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  • Gödel, Escher, Bach - Gödel's string

    - by Brad Urani
    In the book Gödel, Escher, Bach: An Eternal Golden Braid by Douglas Hofstadter, the author gives us a representation of the precursor to Gödel's string (Gödel's string's uncle) as: ~Ea,a': (I don't have the book in front of me but I think that's right). All that remains to get Gödel's string is to plug the Gödel number for this string into the free variable a''. What I don't understand is how to get the Gödel number for the functions PROOF-PAIR and ARITHMOQUINE. I understand how to write these functions in a programming language like FlooP (from the book) and could even write them myself in C# or Java, but the scheme that Hofstadter defines for Gödel numbering only applies to TNT (which is just his own syntax for natural number theory) and I don't see any way to write a procedure in TNT since it doesn't have any loops, variable assignments etc. Am I missing the point? Perhaps Gödel's string is not something that can actually be printed, but rather a theoretical string that need not actually be defined? I thought it would be neat to write a computer program that actually prints Gödel's string, or Gödel's string encoded by Gödel numbering (yes, I realize it would have a gazillion digits) but it seems like doing so requires some kind of procedural language and a Gödel numbering system for that procedural language that isn't included in the book. Of course once you had that, you could write a program that plugs random numbers into variable "a" and run procedure PROOF-PAIR on it to test for theoromhood of Gödel's string. If you let it run for a trillion years you might find a derivation that proves Gödel's string.

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  • Getting the alternative to the 200-Line Linux Kernel patch to work

    - by Gödel
    Apparently, there is a comparable alternative to the 200-line kernel patch that involves no kernel upgrade. It is presented here and discussed here. However, I am not sure if webupd8's solution (under the section "Use it in Ubuntu") on Ubuntu actually works or not. In particular, one commenter on ./ is saying he's getting an error message. Could anyone post the "correct" method that actually works? Suggested solution: Based on the comments I've read so far, the following seems to work. (1) In /etc/rc.local, add the following lines to above exit 0: mkdir -p /dev/cgroup/cpu mount -t cgroup cgroup /dev/cgroup/cpu -o cpu mkdir -m 0777 /dev/cgroup/cpu/user echo "/usr/local/sbin/cgroup_clean" > /dev/cgroup/cpu/release_agent (2) Create a file named /usr/local/sbin/cgroup_clean with the following content: #!/bin/sh rmdir /dev/cgroup/cpu/$1 (3) In your ~/.bashrc, add: if [ "$PS1" ] ; then mkdir -m 0700 /dev/cgroup/cpu/user/$$ echo $$ > /dev/cgroup/cpu/user/$$/tasks echo "1" > /dev/cgroup/cpu/user/$$/notify_on_release fi (4) (To make sure the execution bit is on) execute sudo chmod +x /usr/local/sbin/cgroup_clean /etc/rc.local (5) Reboot.

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  • How to install my current Ubuntu based OS on to an external drive? [closed]

    - by Godel Fishbreath
    Possible Duplicate: How do I install Ubuntu to a USB key? How to copy an Ubuntu install from one laptop to another I have found urls to install ubuntu to a HD. But my current system has been upgraded and updated so often that it does not resemble anything on the web or on my drive disks. So giving my a url to how to install ubuntu will fail. Give me instead 'how to install my current Linux/Ubuntu based system (11.04) and all the upgrades to my external HD. Or alternately how to back up the OS into a bootable external HD. I am looking for either urls or a very complete explanation.

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  • How to install my currrent Ubuntu based OS on to an extenal drive?

    - by Godel Fishbreath
    I have found urls to install ubuntu to a HD. But my current system has been upgraded and updated so often that it does not resemble anything on the web or on my drive disks. So giving my a url to how to install ubuntu will fail. Give me instead 'how to install my current Linux/Ubuntu based system (11.04) and all the upgrades to my external HD. Or alternately how to back up the OS into a bootable external HD. I am looking for either urls or a very complete explanation.

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  • Theoretically bug-free programs

    - by user2443423
    I have read lot of articles which state that code can't be bug-free, and they are talking about these theorems: Halting problem Gödel's incompleteness theorem Rice's theorem Actually Rice's theorem looks like an implication of the halting problem and the halting problem is in close relationship with Gödel's incompleteness theorem. Does this imply that every program will have at least one unintended behavior? Or does it mean that it's not possible to write code to verify it? What about recursive checking? Let's assume that I have two programs. Both of them have bugs, but they don't share the same bug. What will happen if I run them concurrently? And of course most of discussions talked about Turing machines. What about linear-bounded automation (real computers)?

