HUMAN INTELLIGENCE VERSUS MACHINE INTELLIGENCE Mohammad Ali Saniee Monfared Simon J. Steiner Jian-Bo Yang University of Birmingham, UK, B15 2TT Tel: 0121-414-3684, Fax: 0121-414-3688; Email:
[email protected]
distinguish between “fuzzy based intelligence” from “AI based intelligence”, if one is interested only in performance? Is intelligence a “property” which a system holds (or illustrates), or is it a “technology” which the system has as an ingredient?
Abstract- This paper investigates artificial intelligence, fuzzy logic, and neural network based intelligent systems, to develop a better understanding of their scope, capabilities, and limitations and to put the human-machine-intelligence dilemma into a new perspective.. Upon this understanding, a new framework for intelligence and intelligent systems is suggested., where the designation and identification of an intelligent system can be carried out. The important aspect of our framework is that its validity can be scientifically verified ( that is, one can argue for or against it on solid foundations).
These questions have a direct bearing not only on the cognitive side of intelligent systems, but also on the engineering aspect of intelligent systems. For instance, if intelligence is considered as a property (or a collection of properties), then what is that property (or properties), and who has identified it? AI has taken an open definition for intelligence, which can lead either to triviality or to redundancy.
I. INTRODUCTION One could argue, however, that since the nature of intelligence is so unravelled such that openness and vagueness is inescapable, and one should also accept the triviality the redundancy in the term “intelligent” at the same time. Simply because any useful man-made system is a manifestation of the intelligence of its creator(s), then the term “intelligent” can no longer convey any specific information (i.e., the word “intelligent” becomes trivial). Similar openness (and vagueness) is happening also with the fuzzy and neural networks definitions of intelligent systems.
With the introduction of digital computers in the 1970’s, a dilemma regarding the nature, the definition and the extent to which human intelligence can be transferred into machines has emerged. Digital computers were considered as the “hardware” of the brain, where if integrated into machines, they would provide them with intelligence analogous or even superior to human intelligence. Artificial Intelligence (AI) in the 1980’s has provided the second platform for machine intelligence, this time particularly by using the so called “software” of the brain (i.e., human problem solving methods).
On the other hand, if intelligence is not a property but a technology, then by the same token any man-made system which uses a certain technology (either fuzzy logic, or an expert system) could be called an intelligent system, and hence the word “intelligent” becomes redundant. A trivial way could also be open, where different communities attribute the term “intelligent” to their own technology and methodology.
However, as manufacturing technology evolves it has become clear that digital computers and AI techniques could not fully accommodate the emerging needs of the 1990’s. Fuzzy logic and neural networks have, thus, introduced a fresh impetus in building intelligent systems by providing a completely different scope and new methodologies. These newly formulated intelligent systems, since they do not comply with the old paradigm of intelligence and intelligent systems (e.g., expert systems), have brought confusion to many disciplines, including engineering.
To avoid triviality and redundancy as discussed above, how would it be possible to define intelligence whereby the designation and the identification of an intelligent system can be handled. It is the intention of this paper to resolve this problem, provide a new perspective for human-machine intelligence under the light of current developments, and establish a firm framework under which the designation, and hence the identification, of an intelligent system can be recognised.
In particular, some of the advocates of fuzzy logic and neural networks have suggested that “truly” intelligent systems can only be accomplished by using new technologies and AI based systems which do not increase the machine intelligent quotients (MIQ) (see e.g.,[1]). Although such controversies help to understand the cognitive aspects of human intelligence, they also invite questions (and hence confusions) as far as engineering is concerned. Questions such as the following can be considered to illustrate some of the confusions. How can an intelligent machine be identified? How is it possible to
This paper is organised as follows; where in Section II, two opposite views on intelligence and intelligent systems are considered, in Section III, intelligent systems under the pioneer work of AI are discussed, and in Section IV intelligent systems under fuzzy logic and neural networks 1
are studied. In Section V a new cognitive model of intelligence is considered and necessary and sufficient conditions for the designation and identification of intelligent systems are discussed.
