Development of Computers in Simulating the Human Brain - Essay Example

Describe the developments that are taking place to use computers to simulate the human brain and discuss the reasons why you think computers will or will not eventually emulate human intelligence. Since the inception of computer technology, researchers have anticipated about the probability of constructing smart machines that could compete with human intelligence. Provided the current pace of progresses in Artificial Intelligence (AI) and neural computing, such an evolution appears to be a more strong possibility. Many individuals now think that artificial awareness is possible and that, in the future, it will crop up in complicated computing machines (Buttazzo, n.d., p. 24). Our general ways of talking about ourselves and other individuals, of validating our conduct and elaborating that of others, signify a certain notion of human life that is very close to us. It is a part of common sense that we rarely can view it. It is an idea according to which each individual has a mind. The constituents of the mind include faiths, apprehensions, anticipations, motivations, yearnings, etc. The continuity of our minds is the source of our personality and identity as individuals. In the past couple of centuries we have also become convinced that this common-sense psychology is rooted in the brain. These mental conditions and occurrences are somehow going on in the neurophysiological systems of the brain. So this leaves us with two stages at which we can illustrate and elaborate human beings: a level of common-sense psychology, which seems to operate well enough in practice although not scientific; and a level of neurophysiology, which is definitely scientific. However, the most modern specialists know very little about the level of neurophysiology (Searle, 2007, p. 1). What we call minds are merely very complicated digital computer programs. Mental states are only computer states and mental procedures are computational systems. Any process whatever that had the correct program, with the correct input and output, would have to have mental conditions and systems in the same literal sense that we do. The programs in question are "self-modifying" or "self-structuring" "systems of representations" (Searle, 2007, p. 2; Calvin, 1987). The fast progress of computers may indicate the possibility of these machines replacing human brain and emulate human intelligence. Brain: The Digital Computer It is apparent that at least some human mental capabilities are algorithmic. It follows that a person could not find out that the brain or anything else was inherently a digital computer. A person can allocate a computational interpretation to it. Some physical arrangements make possible the computational application much better than others. That is why we put up, program and apply them. According to Searle, the brain has the capability of performing "information processing". The brain, as far as its inherent functions are concerned, does no information processing. It is an exact biological organ and its explicit neurobiological systems leads to specific types of intentionality. In the brain, inherently, there are neurobiological systems and sometimes they cause awareness (Searle, 2004, p. 2, 13; Searle, 1990). How computers simulate human brain? Comprehending others’ intentions is a significant ability that entails representing the mental conditions of others in one’s individual mind (creating a ‘theory of mind’). There are 2 contending opinions of how we do so. ‘Simulation theory’ proposes that we directly simulate others’ cognitive systems by organizing the same cognitive methods. On the other hand, “Theory theory” proposes that we apply inferential and deductive procedures that do not entail simulation. The problem of comprehending others’ intentions can be interpreted into the more concrete problem of forecasting others’ activities. Recognizing the neural mechanisms applied to envisage the activities of others may then allow us to differentiate between the two theories (Ramnani and Miall, 2004, p. 85; Cognitive Psychology, n.d.; Simon, 1990). When we try to simulate how individuals actually spend their time and how they interrelate with their surroundings, we find that the variety of reasons underlying human behavior and accordingly the formulation of objectives has been insufficiently typified by problem solving theory. Furthermore, we discover that the harmonization mechanism controlling moment-by-moment performance goes beyond consideration and learned series to incorporate ritual forms of activities and vigorously coupled perceptual-motor action (such as, playing a video game). Thus, not all goal-directed actions is assumed or  compiled,  some activities only generate cultural patterns and some are carefully and adaptively