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COGNITIVE SCIENCE : YEAR-BY-YEAR

As a result of this course, the graduate will have acquired the skills to be able to;

1. Design a computer game using specialized knowledge of how the brain processes the information presented on the screen;

2. Help to diagnose a patient based on a print out of the relevant genetic information, an interview, and overt behaviour analysis;

3. Design a computer interface (CI) for specialized use, including for mentally and physically challenged individuals, as US law requires they have unrestricted access to information;

4. Assist – and of course direct – R+D in the multitude of applications relating to cognition that will emerge over the next several generations, including (but not restricted to) intelligent search, machine translation, human CI in general (including neural implants), speech processing, bioinformatics approaches to brain, and so on

5. Participate in an informed way on debate about how science impacts on society, particularly focusing on their own cutting-edge area.

6. Work as an applications programmer in a range of commercial and industrial environments

As in all these proposed courses, the student can take subjects from edX and/or Coursera*, and signatures of completion will be accepted as tentative proof that the student has mastered the material. However, in project assessments, those students that have no proof of mastery other than these signatures may be asked questions in oral examinations relating to the content they claim to have mastered. Alternatively, and particularly in the case of Coursera courses that do not give certs the students may ask to be examined on the material at UOI, and an exam will be prepared for them

It is envisaged that 30 academic units will be taken in the two introductory “years” which may of course reflect more or less chronological time There will be a minor thesis, with subject proposed by either faculty or student (7 credits) and a major thesis to be entirely conceived by the student (10 credits)

The challenges are these:

  • To begin the process of mathematical formation, initially through relatively introductory courses in calculus and algebra
  • Likewise, to familiarize the students with computers, beginning with introductory courses
  • To continue the computational formation with algorithms and applications development courses
  • To culminate with a set of subjects focused on cognitive science
  • Finally, a set of projects and advanced seminars

Focus specialisations are Symbolic Systems and Cognitive Neuroscience.

*We do not claim to own content linked at Coursera and edX. We simply suggest courses you can take online that satisfy our requirements. There are zero fees for full-time students. Others who wish to take one our own modules, like consciousness studies, may pay a suggested donation. Nobody turned away for lack of funds!
 

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1. Computer science including Algorithms:( from elements of searching and sorting onward) then applications development

2. Biosemiotics one: will focus on the specifics of human symbolic behaviour in a classical linguistics course. History of the area, primitive signalling systems in nature, the roots ofhuman symbolic behaviour.

3. Mathematics: Introduction to logic and linear systems

4. Introduction to the molecular biology of the gene, with classical data base, search, and signal processing issues introduced as mediated through biological process later in the course

5. Complementary Studies: students will have the option of taking a language, or studying universal cultural issues(In the manner of American studies at UC Berkeley, students have to take a module on cultural studies before graduating)

This entire first year plan may be implemented as follows;

Preparatory/year 1

FIRST SEMESTER

1. Mathematics

A preparatory course is at

https://www.coursera.org/learn/pre-calculus
https://www.coursera.org/learn/trigonometry

Calculus may be handled by

https://www.coursera.org/course/calcsing

 

or

https://www.coursera.org/course/calc1

 

2. Computer Science

There is good introductory material at

https://x.cs50.net/2013/syllabus

 

Either that or

https://www.edx.org/courses/MITx/6.00x/2012_Fall/info

will suffice for the first semester

An alternative is at

https://class.coursera.org/cs101/class/index

 

3. Logic

This course will satisfy the requirements

https://class.coursera.org/intrologic/class/index

 

4. Linear algebra

This course will satisfy the requirements here

https://www.coursera.org/course/matrix

 

SECOND SEMESTER

1. Algorithms 1

https://class.coursera.org/algo/class/index

is a very good start

compare https://www.coursera.org/course/algs4partI

which does not issue Certs

 

2. Applications development

SAAS gives a good intro

https://www.edx.org/courses/BerkeleyX/CS169.1x/2012_Fall/about

 

3. Introduction to genetics

https://www.coursera.org/course/geneticsevolution

 

4. BIOSEMIOTICS

The biosemiotics course, which incorporates an introduction to linguistics, rounds out the year

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1. Mathematics will cover the emerging sciences of chaoplexity as well as develop the first years material. Non-linearity will be a focus.

