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Complexity: A Guided Tour by Melanie Mitchell
Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, Albert Einstein, Albert Michelson, Alfred Russel Wallace, anti-communist, Arthur Eddington, Benoit Mandelbrot, bioinformatics, cellular automata, Claude Shannon: information theory, clockwork universe, complexity theory, computer age, conceptual framework, Conway's Game of Life, dark matter, discrete time, double helix, Douglas Hofstadter, en.wikipedia.org, epigenetics, From Mathematics to the Technologies of Life and Death, Geoffrey West, Santa Fe Institute, Gödel, Escher, Bach, Henri Poincaré, invisible hand, Isaac Newton, John Conway, John von Neumann, Long Term Capital Management, mandelbrot fractal, market bubble, Menlo Park, Murray Gell-Mann, Network effects, Norbert Wiener, Norman Macrae, Paul Erdős, peer-to-peer, phenotype, Pierre-Simon Laplace, Ray Kurzweil, reversible computing, scientific worldview, stem cell, The Wealth of Nations by Adam Smith, Thomas Malthus, Turing machine
(Photograph courtesy of Wolfram Research, Inc.) Whew. With all that fame, funding, and the freedom to do whatever he wanted, Wolfram chose to study the dynamics of cellular automata. In the spirit of good theoretical physics, Wolfram set out to study the behavior of cellular automata in the simplest form possible—using one-dimensional, two-state cellular automata in which each cell is connected only to its two nearest neighbors (figure 10.4a). Wolfram termed these “elementary cellular automata.” He figured that if he couldn’t understand what was going on in these seemingly ultra-simple systems, there was no chance of understanding more complex (e.g., two-dimensional or multistate) cellular automata. Figure 10.4 illustrates one particular elementary cellular automaton rule. Figure 10.4a shows the lattice—now just a line of cells, each connected to its nearest neighbor on either side.
A cellular automaton rule—also called a cell update rule—is simply the identical rule followed by each cell, which tells the cell what its state should be at the next time step as a function of the current states in its local neighborhood. Why do I say that such a simple system is an idealized model of a complex system? Like complex systems in nature, cellular automata are composed of large numbers of simple components (i.e., cells), with no central controller, each of which communicates with only a small fraction of the other components. Moreover, cellular automata can exhibit very complex behavior that is difficult or impossible to predict from the cell update rule. Cellular automata were invented—like so many other good ideas—by John von Neumann, back in the 1940s, based on a suggestion by his colleague, the mathematician Stan Ulam. (This is a great irony of computer science, since cellular automata are often referred to as non -von-Neumann-style architectures, to contrast with the von-Neumann-style architectures that von Neumann also invented.)
And even if it were possible, the ensuing computation would be achingly slow, not to mention wasteful, since the huge parallel, non-von-Neumann-style computing resources of the cellular automaton would be used to simulate, in a very slow manner, a traditional von-Neumann-style computer. For these reasons, people don’t use Life (or other “universal” cellular automata) to perform real-world computations or even to model natural systems. What we really want from cellular automata is to harness their parallelism and ability to form complex patterns in order to achieve computations in a nontraditional way. The first step is to characterize the kinds of patterns that cellular automata can form. The Four Classes In the early 1980s, Stephen Wolfram, a physicist working at the Institute for Advanced Study in Princeton, became fascinated with cellular automata and the patterns they make. Wolfram is one of those legendary former child prodigies whom people like to tell stories about. Born in London in 1959, Wolfram published his first physics paper at age 15.
Think Complexity by Allen B. Downey
Benoit Mandelbrot, cellular automata, Conway's Game of Life, Craig Reynolds: boids flock, discrete time, en.wikipedia.org, Frank Gehry, Gini coefficient, Guggenheim Bilbao, Laplace demon, mandelbrot fractal, Occupy movement, Paul Erdős, peer-to-peer, Pierre-Simon Laplace, sorting algorithm, stochastic process, strong AI, Thomas Kuhn: the structure of scientific revolutions, Turing complete, Turing machine, Vilfredo Pareto, We are the 99%
This book is also about complexity science, which is an interdisciplinary field (at the intersection of mathematics, computer science, and natural science) that focuses on discrete models of physical systems. In particular, it focuses on complex systems, which are systems with many interacting components. Complex systems include networks and graphs, cellular automata, agent-based models and swarms, fractals and self-organizing systems, chaotic systems, and cybernetic systems. These terms might not mean much to you at this point. We will get to them soon, but you can get a preview at http://en.wikipedia.org/wiki/Complex_systems. A New Kind of Science In 2002, Stephen Wolfram published A New Kind of Science, where he presents his and others’ work on cellular automata and describes a scientific approach to the study of computational systems. We’ll get back to Wolfram in Chapter 6, but I want to borrow his title for something a little broader. I think complexity is a “new kind of science” not because it applies the tools of science to a new subject, but because it uses different tools, allows different kinds of work, and ultimately changes what we mean by “science.”
Can you summarize one or more of the objections that philosophers and historians of science have raised to Popper’s claim? Do you get the sense that practicing philosophers think highly of Popper’s work? What Is This a Model Of? Some cellular automata are primarily mathematical artifacts. They are interesting because they are surprising, useful, or pretty, or because they provide tools for creating new mathematics (like the Church-Turing thesis). But it is not clear that they are models of physical systems. If they are, they are highly abstracted, which is to say that they are not very detailed or realistic. For example, some species of cone snail produce a pattern on their shells that resembles the patterns generated by cellular automata (see http://en.wikipedia.org/wiki/Cone_snail). It is natural to suppose that a CA is a model of the mechanism that produces patterns on shells as they grow.
beetles, Falsifiability behavior, Explanatory Models BetterMap, Hashtables BFS, Connected Graphs, Analysis of Graph Algorithms, Dijkstra Big O notation, Order of Growth The Big Sort, Thomas Schelling bin size, Cumulative Distributions bisect module, Analysis of Search Algorithms bisection search, Analysis of Search Algorithms Bishop, Bill, Thomas Schelling boid, Boids bond percolation, Percolation bottom-up, A New Kind of Engineering bounded, Hashtables box-counting dimension, Fractals breadth-first search, Connected Graphs, Analysis of Graph Algorithms, Dijkstra brick wall, Emergence broadcast service, A New Kind of Engineering bubble sort, Analysis of Algorithms busy beaver, Universality C CA, Implementing CAs CADrawer, CADrawer caffeine, Paul Erdős: Peripatetic Mathematician, Speed Freak canonical ensemble, Percolation carrot, Boids causation, Determinism, SOC, Causation, and Prediction, Thomas Schelling Cdf class, Cumulative Distributions CDFs, Cumulative Distributions, Cumulative Distributions plotting, Cumulative Distributions cells, Universality cellular automaton, Cellular Automata centralized system, A New Kind of Engineering chaos, Determinism Church-Turing thesis, Universality circular buffer, FIFO Implementation CircularCA, CADrawer Class 3 behavior, Randomness Class 4 behavior, Universality, Conway’s Conjecture classifying cellular automata, Classifying CAs client-server architecture, A New Kind of Engineering clique, Watts and Strogatz clustering, Watts and Strogatz clustering coefficient, Watts and Strogatz color map, Implementing Life comparing algorithms, Analysis of Algorithms comparison sort, Analysis of Basic Python Operations complementary CDF, Zipf, Pareto, and Power Laws complementary distribution, Continuous Distributions complete graph, What’s a Graph?
Turing's Vision: The Birth of Computer Science by Chris Bernhardt
Ada Lovelace, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, Albert Einstein, Andrew Wiles, British Empire, cellular automata, Claude Shannon: information theory, complexity theory, Conway's Game of Life, discrete time, Douglas Hofstadter, Georg Cantor, Gödel, Escher, Bach, Henri Poincaré, Internet Archive, Jacquard loom, John Conway, John von Neumann, Joseph-Marie Jacquard, Norbert Wiener, Paul Erdős, Turing complete, Turing machine, Turing test, Von Neumann architecture
First we look at Church’s λ-calculus (lambda calculus), then Post’s tag systems, and finally cellular automata. All of these have been shown to be able to do computations. In fact, rather surprisingly, all have computational power equivalent to Turing machines. We start with the λ-calculus. This plays an important role in computer science, especially in the design of computer languages. After our brief foray into λ-calculus we move on to tag systems. Tag systems are easy to describe, but despite their simplicity, they are able to do any computation. Their simplicity is often useful in proving the equivalence of computational systems. For example, the proof that cellular automata can do anything that Turing machines can do involves emulating Turing machines by tag systems. The final topic is the study one-dimensional cellular automata. These are interesting because they yield two-dimensional pictures that show the entire computation.
Quantum Computing since Democritus by Aaranson, The Beginning of Infinity: Explanations That Transform the World by Deutsch, and Gödel, Escher, Bach by Hofstadter are all fascinating. Cellular automata We only looked briefly looked at cellular automata, but they have a long and interesting history. They were first studied by Ulam and von Neumann as the first computers were built. Nils Barricelli was at Princeton during the 1950s and used the computer to simulate the interaction of cells. George Dyson’s Turing’s Cathedral gives a good historical description of this work John Conway, in 1970, defined Life involving two-dimensional cellular automata. These were popularized by Martin Gardner in Scientific American. William Poundstone’s The Recursive Universe is a good book on the history of these automata and how complexity can arise from simple rules.
Chapter 5 In addition to Turing’s approach, there are several different ways at looking at computation. In this chapter we pause to examine at three of these. We begin with the lambda calculus (λ-calculus) of Alonzo Church, then look at an example of a tag system, finally we consider cellular automata. These views of computation seem very different, but each perspective has its own strengths. The λ-calculus leads to programming languages; tag systems are useful for proving different systems equivalent; cellular automata give pictures of complete computations. After this brief detour we return to Turing’s arguments. Chapter 6 Up until now, machines have been described by diagrams. This chapter starts by showing how finite automata and Turing machines can be described by finite sequences of digits, called encodings.
The Singularity Is Near: When Humans Transcend Biology by Ray Kurzweil
additive manufacturing, AI winter, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, Albert Einstein, anthropic principle, Any sufficiently advanced technology is indistinguishable from magic, artificial general intelligence, Asilomar, augmented reality, autonomous vehicles, Benoit Mandelbrot, Bill Joy: nanobots, bioinformatics, brain emulation, Brewster Kahle, Brownian motion, business cycle, business intelligence, c2.com, call centre, carbon-based life, cellular automata, Claude Shannon: information theory, complexity theory, conceptual framework, Conway's Game of Life, coronavirus, cosmological constant, cosmological principle, cuban missile crisis, data acquisition, Dava Sobel, David Brooks, Dean Kamen, disintermediation, double helix, Douglas Hofstadter, en.wikipedia.org, epigenetics, factory automation, friendly AI, George Gilder, Gödel, Escher, Bach, informal economy, information retrieval, invention of the telephone, invention of the telescope, invention of writing, iterative process, Jaron Lanier, Jeff Bezos, job automation, job satisfaction, John von Neumann, Kevin Kelly, Law of Accelerating Returns, life extension, lifelogging, linked data, Loebner Prize, Louis Pasteur, mandelbrot fractal, Marshall McLuhan, Mikhail Gorbachev, Mitch Kapor, mouse model, Murray Gell-Mann, mutually assured destruction, natural language processing, Network effects, new economy, Norbert Wiener, oil shale / tar sands, optical character recognition, pattern recognition, phenotype, premature optimization, randomized controlled trial, Ray Kurzweil, remote working, reversible computing, Richard Feynman, Robert Metcalfe, Rodney Brooks, scientific worldview, Search for Extraterrestrial Intelligence, selection bias, semantic web, Silicon Valley, Singularitarianism, speech recognition, statistical model, stem cell, Stephen Hawking, Stewart Brand, strong AI, superintelligent machines, technological singularity, Ted Kaczynski, telepresence, The Coming Technological Singularity, Thomas Bayes, transaction costs, Turing machine, Turing test, Vernor Vinge, Y2K, Yogi Berra
However, expressing continuous formulas in this way is an inherent complication and would violate Einstein's dictum to express things "as simply as possible, but no simpler." So the real question is whether we can express the basic relationships that we are aware of in more elegant terms, using cellular-automata algorithms. One test of a new theory of physics is whether it is capable of making verifiable predictions. In at least one important way, that might be a difficult challenge for a cellular automata-based theory because lack of predictability is one of the fundamental features of cellular automata. Wolfram starts by describing the universe as a large network of nodes. The nodes do not exist in "space," but rather space, as we perceive it, is an illusion created by the smooth transition of phenomena through the network of nodes. One can easily imagine building such a network to represent "naive" (Newtonian) physics by simply building a three-dimensional network to any desired degree of granularity.
I don't have in hand, but I know he's there....What I see is so compelling that it can't be a creature of my imagination.62 In commenting on Fredkin's theory of digital physics, Wright writes, Fredkin ... is talking about an interesting characteristic of some computer programs, including many cellular automata: there is no shortcut to finding out what they will lead to. This, indeed, is a basic difference between the "analytical" approach associated with traditional mathematics, including differential equations, and the "computational" approach associated with algorithms. You can predict a future state of a system susceptible to the analytic approach without figuring out what states it will occupy between now and then, but in the case of many cellular automata, you must go through all the intermediate states to find out what the end will be like: there is no way to know the future except to watch it unfold....Fredkin explains: "There is no way to know the answer to some question any faster than what's going on. "...
It sounds like a good-news/bad-news joke: the good news is that our lives have purpose; the bas news is that their purpose is to help some remote hacker estimate pi to nine jillion decimal places. 63 Fredkin went on to show that although energy is needed for information storage and retrieval, we can arbitrarily reduce the energy required to perform any particular example of information processing. and that this operation has no lower limit.64 That implies that information rather than matter and energy may be regarded as the more fundamental reality.65 I will return to Fredkin's insight regarding the extreme lower limit of energy required for computation and communication in chapter 3, since it pertains to the ultimate power of intelligence in the universe. Wolfram builds his theory primarily on a single, unified insight. The discovery that has so excited Wolfram is a simple rule he calls cellular automata rules 110 and its behavior. (There are some other interesting automata rules, but rule 110 makes the point well enough.) Most of Wolfram's analyses deal with the simplest possible cellular automata, specifically those that involve just a one-dimensional line of cells, two possible colors (black and white), and rules based only on the two immediately adjacent cells. For each transformation, the color of a cell depends only on its own previous color and that of the cell on the left and the cell on the right.
The Deep Learning Revolution (The MIT Press) by Terrence J. Sejnowski
AI winter, Albert Einstein, algorithmic trading, Amazon Web Services, Any sufficiently advanced technology is indistinguishable from magic, augmented reality, autonomous vehicles, Baxter: Rethink Robotics, bioinformatics, cellular automata, Claude Shannon: information theory, cloud computing, complexity theory, computer vision, conceptual framework, constrained optimization, Conway's Game of Life, correlation does not imply causation, crowdsourcing, Danny Hillis, delayed gratification, discovery of DNA, Donald Trump, Douglas Engelbart, Drosophila, Elon Musk, en.wikipedia.org, epigenetics, Flynn Effect, Frank Gehry, future of work, Google Glasses, Google X / Alphabet X, Guggenheim Bilbao, Gödel, Escher, Bach, haute couture, Henri Poincaré, I think there is a world market for maybe five computers, industrial robot, informal economy, Internet of things, Isaac Newton, John Conway, John Markoff, John von Neumann, Mark Zuckerberg, Minecraft, natural language processing, Netflix Prize, Norbert Wiener, orbital mechanics / astrodynamics, PageRank, pattern recognition, prediction markets, randomized controlled trial, recommendation engine, Renaissance Technologies, Rodney Brooks, self-driving car, Silicon Valley, Silicon Valley startup, Socratic dialogue, speech recognition, statistical model, Stephen Hawking, theory of mind, Thomas Bayes, Thomas Kuhn: the structure of scientific revolutions, traveling salesman, Turing machine, Von Neumann architecture, Watson beat the top human players on Jeopardy!, X Prize, Yogi Berra
Is there an even simpler system that exhibits a complexity that is easier to analyze? Cellular Automata Another colorful character with a scientifically serious approach to complexity, Stephen Wolfram (figure 13.1) was a wunderkind, the youngest person ever to earn a doctorate in physics from Caltech at the age of 20, and the founder of the Center for Complex Systems Research at the University of Illinois in 1986. Wolfram thought that neural networks were too complex and decided instead to explore cellular automata. The Age of Algorithms 197 Figure 13.1 Stephen Wolfram at his home in Concord, Massachusetts, standing on an algorithmically generated floor. Wolfram was a pioneer in complexity theory and showed that even simple programs can give rise to the complexities of the kind we encounter in the world. Courtesy of Stephen Wolfram. Cellular automata typically have only a few discrete values that evolve in time, depending on the states of the other cells.
In the past, such algorithms were handcrafted by mathematicians and computer scientists working like artisans in guilds. Stephen Wolfram automated the finding of algorithms for cellular automata by exhaustive search, starting with the simplest automata, some of which produced highly complex patterns. This insight can be summarized by Wolfram’s law, which states that you don’t have to travel far in the space of algorithms to find one that solves an interesting class of problems. This is like sending bots to play games like StarCraft on the Internet to try all possible strategies. According to Wolfram’s law, there should be a galaxy of algorithms somewhere in the universe of algorithms that can win the game. Wolfram focused on the space of cellular automata, a small subspace in the space of all possible algorithms. But what if cellular automata are atypical algorithms that exhibits more universality than other classes of algorithms?
are critically important for finding a configuration that displays complex patterns. How common are rules that generate complexity? Wolfram wanted to know the simplest cellular automata rule that could lead to complex behaviors and so he set out to search through all of them. Rules 0 to 29 produced patterns that would always revert to boring behaviors: all the cells would end up in a repeating pattern or some nested fractal pattern. But rule 30 produced unfolding patterns and rule 110 dazzled with continually evolving complex patterns (box 13.1).8 It was eventually proved that rule 110 was capable of universal computation; that is, some of the simplest of all possible cellular automata have the power of a Turing machine that can compute any computable function, so it is in principle as powerful as any computer. One of the implications of this discovery is that the remarkable complexity we find in living things could have evolved by sampling the simplest space of chemical interactions between molecules.