organized without consideration Accordingly, Brahms models explain the activities of individuals belonging to manifold groups, situated in a physical background (geographic areas, buildings, conveyance vehicles, etc.) consisting of instruments, documents, and computer methods. The emphasis is on simulating human behavior, not cognitive procedures in the usual sense of arrangements and processes in the brain. In particular, we infer model building (for example, situation-action rules) as behavior prototypes, not knowledge (Clancey, n.d., p. 2-3). Cognitive science was introduced during 1950s. Psychology could not contribute in the cognitive revolution until it had unchained itself from behaviorism, thus reinstating cognition to scientific respectability. It was becoming apparent in a number of disciplines that the solution to a number of their problems counted critically on solving problems conventionally assigned to other disciplines (Miller, 2003, p. 141). A Brahms model of work practice exposes circumstantial, interactional manipulations on how work really gets done, especially how individuals casually involve each other in their work, thus altering the quality of the outcome. Constructing a Brahms model leads human-computer structure designers to inquire how tasks and details in reality flow between individuals and machines, what work is needed to harmonize individual contributions, and how instruments obstruct or help this procedure. It has been observed that information processing models are concepts that omit necessary interactions between individuals, instruments, and facilities. It also overlooks relations that determine whether an implement fits in the workplace. Brahms activity models are supposed to be particularly helpful for system requirements evaluation, training, and implementing software instruments. Brahms has been useful at NASA for simulating astronaut actions on the moon, in addition to human-robot system design (Clancey, n.d., p. 3). Over 30 years of research into the mechanical characteristics of the brain, brain tissue was stimulated by traumatic injury avoidance (for example, automotive accidents) and comprehending of brain structural deceases (for example, hydrocephalus). In recent years, driven by enhancements in virtual reality systems and the appearance of automatic surgical tools and robots, new thrilling area of study has appeared computer simulation of surgical procedures. The precondition for such a simulation is a suitable mathematical model of the brain mechanical properties. This incorporates faithful illustration of geometry, boundary and loading provisions as well as material characterestics of the brain (Miller, Chinzei, Orssengo and Bednarz, 2003, p. 1369). Computer programs facilitating perfect modeling of soft tissue deformation may discover applications, for instance, in a surgical robot control process, where the forecast of deformation is required. Surgical operation preparation and surgeon training processes based on the virtual reality methods (where force feedback is required) and registration (where information of local deformation is needed) are also some paradigms of computer programming. The characteristic feature of the mathematical model of the brain proposed for the simulation of neurosurgery is the strain rate range (the loading velocity range) considered - 0.01-1.0 s -1 - orders of magnitude lower than that experienced in circumstances causing injury (Miller, Chinzei, Orssengo and Bednarz, 2003, p. 1369-1370; Neumann, p. 42). What psychological and philosophical implication should we connect to recent attempts at computer simulations of cognitive capabilities of human beings? In answering this question, Searle (2003) differentiated "strong" AI from "weak" AI. According to weak AI, the main value of the computer in the study of the mind is that it provides us a very powerful instrument. For instance, it allows us to devise and examine hypotheses in a more thorough and accurate fashion. However, according to strong AI, the computer is not just an instrument in the study of the mind; rather, the suitably programmed computer in fact, is a mind. The computers that provided the right programs can be said to comprehend and have other cognitive situations. In strong AI, because the programmed computer has cognitive situations, the programs are not mere implements that permit us to test psychological clarifications; rather, the programs are themselves the elucidations (Searle, 2003, p. 2). Confrontation of computers to humans in several domains: Besides chess, computers have begun advancing towards human aptitude in an increasing number of other fields. In music, for instance, several commercial programs can form melodic lines or complete songs according to specific approaches, ranging from Bach to jazz. Computers now move toward human