Turing complete blockchain
But We are all positive now, but I think that some smart people should also think in that direction. Like we do in business to stress test the system.
Keep with the good work ivanli. Ethereum Turing Complete, Bitcoin is not Programmer explains. Authors get paid when people like you upvote their post.
Thank you very much, glad you like it! Nice to hear friend, glad you like it, see you around here: Hi Ivan, Joined your channel 2 weeks ago on youtube, glad to see you're on steemit. Keep up the great work and keep riding the ether dragon! Thanks, glad you like it!: Something tells me that this can be very interesting: Wow, very interesting, didn't know, going to take a look, thanks!!
Very interesting, where can I read it? Real computers constructed so far can be functionally analyzed like a single-tape Turing machine the "tape" corresponding to their memory ; thus the associated mathematics can apply by abstracting their operation far enough. However, real computers have limited physical resources, so they are only linear bounded automaton complete. In contrast, a universal computer is defined as a device with a Turing complete instruction set, infinite memory, and infinite available time.
In computability theory , several closely related terms are used to describe the computational power of a computational system such as an abstract machine or programming language:.
Turing completeness is significant in that every real-world design for a computing device can be simulated by a universal Turing machine. This says nothing about the effort needed to write the program , or the time it may take for the machine to perform the calculation, or any abilities the machine may possess that have nothing to do with computation.
Charles Babbage 's analytical engine s would have been the first Turing-complete machine if it had been built at the time it was designed. Babbage appreciated that the machine was capable of great feats of calculation, including primitive logical reasoning, but he did not appreciate that no other machine could do better. From the s until the s, mechanical calculating machines such as adders and multipliers were built and improved, but they could not perform a conditional branch and therefore were not Turing complete.
In the late 19th century, Leopold Kronecker formulated notions of computability, defining primitive recursive functions. These functions can be calculated by rote computation, but they are not enough to make a universal computer, because the instructions which compute them do not allow for an infinite loop.
In the early 20th century, David Hilbert led a program to axiomatize all of mathematics with precise axioms and precise logical rules of deduction which could be performed by a machine. Soon, it became clear that a small set of deduction rules are enough to produce the consequences of any set of axioms. This theorem showed that axiom systems were limited when reasoning about the computation which deduces their theorems. Church and Turing independently demonstrated that Hilbert's Entscheidungsproblem decision problem was unsolvable, [1] thus identifying the computational core of the incompleteness theorem.
In Konrad Zuse completed the Z3 computer , the first working Turing-complete machine; this was the first digital computer in the modern sense.
Computability theory characterizes problems as having, or not having, computational solutions. The first result of computability theory is that there exist problems for which it is impossible to predict what a Turing-complete system will do over an arbitrarily long time.
The classic example is the halting problem: It is trivial to create an algorithm that can do this for some inputs, but impossible to do this in general. For any characteristic of the program's eventual output, it is impossible to determine whether this characteristic will hold.
This impossibility poses problems when analyzing real-world computer programs. For example, one cannot write a tool that entirely protects programmers from writing infinite loops, or protects users from supplying input that would cause infinite loops. One can instead limit a program to executing only for a fixed period of time timeout , or limit the power of flow control instructions for example, providing only loops that iterate over the items of an existing array.
However, another theorem shows that there are problems solvable by Turing-complete languages that cannot be solved by any language with only finite looping abilities i. So any such language is not Turing complete. For example, a language in which programs are guaranteed to complete and halt cannot compute the computable function which is produced by Cantor's diagonal argument on all computable functions in that language. A computer with access to an infinite tape of data may be more powerful than a Turing machine: Such an infinite tape of data is called a Turing oracle.
Even a Turing oracle with random data is not computable with probability 1 , since there are only countably many computations but uncountably many oracles. So a computer with a random Turing oracle can compute things that a Turing machine cannot.
All known laws of physics have consequences that are computable by a series of approximations on a digital computer. A hypothesis called digital physics states that this is no accident, because the universe itself is computable on a universal Turing machine.
This would imply that no computer more powerful than a universal Turing machine can be built physically. The computational systems algebras, calculi that are discussed as Turing complete systems are those intended for studying theoretical computer science. They are intended to be as simple as possible, so that it would be easier to understand the limits of computation.
Here are a few:. Most programming languages , conventional and unconventional, are Turing-complete. Rewrite systems are also Turing-complete.
Turing completeness is an abstract statement of ability, rather than a prescription of specific language features used to implement that ability. The features used to achieve Turing completeness can be quite different; Fortran systems would use loop constructs or possibly even goto statements to achieve repetition; Haskell and Prolog, lacking looping almost entirely, would use recursion. Most programming languages are describing computations on von Neumann architectures, which have memory RAM and register and a control unit.
These two elements make this architecture Turing-complete. Even pure functional languages are Turing-complete. Turing completeness in declarative SQL is implemented through recursive common table expressions. This illustrates one reason why relatively powerful non-Turing-complete languages are rare: The untyped lambda calculus is Turing-complete, but many typed lambda calculi, including System F , are not.
The value of typed systems is based in their ability to represent most typical computer programs while detecting more errors. Rule and Conway's Game of Life , both cellular automata , are Turing complete. Many computational languages exist that are not Turing complete.