In Our Own Image: Savior or Destroyer? The History and Future of Artificial Intelligence by George Zarkadakis
3D printing, Ada Lovelace, agricultural Revolution, Airbnb, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, animal electricity, anthropic principle, Asperger Syndrome, autonomous vehicles, barriers to entry, battle of ideas, Berlin Wall, bioinformatics, British Empire, business process, carbon-based life, cellular automata, Claude Shannon: information theory, combinatorial explosion, complexity theory, continuous integration, Conway's Game of Life, cosmological principle, dark matter, dematerialisation, double helix, Douglas Hofstadter, Edward Snowden, epigenetics, Flash crash, Google Glasses, Gödel, Escher, Bach, income inequality, index card, industrial robot, Internet of things, invention of agriculture, invention of the steam engine, invisible hand, Isaac Newton, Jacquard loom, Jacques de Vaucanson, James Watt: steam engine, job automation, John von Neumann, Joseph-Marie Jacquard, Kickstarter, liberal capitalism, lifelogging, millennium bug, Moravec's paradox, natural language processing, Norbert Wiener, off grid, On the Economy of Machinery and Manufactures, packet switching, pattern recognition, Paul Erdős, post-industrial society, prediction markets, Ray Kurzweil, Rodney Brooks, Second Machine Age, self-driving car, Silicon Valley, social intelligence, speech recognition, stem cell, Stephen Hawking, Steven Pinker, strong AI, technological singularity, The Coming Technological Singularity, The Future of Employment, the scientific method, theory of mind, Turing complete, Turing machine, Turing test, Tyler Cowen: Great Stagnation, Vernor Vinge, Von Neumann architecture, Watson beat the top human players on Jeopardy!, Y2K
The mathematics of these reactions involves recursive computations emerging spontaneously at the edge of chaos. It looks as if nature is a lover of extreme sports. It adores pushing everything that is precious to the point of breaking up. This deeper link between the emergence of complex behaviour at criticality and recursive computations has also been demonstrated in digital computers using cellular automata, another great invention by John von Neumann. Cellular automata are patterns of 0s and 1s that evolve step-by-step according to a simple set of rules. A new pattern, or ‘generation’, of a cellular automaton emerges after each step. Points on the new patterns will be either 0 or 1 depending on their current value as well as the value of their neighbours. In the early 1980s, the English mathematician Stephen Wolfram conjectured that a particular cellular automaton called ‘Rule 110’ might be ‘Turing complete’,21 a conjecture that was later proved by Matthew Cook.
Platonic thinking, so deeply entrenched in mathematical culture, is evidently at play here! Nevertheless, the correlation between computation and life is indisputable. It is the interpretation of this connection that polarises empiricists and idealists. Perhaps the discovery of Rule 110 is one giant step towards the discovery of a general, mathematical, law for life. There are too many things about cellular automata that make them profoundly similar to physical, living, things. By operating near the edge of chaos, cellular automata evolve with time by responding to their changing environment. They look like a form of ‘artificial life’ existing in the computer that runs the calculations that make and sustain it. Could this artificial life evolve to the point of becoming conscious? And, if so, how similar would this artificial consciousness be to ours? Imagining true AI Let us summarise what we have explored so far.
The journey of the individual parts towards forming a self-organised system appears then to be algorithmically determined: they are ‘attracted’ to self-organisation and, ultimately, to life. We do not yet know whether this attraction is governed by a general law for biology. However, we have discovered something that seems to point towards such a law: Rule 110, a recursive algorithm that is Turing complete and lifelike – and there might be more.23 This profound correlation between cellular automata and biological phenomena suggests that life is governed by recursive computations, probably similar – or identical – to cellular automata. There is one more special feature of complex computations that is worth noting. They are fractal-like and scale-invariant. This means that they repeat themselves at every scale. From microscopic organisms to weather systems and the formation of galactic clusters nature creates similar patterns of organisation and behaviour.
Applied Cryptography: Protocols, Algorithms, and Source Code in C by Bruce Schneier
active measures, cellular automata, Claude Shannon: information theory, complexity theory, dark matter, Donald Davies, Donald Knuth, dumpster diving, Exxon Valdez, fault tolerance, finite state, invisible hand, John von Neumann, knapsack problem, MITM: man-in-the-middle, NP-complete, P = NP, packet switching, RAND corporation, RFC: Request For Comment, software patent, telemarketer, traveling salesman, Turing machine, web of trust, Zimmermann PGP
Keyword Brief Full Advanced Search Search Tips (Publisher: John Wiley & Sons, Inc.) Author(s): Bruce Schneier ISBN: 0471128457 Publication Date: 01/01/96 Search this book: Go! Previous Table of Contents Next ----------- 20.7 Cellular Automata A new and novel idea, studied by Papua Guam , is the use of cellular automata in public-key cryptosystems. This system is still far too new and has not been studied extensively, but a preliminary examination suggests that it may have a cryptographic weakness similar to one seen in other cases . Still, this is a promising area of research. Cellular automata have the property that, even if they are invertible, it is impossible to calculate the predecessor of an arbitrary state by reversing the rule for finding the successor. This sounds a whole lot like a trapdoor one-way function. 20.8 Other Public-Key Algorithms Many other public-key algorithms have been proposed and broken over the years.
Daemen suggests that anyone interested in improving MMB should first do an analysis of modular multiplication with respect to linear cryptanalysis and choose a new multiplication factor, and then make the constant C different for each round . Then, improve the key scheduling by adding constants to the round keys to remove the bias. He’s not going to do it; he designed 3-Way instead (see Section 14.5). 13.11 CA-1.1 CA is a block cipher built on cellular automata, designed by Howard Gutowitz [677, 678, 679]. It encrypts plaintext in 384-bit blocks and has a 1088-bit key (it’s really two keys, a 1024-bit key and a 64-bit key). Because of the nature of cellular automata, the algorithm is most efficient when implemented in massively parallel integrated circuits. CA-1.1 uses both reversible and irreversible cellular automaton rules. Under a reversible rule, each state of the lattice comes from a unique predecessor state, while under an irreversible rule, each state can have many predecessor states.
Each exponent is the result of the computation with the previous block; the first exponent is given by an IV. Ivan Damgård designed a one-way hash function based on the knapsack problem (see Section 19.2) ; it can be broken in about 232 operations [290, 1232, 787]. Steve Wolfram’s cellular automata  have been proposed as a basis for one-way hash functions. An early implementation  is insecure [1052, 404]. Another one-way hash function, Cellhash [384,404], and an improved version, Subhash [384,402, 405], are based on cellular automata; both are designed for hardware. Boognish mixes the design principles of Cellhash with those of MD4 [402, 407]. StepRightUp can be implemented as a hash function as well . Claus Schnorr proposed a one-way hash function based on the discrete Fourier transform, called FFT-Hash, in the summer of 1991 ; it was broken a few months later by two independent groups [403, 84].
Turing's Cathedral by George Dyson
1919 Motor Transport Corps convoy, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, Albert Einstein, anti-communist, Benoit Mandelbrot, British Empire, Brownian motion, cellular automata, cloud computing, computer age, Danny Hillis, dark matter, double helix, fault tolerance, Fellow of the Royal Society, finite state, Georg Cantor, Henri Poincaré, housing crisis, IFF: identification friend or foe, indoor plumbing, Isaac Newton, Jacquard loom, John von Neumann, mandelbrot fractal, Menlo Park, Murray Gell-Mann, Norbert Wiener, Norman Macrae, packet switching, pattern recognition, Paul Erdős, Paul Samuelson, phenotype, planetary scale, RAND corporation, random walk, Richard Feynman, SETI@home, social graph, speech recognition, Thorstein Veblen, Turing complete, Turing machine, Von Neumann architecture
“My incredible luck,” he bragged to von Neumann from Los Alamos in February 1952, “was evident in poker (8 successive + earnings) this year.”70 Four of the twentieth century’s most imaginative ideas for leveraging our intelligence—the Monte Carlo method, the Teller-Ulam invention, self-reproducing cellular automata, and nuclear pulse propulsion—originated with help from Stan. Three of the four proved to be wildly successful, and the fourth was abandoned before it had a chance. Monte Carlo was the realization, through digital computing, of what Maxwell could only imagine: a way to actually follow the behavior of a physical system at its elemental levels, as “if our faculties and instruments were so sharpened that we could detect and lay hold of each molecule and trace it through all its course.”71 The Teller-Ulam invention invoked a form of Maxwell’s demon to heat a compartment to a temperature hotter than the sun by letting a burst of radiation in, and then, for an equilibrium-defying instant, not letting radiation out. Ulam’s self-reproducing cellular automata—patterns of information persisting across time—evolve by letting order in but not letting order out.
“Information was never ‘volatile’ in transit; it was as secure as an acrophobic inchworm on the crest of a sequoia.” Data were handled the way ships are moved through locks in a canal. “We enjoyed some interesting speculative discussions with von Neumann at this time about information propagation and switching among hypothetical arrays of cells,” remembers Bigelow, “and I believe that some germs of his later cellular automata studies may have originated here.”20 “We did not move information from one place to another except in a positive way,” emphasizes James Pomerene. “That is used absolutely universally now. I think we were the first to do it. And I regret that we didn’t patent it.” Patentable inventions were being generated right and left. “The original patent agreement provided that the Institute would have title to patents, but they would pay to the inventor all royalties in excess of their costs,” Pomerene adds.
They evidently had a long conversation on a bench in Central Park, leaving no record of their discussion of the Ivy Mike test, but a subsequent exchange of letters hinted at the conversation having extended to the possibility of a digital universe being brought to life. “Only because of our conversation on the bench in Central Park I was able to understand…[that] given is an actually infinite system of points (the actual infinity is worth stressing because nothing will make sense on a finite no matter how large model),” noted Ulam, who then sketched out how he and von Neumann had hypothesized the evolution of Turing-complete (or “universal”) cellular automata within a digital universe of communicating memory cells. The definitions had to be made mathematically precise: A “universal” automaton is a finite system which given an arbitrary logical proposition in form of (a linear set L) tape attached to it, at say specified points, will produce the true or false answer. (Universal ought to have relative sense: with reference to a class of problems it can decide.)
Rage Inside the Machine: The Prejudice of Algorithms, and How to Stop the Internet Making Bigots of Us All by Robert Elliott Smith
Ada Lovelace, affirmative action, AI winter, Alfred Russel Wallace, Amazon Mechanical Turk, animal electricity, autonomous vehicles, Black Swan, British Empire, cellular automata, citizen journalism, Claude Shannon: information theory, combinatorial explosion, corporate personhood, correlation coefficient, crowdsourcing, Daniel Kahneman / Amos Tversky, desegregation, discovery of DNA, Douglas Hofstadter, Elon Musk, Fellow of the Royal Society, feminist movement, Filter Bubble, Flash crash, Gerolamo Cardano, gig economy, Gödel, Escher, Bach, invention of the wheel, invisible hand, Jacquard loom, Jacques de Vaucanson, John Harrison: Longitude, John von Neumann, Kenneth Arrow, low skilled workers, Mark Zuckerberg, mass immigration, meta analysis, meta-analysis, mutually assured destruction, natural language processing, new economy, On the Economy of Machinery and Manufactures, p-value, pattern recognition, Paul Samuelson, performance metric, Pierre-Simon Laplace, precariat, profit maximization, profit motive, Silicon Valley, social intelligence, statistical model, Stephen Hawking, stochastic process, telemarketer, The Bell Curve by Richard Herrnstein and Charles Murray, The Future of Employment, the scientific method, The Wealth of Nations by Adam Smith, The Wisdom of Crowds, theory of mind, Thomas Bayes, Thomas Malthus, traveling salesman, Turing machine, Turing test, twin studies, Vilfredo Pareto, Von Neumann architecture, women in the workforce
Inspired by Turing’s papers on computation, von Neumann also came up with the modern conception of Babbage’s ‘store’ and ‘mill’ computer structure, in what is now called the ‘von Neumann architecture’, the architecture at the heart of almost all modern computers. Amongst this world-changing productivity, von Neumann also speculated about how computer programs, like genetic organisms, might be able to self-replicate. His ‘cellular automata’ theory closely parallels the actual replication methods of biological DNA, despite the fact that von Neumann’s work was done in advance of the actual structure of DNA being discovered by Watson and Crick in 1953.7 At the same time, statistician George Box suggested ‘evolutionary operations’8 as a methodology for optimizing industrial processes in the late 1950s, though he never implemented the procedure as a computer algorithm, and there are a number of other scientists who also struck close to the ideas that would eventually emerge as evolutionary computation.
So the evolutionary metaphor for computational algorithms was a firmly set context from the very start of computation, but it wasn’t until the late 1960s that the foundational idea of evolutionary algorithms really began to gain traction in various research centres around the world.9 In Germany, a group led by Ingo Rechenberg developed a kind of optimization algorithm called ‘evolutionsstrategies’.10 On the West Coast of the US, a group led by Lawrence Fogel developed another called ‘evolutionary programming’.11 And, at the University of Michigan, John Holland pioneered another form called ‘genetic algorithms’.12 Of all of these researchers, it is John Holland who has the most direct connection with Von Neumann’s early theories, since his PhD supervisor was Arthur Burks,13 who worked on the ENIAC engineering team and extended von Neumann’s work on ‘cellular automata’.14 Furthermore, Holland was granted the first PhD in computer science and went on to teach at the University of Michigan where he advised many students on PhDs that involved evolutionary computation, including my own PhD supervisor, David Goldberg. Goldberg wrote the first textbook on genetic algorithms while I was a part of his research group in 1989. Lots of work from Dave’s research group was in the text, including some of my own, and I helped edit the book’s first draft (by making red ink marks on a ream of dot matrix paper).
These models and algorithms seem to imply that, to be effective, evolution requires a balance between the discipline of using the best-known idea and the chaos of mixing ideas up. Other scientific studies also show that striking a precise balance between the two may be the vital characteristic of living systems. The term edge of chaos was first used by the SFI researchers Doyne Farmer and Chris Langton, while looking at cellular automata models, simplified algorithms invented by John von Neumann to represent self-replicating biology.4 While experimenting with parameters that controlled these programs, they observed that for some parameter settings, the algorithms would settle into uninteresting equilibria, essentially static states. For others, the algorithms just generated complete randomness, never seeming to settle down into any recognizable patterns.
How to Create a Mind: The Secret of Human Thought Revealed by Ray Kurzweil
Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, Albert Einstein, Albert Michelson, anesthesia awareness, anthropic principle, brain emulation, cellular automata, Claude Shannon: information theory, cloud computing, computer age, Dean Kamen, discovery of DNA, double helix, en.wikipedia.org, epigenetics, George Gilder, Google Earth, Isaac Newton, iterative process, Jacquard loom, John von Neumann, Law of Accelerating Returns, linear programming, Loebner Prize, mandelbrot fractal, Norbert Wiener, optical character recognition, pattern recognition, Peter Thiel, Ralph Waldo Emerson, random walk, Ray Kurzweil, reversible computing, selective serotonin reuptake inhibitor (SSRI), self-driving car, speech recognition, Steven Pinker, strong AI, the scientific method, theory of mind, Turing complete, Turing machine, Turing test, Wall-E, Watson beat the top human players on Jeopardy!, X Prize
Most incompatibilists would find the concept of free will to also be incompatible with our decisions’ being essentially accidental. Free will seems to imply purposeful decision making. Dr. Wolfram proposes a way to resolve the dilemma. His book A New Kind of Science (2002) presents a comprehensive view of the idea of cellular automata and their role in every facet of our lives. A cellular automaton is a mechanism in which the value of information cells is continually recomputed as a function of the cells near it. John von Neumann created a theoretical self-replicating machine called a universal constructor that was perhaps the first cellular automaton. Dr. Wolfram illustrates his thesis with the simplest possible cellular automata, a group of cells in a one-dimensional line. At each point in time, each cell can have one of two values: black or white. The value of each cell is recomputed for each cycle. The value of a cell for the next cycle is a function of its current value as well as the value of its two adjacent neighbors.
The reason that his book is titled A New Kind of Science is because this theory contrasts with most other scientific laws. If there is a satellite orbiting Earth, we can predict where it will be five years from now without having to run through each moment of a simulated process by using the relevant laws of gravity and solve where it will be at points in time far in the future. But the future state of class IV cellular automata cannot be predicted without simulating every step along the way. If the universe is a giant cellular automaton, as Dr. Wolfram postulates, there would be no computer big enough—since every computer would be a subset of the universe—that could run such a simulation. Therefore the future state of the universe is completely unknowable even though it is deterministic. Thus even though our decisions are determined (because our bodies and brains are part of a deterministic universe), they are nonetheless inherently unpredictable because we live in (and are part of) a class IV automaton.
., 96–97, 98 auditory association, 77 auditory cortex, 7, 77, 97, 128 auditory information processing, 96–97, 97 auditory nerve, 97, 128 data reduction in, 138 auditory pathway, 97 autoassociation, 59–61, 133, 173 automobiles, self-driving, 7, 159, 261, 274 axons, 36, 42, 43, 66, 67, 90, 100, 113, 150, 173 as digital processors, 191 Babbage, Charles, 189–90 Bainbridge, David, 179 bandwidth, of Internet, 254 basis functions, 103–4 Bedny, Marina, 87 Bell System Technical Journal, 184 Berger, Theodore, 102 Berners-Lee, Tim, 172 Bernoulli’s principle, 5, 8 Better Angels of Our Nature: Why Violence Has Declined (Pinker), 27 Bierce, Ambrose, 66 BINAC, 189 Bing, 171 biology, 37 DNA as unifying theory of, 17 reverse-engineering of, 4–5 biomedicine, LOAR and, 251, 252, 253 Blackmore, Susan, 211 Blade Runner (film), 210 Blakeslee, Sandra, 73, 156 Blue Brain Project, 63, 80, 124–28, 125 Bombe, 187 Bostrom, Nick, 129–30, 222 Boyden, Ed, 126 brain, evolution of, 2 brain, human: analog computing in, 274 complexity of, 8–9, 181, 272 digital implants in, 243–44 digital neocortex as extension of, 172, 276 hemispheres of, 77, 224–49 LOAR as applied to, 261–63, 263, 264, 265 prediction by, 250 redundancy of, 9 reverse-engineering of, see brain, human, computer emulation of; neocortex, digital structure of, 77 brain, human, computer emulation of, 5, 7, 179–98, 273, 280 invariance and, 197 memory requirements of, 196–97 parallel processing in, 197 processing speed in, 195–96 redundancy in, 197 singularity and, 194 Turing test and, 159–60, 169, 170, 178, 191, 213, 214, 233, 276, 298n von Neumann on, 191–95 see also neocortex, digital brain, mammalian: hierarchical thinking as unique to, 2–3, 35 neocortex in, 78, 93, 286n brain plasticity, 79, 87–89, 91, 182, 193, 197, 225, 280 as evidence of universal neocortical processing, 86, 88, 152 limitations on, 88–89 brain scanning, 7, 263, 308n destructive, 264, 265, 309n–11n LOAR and, 262–63, 263, 264, 265 nondestructive, 127, 129, 264, 312n–13n noninvasive, 273 Venn diagram of, 262 brain simulations, 124–31, 262 brain stem, 36, 99 Bremermann, Hans, 316n Britain, Battle of, 187 Brodsky, Joseph, 199 Burns, Eric A., 113 busy beaver problem, 207 Butler, Samuel, 62, 199–200, 224, 248–49 Byron, Ada, Countess of Lovelace, 190, 191 California, University of, at Berkeley, 88 “CALO” project, 162 carbon atoms, information structures based on, 2 Carroll, Lewis, 109 cells, replacement of, 245, 246 cellular automata, 236–39 cerebellum, 7, 77, 103–4 uniform structure of, 103 cerebral cortex, 7–8 see also neocortex Chalmers, David, 201–2, 218, 241 “chatbots,” 161 chemistry, 37 chess, AI systems and, 6, 38–39, 165–66, 257 chimpanzees: language and, 3, 41 tool use by, 41 “Chinese room” thought experiment, 170, 274–75 Chomsky, Noam, 56, 158 Church, Alonzo, 186 Church-Turing thesis, 186 civil rights, 278 cloud computing, 116–17, 123, 246, 279–80 cochlea, 96, 97, 135, 138 cochlear implants, 243 Cockburn, David, 214 Cold Spring Harbor Laboratory, 129 Colossus, 187, 188 “common sense,” 40 communication, reliability of, 182–85, 190 communication technology, LOAR and, 253, 254 compatibilism, 234 complexity, 198, 233 of human brain, 8–9, 181, 272 modeling and, 37–38 true vs. apparent, 10–11 computation: price/performance of, 4–5, 250–51, 257, 257, 267–68, 301n–3n thinking compared with to, 26–27 universality of, 26, 181–82, 185, 188, 192, 207 Computer and the Brain, The (von Neumann), 191 computers: brain emulated by, see brain, human, computer emulation of consciousness and, 209–11, 213–15, 223 intelligent algorithms employed by, 6–7 knowledge base expanded by, 4, 246, 247 logic gates in, 185 memory in, 185, 259, 260, 268, 301n–3n, 306n–7n reliability of communication by, 182–85, 190 see also neocortex, digital “Computing Machinery and Intelligence” (Turing), 191 conditionals, 65, 69, 153, 189, 190 confabulation, 70, 217, 227, 228, 229 connectionism, 133, 191 “connectome,” 262 consciousness, 11, 199–209 cerebral hemispheres and, 226–29 computers and, 209–11, 213–15, 223, 233 Descartes on, 221–22 dualist views of, 202–3 Eastern vs.
