Year 2020 This holds promise for near infinite lifespans for humans still in the first stages but still very promising for immortality and so much more.
The microbes had survived on trace amounts of oxygen and were able to feed and multiply once revived in the lab.
In the vast and ever-evolving landscape of technology, neuromorphic computing emerges as a groundbreaking frontier, reminiscent of uncharted territories awaiting exploration. This novel approach to computation, inspired by the intricate workings of the human brain, offers a path to traverse the complex terrains of artificial intelligence (AI) and advanced data processing with unprecedented efficiency and agility.
Neuromorphic computing, at its core, is an endeavor to mirror the human brain’s architecture and functionality within the realm of computer engineering. It represents a significant shift from traditional computing methods, charting a course towards a future where machines not only compute but also learn and adapt in ways that are strikingly similar to the human brain. This technology deploys artificial neurons and synapses, creating networks that process information in a manner akin to our cognitive processes. The ultimate objective is to develop systems capable of sophisticated tasks, with the agility and energy efficiency that our brain exemplifies.
The genesis of neuromorphic computing can be traced back to the late 20th century, rooted in the pioneering work of researchers who sought to bridge the gap between biological brain functions and electronic computing. The concept gained momentum in the 1980s, driven by the vision of Carver Mead, a physicist who proposed the use of analog circuits to mimic neural processes. Since then, the field has evolved, fueled by advancements in neuroscience and technology, growing from a theoretical concept to a tangible reality with vast potential.
To be clear, humans are not the pinnacle of evolution. We are confronted with difficult choices and cannot sustain our current trajectory. No rational person can expect the human population to continue its parabolic growth of the last 200 years, along with an ever-increasing rate of natural resource extraction. This is socio-economically unsustainable. While space colonization might offer temporary relief, it won’t resolve the underlying issues. If we are to preserve our blue planet and ensure the survival and flourishing of our human-machine civilization, humans must merge with synthetic intelligence, transcend our biological limitations, and eventually evolve into superintelligent beings, independent of material substrates—advanced informational beings, or ‘infomorphs.’ In time, we will shed the human condition and upload humanity into a meticulously engineered inner cosmos of our own creation.
Much like the origin of the Universe, the nature of consciousness may appear to be a philosophical enigma that remains perpetually elusive within the current scientific paradigm. However, I emphasize the term “current.” These issues are not beyond the reach of alternative investigative methods, ones that the next scientific paradigm will inevitably incorporate with the arrival of Artificial Superintelligence.
The era of traditional, human-centric theoretical modeling and problem-solving—developing hypotheses, uncovering principles, and validating them through deduction, logic, and repeatable experimentation—may be nearing the end. A confluence of factors—Big Data, algorithms, and computational resources—are steering us towards a new type of discovery, one that transcends the limitations of human-like logic and decision-making— the one driven solely by AI superintelligence, nestled in quantum neo-empiricism and a fluidity of solutions. These novel scientific methodologies may encompass, but are not limited to, computing supercomplex abstractions, creating simulated realities, and manipulating matter-energy and the space-time continuum itself.
Join us on Patreon! https://www.patreon.com/MichaelLustgartenPhDDiscount Links: Epigenetic, Telomere Testing: https://trudiagnostic.com/?irclickid=U-s3Ii2r7x…
A molecular biologist at the USC Dornsife College of Letters, Arts and Sciences may have found a new “rule of biology.”
Artificial neural networks (ANNs) show a remarkable pattern when trained on natural data irrespective of exact initialization, dataset, or training objective; models trained on the same data domain converge to similar learned patterns. For example, for different image models, the initial layer weights tend to converge to Gabor filters and color-contrast detectors. Many such features suggest global representation that goes beyond biological and artificial systems, and these features are observed in the visual cortex. These findings are practical and well-established in the field of machines that can interpret literature but lack theoretical explanations.
Localized versions of canonical 2D Fourier basis functions are the most observed universal features in image models, e.g. Gabor filters or wavelets. When vision models are trained on tasks like efficient coding, classification, temporal coherence, and next-step prediction goals, these Fourier features pop up in the model’s initial layers. Apart from this, Non-localized Fourier features have been observed in networks trained to solve tasks where cyclic wraparound is allowed, for example, modular arithmetic, more general group compositions, or invariance to the group of cyclic translations.
