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Category: biological – Page 78

Rational Virology Research For Human Health & Pandemic Prevention — Dr. Felicia Goodrum Sterling, Ph.D. Professor, Department of Immunobiology, The University of Arizona.

Dr. Felicia Goodrum, Ph.D. (https://profiles.arizona.edu/person/fgoodrum) is Interim Associate Department Head and Professor of Immunobiology, as well as Professor, BIO5 Institute, Cellular and Molecular Medicine, Molecular and Cellular Biology, Cancer Biology And Genetics Graduate Interdisciplinary Programs, at the University of Arizona.

Dr. Goodrum earned her Ph.D. from Wake Forest University School of Medicine studying cell cycle restrictions to adenovirus replication and then trained as a postdoctoral fellow at Princeton University in the laboratory of Dr. Thomas Shenk studying human cytomegalovirus latency.

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The Neuroscience of Creativity, Perception, and Confirmation Bias.
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To ensure your survival, your brain evolved to avoid one thing: uncertainty. As neuroscientist Beau Lotto points out, if your ancestors wondered for too long whether that noise was a predator or not, you wouldn’t be here right now. Our brains are geared to make fast assumptions, and questioning them in many cases quite literally equates to death. No wonder we’re so hardwired for confirmation bias. No wonder we’d rather stick to the status quo than risk the uncertainty of a better political model, a fairer financial system, or a healthier relationship pattern. But here’s the catch: as our brains evolved toward certainty, we simultaneously evolved away from creativity—that’s no coincidence; creativity starts with a question, with uncertainty, not with a cut and dried answer. To be creative, we have to unlearn millions of years of evolution. Creativity asks us to do that which is hardest: to question our assumptions, to doubt what we believe to be true. That is the only way to see differently. And if you think creativity is a chaotic and wild force, think again, says Beau Lotto. It just looks that way from the outside. The brain cannot make great leaps, it can only move linearly through mental possibilities. When a creative person forges a connection between two things that are, to your mind, so far apart, that’s a case of high-level logic. They have moved through steps that are invisible to you, perhaps because they are more open-minded and well-practiced in questioning their assumptions. Creativity, it seems, is another (highly sophisticated) form of logic. Beau Lotto is the author of Deviate: The Science of Seeing Differently.

BEAU LOTTO:

Beau Lotto is a professor of neuroscience, previously at University College London and now at the University of London, and a Visiting Scholar at New York University.

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For fans of bioethical nightmares, it’s been a real stonker of a month. First, we had the suggestion that we use comatose women’s wombs to house surrogate pregnancies. Now, it appears we might have a snazzy idea for what to do with their brains, too: to turn them into hyper-efficient biological computers.

Lately, you see, techies have been worrying about the natural, physical limits of conventional, silicon-based computing. Recent developments in ‘machine learning’, in particular, have required exponentially greater amounts of energy – and corporations are concerned that further technological progress will soon become environmentally unsustainable. Thankfully, in a paper published this week, a team of American scientists pointed out something rather nifty: that the walnut-shaped, spongy computer in your skull doesn’t appear to be bound by anything like the same limitations – and that it might, therefore, provide us with something of a solution.

The human brain, the paper explains, is slower than machines at performing basic tasks (like mathematical sums), but much, much better at processing complex problems that involve limited, or ambiguous, data. Humans learn, that is, how to make smart decisions quickly, even when we only have small fragments of information to go on, in a way that computers simply can’t. For anything more sophisticated than arithmetic, sponge beats silicon by a mile.

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Yuli Ban is talking about the prediction and emergence of generative AIs, the extent to which those can disrupt humanities reliance on creativity and productivity. He mentions ‘the dead internet theory’ that postulates that most content is autogenerated, obfuscating the actual people using the internet and reducing their actual exposure.

I think we already see this in social media, internet forums and other areas where fake content and profiles are detected. and this can spread to youtube and short form video platforms, telemarketing and scams. as well as use by political groups and states.

Yuli also mentions the long term implications — peaking human population and the notion of Transhumanism where humans merge with an infinitely more capable AI which assumes control. he mentions how biology is a quality many would like to preserve, to varying degrees.

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Everyone is now scrambling to integrate AI with as many facets of human life as possible. Neural nets and machine learning can offer greatly improved processing speeds, yet these aspects still rely on digital pathways that may never fully mimic the biological structure of the human brain. The next step in AI improvement would be to combine the best of both the digital world and the biological world. Some scientists are already experimenting with this possibility, as a new article published in the academic journal Frontiers of Science is deep diving into the realm of biocomputers and organoid intelligence (OI).

All AI applications today rely on computing power provided by powerful CPUs or GPUs. OI, on the other hand, is seeking to bring “unprecedented advances in computing speed, processing power, data efficiency and storage capabilities” by harnessing the complexity of lab-grown cell-cultures repurposed from adult skin cells that consist of 3D clusters of neurons and other brain cells.