2. Cognitive science, likewise, will begin to explore the areas classically
considered as its constituent disciplines : philosophy, psychology,
linguistics, neuroscience, anthropology, Consciousness – Introduction to methodologies and thrust of the disciplines

3. Bioinformatics will continue to familiarise the students with the details of molecular, developmental, cell, and integrative biology, while demonstrating the application of computational techniques to these areas and their large overlapping areas, It will be introduced by familiarizing the students with statistical methods

4. Science and society ; An overview of contemporary science and its controversies, with an emphasis in ethical and philosophical Issues
It will focus on controversies in one subject as an exemplar; given its current centrality, biology is used..

5. Computing; Software engineering as applications development will follow up on the themes from the computing modules with an applied orientation, and the student’s skills in algorithms will be honed. Complier theory will place the applied work in computational linguistics in a formal languages context. Again, Complementary studies will either be a language or culture In this year, it is optional.

 

One implementation of this plan is as follows;

YEAR 2

Semester 1

1. Algorithms 2

https://www.coursera.org/course/algs4partII

This will not involve a certificate

 

2. App Dev 2

SAAS2 https://www.edx.org/courses/BerkeleyX/CS169.2x/2012_Fall/about

 

3. Computational linguistics

https://class.coursera.org/nlp/wiki/view?page=syllabus

 

4. Cognitive Science

This subject focuses on the subjects that comprise cognitive science; philosophy, psychology, linguistics, neuroscience, AI, anthropology, paramedical studies, engineering and biocomputing. The text uses is “the search for Mind”, third edition; chapters 1-3 will be covered in this semester

 

SEMESTER 2

1. Science and society

See summary and sample

 

2.Stats in the age of Big data

Non-certificate courses include

Introduction

https://www.coursera.org/course/compdata

More advanced

https://www.coursera.org/course/dataanalysis

https://www.coursera.org/course/datasci

 

3. COMPILERS

https://www.coursera.org/course/compilers

 

4. Cognitive Science

This subject focuses on the subjects that comprise cognitive science; philosophy, psychology, linguistics, neuroscience, AI, anthropology, paramedical studies, engineering and biocomputing. The text uses is “the search for Mind”, third edition; chapters 4-9 will be covered in this semester.

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This will involve a year studying or working abroad for students who have taken a foreign language, and otherwise job experience . Students will also do a project, and can take one or more subjects from their final year if they wish while on work placement. Alternatively, they may wish to skip this year and submit a minor thesis in the first semester of final year.

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1. Human Computer Interaction: multimodality, enablement, making computers invisible.

https://class.coursera.org/hci/wiki/view?page=Syllabusandcalendar

2. AI ;classical Symbolic approaches versus subsymbolic and statistical approaches

https://www.edx.org/courses/BerkeleyX/CS188.1x/2012_Fall/about

3. Consciousness studies — Philosophy of mind Classical approaches to mind and brain, recent developments
Introduction, sample lecture, Executive Summary

4. Computational linguistics ; exemplified in practical approaches to machine translation and speech processing.

https://www.coursera.org/course/nlangp

5. Final year project for entire last semester

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1. Neural networks: Bep, Art, Sigma pi, etc.

https://www.coursera.org/course/neuralnets

2. Computational neuroscience;

https://www.coursera.org/course/compneuro

3. Consciousness studies — Philosophy of mind Classical approaches to mind and brain, recent developments
Introduction, sample lecture, Executive Summary

4. Clinical work It is intended to get psychology certification for the course that will motivate practical work from “test bashing ” and
elementary first aid to preliminary diagnosis

5. Final year project for entire last semester

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Cog Sci studies mind as an informational system. The area of classical Cog Sci is well covered in such books as “The search for Mind”; what this final section involves is an unpacking of the contextual material that will ensure that graduates are ready for technical challenges for several decades, both in their professional work and as engaged citizens seeking to understand he effects of science on society.