Researchers from KTH, Redwood Center for Theoretical Neuroscience, and UC Santa Barbara introduced a mathematical explanation for the rise of Fourier features in learning systems like neural networks. This rise is due to the downstream invariance of the learner that becomes insensitive to certain transformations, e.g., planar translation or rotation. The team has derived theoretical guarantees regarding Fourier features in invariant learners that can be used in different machine-learning models. This derivation is based on the concept that invariance is a fundamental bias that can be injected implicitly and sometimes explicitly into learning systems due to the symmetries in natural data.
How does human activity influence the ocean biodiversity for marine protected areas (MPAs)? This is what a recent study published in Conservation Letters hopes to address as a team of international researchers investigated current conservation efforts aimed at further strengthening MPAs around the world. This study holds the potential to help scientists, conservationists, legislators, and the public better understand the global impact of ocean biodiversity, as the United Nations has called for protecting 30 percent of the ocean by 2030.
“Now more than ever we need healthy and biodiverse areas in the ocean to benefit people and help buffer threats to ocean ecosystems,” said Dr. Kirsten Grorud-Colvert, who is an associate professor in the Department of Integrative Biology at Oregon State University and a co-author on the study. “Marine protected areas can only achieve this if they are set up to be effective, just and durable. Our assessment shows how some of the largest protected areas in the world can be strengthened for lasting benefits.”
For the study, the researchers analyzed the 100 largest MPAs in the world using The MPA Guide, the former of which represents 90 percent of the global MPAs. For each MPA, the researchers collected data on the protection status, regulation documents, and management plan, along with analyzing scientific literature pertaining to human activities in those MPAs. In the end, the researchers found that 25 percent of the analyzed MPAs lacked proper implementation while they determined that 33 percent of the analyzed MPAs did not meet criteria for being compatible with nature conservation. They concluded these results were from either decreased regulations or increased levels of human activity.
Start speaking a new language in 3 weeks with Babbel 
. Get up to 60% OFF your subscription 
Here: https://bit.ly/sabinebabbel05
When Roger Penrose originally came out with the idea that the human brain uses quantum effects in microtubules and that was the origin of consciousness, many thought the idea was a little crazy. According to a new study, it turns out that Penrose was actually right… about the microtubules anyways. Let’s have a look.
Paper: https://pubs.acs.org/doi/10.1021/acs…
Check out my new quiz app ➜ http://quizwithit.com/
Support me on Donorbox ➜ https://donorbox.org/swtg.
Transcripts and written news on Substack ➜ https://sciencewtg.substack.com/
Transcript with links to references on Patreon ➜ / sabine.
Free weekly science newsletter ➜ https://sabinehossenfelder.com/newsle…
Audio only podcast ➜ https://open.spotify.com/show/0MkNfXl…
Join this channel to get access to perks ➜
/ @sabinehossenfelder.
On instagram ➜ / sciencewtg.
#science #sciencenews #quantum #biology
A groundbreaking study reveals that Rhizobia bacteria can fix nitrogen in partnership with marine diatoms, a discovery that could have significant implications for agriculture and marine ecosystems.
Nitrogen is an essential component of all living organisms. It is also the key element controlling the growth of crops on land, as well as the microscopic oceanic plants that produce half the oxygen on our planet.
Atmospheric nitrogen gas is by far the largest pool of nitrogen, but plants cannot transform it into a usable form. Instead, crop plants like soybeans, peas and alfalfa (collectively known as legumes) have acquired Rhizobial bacterial partners that “fix” atmospheric nitrogen into ammonium. This partnership makes legumes one of the most important sources of proteins in food production.
A breakthrough study by the Institut Curie reveals that embryonic cell compaction in humans is caused by cell contraction, offering new insights to enhance assisted reproductive technology success rates.
In human development, the compaction of embryonic cells is a vital process in the early stages of an embryo’s formation. Four days post-fertilization, the cells tighten together, helping to form the embryo’s initial structure. If compaction is flawed, it can hinder the development of the essential structure needed for the embryo to attach to the uterus. During assisted reproductive technology (ART), this stage is meticulously observed before the embryo is implanted.
An interdisciplinary research team led by scientists at the Genetics and Developmental Biology Unit at the Institut Curie (CNRS/Inserm/Institut Curie) studying the mechanisms at play in this still little-known phenomenon has made a surprising discovery: human embryo compaction is driven by the contraction of embryonic cells. Compaction problems are therefore due to faulty contractility in these cells, and not a lack of adhesion between them, as was previously assumed. This mechanism had already been identified in flies, zebrafish, and mice, but is a first in humans.
