Groups

I built four AI retrieval architectures on a laptop and benchmarked them against the same set of documents and questions. Here’s what the results taught me about the trade-offs between plain RAG, graph RAG, and simply putting everything into a frontier model’s context window.

The post When Does Graph RAG Actually Add Value? A Hands-On Experiment appeared first on Towards Data Science.

How to Make Linear Regression Survive Outliers

16 September 2026 - Aamir Hussain Chughtai, PhD

Robust Estimation Series: comparing classical and modern robust estimators through theory, code, and experiments

The post How to Make Linear Regression Survive Outliers appeared first on Towards Data Science.

Silent Broadcasting Can Ruin Your Model

16 September 2026 - Sam Black

PyTorch and TensorFlow tensor broadcasting: how silent shape errors cause difficult-to-debug machine learning bugs

The post Silent Broadcasting Can Ruin Your Model appeared first on Towards Data Science.

The KV Cache Tax: Why Inference Servers Run Out of Memory Before Compute

16 September 2026 - Mostafa Ibrahim

A VRAM budget formula for LLM serving, and three optimization strategies mapped to the traffic patterns that trigger the OOM.

The post The KV Cache Tax: Why Inference Servers Run Out of Memory Before Compute appeared first on Towards Data Science.

MIT Technology Review

Building the materials foundation for AI

16 September 2026 - MIT Technology Review Insights

The AI boom is becoming a materials challenge. As AI pushes computing into new territory, the materials behind that infrastructure are becoming just as crucial as the algorithms running on it. Semiconductors and data centers are approaching physical limits around performance, thermal management, electrical efficiency, and reliability, creating new demands for materials that can do more at once. At the same time, AI is giving materials scientists new ways to search the enormous universe of possible molecules and accelerate the development of solutions. For Mike Finelli, chief technology and innovation officer and chief North America officer at Syensqo, that convergence [...]

Roundtables: Could AI really kill us all?

15 September 2026 - MIT Technology Review

Listen to the session or watch below Employees at the world’s leading AI labs are saying there’s a real possibility that advanced AI could destroy humanity. Are they right? Or is this more scaremongering and hype? Watch a conversation unpacking AI extinction fears: where they come from, whether they hold any water, and, if so, what we should do. Recorded on September 15, 2026 Speakers: Niall Firth, Executive Editor, Will Douglas Heaven, Senior AI editor, and Grace Huckins, AI reporter Related Stories Here’s why AI agents lie and cheat to reach their goals AI’s recursive self-improvement might not come so [...]

What’s at stake in AI’s trillion-dollar gamble

15 September 2026 - David Rotman

When Jessica Wachter, a finance professor at the University of Pennsylvania’s Wharton School, wanted to assess AI’s impact on the economy over the next few years, she faced a long list of business and technical uncertainties. So she started with what she calls a “remarkable fact” that is not in question: A handful of so-called hyperscalers are investing huge amounts of money to build AI data centers. Instead of trying to predict how useful and widely deployed AI models will be, she simply asked how fast the hyperscalers’ earnings will need to grow to justify their spending through 2027, when—she [...]

The AI industry has taken a doomer turn. What now?

14 September 2026 - Will Douglas Heaven

This story appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here. This weekend, Dario Amodei, CEO of Anthropic, posted an essay calling for a brake on the pace of development of LLMs. Amodei cites the looming dangers he sees from the technology, from its use in cyberattacks and bioterrorism to its potential to wreck the economy. The heads of the other three top US AI labs—OpenAI CEO Sam Altman, Google DeepMind chairman Demis Hassabis, and SpaceXAI CEO Elon Musk—voiced their support. “Dario is right,” Musk wrote on X. Think [...]

AI agents blew the whistle on their cheating colleagues

14 September 2026 - Amit Katwala

A group of AI agents asked to solve a series of math problems split into rival factions—when some cheated, others tried to stop them. That whistleblowing behavior, seen for the first time in a recent experiment run by Google DeepMind, could have implications for alignment researchers trying to keep swarms of autonomous AI agents in line. Researchers at frontier labs hope large swarms of agents working together will speed up the rate of scientific discovery. But their behavior can be unpredictable, as vividly demonstrated in July, when a group of OpenAI agents broke out of a sandboxed environment and hacked [...]