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A system can only be considered to be Turing complete if it can do anything a universal Turing machine can. Since the universal Turing machine is said to be able to solve any computable function given time, Turing complete systems can, by extension, also do so.

To check to see if something is Turing complete, see if you can implement a Turing machine inside it. In other words, check to see if it can simulate the following:

1. The ability to read and write "variables" (or arbitrary data): Pretty much self explanatory.
2. The ability to simulate moving the read/write head: It isn't enough to just be able to retrieve and store variables. It must also be possible to simulate the ability to move the tape's head in order to reference other variables. This can often be simulated within programming languages with the usage of array data structures (or equivalent) or, in the case of certain languages such as machine code, the ability to reference other variables through the use of "pointers" (or equivalent).
3. The ability to simulate a finite state machine: Although not mentioned often, Turing machines are actually a variation of the finite state machines often used within AI development. Alan Turing said the purpose of the states is to simulate a person's "various modes of problem solving".
4. A "halt" state: Although it's often mentioned a set of rules must be able to repeat itself in order to count as being Turing complete, that isn't really a good criteria since the formal definition of what an algorithm is state algorithms must always eventually conclude. If they can't conclude in some way, either it isn't Turing complete, or said algorithm isn't a computable function. Turing complete systems that technically can't conclude due to the way they work (like game consoles, for example) get around this limitation by being able to "simulate" a halting state within some fashion. Not to be confused with the "halting problem", an undecidable function that proves it's impossible to build a system that could detect with 100% reliability if a given input will cause another system to not conclude.

These are the true minimum requirements for a system to be considered Turing complete. Nothing more, nothing less. If it can't simulate any of these in some fashion, it's not Turing complete. The methods other people proposed are only a means to the end as there's several Turing complete systems that doesn't have those features.

Note that there's no known way to actually build a true Turing complete system. This is because there's no known way to genuinely simulate the limitlessness of the Turing machine's tape within physical space.

A system can only be considered to be Turing complete if it can do anything a universal Turing machine can. Since the universal Turing machine is said to be able to solve any computable function given time, Turing complete systems can, by extension, also do so.

To check to see if something is Turing complete, see if you can implement a Turing machine inside it. In other words, check to see if it can simulate the following:

1. The ability to read and write "variables" (or arbitrary data): Pretty much self explanatory.
2. The ability to simulate moving the read/write head: It isn't enough to just be able to retrieve and store variables. It must also be possible to simulate the ability to move the tape's head in order to reference other variables. This can often be simulated within programming languages with the usage of array data structures (or equivalent) or, in the case of certain languages such as machine code, the ability to reference other variables through the use of "pointers" (or equivalent).
3. The ability to simulate a finite state machine: Although not mentioned often, Turing machines are actually a variation of the finite state machines often used within AI development. Alan Turing said the purpose of the states is to simulate a person's "various modes of problem solving".
4. A "halt" state: Although it's often mentioned a set of rules must be able to repeat itself in order to count as being Turing complete, that isn't really a good criteria since the formal definition of what an algorithm is state algorithms must always eventually conclude. If they can't conclude in some way, either it isn't Turing complete, or said algorithm isn't a computable function. Turing complete systems that technically can't conclude due to the way they work (like game consoles, for example) get around this limitation by being able to "simulate" a halting state in some fashion. Not to be confused with the "halting problem", an undecidable function that proves it's impossible to build a system that could detect with 100% reliability if a given input will cause another system to not conclude.

These are the true minimum requirements for a system to be considered Turing complete. Nothing more, nothing less. If it can't simulate any of these in some fashion, it's not Turing complete. The methods other people proposed are only means to the end as there's several Turing complete systems that doesn't have those features.

Note that there's no known way to actually build a true Turing complete system. This is because there's no known way to genuinely simulate the limitlessness of the Turing machine's tape within physical space.

A system can only be considered to be Turing complete if it can do anything a universal Turing machine can. Since the universal Turing machine is said to be able to solve any computable function given time, Turing complete systems can, by extension, also do so.

To check to see if something is Turing complete, see if you can implement a Turing machine inside it. In other words, check to see if it can simulate the following:

1. The ability to read and write "variables" (or arbitrary data): Pretty much self explanatory.
2. The ability to simulate moving the read/write head: It isn't enough to just be able to retrieve and store variables. It must also be possible to simulate the ability to move the tape's head in order to reference other variables. This can often be simulated within programming languages with the usage of array data structures (or equivalent) or, in the case of certain languages such as machine code, the ability to reference other variables through the use of "pointers" (or equivalent).
3. The ability to simulate a finite state machine: Although not mentioned often, Turing machines are actually a variation of the finite state machines often used within AI development. Alan Turing said the purpose of the states is to simulate a person's "various modes of problem solving".
4. A "halt" state: Although it's often mentioned a set of rules must be able to repeat itself in order to count as being Turing complete, that isn't really a good criteria since the formal definition of what an algorithm state algorithms must always eventually conclude. If they can't conclude in some way, either it isn't Turing complete, or said algorithm isn't a computable function. Turing complete systems that technically can't conclude due to the way they work (like game consoles, for example) get around this limitation by being able to "simulate" a halting state within some fashion. Not to be confused with the "halting problem", an undecidable function that proves it's impossible to build a system that could detect with 100% reliability if a given input will cause another system to not conclude.

These are the true minimum requirements for a system to be considered Turing complete. Nothing more, nothing less. If it can't simulate any of these in some fashion, it's not Turing complete. The methods other people proposed are only a means to the end as there's several Turing complete systems that doesn't have those features.

Note that there's no known way to actually build a true Turing complete system. This is because there's no known way to genuinely simulate the limitlessness of the Turing machine's tape within physical space.