When Things Start to Think by Neil A. Gershenfeld
3D printing, Ada Lovelace, Bretton Woods, cellular automata, Claude Shannon: information theory, Dynabook, Hedy Lamarr / George Antheil, I think there is a world market for maybe five computers, invention of movable type, Iridium satellite, Isaac Newton, Jacquard loom, Johannes Kepler, John von Neumann, low earth orbit, means of production, new economy, Nick Leeson, packet switching, RFID, speech recognition, Stephen Hawking, Steve Jobs, telemarketer, the medium is the message, Turing machine, Turing test, Vannevar Bush
Like the checkers on a checkerboard, tokens that each represent a collection of molecules get moved among sites based on how the neighboring sites are occupied. This idea has come to be known as Cellular Automata (CAs). From the 1970s onward, the group of Ed Fredkin, Tomaso Toffoli, and Norm Margolus at MIT started to make special-purpose computers designed for CAs. Because these machines entirely dispense with approximations of continuous functions, they can be much simpler and faster. And because a Turing machine can be described this way, a CA can do anything that can be done with a conventional computer. A cellular automata model of the universe is no less fundamental than one based on calculus. It's a much more natural description if a computer instead of a pencil is used to work with the model. BIT BELIEFS + 133 And the discretization solves another problem: a continuous quantity can represent an infinite amount of information.
Index adding machine, Pascal's, 131-32 Adelson, Ted, 180-81 affective states, computer's perception of, 53-54 agents, 107, 109, 116-17 airbag, smart, 170-71, 180 analog circuits, 165, 166 Analytical Engine, 125 Any Thing project, 70, 71, 73 Apple Macintosh, 139 Argonne National Laboratory, 158 ARPANET, 79 artificial intelligence, 108, 128, 129-30, 135, 201 sensory perception and, 135, 201 assembly line, 180 AT&T, 158, 203 autonomy and wearable computers, 57-58 Babbage, Charles, 124-27, 132 Barings Bank, 77, 86 bassoon, 29-32 Begin Again Again, 34 Bell Labs, 36, 162, 174 Bender, Walter, 202-3 Benioff, Paul, 158 Bennett, Charles, 159, 176, 177 Benton, Steve, 14 2 Bill of Rights, 98-99 Bill of Things' Rights, 104 Bill of Things Users' Rights, 102 Birnbaum, Joel, 52 Bitnet, 89 "bit" of information, 176 "blessing of dimensionality," 164-65 Boltzmann, Ludwig, 175 books, printed, 13-25 competing technologies, 10, 13-25 dimensions of, 20 future of computing, lesson to be derived for, 14 Gutenberg and movable metal type, 18-19 as historical artifacts, 23-24 libraries and electronic book, 20-23 lighting for reading, 15 papal bull to require certification of, 96 paper for, 15 specifications of, 13-14 state of book business, 13 universal book, 18-20 Borden, David, 30-31 218 +INDEX Boyden, Edward, 196, 197 brain, 212 brain, human, 135, 163-64 Brain Opera, 206-7 Bretton Woods Agreement, 79 Bunka Fashion College, 55 Bush, Vannevar, 139,171-74,180 Buxton, Bill, 140 buzz words, technology, 107-21 CAD software, 73 calculus, 131, 132 Caltech, 158 carbonless copy paper, 15-16 card catalogs, 20-22 Carnegie Mellon, 129 CD-ROM, 10 as competitor of printed book, 13 cello: comparison of computer mouse to violin bow, 142-43 critical reaction to digital cello, 37-38 designing a smart, 27-44, 143-44, 187 limits of classic, 33, 37 Cellular Automata (CAs), 132-33 Census Bureau, 78 central planning, 88-89 chaos theory, 109, 112-16 chess-playing computer programs, 128-30, 134-35 children: learning methods of, 137-38 LOGO programming language, 138, 147 China, Internet access in, 99 Chuang, Isaac, 160-61 Chung, joe, 34 Citron, Robert, 78 Clarke, Arthur C., 51 Clausius, Rudolf, 175 clocks, 104 clothing and wearable computers, 50, 52,55-56,61,102-3,179 coffeemaker, intelligent, 201 communications: imposing on our lives, 95, 100-2 performance limit of a channel of, 176 privacy issues and, 100-1 regulation of, 99-100 see also specific forms of communication, e.g. e-mail; telephones Communications Act of 1934, 99 Communications Assistance for Law Enforcement Act (CALEA), 208 compact disc players, 4 Compumachine, 67 computer chips: entropy and, 177 future uses of, 152 lowering the cost of, 152-56 computers: affective states and, 53-54 Babbage's contribution in developing, 124-25, 132 battle of operating systems, 146 chips, see computer chips cost of, 4, 103 desktop, 5 difficulty using, 4, 7, 103 division of industry into software and hardware, 7 ease of use, 4, 7, 103 educational use of, 201 expectations from, 4 inability to anticipate your needs, 7-8 interfaces, see interfaces, computer irritation with, 199-200 laptop, see laptop computers mainframes, see mainframes minicomputers, 52, 138 Moore's law, 155-57, 163 music and, see musiC and computers parallel, 68, 157 PCs, see personal computers (PCs) peripherals, 52-53 INDEX+ pnvacy issue and, 56-57, 100-1 productivity and, 7 pyramid of information technology, 151 quantum, 157-63, 177 software, see software speed of, 7 standards, 88-90, 126 supercomputers, 151, 177, 199 unobtrusive computing, 44, 200, 211 upgrades, 98 wearable, see wearable computers "Computing Machinery and Intelligence," 128, 135 consciousness, quantum mechanics to describe human, 130-31 Constitution, U.S., 98-99 Copernicus, 113-14 copyrights, 181 Creapole, 55 credit cards: electronic commerce and, 80-81 privacy and use of, 100-1 reflective holograms on, 142 cryptography, 80-81, 156, 207-8 "curse of dimensionality," 164 Daiwa Bank, 77, 86 Darwin, Charles, 125 Data Glove, 49 "Deep Blue," 129-30 "Deep Thought," 129 Defense Advanced Research Projects Agency (DARPA), 79, 129 derivative~ 78, 85-86 Deutsch, David, 158 Deutsche Telekom, 203 developing countries, 210-11 Dickinson, Becton, 204 Difference Engine, 124-25, 132 digital evolution, 10 digital money, see smart money digital representation, effect of time and use on, 5-6 219 Digital Revolution: disturbance resulting from, 10 promise and reality of, 3, 5 disabled, wearable computers and, 58 discovery, the business of, 169-84 Disney, 203 distance learning, 19 3 distribution of wealth, 78 division of labor between people and machines, 8 DNA molecules, 157 Domus, 55 Doom (computer game), 89 Dynabook, 138 e-broidery, 55 Economist, 115 economy, electronic, 79 education: classroom, 188, 197 departmental organization of, 190-91 distance learning, 193 just-in-time, 192 local learning, 193 at Massachusetts Institute of Technology (MIT) Media Lab, 187-97 use of computers for, 201 Einstein's theory of relativity, 178 electronic books, 15-25, 38, 72 electronic commerce, 80-81, 152, 156 cryptography and, 80-81 paying-as-you-go, 82 electronic funds transfers, 80 electronic ink, 16, 17, 200 universal book and, 18-20 e-mail, 101-2, 104-6 encryption, 80-81 Engelhart, Doug, 139 English Bill of Rights, 98 entanglement, 159 entropy, 175, 176, 177, 188-90 "Entschedidungsproblem," 127 Equifax, 101 220 + Ernst, Richard R.
Some Remarks by Neal Stephenson
airport security, augmented reality, barriers to entry, British Empire, cable laying ship, call centre, cellular automata, edge city, Eratosthenes, Fellow of the Royal Society, Hacker Ethic, impulse control, Iridium satellite, Isaac Newton, Jaron Lanier, John von Neumann, Just-in-time delivery, Kevin Kelly, music of the spheres, Norbert Wiener, offshore financial centre, oil shock, packet switching, pirate software, Richard Feynman, Saturday Night Live, shareholder value, Silicon Valley, Skype, slashdot, social web, Socratic dialogue, South China Sea, special economic zone, Stephen Hawking, the scientific method, trade route, Turing machine, undersea cable, uranium enrichment, Vernor Vinge, X Prize
In particular, the monads’ production rule scheme clearly presages the modern concept of cellular automata. Quoting from Mercer’s work: “The Production Rule of F is a rule for the continuous production of the discrete states of F so that it instructs F about exactly what to think at every moment of F’s existence. Following Leibniz’s suggestion, if F exists from t1 to tn and has a different thought at each moment of its existence, then at every moment, there will be an instruction about what to think next. The present thought occurring at t1, together with the Production Rule, will determine what F will think at t2.” Combined with the monadic property of being able to perceive the states of all other monads, this comes close to being a mathematically formal definition of cellular automata, a branch of mathematics generally agreed to have been invented by Stanislaw Ulam and John von Neumann during the 1940s as an outgrowth of work at Los Alamos.
Combined with the monadic property of being able to perceive the states of all other monads, this comes close to being a mathematically formal definition of cellular automata, a branch of mathematics generally agreed to have been invented by Stanislaw Ulam and John von Neumann during the 1940s as an outgrowth of work at Los Alamos. The impressive capabilities of such systems have, in subsequent decades, drawn the attention of many luminaries from the worlds of mathematics and physics, some of whom have proposed that the physical universe might, in fact, consist of cellular automata carrying out a calculation—a hypothesis known as Digital Physics, or It from Bit. 4. Leibniz insisted that each monad perceived the states of all of the others, a premise that runs counter to intuition, given that this would seem to require that an infinite amount of information be transmitted to and stored in each monad. Of all the claims of monadology, this must have seemed the easiest to refute a hundred years ago. Since then, however, it has been given a new lease on life by quantum mechanics.
That’s a description of how all science has been done for a long time. He’s making the argument that a lot of science doesn’t necessarily fit that mold: biological science, psychology. There are plenty of cases you can point to, even in mathematics, where being able to break things down into its smallest components doesn’t really get you anywhere. It doesn’t give you an explanation that’s really worth anything. If you look at cellular automata, for example: Sure, each automaton can be explained as a unit, but that’s not what’s interesting. What’s interesting is the really complicated emergent behaviors that you can get out of a whole bunch of these things acting at once. There’s really no grid to cross that gap. Yet we’re often led to believe that these things are better understood than they are. Biologists complain that it doesn’t make much sense to talk about having “decoded” the genome when how the coding in genes is used to make proteins is still something of a mystery.
The Blockchain Alternative: Rethinking Macroeconomic Policy and Economic Theory by Kariappa Bheemaiah
accounting loophole / creative accounting, Ada Lovelace, Airbnb, algorithmic trading, asset allocation, autonomous vehicles, balance sheet recession, bank run, banks create money, Basel III, basic income, Ben Bernanke: helicopter money, bitcoin, blockchain, Bretton Woods, business cycle, business process, call centre, capital controls, Capital in the Twenty-First Century by Thomas Piketty, cashless society, cellular automata, central bank independence, Claude Shannon: information theory, cloud computing, cognitive dissonance, collateralized debt obligation, commoditize, complexity theory, constrained optimization, corporate governance, creative destruction, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, crowdsourcing, cryptocurrency, David Graeber, deskilling, Diane Coyle, discrete time, disruptive innovation, distributed ledger, diversification, double entry bookkeeping, Ethereum, ethereum blockchain, fiat currency, financial innovation, financial intermediation, Flash crash, floating exchange rates, Fractional reserve banking, full employment, George Akerlof, illegal immigration, income inequality, income per capita, inflation targeting, information asymmetry, interest rate derivative, inventory management, invisible hand, John Maynard Keynes: technological unemployment, John von Neumann, joint-stock company, Joseph Schumpeter, Kenneth Arrow, Kenneth Rogoff, Kevin Kelly, knowledge economy, large denomination, liquidity trap, London Whale, low skilled workers, M-Pesa, Marc Andreessen, market bubble, market fundamentalism, Mexican peso crisis / tequila crisis, MITM: man-in-the-middle, money market fund, money: store of value / unit of account / medium of exchange, mortgage debt, natural language processing, Network effects, new economy, Nikolai Kondratiev, offshore financial centre, packet switching, Pareto efficiency, pattern recognition, peer-to-peer lending, Ponzi scheme, precariat, pre–internet, price mechanism, price stability, private sector deleveraging, profit maximization, QR code, quantitative easing, quantitative trading / quantitative ﬁnance, Ray Kurzweil, Real Time Gross Settlement, rent control, rent-seeking, Satoshi Nakamoto, Satyajit Das, savings glut, seigniorage, Silicon Valley, Skype, smart contracts, software as a service, software is eating the world, speech recognition, statistical model, Stephen Hawking, supply-chain management, technology bubble, The Chicago School, The Future of Employment, The Great Moderation, the market place, The Nature of the Firm, the payments system, the scientific method, The Wealth of Nations by Adam Smith, Thomas Kuhn: the structure of scientific revolutions, too big to fail, trade liberalization, transaction costs, Turing machine, Turing test, universal basic income, Von Neumann architecture, Washington Consensus
Emergence relates to the dynamic nature of interactions between components in a system (Gallegati and Kirman, 2012). The dynamic character of emergent phenomena is not a property of a pre-established, given whole - but arises and becomes apparent as a complex system evolves over time (Goldstein, 1999). Complexity in a system can arise, after all, from simple rules - this is seen in examples such as cellular automata, neural networks and genetic algorithms (Refer Notes). This is because of the non-linearity of the systems relations. As the system becomes complex, emergence manifests itself in the form of self- organization. In other words, no external forces are needed for the system to take on certain properties and traits and these systemic properties arise from far-fromequilibrium conditions (Morçöl, 2008).
All equations are closely based on assumptions that households maximize their own welfare and firms maximize profits. Examples are models developed by Kydland and Prescott and by Christiano and Eichenbaum. Vector autoregression (VAR) models employ a small number of estimated equations to summarize the dynamic behaviour of the entire macroeconomy, with few restrictions from economic theory beyond the choice of variables to include in the model. Sims is the original proponent of this type of model. Cellular automata (CA): “Automaton” (plural: “automata") is a technical term used in computer science and mathematics for a hypothetical machine that changes its internal state based on inputs and its previous state. (Sayama, 2015). A cellular automaton consists of a regular grid of cells, each in one of a finite number of states, such as on and off. Each cell is surrounded by a set of cells called it neighbourhood.
Bheemaiah, The Blockchain Alternative, DOI 10.1007/978-1-4842-2674-2 241 ■ INDEX Capitalism (cont.) cashlessenvironment (see (Multiple currencies)) categories, 88 classification, 88 definition of, 83 de-skilling process, 91 economic hypothesis, 86 education and training levels, 89 EMN, 88 fiat currency, 123 CBDC, 129 commercial banks, 129 debt-based money, 124 digital cash, 129 digital monetary framework, 125 fractional banking system, 124 framework, 124 ideas and methods, 130 non-bank private sector, 124 sovereign digital currency, 125–128 transition, 124 fiscal policy, 136 cashless environment, 136 central bank, 136 concept of, 136 control spending, 138 definition of, 140 exogenous and endogenous function, 137 fractional banking system, 137 Kelton, Stephanie, 139 near-zero interest rates, 136 policy instrument, 136 QE and QQE, 138 tendency, 136 ultra-low inflation, 136 helicopter drops business insider, 141 ceteris paribus, 142 Chatbots, 140–141 Chicago Plan, 145 comparative charts, 142 fractional banking, 145 keywords, 140 technology, 143 UBI, 143–144, 146 higher-skilled workers, 91 ICT technology, 85 industry categories, 90 242 Jiggery Pokery accounts, 106 advantages, 111 bias information, 106 Blockchain, 107 CFTC, 109 digital environment, 108 Enron scandal, 106 limitations, 107 private/self-regulation, 107 public function, 107 regulatory framework, 108 tech-led firms, 109 lending and payments CAMELS evaluation, 94 consumers and SMEs, 95 cryptographic laws, 97 fundamental limitations, 96 governments, 98 ILP, 97 KYB process, 97 lending sector, 95 mobile banking, 96 payments industry, 96 regulatory pressures, 95 rehypothecation, 96 ripple protocol, 97 sectors share, 94 leveraging effect technology, 88 marketing money, 119 cashless system, 120 crime and taxation, 123 economy, 122 IRS, 121 money, 119 Seigniorage, 122 tax evasion, 121 markets and regulation, 84 market structure, 92–93 multiple currency mechanisms, 153 occupational categories, 90 ONET database, 89 policies, 112 economic landscape, 112 financialization, 113 monetary and fiscal policy, 112 money creation methods, 114 The Chicago Plan, 114 transformation, 113 probabilities, 148 regulation, 105 routine and non-routine, 88 ■ INDEX routinization hypothesis, 88 Sarbanes-Oxley Act, 153 SBTC, 92 scalability issue, 152 skill-biased employment, 89 skills and technological advancement, 87 skills downgrading process, 91 trades (see (Trade finance)) UBI Alaska, 147 deployment, 148 Mincome, Canada, 147 Namibia, 147 Cashless system, 120 Cellular automata (CA), 221 Central bank digital currency (CBDC), 125–128 Centre for Economic Policy Research (CEPR), 177 Chicago Plan, 145 Clearing House Interbank Payments System (CHIPS), 48 Collateralised Debt Obligations (CDOs), 29 Collateralized Loan Obligations (CLOs), 29 Complexity economics agent, 193–195 challenges, 184 consequential decisions, 184 deterministic and axiomatized models, 184 dynamics, 187 education, 186 emergence, 192 exogenous and endogenous changes, 184 feedback loops, 191 information affects agents, 185 macroeconoic movements, 182 network science, 189–190 non-linearity, 187 path dependence, 192 power laws, 188 self-adapting individual agents, 185 technology andinvention (see (Technology and invention)) Walrasian approach, 185 Computing, 218–220 Congressional Research Service (CRS), 2 Constant absolute risk aversion (CARA), 206 Contingent convertible (CoCo), 95, 151 Credit Default Swaps (CDSs), 29, 32 CredyCo, 69 Cryptid, 69 Cryptographic law, 97 Currency mechanisms, 153 Current Account Switching System (CASS), 73 D Data analysis techniques, 163 Debt and money broad and base money, 10 China’s productivity, 18 credit, 14 economic pressures, 13 export-led growth, 17 fractional banking,13 (see also (Fractional Reserve banking)) GDP growth, 18 households, 14–15 junk bonds, 11 long-lasting effects, 15 private and public sectors, 16 problems, 19 pubilc and private level, 17 reaganomics, 11 real estate industry, 14, 19 ripple effects, 18 security and ownership, 13 societal level, 17 UK, 10 DigID, 78 Digital trade documents (DOCS), 99 Dodd-Frank Act, 34, 35, 105 Dynamic Stochastic General Equilibrium (DSGE) model, 22, 167, 168 E EBM.
