Since we don’t want to limit our thinking about the future of life to the species we’ve encountered so far, let’s instead define life very broadly, simply as a process that can retain its complexity and replicate. What’s replicated isn’t matter (made of atoms) but information (made of bits) specifying how the atoms are arranged. When a bacterium makes a copy of its DNA, no new atoms are created, but a new set of atoms are arranged in the same pattern as the original, thereby copying the information. In other words, we can think of life as a self-replicating information-processing system whose information (software) determines both its behavior and the blueprints for its hardware.
Swedish-American physicist and cosmologist
Max Tegmark (born May 5, 1967) is a Swedish-American physicist, cosmologist and machine learning researcher. He is a professor at the Massachusetts Institute of Technology and the scientific director of the Foundational Questions Institute. He is also a co-founder of the Future of Life Institute and a supporter of the effective altruism movement, and has received research grants from Elon Musk to investigate existential risk from advanced artificial intelligence.
From: Wikiquote (CC BY-SA 4.0)
Alternative Names:
Tegmark
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Max Shapiro
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Max Erik Tegmark
From Wikidata (CC0)
A great way for a particle arrangement to further this goal is to make copies of itself, to produce more energy absorbers. There are many known examples of such emergent self-replication: for example, vortices in turbulent fluids can make copies of themselves, and clusters of microspheres can coax nearby spheres into forming identical clusters. At some point, a particular arrangement of particles got so good at copying itself that it could do so almost indefinitely by extracting energy and raw materials from its environment. We call such a particle arrangement life.
Gradual declassification of records has revealed that some of these nuclear incidents carried greater risk than was appreciated at the time. For example, it became clear only in 2002 that during the Cuban Missile Crisis, the USS Beale had depth-charged an unidentified submarine that was in fact Soviet and armed with nuclear weapons, and whose commanders argued over whether to retaliate with a nuclear torpedo.
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"The rate of time flow perceived by an observer in the simulated universe is completely independent of the rate at which a computer runs the simulation, a point emphasized in Greg Egan's science-fiction novel Permutation City. Moreover, as we discussed in the last chapter and as stressed by Einstein, it's arguably more natural to view our Universe not from the frog perspective as a three-dimensional space where things happen, but from the bird perspective as a four-dimensional spacetime that merely is. There should therefore be no need for the computer to compute anything at all-it could simply store all the four-dimensional data, that is, encode all properties of the mathematical structure that is our Universe. Individual time slices could then be read out sequentially if desired, and the "simulated" world should still feel as real to its inhabitants as in the case where only three-dimensional data is stored and evolved. In conclusion: the role of the simulating computer isn't to compute the history of our Universe, but to specify it.
How specify it? The way in which the data are stored (the type of computer, the data format, etc.) should be irrelevant, so the extent to which the inhabitants of the simulated universe perceive themselves as real should be independent of whatever method is used for data compression. The physical laws that we've discovered provide great means of data compression, since they make it sufficient to store the initial data at some time together with the equations and a program computing the future from these initial data. As emphasized on pages 340-344, the initial data might be extremely simple: popular initial states from quantum field theory with intimidating names such as the Hawking-Hartle wavefunction or the inflationary Bunch-Davies vacuum have very low algorithmic complexity, since they can be defined in brief physics papers, yet simulating their time evolution would simulate not merely one universe like ours, but a vast decoherin
We encountered many beautiful examples of substrate-independent patterns in chapter 2, including waves, memories and computations. We saw how they weren’t merely more than their parts (emergent), but rather independent of their parts, taking on a life of their own. For example, we saw how a future simulated mind or computer-game character would have no way of knowing whether it ran on Windows, Mac OS, an Android phone or some other operating system, because it would be substrate-independent.
It’s easy for you to tell what it’s a photo of, but to program a function that inputs nothing but the colors of all the pixels of an image and outputs an accurate caption such as “A group of young people playing a game of frisbee” had eluded all the world’s AI researchers for decades. Yet a team at Google led by Ilya Sutskever did precisely that in 2014. Input a different set of pixel colors, and it replies “A herd of elephants walking across a dry grass field,” again correctly. How did they do it? Deep Blue–style, by programming handcrafted algorithms for detecting frisbees, faces and the like? No, by creating a relatively simple neural network with no knowledge whatsoever about the physical world or its contents, and then letting it learn by exposing it to massive amounts of data. AI visionary Jeff Hawkins wrote in 2004 that “no computer can…see as well as a mouse,” but those days are now long gone.