilizes automatic differentiation via text to optimize various components within AI systems. Developed by researchers at Stanford University, TEXTGRAD is designed to improve the performance of Large Language Models (LLMs) by leveraging natural language feedback.

Machine Teaching Part 1 - Linear and Non-linear control

Linear and Non Linear control policies.

Optimizing Gen-AI Applications with DSPy and Haystack - A Practical Guide

Building Gen-AI applications often involves the challenging and time-consuming task of manually optimizing prompts. DSPy, an open-source library, addresses this by transforming prompt engineering into an optimization problem, making it more scalable and robust.

Dynamic Movement Primitives

Dynamic Movement Primitives (DMPs) are a framework used in robotics and computational neuroscience to model and generate complex motor behaviors. This framework was designed to capture the essence of movement skills, making it easier to learn and reproduce various motions.