This would have meant so much to those like my Grandfather in his later years... (If it were affordable. That's always the issue, isn't it?) When I look back I wonder if it might have been a flu vaccine that caused his stroke.
Physicist: The Entire Universe Might Be A Neural Network (Not A 'Simulation') But in a provocative preprint uploaded to arXiv this summer, a physics professor at the University of Minnesota Duluth named Vitaly Vanchurin attempts to reframe reality in a particularly eye-opening way - suggesting that we’re living inside a massive neural network that governs everything around us. n other words, he wrote in the paper, it’s a “possibility that the entire universe on its most fundamental level is a neural network.” For years, physicists have attempted to reconcile quantum mechanics and general relativity. The first posits that time is universal and absolute, while the latter argues that time is relative, linked to the fabric of space-time. In his paper, Vanchurin argues that artificial neural networks can “exhibit approximate behaviors” of both universal theories. Since quantum mechanics “is a remarkably successful paradigm for modeling physical phenomena on a wide range of scales,” he writes, “it is widely believed that on the most fundamental level the entire universe is governed by the rules of quantum mechanics and even gravity should somehow emerge from it.” “We are not just saying that the artificial neural networks can be useful for analyzing physical systems or for discovering physical laws, we are saying that this is how the world around us actually works,” reads the paper’s discussion. “With this respect it could be considered as a proposal for the theory of everything, and as such it should be easy to prove it wrong.” The concept is so bold that most physicists and machine learning experts we reached out to declined to comment on the record, citing skepticism about the paper’s conclusions. But in a Q&A with Futurism, Vanchurin leaned into the controversy — and told us more about his idea. Futurism: Your paper argues that the universe might fundamentally be a neural network. How would you explain your reasoning to someone who didn’t know very much about neural networks or physics? “Towards a theory of machine learning”. The initial idea was to apply the methods of statistical mechanics to study the behavior of neural networks, but it turned out that in certain limits the learning (or training) dynamics of neural networks is very similar to the quantum dynamics we see in physics. At that time I was (and still is) on a sabbatical leave and decided to explore the idea that the physical world is actually a neural network. The idea is definitely crazy, but if it is crazy enough to be true? That remains to be seen. In the paper you wrote that to prove the theory was wrong, “all that is needed is to find a physical phenomenon which cannot be described by neural networks.” What do you mean by that? Why is such a thing “easier said than done?” Well, there are many “theories of everything” and most of them must be wrong. In my theory, everything you see around you is a neural network and so to prove it wrong all that is needed is to find a phenomenon which cannot be modeled with a neural network. But if you think about it it is a very difficult task mainly because we know so little about how the neural networks behave and how the machine learning actually works. That was why I tried to develop a theory of machine learning on the first place. How does your research relate to quantum mechanics, and does it address the observer effect? There are two main lines of thought the Everett’s (or many-world’s) interpretation of quantum mechanics and Bohm’s (or hidden variables) interpretation. I have nothing new to say about the many-worlds interpretation, but I think I can contribute something to the hidden variables theories. In the emergent quantum mechanics which I considered, the hidden variables are the states of the individual neurons and the trainable variables (such as bias vector and weight matrix) are quantum variables. Note that the hidden variables can be very non-local and so the Bell’s inequalities are violated. An approximated space-time locality is expected to emerge, but strictly speaking every neuron can be connected to every other neuron and so the system need not be local. Do you mind expanding on the way this theory relates to natural selection? How does natural selection factor into the evolution of complex structures/biological cells? What I am saying is very simple. There are structures (or subnetworks) of the microscopic neural network which are more stable and there are other structures which are less stable. The more stable structures would survive the evolution, and the less stable structure would be exterminated. On the smallest scales I expect that the natural selection should produce some very low complexity structures such as chains of neurons, but on larger scales the structures would be more complicated. I see no reason why this process should be confined to a particular length scale and so the claim is that everything that we see around us (e.g. particles, atoms, cells, observers, etc.) is the outcome of natural selection. https://www.zerohedge.com/technolog...iverse-might-be-neural-network-not-simulation
NASA announced this week that it had discovered in older data evidence for an Earth-sized planet orbiting the habitable zone of a relatively nearby star, an encouraging sign for astronomers seeking evidence of planets elsewhere in the universe that may harbor and support life. The agency said in a press release on Wednesday that it had made the discovery when reviewing older data from the Kepler space telescope. Launched in 2009 and retired two years ago, that instrument was specifically designed to detect extrasolar planets, or "exoplanets," around stars in the Milky Way. Astronomers are continually searching for planets orbiting around the "habitable zones" of stars, an area in which a planet is close enough to receive a star's heat but far enough away that it can sustain the processes and materials---most importantly liquid water---necessary for life as we know it. The Kepler telescope detected several thousand such planets during its nine-year mission. NASA officials said a review of Kepler's archived data led them to discover a previously unnoticed planet which they titled Kepler-1649c. The planet "is only 1.06 times larger than our own planet. Also, the amount of starlight it receives from its host star is 75% of the amount of light Earth receives from our Sun – meaning the exoplanet's temperature may be similar to our planet’s, as well," the agency said in its press release. Kepler-1649c orbits its star much closer than our planet does to the Sun. Yet the exoplanet's star, which lies about 300 light years from Earth, is a red dwarf, with less volume and a lower energy output than the sun, meaning its habitable zone is much smaller. University of Texas at Austin researcher Andrew Vanderburg said information gathered by scientists over the years indicate that there are possibly a great many planets throughout the galaxy that humans could theoretically populate. "The more data we get, the more signs we see pointing to the notion that potentially habitable and Earth-size exoplanets are common around these kinds of stars," he said. Source
