Machine learning and artificial intelligence are increasingly taking the stage, with huge philosophical implications. We have been following this issue in our RSF science blog, first through the article Between the Holographic Approach and Data Science where we addressed the potential of trained artificial neural networks to replace our scientific models, and the possibility of reality being a numerical simulation was discussed. Somehow we had anticipated the work from Vitaly Vanchurin, from the University of Minnesota Duluth, proposing that we live in a neural network and affirming that only through neural networks we could find the theory of everything and grand unification theory. So, our second article entitled Is the universe a Neural network? addressed this later possibility.
Today it was published in Phys.org an article entitled New machine learning method raises question...
Just a couple of years ago, astronomers and astrophysicists were baffled by the observation of a synchronized behavior in galaxies, which can not be explained by their individual gravitational fields. Such was the case of a study lead by Joon Hyeop Lee, an astronomer at the Korea Astronomy and Space Science Institute, and published in The Astrophysical Journal in October 2018, reporting hundreds of galaxies rotating in sync with the motions of galaxies that were tens of millions of light years away.
Given the fact that from our known theories, in principle it would be impossible that galaxies separated by megaparsecs (millions of light years) could directly interact with each other, their interaction happens across distances that are too large to be explained by their gravitational force. It is then speculated that some unacknowledged force must be acting.
This discovery came...
Graphene, one of the most important nanomaterials developed so far, continues to surprise the scientific community. This time, thanks to the extraordinary phenomena found by a group of physicists from the University of Arkansas. We are talking specifically about the capacity to use the thermal motion of atoms in graphene as a source of energy!
In this recent work, published in Physical Review E under the title Fluctuation-induced current from freestanding graphene, the team of researchers have successfully developed a circuit capable of capturing graphene's thermal motion and converting it into an electrical current.
As it is said in this article: "The idea of harvesting energy from graphene is controversial because it refutes physicist Richard Feynman's well-known assertion that the thermal motion of atoms, known as Brownian motion, cannot do work. Thibado's team found that at room...
In a former RSF article entitled Between the Generalized Holographic approach and Data Science, we addressed the potential of trained artificial neural networks to replace our scientific models, and the possibility of reality being a numerical simulation was discussed. Somehow we had anticipated this next and very recent work from Vitaly Vanchurin, from the University of Minnesota Duluth, proposing that we live in a neural network. It is an audacious idea!
In our prior article we had anticipated the impact of artificial neural networks and deep machine learning … what we had not foreseen was that they would be used literally as the framework for the theory of everything! There is a saying: "better be a historian, than a prophet", meaning that a historian writes about past events, and so taking small risk, while a prophet takes a huge risk with his predictions. Though, we should not brag about this...
By Ines Urdaneta, PhD in Physics and Researcher at Resonance Science Foundation.
Image: Evan Leeson/Bob Peterson/lowjumpingfrog. None of these animals contain a single trace of blue pigment.
Colors in nature come mainly from three sources: pigments, structural colors, and bioluminescence.
Have you noticed that some colors are more intense than others in nature?
Such is the case of blue and green colors, compared to reds and the rest. The main reason is that blue and green can be structural colors, while the remaining colors seem to not be part of the team.
Structural coloring is the result of microscopically fine structured surfaces that interfere with visible light, sometimes in combination with pigments. For example, peacock tail feathers are brown pigmented, but because of their microscopic structure, they also reflect blue, turquoise and green light. And they are often iridescent. Thus, structural coloring is a classic optical effect of interference and diffraction, rather than a...
Image Credit: NASA/JPL-Caltech
In 1969, Roger Penrose proposed a method to extract rotational energy of a rotating black hole, and suggested that an advanced civilization could achieve it by lowering and then releasing a mass from a structure that is co-rotating with the black hole. The process would occur in the region just outside the event horizon, called the ergosphere, where frame-dragging is at its strongest, being able to tear apart an object; one part would enter the event horizon while the remaining one would be accelerated outwards with an additional impulse given by the rotational energy of the black hole. The excess energy calculated by Penrose was estimated to be 21 percent more than the incoming energy.
The process is brilliantly explained in this video: https://www.youtube.com/watch?time_continue=23&v=ES2VxhRAkUM&feature=emb_logo
Inspired by Penrose’s idea, Yakov Zel’dovich...
The question above could start with the following one: Is space an illusion?
Since the magnitude of a force like electromagnetic and gravity between two objects is inversely proportional to the distance between them, it seems plausible to conclude objects only interact with other objects when they are close, and the closer they are, the stronger the interaction. For instance, when bringing two magnets towards each other, one can feel the increase in the rejection between them (if approached by the same pole) or attraction between them (if opposite polarity). And since the force can be felt when the objects are still not in contact, one could say that the force is mediated by a field. Fields spread out as they propagate outside of the object.
This dependence of forces and interactions upon distance is the main characteristic of the principle of locality. Locations and speeds of objects are defined with...
The first steps to achieving efficient electroluminescence necessary for quantum computing have just been made.
Quantum computers encode information in quantum bits otherwise known as qubits. These qubits can exist in the form of a photon or an electron, where the polarisation state of the photon or the spin state of the electron is taken as two bits of information. However, as opposed to classical bits, qubits can also exist in a superposition of states which allows the computer to process significantly more information and at a faster rate. This rate is limited by the transfer of information, which for an electron-spin qubit has so far proven difficult. Currently this has been achieved for distances up to millimetre scales, which although large from the qubit’s perspective, it is too small for practical applications.
To achieve the long-distance kilometre-scale transfer of quantum information encoded as...
Image by: Arkadiusz Jadczyk
The word fractal has become increasingly popular, although the concept started more than two centuries ago in the 17th century with prominent and prolific mathematician and philosopher Gottfried Wilhelm Leibnitz. Leibnitz is believed to have addressed for the first time the notion of recursive self-similarity, and it wasn’t until 1960 that the concept was formally stabilized both theoretically and practically, through the mathematical development and computerized visualizations by Benoit Mandelbrot, who settled on the name “fractal”.
Fractals are defined mainly by three characteristics:
by Dr., Resonance Science Foundation Research Scientist
As many theoretical and computational chemists and physicists know, quantum chemical calculations involving more than an electron and nuclei are very difficult to solve. They belong to a field called many body problems and require an extensive amount of computational infrastructure and hours of calculations depending on the size (the number of particles) of the system.
Here is where artificial intelligence – a combination of artificial neural networks and machine learning – comes into play. Neural networks have been around for more than 50 years, and they are more actualized than ever before. This is because they can learn through something called backward propagation, reaching a high level of predictability and increasing accuracy by training the network.
Quantum theoretical models, together with their computational packages, have been outstandingly successful in describing the quantum...