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Before reaching for an LLM API on every classification problem, it's worth knowing what a decades-old baseline can already do with the labeled data you have — and exactly how much more data buys you.

The post How Many Labeled Examples Does a Text Classifier Actually Need? I Measured It. appeared first on Towards Data Science.

Your Model’s MSE Is Lying to You

14 September 2026 - Waleed Esmail

First in a series on probabilistic forecasting for physical signals. Next: what happens when you roll the forecast forward more than one step.

The post Your Model’s MSE Is Lying to You appeared first on Towards Data Science.

When to Use One Model and When to Use a Team of Agents

14 September 2026 - Naveen Goel

When Codex is the right shape for the problem, when Claude Code is, and how I split 5 specialist agents between them on dense AI capacity work.

The post When to Use One Model and When to Use a Team of Agents appeared first on Towards Data Science.

Graph Engineering for AI Agents: From Prompts and Loops to Workflows

14 September 2026 - Nhu Hoang

A viral debate over loops versus graphs points to a bigger shift in how we build AI systems. Here’s what graph engineering actually means, how it differs from prompt, context, and loop engineering, and why it matters.

The post Graph Engineering for AI Agents: From Prompts and Loops to Workflows appeared first on Towards Data Science.

MIT Technology Review

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 [...]

Roundtables: Could AI really kill us all?

11 September 2026 - MIT Technology Review

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? Join MIT Technology Review executive editor Niall Firth for a conversation with senior AI editor Will Douglas Heaven and AI reporter Grace Huckins unpacking AI extinction fears: where they come from, whether they hold any water, and, if so, what we should do. Register now Going live on Tuesday, September 15 at 16:00 BST / 11:00am EST / 8:00am PST Speakers: Niall Firth, executive editor, Will Douglas Heaven, senior AI editor, and [...]

Powering AI is an architecture problem

10 September 2026 - Ricardo De Azevedo

On July 22, 2026, a transmission line fault in Ashburn, Virginia—the heart of the world’s largest data center cluster—knocked more than 3 gigawatts of load off the grid in seconds. And it wasn’t the first time. Two years earlier, a single failed surge arrester dropped roughly 60 Virginia facilities and 1,500 megawatts at once. No one could anticipate so much uniform load responding to grid faults the same way, at the same time. The AI power debate is mostly about generation: more turbines, more solar, more transmission. The grid needs more electrons. But the outages in Virginia weren’t supply failures; [...]