Accelerando by Stross, Charles
business cycle, call centre, carbon-based life, cellular automata, cognitive dissonance, commoditize, Conway's Game of Life, dark matter, dumpster diving, Extropian, finite state, Flynn Effect, glass ceiling, gravity well, John von Neumann, Kickstarter, knapsack problem, Kuiper Belt, Magellanic Cloud, mandelbrot fractal, market bubble, means of production, MITM: man-in-the-middle, orbital mechanics / astrodynamics, packet switching, performance metric, phenotype, planetary scale, Pluto: dwarf planet, reversible computing, Richard Stallman, SETI@home, Silicon Valley, Singularitarianism, slashdot, South China Sea, stem cell, technological singularity, telepresence, The Chicago School, theory of mind, Turing complete, Turing machine, Turing test, upwardly mobile, Vernor Vinge, Von Neumann architecture, web of trust, Y2K, zero-sum game
He's not sure Pamela ever let him see her fully naked: She thought skin was more sexy when it was covered. Annette squeezes him again, and he stiffens. "More!" By the time they finish, he's aching, and she shows him how to use the bidet. Everything is crystal clear, and her touch is electrifying. While she showers, he sits on the toilet seat lid and rants about Turing-completeness as an attribute of company law, about cellular automata and the blind knapsack problem, about his work on solving the Communist Central Planning problem using a network of interlocking unmanned companies. About the impending market adjustment in integrity, the sinister resurrection of the recording music industry, and the still-pressing need to dismantle Mars. When she steps out of the shower, he tells her that he loves her. She kisses him and slides his glasses and earpieces off his head so that he's really naked, sits on his lap, and fucks his brains out again, and whispers in his ear that she loves him and wants to be his manager.
Each of these companies – and there are currently more than sixteen thousand of them, although the herd is growing day by day – has three directors and is the director of three other companies. Each of them executes a script in a functional language Manfred invented; the directors tell the company what to do, and the instructions include orders to pass instructions on to their children. In effect, they are a flock of cellular automata, like the cells in Conway's Game of Life, only far more complex and powerful. Manfred's companies form a programmable grid. Some of them are armed with capital in the form of patents Manfred filed, then delegated rather than passing on to one of the Free Foundations. Some of them are effectively nontrading, but occupy directorial roles. Their corporate functions (such as filing of accounts and voting in new directors) are all handled centrally through his company-operating framework, and their trading is carried out via several of the more popular B2B enabler dot-coms.
Boris lumbers round in place to face her; today he's wearing a velociraptor, and they don't turn easily in confined spaces. He snarls irritably: "Give me some space!" He coughs, a threatening noise from the back of his wattled throat, "Searching the sail's memory now." The back of the soap-bubble-thin laser sail is saturated with tiny nanocomputers spaced micrometers apart. Equipped with light receptors and configured as cellular automata, they form a gigantic phased-array detector, a retina more than a hundred meters in diameter. Boris is feeding them patterns describing anything that differs from the unchanging starscape. Soon the memories will condense and return as visions of darkness in motion – the cold, dead attendants of an aborted sun. "But where is it going to be?" asks Sadeq. "Do you know what you are looking for?"
The Road to Ruin: The Global Elites' Secret Plan for the Next Financial Crisis by James Rickards
"Robert Solow", Affordable Care Act / Obamacare, Albert Einstein, asset allocation, asset-backed security, bank run, banking crisis, barriers to entry, Bayesian statistics, Ben Bernanke: helicopter money, Benoit Mandelbrot, Berlin Wall, Bernie Sanders, Big bang: deregulation of the City of London, bitcoin, Black Swan, blockchain, Bonfire of the Vanities, Bretton Woods, British Empire, business cycle, butterfly effect, buy and hold, capital controls, Capital in the Twenty-First Century by Thomas Piketty, Carmen Reinhart, cellular automata, cognitive bias, cognitive dissonance, complexity theory, Corn Laws, corporate governance, creative destruction, Credit Default Swap, cuban missile crisis, currency manipulation / currency intervention, currency peg, Daniel Kahneman / Amos Tversky, David Ricardo: comparative advantage, debt deflation, Deng Xiaoping, disintermediation, distributed ledger, diversification, diversified portfolio, Edward Lorenz: Chaos theory, Eugene Fama: efficient market hypothesis, failed state, Fall of the Berlin Wall, fiat currency, financial repression, fixed income, Flash crash, floating exchange rates, forward guidance, Fractional reserve banking, G4S, George Akerlof, global reserve currency, high net worth, Hyman Minsky, income inequality, information asymmetry, interest rate swap, Isaac Newton, jitney, John Meriwether, John von Neumann, Joseph Schumpeter, Kenneth Rogoff, labor-force participation, large denomination, liquidity trap, Long Term Capital Management, mandelbrot fractal, margin call, market bubble, Mexican peso crisis / tequila crisis, money market fund, mutually assured destruction, Myron Scholes, Naomi Klein, nuclear winter, obamacare, offshore financial centre, Paul Samuelson, Peace of Westphalia, Pierre-Simon Laplace, plutocrats, Plutocrats, prediction markets, price anchoring, price stability, quantitative easing, RAND corporation, random walk, reserve currency, RFID, risk-adjusted returns, Ronald Reagan, Silicon Valley, sovereign wealth fund, special drawing rights, stocks for the long run, The Bell Curve by Richard Herrnstein and Charles Murray, The Wealth of Nations by Adam Smith, The Wisdom of Crowds, theory of mind, Thomas Bayes, Thomas Kuhn: the structure of scientific revolutions, too big to fail, transfer pricing, value at risk, Washington Consensus, Westphalian system
These originals were transformative, but the postwar variations are limited, obsolete, and if used doctrinally, dangerous. The Austrian understanding of the superiority of free markets over central planning is sound. Still, the Austrian school needs updating using new science and twenty-first-century technology. Christopher Columbus was the greatest dead-reckoning navigator ever. Yet no one disputes he would use GPS today. If Friedrich Hayek were alive, he would use new instruments, network theory, and cellular automata to refine his insights. His followers should do no less. Neo-Keynesian models are the reigning creed. Interestingly, they have little to do with John Maynard Keynes. He was above all a pragmatist; those who follow in his name are anything but. Keynes advocated for gold in 1914, counseled for a higher gold price in 1925, opposed gold in 1931, and offered a modified gold standard in 1944. Keynes had pragmatic reasons for each position.
There may be a cause-and-effect relationship between catalyst and collapse. Still, it is too small to observe and the timing is difficult to forecast. Predicting market crashes is like predicting earthquakes. One may be certain the event will occur, and can estimate its magnitude, yet one will never know exactly when. Laboratory science, in particular sand pile experiments (similar to a snowflake-avalanche dynamic) and computer simulations using cellular automata, reveal degree distributions of extreme events. Still, a million experiments will not let you predict which particular grain of sand causes a certain sand pile to collapse. Systemic instability, not an individual catalyst, destroys your wealth. Anxious investors should not focus on snowflakes, they should stay alert for an avalanche. Nonetheless, the search for snowflakes is seductive. The most sensational snowflake may be a publicized failure to deliver physical gold by a prominent bank.
Schumpeter’s consideration of capitalism’s rise and fall, while not specific to one civilization, is the kind of study to which complexity theory lends valuable tools. Schumpeter eschewed Keynesian models because of the artifice in holding most variables constant while monotonically isolating one as the “cause” of the phenomena under study. Today a twenty-first-century mélange of a Schumpeterian long view and massive computing power—unavailable to Schumpeter—allows for a rapid expansion of recursive functions and simulation of human action via cellular automata. Schumpeter would surely look benignly on such efforts as a reasonable simulacrum of his deep historical processes. Society stands on Schumpeter’s shoulders with new tools of complexity theory to look over a ridgeline at the rise of socialism and fascism, one and the same. The New Praetorians In ancient Rome, the Praetorian Guard were an elite military unit that provided personal protection to emperors.
Epigenetics: How Environment Shapes Our Genes by Richard C. Francis
agricultural Revolution, cellular automata, double helix, Drosophila, epigenetics, experimental subject, longitudinal study, Machine translation of "The spirit is willing, but the flesh is weak." to Russian and back, meta analysis, meta-analysis, phenotype, stem cell, twin studies
As we have seen, the epigenetic changes in gene expression that determine a cell’s fate are largely determined by the position of that cell in the developing embryo. Therefore, it would be more apt to say that the genes are programmed by cellular interactions. There are other, very minimalist senses of “program” that have become popular in the fields of artificial intelligence and artificial life.26 On this minimalist notion of program, a program provides a few basic rules, and the robots or cellular automata take it from there through interactions with their neighbors and the rest of their environment. But the idea of a central director is gone. This minimalist sense of program looks a lot like epigenesis. So the preformationist notion of an epigenetic (or genetic) program is either false or too squishy to be distinguished from epigenesis. In either case, we would be better served to drop the “program” metaphor altogether, and with it, the temptation to think of genes as software.
From these stem cells, they generated normal-appearing mice. 24. Kulesa, Kasemeier-Kulesa, et al. (2006); Hendrix, Seftor, et al. (2007). 25. Collas (2010) is typical in this regard, in the context of cellular differentiation. 26. For the minimalist notion of programming in situated robotics, see Hendriks-Jansen (1996). Wolfram (2002) is an expanded, semi-mystical view of the significance of a minimalist program, motivated by Wolfram’s research on cellular automata. 27. Passier and Mummery (2003). 28. Moreover, there are subtle differences between embryonic stem cells (ESCs) and induced embryonic stem cells (iPSCs) that are clinically relevant. For example, ESCs have proved much more efficient in promoting neuronal redifferentiation than iPSCs have (Tokumoto, Ogawa, et al. 2010). 29. This is how Conrad Waddington, who coined the term epigenetic, describes its derivation: “Some years ago I introduced the word ‘epigenetic,’ derived from the Aristotelian word ‘epigenesis,’ which had more or less passed into disuse, as a suitable name for the branch of biology which studies the causal interactions between genes and their products which bring the phenotype into being” (Waddington 1968). 30.
Radical Technologies: The Design of Everyday Life by Adam Greenfield
3D printing, Airbnb, augmented reality, autonomous vehicles, bank run, barriers to entry, basic income, bitcoin, blockchain, business intelligence, business process, call centre, cellular automata, centralized clearinghouse, centre right, Chuck Templeton: OpenTable:, cloud computing, collective bargaining, combinatorial explosion, Computer Numeric Control, computer vision, Conway's Game of Life, cryptocurrency, David Graeber, dematerialisation, digital map, disruptive innovation, distributed ledger, drone strike, Elon Musk, Ethereum, ethereum blockchain, facts on the ground, fiat currency, global supply chain, global village, Google Glasses, IBM and the Holocaust, industrial robot, informal economy, information retrieval, Internet of things, James Watt: steam engine, Jane Jacobs, Jeff Bezos, job automation, John Conway, John Markoff, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, John von Neumann, joint-stock company, Kevin Kelly, Kickstarter, late capitalism, license plate recognition, lifelogging, M-Pesa, Mark Zuckerberg, means of production, megacity, megastructure, minimum viable product, money: store of value / unit of account / medium of exchange, natural language processing, Network effects, New Urbanism, Occupy movement, Oculus Rift, Pareto efficiency, pattern recognition, Pearl River Delta, performance metric, Peter Eisenman, Peter Thiel, planetary scale, Ponzi scheme, post scarcity, post-work, RAND corporation, recommendation engine, RFID, rolodex, Satoshi Nakamoto, self-driving car, sentiment analysis, shareholder value, sharing economy, Silicon Valley, smart cities, smart contracts, social intelligence, sorting algorithm, special economic zone, speech recognition, stakhanovite, statistical model, stem cell, technoutopianism, Tesla Model S, the built environment, The Death and Life of Great American Cities, The Future of Employment, transaction costs, Uber for X, undersea cable, universal basic income, urban planning, urban sprawl, Whole Earth Review, WikiLeaks, women in the workforce
These copies would immediately set about making further copies, underwriting an exponential increase in production capacity. Von Neumann’s constructors existed in an entirely virtual universe of possibility. At the time he imagined them, there wasn’t enough computing power in the world to simulate their behavior, let alone physically realize them as anything other than painstakingly inked grids of pen on paper, so-called cellular automata.2 But with its implication of a continuously and geometrically expanding productivity, his vision of a universal constructor inspired generations of engineers. Sometime around the turn of the twenty-first century, one of them—a mechanical engineering professor at the University of Leeds named Adrian Bowyer, troubled by the thought of a world riven by material scarcity—finally took von Neumann at his word.
Casey Newton, “Seattle dive bar becomes first to ban Google Glass,” CNET, March 8, 2013. 23.Dan Wasserman, “Google Glass Rolls Out Diane von Furstenberg frames,” Mashable, June 23, 2014. 4Digital fabrication 1.John Von Neumann, Theory of Self-Reproducing Automata, Urbana: University of Illinois Press, 1966, cba.mit.edu/events/03.11.ASE/docs/VonNeumann.pdf. 2.You may be familiar with cellular automata from John Conway’s 1970 Game of Life, certainly the best-known instance of the class. See Bitstorm.org, “John Conway’s Game of Life,” undated, bitstorm.org. 3.Adrian Bowyer, “Wealth Without Money: The Background to the Bath Replicating Rapid Prototyper Project,” February 2, 2004, reprap.org/wiki/Wealth_Without_Money; RepRap Project, “Cost Reduction,” December 30, 2014, reprap.org/wiki/Cost_Reduction.
., 103 Brown, Joshua, 223–4, 254 Brown, Michael, 231 “buddy punching,” 198 bullshit jobs, 203, 205 Bui, Quoctrung, 192–3 bushido, 266–7 Bushido Project, the, 266 Business Microscope, 197 Buterin, Vitalik, 147–50, 152, 154, 162–4, 167, 169, 172, 175, 177, 179, 303, 311 Byzantium, 69 CAD-Coin, 157 Californian Ideology, the, 283 Carmack, John, 82 cartography, 20 cats, 214 CCTV, 49–50, 54, 241 cellular automata, 86 Champs-Élysées, 1 Chaum, David, 121 Checkpoint Charlie, 70 chess, 263 Chevrolet Camaro, 216–18 Chicago Police Department, 230–1 China, 87, 102, 190, 194, 278–9, 286, 290, 306 Churchill, Winston, 28 circular economy, 92, 96, 99, 288 Ciutat Meridiana, Barcelona neighborhood, 109 climax community, 289 Cockney rhyming slang, 311 code library, 274–5 commons, the, 171–3 computer numerical control, CNC milling, 86, 93, 95, 97, 108, 110, 273 Container Store, 196 cooperatives, 171 cooperative motility, 80 Copenhagen, 31, 51 Cornell Law School, 151 Cortana virtual assistant, 39 Costco, 45 cozy catastrophe, 291 cradle-to-cradle industrial ecosystem.
From eternity to here: the quest for the ultimate theory of time by Sean M. Carroll
Albert Einstein, Albert Michelson, anthropic principle, Arthur Eddington, Brownian motion, cellular automata, Claude Shannon: information theory, Columbine, cosmic microwave background, cosmological constant, cosmological principle, dark matter, dematerialisation, double helix, en.wikipedia.org, gravity well, Harlow Shapley and Heber Curtis, Henri Poincaré, Isaac Newton, Johannes Kepler, John von Neumann, Lao Tzu, Laplace demon, lone genius, low earth orbit, New Journalism, Norbert Wiener, pets.com, Pierre-Simon Laplace, Richard Feynman, Richard Stallman, Schrödinger's Cat, Slavoj Žižek, Stephen Hawking, stochastic process, the scientific method, wikimedia commons
As a general rule, the more symmetries you have, the simpler things become. 110 This whole checkerboard-worlds idea sometimes goes by the name of cellular automata. A cellular automaton is just some discrete grid that follows a rule for determining the next row from the state of the previous row. They were first investigated in the 1960s, by John von Neumann, who is also the guy who figured out how entropy works in quantum mechanics. Cellular automata are fascinating for many reasons having little to do with the arrow of time; they can exhibit great complexity and can function as universal computers. See Poundstone (1984) or Shalizi (2009). Not only are we disrespecting cellular automata by pulling them out only to illustrate a few simple features of time reversal and information conservation, but we are also not speaking the usual language of cellular-automaton cognoscenti.
bosons bouncing-universe cosmology boundary conditions and cause and effect described and initial conditions of the universe and irreversibility and Maxwell’s Demon and recurrence theorem and time symmetry Bousso, Raphael Brahe, Tycho branes Brillouinéon brown dwarfs Brownian motion Bruno, Giordano bubbles of vacuum Buddhism Bureau of Longitude Callender, Craig Callisto caloric Calvin, John Calvino, Italo Carnot, Lazare Carnot, Nicolas Léonard Sadi Carrey, Jim Carroll, Lewis cause and effect celestial mechanics cellular automata CERN C-field Chandrasekhar Limit chaotic dynamics The Character of Physical Law (Feynman) charge charge conjugation checkerboard world exercise and arrow of time background of and conservation of information and Hawking radiation and holographic principle and information loss and interaction effects and irreversibility and Principle of Indifference and symmetry and testing hypotheses chemistry Chen, Jennifer choice Chronology Protection Conjecture circles in time.
