Browsing: Learning

In the current landscape of generative AI, the ‘scaling laws’ have generally dictated that more parameters equal more intelligence. However, Liquid AI is challenging this convention…

NVIDIA researchers introduced ProRL AGENT, a scalable infrastructure designed for reinforcement learning (RL) training of multi-turn LLM agents. By adopting a ‘Rollout-as-a-Service’ philosophy, the system decouples…

async def run_warm_start_task(): print(“=”*60) print(“🔥 WARM START: Reusing previously evolved skills”) print(“=”*60) task = ( “Create a Python script that analyzes a CSV file containing “…

With so much happening in AI and machine learning today, figuring out where to start can feel overwhelming. Different learners prefer different approaches! Some want visuals,…

In this tutorial, we implement a reinforcement learning agent using RLax, a research-oriented library developed by Google DeepMind for building reinforcement learning algorithms with JAX. We…

Study 1: Standalone performance and integration feasibilityThe first study was split into two phases. In the first phase, we conducted a large-scale multi-center retrospective evaluation of…

For different learning styles, goals, and comfort levels, finding a course that matches how you learn is HARD. Some people need visuals. While others wanna jump straight into code.…

Stanford researchers have introduced OpenJarvis, an open-source framework for building personal AI agents that run entirely on-device. The project comes from Stanford’s Scaling Intelligence Lab and…

In this tutorial, we implement a Colab-ready version of the AutoResearch framework originally proposed by Andrej Karpathy. We build an automated experimentation pipeline that clones the…

In this tutorial, we explore how we use Daft as a high-performance, Python-native data engine to build an end-to-end analytical pipeline. We start by loading a…