Descart [4] considers the universe to comprise of material and immaterial substances. Immaterial substances such as the soul, the mind, thinking, cognition, and consciousness are not accessible to us, so any attempts to imitate them (i.e., building thinking machines, intelligent systems, etc.) are bound to fail. Artificial intelligence, however, following Darwinian evolutionary theories, has challenged these long lasting ideas.
II. OPPOSITE VIEWS ON INTELLIGENCE The word intelligence has been eluding mankind for thousands of years (see e.g. [2]). Recently intelligence has been addressed by disciplines such as philosophy, psychology, biology, artificial intelligence, neuroscience, and engineering [3]. Researchers in these fields have found intelligence complex and usually hard to define. It is often the case that when they tried to put forward their own definitions of intelligence, they fell prey by adopting different terminology (such as cognition, thinking, conscious, self awareness, etc. instead of intelligence). Although the adoption of new terminology has served to clarify new aspects of intelligence from different viewpoints, it has brought many confusions as well, indicating the complexity associated with the concept of intelligence.
With this philosophical background, most mental activities were considered impossible to be imitated (e.g.,“Thinking is a function of man’s God given immortal soul. Machines have no soul, hence cannot think.”;”To be truly intelligent machine would have to be consciously aware of the thought processes rather than blindly executing its mechanical procedure”; ‘What you get out of a computer is at best what you put into it, Computers might approach human intelligence but never match or surpass it”(quoted from Alan Turing by [5]). To eliminate the difficulties associated with the definition of intelligence, Alan Turing [5] has formulated a test where intelligent system (or thinking machine) is the one which can fool a human. Thus, any system that can perform a task which is hard to distinguish whether being performed by a human or by a machine can be taken as an intelligent task. Turing’s test worked very well to the favour of AI practitioners, and computer programs which illustrate elements of human intelligence started to appear.
AI considers the universe, including humans, to consist of only material substances. Intelligence, the mind, and the soul are all considered properties of material, as for instance mutual attraction between masses has been considered as the innate properties of material. The difference between a stone and a plant is, for example, that the latter is many orders of magnitude more complex than the former, just as human intelligence is many orders of magnitude more complex than his ancestor (e.g., the Chimpanzee).
Hao Wang programmed a computer to prove theorems in prepositional logic. In less than eight and half minutes his program found proofs to all of the approximately 350 theorems in the first thirteen chapters of Principia Mathematica by Russell and Whitehead. Samuel’s checkers program plays at the master’s level, Guard’s theorem prover has proved a new lemma in lattice theory, and the heuristic DENDRAL program of Feigenbaum et al. carried out spectrochemical analysis worthy of publication in the chemical literature [5].
AI conceptual theories about the mind are supported by the emerging power of digital computers technology. Since, presumably, digital computers have adopted the structure of the human brain, and because the computers can perform even faster, artificial systems which are equipped with this technology can replace human intelligence with better performance.
Research in AI within the last few decades has resulted in the discovery and development of efficient tree search methods, advances in formal logic (resolution) and formal reasoning mechanisms (non-monotonic reasoning, certainty logics, Bayesian belief networks, etc.), string processing and pattern matching (useful in compilers, etc.), advances in linguistics, advances in probability theory (D.G. Willis established a correspondence between formal probability theory and the theory of computable functions in his work on inductive inference), advances in recursive function theory, integral calculus (Slagle elucidated substitution methods for indefinite integration in his workSAINT), advances in programming technology (common lisp, prolog, object oriented programming, etc.), tools (bit mapped display, character generator, mouse, windows, context editors, etc.), and development of expert systems of all kinds [5].
III. AI BASED INTELLIGENT SYSTEMS AI was born to build artificially intelligent systems, when imitating such systems was considered impossible. This was due mainly to philosophical attitudes which formed the intellectual environment of modern science. According to Descartes’ philosophical theory, the universe is divided into two ontological categories, i.e.,/ material substance and immaterial substance (or inanimate and animate). The motion of the planets, mutual repulsion, and attraction of magnets, as examples, can be explained in terms of prior causes, universal laws, and innate properties of bodies. Behaviour of animate or immaterial substances are the consequence of their volitions, their attitudes and other attributes of soul. Thus soul, mind, intelligence, and other activities of mind are confirmed in this theory as immaterial substances [4].