The mathematics used clearly should use logic, with grounding in set theory. While this introduces the student to the world explored in the 2oth century by many greats, it also gives them a deeper sense of the Boolean operations central to computation, the concept of what a proof is that will be vital for programming, and the concept of a rule that will be critical for grammar in linguistics.

Secondly, there is a mathematical consensus on how to go from analog to digital, and how to analyze the elements of a time series with a view to prediction. Matrix theory opened the door to the recent breakthroughs in sparse coding that has seen a vast range in applications from the study of perception through to the engineering-based analysis of signals. Given that this branch of math deals with vector bases, it is inevitable that the linear systems courses will need to be thorough.

Thirdly, the Fourier transform led the way for analog to digital conversion, and is now complemented by more precise methods like the Hilbert transform. Knowing this area will assist the graduates if they decide later in their careers to become electronic engineers. A grounding in probability and statistics will help no matter what thy decide to do.

Fourthly, it is increasingly clear that the perceptual space in which we function has non-Euclidean characteristics; it is also plausible that our symbolic functioning exploits the same mechanisms as our spatial functioning. Students should be familiar with formalisms like Lie groups that can handle non-Euclidean spaces, while also giving them an exciting entrée into the techniques used in quantum mechanics

Likewise, they may decide to become programmers. That requires not only the five-finger exercises in algorithms like searching and sorting and the later For close to two generations, there has been an aesthetically pleasing approach based on the LISP language, and culminating in current subsets of Lisp like Scheme and pedagogical tools like BYOB that introduce even high school students to the notion that data and programs can have the same form – like DNA.

It is vital that students learn central biological mechanisms like gene expression, epigenetics, and micro-evolution. It is also desirable that they have a sense of such theses as that molecular biology is a reduction of biology to physics and chemistry, and that they be able to make up their minds as to the degree of success of this reduction.

The resulting skillset

Cognitive Science graduates should be able to perform the following tasks, among others;

1. Design a computer game using specialized knowledge of how the brain processes the information presented on the screen;

2. Help to diagnose a patient based on a print out of the relevant genetic information, an interview, and overt behaviour analysis;

3. Design a computer interface (CI) for specialized use, including for mentally and physically challenged individuals, as US law requires they have unrestricted access to information;

4. Assist – and of course direct – R+D in the multitude of applications relating to cognition that will emerge over the next several generations, including (but not restricted to) intelligent search, machine translation, human CI in general (including neural implants), speech processing, bioinformatics approaches to brain, and so on

5. Participate in an informed way on debate about how science impacts on society, particularly focusing on their own cutting-edge area.

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A cutting-edge cognitive science program

 

 

1: Introduction / 2: The institutional framework /3: Social Context/4: The Arts

 

Executive Summary

 

The native software industry in the USA, and with it other initiatives like biocomputing, has reached a crossroads. The USA is not producing enough gradutes and there is a cautionary example in Ireland where the attempt to expand student numbers without bringing in new, innovative courses was botched. A cost-effective way forward that creates a degree programme in cognitive science and offshoots in applications is outlined. The central point is that, instead of the cognitive science programme being divided over numerous university departments and the subject of endless turf wars, it is considered primarily as a set of technologies and administered in a cost-effective way that facilitates distance learning. That allows responsive curriculum change in a way that reflects the exponential advances being made in the disciplines contributory to cognitive science.

 

 

 

 

 

 

1: Introduction

 

The fears that the USA would end up outsourcing its call centers to countries with low corporate tax rates, and an uncertain future where basic costs are lower elsewhere, have proven justified. A generation’s living standards can be protected only once by passing on the bill in the shape of vastly higher property prices to the next generation, and that has been done culminating in the 2008 disaster. In parallel with this, entrepeneurship within the universities has been stifled by a management structure that rolls administrative, academic, and technical issues into a single hierarchy. The understandable response of many academics has been to opt for a quiet and civilized, if underachieving existence, in preference to diving into the crucible of dealing with what frankly are often under-qualified administrators in order to implement initiatives. In particular, subjects that change radically and quickly like cognitive science are ill-served by current US university structures. A new model is necessary for such multidisciplinary degrees.