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  • A Guided Tour of Complexity

    - by JoshReuben
    I just re-read Complexity – A Guided Tour by Melanie Mitchell , protégé of Douglas Hofstadter ( author of “Gödel, Escher, Bach”) http://www.amazon.com/Complexity-Guided-Tour-Melanie-Mitchell/dp/0199798109/ref=sr_1_1?ie=UTF8&qid=1339744329&sr=8-1 here are some notes and links:   Evolved from Cybernetics, General Systems Theory, Synergetics some interesting transdisciplinary fields to investigate: Chaos Theory - http://en.wikipedia.org/wiki/Chaos_theory – small differences in initial conditions (such as those due to rounding errors in numerical computation) yield widely diverging outcomes for chaotic systems, rendering long-term prediction impossible. System Dynamics / Cybernetics - http://en.wikipedia.org/wiki/System_Dynamics – study of how feedback changes system behavior Network Theory - http://en.wikipedia.org/wiki/Network_theory – leverage Graph Theory to analyze symmetric  / asymmetric relations between discrete objects Algebraic Topology - http://en.wikipedia.org/wiki/Algebraic_topology – leverage abstract algebra to analyze topological spaces There are limits to deterministic systems & to computation. Chaos Theory definitely applies to training an ANN (artificial neural network) – different weights will emerge depending upon the random selection of the training set. In recursive Non-Linear systems http://en.wikipedia.org/wiki/Nonlinear_system – output is not directly inferable from input. E.g. a Logistic map: Xt+1 = R Xt(1-Xt) Different types of bifurcations, attractor states and oscillations may occur – e.g. a Lorenz Attractor http://en.wikipedia.org/wiki/Lorenz_system Feigenbaum Constants http://en.wikipedia.org/wiki/Feigenbaum_constants express ratios in a bifurcation diagram for a non-linear map – the convergent limit of R (the rate of period-doubling bifurcations) is 4.6692016 Maxwell’s Demon - http://en.wikipedia.org/wiki/Maxwell%27s_demon - the Second Law of Thermodynamics has only a statistical certainty – the universe (and thus information) tends towards entropy. While any computation can theoretically be done without expending energy, with finite memory, the act of erasing memory is permanent and increases entropy. Life & thought is a counter-example to the universe’s tendency towards entropy. Leo Szilard and later Claude Shannon came up with the Information Theory of Entropy - http://en.wikipedia.org/wiki/Entropy_(information_theory) whereby Shannon entropy quantifies the expected value of a message’s information in bits in order to determine channel capacity and leverage Coding Theory (compression analysis). Ludwig Boltzmann came up with Statistical Mechanics - http://en.wikipedia.org/wiki/Statistical_mechanics – whereby our Newtonian perception of continuous reality is a probabilistic and statistical aggregate of many discrete quantum microstates. This is relevant for Quantum Information Theory http://en.wikipedia.org/wiki/Quantum_information and the Physics of Information - http://en.wikipedia.org/wiki/Physical_information. Hilbert’s Problems http://en.wikipedia.org/wiki/Hilbert's_problems pondered whether mathematics is complete, consistent, and decidable (the Decision Problem – http://en.wikipedia.org/wiki/Entscheidungsproblem – is there always an algorithm that can determine whether a statement is true).  Godel’s Incompleteness Theorems http://en.wikipedia.org/wiki/G%C3%B6del's_incompleteness_theorems  proved that mathematics cannot be both complete and consistent (e.g. “This statement is not provable”). Turing through the use of Turing Machines (http://en.wikipedia.org/wiki/Turing_machine symbol processors that can prove mathematical statements) and Universal Turing Machines (http://en.wikipedia.org/wiki/Universal_Turing_machine Turing Machines that can emulate other any Turing Machine via accepting programs as well as data as input symbols) that computation is limited by demonstrating the Halting Problem http://en.wikipedia.org/wiki/Halting_problem (is is not possible to know when a program will complete – you cannot build an infinite loop detector). You may be used to thinking of 1 / 2 / 3 dimensional systems, but Fractal http://en.wikipedia.org/wiki/Fractal systems are defined by self-similarity & have non-integer Hausdorff Dimensions !!!  http://en.wikipedia.org/wiki/List_of_fractals_by_Hausdorff_dimension – the fractal dimension quantifies the number of copies of a self similar object at each level of detail – eg Koch Snowflake - http://en.wikipedia.org/wiki/Koch_snowflake Definitions of complexity: size, Shannon entropy, Algorithmic Information Content (http://en.wikipedia.org/wiki/Algorithmic_information_theory - size of shortest program that can generate a description of an object) Logical depth (amount of info processed), thermodynamic depth (resources required). Complexity is statistical and fractal. John Von Neumann’s other machine was the Self-Reproducing Automaton http://en.wikipedia.org/wiki/Self-replicating_machine  . Cellular Automata http://en.wikipedia.org/wiki/Cellular_automaton are alternative form of Universal Turing machine to traditional Von Neumann machines where grid cells are locally synchronized with their neighbors according to a rule. Conway’s Game of Life http://en.wikipedia.org/wiki/Conway's_Game_of_Life demonstrates various emergent constructs such as “Glider Guns” and “Spaceships”. Cellular Automatons are not practical because logical ops require a large number of cells – wasteful & inefficient. There are no compilers or general program languages available for Cellular Automatons (as far as I am aware). Random Boolean Networks http://en.wikipedia.org/wiki/Boolean_network are extensions of cellular automata where nodes are connected at random (not to spatial neighbors) and each node has its own rule –> they demonstrate the emergence of complex  & self organized behavior. Stephen Wolfram’s (creator of Mathematica, so give him the benefit of the doubt) New Kind of Science http://en.wikipedia.org/wiki/A_New_Kind_of_Science proposes the universe may be a discrete Finite State Automata http://en.wikipedia.org/wiki/Finite-state_machine whereby reality emerges from simple rules. I am 2/3 through this book. It is feasible that the universe is quantum discrete at the plank scale and that it computes itself – Digital Physics: http://en.wikipedia.org/wiki/Digital_physics – a simulated reality? Anyway, all behavior is supposedly derived from simple algorithmic rules & falls into 4 patterns: uniform , nested / cyclical, random (Rule 30 http://en.wikipedia.org/wiki/Rule_30) & mixed (Rule 110 - http://en.wikipedia.org/wiki/Rule_110 localized structures – it is this that is interesting). interaction between colliding propagating signal inputs is then information processing. Wolfram proposes the Principle of Computational Equivalence - http://mathworld.wolfram.com/PrincipleofComputationalEquivalence.html - all processes that are not obviously simple can be viewed as computations of equivalent sophistication. Meaning in information may emerge from analogy & conceptual slippages – see the CopyCat program: http://cognitrn.psych.indiana.edu/rgoldsto/courses/concepts/copycat.pdf Scale Free Networks http://en.wikipedia.org/wiki/Scale-free_network have a distribution governed by a Power Law (http://en.wikipedia.org/wiki/Power_law - much more common than Normal Distribution). They are characterized by hubs (resilience to random deletion of nodes), heterogeneity of degree values, self similarity, & small world structure. They grow via preferential attachment http://en.wikipedia.org/wiki/Preferential_attachment – tipping points triggered by positive feedback loops. 2 theories of cascading system failures in complex systems are Self-Organized Criticality http://en.wikipedia.org/wiki/Self-organized_criticality and Highly Optimized Tolerance http://en.wikipedia.org/wiki/Highly_optimized_tolerance. Computational Mechanics http://en.wikipedia.org/wiki/Computational_mechanics – use of computational methods to study phenomena governed by the principles of mechanics. This book is a great intuition pump, but does not cover the more mathematical subject of Computational Complexity Theory – http://en.wikipedia.org/wiki/Computational_complexity_theory I am currently reading this book on this subject: http://www.amazon.com/Computational-Complexity-Christos-H-Papadimitriou/dp/0201530821/ref=pd_sim_b_1   stay tuned for that review!

