Lifeboat Foundation

Neuromorphic computers are devices that try to achieve reasoning capability by emulating a human brain. They are a different type of computer architecture that copies the physical characteristics and design principles of biological nervous systems. Although neuromorphic computations can be emulated, it’s very inefficient for classical computers to simulate. Typically new hardware is required.

The first neuromorphic computer at the scale of a full human brain is about to come online. It’s called DeepSouth, and will be finished in April 2024 at Western Sydney University. This computer should enable new research into how our brain actually functions, potentially leading to breakthroughs in how AI is created.

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“These spots are a big surprise,” said Dr. David Flannery. “On Earth, these types of features in rocks are often associated with the fossilized record of microbes living in the subsurface.”

Did Mars once have life billions of years ago? This is what NASA’s Perseverance (Percy) rover hopes to figure out, and scientists might be one step closer to answering that question with a recent discovery by the car-sized robotic explorer that found a unique rock with “leopard spots” that have caused some in the scientific community to claim this indicates past life might have once existed on the now cold and dry Red Planet. However, others have just as quickly rushed to say that further evidence is required before jumping to conclusions.

Upon analyzing the rock using Percy’s intricate suite of scientific instruments, scientists determined that it contained specific chemical signatures indicative of life possibly having existed billions of years ago when liquid water flowed across the surface. However, the science team is also considering other reasons for the rock’s unique appearance, including further research to determine if the findings are consistent with potential ancient life.

Continue reading “Peculiar Rock Found by NASA’s Perseverance Rover Leaves Scientists Puzzled” »

Putting 50 billion transistors into a microchip the size of a fingernail is a feat that requires manufacturing methods of nanometer level precision—layering of thin films, then etching, depositing, or using photolithography to create the patterns of semiconductor, insulator, metal, and other materials that make up the tiny working devices within the chip.

The process relies heavily on solvents that carry and deposit materials in each layer—solvents that can be difficult to handle and toxic to the environment.

Now researchers led by Fiorenzo Omenetto, Frank C. Doble Professor of Engineering at Tufts, have developed a nanomanufacturing approach that uses water as the primary solvent, making it more environmentally compatible and opening the door to the development of devices that combine inorganic and biological materials.

In a study published in Device (“Self-powered electrostatic tweezer for adaptive object manipulation”), a research team led by Dr. DU Xuemin from the Shenzhen Institute of Advanced Technology (SIAT) of the Chinese Academy of Sciences has reported a new self-powered electrostatic tweezer that offers superior accumulation and tunability of triboelectric charges, enabling unprecedented flexibility and adaptability for manipulating objects in various working scenarios.

The ability to manipulate objects using physical tweezers is essential in fields such as physics, chemistry, and biology. However, conventional tweezers often require complex electrode arrays and external power sources, have limited charge-generation capabilities, or produce undesirable temperature rises.

The newly proposed self-powered electrostatic tweezer (SET) features a polyvinylidene fluoride trifluoroethylene (P(VDF-TrFE))-based self-powered electrode (SE) that generates large and tunable surface charge density through the triboelectric effect, along with a dielectric substrate that functions as both a tribo-counter material and a supportive platform, and a slippery surface to reduce resistance and biofouling during object manipulation.

Aging is a universal experience, evident through changes like wrinkles and graying hair. However, aging goes beyond the surface; it begins within our cells. Over time, our cells gradually lose their ability to perform essential functions, leading to a decline that affects every part of our bodies, from our cognitive abilities to our immune health.

To understand how cellular changes lead to age-related disorders, Calico scientists are using advanced RNA sequencing to map molecular changes in individual cells over time in the roundworm, C. elegans. Much like mapping networks of roads and landscapes, we’re charting the complexities of our biology. These atlases uncover cell characteristics, functions, and interactions, providing deeper insights into how our bodies age.

In the early 1990s, Cynthia Kenyon, Vice President of Aging Research at Calico, and her former team at UCSF discovered genes in C. elegans that control lifespan; these genes, which influence IGF1 signaling, function similarly to extend lifespan in many other organisms, including mammals. The genetic similarities between this tiny worm and more complex animals make it a useful model for studying the aging process. In work published in Cell Reports last year, our researchers created a detailed map of gene activity in every cell of the body of C. elegans throughout its development, providing a comprehensive blueprint of its cellular diversity and functions. They found that aging is an organized process, not merely random deterioration. Each cell type follows its own aging path, with many activating cell-specific protective gene expression pathways, and with some cell types aging faster than others. Even within the same cell type, the rate of aging can vary.

The idea of the brain as a computer is everywhere. So much so we have forgotten it is a model and not the reality. It’s a metaphor that has lead some to believe that in the future they’ll be uploaded to the digital ether and thereby achieve immortality. It’s also a metaphor that garners billions of dollars in research funding every year. Yet researchers argue that when we dig down into our grey matter our biology is anything but algorithmic. And increasingly, critics contend that the model of the brain as computer is sending scientists (and their resources) nowhere fast. Is our attraction to the idea of the brain as computer an accident of current human technology? Can we find a better metaphor that might lead to a new paradigm?

If there’s one phrase the June 2024 U.S. presidential debate may entirely eliminate from the English vocabulary it’s that age is a meaningless number. Often attributed to boxer Muhammad Ali, who grudgingly retired at age 39, this centuries-old idea has had far-reaching consequences in global politics, as life expectancy more than doubled since the start of the 20th century, and presidents’ ages shifted upwards. We say “age is what we make of it” to ourselves and to policymakers, and think it’s a harmless way to dignify the aged. But how true is it? And if it isn’t true, why would we lie?

For centuries, we have confused our narrative of what aging should be with what its ruthless biology is. Yet pretending that biological age does not matter is at best myopic, and at worst, it’s a dangerous story to our governments, families, and economies. In just 11 years — between 2018 and 2029 — U.S. spending on Social Security and Medicare will more than double, from $1.3 trillion to $2.7 trillion per year. As we age, our odds of getting sick and dying by basically anything go up exponentially. If smoking increases our chances of getting cancer by a factor of 15, aging does so 100-fold. At age 65, less than 5% of people are diagnosed with Alzheimer’s. Beyond age 85, nearly half the population has some form of dementia. Biological aging is the biggest risk factor for most chronic diseases; it’s a neglected factor in global pandemics; and it even plays a role in rare diseases.

This explains why in hospitals, if there’s one marker next to a patient’s name, it’s their age. How many birthday candles we have blown out is an archaic surrogate marker of biological aging. Yet it’s the best we have. Chronological age is so telling of overall health that physicians everywhere rely on it for life-or-death decisions, from evaluating the risks of cancer screening to rationing hospital beds.

Neural networks biological and artificial.

Neural Networks have found applications across various domains due to their ability to learn from data and improve over time without human intervention. They can solve challenging problems that are hard or impossible to solve using traditional methods. Here are some of the examples of how neural networks and artificial neurons are used in real-world scenarios:

Voice assistants: Voice assistants like Siri and Alexa use neural networks to understand spoken language commands and questions. They use trained models based on artificial neurons processing vast datasets of speech and text data. They can also generate natural-sounding responses and perform various tasks, such as playing music, setting reminders, searching the web, etc.

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A team of scientists from Montana State University has provided the first experimental evidence that two new groups of microbes thriving in thermal features in Yellowstone National Park produce methane—a discovery that could one day contribute to the development of methods to mitigate climate change and provide insight into potential life elsewhere in our solar system.