One alternative is currently being worked out in the USA. Having put their institutional credibility at risk, public universities are now competing with the likes of the University of Phoenix (UP), with often disastrous results for students. In particular, the level of students debt in the USA, at over $1 trillion, now approximates that of total credit card debt. Nor do these private colleges guarantee jobs for their graduates, who are often poorly trained. UP finished a lawsuit from the government with a $67.5 million payment; 17 students have joined a lawsuit against Argosy; the list is for all practical purposes endless. In terms of the quality of their accreditation, there is nothing distinguishing the universities from these colleges.

There is room for private colleges to do cutting-edge programmes in areas that the unis cannot cater for. Instead, the former tend to put their money into sales ; UP’s marketing budget in 2009 at $130 million exceeded Revlon. Grand Canyon’s was $25 million, about twice what it spent on its teaching staff.

Stanford, a private university – and one of the world’s finest – resembles a coalition of small businesses with little top-down control. Thus, in founding Google, the entrepreneurs Larry Page and Sergei Brin capitalized on the world-class educational and technical expertise at Stanford, but were free to remove themselves from its control, and IP claims. The response of Stanford’s president, John Hennessy, has been personally to buy Google stock. Yet Stanford initially failed to see the potential of the Google project, and in fact Page and Brin were discouraged by their advisors in Stanford. Similarly, the symbolic systems program at Stanford, on which I have taught, is very badly administered, falling between departments and with no permanent staff assigned to it.

 

 

I propose an entirely new model of academia, entrepeneurship, and research implemented in an intersecting set of structures. While the specifics of the model make reference to particular locations, the founding impulses are much deeper in origin. I propose that an independent college specializing in cognitive science be initiated. The independent college should initially run a variety of courses, described below, that are currently not catered for by the state, and should seek university-level accreditation for them. The initial courses should service a research and development centre analogously to how the symbolic systems program serves Stanford. They will be taught over the web as well as in classrooms.

For the next section, we shall outline what cognitive science is, its importance to the knowledge economy, and the courses initially to be run over the web, and then in the College . While the buildings are being commissioned, a graduate wing can be set up at short notice. Moreover, the courses can be put on the web at short notice using the current Coursera and EDX offerings; as a proof of concept, I have already done so, mixing my Stanford and Berkeley courses with others that are available under Creative commons licenses and thus free for me to use. Indeed, it seems to be the case that the cost per hour of teaching can be cut by over 99% with judicious use of web resources as I outline.

Cognitive Science

 

Cognitive Science attempts to describe the mind as an informational system. It brings the scientific method to bear on problems of philosophy, psychology and artificial intelligence which have recently become empirically tractable with advances in modern brain imaging technology and the growth of previously unimagined computational resources. It is agnostic as to whether this description captures all aspects of mind; for example, higher-order consciousness may be outside its purview.

 

Cognitive science studies the intentional apprehension of the world in our minds; that is, how mental structures can refer to the world . It also is related to a set of useful technologies. Physics cannot be “reduced” to it, and in fact it is constrained to explain the concepts used in physics in terms no less complex than those of the mathematical physics involved, and also explain in cognitive and eventually neural terms how such mathematical physics are implemented in the brain.

 

 

While Cognitive science completes the explanatory circle in science by showing how the mind works, physics, biology – and indeed the social sciences – are independent of it and cannot be reduced to it. We live in a physical, biological and social world and experience ourselves as objects in all three

 

Cognitive Science graduates should be able to perform the following tasks, among others;

 

1. Design a computer game using specialized knowledge of how the brain processes the information presented on the screen;

2. Help to diagnose a patient based on a print out of the relevant genetic information, an interview, and overt behaviour analysis;

3. Design a computer interface (CI) for specialized use, including for mentally and physically challenged individuals, as US law requires they have unrestricted access to information;

4. Assist – and of course direct – R+D in the multitude of applications relating to cognition that will emerge over the next several generations, including (but not restricted to) intelligent search, machine translation, human CI in general (including neural implants), speech processing, bioinformatics approaches to brain, and so on

5. Participate in an informed way on debate about how science impacts on society, particularly focusing on their own cutting-edge area.

