Artificial intelligence has already shaved years off research into protein engineering. Now, for the first time, scientists have synthesized proteins predicted by an AI model in the lab, and found them to work just as well as their natural counterparts.
The first time a language model was used to synthesize human proteins.
Of late, AI models are really flexing their muscles. We have recently seen how ChatGPT has become a poster child for platforms that comprehend human languages. Now a team of researchers has tested a language model to create amino acid sequences, showcasing abilities to replicate human biology and evolution.
The language model, which is named ProGen, is capable of generating protein sequences with a certain degree of control. The result was achieved by training the model to learn the composition of proteins. The experiment marks the first time a language model was used to synthesize human proteins.
The man himself:
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 “Why I Am Spending Millions To Be 18 Again” »
(http://www.evol-net.fr/index.php?option=com_tlpteam&view=team&id=2&Itemid=559) is a Research Director at the French National Centre for Scientific Research (CNRS), the French state research organization and the largest fundamental science agency in Europe.
Dr. Bapteste has both a Ph.D. in evolutionary biology from Pierre and Marie Curie University and a Ph.D. in the philosophy of biology from Pantheon-Sorbonne University.
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.
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SOURCES:
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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.
