Lifeboat Foundation

What led to the emergence of complex organisms on Earth? It’s a significant unanswered question in biology. Researchers from Christa Schleper’s team at the University of Vienna and Martin Pilhofer’s team at ETH Zurich have taken a step towards resolving it. The scientists succeeded in cultivating a special archaeon and characterizing it more precisely using microscopic methods.

This member of the Asgard archaea exhibits unique cellular characteristics and may represent an evolutionary “missing link” to more complex life forms such as animals and plants. The study was recently published in the journal Nature.

All life forms on earth are divided into three major domains: eukaryotes, bacteria and archaea. Eukaryotes include the groups of animals, plants and fungi. Their cells are usually much larger and, at first glance, more complex than the cells of bacteria and archaea. The genetic material of eukaryotes, for example, is packaged in a cell nucleus and the cells also have a large number of other compartments. Cell shape and transport within the eukaryotic cell are also based on an extensive cytoskeleton. But how did the evolutionary leap to such complex eukaryotic cells come about?

A model for information storage in the brain reveals how memories decay with age.

Theoretical constructs called attractor networks provide a model for memory in the brain. A new study of such networks traces the route by which memories are stored and ultimately forgotten [1]. The mathematical model and simulations show that, as they age, memories recorded in patterns of neural activity become chaotic—impossible to predict—before disintegrating into random noise. Whether this behavior occurs in real brains remains to be seen, but the researchers propose looking for it by monitoring how neural activity changes over time in memory-retrieval tasks.

Memories in both artificial and biological neural networks are stored and retrieved as patterns in the way signals are passed among many nodes (neurons) in a network. In an artificial neural network, each node’s output value at any time is determined by the inputs it receives from the other nodes to which it’s connected. Analogously, the likelihood of a biological neuron “firing” (sending out an electrical pulse), as well as the frequency of firing, depends on its inputs. In another analogy with neurons, the links between nodes, which represent synapses, have “weights” that can amplify or reduce the signals they transmit. The weight of a given link is determined by the degree of synchronization of the two nodes that it connects and may be altered as new memories are stored.

This video covers the timelapse of metaverse technologies from 2030 to 3000+. Watch this next video about the Future of Virtual Reality (2030 – 3000+): https://bit.ly/3zfjybO.
► Support This Channel: https://www.patreon.com/futurebusinesstech.
► Udacity: Up To 75% Off All Courses (Biggest Discount Ever): https://bit.ly/3j9pIRZ
► Brilliant: Learn Science And Math Interactively (20% Off): https://bit.ly/3HAznLL
► Jasper AI: Write 5x Faster With Artificial Intelligence: https://bit.ly/3MIPSYp.

SOURCES:
• https://www.futuretimeline.net.
• The Singularity Is Near: When Humans Transcend Biology (Ray Kurzweil): https://amzn.to/3ftOhXI

Continue reading “Future of the Metaverse (2030 — 10,000 A.D.+)” »

Bryan Johnson releases his rejuvenation protocol:

Blueprint is a public science experiment to determine whether it’s possible to stay the same biological age. This requires slowing down aging processes as much as possible and then reversing the aging that has happened. Currently my speed of aging is .76 (DunedinPACE). That means for every 365 days each year, I age 277 days. My goal is to remain the same age biologically for every 365 days that pass.

Continue reading “My Anti-Aging Protocol Broke a World Record… — YouTube” »

Health tests and reports suggest that Bryan Johnson, 45, has the heart of a 37-year-old and gets erections like a teenager, Bloomberg reported.

Ever since the invention of computers in the 1940s, machines matching general human intelligence have been greatly anticipated. In other words, a machine that possesses common sense and an effective ability to learn, reason, and plan to meet complex information-processing challenges across a wide range of natural as well as abstract domains, would qualify as having a human-level machine intelligence. Currently, our machines are far inferior to humans in general intelligence. However, according to philosopher Nick Bostrom at the University of Oxford, there are several pathways that could lead to human-level intelligence in machines such as whole brain emulation, biological cognition, artificial intelligence, human-machine interfaces, as well as networks and organizations. Once this happens, it would only be a matter of time until superhuman-level machine intelligence, or simply, superintelligence is unlocked. But what exactly do we mean by ‘superintelligence’? And are there different forms of superintelligence that our A.I.s can attain in the future? Let’s take a look at what Nick Bostrom has to say in this matter!

