Artificial Intelligence (AI)
AI helps astronomers better explore the universe
Science Published 4 September 2025 Authors Brendan Tracey, Jonas Buchli Our novel Deep Loop Shaping method improves control of gravitational wave observatories, helping astronomers better understand the dynamics and formation of the universe. To help astronomers study the universe’s most powerful processes, our teams have been using AI to stabilize one of the most sensitive…
A greener way to 3D print stronger stuff
3D printing has come a long way since its invention in 1983 by Chuck Hull, who pioneered stereolithography, a technique that solidifies liquid resin into solid objects using ultraviolet lasers. Over the decades, 3D printers have evolved from experimental curiosities into tools capable of producing everything from custom prosthetics to complex food designs, architectural models,…
What is OLMoASR and How Does It Compare to OpenAI’s Whisper in Speech Recognition?
The Allen Institute for AI (AI2) has released OLMoASR, a suite of open automatic speech recognition (ASR) models that rival closed-source systems such as OpenAI’s Whisper. Beyond just releasing model weights, AI2 has published training data identifiers, filtering steps, training recipes, and benchmark scripts—an unusually transparent move in the ASR space. This makes OLMoASR one…
The AI Gold Rush Is Here—But 95% of Companies Are Digging in the Wrong Place – Spritle software
A recent MIT Technology Review Insights report, “State of AI in Business 2025,” reveals a stark reality: Billions are being poured into GenAI — yet the uncomfortable truth is that most enterprises are running in circles while only a select few sprint ahead. Welcome to the GenAI Divide—a widening chasm between companies trapped in endless…
3 Questions: The pros and cons of synthetic data in AI
Synthetic data are artificially generated by algorithms to mimic the statistical properties of actual data, without containing any information from real-world sources. While concrete numbers are hard to pin down, some estimates suggest that more than 60 percent of data used for AI applications in 2024 was synthetic, and this figure is expected to grow…
Meet Elysia: A New Open-Source Python Framework Redefining Agentic RAG Systems with Decision Trees and Smarter Data Handling
If you’ve ever tried to build a agentic RAG system that actually works well, you know the pain. You feed it some documents, cross your fingers, and hope it doesn’t hallucinate when someone asks it a simple question. Most of the time, you get back irrelevant chunks of text that barely answer what was asked….
Alibaba Qwen Team Releases Mobile-Agent-v3 and GUI-Owl: Next-Generation Multi-Agent Framework for GUI Automation
Image source: Marktechpost.com Introduction: The Rise of GUI Agents Modern computing is dominated by graphical user interfaces across devices—mobile, desktop, and web. Automating tasks in these environments has traditionally been limited to scripted macros or brittle, hand-engineered rules. Recent advances in vision-language models offer the tantalizing possibility of agents that can understand screens, reason about…
Step-by-Step Guide to AI Agent Development Using Microsoft Agent-Lightning
In this tutorial, we walk through setting up an advanced AI Agent using Microsoft’s Agent-Lightning framework. We are running everything directly inside Google Colab, which means we can experiment with both the server and client components in one place. By defining a small QA agent, connecting it to a local Agent-Lightning server, and then training…
How to Build a Conversational Research AI Agent with LangGraph: Step Replay and Time-Travel Checkpoints
In this tutorial, we aim to understand how LangGraph enables us to manage conversation flows in a structured manner, while also providing the power to “time travel” through checkpoints. By building a chatbot that integrates a free Gemini model and a Wikipedia tool, we can add multiple steps to a dialogue, record each checkpoint, replay…
Can large language models figure out the real world?
Back in the 17th century, German astronomer Johannes Kepler figured out the laws of motion that made it possible to accurately predict where our solar system’s planets would appear in the sky as they orbit the sun. But it wasn’t until decades later, when Isaac Newton formulated the universal laws of gravitation, that the underlying…