 Hello everybody, today I am going to discuss about artificial intelligence and the functionality theory of mind, machine intelligence, competency and creativity mind in artificial intelligence, strong artificial intelligence and weak artificial intelligence, strong AI is reductionism and weak AI is non reductionism. Let us see first, what is the relationship between artificial intelligence and the functionality theory of mind, although I have already explained about the concept of artificial intelligence and the functionality theory of mind, but here I will be drawing or discussing some of the main issues, the relationship between artificial intelligence and the functionality theory of mind, whether they are going together or not, that I am going to discuss here. As we know that functionalism arose as a result of the phenomena rise of interest in computing machines and artificial intelligence. All the functionalists says that the mental processes are computational processes realized in a machine. Functionalism is a theory that explains mental phenomena in terms of the external input and the observable outputs, it explains the mind as a machines. This I have already explained in the lectures on functionalism, but here we have to see how this functionalistic model of mind is very much related to the AI model of mind. Let us see first the functionalism and how it is explaining the concept of mind. Functionalism as a theory of mind is supported by various scientific theories like those of artificial intelligence, cognitive science and neuroscience. We need to see cognitive psychology and many other theses which are related to mechanistic model of mind, but artificial intelligence advocates a computational theory of mind which argues in favor of the functional similarity between the computations state of the artificial system and neuro physiological state of the brain. The hypothesis of artificial intelligence that machine can think become very popular after two rings are taking on computing machinery and intelligence. And this two ring hypothesis I have already explained extensively while I was explaining the concept of AI, but here I will be introducing some of the things how this two ring hypothesis is also part of the functionalistic model of mind, because two ring hypothesis hypothesis is that machines thinks intelligently like human beings, but as we know that Patnam is one of the thinkers or one of the propellant of AI model of mind as well as the functionalistic thesis of mind for him. Probabilistic automations has been generalized to allow for sensory inputs and motor outputs that is the machine table specifies for every possible combination of state and the complete state of sensory inputs and instruction which determines the probability of the next state and also probabilities of the motor outputs. There are some steps which explain how a machine functions in general whenever the AI scientist or even if a functionalistic model of mind is explaining mind. They are talking about different states of the functions, different states even if any functions, any kind of systems there are three states input states, output states and hidden states are there and you give some inputs and hidden units is there and output states. You can see the output states, you can see even if the input states, but you cannot see the hidden units, but in the case of any kind of all the states its function is to follow some kind of procedures and that is the sequence of states and that is one kind of procedure and in that sequence of states there are description rules are there, they have to follow some kind of rules and that is rational of their entire procedure. The computing machine therefore, is a system constructed out of different subsystems that function inside it to process the inputs and the and to produce the output once the input is simulated in the machine. It tries to match the simulating state with the states already computed and mapped in the system. This mapping order follows certain syntax or rules, the syntax is responsible for the correlation of the total cognitive states. Thus the entire process of stimulation can be called an intelligent process. This simulation process take place between the function of the two functional isoporamic systems. This already I have explained while I was explaining different models of cognitions from the cognitive science perspective and this model has been proposed by Hilary Putnam. This model says that there is a functional isomorphic according to Putnam between the brain, mind and a machine. This functional isoporamism due to the causal capacity of the functional state of the machine. For example, when I have a pain there is a neurophysiological process corresponding to the mental state because of the firing of the C fiber. The brain mind identity follows as there is a functional identity between the two. Even if there is a earlier we were talking about the identity between brain and body, brain and mind, then now we are talking about the identity between mind and machines and this kind of identity is there according to functionistic model of mind. But identity between the mental states and the physical processes of the brain is established from the functional point of view. That is in the function terms the brain state isoporamity with the mental state. There is an identity between software that constitute the program and the hardware of the machines which helps the software to be realized in the machines. But if we see in the case of Daniel C. Dennett has suggested that a multiple draft model approach to the nature of mind and which is one of the functionalistic model of mind also. This model says that there is a similarity between the function of the human mind and those of the computer. The brain system functions in relation to different subsystems. So, there are multiple drafts which operate within an artificial system. Such analysis is beneficial because it analyzes consciousness from the point of view of language processing. This is given important in the sense that a linguistic or a language speaking being is considered not only as a conscious being but also a rational being. Even the robot as a information processing systems can also be characterized as intelligent systems for Dennett. But Dennett says that we are also like machines. We human beings are like machines. We are just very very complicated evolved machines made of