Artificial Intelligence (AI) pioneers, however, decided to “conquer the castle” (i.e., intelligence, then an untouchable entity). They believed that digital computers 2
were already in existence which imitated the hardware of the brain. It was just a matter of imitating the software of the brain (i.e., human knowledge). AI equipped with the Turing test has taken the initiative to demonstrate intelligent systems in practice. Computer programs were developed each capable of illustrating attributes of human intelligence (e.g., games, theorem proving, scientific analysis).
One way that was open for expert systems to develop further was the area of human knowledge that deals with structured knowledge. This marked the era of a second generation of expert system, by which structured knowledge was used to develop the expert system; hence, expert systems could be built out of the integration of procedures and algorithms that have been developed in domain specific areas. The application of such expert systems in engineering flourished, where conventional approaches were enhanced by the application of expert systems. Examples are many, including the integration of expert systems with CAD, with CAM, with scheduling, and with production control, to name just a few.
The key element in AI methodology was the development of Expert Systems (ES) which have been a general method to be used to develop artificial intelligent systems. Attempts to build expert systems were widespread and covered many aspects of human intelligence in practice, including law, medicine, and engineering. The primary intention of building an expert system was to replace human intelligence; however, the definition of human intelligence was changing from one generation of expert system to another, as will be discussed below.
The introduction of second generation expert systems was a significant step forward because such systems (i.e., computer-based expert systems) were used to assist human experts. Although this new generation of expert system could hardly replace humans, as was claimed, it did not reduce the great impact that they had on assisting human experts in performing more complex tasks with higher effectivity and efficiency.
A. First Generation Expert Systems: Heuristics! The most provocative generation of expert system was the first, where expert systems were considered to offer alternative solutions to most engineering problems. The basic assumption was that current analytical problemsolving techniques were poor. For instance, most problemsolving techniques were linear, while real world problems are non-linear, and so linear techniques are mostly not applicable. On the other hand, human experts often attempt to design and develop new systems by using their intuition rather than analytical techniques and the rigor mathematical approaches. This intuition and specifically developed heuristics can only then be extracted from the human expert, not from a textbook.
The only problem that must be recognised with these expert systems is the problem “in tendency” when they were designed. Design of a system that is to replace a human is by nature different from the design of a system that is there to assist a human. It is clear that the two systems must have different objectives, and hence methodologies.
Expert systems thus can be developed by extracting expertise from the human (i.e., knowledge acquisition) by building an emulated human expert (i.e., an expert system). If this idea would work, the development of such expert systems would not be limited to the engineering field, but it could also apply in medicine, in law, in politics, to name just a few; This could lead to the replacement of virtually all human efforts. New systems which are built using expert systems should then be expected to perform better than their counterpart systems with human presence. This was, according to such arguments, because the expert system has, however, been developed using the best expertise available.
In practice, expert systems were then developed to work in an interactive manner with human experts, that is, to extend or enhance human capabilities. This tendency produced a new confusion about what an expert system actually was, and what makes it different from other types of system. It is hard to distinguish, for instance, between an Expert System (ES) in AI terminology and a Decision Support System (DSS) in OR terminology [6]. For instance, an ES that helps a human scheduler in allocating jobs to machines is considered an intelligent system, while a DSS that uses an optimal scheduling algorithm to handle a scheduling task is not considered an intelligent system, even though the sophistication and effectiveness of the DSS is superior to the heuristic based ES.
The focus of expert systems developers was to build something that could replace human experts, and this was mainly a failure. However, they could have built useful systems instead if their focus was on developing systems to assist human experts.
Tremendous effort was put into developing such expert systems. It was soon realised that the great difficulty associated with expert systems lay in the way human expertise was acquired; Knowledge acquisition, therefore, became the bottleneck of AI and ES. This was due to the fact that most human knowledge is of a tacit form, which cannot be explicitly presented.
