Lifeboat Foundation

Pacemakers are medical devices that make sure that someone’s heart beats the way it should. If the heart rhythm is off, the pacemaker delivers a surge of electricity to bring the heart back into rhythm. The pacemaker takes effort into account and delivers faster pulses when needed. For example, when you’re exercising. For these electric pulses, the pacemaker needs a capacitor to rapidly charge and discharge. This provides a high enough electric charge to reset the heart.

Researcher Minh Duc Nguyen and his colleagues worked on a new design strategy for these capacitors to improve their energy storage, decrease the amount of energy lost every time it is charged or discharged, and increase the number of times they can reliably charge and discharge.

“It needs to keep up with your heartbeat, so it should be able to charge and discharge up to billions of times. Otherwise, you’ll have to replace the pacemaker every few months”, explains Nguyen.

This unveil marks a significant step towards the production of its new ultra-advanced 1.6-nanometer (nm) chips by 2026.

With its focus on nanosheet transistors and innovative backside power delivery, A16 paves the way for the production of 1.6nm chips by 2026.

Studying tissues, cells, and proteins under a microscope is essential for disease prevention and treatment. This research requires accurately measuring the dimensions of these biological structures. However, when viewed through a light microscope, these samples can sometimes appear more flattened than their true form.

Researchers at Delft University of Technology have now demonstrated for the first time that this distortion is not constant, contrary to what many scientists have assumed for decades. The breakthrough, published in Optica, confirms a prediction by Nobel laureate Stefan Hell from the 90s. With an online calculation tool and software, every researcher can now determine the correct depth of a biological sample.

That led the Microsoft Research machine learning expert to wonder how much an AI model could learn using only words a 4-year-old could understand – and ultimately to an innovative training approach that’s produced a new class of more capable small language models that promises to make AI more accessible to more people.

Large language models (LLMs) have created exciting new opportunities to be more productive and creative using AI. But their size means they can require significant computing resources to operate.

While those models will still be the gold standard for solving many types of complex tasks, Microsoft has been developing a series of small language models (SLMs) that offer many of the same capabilities found in LLMs but are smaller in size and are trained on smaller amounts of data.

Scientists made a major breakthrough in understanding the incredible mechanics of insect flight — A complex but efficient wing hinge system.

SpaceX is making rapid progress in the development of their Starship, with improvements in heat shield tiles, construction of a second launch tower, and multiple successful launches, showcasing their commitment to innovation and progress in space exploration.

Questions to inspire discussion.

Continue reading “SpaceX’s Latest Progress in Starship Development and Lunar Exploration” »

A new video of researchers communicating with a humpback whale in Alaska over 20 minutes. The researchers hope the breakthrough could lead to communication with aliens.

Google DeepMind has recently introduced Penzai, a new JAX library that has the potential to transform the way researchers construct, visualize, and alter neural networks. This innovative tool is designed to smoothly integrate with Google Colab and the JAX ecosystem, which is a major step forward in the accessibility and manipulability of AI models.

Penzai is a new approach to neural network development that emphasizes transparency and functionality. It allows users to view and edit models as legible pytree data structures, making it easier than ever to delve into the inner workings of a model. This feature is especially useful after a model has been trained, as it provides insights into how the model operates and allows for modifications that can help achieve desired outcomes.

Penzai aims to make AI research more accessible to researchers by simplifying the process of modifying pre-trained neural networks. This would enable a wider range of researchers to experiment and innovate on existing AI technologies, which is crucial for advancing the field and discovering new AI applications. Penzai’s user-friendly interface breaks down the barriers to AI research and makes it easier for everyone to benefit from the technology.

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