organized molecules instead of metal and silicon and we are conscious. So, there can be a conscious machines like us and the way Dennett is arguing that there is no distinction between mind and machines. Therefore, all the mental activities can be explainable in terms of machines. So, the human thought processes and the language processing in the artificial systems are analogous to each other. In the case of the conscious thought process we are aware of our thoughts. At the same time there is psychochemical processes which goes on in our brain. But Dennett's functional analysis of consciousness is divided into two parts. There are the sub personal view of consciousness and the multiple model of consciousness respectively. Because in the case of the sub personal model explains consciousness and the other activities through the help of neuro logical states and processes of the organism. Whereas, the multiple that model discusses how one artificial system behaves intelligently. Therefore, Dennett's provides or he is providing a functional explanation of consciousness at the sub personal level. But in this sub personal level of explanation of consciousness tries to explain not only how the human beings how the human beings are system of organism, but also how the system is being constituted and how the various function involved in different physiological parts of the organism functions together. And the that functional structure would help us in defining the capacity involved in causing consciousness or according to Dennett he says that what we call as conscious behavior. A state of consciousness is simply one which exhibit a certain characteristics patterns of causal relations to the other states both and to the other states. Those other states are both mental and physical. We know that human beings perform various activities. They are language acquire various knowledge states, belief states. There are changes in their belief states and soon many kind of belief states are there in the case of human. And according to Dennett for many thinkers all these activities are very much independent of the biological activities of human life. Dennett anticipates that there would be a system whose programs would be such that it would be self dependent in all its functions that would be able to replace or stand parallel to human intelligence. Again even if in a kind of functional system which helps in explaining the various mysterious features that that are ascribed to the human life as I have already explained in the concept of artificial intelligence model of mind. The main aim of artificial intelligence is not only to develop different kind of program to help our day to day life, but also it helps understanding of the human mind. Therefore, the strong notion of functionalism at work is the identity between the mental state and the brain processes. It also explains the different basic features of the human being such as consciousness, intentionality, subjectivity and many other mental activities which we can ascribe to only mind or to a conscious subject and which belongs to a self not to a any other being. And by bringing these features the functional isomeric way into account according to functionalistic model this can be bring into a isomeric way account. And this functionalistic model holds that the mental states are abstract functionalistic characterized slowly in terms of their causal relationship to each other, to input and to output. Human purposive behavior is then explained in terms of how this hypothesized system of states take the organism from central inputs to behavior output. Because functions insist upon a network of mental states, it insist upon the whole of the mental upon the way in which mental states operate to get to explain behavior. And it accepts structure of mental state in which each is necessarily connected with other. The mental state do not function in isolations, rather they function within the causal core relationship with other mental states. The function of mental states also takes into account the effect of the environment in which the subject or agent is placed with the system that must be well equipped to receive the input from the environment and to produce the output. The functions program has been strongly influenced by analogies drawn from computer science and artificial intelligence, both in its general outlook and in several of its specific applications to problem about the nature of mind. Because a functionalist state like a computational state of a computer, a computer program can be described as a functional organization of the hardware. As already discussed the functionality argues that mental states are like the intervention processing state of a computer according for functionalism. Our artificial intelligence, the brain is a computer and the mind is the computer program implemented in the brain. Thus artificial intelligence is a strong founded on a functionalistic conception of mind. It is dependent on the idea that human functions like a digital computer with multi functions computational abilities. Therefore, in this way this functionalistic model of mind is very much related to artificial intelligence model of mind. Now, we have to see machine intelligence competency and creativity, although there is something called machine intelligence or machine competency and machine creativity. Can we say a machine is a creative or a machine is something which can do better activity than human beings, but the way computer science or artificial intelligence is doing the activity in the day to day life which is 1000 better than any human beings. Then the question is can we ascribe competency, creativity to machines. As discussed machine intelligence or artificial intelligence is a symbol system is a physical symbol system which has the necessary sufficient means for general intelligent actions. The above statement shows that machine intelligence has the capacity of competency and creativity. However, the system that exhibit general intelligence will prove upon analysis to be a physical symbol system. The physical symbol system of any kind of physical system system has a sufficient size can be organized further to exhibit this general intelligent actions. The general intelligent actions the same scope of intelligence as we have seen in the human actions. Therefore, the symbol system hypothesis implies that the intelligence will be realized