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  • How can I be certain that my code is flawless? [duplicate]

    - by David
    This question already has an answer here: Theoretically bug-free programs 5 answers I have just completed an exercise from my textbook which wanted me to write a program to check if a number is prime or not. I have tested it and seems to work fine, but how can I be certain that it will work for every prime number? public boolean isPrime(int n) { int divisor = 2; int limit = n-1 ; if (n == 2) { return true; } else { int mod = 0; while (divisor <= limit) { mod = n % divisor; if (mod == 0) { return false; } divisor++; } if (mod > 0) { return true; } } return false; } Note that this question is not a duplicate of Theoretically Bug Free Programs because that question asks about whether one can write bug free programs in the face of the the limitative results such as Turing's proof of the incomputability of halting, Rice's theorem and Godel's incompleteness theorems. This question asks how a program can be shown to be bug free.

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  • Best non-development book for software developers

    - by Dima Malenko
    What is the best non software development related book that you think each software developer should read? Note, there is a similar, poll-style question here: What non-programming books should programmers read? Update: Peopleware is a great book, must read, no doubt. But it is about software development so does not count. Update: We ended up suggesting more than one book and that's great! Below is summary (with links to Amazon) of the books you should consider for your reading list. The Design of Everyday Things by Donald Norman Getting Things Done by David Allen Godel, Escher, Bach by Douglas R. Hofstadter The Goal and It's Not Luck by Eliyahu M. Goldratt Here Comes Everybody by Clay Shirky ...to be continued.

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  • How do people know so much about programming?

    - by Luciano
    I see people in this forums with a lot of points, so I assume they know about a lot of different programming stuff. When I was young I knew about basic (commodore) and the turbo pascal (pc). Then in college I learnt about C, memory management, x86 set, loop invariants, graphs, db query optimization, oop, functional, lambda calculus, prolog, concurrency, polymorphism, newton method, simplex, backtracking, dynamic programming, heuristics, np completeness, LR, LALR, neural networks, static & dynamic typing, turing, godel, and more in between. Then in industry I started with Java several years ago and learnt about it, and its variety of frameworks, and also design patterns, architecture patterns, web development, server development, mobile development, tdd, bdd, uml, use cases, bug trackers, process management, people management if you are a tech lead, profiling, security concerns, etc. I started to forget what I learnt in college... And then there is the stuff I don't know yet, like python, .net, perl, JVM stuff like groovy or scala.. Of course Google is a must for rapid documentation access to know if a problem has been solved already and how, and to keep informed about new stuff by blogs and places like this one. It's just too much or I just have a bad memory.. how do you guys manage it?

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