In his book, ‘Superintelligence’ Nick Bostrom defines the term ‘superintelligence’ “to refer to intellects that greatly outperform the best current human minds across many very general cognitive domains.” So, a super-intelligent intellect, would in principle, have the capacity to completely surpass the best human minds in practically every field, including science, philosophy, arts, general wisdom, and even social skills.

The ability to see invisible structures in our bodies, like the inner workings of cells, or the aggregation of proteins, depends on the quality of one’s microscope. Ever since the first optical microscopes were invented in the 17th century, scientists have pushed for new ways to see more things more clearly, at smaller scales and deeper depths.

Randy Bartels, professor in the Department of Electrical Engineering at Colorado State University, is one of those scientists. He and a team of researchers at CSU and Colorado School of Mines are on a quest to invent some of the world’s most powerful light microscopes—ones that can resolve large swaths of biological material in unimaginable detail.

The name of the game is super–resolution microscopy, which is any optical imaging technique that can resolve things smaller than half the wavelength of light. The discipline was the subject of the 2014 Nobel Prize in Chemistry, and Bartels and others are in a race to keep circumventing that diffraction limit to illuminate biologically important structures inside the body.

With so much death all around us, from the pandemic to the war in Ukraine to all the mass shootings, you might wonder what it all means. Queen Elizabeth gone. Betty White gone. And perhaps even a loved one of yours gone. They no longer exist, right? They are just memories, at least from a rational scientific perspective. But what if you’re wrong?

Dr. Caroline Soames-Watkins also believed that the world around her existed as a hard, cold reality ticking away like a clock. Death was a foregone conclusion—until she learned different. Caro, the protagonist of my new novel co-written with award-winning sci-fi author Nancy Kress, also thought she had the world figured out. Not her personal world, which has been upended by controversy, but how the physical world works and how her consciousness operates within it. Broke and without a job, she accepts a job offer from her great-uncle, a Nobel Prize-winning scientist who runs a research facility studying the space between biology and consciousness—between the self and what we assume is reality. They are on the verge of a humanity-altering discovery, which throws Caro into danger—love, loss, and death—that she could never have imagined possible.

Observer takes Caro on a mind-expanding journey to the very edge of science, challenging her to think about life and the power of the imagination in startling new ways. The ideas behind Observer are based on real science, starting with the famous two-slit experiments, in which the presence of an observer affects the path taken by a sub-atomic particle, and moves step-by-step into cutting-edge science about quantum entanglement, on-going experiments applying quantum-level physics to the macro-world, the multiverse, and the nature of time and consciousness itself.

The brain is often regarded as a soft-matter chemical computer, but the way it processes information is very different to that of conventional silicon circuits. Three groups now describe chemical systems capable of storing information in a manner that resembles the way that neurons communicate with one another at synaptic junctions. Such ‘neuromorphic’ devices could provide very-low-power computation and act as interfaces between conventional electronics and ‘wet’ chemical systems, potentially including neurons and other living cells themselves.

At a synapse, the electrical pulse or action potential that travels along a neuron triggers the release of neurotransmitter molecules that bridge the junction to the next neuron, altering the state of the second neuron by making it more or less likely to fire its own action potential. If one neuron repeatedly influences another, the connection between them may become strengthened. This is how information is thought to become imprinted as a memory, a process called Hebbian learning. The ability of synapses to adjust their connectivity in response to input signals is called plasticity, and in neural networks it typically happens on two timescales. Short-term plasticity (STP) creates connectivity patterns that fade quite fast and are used to filter and process sensory signals, while long-term plasticity (LTP, also called long-term potentiation) imprints more long-lived memories. Both biological processes are still imperfectly understood.

Neuromorphic circuits that display such learning behaviour have been developed previously using solid-state electronic devices called memristors, two-terminal devices in which the relationship between the current that passes through and the voltage applied depends on the charge that passed through previously. Memristors may retain this memory even when no power is applied – they are ‘non-volatile’ – meaning that neuromorphic circuits can potentially process information with very low power consumption, a feature crucial to the way our brains can function without overheating. Typically, memristor behaviour manifests as a current–voltage relationship on a loop, and the response varies depending on whether the voltage is increasing or decreasing: a property called hysteresis, which itself represents a kind of memory as the device behaviour is contingent on its history.