by a universal computer. It also asserts that the intelligent machine is a symbol system. When machine acquired enough memory to make it practicable to locate actual symbol system with the help of a program that produce intelligent behavior and solve many problems. Many machine intelligent scientist use computer as tools to help them create things they could not have created. For example, many scientist use sounds which no tester can produce. A visual artist may get ideas from a computer graphics. We are concerned with those of programs which produce aesthetically interesting creations. There are number of programs which explore artistically interesting spaces and a few which produce aesthetically acceptable results. Borden is one of the most important philosopher in the area of philosophy of mind. Borden has given one kind of one kind of famous example and he has given the example of a story which is written by Harlott Cohen and Cohen has written a series of programs which produce pleasing and unpredictable lines drawing. Cohen's programs explores a certain style of line drawing a certain subject matters. As human artist have to know about the things they are depositing. So, each of Cohen's program need an internal mode of its subject matter. This model is not a physical object. It is a set of a abstract rules which specify not only the anatomy of the human body, but also how the various body parts appear from point of view. The machine intelligence programs that write stories are way full inadequate compared with the human story teller, but the best of them get first strength the process from their internal models of very general aspect of motivation. For example, a program has to write a story with the moral never trust flatters. In this story there are two characters one is the fox and crow. The story follows like this. Once upon a time there are a dishonest fox named Henry who lived in a cave and a van and trusting crow named Joe who lived in an elementary. Joe had gotten a piece of cheese and was holding it in his mouth. One day Henry worked from his cave across the middle to the elementary. He saw Joe crow and the cheese and become hungry. He decided that he might get the cheese if Joe crow spoke. So, he told Joe that he liked his singing very much and wanted to hear him that song. Kindly sing for me. Joe was very pleased with Henry and began to sing. The cheese fell out of his mouth down to the ground. Henry picked up the cheese and told Joe crow that he was stupid. Joe was angry and did not trust Henry anymore. Henry returned to his cave and the story end and here there is a kind of trustness is not there in the case of Joe and Henry. But this kind of story program can construct hierarchical plans ascribe them to the individual characters and according to the sort of motivations one would expect them to have. It can give one character a role in another plans but these roles need not be allocated randomly but can depend on background interpersonal relations and it can represent different sort of communications between the characters which constrain what follows in different ways. All these matters are represented as abstract schemata which are used to produce the story structure. Thus the above programs shows that machines have the capacity to produce creativity and the question may be raised as whether programs have scientific discovery, programs designed can find a simple mathematical and a classificator relations as rediscovered with the help of physical and chemical laws and net talk is another famous and important of machine creativity and competencies also. As we know this net talk or this model's goal is successful to negotiate the problem domain of text to speech transformations. Net talk took seven later segments of text as inputs and mapped the target window of the of data input onto an output which coded for phonemes. A connection system is not trained but programmed let us raise a question what does net talk know according to clerk net talk does not any sense understand what it is saying but this is not the point likewise I might learn roughly how to pronounce Chinese sequences without understanding them. Nevertheless net talk has gone from bubble to an output which is lawfully disciplined with respect to its input that strongly suggest that it has learned something. The question is that the automatic mathematics is a system that does not produce proofs nor produce mathematical problems rather it generates and explains mathematical ideas. Artificial machines starts with very primitive and artificial intelligence mathematical concepts draws from a set theory including sets, lists, equality and operations. These concepts are so basic that they do not even include the ideas of eliminating arithmetic to begin with the program does not know what an integer is till less additions, subtractions, multiplications and divisions. Therefore, artificial machines hunches like human hunches are sometimes wrong nevertheless it has come up with some extremely powerful notions it produce many mathematical concepts including integer, square root, addition and multiplications. It generates the fundamental theorem of number theory through though did not prove it is suggest that interesting ideas that every event number greater than two is the sum of two different primes. It has originated one major theorem which no one had ever thought of before here artificial machines appears to be p creativity or psychological creativity or psychological creative. According to Borden there are two sense of creativity one is psychological creativity or p creativity and the other is historical creativity or h creativity. This p creativity and h creativity I will be explaining in the next lectures. Now, let us see how this p creativity and h creativity is related to machine creativity and the idea is that p creativity if the person an idea is p creative if the person in whose mind it arise could not have had it before. It does not matter how many times other people have already had the same idea. On the other hand an idea is h creativity if it is p creative and no one else have has ever had it before. Those artificial machines creative is p creative or psychological creativity and another example in the case of copy cat program which can generate many different analogies where contextual actually appropriate comparisons are favored over in appropriate ones. It does not rely on the