levels of comprehending in incessant speech, electrocardiogram diagnostics, theorem verifying, and aircraft supervision. In the future, we can anticipate similar levels of computer performance in domains that incorporate difficult tasks such as driving, immediate language conversion, home cleaning, surgery, scrutinizing, and law enforcement. However, though machines turn out to be as skilled as humans in a lot of disciplines, such that we cannot differentiate between their performance and that of human beings, we cannot presume that they have turned out to be self-conscious. Simultaneously, we cannot imagine that such machines are not self- conscious. In fact, while intelligence is an expression of a peripheral behavior that we can compute with particular tests, self-awareness is a property of an interior brain state, which we cannot determine. Hence, to resolve this query, we must refer to philosophy (Buttazzo, n.d., p. 26). From a purely philosophical viewpoint, we cannot confirm the presence of awareness in another brain, either human or artificial, since only the carrier itself can confirm this property. As we cannot get into another being’s mind, we cannot be convinced about its awareness. We base our faith that humans are self-aware on our intrinsic similarities. As we have the similar organs and identical brains, it is sensible to conclude that if each of us is self-aware, so is everybody else. If, however, the creature in front of us, even though behaving like a human, were encompassed with synthetic tissues, mechatronic limbs, and neural processors, we might react differently. The most common opposition to awarding electroniccircuit-driven computers self-aware status is the insight that, operating in a fully automated form, they cannot display originality, emotions, or free determination. A computer, similar to a washing machine, is a slave functioned by its constituents. It is rational that we must apply this analysis to machines’ biological complements. At a neural stage, the similar electrochemical reactions existing in machinery function in the human brain. Each neuron mechanically retorts to its inputs according to fixed rules. However, these methods do not prevent us from undergoing happiness, adoration, or illogical behaviors. With the appearance of artificial neural systems, the problem of artificial awareness becomes even more interesting because neural systems imitate the brain’s fundamental electrical behavior and offer the appropriate support for apprehending a processing system similar to the one accepted by the brain (Buttazzo, n.d., p. 26). A common supposition in the philosophy of mind is that of substrate-independence. The concept is that mental conditions can occur on any of a broad category of physical substrates. It is a system that employs the accurate sort of computational structures and systems. It can be related to awareness experiences (Bostrom, 2003, p. 2). The Brain and the machines: Over the years, chip development not only sustained, it sped up. Shorter-wavelength light was replaced, a more exact way of embedding impurities was planned, voltages were decreased, improved insulators, guarding designs, more well-organized transistor designs, enhanced heat sinks, denser pin prototypes and non-radioactive packaging substances were found. Molecular and quantum computers will be significant sooner or later, but humanlike robots are expected to turn up without their help. Research within semiconductor corporations, including operating prototype chips, makes it quite apparent that existing methods can be nurtured along for another decade. The chip which has traits below 0.1 micrometers, memory chips with 10s of billions of bits and multiprocessor chips with more than 100,000 Million Instructions per Second (MIPS) will be applied in the future. The circuitry will most likely slot in a rising number of quantum intervention components. As production methods for those tiny constituents are perfected, they will begin to capture the chips, and the speed of computer development may steepen further. The 100 million MIPS to equalize human brain power is anticipated to reach at home computers before 2030 (Moravec, 1998). How enviable and how viable is speech communication between human operatives and computing machines? This question often surfaces whenever refined data-processing structures are talked about. Engineers who deal with computers take a conventional attitude toward the attraction. Engineers having the experience in the domain of automatic speech identification take a conventional attitude towards the viability. Yet there is an ongoing interest in the concept of talking with calculating machines. If calculating machines are ever to be employed directly by top-level managers, it may be useful to provide communication by means of the most natural