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Tiny insects known as sharpshooters excrete by catapulting urine drops at incredible accelerations. Their excretion is the first example of superpropulsion discovered in a biological system.

Saad Bhamla was in his backyard when he noticed something he had never seen before: an insect urinating. Although nearly impossible to see, the insect formed an almost perfectly round droplet on its tail and then launched it away so quickly that it seemed to disappear. The tiny insect relieved itself repeatedly for hours.

It’s generally taken for granted that what goes in must come out, so when it comes to fluid dynamics in animals, the research is largely focused on feeding rather than excretion. But Bhamla, an assistant professor in the School of Chemical and Biomolecular Engineering at the Georgia Institute of Technology (Georgia Tech), had a hunch that what he saw wasn’t trivial.

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Trying to make computers more like human brains isn’t a new phenomenon. However, a team of researchers from Johns Hopkins University argues that there could be many benefits in taking this concept a bit more literally by using actual neurons, though there are some hurdles to jump first before we get there.

In a recent paper, the team laid out a roadmap of what’s needed before we can create biocomputers powered by human brain cells (not taken from human brains, though). Further, according to one of the researchers, there are some clear benefits the proposed “organoid intelligence” would have over current computers.

“We have always tried to make our computers more brain-like,” Thomas Hartung, a researcher at Johns Hopkins University’s Environmental Health and Engineering department and one of the paper’s authors, told Ars. “At least theoretically, the brain is essentially unmatched as a computer.”

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Is self-consciousness necessary for consciousness? The answer is yes. So there you have it—the answer is yes. This was my response to a question I was asked to address in a recent AEON piece (https://aeon.co/essays/consciousness-is-not-a-thing-but-a-process-of-inference). What follows is based upon the notes for that essay, with a special focus on self-organization, self-evidencing and self-modeling. I will try to substantiate my (polemic) answer from the perspective of a physicist. In brief, the argument goes as follows: if we want to talk about creatures, like ourselves, then we have to identify the characteristic behaviors they must exhibit. This is fairly easy to do by noting that living systems return to a set of attracting states time and time again. Mathematically, this implies the existence of a Lyapunov function that turns out to be model evidence (i.e., self-evidence) in Bayesian statistics or surprise (i.e., self-information) in information theory. This means that all biological processes can be construed as performing some form of inference, from evolution through to conscious processing. If this is the case, at what point do we invoke consciousness? The proposal on offer here is that the mind comes into being when self-evidencing has a temporal thickness or counterfactual depth, which grounds inferences about the consequences of my action. On this view, consciousness is nothing more than inference about my future; namely, the self-evidencing consequences of what I could do.

There are many phenomena in the natural sciences that are predicated on the notion of “self”; namely, self-information, self-organization, self-assembly, self-evidencing, self-modeling, self-consciousness and self-awareness. To what extent does one entail the others? This essay tries to unpack the relationship among these phenomena from first (variational) principles. Its conclusion can be summarized as follows: living implies the existence of “lived” states that are frequented in a characteristic way. This mandates the optimization of a mathematical function called “surprise” (or self-information) in information theory and “evidence” in statistics. This means that biological processes can be construed as an inference process; from evolution through to conscious processing. So where does consciousness emerge? The proposal offered here is that conscious processing has a temporal thickness or depth, which underwrites inferences about the consequences of action.

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“Computers that run on this ‘biological hardware’ could in the next decade begin to alleviate energy-consumption demands of supercomputing.”

Johns Hopkins University researchers have outlined plans for a “bio-computer” that is highly feasible in our lifetime.

“Computing and artificial intelligence have been driving the technology revolution, but they are reaching a ceiling,” Thomas Hartung, a professor of environmental health sciences at the Johns Hopkins Bloomberg School of Public Health and Whiting School of Engineering, who is spearheading the work, said in a statement.

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An updated metric for prioritizing species’ conservation that incorporates scientific uncertainty and complementarity between species, in addition to extinction risk and evolutionary distinctiveness, has been published on February 28 in the open access journal PLOS Biology, authored by Rikki Gumbs from the Zoological Society of London (ZSL), U.K., and colleagues.

In 2007, ZSL established the Evolutionarily Distinct and Globally Endangered (EDGE) metric to prioritize species for conservation based on preserving embodied within . The approach allocates each species a score based on the evolutionary distance, measured in millions of years, that separates a species from its closest living relatives, and its conservation status in the IUCN Red List.

EDGE has since been applied to mammals, amphibians, birds, sharks and rays, corals, and flowering plants, and is used to allocate conservation funding. To update the EDGE metric to incorporate recent advances in and conservation, ZSL hosted a workshop for conservation scientists and practitioners, who reached a consensus on EDGE2—an updated metric that includes the of closely related species and uncertainty in species’ relationships and conservation status.

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