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AI models have, within hours, created more efficient wireless chips through deep learning, but it is unclear how their ‘randomly shaped’ designs were produced.

This approach significantly enhances performance, as observed in Atari video games and several other tasks involving multiple potential outcomes for each decision.

“They basically asked what happens if rather than just learning average rewards for certain actions, the algorithm learns the whole distribution, and they found it improved performance significantly,” explained Professor Drugowitsch.

In the latest study, Drugowitsch collaborated with Naoshige Uchida, a professor of molecular and cellular biology at Harvard University. The goal was to gain a better understanding of how the potential risks and rewards of a decision are weighed in the brain.

Using machine learning, a team of researchers in Canada has created ultrahigh-strength carbon nanolattices, resulting in a material that’s as strong as carbon steel, but only as dense as Styrofoam.

The team noted last month that it was the first time this branch of AI had been used to optimize nano-architected materials. University of Toronto’s Peter Serles, one of the authors of the paper describing this work in Advanced Materials, praised the approach, saying, “It didn’t just replicate successful geometries from the training data; it learned from what changes to the shapes worked and what didn’t, enabling it to predict entirely new lattice geometries.”

To quickly recap, nanomaterials are engineered by arranging atoms or molecules in precise patterns, much like constructing structures with extremely tiny LEGO blocks. These materials often exhibit unique properties due to their nanoscale dimensions.

Scientists are exploring gene editing as a way to correct trisomy at the cellular level. Using CRISPR-Cas9, researchers successfully removed extra copies of chromosome 21 in Down syndrome cell lines, restoring normal gene expression.

This breakthrough suggests that, with further development, similar approaches could be applied to neurons and glial cells, offering a potential treatment for those with the condition.

Gene Editing for Trisomy Treatment.

But other calculations say that applies only in limited cases and that if you ramp up the warp engine slowly enough, you’ll be fine.

Yet more calculations sidestep all of this and just look at how much negative energy you actually need to construct your warp drive. And the answer is, for a single macroscopic bubble — say, 30 feet (100 meters) across — you would need 10 times more negative energy than all of the positive energy contained in the entire universe, which isn’t very promising.

However, still other calculations show that this immense amount applies only to the traditional warp bubble as defined by Alcubierre. It might be possible to reshape the bubble so there’s a tiny “neck” in the front that’s doing the work of compressing space and then it balloons out to an envelope to contain the warp bubble. This minimizes any quantum weirdness so that you need only about a star’s worth of negative energy to shape the drive.