ways, even at substantial cost (Licklider, 1960). On 1 July 2005, the Brain Mind Institute and IBM commenced the Blue Brain Project. Using the massive calculating power of IBM’s archetype Blue Gene/L supercomputer, the objectives of this ambitious proposal are to replicate the brains of mammals with a high degree of biological accuracy and eventually, to study the steps caught up in the appearance of biological intelligence. IBM established the computer Deep Blue to contend against and ultimately defeat Garry Kasparov at chess, shaking the bases of our notions of intelligence. Deep Blue combined conservative techniques from computer science, but was able to win by animal force, considering 200 million moves per second. Nonetheless, this defeat of the master of chess by a computer on such an intricate cognitive task created the question of whether the pertinent world of an organism could merely be explained by adequate if–then conditions. It could perhaps be debated that artificial intelligence (AI), robotics and even the most sophisticated computational neuroscience viewpoints that have been applied to model brain function are just if–then-like conditions in different kinds. Variation and learning algorithms have particularly improved the power of these networks, but it could also be stated that these viewpoints simply enable the network to automatically obtain more if–then laws. Regardless of the intricacy of such a process, the quality of the process is much the same during any phase of the calculation, and this type of intelligence could thus be regarded as ‘linear intelligence’ (Markram, 2006, p. 153). The main restrictions for digital computers in the imitation of biological procedures are the severe temporal and spatial decree demanded by some biological procedures, and the restrictions of the algorithms that are applied to model biological procedures. If each atomic collision is replicated, the most influential supercomputers still take days to replicate a microsecond of protein folding. Thus, it is not probable to replicate complex biological processes at the atomic level. However, models at higher scales, such as the molecular or cellular stages, can detain lower-level systems and permit complex large-scale imitations of biological procedures. The Blue Brain Project’s Blue Gene can replicate a neocortical column (NCC) of up to 100,000 highly intricate neurons at the cellular stage (about 5 times the number of neurons in Aplysia californica), or as many as 100 million uncomplicated neurons (nearly the same number of neurons derived from a mouse brain). However, imitating neurons implanted in microcircuits, microcircuits implanted in brain regions, and brain areas implanted in the whole brain as portion of the process of comprehending the appearance of complex behaviors of animals is an expected development in comprehending brain operation and dysfunction, and the question is whether whole-brain replications are at all achievable. Computational power requires enhancing approximately 1-million-fold before we will be able to replicate the human brain, with 100 billion neurons, at the same stage of detail as the Blue Column. Algorithmic and replication competence (which guarantee that all possible FLOPS are used) could decrease this requirement by 2 to 3 orders of magnitude. Replicating the NCC could also act as a test-bed to process algorithms necessary to replicate brain function. This can be applied to generate field programmable gate array (FPGA)-based chips. FPGAs could augment computational paces by as much as 2 orders of magnitude. The FPGAs could, consecutively, offer the testing floor for the production of particular NEURON solver application-specific integrated circuits (ASICs) that could further augment computational pace by another 1 to 2 orders of magnitude. It could therefore be probable, in principle, to replicate the human brain even with existing technology (Markram, 2006, p. 158). One of the advancements in computer is the introduction of Electrical impedance tomography (EIT). It is a comparatively new method by which representations of the internal allocation of an object’s impedance are rebuilt from impedance computations made through electrodes located on the object’s surface. It has formerly been applied to measure alterations produced during usual and pathological brain action using an electrode ring located on the uncovered cortex of anaesthetized rabbits. Impedance reductions of 2–5 percent were calculated during electrical impetus of the forepaw or visual stimulus with an 8 Hz flashing light and impedance augmentations of up to 10 percent were computed during epilepsy. These alterations are mainly due to alterations in cerebral blood volume during functional movement or cell enlarging during epilepsy. As the resistivity of blood (170 Ω cm) is