Church/Strong Turing Thesis, Searle's Periscope, Grounding, Computation And Cognition

1. According to the Strong/Church Turing Thesis. just about anything in the world can be simulated computationally: What is the difference between a thing and its computational simulation?

2. What is Searle’s Periscope? On what does it work and on what does it not work, and why?

3. What are the important similarities and differences between computation and language?

4. What is the difference between grounding and meaning?

5. Can studying the brain explain cognition? How? Or why not?

6. Why does computationalism have a symbol grounding problem but computation does not?

What is the Church/Strong Turing thesis?

The Church/Strong Turing thesis is based on the argument that everything can be imitated to get a new and similar feature through the use of a Turing machine. Its argument is based on what makes a computational simulation mentioning that the computational simulation itself is a manipulation of the symbols that can mean something when interpreted. This means that the result of what is being simulated gives the computational simulation. Therefore, what is being simulated means it is the thing. This is because it has natural or its own existence that does not depend on simulation to exist. However, the meaning to the shape of the symbol is not relevant in understanding the simulation.

Moreover, to understand the differences between a computation simulation and a thing, it is essential first to simplify the more complex word then understand each word independently. Here, we can make it easier by understanding what computation independently means and also the term simulation. After that, we can then combine the two to and try to differentiate with the term thing. According to Turing (Magyar Pszichologiai Szemle 37) the term computation means the use of technological devices such as the computers to perform a task.  On the other hand, simulation refers to the technique of representing or imitating the real world through the use of computerized programs (Cook et al. 181). Being able to understand the two terms, now the word computational simulation refers to the use of technological devices in representing the real-world situation through simulation. On the other hand, a thing refers to the attribute, object, or any quality preferred to have its own existence. According to Harnad (Journal of Consciousness Studies 71) the existence of things does not depend on a computer algorithm as it is the case in computational simulation.

A Searle's Periscope is more concerned with the minds in which both the people and the computer understands. It is a computationalism soft underbelly. For an instant, failing of the Turing Test (TT) is not a guarantor of lacking mind or the passing is not a guarantor of having a mind (Turing 31). Its argument is however based on the rights and wrongs about the Searches Chinese Room Argument. It argues on the fatal flow in computationalism such that the computational states have no difference to the mental states. According to Harnad (philosophical and methodological issues in the quest for the thinking computer 240) the Artificial intelligence also argues on the mind thinking of all the programs with cognitive states. In this point, it is, therefore, true to say that any Chinese understanding computer program does not mean that it really understands Chinese. According to Harnad (Essays on Searle's Chinese room argument 47) this is because, for example, a person like the Searle who did not understand the language could also execute a similar program without the ability to understand the Chinese. Hence the execution of the program by the computer does not mean that it understands the Chinese.

What is Searle's Periscope?

However, the functioning of the Searle's Periscope does not work for a T3 robot but works for the T2 robots and the computational part. A T3 refers to the sensorimotor interaction where things interact through manipulation, touching and seeing. The computers are however unable to do that. T2, on the other hand, is a symbol in symbol out with an ability of computation (symbol manipulation) between them. For the T2, the activities of manipulation are therefore supported by the computer.

To be able to understand the similarities between the two, it is essential first to understand the meaning of each term and what is composed of. According to the Church-Turing Thesis, the term computation refers to the manner in which something can be manipulated to have a clear meaning especially based on the shape of the original object or thing that has been manipulated. This manipulation involves something like the simulation where a new product is achieved upon manipulating the original item. Harnad (arXiv preprint arXiv:1712.05881 601) it can also be said computation is what the mathematicians do as they try to solve and get a final and new result solution from the original problem. This solution resembles the original problem. It is however impossible when it comes to the manipulation of the functions that are non-recursive.

On the other hand, language refers to the remarkable and unique abilities that allow us to make either a true or false statement. Acording to Harnad (Kaziemierz Naturalized Epistemology Workshop (KNEW), Kaziemierz 327), this means that the reliability of the outcome whether it is true or false depends on the input. Therefore, one of the similarities between the computation and language is that they both involve some input to get an output. The quality of the computational output depends on the input similar to the language where the reliability of the output whether true or false depends on the input. The difference between the two is that in computation, manipulation can be done through simulation where Turing machines can be used while for the language, the Turing machines can be omitted to get the false or the true statement.

Grounding is more concerned with the techniques that are used in giving certain words meaning or matching the meaning of a word to its referent. The grounding involves more than the meaning, and it is the source for meaning. The main problem associated with the grounding is how to get the meaning of the words or the symbols hence the problem of what meaning itself really is. However, Harnad (Scholarpedia2.7 1033) states that according to the Chinese Room Argument, the grounding has been used to refer to an intrinsic meaning as a result of semantic interpretation. On the other hand, meaning refers to a combination of both the feelings and the symbol grounding. It moreover refers to our ability in pointing or finding the referent.  

What is grounding?

In grounding, the is one of the vital requirements as it is used in avoiding some of the semantic misunderstandings between any two people as they have to get a close enough meaning. When it comes to T2 and T3 in grounding, T3 can ground and reply questions correctly. However, when it comes to meaning, the same T3 is unable actually to have feelings or understand. Moreover, T3 allows doing of all things in grounding, but when it comes to the meaning, it has some limitations to the things it can do.

It is true to say that cognition can be explained through studying the brain. Harnad (Computation at 70, cognition at 20" 251), states that cognition refers to the way human beings think in which they understand and acquire knowledge through experience and senses. The understanding of the cognition among human beings is enhanced through the study of how the human brain think, remember and learn. The behavior of the mind is quite complicated as different people think and understand differently and, therefore, to understand how it occurs, it is vital to do some study research (Searle 421). Looking directly at how people do their daily task is not enough to explain the full cognition, and therefore it requires the brain scientist also known as the cognitive physiologist to study and provide clear information concerning the human brain thinking and understanding.

The cognitive psychologist study how people store information in their brains, how they perceive and process any information. Harnad (Computation, cognition, and Pylyshyn 245) states that to make it easy to understand the brain cognition, the scientist uses the new technology resources such as the Magnetic Resonance Imaging (MRI) which have been designed such that it can capture the picture the brain at work (Harnad 245). This enables the scientist to see the how the brain works thus helping them to understand how the brain reacts to a particular stimulus. They are also able to use the picture in understanding how brain structure can affect a person's cognitive, personality or health functioning.

When a comparison between the computationalism and the computation is made, it is found that the computationalism involves more activities as compared to the computation as an independent. Computationalism is more recognized as a strong Artificial Intelligence compared to the computation as it applies a lot of intelligence. In this computationalism, the understanding of how human brains thinks understands and stores information in computational. According to Harnad (422) the implementation of the computation does not depend on other computations hence it is independent. Also, the intelligence of the computer through the use of replies to the questions put on computers to be unable to distinguish a machine from another human being is decisive (Harnad 525). This means that the intelligence of the machine has the ability to make a decision quickly and effective hence the importance of symbol grounding.

On the other hand, the computation lacks the symbol grounding due to a lot of dependencies and less complex requirement compared to the computationalism. The Turin machine is used in computation only for less task. In computation, the machine is limited to what it can read as it only reads what is on its reader or the head. It is also tasked with writing on the tape, erasing what is on such tape and finally stopping the work. This less and independent functionality to the computation does not, therefore, allow the symbol grounding.

Reference

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Harnad S., Zahra, Jack Felag, and Josh Bongard. "Morphology dictates a robot's ability to ground crowd-proposed language." arXiv preprint arXiv:1712.05881, 2013.

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