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In 1959, Richard Feynman gave a lecture imagining machines built at the scale of atoms. For decades, the dream stalled on a deceptively simple problem: nobody could build a switch.

Individual molecules never sit still. Thermal energy jostles them constantly, which makes positioning a tiny structure, flipping it cleanly, and having it actually stay in place nearly impossible at that scale.

A team at the Technical University of Munich just built one anyway, using folded strands of DNA. The switch holds a stable position for up to an hour, then flips in milliseconds when triggered. One version survived more than 200,000 flips in five and a half hours. A simplified version withstood a million cycles in three hours, still working 85 percent of the time.

Some devices failed quickly. Others kept going for days. And a handful of switches that stopped working later started again on their own, which the researchers say points to something unexpected: self-repair.

The team has already shown the switch can do something useful, beyond simply flipping. What that capability is, and what it could mean for controlling chemical reactions at the molecular scale, is the part worth reading.

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Researchers gave GPT-4o and Claude a psychology test invented in 1935. On short versions, the AI performed exceptionally well. Then the test got longer.

The Stroop task is simple: you see a color word printed in a different ink color, and your job is to name the ink, not read the word. It sounds easy. It isn't. Even humans slow down, and the effect never fully goes away with practice — which is why psychologists use it to study how the brain resolves conflicting information.

On five-word lists, GPT-4o was over 90 percent accurate. On 40-word incongruent lists, accuracy fell to roughly 15 percent. In mixed-condition tests, both models nearly collapsed to zero.

The detail worth pausing on: some models correctly explained what the Stroop test was and described exactly what they were supposed to do. Then they failed it anyway. Understanding the rules offered no protection against the breakdown.

Researchers say this points to something missing in how AI processes attention — something the human brain handles automatically that current AI architectures don't have a real equivalent for.

What that missing ingredient is, and what it would take to build it in, is the more consequential part of the story.

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A handful of healthy senior citizens are about to trip on psylocibin to see if the psychedelic protects aging brains.

Psylocibin, the active ingredient in magic mushrooms, is best known for its ties to 1960s counterculture. But now it may also herald a new genre of mental health treatment. From severe depression to post-traumatic stress disorder, studies have highlighted psychedelic drugs’ ability to reshape brain networks and relieve debilitating symptoms.

Most of these studies have focused on younger people with mental health conditions that don't respond to standard treatment. The field’s success is prompting scientists to ask if psychedelics could also help healthy brains age better.

In a first-of-its-kind study focused on adults between the ages of 60 and 85, they’ll investigate how psilocybin affects perception, emotion, and memory using a battery of psychological tests.

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In 2018, a Chinese scientist announced the birth of gene-edited babies. Global outrage followed. He served three years in prison. The scientific community called for an international moratorium on editing human embryos.

Last week, researchers at Columbia University published a new study doing exactly that.

The technology is different this time — more precise, less likely to cause the collateral DNA damage that made earlier experiments so alarming. The goal was research, not pregnancy. And the lead scientist has been among the most vocal advocates for open public debate before any clinical use. "You can't use it," he told Nature. "It's as clear as day and night."

But the study has already attracted commercial interest from a company that screens IVF embryos and has developed predictive models for traits like intelligence. One prominent genomics researcher called the work's commercial ties the detail most likely to matter long-term, regardless of the science.

The safety questions are real and unresolved. The ethical questions are older and harder. And the line between preventing disease and selecting for traits is one that scientists, regulators, and the public have never fully agreed on where to draw.

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