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The Download: introducing the Nature issue

The Download: introducing the Nature issue

This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. Introducing: the Nature issue When we talk about “nature,” we usually mean something untouched by humans. But little of that world exists...

Shifting to AI model customization is an architectural imperative

Shifting to AI model customization is an architectural imperative

In the early days of large language models (LLMs), we grew accustomed to massive 10x jumps in reasoning and coding capability with every new model iteration. Today, those jumps have flattened into incremental gains. The exception is domain-specialized intelligence, where true step-function...

Meet the new biologists treating LLMs like aliens

Meet the new biologists treating LLMs like aliens

How large is a large language model? Think about it this way. In the center of San Francisco there’s a hill called Twin Peaks from which you can view nearly the entire city. Picture all of it—every block and intersection, every neighborhood and park, as far as you can see—covered in sheets of...

The AI doomers feel undeterred

It’s a weird time to be an AI doomer. This small but influential community of researchers, scientists, and policy experts believes, in the simplest terms, that AI could get so good it could be bad—very, very bad—for humanity. Though many of these people would be more likely to describe themselves...

The State of AI: A vision of the world in 2030

The State of AI: A vision of the world in 2030

Welcome back to The State of AI, a new collaboration between the Financial Times and MIT Technology Review. Every Monday, writers from both publications debate one aspect of the generative AI revolution reshaping global power. You can read the rest of the series here. In this final edition,...

OpenAI has trained its LLM to confess to bad behavior

OpenAI has trained its LLM to confess to bad behavior

OpenAI is testing another new way to expose the complicated processes at work inside large language models. Researchers at the company can make an LLM produce what they call a confession, in which the model explains how it carried out a task and (most of the time) owns up to any bad behavior....

From pilot to scale: Making agentic AI work in health care

From pilot to scale: Making agentic AI work in health care

Over the past 20 years building advanced AI systems—from academic labs to enterprise deployments—I’ve witnessed AI’s waves of success rise and fall. My journey began during the “AI Winter,” when billions were invested in expert systems that ultimately underdelivered. Flash forward to today: large...

Why we should thank pigeons for our AI breakthroughs

Why we should thank pigeons for our AI breakthroughs

In 1943, while the world’s brightest physicists split atoms for the Manhattan Project, the American psychologist B.F. Skinner led his own secret government project to win World War II.  Skinner did not aim to build a new class of larger, more destructive weapons. Rather, he wanted to make...

Five ways that AI is learning to improve itself

Five ways that AI is learning to improve itself

Last week, Mark Zuckerberg declared that Meta is aiming to achieve smarter-than-human AI. He seems to have a recipe for achieving that goal, and the first ingredient is human talent: Zuckerberg has reportedly tried to lure top researchers to Meta Superintelligence Labs with nine-figure offers. The...