Is God a Mathematician? by Mario Livio
Albert Einstein, Antoine Gombaud: Chevalier de Méré, Brownian motion, cellular automata, correlation coefficient, correlation does not imply causation, cosmological constant, Dava Sobel, double helix, Edmond Halley, Eratosthenes, Georg Cantor, Gerolamo Cardano, Gödel, Escher, Bach, Henri Poincaré, Isaac Newton, Johannes Kepler, John von Neumann, music of the spheres, Myron Scholes, probability theory / Blaise Pascal / Pierre de Fermat, Russell's paradox, Thales of Miletus, The Design of Experiments, the scientific method, traveling salesman
Most mathematicians agree that mathematics as we know it has evolved from the basic branches of geometry and arithmetic that were practiced by the ancient Babylonians, Egyptians, and Greeks. However, was it truly inevitable that mathematics would start with these particular disciplines? Computer scientist Stephen Wolfram argued in his massive book A New Kind of Science that this was not necessarily the case. In particular, Wolfram showed how starting from simple sets of rules that act as short computer programs (known as cellular automata), one could develop a very different type of mathematics. These cellular automata could be used (in principle, at least) as the basic tools for modeling natural phenomena, instead of the differential equations that have dominated science for three centuries. What was it, then, that drove the ancient civilizations toward discovering and inventing our special “brand” of mathematics? I don’t really know, but it may have had much to do with the particulars of the human perceptual system.
Possible Minds: Twenty-Five Ways of Looking at AI by John Brockman
AI winter, airport security, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, artificial general intelligence, Asilomar, autonomous vehicles, basic income, Benoit Mandelbrot, Bill Joy: nanobots, Buckminster Fuller, cellular automata, Claude Shannon: information theory, Daniel Kahneman / Amos Tversky, Danny Hillis, David Graeber, easy for humans, difficult for computers, Elon Musk, Eratosthenes, Ernest Rutherford, finite state, friendly AI, future of work, Geoffrey West, Santa Fe Institute, gig economy, income inequality, industrial robot, information retrieval, invention of writing, James Watt: steam engine, Johannes Kepler, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, John von Neumann, Kevin Kelly, Kickstarter, Laplace demon, Loebner Prize, market fundamentalism, Marshall McLuhan, Menlo Park, Norbert Wiener, optical character recognition, pattern recognition, personalized medicine, Picturephone, profit maximization, profit motive, RAND corporation, random walk, Ray Kurzweil, Richard Feynman, Rodney Brooks, self-driving car, sexual politics, Silicon Valley, Skype, social graph, speech recognition, statistical model, Stephen Hawking, Steven Pinker, Stewart Brand, strong AI, superintelligent machines, supervolcano, technological singularity, technoutopianism, telemarketer, telerobotics, the scientific method, theory of mind, Turing machine, Turing test, universal basic income, Upton Sinclair, Von Neumann architecture, Whole Earth Catalog, Y2K, zero-sum game
He is the creator of the symbolic computation program Mathematica and its programming language, Wolfram Language, as well as the knowledge engine Wolfram|Alpha. He is also the author of A New Kind of Science. The following is an edited transcript from a live interview with him conducted in December 2015. Over nearly four decades, Stephen Wolfram has been a pioneer in the development and application of computational thinking and responsible for many innovations in science, technology, and business. His 1982 paper “Cellular Automata as Simple Self-Organizing Systems,” written at the age of twenty-three, was the first of numerous significant scientific contributions aimed at understanding the origins of complexity in nature. It was around this time that Stephen briefly came into my life. I had established The Reality Club, an informal gathering of intellectuals who met in New York City to present their work before peers in other disciplines.
But even with a smart enough machine and smart enough mathematics, we can’t get to the endpoint without going through the steps. Some details are irreducible. We have to irreducibly follow those steps. That’s why history means something. If we could get to the endpoint without going through the steps, history would be, in some sense, pointless. So it’s not the case that we’re intelligent and everything else in the world is not. There’s no enormous abstract difference between us and the clouds or us and the cellular automata. We cannot say that this brainlike neural network is qualitatively different from this cellular-automaton system. The difference is a detailed difference. This brainlike neural network was produced by the long history of civilization, whereas the cellular automaton was created by my computer in the last microsecond. The problem of abstract AI is similar to the problem of recognizing extraterrestrial intelligence: How do you determine whether or not it has a purpose?
The End of Theory: Financial Crises, the Failure of Economics, and the Sweep of Human Interaction by Richard Bookstaber
"Robert Solow", asset allocation, bank run, bitcoin, business cycle, butterfly effect, buy and hold, capital asset pricing model, cellular automata, collateralized debt obligation, conceptual framework, constrained optimization, Craig Reynolds: boids flock, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, dark matter, disintermediation, Edward Lorenz: Chaos theory, epigenetics, feminist movement, financial innovation, fixed income, Flash crash, Henri Poincaré, information asymmetry, invisible hand, Isaac Newton, John Conway, John Meriwether, John von Neumann, Joseph Schumpeter, Long Term Capital Management, margin call, market clearing, market microstructure, money market fund, Paul Samuelson, Pierre-Simon Laplace, Piper Alpha, Ponzi scheme, quantitative trading / quantitative ﬁnance, railway mania, Ralph Waldo Emerson, Richard Feynman, risk/return, Saturday Night Live, self-driving car, sovereign wealth fund, the map is not the territory, The Predators' Ball, the scientific method, Thomas Kuhn: the structure of scientific revolutions, too big to fail, transaction costs, tulip mania, Turing machine, Turing test, yield curve
“Investment under Uncertainty.” Econometrica 39, no. 5: 659–81. doi: 10.2307/1909571. Luria, A. R. 1968. The Mind of a Mnemonist: A Little Book about a Vast Memory. Trans. Lynn Solotaroff. New York: Basic Books. Lynch, Peter. 2008. “The Origins of Computer Weather Prediction and Climate Modeling.” Journal of Computational Physics 227: 3431–44. doi: 10.1016/j.jcp.2007.02.034. Maerivoet, Sven, and Bart De Moor. 2005. “Cellular Automata Models of Road Traffic.” Physics Report 419, no. 1: 1–64. doi: 10.1016/j.physrep.2005.08.005. Majdandzic, Antonio, Boris Podobnik, Sergey V. Buldyrev, Dror Y. Kenett, Shlomo Havlin, and H. Eugene Stanley. 2014. “Spontaneous Recovery in Dynamical Networks.” Nature Physics 10, no. 1: 34–38. doi: 10.1038/nphys2819. Martel, Robert. 1996. “Heterogeneity, Aggregation, and a Meaningful Macroeconomics.”
See bank/dealer bank/dealer, 135; derivatives desk of, 135–136; during crises, 187; failure of, 135; and funding, 135–136; and heuristics, 135–136; market making by, 135–136; prime broker of, 135; structure of, 131; trading desk of, 135–136 Basel Committee on Banking Supervision, 156 Bear Stearns, 10, 160; Goldman Sachs and the failure of, 167; market’s loss of confidence in, 167; use of repo for funding, 166 Bear Stearns Asset Management (BSAM), 161; collateral seized by Merrill Lynch, 162–163; Enhanced Leverage Fund, 162–163; hedge funds’ bankruptcy, 166–167 Becker, Gary, 81, 182 behavioral economics, 42, 102 Beinhocker, Eric, 89–90, 114, 175 Bentham, Jeremy, 6 Bernanke, Ben, 10–11 Big Short, The, 185 boids, 104 Borges, Jorge Luis, 182; The Art of Cartography, 25; Funes, the Memorious, 75 (see also Funes, the Memorious); The Library of Babel, 61–63 (see also Library of Babel); and example of Suarez Miranda, 25; Tlön, Uqbar, Orbis Tertius, 92–93 Bousquet, Antoine, 174 Boyd, John, 173–174; at the Air War College, 173; and ambiguity in warfare, 174; and Boyd Cycle, 119 (see also OODA loop); F-86 sabre strategy and, 119; at Fighter Weapons School, 119; and development of the OODA loop, 119, 179 (see also Boyd Cycle); and the Strategic Game of ? and ?, 118–119, 174 Calvinism, 58 Cambridge University Press, 52 cascades: and the financial crisis of 2008, 159–160 (see also financial crisis of 2008); fire sales and, 140; liquidity during, 128, 152 (see also liquidity); margin calls during, 14; occurrence of, 159; portfolio insurance and, 146–147; cash providers, 136 CDOs, 161, 164 cellular automata, 37, 95, 97–98 chaos, 29; limits to knowledge and, 51 chatbot. See MGonz Chernobyl, nuclear accident of, 112 Church, Alonzo, 54 Citigroup, 11, 166 Clower, Robert, 85 cockroach, 68, 74; defense mechanism of, 66; and omniscient planner, 66–67 Coleman, Henry, 5 collateral: haircuts and, 131; risk reduction of, 204; transformations of, 131 commercial paper, 136 Commodity Futures Trading Commission (CFTC), 147–148 complexity: and chaos theory, 110–111; in comparison to computational irreducibility, 108, 122; description of, 109–112; and emergence, 108, 122; and ergodicity, 111, 122; and financial crises, 112 (see also financial crises); and informational irreducibility, 109–110; investigation of by Gotfried Wilhelm Leibniz, 109; and neoclassical economics, 123–124 (see also neoclassical economics); and network theory, 110; and nonlinear systems, 110–111; and the OODA loop, 122 (see also OODA Loop); and radical uncertainty, 112, 122; and strategic complexity, 122–124 computational irreducibility, 12, 18, 33; crises and, 105; and heuristics, 65; and the Library of Babel, 62–63; and maps, 26; and mathematical shortcuts, 26; and neoclassical economics, 83 (see also neoclassical economics); and three-body problem, 27–28 (see also three-body problem); and Turing’s halting problem, 55 computers, and the universal Turing machine, 54.
Chaos: Making a New Science by James Gleick
Benoit Mandelbrot, business cycle, butterfly effect, cellular automata, Claude Shannon: information theory, discrete time, Edward Lorenz: Chaos theory, experimental subject, Georg Cantor, Henri Poincaré, Isaac Newton, iterative process, John von Neumann, Louis Pasteur, mandelbrot fractal, Murray Gell-Mann, Norbert Wiener, pattern recognition, Richard Feynman, Stephen Hawking, stochastic process, trade route
When Yaneer Bar-Yam wrote a kilopage textbook, Dynamics of Complex Systems, in 2003, he took care of chaos proper in the first section of the first chapter. (“The first chapter, I have to admit, is 300 pages, okay?” he says.) Then came Stochastic Processes, Modeling Simulation, Cellular Automata, Computation Theory and Information Theory, Scaling, Renormalization, and Fractals, Neural Networks, Attractor Networks, Homogenous Systems, Inhomogenous Systems, and so on. Bar-Yam, the son of a high-energy physicist, had studied condensed matter physics and become an engineering professor at Boston University, but he left in 1997 to found the New England Complex Systems Institute. He had been exposed to Stephen Wolfram’s work on cellular automata and Robert Devaney’s work in chaos and discovered that he was less interested in polymers and superconductors than in neural networks and—he says this with no sense of grandiosity—the nature of human civilization.
Singularity Sky by Stross, Charles
anthropic principle, cellular automata, Conway's Game of Life, cosmological constant, Doomsday Clock, Extropian, gravity well, Kuiper Belt, life extension, means of production, new economy, phenotype, prisoner's dilemma, skinny streets, technological singularity, uranium enrichment
She'd spoken to Martin about it, piecing together his information with her own. Together they'd pieced together a terrifying hypothesis. "Herman was unusually vague about it," Martin admitted. "Normally he has a lot of background detail. Every word means something. But it's as if he doesn't want to say too much about the Festival. They're—he called them, uh, glider-gun factories. I don't know if you know about Life—" "Cellular automata, the game?" "That's the one. Glider guns are mobile cellular automata. There are some complex life structures that replicate themselves, or simpler cellular structures; a glider-gun factory is a weird one. It periodically packs itself into a very dense mobile system that migrates across the grid for a couple of hundred squares, then it unpacks itself into two copies that then pack down and fly off in opposite directions. Herman said that they're a real-space analogue: he called them a Boyce-Tipler robot.
Darwin's Dangerous Idea: Evolution and the Meanings of Life by Daniel C. Dennett
Albert Einstein, Alfred Russel Wallace, anthropic principle, assortative mating, buy low sell high, cellular automata, combinatorial explosion, complexity theory, computer age, conceptual framework, Conway's Game of Life, Danny Hillis, double helix, Douglas Hofstadter, Drosophila, finite state, Gödel, Escher, Bach, In Cold Blood by Truman Capote, invention of writing, Isaac Newton, Johann Wolfgang von Goethe, John von Neumann, Murray Gell-Mann, New Journalism, non-fiction novel, Peter Singer: altruism, phenotype, price mechanism, prisoner's dilemma, QWERTY keyboard, random walk, Richard Feynman, Rodney Brooks, Schrödinger's Cat, selection bias, Stephen Hawking, Steven Pinker, strong AI, the scientific method, theory of mind, Thomas Malthus, Turing machine, Turing test
His description (posthumously published, 1966) of how an automaton would read its own blueprint and then copy it into its new creation anticipated in impressive detail many of the later discoveries about the mechanisms of DNA expression and replication, but in order to make his proof of the possibility of a self-reproducing automaton mathematically rigorous and tractable, von Neumann had switched to simple, two-dimensional abstractions, now known as cellular automata. Conway's Life-world cells are a particularly agreeable example of cellular automata. Conway and his students wanted to confirm von Neumann's proof in detail by actually constructing a two-dimensional world with a simple physics in which such a self-replicating construction would be a stable, working structure. Like von Neumann, they wanted their answer to be as general as possible, and hence as independent as possible of actual (Earthly?
The task of the wise God required to put this world into motion is a task of discovery, not creation, a job for a Newton, not a Shakespeare. What Newton found — and what Conway found — are eternal Platonic fixed points that anybody else in principle could have discovered, not idiosyncratic creations that depend in any way on the particularities of the minds of their authors. If Conway had never turned his hand to designing cellular-automata worlds — if Conway had never even existed — some other mathematician might very well have hit upon exactly the Life world that Conway gets the credit for. So, as we follow the Darwinian down this path, God the Artificer turns first into God the Lawgiver, who now can be seen to merge with God the Lawfinder. God's hypothesized contribution is thereby becoming less personal — and hence more readily performable by something dogged and mindless!
Blockchain: Blueprint for a New Economy by Melanie Swan
23andMe, Airbnb, altcoin, Amazon Web Services, asset allocation, banking crisis, basic income, bioinformatics, bitcoin, blockchain, capital controls, cellular automata, central bank independence, clean water, cloud computing, collaborative editing, Conway's Game of Life, crowdsourcing, cryptocurrency, disintermediation, Edward Snowden, en.wikipedia.org, Ethereum, ethereum blockchain, fault tolerance, fiat currency, financial innovation, Firefox, friendly AI, Hernando de Soto, intangible asset, Internet Archive, Internet of things, Khan Academy, Kickstarter, lifelogging, litecoin, Lyft, M-Pesa, microbiome, Network effects, new economy, peer-to-peer, peer-to-peer lending, peer-to-peer model, personalized medicine, post scarcity, prediction markets, QR code, ride hailing / ride sharing, Satoshi Nakamoto, Search for Extraterrestrial Intelligence, SETI@home, sharing economy, Skype, smart cities, smart contracts, smart grid, software as a service, technological singularity, Turing complete, uber lyft, unbanked and underbanked, underbanked, web application, WikiLeaks
One possible path is bringing existing non-AI and non-blockchain rule-based systems onto the blockchain to further automate and empower their operations. This could include systems like chaining together simple if-this-then-that (or IFTTT) behavior and the open source Huginn platform for building agents that monitor situations and act on your behalf. A second possible path is implementing programmatic ideas from AI research fields such as Wolfram’s cellular automata, Conway’s Game of Life, Dorigo’s Ant Colony Optimization and Swarm Intelligence, Andy Clark’s embodied cognitive robots, and other general agent-based systems. Chapter 3. Blockchain 3.0: Justice Applications Beyond Currency, Economics, and Markets Blockchain Technology Is a New and Highly Effective Model for Organizing Activity Not only is there the possibility that blockchain technology could reinvent every category of monetary markets, payments, financial services, and economics, but it might also offer similar reconfiguration possibilities to all industries, and even more broadly, to nearly all areas of human endeavor.
Practical OCaml by Joshua B. Smith
cellular automata, Debian, domain-specific language, general-purpose programming language, Grace Hopper, hiring and firing, John Conway, Paul Graham, slashdot, SpamAssassin, text mining, Turing complete, type inference, web application, Y2K
The third is the logical and of the sample and the random (Figure 27-4). Figure 27-2. Random BMP 620Xch27final.qxd 9/22/06 1:22 AM Page 389 CHAPTER 27 ■ PROCESSING BINARY FILES Figure 27-3. xor BMP Figure 27-4. and BMP 389 620Xch27final.qxd 390 9/22/06 1:22 AM Page 390 CHAPTER 27 ■ PROCESSING BINARY FILES Conway’s Game of Life In 1970, a British mathematician named John Conway created the field of cellular automata when he published the first article on the subject. Conway’s “game” isn’t so much a game played by people as it is a mathematical experiment. The game is an example of emergent behavior because there are only four simple rules that generate an amazing amount of complexity. Conway’s game is also Turing Complete, which means that (given the right initial conditions) the game is as powerful as any “real” computer.
See primitive types batch compilers, 405 Bayes’ Theorem, 169 binary files bitmaps and, 383–389 comparing two, 380–383 outputting, 377 parsing, 295 processing, 375– 399 reading, 383–389 bitmap header definitions, 384 blog server example, 278–288 BNF (Backus-Naur Form), 210 Boolean type (bool), 67 bottom-up design, 267 bprintf function, 76 broadcast function, 318 bscanf function, 77, 253 buffers, 80, 110 build tools, 401–409 buildmap function, 172 build_list function, 324 445 620Xidxfinal.qxd 446 9/22/06 4:19 PM Page 446 ■INDEX built-in exceptions, 125, 131 built-in functions, 48 built-in types, 24 buy function, for securities trades database, 54 ■C C code, interfacing with, 349–358 -c compiler flag, 405 .c files, 408 C functions, defining, 352–355 C preprocessor (cpp), 411 C++ Foreign Function Interfaces and, 349 polymorphic classes and, 26 templates and, 25, 160 calculators four-function, 42 guards and, 48 Calendar module, 360 CAM (Categorical Abstract Machine), 3 CAM-ML, 3 Cameleon, 14 Caml Light, 3 Caml Special Light, 4 camlbrowser, 402, 405 CamlIDL library, 359 camlidl tool, 349, 355, 357 Camlp4, 411–429 camlp4o command, 418, 425 CAMLparam macro, 352 CAMLprim, 362 caml_alloc data, 352 caml_alloc_string(length) function, 351 caml_alloc_string(n) function, 352 caml_alloc_tuple(n) function, 352 caml_copy_double(d) function, 352 caml_copy_double(initial_value) function, 352 caml_copy_string(str) string, 352 caml_failwith(argument_string) function, 352 caml_invalid_argument(argument_string) function, 352 caml_raise_end_of_file (void) exception, 352 caml_raise_not_found ( void ) exception, 352 Capability Maturity Model (CMM), 271 Categorical Abstract Machine (CAM), 3 -cc <CCNAME> flag, 406 -cclib -lLIBNAME flag, 406 -ccopt OPTION flag, 406 cellular automata, 390 CGI (Common Gateway Interface), 273–291 advantages/disadvantages of, 274 writing your own functions and, 277–284 channels, 52, 113–117 chars, 64, 377 check_suffix function, 137 choose function, 320 chop_extension function, 137 chop_suffix function, 137 Church, Alonzo, 263 class keyword, 226, 228 classes, 25, 225–229 internal, 233 vs. objects, 226 parameterized, 234 polymorphism and, 230 reasons for using, 227 virtual, 234, 241 client class, 334 client functions, 189 clients, OCaml support for, 179–191 client_app.ml file, 190 cloning objects, 241 close_in function, 114 close_out function, 114 .cma files, 20, 408 .cmi files, 20 CMM (Capability Maturity Model), 271 .cmo files, 20, 408 Cocanwiki web application, 291 code linking options for, 356 coding rules and, 130–133 obtaining line numbers/function names and, 134 ocamllex processing and, 197–201, 222 ocamlyacc processing and, 206, 222 reuse and, 225–228, 267 code completion, 14 code files, 12, 18 collapse function, 46 collections, 89–111 combine function, 96 command-line flags, 311 command-line toplevel, 13 comments documentation extracted from, 145 importance of, 154 ocamldoc for, 146 Common Gateway Interface.
