Lifeboat Foundation

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.”

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.”

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/…

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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.

Envision AI evolving beyond mere imitation, surpassing human intelligence to redefine the boundaries of consciousness and ethics.

Gene expression is inherently dynamic, due to complex regulation and stochastic biochemical events. Here the authors train a deep neural network to predict and dynamically control gene expression in thousands of individual bacteria in real-time which they then apply to control antibiotic resistance and study single-cell survival dynamics.

A team of roboticists at the University of Tokyo has taken a new approach to autonomous driving—instead of automating the entire car, simply put a robot in the driver’s seat. The group built a robot capable of driving a car and tested it on a real-world track. They also published a paper describing their efforts on the arXiv preprint server.

https://www.youtube.com/global5gevolution

Elon Musk says Tesla could make 100 million Optimus robots a year, costing $10k-20k each, to do everything from babysitting to working in factories, leading to the population of humanoid robots exceeding that of humans.

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Predicting how proteins bind to other molecules could revolutionize biochemistry, drug discovery.