A system can only be considered to be Turing complete if it can do anything a universal Turing machine can. Since the universal Turing machine is said to be able to solve any computable function given time, Turing complete systems can, by extension, also do so.

To check to see if something is Turing complete, see if you can implement a Turing machine inside it. In other words, check to see if it can simulate the following:

1. The ability to read and write "variables" (or arbitrary data): Pretty much self explanatory.
2. The ability to simulate moving the read/write head: It isn't enough to just be able to retrieve and store variables. It must also be possible to simulate the ability to move the tape's head in order to reference other variables. This can often be simulated within programming languages with the usage of array data structures (or equivalent) or, in the case of certain languages such as machine code, the ability to reference other variables through the use of "pointers" (or equivalent).
3. The ability to simulate a finite state machine: Although not mentioned often, Turing machines are actually a variation of the finite state machines often used within AI development. Alan Turing said the purpose of the states is to simulate a person's "various modes of problem solving".
4. A "halt" state: Although it's often mentioned a set of rules must be able to repeat itself in order to count as being Turing complete, that isn't really a good criteria since the formal definition of what an algorithm is state algorithms must always eventually conclude. If they can't conclude in some way, either it isn't Turing complete, or said algorithm isn't a computable function. Turing complete systems that technically can't conclude due to the way they work (like game consoles, for example) get around this limitation by being able to "simulate" a halting state within some fashion. Not to be confused with the "halting problem", an undecidable function that proves it's impossible to build a system that could detect with 100% reliability if a given input will cause another system to not conclude.

These are the true minimum requirements for a system to be considered Turing complete. Nothing more, nothing less. If it can't simulate any of these in some fashion, it's not Turing complete. The methods other people proposed are only a means to the end as there's several Turing complete systems that doesn't have those features.

Note that there's no known way to actually build a true Turing complete system. This is because there's no known way to genuinely simulate the limitlessness of the Turing machine's tape within physical space.

A system can only be considered to be Turing complete if it can do anything a universal Turing machine can. Since the universal Turing machine is said to be able to solve any computable function given time, Turing complete systems can, by extension, also do so.

To check to see if something is Turing complete, see if you can implement a Turing machine inside it. In other words, check to see if it can simulate the following:

1. The ability to read and write "variables" (or arbitrary data): Pretty much self explanatory.
2. The ability to simulate moving the read/write head: It isn't enough to just be able to retrieve and store variables. It must also be possible to simulate the ability to move the tape's head in order to reference other variables. This can often be simulated within programming languages with the usage of array data structures (or equivalent) or, in the case of certain languages such as machine code, the ability to reference other variables through the use of "pointers" (or equivalent).
3. The ability to simulate a finite state machine: Although not mentioned often, Turing machines are actually a variation of the finite state machines often used within AI development. Alan Turing said the purpose of the states is to simulate a person's "various modes of problem solving".
4. A "halt" state: Although it's often mentioned a set of rules must be able to repeat itself in order to count as being Turing complete, that isn't really a good criteria since the formal definition of what an algorithm state algorithms must always eventually conclude. If they can't conclude in some way, either it isn't Turing complete, or said algorithm isn't a computable function. Turing complete systems that technically can't conclude due to the way they work (like old gaming consoles, for example) get around this limitation by being able to "simulate" a halting state within some fashion. Not to be confused with the "halting problem", an undecidable function that proves it's impossible to build a system that could detect with 100% reliability if a given input will cause another system to not conclude.

These are the true minimum requirements for a system to be considered Turing complete. Nothing more, nothing less. If it can't simulate any of these in some fashion, it's not Turing complete. The methods other people proposed are only a means to the end as there's several Turing complete systems that doesn't have those features.

Note that there's no known way to actually build a true Turing complete system. This is because there's no known way to genuinely simulate the limitlessness of the Turing machine's tape within physical space.

A system can only be considered to be Turing complete if it can do anything a universal Turing machine can. Since the universal Turing machine is said to be able to solve any computable function given time, Turing complete systems can, by extension, also do so.

To check to see if something is Turing complete, see if you can implement a Turing machine inside it. In other words, check to see if it can simulate the following:

1. The ability to read and write "variables" (or arbitrary data): Pretty much self explanatory.
2. The ability to simulate moving the read/write head: It isn't enough to just be able to retrieve and store variables. It must also be possible to simulate the ability to move the tape's head in order to reference other variables. This can often be simulated within programming languages with the usage of array data structures (or equivalent) or, in the case of certain languages such as machine code, the ability to reference other variables through the use of "pointers" (or equivalent).
3. The ability to simulate a finite state machine: Although not mentioned often, Turing machines are actually a variation of the finite state machines often used within AI development. Alan Turing said the purpose of the states is to simulate a person's "various modes of problem solving".
4. A "halt" state: Although it's often mentioned a set of rules must be able to repeat itself in order to count as being Turing complete, that isn't really a good criteria since the formal definition of what an algorithm state algorithms must always eventually conclude. If they can't conclude in some way, either it isn't Turing complete, or said algorithm isn't a computable function. Turing complete systems that technically can't conclude due to the way they work (like game consoles, for example) get around this limitation by being able to "simulate" a halting state within some fashion. Not to be confused with the "halting problem", an undecidable function that proves it's impossible to build a system that could detect with 100% reliability if a given input will cause another system to not conclude.

These are the true minimum requirements for a system to be considered Turing complete. Nothing more, nothing less. If it can't simulate any of these in some fashion, it's not Turing complete. The methods other people proposed are only a means to the end as there's several Turing complete systems that doesn't have those features.

Note that there's no known way to actually build a true Turing complete system. This is because there's no known way to genuinely simulate the limitlessness of the Turing machine's tape within physical space.