Coders: The Making of a New Tribe and the Remaking of the World by Clive Thompson
2013 Report for America's Infrastructure - American Society of Civil Engineers - 19 March 2013, 4chan, 8-hour work day, Ada Lovelace, AI winter, Airbnb, Amazon Web Services, Asperger Syndrome, augmented reality, Ayatollah Khomeini, barriers to entry, basic income, Bernie Sanders, bitcoin, blockchain, blue-collar work, Brewster Kahle, Brian Krebs, Broken windows theory, call centre, cellular automata, Chelsea Manning, clean water, cloud computing, cognitive dissonance, computer vision, Conway's Game of Life, crowdsourcing, cryptocurrency, Danny Hillis, David Heinemeier Hansson, don't be evil, don't repeat yourself, Donald Trump, dumpster diving, Edward Snowden, Elon Musk, Erik Brynjolfsson, Ernest Rutherford, Ethereum, ethereum blockchain, Firefox, Frederick Winslow Taylor, game design, glass ceiling, Golden Gate Park, Google Hangouts, Google X / Alphabet X, Grace Hopper, Guido van Rossum, Hacker Ethic, HyperCard, illegal immigration, ImageNet competition, Internet Archive, Internet of things, Jane Jacobs, John Markoff, Jony Ive, Julian Assange, Kickstarter, Larry Wall, lone genius, Lyft, Marc Andreessen, Mark Shuttleworth, Mark Zuckerberg, Menlo Park, microservices, Minecraft, move fast and break things, move fast and break things, Nate Silver, Network effects, neurotypical, Nicholas Carr, Oculus Rift, PageRank, pattern recognition, Paul Graham, paypal mafia, Peter Thiel, pink-collar, planetary scale, profit motive, ransomware, recommendation engine, Richard Stallman, ride hailing / ride sharing, Rubik’s Cube, Ruby on Rails, Sam Altman, Satoshi Nakamoto, Saturday Night Live, self-driving car, side project, Silicon Valley, Silicon Valley ideology, Silicon Valley startup, single-payer health, Skype, smart contracts, Snapchat, social software, software is eating the world, sorting algorithm, South of Market, San Francisco, speech recognition, Steve Wozniak, Steven Levy, TaskRabbit, the High Line, Travis Kalanick, Uber and Lyft, Uber for X, uber lyft, universal basic income, urban planning, Wall-E, Watson beat the top human players on Jeopardy!, WikiLeaks, women in the workforce, Y Combinator, Zimmermann PGP, éminence grise
One of the latter was Margaret Hamilton, a young MIT coder who would later become a famous programmer engineering mission-critical NASA systems, helping to land Apollo missions safely on the moon. Back in those early MIT days, she was trying to run a weather-simulation model, but it kept on crashing. Why? Eventually Hamilton learned it was because the hackers had rejiggered the computer’s assembler to suit their desires and hadn’t switched it back. They wanted to muck around with pretty cellular automata; she was trying to do weather science. But the hackers simply hadn’t appeared to think about the repercussions their tinkering had for other people. These guys were companionable with each other but mostly uninterested in talking about their own or others’ inner lives. “I spent my lifetime walking around talking like a robot, talking to a bunch of other robots,” as one of them later said with a sigh.
When you take cheap machines that can do nearly anything you tell them to and hand them over to teenagers with essentially no adult supervision—because their parents had no idea what computers were—you create the infinite-monkeys experiment of software. Soon, teenage coders were cobbling together everything and anything: chatbots that would curse and swear, spellbinding forms of artificial life known as “cellular automata,” casino games, little databases and accounting programs, computer music, and endless varieties of games. Everingham was desperate to join this scene. He was from a lower-middle-class family whose parents couldn’t afford to buy him a computer. So he began frantically picking up every spare job he could—mowing lawns, then shoveling snow when winter came—until he’d saved enough, with a contribution from his mother, to get his own VIC-20.
Future Politics: Living Together in a World Transformed by Tech by Jamie Susskind
3D printing, additive manufacturing, affirmative action, agricultural Revolution, Airbnb, airport security, Andrew Keen, artificial general intelligence, augmented reality, automated trading system, autonomous vehicles, basic income, Bertrand Russell: In Praise of Idleness, bitcoin, blockchain, brain emulation, British Empire, business process, Capital in the Twenty-First Century by Thomas Piketty, cashless society, Cass Sunstein, cellular automata, cloud computing, computer age, computer vision, continuation of politics by other means, correlation does not imply causation, crowdsourcing, cryptocurrency, digital map, distributed ledger, Donald Trump, easy for humans, difficult for computers, Edward Snowden, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, Ethereum, ethereum blockchain, Filter Bubble, future of work, Google bus, Google X / Alphabet X, Googley, industrial robot, informal economy, intangible asset, Internet of things, invention of the printing press, invention of writing, Isaac Newton, Jaron Lanier, John Markoff, Joseph Schumpeter, Kevin Kelly, knowledge economy, lifelogging, Metcalfe’s law, mittelstand, more computing power than Apollo, move fast and break things, move fast and break things, natural language processing, Network effects, new economy, night-watchman state, Oculus Rift, Panopticon Jeremy Bentham, pattern recognition, payday loans, price discrimination, price mechanism, RAND corporation, ransomware, Ray Kurzweil, Richard Stallman, ride hailing / ride sharing, road to serfdom, Robert Mercer, Satoshi Nakamoto, Second Machine Age, selection bias, self-driving car, sexual politics, sharing economy, Silicon Valley, Silicon Valley startup, Skype, smart cities, Smart Cities: Big Data, Civic Hackers, and the Quest for a New Utopia, smart contracts, Snapchat, speech recognition, Steve Jobs, Steve Wozniak, Steven Levy, technological singularity, the built environment, The Structural Transformation of the Public Sphere, The Wisdom of Crowds, Thomas L Friedman, universal basic income, urban planning, Watson beat the top human players on Jeopardy!, working-age population
One approach has been to use non-silicon materials in chips for the first time.53 Another possibility is to move from the current paradigm of ‘2D’ integrated circuits—where transistors are arranged side-by-side on a wafer of silicon—to a ‘3D’ approach where transistors are piled high.54 Another approach might be to abandon silicon altogether in favour of carbon nanotubes as the material for building even smaller, more efficient transistors.55 Yet another approach, currently taken by Google, would be to use more special-purpose computer chips for particular functions—chips that do fewer things but much faster.56 Microsoft increasingly uses a new type of chip that could combine much greater speed with flexibility.57 Looking further ahead, engineers at Google and elsewhere are already hard at work developing ‘quantum computers’ which, in certain tasks, are expected to be able to compute well beyond the capabilities of classical computers.58 Another possible alternative to OUP CORRECTED PROOF – FINAL, 26/05/18, SPi РЕЛИЗ ПОДГОТОВИЛА ГРУППА "What's News" VK.COM/WSNWS Increasingly Capable Systems 41 silicon might be to use 2D graphene-like compounds and ‘spintronic’ materials, which compute by harnessing the spin of electrons rather than moving them around.59 There’s also the growing field of neuroelectronics, which seeks to reverse-engineer the neural networks of the human brain while potentially requiring less power than silicon.60 In the still longer term, Quantum Dot Cellular Automata (‘QDCA’) technology may yield an unimaginably small semiconductor capable of doing the work of a transistor, but using much less power and wasting little energy.61 Many of these technologies are still in their infancy and nothing certain can be said about the future of Moore’s Law. But the least likely outcome is that computer science simply grinds to a halt, with hungry young Silicon Valley engineers hanging up their circuit boards and heading for retirement.
A. 389 Pokémon Go 58 political campaigning 219–20 political concepts 74–80 political hacking 180–2 political speeches 31, 360–1 political theory 80–5 conceptual analysis 81–3, 84–5 contextual analysis 84–5 future of 84–5 normative analysis 83–5 promise of 9–11 politicians Direct Democracy 240–1, 243 technocratic 251 politics definition 74 nature of 70–4 of politics 72 post-truth 230–1, 237 of prediction 172–6 task of 346 of tech firms 156–9 Popper, Ben 381 Portugal 50 post-politics 362–6 post-truth politics 230–1, 237 Potts, Amanda 422 power 3, 10, 22–3, 89, 345–6 code as 95–7, 154–5 concept 75, 76 conceptual analysis 81 definition 92 digital technology 94–8 faces of 92–3 force 100–21 and liberty 189–94 nature of 90–2 nature of politics 74 perception-control 142–52 private 153–60, 189–94 public 153–60 range of 91–2, 158 scrutiny 122–41 separation of powers 358–9 and significance 92, 158 stability of 92, 158 structural regulation 356, 357–9 supercharged state 347–8 tech firms 348–54 pragmatism 349 predictability of behaviour 127, 138–9 prediction Data Democracy 250 politics of 172–6 totalitarianism 177 predictive policing 174, 176 predictive sentencing 174, 176 preliterate societies 111–12 Preotiuc, Daniel 393 pricing mechanism 269–70, 286 Prince, Matthew 414 Princeton Review 286 printing technology 3D printing 56–7, 178, 329 4D printing 57 Gutenberg’s press 20, 62–3 prioritarians 260 Pritchard, Tom 405 Private Property Paradigm 323–7, 336 privatization of force 100, 114–19 OUP CORRECTED PROOF – FINAL, 28/05/18, SPi РЕЛИЗ ПОДГОТОВИЛА ГРУППА "What's News" VK.COM/WSNWS Index productive technologies 316–17 state ownership 329 taxation 328 profit, rights of 330–1 Promobot 55–6 property 313–41 capital 314–17 concentration of 318–22 concept 77, 78 conceptual analysis 82–3 future 327 new paradigm 327–40 Private Property Paradigm 323–7 types of 324 Wealth Cyclone 322–3 ProPublica 174 Proteus Biomedical 51 Protocols of the Elders of Zion 232 proxy votes 242 public utilities, similarity of tech firms to 157–8 Qin dynasty 131 quantum computers 40 Quantum Dot Cellular Automata (QDCA) technology 41 race/racism data-based injustice 282 neutrality fallacy 288, 289, 290 recidivism prediction 174 rule-based injustice 283, 285 Radicati Group Inc. 387 Ralph Lauren 44 ranking, digital 276–8 algorithmic injustice 289–90 ransomware 182 rateability of life 139–40, 277 rational ignorance, problem of 241 Ratner, Paul 383 Rawls, John 389, 404, 417, 419, 432 justice 257, 258, 262–3 political hacking 181 political theory 9 reality, fragmented 229–31, 237 real property 324 509 recognition, algorithms of 260, 275–8 Reddit 77 regulation of tech firms 350–1, 354–9 reinforcement learning (AI) 35 Remnick, David 367, 412 representative democracy 218, 240, 248 republican freedom 167–8, 184 and democracy 222 and private power 191 wise restraints 185 Republican Party (US) 229 reputation.com 290 reputation systems 289–90 resources, limited 365 responsibility, individual 346–7 Reuters 405 revolution concept 77, 78 Richards, Thomas 369 Rieff, David 397 right to explanation 354 usufructuary 330–1 to work 304–5, 307 Riley v.
Thinking Machines: The Inside Story of Artificial Intelligence and Our Race to Build the Future by Luke Dormehl
Ada Lovelace, agricultural Revolution, AI winter, Albert Einstein, Alexey Pajitnov wrote Tetris, algorithmic trading, Amazon Mechanical Turk, Apple II, artificial general intelligence, Automated Insights, autonomous vehicles, book scanning, borderless world, call centre, cellular automata, Claude Shannon: information theory, cloud computing, computer vision, correlation does not imply causation, crowdsourcing, drone strike, Elon Musk, Flash crash, friendly AI, game design, global village, Google X / Alphabet X, hive mind, industrial robot, information retrieval, Internet of things, iterative process, Jaron Lanier, John Markoff, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, John von Neumann, Kickstarter, Kodak vs Instagram, Law of Accelerating Returns, life extension, Loebner Prize, Marc Andreessen, Mark Zuckerberg, Menlo Park, natural language processing, Norbert Wiener, out of africa, PageRank, pattern recognition, Ray Kurzweil, recommendation engine, remote working, RFID, self-driving car, Silicon Valley, Skype, smart cities, Smart Cities: Big Data, Civic Hackers, and the Quest for a New Utopia, social intelligence, speech recognition, Stephen Hawking, Steve Jobs, Steve Wozniak, Steven Pinker, strong AI, superintelligent machines, technological singularity, The Coming Technological Singularity, The Future of Employment, Tim Cook: Apple, too big to fail, Turing machine, Turing test, Vernor Vinge, Watson beat the top human players on Jeopardy!
‘It was so incompetent when faced with any real-world problems, I didn’t feel I needed to learn about it.’ For years, however, he had been searching for what he refers to as the ‘problem of a lifetime’ to sink his teeth into. An interest in the brain made him consider everything from primate neuroanatomy and insect flight behaviour, to learning in the rat hippocampus or curing Alzheimer’s disease. For a while, Hopfield was fascinated by cellular automata and the prospect of robots that could build copies of themselves. However, after months of research, it led him to a dead end. ‘It is surprisingly difficult to give up on a wrong idea that has been nurtured for a year,’ Hopfield says. But the idea of creating a model of life inside a computer stayed with him. He was fascinated by the idea of using a network to accomplish a task which the brain does rapidly and easily, but which computers were incapable of.
Skin in the Game: Hidden Asymmetries in Daily Life by Nassim Nicholas Taleb
availability heuristic, Benoit Mandelbrot, Bernie Madoff, Black Swan, Brownian motion, Capital in the Twenty-First Century by Thomas Piketty, Cass Sunstein, cellular automata, Claude Shannon: information theory, cognitive dissonance, complexity theory, David Graeber, disintermediation, Donald Trump, Edward Thorp, equity premium, financial independence, information asymmetry, invisible hand, knowledge economy, loss aversion, mandelbrot fractal, mental accounting, microbiome, moral hazard, Murray Gell-Mann, offshore financial centre, p-value, Paul Samuelson, Ponzi scheme, price mechanism, principal–agent problem, Ralph Nader, random walk, rent-seeking, Richard Feynman, Richard Thaler, Ronald Coase, Ronald Reagan, Rory Sutherland, Silicon Valley, Steven Pinker, stochastic process, survivorship bias, The Nature of the Firm, transaction costs, urban planning, Yogi Berra
There has been a storm around work by Martin Nowack and his colleagues (which include the biologist E. O. Wilson) about the terminal flaws in the selfish gene theory.fn2 The question is: could it be that much of what we have read about the advances in behavioral sciences is nonsense? Odds are it is. Many people have been accused of racism, segregationism, and somethingism without merit. Using cellular automata, a technique similar to renormalization, the late Thomas Schelling showed a few decades ago how a neighborhood can be segregated without a single segregationist among its inhabitants. ZERO-INTELLIGENCE MARKETS The underlying structure of reality matters much more than the participants, something policymakers fail to understand. Under the right market structure, a collection of idiots produces a well-functioning market.
Our Final Invention: Artificial Intelligence and the End of the Human Era by James Barrat
AI winter, AltaVista, Amazon Web Services, artificial general intelligence, Asilomar, Automated Insights, Bayesian statistics, Bernie Madoff, Bill Joy: nanobots, brain emulation, cellular automata, Chuck Templeton: OpenTable:, cloud computing, cognitive bias, commoditize, computer vision, cuban missile crisis, Daniel Kahneman / Amos Tversky, Danny Hillis, data acquisition, don't be evil, drone strike, Extropian, finite state, Flash crash, friendly AI, friendly fire, Google Glasses, Google X / Alphabet X, Isaac Newton, Jaron Lanier, John Markoff, John von Neumann, Kevin Kelly, Law of Accelerating Returns, life extension, Loebner Prize, lone genius, mutually assured destruction, natural language processing, Nicholas Carr, optical character recognition, PageRank, pattern recognition, Peter Thiel, prisoner's dilemma, Ray Kurzweil, Rodney Brooks, Search for Extraterrestrial Intelligence, self-driving car, semantic web, Silicon Valley, Singularitarianism, Skype, smart grid, speech recognition, statistical model, stealth mode startup, stem cell, Stephen Hawking, Steve Jobs, Steve Wozniak, strong AI, Stuxnet, superintelligent machines, technological singularity, The Coming Technological Singularity, Thomas Bayes, traveling salesman, Turing machine, Turing test, Vernor Vinge, Watson beat the top human players on Jeopardy!, zero day
Humans should not stand in the way: Kristof, Nicholas D., “Robokitty,” New York Times Magazine, August 1, 1999. In fact, de Garis: De Garis, Hugo, Brain Builder Group, Evolutionary Systems Department, ATR Human Information Processing Research Laboratories, “CAM-BRAIN The Evolutionary Engineering of a Billion Neuron Artificial Brain by 2001 which Grows/Evolves at Electronic Speeds inside a Cellular Automata Machine (CAM),” last modified 1995, http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.48.8902 (accessed June 22, 2011). a system will consider stealing: Omohundro, “Foresight Vision Talk: Self-Improving AI and Designing 2030.” They are going to want: Omohundro, “The Nature of Self-Improving Artificial Intelligence.” There is a first-mover advantage: Ibid. That’s because M13 will: Steele, Bill, Cornell News, “It’s the 25th anniversary of Earth’s first (and only) attempt to phone E.T.,” last modified November 12, 1999, http://web.archive.org/web/20080802005337/http://www.news.cornell.edu/releases/Nov99/Arecibo.message.ws.html (accessed July 2, 2011).