As another example, a program that assists an engineer in selecting a suitable robot, by asking questions and narrowing down the list of available robots to find the nearest one can be called an intelligent system, while another program which is a curve fitting system, which examines a series of data against different mathematical models to find the proper model that has the maximum fitness (i.e., least square error), is not considered an intelligent system by default.
B. Second Generation Expert Systems: Integrated Systems
3
The only departure point between an ES and a DSS can be associated with their architectures, not the type of task which they perform. An expert system has a distinct architecture, and includes certain components such as a knowledge base, and an inference engine. This is to the benefit of expert systems against its conventional counterparts, such as a DSS. But the notion that an expert system could turn a “dump system” into an intelligent system has proven to be rather naive.
For this wide spectrum of difficulties, there are some who would rather consider only those intelligent systems which offer new solutions, using new fuzzy and neural based methdologies, and hence they discard any AI based systems as being pseudo-intelligent systems. However, recognising the historical background, it is more reasonable to consider newly emerging intelligent systems as new methods for building artificial intelligent systems based upon a new understanding of human intelligence,which can explore new dimensions of human intelligence.
C. Third Generation Expert Systems: Learning Systems Confusion around what makes an expert system to be intelligent led to third generation expert systems, where the concept of learning plays a central role. Expert systems thus are now defined as learning systems, and because of this they are entitled to be called intelligent systems. Learning systems can improve their performance as they are exposed to new information.
New methods are based on new modelling approaches, which differ from conventional physic-like modelling approaches. The conventional modelling approach usually adopted proceeds as follows. The system under consideration is viewed as an interconnection of component subsystems. To build a specific model, individual subsystems are zoomed in and their immediate and important interconnections are taken into account.
Learnability has been long recognised as an important attribute of biological intelligent entities. In addition to its concept in AI, learnability has become a pivotal element in newly emerging approaches of fuzzy logic and neural networks.
Models built in this way will typically involve the constitutive equations of the elements in the subsystems, field equations, conservation laws, etc. Perhaps there may also be a number of numerical values in the constitutive laws which are uncertain, and so either may be left as parameters or could be determined by means of direct measurements.Thus the laws of physics play a key role, and observations play an ancillary role in this procedure. This view of modeling has served very well as far as natural physical systems are concerned [7].
IV. FUZZY AND NEURAL BASED INTELLIGENT SYSTEMS Artificially intelligent systems have been the monopoly of AI for the last few decades. Since such a monopolistic perspective has suffered from its own intrinsic limitations, there have been endeavours to explore new avenues for building intelligent systems and intelligent machines. These efforts have led to today’s resurgent interest in neural and fuzzy based intelligent technologies.
However, in the areas of man-made systems (such as in adaptive control, pattern recognition, signal processing, and econometrics), there is an increasing emphasis on the use of different modelling procedures, such as building the model from directly observed data, and building the model using human problem-solving methods (i.e., heuristics).
It is worth noting, however, that these newly emerging technologies do not dispute the philosophical principles behind AI in building artificial intelligent systems, but rather methodological principles. Neural network and fuzzy logic practitioners share similar views as the AI practitioner about intelligence. They too consider the building of intelligent systems to be possible.
These modeling approaches are not new (e.g., black-box modeling, and techniques such as frequency analysis in system identification, time series analysis in econometry, and expert systems). The most important feature of neural networks and fuzzy logic is that both break the traditional physic-like modeling platform and provide a wider spectrum for building intelligent systems. This does not, however, imply that traditional methodologies are not useful or must be discarded. But the argument is that these emerging methodologies can tackle new problems and, when they are integrated with other methods, the result can provide superior performance.
They argue instead about the various difficulties of the current modeling approaches. This includes: i) questioning the viability of the digital computer as a model of the human mind, ii) the fact that human capabilities and intelligence in most cases are the result of long term learning and mastering skills (so that these acquired capabilities are not easily definable in clear mathematical or even algorithmic terms), iii) limitations of the symbolic processing approach adopted in AI, iv) limitation of the inferencing method (i.e., bivalent logic), v) and the fact that since natural language is inherently fuzzy (where most human intelligence is manipulated and delivered in such a medium), it may be necessary that a fuzzy modeling approach which is compatible with such fuzzy language be adopted.
