Lifeboat Foundation: Safeguarding Humanity
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For the first time, researchers have measured the shape of an electron as it moves through a solid. This achievement could open a new way of looking at how electrons behave inside different materials.

Their discovery highlights many effects that could be relevant to everything from quantum information science to electronics manufacturing.

Those findings come from a team led by physicist Riccardo Comin, MIT’s Class of 1947 Career Development Associate Professor of Physics and leader of the work, in collaboration with other institutions.

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El Capitan can reach a peak performance of 2.746 exaFLOPS, making it the National Nuclear Security Administration’s first exascale supercomputer. It’s the world’s third exascale machine after the Frontier supercomputer at Oak Ridge National Laboratory in Tennessee and the Aurora supercomputer at the Argonne Leadership Computing Facility, also in Illinois.

The world’s fastest supercomputer is powered by more than 11 million CPU and GPU cores integrated into 43,000+ AMD Instinct MI300A accelerators. Each MI300A APU comprises an EPYC Genoa 24-core CPU clocked at 1.8GHz and a CDNA3 GPU integrated onto a single organic package, along with 128GB of HBM3 memory.

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LEV is upon us.

OpenAI chief executive Sam Altman, who provided the initial $180mn to seed the start-up, will put in more money in the series A. The company is in talks with family offices, venture capitalists and sovereign wealth funds, as well as a US “hyperscaler” data centre to provide computing power to run the AI models it uses to create and test its treatments.

In partnership with OpenAI, the start-up has built a bespoke AI model that designs proteins to temporarily turn regular cells into stem cells, which it says can reverse their ageing process.

The San Francisco-based biotech will use the money to fund clinical trials for three drugs, including a potential treatment for Alzheimer’s disease, which will be tested in an early stage study in Australia this year. It is also working on drugs for rejuvenating blood and brain cells.

Continue reading “Sam Altman-backed Retro Biosciences to raise

LEV is upon us.

OpenAI chief executive Sam Altman, who provided the initial $180mn to seed the start-up, will put in more money in the series A. The company is in talks with family offices, venture capitalists and sovereign wealth funds, as well as a US “hyperscaler” data centre to provide computing power to run the AI models it uses to create and test its treatments.

In partnership with OpenAI, the start-up has built a bespoke AI model that designs proteins to temporarily turn regular cells into stem cells, which it says can reverse their ageing process.

The San Francisco-based biotech will use the money to fund clinical trials for three drugs, including a potential treatment for Alzheimer’s disease, which will be tested in an early stage study in Australia this year. It is also working on drugs for rejuvenating blood and brain cells.bn for project to extend human life” | >

When it rains, it pours. OpenAI Operator tested and reviewed, with full paper analysis. Perplexity Assistant is useful. Then Stargate, is it all smoke and mirrors? Strong rumours of an o3+ model from Anthropic. Then a full breakdown of Deepseek R1, and what it’s training method says about the state of AI. It’s not open source BTW. Plus Humanity’s Last Exam, and Hassabis Accelerates his AGI timeline.

https://app.grayswan.ai/arena/chat/ha…
https://app.grayswan.ai/arena.

AI Insiders ($9!): / aiexplained.

Chapters:

Continue reading “Nothing Much Happens in AI, Then Everything Does All At Once” | >

Engineered enzymes are poised to have transformative impacts across applications in energy, materials, biotechnology, and medicine. Recently, machine learning has emerged as a useful tool for enzyme engineering. Now, a team of bioengineers and synthetic biologists says they have developed a machine-learning guided platform that can design thousands of new enzymes, predict how they will behave in the real world, and test their performance across multiple chemical reactions.

Their results are published in Nature Communications in an article titled, “Accelerated enzyme engineering by machine-learning guided cell-free expression,” and led by researchers at Stanford University and Northwestern University.

“Enzyme engineering is limited by the challenge of rapidly generating and using large datasets of sequence-function relationships for predictive design,” the researchers wrote. “To address this challenge, we develop a machine learning (ML)-guided platform that integrates cell-free DNA assembly, cell-free gene expression, and functional assays to rapidly map fitness landscapes across protein sequence space and optimize enzymes for multiple, distinct chemical reactions.”

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