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Category: biological – Page 50

Complexity of biological forms has fascinated humankind over the years. Different species of plants have different leaf shapes. Have you ever wondered why it is so? Why does this shape diversity exist? Plants can change their leaf shapes over time and space. But how?

Does the distinct of forms play a significant role in energy optimization? In fact, the shape of leaves has a lot to do with adapting to their surrounding environment. How is the unfolding of shape related to the evolutionary process of nature? These intriguing questions have led us to focus on quantitative approaches to the complexity of plant leaves.

Quantifying leaf shapes using Euclidean shapes, such as circles, triangles, etc., are appropriate to only a few . Therefore, various quantitative measures of leaf shapes have been developed with varying accuracy. But Is the shape of an object really its actual shape? Visual perception of definite shape or geometry of physical objects is only an abstraction.

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Dr. Avshalom Cyrus Elitzur (Hebrew: אבשלום כורש אליצור; born 30 May 1957) is an Israeli physicist, philosopher and professor at Chapman University. He is also the founder of the Israeli Institute for Advanced Physics. He obtained his PhD under Yakir Aharanov. Elitzur became a household name among physicists for his collaboration with Lev Vaidman in formulating the “bomb-testing problem” in quantum mechanics, which has been validaded by two Nobel-prize-winning physicists. Elitzur’s work has sparked extensive discussions about the foundations of quantum mechanics and its interpretations, including the Copenhagen interpretation, many-worlds interpretation, and objective collapse models. His contributions have had a profound impact on both physics and philosophy, influencing debates about measurement, the role of observers, and the ontology of quantum states. Elitzur has also engaged in discussions about consciousness, the arrow of time, and other foundational topics, including a recent breakthrough in bio-thermodynamics and the “ski-lift” pathway.

Elitzur’s Google Scholar page: https://tinyurl.com/5n7a8hd6
Elitzur’s Wikipedia page: https://en.wikipedia.org/wiki/Avshalo…
IAI Article: https://iai.tv/articles/a-radical-new…

Powerpoint presentation: Pending.

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A new study shows how quantum computing can be harnessed to discover new properties of polymer systems central to biology and material science.

The advent of quantum computing is opening previously unimaginable perspectives for solving problems deemed beyond the reach of conventional computers, from cryptography and pharmacology to the physical and chemical properties of molecules and materials. However, the computational capabilities of present-day quantum computers are still relatively limited. A newly published study in Science Advances fosters an unexpected alliance between the methods used in quantum and traditional computing.

The research team, formed by Cristian Micheletti and Francesco Slongo of SISSA in Trieste, Philipp Hauke of the University of Trento, and Pietro Faccioli of the University of Milano-Bicocca, used a mathematical approach called QUBO (from “Quadratic Unconstraint Binary Optimization”) that is ideally suited for specific quantum computers, called “quantum annealers.”

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Over the past few decades, it has become quite obvious that humans are not the only living organisms with intelligence.

The story of intelligence you are about to experience goes back 13.8 billion years, back to the moment the universe was born: the Big Bang. It’s a story of time and space, matter and energy. It is a story of unfolding, It’s the story of how the very nature of the physical universe from its very inception led to the universe getting to know itself and eventually, to reflect.

Complexity, Evolution, and Intelligence is comprised of five parts, each corresponding to a movement in Dan Forrest’s “Requiem For The Living.” This composition was performed August 2, 2013 in Raleigh, NC by Bel Canto, conducted by Dr. Bill Young.

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In this introduction to quantum consciousness, Justin Riddle presents six arguments that quantum consciousness is an important theory of mind.\
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To summarize them briefly, People always identify as their latest technology and so most people believe that they are a digital computer. Time to update those models of self, because… Quantum computers are here. We wouldn’t want the brick of metal in our pocket to have greater computational power than our brain. People say the brain is too warm, wet, and noisy for quantum effects; yet, evidence keeps emerging for quantum effects in biology (such as photosynthesis). Where do we draw the line? Evolution might be selecting for quantum systems that can maintain quantum coherence. The debate around the role of quantum mechanics in consciousness has been raging for 100 years. Many key historical figures like Bohr, Schrodinger, Heisenberg, von Neumann entertained the idea that quantum mechanics might relate to our mind. Physical theories that are purely deterministic have failed to account for key aspects of subjective experience. There may be novel answers from a perspective that incorporate new physics.\
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0:00 Introduction\
1:26 1. People identify as their latest technology\
4:07 2. Quantum computers are here\
7:30 3. Biology utilizes quantum properties\
12:00 4. Evolution selects for quantum systems\
14:10 5. Historical precedent for quantum consciousness\
16:30 6. Failure of physical theories to explain\
a. Sense of self\
b. Freewill\
c. Meaning\
21:07 Outro\
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#quantum\
#consciousness\
#philosophy\
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Website: www.justinriddlepodcast.com\
Email: [email protected]\
Twitter: @JRiddlePodcast\
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Music licensed from and created by Baylor Odabashian. BandCamp: @UnscrewablePooch\
Painting behind me by Paul Seli. IG: @paul.seli.art\
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Relevant external link:\
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A new biohybrid computer combining a “brain organoid” and a traditional AI was able to perform a speech recognition task with 78% accuracy — demonstrating the potential for human biology to one day boost our computing capabilities.

The background: The human brain is the most energy efficient “computer” on Earth — while a supercomputer needs 20 mega watts of power to process more than a quintillion calculations per second, your brain can do the equivalent with just 20 watts (a megawatt is 1 million watts).

This has given researchers the idea to try boosting computers by combining them with a three-dimensional clump of lab-grown human brain cells, known as a brain organoid.

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Though highly capable – far outperforming humans in big-data pattern recognition tasks in particular – current AI systems are not intelligent in the same way we are. AI systems aren’t structured like our brains and don’t learn the same way.

AI systems also use vast amounts of energy and resources for training (compared to our three-or-so meals a day). Their ability to adapt and function in dynamic, hard-to-predict and noisy environments is poor in comparison to ours, and they lack human-like memory capabilities.

Our research explores non-biological systems that are more like human brains. In a new study published in Science Advances, we found self-organising networks of tiny silver wires appear to learn and remember in much the same way as the thinking hardware in our heads.

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How the brain adjusts connections between #neurons during learning: this new insight may guide further research on learning in brain networks and may inspire faster and more robust learning #algorithms in #artificialintelligence.

Researchers from the MRC Brain Network Dynamics Unit and Oxford University’s Department of Computer Science have set out a new principle to explain how the brain adjusts connections between neurons during learning. This new insight may guide further research on learning in brain networks and may inspire faster and more robust learning algorithms in artificial intelligence.

The essence of learning is to pinpoint which components in the information-processing pipeline are responsible for an error in output. In , this is achieved by backpropagation: adjusting a model’s parameters to reduce the error in the output. Many researchers believe that the brain employs a similar learning principle.

However, the biological brain is superior to current machine learning systems. For example, we can learn new information by just seeing it once, while artificial systems need to be trained hundreds of times with the same pieces of information to learn them. Furthermore, we can learn new information while maintaining the knowledge we already have, while learning new information in artificial neural networks often interferes with existing knowledge and degrades it rapidly.

Continue reading “New insight into how brain adjusts synaptic connections during learning may inspire more robust AI” | >