less than that of brain (500 Ω cm), an enhancement in blood volume reduces impedance. Cell enlarging aims to decrease the size of the conductive extra-cellular space, which raises impedance. If these alterations occur in humans during functional movement and can be determined with EIT, then EIT has the prospective to be developed into a clinically constructive neuroimaging structure to determine the much larger impedance alterations related to epilepsy (Tidswell, Gibson, Bayford and Holder, 2001, p. 167-168). Can computers emulate human intelligence? From the above discussion we may conclude that it is entirely likely for a computer to be built with the identical degree of intricacy and ability of the human brain. Nanoscale transistors, for example, will permit us to manufacture computers that are as slender as a sheet of paper. With that kind of technology it is entirely probable that a computer will be framed that imitates the functions of the human brain. The precondition for that plan, of course, is a more thorough understanding of how the brain operates. Certainly, the subsequent question to be asked is that do we define consciousness by the level of performance? The majority of computers can work swifter than the human brain at definite tasks. So the question is, ‘will Artificial Intelligence ever become self-conscious?’ Most probably the answer is positive. The games like “The Force Unleashed” apply an AI that intentionally tries to enable itself in a very human method. How that is carried out and how the decisions are taken instantaneously is not revealed so much, but it is sure that these games are not drafted in any manner. Thus, it can be concluded that in fact Artificial Intelligence will possibly become intelligent and self-conscious. References: 1. Bostrom, N, 2003. “Are you living in a computer simulation?” Philosophical Quarterly, Vol. 53, No. 211, pp. 243-255. Available at: http://simulation-argument.com/simulation.pdf (Accessed on Dec. 11, 2009). 2. Buttazzo, G, n.d. “Artificial Consciousness: Utopia or Real Possibility?” Perspectives. 3. Calvin, W.H, 1987. The brain as a Darwin machine. Nature 330 pp. 33-34 Available at: http://cogprints.org/22/0/1987Nature.htm (Accessed on Dec. 11, 2009). 4. Clancey, W.J, n.d. “Simulating Activities: Relating Motives, Deliberation and Attentive Coordination”. 5. “Cognitive Psychology”, n.d. Available at: http://psychology.derby.ac.uk/~Sigrid/cognitive/overview_handout.PDF (Accessed on Dec. 11, 2009). 6. Licklider, J.C.R, March 1960. “Man-Computer Symbiosis”. IRE Transactions on Human Factors in Electronics, volume HFE-1, pages 4-11. Available at: http://www.giacomoricci.it/Man-computer%20symbiosys.pdf (Accessed on Dec. 11, 2009). 7. Markram, H, Feb. 2006. “The Blue Brain Project”. Perspectives, Vol. 7. Available at: http://www.hss.caltech.edu/~steve/markham.pdf (Accessed on Dec. 11, 2009). 8. Miller, G.A, 2003. “The cognitive revolution: The historical perspective”. Trends in Cognitive Science. Vol. 7, No. 3. 9. Miller, K, Chinzei, K, Orssengo, G, Bednarz, P, May 22, 2003. “Mechanical properties of brain tissue in-vivo: experiment and computer simulation”. Journal of Biomechanics Vol.33. pp. 1369-1376. 10. Moravec, H, 1998. “When will computer hardware match the human brain?” Journal of Evolution and Technology, Vol. 1. 11. Neumann, J.V, n.d. “The Computer and the Brain”. Available at: http://cas.buffalo.edu/classes/dms/rtscholz/THEBRAIN.PDF (Accessed on Dec. 11, 2009). 12. Ramnani, N, Miall, R.C, Jan, 2004. “A system in the Human Brain for predicting the actions of others”. Natural Neuroscience. Vol. 7, No. 1. 13. Searle, J.R, April 20, 2004. “Is Brain a Digital Computer?” 14. Searle, J.R, Jan, 1990. “Is Brain’s Mind A Computer Program?” Science American. Available at: http://ia.ucpel.tche.br/~lpalazzo/Classificar/Vanzin/Penrose/Scientific%20American%20-%20Is%20The%20Brains%20Mind%20A%20Computer%20Program.pdf (Accessed on Dec. 11, 2009). 15. Searle, J.R, July 14, 2003. “Minds, brains, and programs”. Behavioral and Brain Sciences 3 (3): 417-457. 16. Searle, J.R, Nov. 23, 2007. “The Myth of the Computer”. The New York Review of Books. Vol. 29, No. 7. Available at: http://unbox.org/wisp/var/timm/08/ai/doc/philosophy/mythOfTheComputer.pdf (Accessed on Dec. 11, 2009). 17. Simon, H. A, 1990. “Invariants of Human Behavior”. Annu. Rev. Psychology. 41: 1-19. Available at: http://wexler.free.fr/library/files/simon%20(1990)%20invariants%20of%20human%20behavior.pdf (Accessed on Dec. 11, 2009). 18. Tidswell1, A.T, Gibson, A, Bayford, R.H, Holder, D.S, 2001. “Electrical impedance tomography of human brain activity with a two-dimensional ring of scalp electrodes”. Institute of Physics Publishing. Physiol. Meas. 22: 167–175. Available at: http://www.medphys.ucl.ac.uk/~agibson/work/papers/2Dheadeit.pdf (Accessed on Dec. 11, 2009). Read More