Lifeboat Foundation

“Forget about essences.” Philosopher Daniel Dennett on how modern-day philosophers should be more collaborative with scientists if they want to make revolutionary developments in their fields.

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In early 2023, following an international conference that included dialogue with China, the United States released a “Political Declaration on Responsible Military Use of Artificial Intelligence and Autonomy,” urging states to adopt sensible policies that include ensuring ultimate human control over nuclear weapons. Yet the notion of “human control” itself is hazier than it might seem. If humans authorized a future AI system to “stop an incoming nuclear attack,” how much discretion should it have over how to do so? The challenge is that an AI general enough to successfully thwart such an attack could also be used for offensive purposes.

We need to recognize the fact that AI technologies are inherently dual-use. This is true even of systems already deployed. For instance, the very same drone that delivers medication to a hospital that is inaccessible by road during a rainy season could later carry an explosive to that same hospital. Keep in mind that military operations have for more than a decade been using drones so precise that they can send a missile through a particular window that is literally on the other side of the earth from its operators.

We also have to think through whether we would really want our side to observe a lethal autonomous weapons (LAW) ban if hostile military forces are not doing so. What if an enemy nation sent an AI-controlled contingent of advanced war machines to threaten your security? Wouldn’t you want your side to have an even more intelligent capability to defeat them and keep you safe? This is the primary reason that the “Campaign to Stop Killer Robots” has failed to gain major traction. As of 2024, all major military powers have declined to endorse the campaign, with the notable exception of China, which did so in 2018 but later clarified that it supported a ban on only use, not development—although even this is likely more for strategic and political reasons than moral ones, as autonomous weapons used by the United States and its allies could disadvantage Beijing militarily.

This is the first lesson that will eventually lead to Grover’s Algorithm and Rotation (Phase) Estimation. We talk about the task of distinguishing between two qubit states, with \.

A research team led by Osaka University discovered that the new organic molecule thienyl diketone shows high-efficiency phosphorescence. It achieved phosphorescence that is more than ten times faster than traditional materials, allowing the team to elucidate this mechanism.

The paper is published in the journal Chemical Science.

Phosphorescence is a valuable optical function used in applications such as organic EL displays (OLEDs) and cancer diagnostics. Until now, achieving high-efficiency phosphorescence without using rare metals such as iridium and platinum has been a significant challenge. Phosphorescence, which occurs when a molecule transitions from a high-energy state to a low-energy state, often competes with non-radiative processes where the molecule loses energy as heat.

In 2006, just a few years after the fruit fly genome had been sequenced, geneticists at the University of California, Davis, made a startling discovery: Several new genes had cropped up, seemingly out of nowhere.

These “de novo genes” weren’t simply new variants of existing ones; they had sprung forth from the supposedly inert spaces in between the coding sections of DNA—regions long dismissed as the junkyards of the double helix. Since the days of Darwin, such sprightly biological change agents had never before been seen.

A young graduate student at the time, Li Zhao was so intrigued that upon graduating in 2011, she set out to join the lab of David Begun, where the discovery was first made. She soon revealed that these little genetic big bangs happen all the time­­—over the past decade, she and her team have identified more than 500 de novo genes in the Drosophila lineage alone.

As the energy consumption of neural networks continues to grow, different approaches to deep learning are needed. A neuromorphic method offering nonlinear computation based on linear wave scattering can be implemented using integrated photonics.

Cyagen has built a one-stop service platform for in vivo and in vitro CAR-T cell therapy research. The platform is available to researchers worldwide, which includes scFv sequence screening, CAR molecular design, and full-process services to develop the foundational research for your CAR-T cell therapy.

Over the recent decades, comprehensive genome-wide association studies (GWAS) have indicated the potential influence of genetic factors on one’s alcohol consumption volume and identified over 100 related variants^6,7. However, a predominant proportion of the identified variants are localized within noncoding regions, and their effect sizes tend to be small, making interpretation and identification of the causal gene challenging⁸. In addition, previous GWAS mainly utilized imputed genotype data, which only cover limited regions of the genome, and thus may have missed many potential genes. Furthermore, GWAS studies focused mainly on common variants, and few studies have investigated rare variants associated with alcohol consumption, which yield greater potential to interpret biological function and elucidate mechanisms⁹. Although there are studies that have attempted to leverage exome chip data to identify rare variants contributing to alcohol consumption, the sample size was small and limited regions of the whole exome were examined¹⁰.

The introduction of whole exome sequencing (WES) provides a great chance to overcome the limitations of previous genetic studies on alcohol consumption with a substantially larger amount of rare and ultra-rare protein-coding variants^11,12,13. Collapsing of loss-of-function (LOF) variants helps estimate the effect direction of associated genes^13,14. When combined with large-scale population cohorts with multi-modal phenotypic data, WES would greatly facilitate our understanding of the genetic underpinnings of alcohol consumption as well as its implication on physical and mental health⁶. However, to our knowledge, there have been few large-scale WES studies on alcohol consumption, let alone elucidating the potential implications of the identified genes^10,15. Meanwhile, as indicated by a previous genome-wide association study, significant genetic associations existed between alcohol consumption and several body health phenotypes⁷. The application of phenome-wide analysis for alcohol-related genes can help extend and deepen our current comprehension of the association between alcohol consumption and human health.

Hence, aiming to refine the genetic architecture of alcohol consumption, we conduct an exome-wide association study (ExWAS) for alcohol consumption among 304,119 individuals from the UK Biobank (UKB). We also examine the rare-variant associations with genes reported by previous GWAS^6,7,16,17. Finally, we provide biological insights into the identified genes via bioinformatics analyses and phenome-wide association analysis (PheWAS).

Genome editing stands as one of the most transformative scientific breakthroughs of our time. It allows us to dive into the very code of life and make precise modifications. Imagine being able to rewrite the genetic instructions that determine almost everything about an organism—how it looks, behaves, interacts with its environment, and its unique characteristics. This is the power of genome editing.

We use genome editing tools to tweak the genetic sequences of microbes, animals, and plants. Our goal? To develop desired traits and eliminate unwanted ones. This technology’s impact has been felt across biotechnology, human therapeutics, and agriculture, bringing rapid advancements and solutions.

The most widely used proteins in genome editing are Cas9 and Cas12a. These proteins are like the scissors of the genetic world, allowing us to cut and edit DNA. However, they are quite bulky, consisting of 1,000–1,350 amino acids. Advanced editing technologies like base editing and prime editing require the fusion of additional proteins with Cas9 and Cas12a, making them even bulkier. This bulkiness poses a challenge to delivering these proteins efficiently into cells, where the genetic material resides.