A. Neural Modeling of Intelligence Neural networks touch upon new dimensions of human intelligence. For example, abstraction/generalisation is one of the most important aspects of biological neural networks. Humans learn concepts ostensibly by pointing out examples, and not by mathematical definitions. A child learns a circle or red colour by abstraction (i.e., to learn a 4
circle by looking at round objects, or to learn the colour red from observing red objects, e.g., red pen, red flower). When the child has learnt to build these abstracted concepts, s/he can gain a generalisation capability.
A. The Cognitive Model The important principles upon which our cognitive model is established are as follows (see also Fig. A1 in Appendix A):
Abstraction itself is the process of converting complex entities into simple ones, which means in engineering terms, less computation and less need for storage capacity. Generalisation, on the other hand, provides the capability to handle unseen entities, or to solve unseen problems. The third aspect of abstraction/generalisation of biological neural networks is that the topology or modeling structure is formed as examples are generated, so that huge primary data collection is not necessary to build such models. As data is generated, the structure is formed, and this property again is important in engineering applications. An artificially intelligent system that can imitate this aspect of human intelligence is the target of neural networks technology (e.g., pattern recognition).
1) Human intelligence is an infinite-dimensional entity (see Fig.A2 in Appendix A). 2) What constitutes human intelligence evolves over time (including both the nature of human intelligence and our perception of human intelligence). 3) Machine intelligence levels follow the human intelligence level. Machine intelligence may precede aspects of human intelligence, but never may precede human intelligence in its wholeness. In other words, it always follows a creator-creation pattern. 4) Certain criteria for machine intelligence must be set; this should be domain-specific, and for a given length of time (e.g., 20-30 years).
B. Fuzzy Modeling of Intelligence Human decision making tends to work with vague and imprecise concepts, which are often expressed linguistically. Fuzzy logic argues that it is this element of imprecision that is an important contributing factor to human intelligence. Mamdani [8], and Zadeh [9] proposed a mathematical framework for dealing with imprecision in his theory of Approximate Reasoning (AR). This theory is based on fuzzy set theory [10], where sets are defined in a way which preserves the approximate nature of human reasoning, rather than trying to avoid it.
5) To accomplish new dimensions (or properties) of human intelligence as has recently been recognised, new technologies must be developed, which must complement the previous methodologies (such as mathematical modelling, expert systems, Fuzzy logic, heuristics, etc.). B. The Engineering Model Our proposed engineering model by which an intelligent system can be designated and identified will have necessary and sufficient conditions, as follows.
A fuzzy control methodology, for example, has utilised fuzzy theory in establishing a link between the human expert imprecise world and the engineering precise world, to build intelligent control systems. The basic idea behind this approach was to incorporate the experience of a human process operator in the design of a controller. From a set of linguistic rules which describes the operator’s control strategy, a control algorithm or system of fuzzy rules is constructed where the words are defined as fuzzy sets. The main advantages of this approach seem to be the possibility of implementing “rules of thumb” experience, intuitions, and heuristics, and the fact that it does not need a model of the process.[8,1,11].
1)The ncessary condition for an intelligent system is that it should be a dynamical system, such as a continuous time variable dynamical systems (CVDS), a discrete time variable dynamical system (DVDS), a discrete event dynamical systems (DEDS), or a hybrid dynamical system (HDS). 2) The sufficient condition for an intelligent system is that it should have an on-line self-optimising (or learning) property. Any system in question can then be examined against this model, to see if it can be considered as an intelligent system This system may use a single technique or a combination of techniques such as AI, fuzzy logic, neural networks,or others to build an intelligent system (see examples in [12,13,14]).
Fuzzy control modeling thus transforms the ideas underlying expert systems in AI (i.e., to extract and to present human knowledge in performing complex tasks), but in a numerical rather than a symbolic method, and by using fuzzy rather than non-fuzzy sets. This has provided the basis for a tremendous number of industrial, and commercial, fuzzy-based systems in recent years[1].
