Lifeboat Foundation: Safeguarding Humanity
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Blog – Page 64

As scientists, we often think we understand a virus—its structure, its tricks, the way it moves through the body. But every once in a while, we stumble upon something unexpected—something that completely changes the way we see an infection.

I have spent years studying the molecular tactics of viruses—how they invade, replicate, and most intriguingly, how they evade our immune system. Some strategies are well documented: antigenic drift, glycan shielding, . But every so often, we stumble upon a novel mechanism that redefines our understanding of viral pathogenesis.

A recent finding in Nature showed that the spike protein of SARS-CoV-2 binds with , leading to thrombo-inflammation. This raises a fundamental question: Why does the virus need to bind with fibrinogen? Could this interaction provide an evolutionary advantage to the virus? Could this be the reason behind post-COVID heart attack cases?

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A genetic mutation in horses that would typically halt protein production has become a molecular asset. Researchers at Johns Hopkins University and Vanderbilt University have identified a rare instance of genetic recoding that enhances oxygen metabolism and energy production in horses, donkeys, and zebras.

The findings, published in Science, provide insight into the genetic foundation of exceptional equine athletic ability, and hint at an entirely new way of dealing with stop codons.

Few mammals match horses in aerobic performance. Muscle tissue in thoroughbreds consumes oxygen at rates exceeding 360 liters per minute. Oxygen uptake per unit of body mass is more than twice that of elite human athletes. While many genes involved in muscle structure and locomotion have been studied, the genetic basis for this level of metabolic output has remained unclear.

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Lithium-ion batteries are part of everyday life. They power small rechargeable devices such as mobile phones and laptops. They enable electric vehicles. And larger versions store excess renewable energy for later use, supporting the clean energy transition.

Australia produces more than 3,000 metric tons of lithium-ion battery a year. Managing this waste is a technical, economic and social challenge. Opportunities exist for and creating a circular economy for batteries. But they come with risk.

That’s because contain manufactured chemicals such as PFAS, or per-and polyfluoroalkyl substances. The chemicals carry the lithium—along with electricity—through the battery. If released into the environment, they can linger for decades and likely longer. This is why they’ve been dubbed “forever chemicals”

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I stormed a castle in Burbank that is home to the Terraformer — a machine that uses air, water, and sunlight to produce all the fuel we’d ever need. It’s cheap and can be run in almost any condition, anywhere in the world. The only problem? It’s wildly inefficient – but for the first time in history, solar power is so cheap that it no longer matters.

Plus, we get to see the misuse of a cake mixer to further the cause of science! Leave a comment to let us know if this is your favorite misuse of a cake mixer.

Timestamps:
0:00 — Welcome to Hard Reset.
1:16 — Meet Casey Handmer.
3:06 — A cheaper kind of fuel.
6:13 — Casey’s plan.
7:08 — The terraformer.
8:27 — The carbon capture system.
10:50 — The power of methane.
12:16 — An inefficient process.
13:50 — Terraform Industries’ next step.

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Summary: ChatGPT4 has demonstrated superiority in various student exams, revealing its potential to support academic learning and improve educational outcomes, particularly in test preparation. With its accessibility and affordability compared to traditional tutoring services, AI tutoring can help address the increasing demand for academic support, especially as universities begin to reinstate standardized testing requirements.

In 2023, OpenAI shook the foundation of the education system by releasing ChatGPT4. The previous model of ChatGPT had already disrupted classrooms K–12 and beyond by offering a free academic tool capable of writing essays and answering exam questions. Teachers struggled with the idea that widely accessible artificial intelligence (AI) technology could meet the demands of most traditional classroom work and academic skills. GPT3.5 was far from perfect, though, and lacked creativity, nuance, and reliability. However, reports showed that GPT4 could score better than 90 percent of participants on the bar exam, LSAT, SAT reading and writing and math, and several Advanced Placement (AP) exams. This showed a significant improvement from GPT3.5, which struggled to score as well as 50 percent of participants.

This marked a major shift in the role of AI, from it being an easy way out of busy work to a tool that could improve your chances of getting into college. The US Department of Education published a report noting several areas where AI could support teacher instruction and student learning. Among the top examples was intelligent tutoring systems. Early models of these systems showed that an AI tutor could not only recognize when a student was right or wrong in a mathematical problem but also identify the steps a student took and guide them through an explanation of the process.

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AI agents need two things to succeed in this space: infinite scalability and the ability to connect agents from different blockchains. Without the former, agents do not have infrastructure with sufficient capacity to transact. Without the latter, agents would be off on their own island blockchains, unable to truly connect with each other. As agent actions become more complex on chain, more of their data will also have to live on the ledger, making optimizing for both of these factors important right now.

Because of all of this, I believe the next frontier of AI agents on blockchains is in gaming, where their training in immersive worlds will inevitably lead to more agentic behavior crossing over to non-gaming consumer spaces.

If the future of autonomous consumer AI agents sounds scary, it is because we have not yet had a way to independently verify LLM training models or the actions of AI agents so far. Blockchain provides the necessary transparency and transaction security so that this inevitable phenomenon can operate on safer rails. I believe the final home for these AI agents will be Web3.

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