Bad Data Handbook by Q. Ethan McCallum
Amazon Mechanical Turk, asset allocation, barriers to entry, Benoit Mandelbrot, business intelligence, cellular automata, chief data officer, Chuck Templeton: OpenTable:, cloud computing, cognitive dissonance, combinatorial explosion, commoditize, conceptual framework, database schema, DevOps, en.wikipedia.org, Firefox, Flash crash, Gini coefficient, illegal immigration, iterative process, labor-force participation, loose coupling, natural language processing, Netflix Prize, quantitative trading / quantitative ﬁnance, recommendation engine, selection bias, sentiment analysis, statistical model, supply-chain management, survivorship bias, text mining, too big to fail, web application
The Delicate Sound of a Combinatorial Explosion… We’ve introduced the problem and sketched out a rudimentary solution in just a few pages, but imagine how a real system like this might evolve over an extended period of months or even years with a team of people involved. It’s easy to see how the complexity could be overlooked or taken for granted as the nature of the problem we set out to solve. A system can start out simple, but very quickly become complex. This fact has been deeply explored in the study of complex systems and cellular automata. To see this idea in action, consider a classic technique for defining a complex graphical object by starting with two simple objects: “One begins with two shapes, an initiator and a generator…each stage of the construction begins with a broken line and consists in replacing each straight interval with a copy of the generator, reduced and displaced so as to have the same end points as those of the interval being replaced.”
The Data Revolution: Big Data, Open Data, Data Infrastructures and Their Consequences by Rob Kitchin
Bayesian statistics, business intelligence, business process, cellular automata, Celtic Tiger, cloud computing, collateralized debt obligation, conceptual framework, congestion charging, corporate governance, correlation does not imply causation, crowdsourcing, discrete time, disruptive innovation, George Gilder, Google Earth, Infrastructure as a Service, Internet Archive, Internet of things, invisible hand, knowledge economy, late capitalism, lifelogging, linked data, longitudinal study, Masdar, means of production, Nate Silver, natural language processing, openstreetmap, pattern recognition, platform as a service, recommendation engine, RFID, semantic web, sentiment analysis, slashdot, smart cities, Smart Cities: Big Data, Civic Hackers, and the Quest for a New Utopia, smart grid, smart meter, software as a service, statistical model, supply-chain management, the scientific method, The Signal and the Noise by Nate Silver, transaction costs
Bates, J. (2012) ‘“This is what modern deregulation looks like”: co-optation and contestation in the shaping of the UK’s Open Government Data Initiative’, The Journal of Community Informatics, 8(2), http://www.ci-journal.net/index.php/ciej/article/view/845/916 (last accessed 6 February 2013). Bates, J. (2013) ‘Opening up public data’, SPERI Comment, 21 May. http://speri.dept.shef.ac.uk/2013/05/21/opening-public-data/(last accessed 18 September 2013). Batty, M. (2007) Cities and Complexity: Understanding Cities with Cellular Automata, Agent Based Models, and Fractals. MIT Press, Cambridge, MA. Batty, M., Axhausen, K.W., Giannotti, F., Pozdnoukhov, A., Bazzani, A., Wachowicz, M., Ouzounis, G. and Portugali, Y. (2012) ‘Smart cities of the future’, European Physical Journal Special Topics, 214: 481–518. Baym, N.K. (2013) ‘Data not seen: the uses and shortcomings of social media metrics’, First Monday, 18(10), http://firstmonday.org/ojs/index.php/fm/article/view/4873/3752 (last accessed 3 January 2014).
Masterminds of Programming: Conversations With the Creators of Major Programming Languages by Federico Biancuzzi, Shane Warden
Benevolent Dictator For Life (BDFL), business intelligence, business process, cellular automata, cloud computing, commoditize, complexity theory, conceptual framework, continuous integration, data acquisition, domain-specific language, Douglas Hofstadter, Fellow of the Royal Society, finite state, Firefox, follow your passion, Frank Gehry, general-purpose programming language, Guido van Rossum, HyperCard, information retrieval, iterative process, John von Neumann, Larry Wall, linear programming, loose coupling, Mars Rover, millennium bug, NP-complete, Paul Graham, performance metric, Perl 6, QWERTY keyboard, RAND corporation, randomized controlled trial, Renaissance Technologies, Ruby on Rails, Sapir-Whorf hypothesis, Silicon Valley, slashdot, software as a service, software patent, sorting algorithm, Steve Jobs, traveling salesman, Turing complete, type inference, Valgrind, Von Neumann architecture, web application
So here was a promising idea, but it just didn’t quite work in the long run. I pulled some of those ideas into UML, but data flow architecture doesn’t seem to replace von Neumann architecture in most cases. So I had my shot and didn’t quite make it. There are also cellular automata. I think over half of my fellow grad students tried to build on them a highly parallel computer. That has to be the right approach, because that’s how the universe is constructed. (Or maybe not. Modern physics is stranger than fiction. The latest speculations suggest that space and time arise out of something more primitive.) But cellular automata seem suited to only certain geometric problems, very important problems to be sure, but not general-case problems. People haven’t figured out how to program them for the general case. Maybe there is no general case. The critical issue seems to be the interface between control flow and data structure.
Mathematics for Finance: An Introduction to Financial Engineering by Marek Capinski, Tomasz Zastawniak
Black-Scholes formula, Brownian motion, capital asset pricing model, cellular automata, delta neutral, discounted cash flows, discrete time, diversified portfolio, fixed income, interest rate derivative, interest rate swap, locking in a profit, London Interbank Offered Rate, margin call, martingale, quantitative trading / quantitative ﬁnance, random walk, short selling, stochastic process, time value of money, transaction costs, value at risk, Wiener process, zero-coupon bond
Springer-Verlag: Mathematica in Education and Research Vol 4 Issue 3 1995 article by Roman E Maeder, Beatrice Amrhein and Oliver Gloor ‘Illustrated Mathematics: Visualization of Mathematical Objects’ page 9 ﬁg 11, originally published as a CD ROM ‘Illustrated Mathematics’ by TELOS: ISBN 0-387-14222-3, German edition by Birkhauser: ISBN 3-7643-5100-4. Mathematica in Education and Research Vol 4 Issue 3 1995 article by Richard J Gaylord and Kazume Nishidate ‘Trafﬁc Engineering with Cellular Automata’ page 35 ﬁg 2. Mathematica in Education and Research Vol 5 Issue 2 1996 article by Michael Trott ‘The Implicitization of a Trefoil Knot’ page 14. Mathematica in Education and Research Vol 5 Issue 2 1996 article by Lee de Cola ‘Coins, Trees, Bars and Bells: Simulation of the Binomial Process’ page 19 ﬁg 3. Mathematica in Education and Research Vol 5 Issue 2 1996 article by Richard Gaylord and Kazume Nishidate ‘Contagious Spreading’ page 33 ﬁg 1.
The Cultural Logic of Computation by David Golumbia
Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, American ideology, Benoit Mandelbrot, borderless world, business process, cellular automata, citizen journalism, Claude Shannon: information theory, computer age, corporate governance, creative destruction, en.wikipedia.org, finite state, future of work, Google Earth, Howard Zinn, IBM and the Holocaust, iterative process, Jaron Lanier, jimmy wales, John von Neumann, Joseph Schumpeter, late capitalism, means of production, natural language processing, Norbert Wiener, packet switching, RAND corporation, Ray Kurzweil, RFID, Richard Stallman, semantic web, Shoshana Zuboff, Slavoj Žižek, social web, stem cell, Stephen Hawking, Steve Ballmer, Stewart Brand, strong AI, supply-chain management, supply-chain management software, Ted Nelson, telemarketer, The Wisdom of Crowds, theory of mind, Turing machine, Turing test, Vannevar Bush, web application
Norwood, NJ: Ablex Publishing. Wittgenstein, Ludwig. 1922. Tractatus Logico-Philosophicus. Trans. D. F. Pears and B. F. McGuinness. First paperback edition. New York: Routledge, 1974. ———. 1953. Philosophical Investigations. Third edition. Cambridge, MA: Blackwell, 2002. ———. 1972. On Certainty. New York: Harper & Row. ———. 1984. Culture and Value. Chicago: University of Chicago Press. Wolfram, Stephen. 1994. Cellular Automata and Complexity: Collected Papers. Boulder, CO: Westview Press. ———. 2002. A New Kind of Science. Champaign, IL: Wolfram Media. Wright, Ronald. 2005. A Short History of Progress. New York: Carroll & Graf Publishers. Ziff, Paul. 1961. Semantic Analysis. Ithaca, NY: Cornell University Press. Zittrain, Jonathan. 2008. The Future of the Internet—and How to Stop It. New Haven, CT: Yale University Press.
The Most Human Human: What Talking With Computers Teaches Us About What It Means to Be Alive by Brian Christian
4chan, Ada Lovelace, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, Bertrand Russell: In Praise of Idleness, carbon footprint, cellular automata, Claude Shannon: information theory, cognitive dissonance, commoditize, complexity theory, crowdsourcing, David Heinemeier Hansson, Donald Trump, Douglas Hofstadter, George Akerlof, Gödel, Escher, Bach, high net worth, Isaac Newton, Jacques de Vaucanson, Jaron Lanier, job automation, l'esprit de l'escalier, Loebner Prize, Menlo Park, Ray Kurzweil, RFID, Richard Feynman, Ronald Reagan, Skype, Social Responsibility of Business Is to Increase Its Profits, starchitect, statistical model, Stephen Hawking, Steve Jobs, Steven Pinker, Thales of Miletus, theory of mind, Thomas Bayes, Turing machine, Turing test, Von Neumann architecture, Watson beat the top human players on Jeopardy!, zero-sum game
You get this from the human-computer dialogues: And this from the human-human dialogues: Now if that difference isn’t night and day, I don’t know what is. Over. 1. Some equations (the Newtonian parabolas that projectiles follow, for instance) are such that you can just plug in any old future value for time and get a description of the future state of events. Other calculations (e.g., some cellular automata) contain no such shortcuts. Such processes are called “computationally irreducible.” Future time values cannot simply be “plugged in”; rather, you have to run the simulation all the way from point A to point Z, including all intermediate steps. Stephen Wolfram, in A New Kind of Science, attempts to reconcile free will and determinism by conjecturing that the workings of the human brain are “irreducible” in this way: that is, there are no Newtonian-style “laws” that allow us shortcuts to knowing in advance what people will do.
All Your Base Are Belong to Us: How Fifty Years of Video Games Conquered Pop Culture by Harold Goldberg
activist lawyer, Alexey Pajitnov wrote Tetris, Apple II, cellular automata, Columbine, Conway's Game of Life, G4S, game design, In Cold Blood by Truman Capote, Mars Rover, Mikhail Gorbachev, Ralph Waldo Emerson, Ray Oldenburg, Saturday Night Live, Silicon Valley, Steve Jobs, Steve Wozniak, The Great Good Place, Thorstein Veblen, urban planning
As well, when Maxis was still a public company, Braun had shown Wright a 1985 Activision game for the Apple II called Little Computer People. Little Computer People was occasionally hilarious and featured a slow-moving cartoonlike character called Darren who would write you letters saying, “I have many hobbies that occupy my time.” To prove it, he watched TV, exercised, and searched for someone to live in his computer with him. Finally, Wright was impressed with John Horton Conway’s theories of cellular automata, which were espoused in The Game of Life. In his 1970s simulation game, Conway showed that you could emulate the complex patterns of the birth and death of organisms living together in society—and everything in between. All these combined to influence Wright as he dreamed up a project whose working title was Home Tactics, the Experimental Domestic Simulator. Wright later tweaked the name to the slightly more appealing Dollhouse.
The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World by Pedro Domingos
Albert Einstein, Amazon Mechanical Turk, Arthur Eddington, basic income, Bayesian statistics, Benoit Mandelbrot, bioinformatics, Black Swan, Brownian motion, cellular automata, Claude Shannon: information theory, combinatorial explosion, computer vision, constrained optimization, correlation does not imply causation, creative destruction, crowdsourcing, Danny Hillis, data is the new oil, double helix, Douglas Hofstadter, Erik Brynjolfsson, experimental subject, Filter Bubble, future of work, global village, Google Glasses, Gödel, Escher, Bach, information retrieval, job automation, John Markoff, John Snow's cholera map, John von Neumann, Joseph Schumpeter, Kevin Kelly, lone genius, mandelbrot fractal, Mark Zuckerberg, Moneyball by Michael Lewis explains big data, Narrative Science, Nate Silver, natural language processing, Netflix Prize, Network effects, NP-complete, off grid, P = NP, PageRank, pattern recognition, phenotype, planetary scale, pre–internet, random walk, Ray Kurzweil, recommendation engine, Richard Feynman, scientific worldview, Second Machine Age, self-driving car, Silicon Valley, social intelligence, speech recognition, Stanford marshmallow experiment, statistical model, Stephen Hawking, Steven Levy, Steven Pinker, superintelligent machines, the scientific method, The Signal and the Noise by Nate Silver, theory of mind, Thomas Bayes, transaction costs, Turing machine, Turing test, Vernor Vinge, Watson beat the top human players on Jeopardy!, white flight, zero-sum game
AND, OR and NOT can all be implemented using NOR gates, so NOR can do everything, and in fact it’s all some microprocessors use. So why can’t it be the Master Algorithm? It’s certainly unbeatable for simplicity. Unfortunately, a NOR gate is not the Master Algorithm any more than a Lego brick is the universal toy. It can certainly be a universal building block for toys, but a pile of Legos doesn’t spontaneously assemble itself into a toy. The same applies to other simple computation schemes, like Petri nets or cellular automata. Moving on to more sophisticated alternatives, what about the queries that any good database engine can answer, or the simple algorithms in a statistical package? Aren’t those enough? These are bigger Lego bricks, but they’re still only bricks. A database engine never discovers anything new; it just tells you what it knows. Even if all the humans in a database are mortal, it doesn’t occur to it to generalize mortality to other humans.
Darwin Among the Machines by George Dyson
Ada Lovelace, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, Albert Einstein, anti-communist, British Empire, carbon-based life, cellular automata, Claude Shannon: information theory, combinatorial explosion, computer age, Danny Hillis, Donald Davies, fault tolerance, Fellow of the Royal Society, finite state, IFF: identification friend or foe, invention of the telescope, invisible hand, Isaac Newton, Jacquard loom, James Watt: steam engine, John Nash: game theory, John von Neumann, low earth orbit, Menlo Park, Nash equilibrium, Norbert Wiener, On the Economy of Machinery and Manufactures, packet switching, pattern recognition, phenotype, RAND corporation, Richard Feynman, spectrum auction, strong AI, the scientific method, The Wealth of Nations by Adam Smith, Turing machine, Von Neumann architecture, zero-sum game
., in four volumes (London: Charles Knight, 1845), 9. 39.John von Neumann, in Arthur Burks, ed., Theory of Self-Reproducing Automata (Urbana: University of Illinois Press, 1966), 47. 40.John Myhill, “The Abstract Theory of Self-Reproduction,” in Mihajlo D. Mesarovic, ed., Views on General Systems Theory, Proceedings of the Second Systems Symposium at Case Institute of Technology, 1964; reprinted in Arthur Burks, ed., Essays on Cellular Automata (Urbana: University of Illinois Press, 1970), 218. 41.John von Neumann, 1948, “The General and Logical Theory of Automata,” in Lloyd A. Jeffress, ed., Cerebral Mechanisms in Behavior: The Hixon Symposium (New York: Hafner, 1951), 31. 42.Robert Chambers, Vestiges of the Natural History of Creation (London: John Churchill, 1844), 222–223. 43.Nils Barricelli, in Paul S. Moorhead and Martin M.
Cities Are Good for You: The Genius of the Metropolis by Leo Hollis
Airbnb, banking crisis, Berlin Wall, Boris Johnson, Broken windows theory, Buckminster Fuller, call centre, car-free, carbon footprint, cellular automata, clean water, cloud computing, complexity theory, congestion charging, creative destruction, credit crunch, Credit Default Swap, crowdsourcing, Deng Xiaoping, digital map, East Village, Edward Glaeser, Enrique Peñalosa, Firefox, Frank Gehry, Geoffrey West, Santa Fe Institute, Gini coefficient, Google Earth, Guggenheim Bilbao, haute couture, Hernando de Soto, housing crisis, illegal immigration, income inequality, informal economy, Internet of things, invisible hand, Jane Jacobs, Kickstarter, knowledge economy, knowledge worker, Long Term Capital Management, M-Pesa, Mahatma Gandhi, Mark Zuckerberg, Masdar, mass immigration, megacity, negative equity, new economy, New Urbanism, Occupy movement, openstreetmap, packet switching, Panopticon Jeremy Bentham, place-making, Ray Oldenburg, Richard Florida, sharing economy, Silicon Valley, Skype, smart cities, smart grid, spice trade, Steve Jobs, technoutopianism, the built environment, The Chicago School, The Death and Life of Great American Cities, The Great Good Place, the High Line, The Spirit Level, The Wisdom of Crowds, Thomas Malthus, trade route, traveling salesman, urban planning, urban renewal, urban sprawl, walkable city, white flight, Y2K, Yom Kippur War
., Developing New Approaches for People-centred Development, Environment and Urbanisation, 2008 Arthur, C., The Thinking City, BBC Focus, January 2012 Barney, S. A. et al, The Etymologies of Isidore of Seville, CUP, 2006 Barros, J. and Sobeiera, F., City of Slums: Self Organisation Across Scales, CASA Working Paper 55, June 2002 Batty, M., Cities and Complexity: Understanding Cities with Cellular Automata, Agent-based Models and Fractals, MIT Press, 2005 Batty, M., Complexity in City Systems: Understanding, Evolution and Design, UCL Working Paper 117, March 2007 Beavan, C., No Impact Man, Piatkus, 2011 Bergdoll, B. and Martin, R., Foreclosed: Rehousing the American Dream, MoMA, 2012 Bettencourt, L. and West, G., ‘A Unified Theory of Urban Living’, Nature, 21 October 2010 Bettencourt, L., Lobo, J. et al, Growth Innovation, Scaling and the Pace of Life in Cities, PNAS, 16 April 2007 Bound, K. and Thornton, I., Our Frugal Future, NESTA, July 2012 Brand, S., Whole Earth Discipline: An Ecopragmatist Manifesto, Atlantic Books, 2010 Bratton, W. and Tumin, Z., Collaborate or Perish: Reaching Across Boundaries in a Networked World, Crown Business, 2012 Brugman, J., Welcome to the Urban Revolution: How cities are changing the world, Bloomsbury Press, 2010 Bucher, U. and Finka, M., The Electronic City, BWV, 2008 Burdett, R. and Rode, P., Cities: Towards a Green Economy, UNEP, 2011 Burnham, S., Trust Design, Part Four, ‘Public Trust’ supplement to Volume 30, 2011, www.premsela.org Burra, S., Towards a Pro-poor Framework for Slum Upgrading in Mumbai, India, Environment and Urbanisation, 2005 Burra, S., Community-based, Built and Managed Toilet Blocks in Indian Cities, Environment and Urbanisation, 2003 Burrell, J., Livelihoods and the Mobile Phone in Rural Uganda, Grameen Foundation, USA, January 2008 Burrows, E.
