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Category: biological – Page 119

Researchers have been trying to build artificial synapses for years in the hope of getting close to the unrivaled computational performance of the human brain. A new approach has now managed to design ones that are 1,000 times smaller and 10,000 times faster than their biological counterparts.

Despite the runaway success of deep learning over the past decade, this brain-inspired approach to AI faces the challenge that it is running on hardware that bears little resemblance to real brains. This is a big part of the reason why a human brain weighing just three pounds can pick up new tasks in seconds using the same amount of power as a light bulb, while training the largest neural networks takes weeks, megawatt hours of electricity, and racks of specialized processors.

That’s prompting growing interest in efforts to redesign the underlying hardware AI runs on. The idea is that by building computer chips whose components act more like natural neurons and synapses, we might be able to approach the extreme space and energy efficiency of the human brain. The hope is that these so-called “neuromorphic” processors could be much better suited to running AI than today’s computer chips.

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Living organisms offer extensive diversity in terms of their phenotypes, metabolic processes, and adaptation to various niches. However, the basic building blocks that create this diversity are remarkably similar. How can we advance our understanding of the fascinating mechanisms that drive biological complexity and how can we harness biological components to build entirely new materials and devices?

A new Special Issue from ACS Synthetic Biology will focus on this dynamic topic, including contributions that deconstruct as well as build up and mimic biological systems. The resulting work serves both to test our scientific understanding and to extend known biology to develop new concepts and applications. The issue will be led by Associate Editor Michael Jewett with Guest Editors Kate Adamala, Marileen Dogterom, and Neha Kamat.

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Patreon: https://www.patreon.com/mlst.
Discord: https://discord.gg/ESrGqhf5CB

The field of Artificial Intelligence was founded in the mid 1950s with the aim of constructing “thinking machines” — that is to say, computer systems with human-like general intelligence. Think of humanoid robots that not only look but act and think with intelligence equal to and ultimately greater than that of human beings. But in the intervening years, the field has drifted far from its ambitious old-fashioned roots.

Dr. Ben Goertzel is an artificial intelligence researcher, CEO and founder of SingularityNET. A project combining artificial intelligence and blockchain to democratize access to artificial intelligence. Ben seeks to fulfil the original ambitions of the field. Ben graduated with a PhD in Mathematics from Temple University in 1990. Ben’s approach to AGI over many decades now has been inspired by many disciplines, but in particular from human cognitive psychology and computer science perspective. To date Ben’s work has been mostly theoretically-driven. Ben thinks that most of the deep learning approaches to AGI today try to model the brain. They may have a loose analogy to human neuroscience but they have not tried to derive the details of an AGI architecture from an overall conception of what a mind is. Ben thinks that what matters for creating human-level (or greater) intelligence is having the right information processing architecture, not the underlying mechanics via which the architecture is implemented.

Ben thinks that there is a certain set of key cognitive processes and interactions that AGI systems must implement explicitly such as; working and long-term memory, deliberative and reactive processing, perc biological systems tend to be messy, complex and integrative; searching for a single “algorithm of general intelligence” is an inappropriate attempt to project the aesthetics of physics or theoretical computer science into a qualitatively different domain.

Panel: Dr. Tim Scarfe, Dr. Yannic Kilcher, Dr. Keith Duggar.

Pod version: https://anchor.fm/machinelearningstreettalk/episodes/58-Dr–…e-e15p20i.

Continue reading “#58 Dr. Ben Goertzel — Artificial General Intelligence” | >

Artificial Intelligence is pretty much THE HOLY GRAIL of Future Technologies.
There is no big Company nor University, which is not working on the development of Artificial Intelligence.
Role models are often the superior performance of the biological brain, but that’s also a lot of work.
So a development team in Australia therefore wants to save tedious development time and insert brain cells into Computers!
You may think that sounds crazy?
But their first prototype is already learning faster than traditional Artificial Intelligences of computers.

How did they even do that? This is exactly what we will talk about in this video.

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The Virus Zoo is my latest educational blog post! I’ve written up ~1 page ‘cheat sheets’ on the molecular biology of specific viruses. I cover genome, structure, and life cycle. So far, my zoo includes adeno-associated virus (AAV), adenovirus, and herpes simplex virus 1 (HSV-1). However, I plan to add more viruses as time goes on! Some others I would like to incorporate later are coronavirus, HIV, anellovirus, lentivirus, ebolavirus, and MS2 bacteriophage. Feel free to suggest other interesting viruses in the comments! All images were created by me. #virology #molecularbiology #biotechnology #genetherapy #virus #biochemistry #genetics

Genome and Structure:

AAV genomes are about 4.7 kb in length and are composed of ssDNA. Inverted terminal repeats (ITRs) form hairpin structures at ends of the genome. These ITR structures are important for AAV genomic packaging and replication. Rep genes (encoded via overlapping reading frames) include Rep78, Rep68, Rep52, Rep40.1 These proteins facilitate replication of the viral genome. As a Dependoparvovirus, additional helper functions from adenovirus (or certain other viruses) are needed for AAVs to replicate.

AAV capsids are about 25 nm in diameter. Cap genes include VP1, VP2, VP3 and are transcribed from overlapping reading frames.2 The VP3 protein is the smallest capsid protein. The VP2 protein is the same as VP3 except that it includes an N-terminal extension with a nuclear localization sequence. The VP1 protein is the same as VP2 except that it includes a further N-terminal extension encoding a phospholipase A2 (PLA2) that facilitates endosomal escape during infection. In the AAV capsid, VP1, VP2, and VP3 are present at a ratio of roughly 1:1:10. It should be noted that this ratio is actually the average of a distribution, not a fixed number.

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Back in March this year, a study published in Evolutionary Biology claimed that fossils categorized as Tyrannosaurus rex represent three separate species. However, a new study published on July 25 in Evolutionary Biology refutes this claim and suggests that the previous research lacked evidence and Tyrannosaurus rex is made of only one species.

The previously controversial research implied that T. rex should be reclassified as three different species, including the standard T. rex, the bulkier “T. imperator,” and the slimmer “T. regina.” Researchers analyzed 38 T. rex fossils that contained leg bones and teeth samples, a press release revealed.

However, paleontologists at the American Museum of Natural History and Carthage College were determined to review the data of the previous research, adding data points from 112 species of living dinosaurs—birds—and from four non-avian theropod dinosaurs.

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Circa 2013

What if your running shoes could really adapt to your feet — and not just in the way that footwear retailers describe to solidify sales. These cutting-edge Protocells Trainers present the fascinating possibilities of wearable living materials that can grow, modify and repair themselves through continuous use.

Shamees Aden has been working with Dr. Martin Hanczyc on these innovative kicks, developing a synthetic biological substance that could be 3D printed to fit the wearer’s feet like gloves. The composite organic fabric would provide surface protection to toes and soles, yet it could also offer support skeletal and muscular. The anatomical tissue of the Protocells Trainers would thicken in areas that experience more pressure, and they could heal their own tears while bottled in a special solution overnight.

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Circa 2016

Penn State scientists made a coating that allows conventional textiles used in everyday clothing to patch themselves up. Derived from squid ring teeth, the coating can turn virtually any fabric into a self-healing one. Simply adding water is enough to kick start the repairing process.

Nano research has already revealed the potential of self-cleaning clothes, and now a new study reveals the potential for similar technology in suits that can be used to protect soldiers from chemical or biological attacks.

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