Lifeboat Foundation: Safeguarding Humanity
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Blog – Page 1204

Centenarians, once considered rare, have become commonplace. Indeed, they are the fastest-growing demographic group of the world’s population, with numbers roughly doubling every ten years since the 1970s.

How long humans can live, and what determines a long and healthy life, have been of interest for as long as we know. Plato and Aristotle discussed and wrote about the ageing process over 2,300 years ago.

The pursuit of understanding the secrets behind exceptional longevity isn’t easy, however. It involves unravelling the complex interplay of genetic predisposition and lifestyle factors and how they interact throughout a person’s life.

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Physicists have delved deeper into the enigmatic world of quantum entanglement and top quarks, bringing a new level of understanding to a phenomenon that even Albert Einstein found perplexing.

This incredible feat has the potential to revolutionize our understanding of the quantum realm and its far-reaching implications.

The experiment, conducted by a team of researchers led by University of Rochester physics professor Regina Demina at the European Center for Nuclear Research (CERN), has yielded a significant result.

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Researchers from Tohoku University and Kyoto University have successfully developed a DNA-based molecular controller that autonomously directs the assembly and disassembly of molecular robots. This pioneering technology marks a significant step towards advanced autonomous molecular systems with potential applications in medicine and nanotechnology.

Details of the breakthrough were published in the journal Science Advances (“Autonomous assembly and disassembly of gliding molecular robots regulated by a DNA-based molecular controller”).

“Our newly developed molecular controller, composed of artificially designed DNA molecules and enzymes, coexists with molecular robots and controls them by outputting specific DNA molecules,” points out Shin-ichiro M. Nomura, an associate professor at Tohoku University’s Graduate School of Engineering and co-author of the study. “This allows the molecular robots to self-assemble and disassemble automatically, without the need for external manipulation.”

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Internet data scraping is one of the biggest fights in AI right now. Tech companies argue that anything on the public internet is fair game, but they are facing a barrage of lawsuits over their data practices and copyright. It will likely take years until clear rules are in place.

In the meantime, they are running out of training data to build even bigger, more powerful models, and to Meta, your posts are a gold mine.

If you’re uncomfortable with having Meta use your personal information and intellectual property to train its AI models in perpetuity, consider opting out. Although Meta does not guarantee it will allow this, it does say it will “review objection requests in accordance with relevant data protection laws.”

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From king’s college london, carnegie mellon, & U birmingham.

Llm-driven robots risk enacting discrimination, violence, and unlawful actions.

Rumaisa Azeem, Andrew Hundt, Masoumeh Mansouri, Martim Brandão June 2024 Paper: https://arxiv.org/abs/2406.08824 Code: https://github.com/SepehrDehdashtian/…

The data and code for paper ‘The Dark Side of Dataset Scaling: Evaluating Racial Classification in Multimodal Models’ — SepehrDehdashtian/the-dark-side-of-dataset-scaling.

Continue reading “The Dark Side of Dataset Scaling” | >

Scientists can’t address the origins of life without having a basic understanding of evolution.

You’d think that would make the origins of life a popular research topic for evolutionary biologists. But Maria Kalambokidis, Ph.D. candidate in Ecology, Evolution and Behavior, and recent recipient of the NASA Future Investigators Fellowship, may be one of only a handful across the globe investigating the topic. She thinks it might be because the origins of life, also called abiogenesis, has mostly been studied by chemists.

“It’s difficult to come into the field when you have a completely different scientific background than someone else,” says Kalambokidis. “There are insights from evolution that you might miss by only taking the perspective of a chemist.”

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This review spotlights the revolutionary role of deep learning (DL) in expanding the understanding of RNA is a fundamental biomolecule that shapes and regulates diverse phenotypes including human diseases. Understanding the principles governing the functions of RNA is a key objective of current biology. Recently, big data produced via high-throughput experiments have been utilized to develop DL models aimed at analyzing and predicting RNA-related biological processes. This review emphasizes the role of public databases in providing these big data for training DL models. The authors introduce core DL concepts necessary for training models from the biological data. By extensively examining DL studies in various fields of RNA biology, the authors suggest how to better leverage DL for revealing novel biological knowledge and demonstrate the potential of DL in deciphering the complex biology of RNA.

This summary was initially drafted using artificial intelligence, then revised and fact-checked by the author.

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