 

Cognitive Science is intimately tied to many previously disparate fields. As such it is in a position to promote interaction between fields and individuals who otherwise would not be in contact. The benefits of such interaction and the quality of academic work which results is in evidence in the proceedings of the international conferences that I hosted every year from 1992 to 1999, when we bowed to the inevitable( in that we were already experiencing enormous obstruction from the state) represented by the Irish government’s decision in setting up Medialab by suspending the conferences.

Moreover, the two conferences involved, CSNLP and Mind, have successfully been transferred to other locations, like Sheffield and Galway. These conferences, which were self-funding non-profit ventures, brought over a million Euro into the state from the attendees. John Benjamins and prestigious journals have accepted all proceedings we’ve offered for book publication. The calls for the conferences always originated from me. in 2003 the Irish government closed down the degree programme of which this proposal is a forerunner, after it had run successfully for 12 years, with recognition from the best universities in the EU manifest in their sending students to us for training, and recognizing our accreditation.

In the context of academia Cognitive Science is positioned to have its findings feed directly into many areas, including applied engineering. Already it has provided the bedrock research which has led to the development of artificial neural networks, expert systems, speech recognition systems, natural language processors, virtual reality interfaces and much more. Two early spin-offs from our own activity include a neural network tool which has been used by developers of cochlear implants (The Brain Construction Kit), and a speech recognition system (Xvoice) which has gained widespread use among quadriplegics and sufferers of repetitive strain injuries.

The next generation of researchers will not simply be designing new and smarter machines, as their forebears did. On a theoretical level, they will ask the question, “What is it to be human?” At the implementation level their work will involve systems which blur the distinction between man and machine. The ethical implications of such work, and of the pursuit of scientific knowledge in general are obvious. What is certain, however, is that a new set of technologies are emerging which emulate aspects of cognitive functioning. The first such are the range of decision support systems, often based on “neural networks”, and language tools like speech interfaces and machine translation aids that currently obtain.

 

The recent emergence of systems which directly capture neural impulses must, however, give pause. Is it possible that we are entering an era of more direct human-machine symbiosis for which this successful attempt directly to interface neural signals with the movement of a cursor is a harbinger? The ethical issues involved require conscious engagement. However, it is also important to note that cognitive science, with its emphasis on mentation (the workings of mind), offers an opportunity for a dialogue involving science, the arts, and consciousness studies.

The questions asked are weighty, and it is less than expedient to scrutinize them from a single viewpoint. As such the contribution of artists, psychologists, philosophers and those with backgrounds in the humanities in general will be drawn upon to bolster and inform the scientific process. Practitioners will need a broad skill set consisting of technical virtuosity coupled with an understanding of the subtleties of behavioral psychology, philosophy and a grounding in the social sciences.

The pace of technological advancement makes the delineation of technical skill sets a full time task; however, while the minutiae constantly change and require monitoring, the basic skills remain the same. A firm founding in mathematics and logic provides the individual with the general basis for a proper appreciation of both hardware and software. The details of the electronic engineering underpinning the computational explosion of recent years must be imparted. The ability to provide quality software solutions to immensely complex problems is an absolute essential for success in the field, and we will equip our students with same.

In particular, the mathematics part of the course needs to be constructed with great care. On the one hand, graduates need to know discrete mathematics like boolean logic and probability theory; on the other, linear algebra has experienced massive development in the first decade of the 21st century with techniques like “compressed sensing” revolutionizing analog to digital conversion. The issues to what extent the brain uses related techniques like “sparse coding” and indeed to what extent the brain’s operations need advanced tensor calculus and Lie groups to be modeled are interesting in the extreme. On the same theme, the differentiability of probability functions and their capacity to expression in some cases as a set of linear equations nuances the discrete/non-discrete dichotomy, showing how complex this area has become.

Similarly, the rapid advances in algorithms due to the requirements of bioinformatics needs to be reflected in the course. Techniques like singular value decomposition, Burrrows-wheeler transform, support vector machines, etc, common in genomic analysis, should be grasped in all their complexity and mathematical elegance as well as being used. Being on the cutting-edge requires studying the details of these techniques. Finally, even the biggest recent software successes – Google and Facebook – manifest a skillset that requires aptitude in linear systems, like the Page-rank algorithm.

