🚨 911 NIGHTMARE: Dad Calls for Help – Cops Handcuff the Victim Instead 😡 #BodyCam #HillCounty
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Video: the LLM 2.0 Revolution
What if you could build a secure, scalable RAG+LLM system – no GPU, no latency, no hallucinations? In this session, Vincent Granville shares how to engineer high-performance, agentic multi-LLMs from scratch using Python. Learn how to rethink everything from token chunking to sub-LLM selection to create AI systems that are explainable, efficient, and designed for enterprise-scale applications. What you’ll learn: How to build LLM systems without deep neural nets or GPUs Real-time fine-tuning, self-tuning, and context-aware retrieval Best […]
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Innovation to Impact: How NVIDIA Research Fuels Transformative Work in AI, Graphics and Beyond
The roots of many of NVIDIA’s landmark innovations — the foundational technology that powers AI, accelerated computing, real-time ray tracing and seamlessly connected data centers — can be found in the company’s research organization, a global team of around 400 experts in fields including computer architecture, generative AI, graphics and robotics. Established in 2006 and led since 2009 by Bill Dally, former chair of Stanford University’s computer science department, NVIDIA Research is unique among corporate research organizations — […]
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The Machine Learning “Advent Calendar” Day 10: DBSCAN in Excel
DBSCAN shows how far we can go with a very simple idea: count how many neighbors live close to each point. It finds clusters and marks anomalies without any probabilistic model, and it works beautifully in Excel. But because it relies on one fixed radius, HDBSCAN is needed to make the method robust on real data. The post The Machine Learning “Advent Calendar” Day 10: DBSCAN in Excel appeared first on Towards Data Science.
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Time Series and Trend Analysis Challenge Inspired by Real World Datasets
See how different time series methods reveal the shifts, surges, and stabilization in inflation expectations.
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🎃 A Full Month of Privacy Tips from EFF | EFFector 37.14
Instead of catching you off-guard with a jump scare this Halloween season, EFF is here to catch you up on the latest digital rights news with our EFFector newsletter! In this issue, we’re helping you take control of your online privacy with Opt Out October; explaining the UK’s attack on encryption and why it’s bad for all users; and covering shocking new details about an abortion surveillance case in Texas. Prefer to listen in? Check out our audio […]
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Trump’s AI executive order promises ‘one rulebook.’ Startups may get legal limbo instead.
Trump signed an AI executive order targeting state laws and promising one national rulebook. Critics warn it could trigger court battles and prolong uncertainty for startups while Congress debates federal rules.
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A smarter way for large language models to think about hard problems
To make large language models (LLMs) more accurate when answering harder questions, researchers can let the model spend more time thinking about potential solutions. But common approaches that give LLMs this capability set a fixed computational budget for every problem, regardless of how complex it is. This means the LLM might waste computational resources on simpler questions or be unable to tackle intricate problems that require more reasoning. To address this, MIT researchers developed a smarter way to allocate […]
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MIT engineers design an aerial microrobot that can fly as fast as a bumblebee
In the future, tiny flying robots could be deployed to aid in the search for survivors trapped beneath the rubble after a devastating earthquake. Like real insects, these robots could flit through tight spaces larger robots can’t reach, while simultaneously dodging stationary obstacles and pieces of falling rubble. So far, aerial microrobots have only been able to fly slowly along smooth trajectories, far from the swift, agile flight of real insects — until now. MIT researchers have demonstrated […]
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MIT researchers propose a new model for legible, modular software
Coding with large language models (LLMs) holds huge promise, but it also exposes some long-standing flaws in software: code that’s messy, hard to change safely, and often opaque about what’s really happening under the hood. Researchers at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) are charting a more “modular” path ahead. Their new approach breaks systems into “concepts,” separate pieces of a system, each designed to do one job well, and “synchronizations,” explicit rules that describe exactly […]
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