Download portable uber turing machine

(Main prize page: The Wolfram 2,3 Turing Machine Research Prize)“And although it will no doubt be very difficult to prove, it seems likely that this Turing machine will in the end turn out to be universal.”So I wrote on page 709 of A New Kind of Science (NKS).I had searched the computational universe for the simplest possible universal Turing machine. And I had found a candidate—that my intuition told me was likely to be universal. But I was not sure.And so as part of commemorating the fifth anniversary of A New Kind of Science on May 14 this year, we announced a $25,000 prize for determining whether or not that Turing machine is in fact universal.I had no idea how long it would take before the prize was won. A month? A year? A decade? A century? Perhaps the question was even formally undecidable (say from the usual axioms of mathematics).But today I am thrilled to be able to announce that after only five months the prize is won—and we have the answer: the Turing machine is in fact universal!Alex Smith—a 20-year-old undergraduate from Birmingham, UK—has produced a 40-page proof.I’m pleased that my intuition was correct. But more importantly, we now have another piece of evidence for the very general Principle of Computational Equivalence (PCE) that I introduced in A New Kind of Science.We are also at the end of a quest that has spanned more than half a century to find the very simplest universal Turing machine.Here it is.

Involve only a finite number of “nonzero bits”—and that the Turing machine must “halt”.But the 2,3 Turing machine—like modern computers (or systems in nature)—doesn’t “halt”. And in Alex Smith’s construction the Turing machine “tape” (i.e., memory) must be filled with an infinite pattern of bits.But the key point is that the pattern of bits can be set up without doing universal computation. So that means that when we see universal computation, it’s really being done by the 2,3 machine, not somehow by the encoding we’re using.What Alex Smith needed to do to establish universality and win the prize was just to show that there’s some way of programming the 2,3 machine to do any computation. That it’s possible to make a “compiler” that compiles “code” for some known class of universal machines to code for the 2,3 machine.He did that. But his “compiler” doesn’t make terribly compact or efficient code. In fact, for anything but the simplest cases, the code tends to be astronomically large and horrendously inefficient.But that isn’t the point here. The point is one of principle: the 2,3 Turing machine is universal.No doubt it’ll be possible to find much better compilers, that make much better code.And that’ll be interesting. Perhaps one day there’ll even be practical molecular computers built from this very 2,3 Turing machine.With tapes a bit like RNA strands, and heads moving up and down like ribosomes.When we think of nanoscale computers, we usually imagine carefully engineering them to mimic the architecture of the computers

No. 1393)" (1936) by Reichsdruckerei Berlin (1879 - 1949)Museum for Communication Frankfurt, Museum Foundation Post and TelecommunicationThe encryption process involved code tables as well as the Enigma machine. At the start of every radio message, the starting position of the rotors would be sent (the message key) and the basic setting of the rotors and plugs confirmed (key identification). This information was encrypted manually using tables.Enigma I rotor cipher machine with 3 rotors, in a box made of high-density plywood (1941 - 1945) by Ertel-Werk für Feinmechanik (1921 - 1984)Museum for Communication Frankfurt, Museum Foundation Post and TelecommunicationThe Enigma machine was believed to be unbreakable because of its 100 quadrillion combinations. During the Second World War, the German armed forces had complete faith in the ingenious design of the Enigma machine—even though some of the enemy's unexplained successes should have given rise to doubt.Photograph of Henryk Zygalski, Jerzy Różycki, and Marian Rejewski, mathematicians at the University of Poznan (1932)Museum for Communication Frankfurt, Museum Foundation Post and TelecommunicationEncryption by Enigma had certain weaknesses, including that a letter was never encrypted to itself. That made it possible to guess the position of expected words in the text, and that was how Polish mathematicians were able to decrypt Enigma messages. In 1932, Henryk Zygalski, Jerzy Różycki, and Marian Rejewski succeeded in working out a formula for Enigma's encryption code and reconstructing the wiring of the rotors.Notes by Alan Turing on breaking the Enigma code (1939 - 1942) by Alan Mathison Turing (1912 - 1954)Museum for Communication Frankfurt, Museum Foundation Post and TelecommunicationBritish citizen Alan Turing (1912–54) was an outstanding mathematician and computing pioneer. From 1938, he worked for the GC&CS secret service, which had set up a decryption "factory" at Bletchley Park.Notes by Alan Turing on breaking the Enigma code (1939 - 1942) by Alan Mathison Turing (1912 - 1954)Museum for Communication Frankfurt, Museum Foundation Post and TelecommunicationAt Bletchley Park, Turing developed mathematical models for decrypting Enigma messages. His Turing Bombe automated the decryption process so that the time required was reduced to just a few hours.Enigma M4 rotor cipher machine with 4

At Go (link resides outside ibm.com).Though these models might represent breakthroughs in artificial superintelligence, they have not achieved artificial "general" intelligence, as such AI systems cannot autonomously learn new tasks or expand their problem-solving capabilities beyond their narrowly defined scope.Furthermore, it’s worth noting that superintelligence is not a prerequisite of AGI. In theory, an AI system that demonstrates consciousness and an intelligence level comparable to that of an average, unremarkable human being would represent both AGI and strong AI—but not artificial superintelligence. Existing definitions of artificial general intelligence There is no consensus among experts regarding what exactly should qualify as AGI, though plenty of definitions have been proposed throughout the history of computer science. These definitions generally focus on the abstract notion of machine intelligence, rather than the specific algorithms or machine learning models that should be used to achieve it.In 2023, a Google Deepmind paper (link resides outside ibm.com) surveyed existing academic literature and identified several categories of frameworks for defining artificial general intelligence:The Turing Test: Machines that can convincingly act like humansStrong AI: Systems possessing consciousnessAnalogies to the human brainHuman-level performance on cognitive tasksAbility to learn new tasksEconomically valuable workFlexible and general capabilities“Artificial Capable Intelligence” (ACI) The Turing Test Alan Turing, a seminal figure in the history of theoretical computer science, published one of the earliest and most influential definitions of machine intelligence in his 1950 paper, “Computer Machinery and Intelligence.” The core of his argument was that intelligence can be defined by behavior, rather than mystical philosophical qualities. Acknowledging the difficulty of pinning down firm definitions of concepts such as machines and thinking, Turing proposed a simple way around the problem based on a party game called the Imitation Game.The “Turing Test” is simple: a human observer must read text samples and determine whether they were generated by a human or by a machine. Turing proposed that if a human cannot distinguish between the program’s output and another human’s output, the program can be said to demonstrate human-like intelligence.Criticisms of the Turing Test Despite its monumental influence, computer scientists today do not consider the Turing Test to be an adequate measure of AGI. Rather than demonstrate the ability of machines to think, the test often simply highlights how easy humans are to fool.For instance, in 1966 Joseph Weizelbaum created a chatbot program called ELIZA that applied simple rules to transform the human language of a user’s input

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