ready made fixed representations, but consist its own representation in a context sensitive way. Its new analogies and new perceptions develops together. As you have seen cognitive science tries to provide computational models of the mind that is computational simulation of human cognitive processes. If creativity is not a computational process it might still be possible to simulate it computationally. Just as it is possible to simulate the digestive process without the simulation itself being a digestive process it might be possible to have machine models of human creative processes even if machine themselves cannot be creative. But there are many scientist even in many philosophers they say that machine cannot be creative because there is a significant sense in which they cannot be intelligent and because machines have their if you say that machines have mind machine can do creativity then you can say machines have minds of their own. And can you ascribe this kind of concept to machine creativity? There are many kinds of limitations which limits to machine creativity, machine competency and machine intelligence also and those things I will be explaining in the next lectures. Now we have to see the place of mind in artificial intelligence. As you have seen that functionistic model of mind and the AI model of mind mind in AI explores that the state of mind in artificial intelligence. As we know that the main aim of artificial intelligence is to reproduce mentality in machines that is to say that artificial intelligence aims at producing machines with minds. If we say that machines have minds then we have to ascribe certain belief, knowledge, free will, intentions, observations etcetera to a machines. In that case the machines will perform intelligent tasks and thus will behave like human beings. We may raise a questions now why should we want ascribe mental qualities to machines at all? But there are AI scientist they say that there are many reasons for ascribing belief and other mental qualities to machines. We may know that epogram but its states are given moment is usual not directly observable and we can explain performance of a machine only by ascribing belief and goes to it. Ascribing belief may allow to derivation of a general treatment about the machines behavior that could not be obtained from any finite number of simulations. The difference between this program and another actual hypothetical program may best be expressed as a difference in belief structures. But according to Hogland thought its safety is not static and random it develops in ways that obey different rules of inferences. Hogland says that since correct applications of the rules of reason to particular thoughts depends on what those thoughts and mean. It seems that these must be some active rules applyer which understand the thoughts even if understand the rules also and which apply the rules to the thought as well as it can. If the activity of these rules applies following the rules of reason is to explain the rationality of our thought processes and then it must be regarded as a complete little person or homunculus in latin which is sitting inside the head like same way according to Hogland there is also a there is also a compunculus which is existing in the human mind and this little computer is there and as you know that cognitive scientist can be materialist and mentalist at the same time. They are materialist because they support the view that the mind is a complicated machines or matter on the other hand some support that mind is exist along with body and mind is not different from the body and mind is identically with the body some of them are supporting they can offer explanation in terms of meaning and rule following without presupposition any unexplained homunculus it would be peculiar to start assigning geometrical shapes and locations to the internal program routines and operations of the systems. These same decisions clearly cause physical behavior yet no one is already that the laws of physics are being violated. But Hogland says that when the machine plays it follows rules in at least two sense it always abides by the rules of the game and it employs various reasonable rules of terms to select plausible moves. Though these rules are in no way laws of nature the machines behavior is explained by citing themselves yet no unexplained compunculus is presupposed like there is no unexplained homunculus is there. Thus this explanation will necessarily invoke those systems internal reasoning processes yet it is far from easy to figure out processes that will consistently lead to the observed behavior observed behavioral response. Dennett rightly says that human mind is a cementing engine that is to say that the way human mind handles the meaning of a word or sentences in the same way a machine handles the literal meaning of a word or sentence. Dennett's view which we have already seen in the functionistic model of mind but he says that human mind is a machine like ordinary machine because both mind and machine have the same quality the difference is only apparent. Therefore this kind of analogy shows that the place of mind in artificial intelligence is completely mechanistic place or mechanistic explanations. Let us see next last section or next section in this lectures on strong artificial intelligence which is completely leads to reductionism and weak AI which is non reductionism. In this sections I am going to discuss about strong artificial intelligence and weak artificial intelligence strong artificial intelligence leads to reductionism and weak AI leads to non reductionism. As we have seen that main thesis of AI is that the human brain is like a digital computer and the human mind is just a computer program. It tries to prove that the relationship between the programs and the computer hardware is like the relationship between mind and brain. Some supporter of artificial intelligence argues that we have every reason to believe that computers have intelligence. At the same time some others argues that the computers intelligence is limited where human intelligence have non limit. Nowadays computers have achieved some modest success in proving theorems, guiding missiles, sorting emails, driving assemblies and many kind of activities like robotics, diagnosis, illness, predicting about weather and economic events and many other events and activities. Computer receives, interprets, process, stores, manipulates