Chemists and computer scientists tapped AI to find new disinfectants to combat the growing threat of dangerous “superbugs.”
The Journal of Chemical Information and Modeling published their computational-experimental framework for developing quaternary ammonium compounds, or QACs, to kill bacteria.
The method yielded 11 new QACs that show activity against antimicrobial-resistant bacteria.
A new type of hologram technology has been developed that uses the motion of light as a key, revealing information only under specific conditions. This is gaining attention as a novel approach that can simultaneously overcome the limitations of existing optical communication and security technologies.
Ever wonder what actually happens inside the AI after you hit “Enter”?
You type a prompt into your favorite generative AI, and within seconds, your screen fills with exactly what you asked for—whether it’s a quarterly report or a cinematic image of a cyberpunk golden retriever. It feels like absolute magic.
But behind that seamless curtain lies a bustling, microscopic economy running entirely on a digital currency you’ve probably heard of but might not fully understand: the token.
Most of us only ever see the input and the output. We don’t see the internal cash register ringing, the mathematical gymnastics, or the sprawling “assembly line” churning through billions of calculations.
What actually happens between the moment you hit send and the moment your final masterpiece appears? In my newest blog post, I peel back the curtain to trace the fascinating journey of an AI token.
I break down this invisible economy—from the “toll booth” of the input phase to the heavy lifting of the output phase—and show you exactly how the machine balances the books.
To try everything Brilliant has to offer—free—for a full 30 days, visit https://brilliant.org/ArtemKirsanov. You’ll also get 20% off an annual premium subscription.
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My name is Artem, I’m a neuroscience PhD student at Harvard University.
🌎 Website and Social links: https://kirsanov.ai/
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A lightning fast catchup on the latest in foundational physics theories, in light of recent news conserning UAP, NHI, and psionics
In 1965, a mathematician who worked alongside Alan Turing wrote a single
paragraph that has haunted AI research ever since. He predicted that one
day, a machine would learn to improve itself, and that everything after
that point would change.
Sixty years later, that loop is starting to close.
In this video, we trace how AI got here: from I.J. Good’s 1965 prediction.
to AlphaGo Zero teaching itself Go in 72 hours, to AlphaEvolve cracking a
math problem that had stood unbeaten for 56 years, and then quietly
speeding up the training of the very model that runs it. We look at the
data behind the trend (autonomous AI task length is doubling every 7
months), the walls AI keeps running into (compute, data, energy), and what
the people building this technology are actually saying about how close
we are.
This video is an honest look at what \.
Now online! A host-filtering and decontamination pipeline was benchmarked and applied to 16,369 tumor genomes, providing a high-resolution atlas of the cancer microbiome. Although most cancer types lacked a detectable microbiome, orodigestive cancers harbored complex multi-kingdom microbial communities that varied by site, subtype, and somatic mutation burden, linking the tumor microbiome to host phenotype and tumor genomic context.