ACKNOWLEDGMENT V. A NEW FRAMEWORK
The first author wishes to express his deep gratitude to the ministry of culture and higher education in Iran for providing the funding for this research.
Our new framework has a cognitive model and an engineering model to represent the philosophical and application aspects of the human-machine intelligence respectively. 5
REFERENCES APPENDIX A [1] B. Kosko, “Fuzzy Thinking”, Flamingo, 1994. [2] D.R. Hofstadter, “Metamagical Themas: Can Inspiration Be Mechanized?” Scientific American, 247(3), Sept. 1982, pp18-31. [3] J. S. Albus, “Outline for a Theory of Intelligence”, IEEE Transaction on Man, Systems, and Cyberbetics, vol 21, no. 3, May/June 1991. [4] A, Mc Clintock, “The Convergence of Machine and Human Nature”, Avebury Series in Philosophy, 1995. [5] N. Cercone, N, C.Chen,, “Artificial Intelligence”, Lecture Notes, University of Regina, Canada, Fall 1993. [6] P.N. Finlay, “Decision Support Systems and Expert Systems: A Comparison of Their Components and Design Methdologies”, Computers Opns Res. vol 17, no. 6, pp535-543, 1990. [7]. J.C. Willems, “Paradigms and Puzzles in the Theory of Dynamical Systems”, IEEE Transactions on Automatic Control, 36(3), 1991. [8] T.J. Procyk, E.H Mamdani, “A Linguistic SelfOrganizing Process Controller”, Automatica, vol. 17, pp. 15-30, 1979. [9] L.A. Zadeh, “Outline of a new approach to the analysis of complex systems and decision processes”, IEEE Trans. Syst. Man Cybern, SMC-1, 28-44, 1973. [10] L.A. Zadeh, “Fuzzy Sets”, Information Control, vol. 8, pp338-353, 1965. [11] B. Kosko, “Neural Networks and Fuzzy Systems: A Dynamical Systems Approach to Machine Intelligence”, Englewood cliffs, NJ: Prentice Hall, 1992. [12]M.A.S. Monfared, S.J. Steiner, “Fuzzy Based Scheduling and Control Systems”, Fuzzy Sets and Systems (accepetd for publication), 1996. [13] M.A.S. Monfared, J.B.Yang, and S.J. Steiner, “The Design of an Intelligent Manufacturing Control System Using an On-line Self-optimisation Approach”,Int. Conf on Intelligent and Cognitive Systems(ICICS,96), Sept 2326, 1996, Tehran, Iran. [14] M. Bakhshi-Joybari, “Intelligent Knowledge Based System for Metal Forming Processes”, PhD Dissertation, October 1995, University of Birmingham, England.
The mind-brain relationship can be illustrated as one of the three models depicted in Fig.A1. Our cognitive model which has been discussed in Section V will comply with both models b) and c).
M
S
a) Descarte Model
M/S b) AI Model
M
S
c) Alternative Model
Fig. A1: Alternative views on Matter (M) and Spirit (S) A model of human intelligence is depicted in the following Fig. A2, where intelligence is a complex entity.
Self-awareness Thinking Conscious
Intelligence
Cognition Creativity
Fig. A2: Human intelligence as an entity with infinite dimensions
BIBLIOGRAPHY 1. Calvin, W., H., The Emergence of Intelligence, Scientific American, October 1994, pp79-85. 2. Deryfus, H.L., Mind Over Machine, The Free Press, 1986. 3.Horgan, J., Can Science Explain Consciousness?, Scientific American, July 1994, pp72-78. 4. Minsky, M., Will Robots Inherit the Earth?, Scientific American, October 1994, pp87-91. 5. Penrose, R.,The Emporor’s New Mind: Concerning Computers, Minds and the Laws of Physics, Oxford University Press, 1989. 6. Searle, J.,R., Minds Brains and Programs, The Behavioral and Brain Sciences, 3, 3, 1980. 7. Steinman, L., Autoimmune Disease, Sci. Ame, Sep. 1993, p75. 6
