FUTURE OF CS

1. FUTURE OF CS CRUM’S ACHIEVEMENTS CRUM’S CHALLENGES

2. FUTURE OF CS . CRUM’S ACCOMPLISHMENTS • CRUM has accomplished well in representing several functions of human mind. • CRUM’S CHALLENGES • CRUM does not consider human mind functions like emotions and consciousness.

3. Other approaches • Any other approach to mind has to surpass CRUM in its ability to explain human problem solving, learning and language. • In past half century cognitive scientists shed lights on many aspects of human thought. But still there are aspects like consciousness and emotions that are elusive.

4. Integration • Integration of research with other fields like psychological computational anthropological linguistic philosophical neurological disciplines can solve such problems of mind research puzzles • 3 kinds of research integrations can be possible

5. Integration • 1) Most general conceptual level cross disciplinary integrations possible,as the researchers from different fields continue to recognize and follow each others work. Students and researchers restricting their interest / learning to a single discipline miss on broader understanding of mind and possibility of creative advances in their own discipline. If study of neurochemicals is important to CS then molecular biology can be an important contributor to CS theory.

6. Integration • 2) CS should continue with experimental integrations thru different data collected by diff disciplines. • Research on language can combine linguistic psychological and neurological data

7. Integration • Diverse kinds of data should be unified conceptually to so that they point to robust conclusions about the nature of language use. • Imagery research and other aspects of thinking are benefiting from a combination of psychological and neurological experiments. • Solving the consciousness problem requires behavioral, neurological and experiential data that we have because of our conscious experience. Experimental data from molecular biology will also become increasingly relevant. • 3) Theoretical integration made from computational ideas and simulations. • Analogy between thinking and computation has made 2 great contributions to the understanding of mind. • 1) It has provided a host of ideas about mental processes and structures generated a complex account of mind that integrated behaviorism and dualism (expand on this) • Mind = computer type concept and how it might be programmed, led researchers produce precise and detailed account of mental operations.

8. Computational hypotheses can be made precise enough to be programmed to and tested by running simulations whose performance compared to human thinking. • Simulations are valuable tools to understand mechanisms at many levels like cognitive neural molecular and social. • Computational models can aid our understanding of relations between different levels of mechanisms ex social to cognitive, cognitive to neural, and neural to molecular. • Models need to incorporate multiple levels to help understand social phenomena in the field of social neuroscience. Molecular mechanisms contributing to consciousness may be relevant to understanding cognition.

9. Computational theories • Computational theories have been integrated in that they cross the boundaries of different approaches. Theories of mental models of, concepts, rules, analogies and imagery are increasingly being tied both experimentally and theoretically to neural activity. Computational models that use multiple representations concepts and rules have been developed. Such cross- fertilization and hybridization enriched by dynamical systems ideas would be a part of CS.

10. Future of Human and Machine Intelligence. • Increasing attention to the physical, biological and social contexts of cognition can be expected. CRUM is being expanded to CRUMBS, The Computational Representational Understanding of Mind Biological Social. Problems like consciousness will yield to an integrated multidisciplinary approach. • Dark side to continuing development of computational models of human and machine intelligence. • Consider the movies Matrix, Terminator – Intelligent machines dominate and mistreat humans. • This is not a crazy scenario.

11. Kurzweil 1999 Moravec 1998 estimated that increase in computer speed will make human intelligence possible in machines. Machine intelligence may treat humans well but there is no reason to expect that super intelligent computers would be programmed to hold importance to human interests. • A program that makes an extraordinarily intelligent computer possible would be large and complex to be written by humans. • It would be the result of generations of evolutionary self improvement. Programs would evolve to further the goal of computers rather than humans. Human ethical beliefs are closely tied in with emotional reactions such as empathy and compassion. We should not expect Artificial Intelligence to have emotions and consciousness like humans. Hence such future generation super intelligent computers might be psychopaths incapable of caring of humans whose intelligence they have surpassed.

12. There are 2 reasons for the scenario in which humans are superseded by intelligent computers to be farther off • 1 They underestimate the computational power of humans • 2 They overestimate the ease with which super intelligent computers can be programmed. • Their prediction is based on exponential increase in processing speed of the computer chips. • Kurzweil estimates the computing speed of the human brain at around 20 million billion calculations per second based 100 billion neurons each with 1000 connections and slow firing rate of 200 calculations per second. Assuming continued exponential increase in chip speed digital computers will reach 20 million billion calculations per second by around 2020.

13. The molecular chemistry of the brain suggests that such estimate of computational power of the human brain can be very misleading both quantitatively and qualitatively. • If we include the number of brain proteins as the processors of human brain not just neurons we get a billion times 100 billion. The number of computational elements in the brain is more than 10* 11 to 10*12 neurons. • The discussion on hormones shows that the number of computationally relevant causal connections is greater than 100 synaptic connections per neuron. • It is difficult to estimate the number of neurons with hormonal receptors that can be influenced by single neuron that activates hormone secreting glands but the number must be huge.

14. If the number of neurons with hormonal receptors is 1 million and if every brain protein is viewed as a miniprocessor than the computational speed of the brain is 10*23 far greater than 10 *15 which Kurzweil expects by 2020. Although less than where he expects computers by 2060. • Thus digital computers are much farther away than Kurzweil and Moravec estimate from reaching the raw computational power of the human brain.