 

 

Over and above traditional technical education, we endeavor to engage the individual’s sense of self and spirit. If we are to study the human mind, we must each begin with our own, and while it may be the case that emotive instincts are epiphenomenal anomalies, we allow for the reality of the subjective, and encourage its exploration. The relationship between science and society naturally follows from such activity, and will be considered at length. We intend to produce reflective human beings; better to engage them in real issues than the dross that has preoccupied Cognitive Science for 30 years with “just so” stories from evolutionary scenarios, the elimination of consciousness an attention-seeking tactic, and so on.

Cognitive Science has roots in and consequences for philosophy, psychology, linguistics, neuroscience, AI, anthropology, paramedical studies, engineering and biocomputing. Many of these fields are changing very rapidly, and a new generation of specialists is necessary. In particular, existing university structures cannot cater to the plethora of new findings which require restructuring of subjects on an annual basis. Universities simply cannot move fast enough, and for the USA to compete in this area, it needs new initiatives like this independent college.

The cognitive neuroscience stream will concern itself with intelligence as manifested in biological systems and the modeling of such systems in computational terms. Parallel distributed processing architectures will be explored at length, as will the details of electrochemical and cytoskeletal processing in the central nervous system. This burgeoning area has many applications from control systems technology to prosthetic implant development.

The applied intelligent systems stream, a counterpart to the symbolic systems program at Stanford, will concern itself with human-computer interaction, robotics and artificial intelligence in general. On a more theoretical front, it is here that we will explore the implications of embodiment for the problem of intelligent machine behaviors. Development of effective artificial perceptual systems in such an environment will present many challenges, but has and will yield many insights into human perceptual structures in addition to the obvious applications of successful models in industry.

 

For the remainder of this short paper, we shall explore the institutional framework proposed, some further philosophical background, and the consequences for the arts.

 

 

 

 

2. The institutional framework

 

There is a danger that, by focusing on programs which develop only specific skills, the USA will soon allow herself be outbid by countries like India for foreign computing investment. It is imperative that we also explore areas in which we can become true world leaders in the next few decades. We need to focus on well-chosen areas of excellence

We made a good start in multimedia. Now, however, companies are actively researching how to include the next stage, intelligent multimedia, in their products. Briefly, this requires that such products should have models of the needs and plans of their users. Consequently, intelligent multimedia research is a creative activity involving not just standard software engineering, but also expertise in artificial intelligence, cognitive science, user interfaces and so on. Moreover, once its world reputation in intellimedia is secured, it will have built in to its staffing structure sufficient flexibility to move on to other areas, as necessary. It is proposed to start immediately with a core staff of ten, and build naturally with respect to numbers on the courses.

The details of how courses can be taught cheaply and remotely is outlined on our home page. Briefly, all teaching material is put on the web – even for students physically on campus who have a chance to attend lectures -and the students themselves decide how much extra tuition they need, and pay accordingly. This prevents the kind of scams “for profit” private colleges risk, and the core fees are much lower.

Costs are also saved by using creative commons courses on the web, and charging only for examination on these courses. An accompanying document indicates how in 2012-2013, course for a full undergraduate programme in cog Sci can be taught for close to nothing.

 

3. Social context

It is indeed possible that many perennial philosophical problems will find themselves restated and perhaps solved due to current scientific research. An obvious one is the extent to which human knowledge is innate; recent scientific research is ever finding further evidence for innate knowledge. The Centre for cognitive science will be alert to such findings. Moreover, a glance at the conferences we’ve run will show more than a passing concern with the ethical.

 

 

Enablement and its associated practices and technologies cover a multitude of human activities. Providing education and health care to a child in a developing country is an enabling process; customising a computer interface so that a quadriplegic can use a computer enables that user; studying human metabolism with the goal of making seniors lead fuller and longer lives is an enablement project. Less obviously, providing personalised information to a CEO through wireless connections to facilitate business decisions enables that CEO; making computer interfaces more responsive, cheap, and flexible enables everybody, as does the provision of sustainable energy sources.