and use information. Thus intelligence behavior is programmed into the computers. On the contrary we have no idea how the brain functions but we have an idea of the general relationship between brain processes and mental processes. Mental processes are caused by the brain activities which are function of the element consisting the brain. Strong AI argues that it is possible that one day a computer will be invented which can function like a mind in the fuller sense of the word. In other words it can think, reason, imagine and do all things that will currently associate with the human minds. On the other hand weak AI argues that computer can only simulate human mind and are not actually conscious in the same ways as human minds are. According to weak artificial intelligence computers having artificial intelligence are very powerful instruments in the hands of man whereas strong artificial intelligence hold that computer is not only merely an instrument in the study of the mind but that the appropriately programmed computer is really a mind in the sense that computers can think and do reasoning like the human beings. In strong AI the programmed computer has cognitive state. So, the programs are not simply tools that allow us to test psychological explanations rather the programs are themselves the explanation of the mind. Strong AI according to Saul basically claims that the appropriately programmed computer literally has cognitive states and that the programs thereby explaining human cognitions and this is the view of strong AI according to John Searle and John Searle is one of the non-reactivity philosopher and he has drawn the distinction between mind and machines and he has shown the importance of the mind especially in the Chinese room argument which I will be discussing in the further lectures. But as we as we know the main aim of artificial intelligence is to produce mentality in computational machines and is try to prove that functions of a machine are similar to the function of the human mind. But the question is could a machine have mental states for artificial intelligence there is a nothing essentially biological about the human mind because the the brain just happens to be one of a indefinitely large number of different kinds of hardware computers that could sustain the programs which make of human intelligence. On this view any physical symbol system may be whatever that had the right program right inputs would get the right output and that right output is exactly in the what the mind you mean or even if I mean what your mind and I have minds also. But this physical symbol system has the right program which right inputs and that can give rise right output exactly which our human mind is doing and this is the view of cognitive scientist and they believe that perhaps they can design the appropriate hardware and programs artificial brains and minds that are comparable to human brain and minds and this is called strong artificial intelligence and this strong artificial intelligence is you one kind of a reductionistic a reductionistic thesis because strong I reduce mind or mentality to physical properties here the term reduce to names a relation between theories when this relations holds between a pair of theories for example, r 1 and r 2 then r 1 is said to be reducer of r 1. If we see in the case of Fodor's explanation of of reductions that will be very clear about to the reactions reductionistic thesis of strong AI. Fodor says that the reduction is transitive and asymmetrical hence irreflexibly by the unity of science I shall mean the doctrine that all sciences except physics reduce to physics by physicalistic reduction I shall mean a certain claims that is entailed by but does not entail the unity of science therefore, the claim that psychology reduces to physics and this kind of reducibility involves a good deal more than a ontological claim that things that satisfy description in the vocabulary of r 1 also satisfy descriptions the vocabulary of r 2. This condition is stronger than the ontological requirement that whatever falls under the variation of r 1 should also fall under the theories of r 2 on this view that is an important sense in which syntax is preserved on those reductions that is to say that reduction permits us to re-describe the event in the vocabulary of r 2 therefore, according to strong artificial intelligence mental states reduce to the computational state of the same way. On the other hand, weak AI is a non-reductionistic because this theory is not reducing the human mind in terms of machines, but it can only simulate human mind and this does not mean exact replications. The above statement shows that the weak artificial intelligence view is non-reductionistic for the physicalist life is higher order properties which emerges out of the physical properties. However, in the case of Jambai, there is an absence of consciousness. In other words, the logical possibility of a Jambai world is considered as a world physically identical to our world, but conscious experience is impossible in this world. The Jambai may be physiological or phenomenal Jambais or psychological Jambais which are physically and functionally identical to human beings, but they lack experiences and this is the view of David Chalmers and he says that there is a logical possibility of Jambais seems equally obvious to me. A Jambai is just something physically identical to me, but which has no conscious experience all is dark inside. The Jambai and human mind have identical physical properties, but differ in high level properties that is consciousness. The Jambai lack consciousness therefore, the high level property being conscious cannot be logical, super emanant on physical properties. In the same way, according to weak artificial intelligence, mind and machines have some identical properties, but differ mainly on some higher qualities. Here weak artificial intelligence is non reductionist because unlike strong artificial intelligence unlike because it is strong artificial intelligence. Whereas, in the case of weak AI is non reductionist and strong AI is reductionist because strong AI is reducing all the mental phenomena into a physical phenomena. Now, I will conclude this lecture here. Some of the limitations to all these functionalistic mechanistic AI model of mind which I will be discussing in the due course of my lectures. Thank you very much.