15. Intelligence is not a matter of raw computational power but requires that the computer have sufficiently powerful program to produce the desired task. • Macintosh G4 laptop computer can calculate 2*100000 calculations per sec in the same amt of time in which human brain can calculate 2*5 but computer lacks programming to understand language and solve complex problems. • Kurzweil and Moravec are aware that it is daunting to write huge software to enable future super fast computers to approach human cognitive capabilities. • They assume that evolutionary algorithms allow computers to develop their own intelligent software. • Evolutionary computation which uses algorithms modeled on human genetics is a powerful means of developing new software (Koza 1992) • Evolutionary computation is limited by the need for humans to provide a criterion of fitness that the generic algorithms serve to maximize. In humans the evaluation of different states is provided by emotions which direct us to what matters for our learning and problem solving. Computers currently lack intrinsic biologically provided motivation and so find it difficult directing their problem solving in non routine directions.

16. Software might be developed that does computers what emotions do for humans. Current computational research on emotions has to decipher complexity of human emotional system, its numerous neurotransmittors and neuromodulators. Researchers are treating emotions as symbolic or electrical phenomenon rather than as chemical one. The complexity of human emotions based on looping interactions among neural, hormonal, and immune systems, may be too complex for people to figure out how to program created by humans to evolve. • We can get an approximate understanding of these systems by using computer simulations and other methods to sketch the mechanisms by which they operate.

17. Computers • Computers of great intelligence in special areas will be developed regardless of the several challenges. It ma be difficult for AI researchers to duplicate human brain, But intelligent computers might be developed by other means like the IBM managed to develop world’s best chess player by combining clever software and fast computing chips. • We should not expect a computer to have all mental capacities of humans or anything like consciousness, which may be intrinsically ties to human emotions and peculiar to human brain chemistry.

18. There is doubt that the present generation is in serious danger of becoming obsolete or subservient to intelligent machines. It would be tragedy to human species if future generations inhabit in a world dominated by intelligent computers. • More optimistic scenarios • People acquire a kind of immortality by virtue of the transfer of their neural structures to computer chips • This may be technologically more feasible than transporting between spaceships in Star trek. • The most scientifically plausible scenario is the one in which people integrate machines more and more into their cognitive operations. • People may some day have a device implanted in their brains that provides direct access to internet and interpersonal communications.

19. Your future in CS • Challenges of CS throw more prospective opportunities for research. Undergraduates studying mind face difficulty since mind can be studied through various disciplines. They can find relevant courses in one of the disciplines.

20. Graduate students forced into specialization can follow one of the following paths. • Pick an aspect of mind that is interesting to you. • Pick a methodology • A) Experimental designing • b) Programming • c) Collecting linguistic examples. • Keep your eyes open to other fields • Integrate and collaborate.

21. CS is interesting and exciting involves multifarious interdisciplinary approaches that center around CRUM which is in need of expansion and supplementation. Many exciting projects in CS are awaiting new investigators. CS has had numerous achievements theoretical and applied. Progress in understanding of mind will require cross disciplinary experimental and theoretical integrations. The development of AI that might surpass human intelligence raises difficult ethical issues. Students contemplating a future in CS have a wealth of problems and approaches to choose.

22. Neuromodulator • A neuromodulator is a relatively new concept in the field, and it can be conceptualized as a neurotransmitter that is not reabsorbed by the pre-synaptic neuron or broken down into a metabolite. Such neuromodulators end up spending a significant amount of time in the CSF (cerebrospinal fluid), influencing (or modulating) the overall activity level of the brain. For this reason, some neurotransmitters are also considered as neuromodulators. Examples of neuromodulators in this category are serotonin and acetylcholine

23. Behaviorism • Behaviorism is a philosophy of psychology based on the proposition that all things which organisms do including acting, thinking and feeling should be regarded as behaviors. • The school of psychology maintains that behaviors as such can be described scientifically without resource either to internal physiological events or to hypothetical constructs such as the mind. • Behaviorism comprises the position that all theories should have observational correlates but that there are no philosophical differences between publicly observable processes such as actions and privately observable processes such as thinking and feeling.

24. Dualism • In philosophy of mind, dualism is a set of views about the relationship between mind and matter, which begins with the claim that mental phenomena are, in some respects, non-physical. • Ideas on mind body dualism originate at least as far back as Zarathushtra. Plato and Aristotle deal with speculations as to the existence of an incorporeal soul that bore the faculties of intelligence and wisdom. They maintained, that people's "intelligence" could not be identified with, their physical body. • A generally well-known version of dualism is attributed to René Descartes (1641), which holds that the mind is a nonphysical substance. Descartes was the first to clearly identify the mind with consciousness and self-awareness and to distinguish this from the brain, which was the seat of intelligence. He was the first to formulate the mind-body problem in the form in which it exists today.

25. Neuromodulation • In neuroscience, neuromodulation is the process in which several classes of neurotransmitters in the nervous system regulate diverse populations of neurons (one neuron uses different neurotransmitters to connect to several neurons). As opposed to direct synaptic transmission, in which one presynaptic neuron directly influences a postsynaptic partner (one neuron reaching one other neuron), neuromodulatory transmitters secreted by a small group of neurons diffuse through large areas of the nervous system, having an effect on multiple neurons. Examples of neuromodulators include dopamine, serotonin, acetylcholine, histamine and others.