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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.
Researchers at the University of Toronto have identified a protein from the quagga mussel that can stick to surfaces underwater, even though it lacks a chemical feature long thought to be essential for this kind of adhesion. The protein, called Dbfp7, is the first freshwater mussel adhesive protein to be functionally characterized.
The finding, published in PNAS, helps explain how some organisms attach themselves in wet environments and could inform the design of future medical glues—such as medical sealants and surgical adhesives—or other materials that need to work reliably in water.
Most studies of underwater adhesion have focused on marine mussels, which use proteins rich in a modified amino acid called 3,4-dihydroxyphenylalanine (DOPA) to bond to surfaces. Freshwater species have been studied less, and whether they rely on the same chemistry has not been clear.
Creating complex molecules usually requires years of experience and countless decisions, but a new AI system is changing that. Synthegy lets chemists guide synthesis and reaction planning using simple language, while powerful algorithms generate and evaluate possible solutions. The AI doesn’t just compute—it reasons, scoring pathways and explaining which ones make the most sense.
A new technology allows light to be “designed” into desired forms, potentially making AI and communication technologies faster and more accurate. A KAIST research team has developed an “integrated photonic resonator”—a core component of next-generation optical integrated circuits that process data using light. Interestingly, the research was led by an undergraduate student. This technology is expected to serve as a key foundation for next-generation security technologies such as highspeed data processing and quantum communication.
The resonator developed by the research team of Professor Sangsik Kim from the School of Electrical Engineering, in collaboration with Professor Jae Woong Yoon’s team from the Department of Physics at Hanyang University, is capable of freely controlling optical signals by utilizing light interference (the phenomenon where two light waves meet and influence each other). Their paper is published in Laser & Photonics Reviews.
Photonic Integrated Circuits (PICs) process data at ultra-high speeds and with low power consumption using light. They are garnering significant attention as a fundamental platform technology for next-generation fields such as AI, data centers, and quantum information processing.
A three-dimensional soft electronic sensor and stimulator array that is integrated with a three-dimensional cultured neural network can be used to record action potential from multiple planes over a period of 6 months, monitor evolving connectivity maps and pharmacological responses, as well as construct a reservoir neural network for biocomputing.