A brief overview on 2 concepts.
Computing Machinery and Intelligence, Alan Turing
Interesting concepts for Alan Turing.
Alan Turing aims to refute a series of theories directed in negating the ability of a computer to think now and in the future, or even emulate thinking in a satisfactory way.
He sets up a test, as a control, later to be known as the Turing test.
The test involves 3 individuals, one acting as an interrogator, aiming at finding out which individual is an imitator.
No voice, no image and no indication is provided, as all responses are delivered standardized.
His proposal is that to consider something intelligent or thinking it must fool the interrogator. If it does so then it passes the test.
He presents some very interesting theories at the end of his paper in simulating human intelligence, by suggesting that the child brain be used as a model. To him it appears that a child’s brain is something like a notebook as one buys it from the stationer’s. It is a little mechanism, so little in fact that it can be easily programmed.
By separating the process as such, he can separate the mechanism of the brain and move forward with programming.
This is an interesting concept as it draws some very interesting anthropomorphic aspects of evolution/mutation and subsequent education to the notion of machine learning and intelligence.
One of the funnies refutiations in my mind is his refusal to even refutiate a statement like Head in the Sand.
I think overall he brings up a lot of very interesting arguments based on a test that he created specifically to determine if intelligence can be mimicked and not only that but also fool an interrogator.
Minds, Brains and Programs, Searle
Weak vs Strong AI
Weak AI is a tool to test psychological explanations.
Strong AI is literally said to understand and have other cognitive powers which become psychological explanations
Criticism of Schanks program in
1. the machine can literally understand the story and provide answers to it
2.what the machine and its program do explains the human ability to understand teh story and answer questions about it.
Answer is the Gedankenexperiment/Chinese Room experiment.
Just because one can correlate symbols to language, does not mean that the language is understood. Performing calculations on formally specified elements, does not equate understanding. The main point being that as long as the program is defined in terms of computational operations on purely formally defined elements, what the example suggests is that these by themselves have no interesting connection with understanding.
The very interesting aspect of his argument is that it passes the Turing test but is disproved based on the lack of relations between the 2 system- English and Chinese.
Another response is the Robot Reply
In fact this is much like the Gedankenexperiment but with the addition of tacit physical manifestations of expressions that aid in the perception of appearing to understand and be human. Even though these expressions exist we still do not have a mode of understanding but rather a redefined consequence to formally specified elements.
The brain simulator reply
If we mimic the synapse structure of the brain, then surely that means that we are achieving understand.
Digression on the idea of Strong AI- not necessary or important to know how the brain works, to figure out how the mind works.
The rebutall, ofcourse is wrapped up in the Chinese Room. We have the same man, but in this case he is operating a complex set of pipes mimicking the synapse structure of a Chinese Man.
The man is still following instructions without actually understanding what he is doing. Clearly following the formal structure of the brain is not indicative of achieving understanding- or assumption of intentionality.