Lifeboat Foundation

For the first time, Stanford researchers have found a way to create and stabilize an extremely rare form of gold that has lost two negatively charged electrons, denoted Au²⁺. The material stabilizing this elusive version of the valued element is a halide perovskite—a class of crystalline materials that holds great promise for various applications including more-efficient solar cells, light sources, and electronics components.

Surprisingly, the Au²⁺ perovskite is also quick and simple to make using off-the-shelf ingredients at room temperature.

“It was a real surprise that we were able to synthesize a stable material containing Au²⁺ —I didn’t even believe it at first,” said Hemamala Karunadasa, associate professor of chemistry at the Stanford School of Humanities and Sciences and senior author of the study published Aug. 28 in Nature Chemistry. “Creating this first-of-its-kind Au²⁺ perovskite is exciting. The gold atoms in the perovskite bear strong similarities to the copper atoms in high-temperature superconductors, and heavy atoms with unpaired electrons, like Au²⁺, show cool magnetic effects not seen in lighter atoms.”

There’s still a lot of mystery surrounding the Apple Car but we know a few things about it, including the fact it won’t be cheap.

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Professional video editing, color correction, visual effects and audio post production all in a single application. Free and paid versions for Mac, Windows and Linux.

Effective compression is about finding patterns to make data smaller without losing information. When an algorithm or model can accurately guess the next piece of data in a sequence, it shows it’s good at spotting these patterns. This links the idea of making good guesses—which is what large language models like GPT-4 do very well —to achieving good compression.

In an arXiv research paper titled “Language Modeling Is Compression,” researchers detail their discovery that the DeepMind large language model (LLM) called Chinchilla 70B can perform lossless compression on image patches from the ImageNet image database to 43.4 percent of their original size, beating the PNG algorithm, which compressed the same data to 58.5 percent. For audio, Chinchilla compressed samples from the LibriSpeech audio data set to just 16.4 percent of their raw size, outdoing FLAC compression at 30.3 percent.

In this case, lower numbers in the results mean more compression is taking place. And lossless compression means that no data is lost during the compression process. It stands in contrast to a lossy compression technique like JPEG, which sheds some data and reconstructs some of the data with approximations during the decoding process to significantly reduce file sizes.

The robot industry is expected to grow significantly in the coming years and with AI powering these machines they are to shape the future.

The narrative around cheating students doesn’t tell the whole story. Meet the teachers who think generative AI could actually make learning better.

Ribonucleic acid (RNA) is a molecule which is present in cells and made of genetic material to help build proteins necessary for cell function. RNA provides a template for the construction of proteins and is essential for cell and organism life. Immune cells rely on these proteins, including CD8⁺ or cytotoxic T cells which are responsible for killing invading pathogens. Importantly, cytotoxic T cells are a major component of the memory immune response. A pool of T cells specifically designed to recognize an invader is stored for future invasion of that particular pathogen. For example, once these cells are exposed to an invading antigen or protein, the immune system will expand T cells specific to that antigen and remember the antigen next time it enters the body. Vaccines work in a similar way by introducing a foreign antigen to the body, so the immune system is ready if the pathogen ever enters your body in the future. Only a small set of T cells that expand survive and it is unclear how this process occurs.

Recently a team of researchers at the University of Massachusetts Amherst (UMass) demonstrated that a single strand of RNA governs a T cells ability to recognize and kill tumors. The single strand of RNA is known as let-7 and is a microRNA, which is responsible for gene expression regulation. The recent discovery may improve vaccine development and cellular memory to enhance immunotherapy against cancers. Immunotherapy is a general term referring to cancer therapies that try to activate the immune system to kill the tumor compared to other drugs that try to directly kill the tumor with chemicals, such as chemotherapy.

The report published in Nature Communications identified that the microRNA, let-7, may enhance memory of T cells. Researchers led by Dr. Leonid Pobezinsky, Associate Professor of Veterinary and Animal Sciences at UMass, further built on our understanding of how T cells form immune memory. Pobezinsky and colleagues found that a small piece of microRNA that has been present throughout evolution is expressed in memory cells. Additionally, they found that more let-7 a cell has, the more likely that cell will recognize a cancer cell and kill it. The increased let-7 also indicates that the cell will turn into a memory cell after being exposed to an antigen. The regulation of enhanced memory T cells by let-7 is an integral process key to fight infections. This is a critical finding, especially because memory cells retain stem-like characteristics and can survive for decades.

Given the staggering pace of generative AI development, it’s no wonder that so many executives are tempted by the possibilities of AI, concerned about finding and retaining qualified workers, and humbled by recent market corrections or missed analyst expectations. They envision a future of work without nearly as many people as today. But this is a miscalculation. Leaders, understandably concerned about missing out on the next wave of technology, are unwittingly making risky bets on their companies’ futures. Here are steps every leader should take to prepare for an uncertain world where generative AI and human workforces coexist but will evolve in ways that are unknowable.

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A framework for making plans in the midst of great uncertainty.

That’s one way to talk about other human beings.

As writer Elizabeth Weil notes in a new profile of OpenAI CEO Sam Altman in New York Magazine, the powerful AI executive has a disconcerting penchant for using the term “median human,” a phrase that seemingly equates to a robotic tech bro version of “Average Joe.”

Altman’s hope is that artificial general intelligence (AGI) will have roughly the same intelligence as a “median human that you could hire as a co-worker.”