Tools for Thought: The History and Future of Mind-Expanding Technology by Howard Rheingold
Ada Lovelace, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, card file, cellular automata, Claude Shannon: information theory, combinatorial explosion, computer age, conceptual framework, Conway's Game of Life, Douglas Engelbart, Dynabook, experimental subject, Hacker Ethic, Howard Rheingold, interchangeable parts, invention of movable type, invention of the printing press, Jacquard loom, John von Neumann, knowledge worker, Marshall McLuhan, Menlo Park, Norbert Wiener, packet switching, pattern recognition, popular electronics, post-industrial society, RAND corporation, Robert Metcalfe, Silicon Valley, speech recognition, Steve Jobs, Steve Wozniak, Stewart Brand, Ted Nelson, telemarketer, Turing machine, Turing test, Vannevar Bush, Von Neumann architecture
The way the task was accomplished by living organisms of the type found on earth was only one way it could be done. In principle, the task could be done by a machine that could follow a plan, because the plan, and not the mechanism that carried it out, was a part of the system with the special, heretofore mysterious property that distinguished life from nonliving matter. Von Neumann approached "cellular automata" on an abstract level, just as Turing did with his first machines. As early as 1948, he showed that any self-replicating system must have raw materials, a program that provides instructions, an automaton that follows the instructions and arranges the symbols in the cells of a Turing-type machine, a system for duplicating instructions, and a supervisory unit -- which turned out to be an excellent description of the DNA direction of protein synthesis in living cells.
Machines of Loving Grace: The Quest for Common Ground Between Humans and Robots by John Markoff
"Robert Solow", A Declaration of the Independence of Cyberspace, AI winter, airport security, Apple II, artificial general intelligence, Asilomar, augmented reality, autonomous vehicles, basic income, Baxter: Rethink Robotics, Bill Duvall, bioinformatics, Brewster Kahle, Burning Man, call centre, cellular automata, Chris Urmson, Claude Shannon: information theory, Clayton Christensen, clean water, cloud computing, collective bargaining, computer age, computer vision, crowdsourcing, Danny Hillis, DARPA: Urban Challenge, data acquisition, Dean Kamen, deskilling, don't be evil, Douglas Engelbart, Douglas Engelbart, Douglas Hofstadter, Dynabook, Edward Snowden, Elon Musk, Erik Brynjolfsson, factory automation, From Mathematics to the Technologies of Life and Death, future of work, Galaxy Zoo, Google Glasses, Google X / Alphabet X, Grace Hopper, Gunnar Myrdal, Gödel, Escher, Bach, Hacker Ethic, haute couture, hive mind, hypertext link, indoor plumbing, industrial robot, information retrieval, Internet Archive, Internet of things, invention of the wheel, Jacques de Vaucanson, Jaron Lanier, Jeff Bezos, job automation, John Conway, John Markoff, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, John von Neumann, Kevin Kelly, knowledge worker, Kodak vs Instagram, labor-force participation, loose coupling, Marc Andreessen, Mark Zuckerberg, Marshall McLuhan, medical residency, Menlo Park, Mitch Kapor, Mother of all demos, natural language processing, new economy, Norbert Wiener, PageRank, pattern recognition, pre–internet, RAND corporation, Ray Kurzweil, Richard Stallman, Robert Gordon, Rodney Brooks, Sand Hill Road, Second Machine Age, self-driving car, semantic web, shareholder value, side project, Silicon Valley, Silicon Valley startup, Singularitarianism, skunkworks, Skype, social software, speech recognition, stealth mode startup, Stephen Hawking, Steve Ballmer, Steve Jobs, Steve Wozniak, Steven Levy, Stewart Brand, strong AI, superintelligent machines, technological singularity, Ted Nelson, telemarketer, telepresence, telepresence robot, Tenerife airport disaster, The Coming Technological Singularity, the medium is the message, Thorstein Veblen, Turing test, Vannevar Bush, Vernor Vinge, Watson beat the top human players on Jeopardy!, Whole Earth Catalog, William Shockley: the traitorous eight, zero-sum game
Alan Turing, for example, had written about the possibility the previous year, to receptive audiences on both sides of the Atlantic. McCarthy was thinking about intelligence as a mathematical abstraction rather than something realizable—along the lines of Turing—through building an actual machine. It was an “automaton” notion of creating human intelligence, but not of the kind of software cellular automata that von Neumann would later pursue. McCarthy focused instead on an abstract notion of intelligence that was capable of interacting with the environment. When he told von Neumann about it, the scientist exclaimed, “Write it up!” McCarthy thought about the idea a lot but never published anything. Years later he would express regret at his inaction. Although his thesis at Princeton would focus on differential equations, he also developed an interest in logic, and a major contribution to the field of artificial intelligence would later come from his application of mathematical logic to common sense reasoning.
Anathem by Neal Stephenson
anthropic principle, cellular automata, Danny Hillis, double helix, interchangeable parts, nuclear winter, orbital mechanics / astrodynamics, pattern recognition, phenotype, selection bias, Stewart Brand, trade route
“I suppose ants can flank,” I said, though I sensed that it was a trick question and that Lio was flanking me with words at this very moment. “Why not?” “By accident, of course they can! You look down on it from above and say, ‘Oh, that looked like flanking.’ But if there’s no commander to see the field and direct their movements, can they really perform coordinated maneuvers?” “That’s a little like Saunt Taunga’s Question,” I pointed out (“Can a sufficiently large field of cellular automata think?”). “Well, can they?” “I’ve seen ants work together to carry off part of my lunch, so I know they can coordinate their actions.” “But if I’m one of a hundred ants all pushing on the same raisin, I can feel the raisin moving, can’t I—so the raisin itself is a way that they communicate with one another. But, if I’m a lone ant on a battlefield—” “Thistlehead, it’s Provener.” “Okay,” he said, and turned his back on me and started walking.
The Orithenans had used a system of computational chanting that, it was plain to see, was rooted in traditions that their founders had brought over from Edhar. To that point, it was clearly recognizable to any Edharian. It was a way of carrying out computations on patterns of information by permuting a given string of notes into new melodies. The permutation was done on the fly by following certain rules, defined using the formalism of cellular automata. After the Second Sack reforms, newly computerless avout had invented this kind of music. In some concents it had withered away, in others mutated into something else, but at Edhar it had always been practiced seriously. We’d all learned it as a sort of children’s musical game. But at Orithena they had been doing new things with it, using it to solve problems. Or rather to solve a problem, the nature of which I didn’t understand yet.
The Age of Spiritual Machines: When Computers Exceed Human Intelligence by Ray Kurzweil
Ada Lovelace, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, Albert Einstein, Any sufficiently advanced technology is indistinguishable from magic, Buckminster Fuller, call centre, cellular automata, combinatorial explosion, complexity theory, computer age, computer vision, cosmological constant, cosmological principle, Danny Hillis, double helix, Douglas Hofstadter, Everything should be made as simple as possible, first square of the chessboard / second half of the chessboard, fudge factor, George Gilder, Gödel, Escher, Bach, I think there is a world market for maybe five computers, information retrieval, invention of movable type, Isaac Newton, iterative process, Jacquard loom, John Markoff, John von Neumann, Lao Tzu, Law of Accelerating Returns, mandelbrot fractal, Marshall McLuhan, Menlo Park, natural language processing, Norbert Wiener, optical character recognition, ought to be enough for anybody, pattern recognition, phenotype, Ralph Waldo Emerson, Ray Kurzweil, Richard Feynman, Robert Metcalfe, Schrödinger's Cat, Search for Extraterrestrial Intelligence, self-driving car, Silicon Valley, social intelligence, speech recognition, Steven Pinker, Stewart Brand, stochastic process, technological singularity, Ted Kaczynski, telepresence, the medium is the message, There's no reason for any individual to have a computer in his home - Ken Olsen, traveling salesman, Turing machine, Turing test, Whole Earth Review, Y2K
A Bibliography of Computer Music: A Reference for Composers. Iowa City: University of Iowa Press, 1981. Toepperwein, L. L., et al. Robotics Applications for Industry: A Practical Guide. Park Ridge: Noyes Data Corporation, 1983. Toffler, Alvin. Powershift. New York: Bantam Books, 1990. ————. The Third Wave: The Classic Study of Tomorrow. New York: Bantam Books, 1980. Toffoli, Tommaso and Norman Margolis. Cellular Automata Machines: A New Environment for Modeling. Cambridge, MA: MIT Press, 1987. Torrance, Stephen B., ed. The Mind and the Machine: Philosophical Aspects of Artificial Intelligence. Chichester, UK: Ellis Horwood, 1986. Traub, Joseph F., ed. Cohabiting with Computers. Los Altos, CA: William Kaufmann, 1985. Truesdell, L. E. The Development of Punch Card Tabulation in the Bureau of the Census, 1890-1940.
Traffic: Why We Drive the Way We Do (And What It Says About Us) by Tom Vanderbilt
Albert Einstein, autonomous vehicles, availability heuristic, Berlin Wall, call centre, cellular automata, Cesare Marchetti: Marchetti’s constant, cognitive dissonance, computer vision, congestion charging, Daniel Kahneman / Amos Tversky, DARPA: Urban Challenge, endowment effect, extreme commuting, fundamental attribution error, Google Earth, hedonic treadmill, hindsight bias, hive mind, if you build it, they will come, impulse control, income inequality, Induced demand, invisible hand, Isaac Newton, Jane Jacobs, John Nash: game theory, Kenneth Arrow, lake wobegon effect, loss aversion, megacity, Milgram experiment, Nash equilibrium, Sam Peltzman, Silicon Valley, statistical model, the built environment, The Death and Life of Great American Cities, traffic fines, ultimatum game, urban planning, urban sprawl, women in the workforce, working poor
From Boris Kerner, The Physics of Traffic: Empirical Freeway Pattern Features, Engineering Applications, and Theory (Berlin: Springer, 2004), p. 69. each car behind it will stop: One simulation compared the “oscillations” and “amplifications” found in stop-and-go traffic to those found in queues. “Perturbations” in the queue, or the way people stopped and started, were often observed to grow larger from the front to the back of the queue in simulators using cellular automata. See Bongsoo Son, Tawan Kim, and Yongjae Lee, “A Simulation Model of Congested Traffic in the Waiting Line,” Computational Science and Its Applications: ICCSA 2005, vol. 3481 (2005), pp. 863–69. the harder it is to predict: An interesting parallel has been drawn between the way nonlinear traffic flows behave and the way supply chains work in the world of business. Supply chains suffer from what has been called the “bullwhip effect”—the farther a supplier is from the consumer, the higher the potential for variability (e.g., oversupply or undersupply).
Debunking Economics - Revised, Expanded and Integrated Edition: The Naked Emperor Dethroned? by Steve Keen
"Robert Solow", accounting loophole / creative accounting, banking crisis, banks create money, barriers to entry, Benoit Mandelbrot, Big bang: deregulation of the City of London, Black Swan, Bonfire of the Vanities, business cycle, butterfly effect, capital asset pricing model, cellular automata, central bank independence, citizen journalism, clockwork universe, collective bargaining, complexity theory, correlation coefficient, creative destruction, credit crunch, David Ricardo: comparative advantage, debt deflation, diversification, double entry bookkeeping, en.wikipedia.org, Eugene Fama: efficient market hypothesis, experimental subject, Financial Instability Hypothesis, fixed income, Fractional reserve banking, full employment, Henri Poincaré, housing crisis, Hyman Minsky, income inequality, information asymmetry, invisible hand, iterative process, John von Neumann, Kickstarter, laissez-faire capitalism, liquidity trap, Long Term Capital Management, mandelbrot fractal, margin call, market bubble, market clearing, market microstructure, means of production, minimum wage unemployment, money market fund, open economy, Pareto efficiency, Paul Samuelson, place-making, Ponzi scheme, profit maximization, quantitative easing, RAND corporation, random walk, risk tolerance, risk/return, Robert Shiller, Robert Shiller, Ronald Coase, Schrödinger's Cat, scientific mainstream, seigniorage, six sigma, South Sea Bubble, stochastic process, The Great Moderation, The Wealth of Nations by Adam Smith, Thorstein Veblen, time value of money, total factor productivity, tulip mania, wage slave, zero-sum game
(1996) Prices, Cycles and Growth, Cambridge, MA: MIT Press. Oakley, A. (1983) The Making of Marx’s Critical Theory, London: Routledge and Kegan Paul. Obama, B. (2009) ‘Obama’s remarks on the economy,’ New York Times, 14 April. O’Brien, Y.-Y. J. C. (2007) ‘Reserve requirement systems in OECD countries,’ SSRN eLibrary. Oda, S. H., K. Miura, K. Ueda and Y. Baba (2000) ‘The application of cellular automata and agent models to network externalities in consumers’ theory: a generalization of life game,’ in W. A. Barnett, C. Chiarella, S. Keen, R. Marks and H. Schnabl (eds), Commerce, Complexity and Evolution, New York: Cambridge University Press. O’Hara, M. (1995) Market Microstructure Theory, Cambridge: Blackwell. Ormerod, P. (1997) The Death of Economics, 2nd edn, New York: John Wiley & Sons.
Rationality: From AI to Zombies by Eliezer Yudkowsky
Albert Einstein, Alfred Russel Wallace, anthropic principle, anti-pattern, anti-work, Arthur Eddington, artificial general intelligence, availability heuristic, Bayesian statistics, Berlin Wall, Build a better mousetrap, Cass Sunstein, cellular automata, cognitive bias, cognitive dissonance, correlation does not imply causation, cosmological constant, creative destruction, Daniel Kahneman / Amos Tversky, dematerialisation, different worldview, discovery of DNA, Douglas Hofstadter, Drosophila, effective altruism, experimental subject, Extropian, friendly AI, fundamental attribution error, Gödel, Escher, Bach, hindsight bias, index card, index fund, Isaac Newton, John Conway, John von Neumann, Long Term Capital Management, Louis Pasteur, mental accounting, meta analysis, meta-analysis, money market fund, Nash equilibrium, Necker cube, NP-complete, P = NP, pattern recognition, Paul Graham, Peter Thiel, Pierre-Simon Laplace, placebo effect, planetary scale, prediction markets, random walk, Ray Kurzweil, reversible computing, Richard Feynman, risk tolerance, Rubik’s Cube, Saturday Night Live, Schrödinger's Cat, scientific mainstream, scientific worldview, sensible shoes, Silicon Valley, Silicon Valley startup, Singularitarianism, Solar eclipse in 1919, speech recognition, statistical model, Steven Pinker, strong AI, technological singularity, The Bell Curve by Richard Herrnstein and Charles Murray, the map is not the territory, the scientific method, Turing complete, Turing machine, ultimatum game, X Prize, Y Combinator, zero-sum game
The statisticians and cryptographers and physicists and computer scientists go to work. There is structure here; it needs only to be unraveled. The masters of causality search for conditional independence, screening-off and Markov neighborhoods, among bits and groups of bits. The so-called “color” appears to play a role in neighborhoods and screening, so it’s not just the equivalent of surface reflectivity. People search for simple equations, simple cellular automata, simple decision trees, that can predict or compress the message. Physicists invent entire new theories of physics that might describe universes projected onto the grid—for it seems quite plausible that a message such as this is being sent from beyond the Matrix. After receiving 32 × 512 × 256 = 4,194,304 bits, around one and a half months, the stars stop flickering. Theoretical work continues.
What if you build your own simulated universe? The classic example of a simulated universe is Conway’s Game of Life. I do urge you to investigate Life if you’ve never played it—it’s important for comprehending the notion of “physical law.” Conway’s Life has been proven Turing-complete, so it would be possible to build a sentient being in the Life universe, although it might be rather fragile and awkward. Other cellular automata would make it simpler. Could you, by creating a simulated universe, escape the reach of God? Could you simulate a Game of Life containing sentient entities, and torture the beings therein? But if God is watching everywhere, then trying to build an unfair Life just results in the God stepping in to modify your computer’s transistors. If the physics you set up in your computer program calls for a sentient Life-entity to be endlessly tortured for no particular reason, the God will intervene.
The Transhumanist Reader by Max More, Natasha Vita-More
23andMe, Any sufficiently advanced technology is indistinguishable from magic, artificial general intelligence, augmented reality, Bill Joy: nanobots, bioinformatics, brain emulation, Buckminster Fuller, cellular automata, clean water, cloud computing, cognitive bias, cognitive dissonance, combinatorial explosion, conceptual framework, Conway's Game of Life, cosmological principle, data acquisition, discovery of DNA, Douglas Engelbart, Drosophila, en.wikipedia.org, endogenous growth, experimental subject, Extropian, fault tolerance, Flynn Effect, Francis Fukuyama: the end of history, Frank Gehry, friendly AI, game design, germ theory of disease, hypertext link, impulse control, index fund, John von Neumann, joint-stock company, Kevin Kelly, Law of Accelerating Returns, life extension, lifelogging, Louis Pasteur, Menlo Park, meta analysis, meta-analysis, moral hazard, Network effects, Norbert Wiener, pattern recognition, Pepto Bismol, phenotype, positional goods, prediction markets, presumed consent, Ray Kurzweil, reversible computing, RFID, Ronald Reagan, scientific worldview, silicon-based life, Singularitarianism, social intelligence, stem cell, stochastic process, superintelligent machines, supply-chain management, supply-chain management software, technological singularity, Ted Nelson, telepresence, telepresence robot, telerobotics, the built environment, The Coming Technological Singularity, the scientific method, The Wisdom of Crowds, transaction costs, Turing machine, Turing test, Upton Sinclair, Vernor Vinge, Von Neumann architecture, Whole Earth Review, women in the workforce, zero-sum game
Yes, as Dawkins says they are creatures naturally evolved in their physical universe and they cannot violate their physics (Dawkins 2006), but they can violate ours if they want. Make this simple experiment: Run a Game of Life program, choose an initial pattern, and let it evolve for a while. Now, stop the program, flip a cell, and resume the program. You have just performed a miracle: something that goes against the physical laws (the simple cellular automata evolution rules of Life) of the lower-level reality that you are simulating. Of course the Game of Life is too simple to contain conscious observers, but hypothetical observers within the game would observe an event that cannot be understood in terms of the physical laws of their universe. A miracle. Berkeley and Moravec are not only saying similar things, they are saying the same thing using different wording.
The system of the world by Neal Stephenson
bank run, British Empire, cellular automata, Edmond Halley, Fellow of the Royal Society, high net worth, Isaac Newton, James Watt: steam engine, joint-stock company, large denomination, MITM: man-in-the-middle, place-making, the market place, trade route, transatlantic slave trade
Many others have, knowingly or not, contributed to a milieu in which it was possible for me to consider writing something like this without seeming completely mad. And here I am tempted to list the names of a lot of mathematicians and physicists. But out of a concern for their privacy and a desire not to seem like I’m clinging to their ankles, I’ll draw a veil over those conversations. Suffice it to say that the Royal Society crowd written about in these books has many descendants and heirs today, who are capable of talking learnedly about monads, cellular automata, the calculus dispute, absolute time and space, &c. at the drop of a hat, and that it’s been my privilege to know a few of them. They seem pleasantly surprised to learn that someone actually wants to write a novel about such topics, and I in turn have been pleasantly surprised to find that they are actually willing to spend time talking to me, and out of this, quite a few good conversations have arisen over the years.