Register for free to get subscriber-only benefits: https://closertotruth.com/If consciousness is 100% physical, we would have to conclude that the same kind…
How humans, animals and even single-celled organisms cooperate to survive suggests there’s more to life than just competition, argues a cheering study of evolutionary biology.
Just when scientists thought they had almost figured out the origins of multicellular life, evolution throws another curveball.
In a serendipitous discovery, a team of researchers has just chanced upon a third type of ‘unconventional’ multicellularity that blends the two kinds we already knew about.
Multicellularity has evolved a staggering 45 times or more across the tree of life. Yet fundamentally, the ancestor of each multicellular lineage relied on just one of two methods — individual cells sticking together as they split, or individual cells that have previously split coming back together.
Many biological structures form through the self-assembly of molecular building blocks. A new theoretical study explores how the shape of these building blocks can affect the formation rate [1]. The simplified model shows that hexagonal blocks can form large structures much faster than triangular or square blocks. The results could help biologists explain cellular behavior, while also giving engineers inspiration for more efficient self-assembly designs.
Certain viruses and cellular structures are made from self-assembling pieces that can be characterized by geometrical shapes. For example, some types of bacteria host carboxysomes, which are icosahedral (20-face) compartments built up from self-assembling hexagonal and pentagonal subunits.
To investigate the role of shape, Florian Gartner and Erwin Frey from Ludwig Maximilian University of Munich simulated self-assembly of two-dimensional structures with three types of building blocks: triangles, squares, and hexagons. The model assumed that the blocks bind along their edges, but these interactions are reversible, meaning that the resulting structures can fall apart before growing very large. Gartner and Frey found that certain shapes were better than others at assembling into larger structures, as they tended to form intermediate structures with more bonds around each block. In particular, hexagonal blocks were the most efficient building material, forming 1000-piece structures at a rate that was 10,000 times faster than triangular blocks.
A theory of consciousness should capture its phenomenology, characterize its ontological status and extent, explain its causal structure and genesis, and describe its function. Here, I advance the notion that consciousness is best understood as an operator, in the sense of a physically implemented transition function that is acting on a representational substrate and controls its temporal evolution, and as such has no identity as an object or thing, but (like software running on a digital computer) it can be characterized as a law. Starting from the observation that biological information processing in multicellular substrates is based on self organization, I explore the conjecture that the functionality of consciousness represents the simplest algorithm that is discoverable by such substrates, and can impose function approximation via increasing representational coherence. I describe some properties of this operator, both with the goal of recovering the phenomenology of consciousness, and to get closer to a specification that would allow recreating it in computational simulations.
As computer vision (CV) systems become increasingly power and memory intensive, they become unsuitable for high-speed and resource deficit edge applications — such as hypersonic missile tracking and autonomous navigation — because of size, weight, and power constraints.
At the University of Pittsburgh, engineers are ushering in the next generation of computer vision systems by using neuromorphic engineering to reinvent visual processing systems with a biological inspiration — human vision.
Rajkumar Kubendran, assistant professor in Pitt’s Swanson School of Engineering and senior member at the Institute of Electrical and Electronics Engineers (IEEE), received a Faculty Early Career Development (CAREER) award from the National Science Foundation (NSF) for his research on energy-efficient and data-efficient neuromorphic systems. Neuromorphic engineering is a promising frontier that will introduce the next generation of CV systems by reducing the number of operations through event-based computation in a biology-inspired framework.
Artificial intelligence (AI) systems like ChatGPT have taken the world by storm. There isn’t much in which they’re not involved, from recommending the next binge-worthy TV show to helping navigate through traffic. But can AI systems learn the language of life and help biologists reveal exciting breakthroughs in science?
Watch more videos on complexity and emergence: https://bit.ly/3TxyQBmThe world works at different levels—fundamental physics, physics, chemistry, biology, ps…
Evolution processes of complex networked systems in biology and social sciences, and their underlying mechanisms, still need better understanding. The authors propose a machine learning approach to reconstruct the evolution history of complex networks.
😗😁😘 year 2023.
The world’s first supercomputer capable of simulating networks at the scale of the human brain has been announced by researchers at the International Centre for Neuromorphic Systems (ICNS) at Western Sydney University.
DeepSouth uses a neuromorphic system which mimics biological processes, using hardware to efficiently emulate large networks of spiking neurons at 228 trillion synaptic operations per second — rivalling the estimated rate of operations in the human brain.