For some years I worked in conjunction with the Archimedes project in Stanford, and the department of molecular and cell biology at UC Berkeley on such projects. In 2001-2002, I established a lab specifically to develop applications. One such was a tool for sufferers from autism that allowed these mainly non-verbal children to communicate using a touch-sensitive pad, done under the auspices of Cure autism now! This tool presaged by fully a decade the ipad application featured in “60 minutes” in late 2011.

I envisage research projects in the areas above, and proactive canvassing of the many biotech, human-computer interaction, clean technology, and wireless start-ups that currently abound in Silicon Valley to encourage them to set up. The relationship with the college will be seen as close to what silicon Valley has with Stanford; while it is better that academic pursuits be given autonomy, students will be encouraged to work during breaks in these companies and, crucially, to start their own such. In the latter case, the college should be seen as a venture capitalist of first option. Coupled with the innovative structure of the College, this should provide the USA with a much-needed shot in the arm, as well as providing an alternative in the third level landscape.

 

4. Cognitive Science and the Arts

 

One cannot explore human cognition without being drawn to its finest manifestations, in the past education placed great emphasis on endowing a capacity for both the scientific and the artistic, and we are beginning to rediscover an appreciation for the wisdom of this in more recent times. The dual acts of perception and production of music provide an example of an increasingly active subject of research. Our 1999 conference “Language, Vision and Music” (published as a book by Benjamins) attracted a wide range of perspectives ranging from performing artists concerned with the emotive aspects of their activity to mathematicians intrigued by the implicit structure they have discovered in the language of music.

 

 

Cognitive scientists are somewhere in the middle, mediating the two approaches much as the perceptual system itself mediates between structured ontological reality and our subjective experience of it. This does not imply that anything less than the most rigorous scientific procedures are brought to bear upon the study of the arts, but we do seek to expand the territory of our inquiry, and are encouraged by the fruits borne of open-mindedness thus far.

That artistic performance endows the individual with heightened awareness of physical surroundings, rhythm and timing, and most interestingly inter-individual emotive power, is undeniable. As such, we have already established contact with people actively engaged in the performing arts at a high level of accomplishment, that we might better glean an understanding of the processes involved in the fine-tuning of the motor and perceptual processes to their highest degree. While we feel that there is great scientific utility to be obtained by a concern for artistic activity, we also feel that it is a worthy end in itself.

In particular, the now relatively moribund music industry will benefit greatly from its many able participants getting a technological fillip. (I myself have performed in some of the world’s top jazz and folk venues and run a successful music business). Cognitive science is primarily the study of human cognition, and therefore the activity of humans themselves.

In short, then, American cognitive science deserves and needs an autonomous third-level institution. There is an enormous market, not tapped by the likes of Kaplan, DeVry , U of Phoenix, and so on, of people who would like to do a world-class degree – even a difficult one – in their own time. The kind of experience I have in many of the best universities and research institutions in the world, with resulting publications in fields as diverse as AI and neuroscience, coupled with my business and administrative experience, makes this project an exciting and vital addition to American commercial and intellectual life.

 

COURSES TAUGHT AT BERKELEY AND STANFORD AND ON WEB

Neuroscience and experience

See summary and sample


 

 

BIO

 

 

ORDER AND CHANGE


 

 

IRISH MUSIC

See summary and sample

 

An undergraduate cognitive science program

 

The degree is a general cognitive science degree. This subject studies mind as an informational system; it incorporates elements of philosophy, psychology, linguistics, AI, ethnoscience and – increasingly – neuroscience and other biological sciences. The approach to cognitive science taken here is more bioinformatics based than its predecessors; yet it is also concerned with imbuing students with sufficient programming skills that they can get a job in that area.

With that in mind, I have had visiting positions in biology, neuroscience,, and computer science at UC Berkeley, AI at NRC Canada, and philosophy at Stanford. I outline the general structure of what I believe 21st century cog sci undergraduate education must be. Ireland is, to my knowledge, the only OECD country without such a degree. I have already written many of the courses; in the cases of the ones accredited by UC Berkeley and Stanford and developed and taught by me at these institutions, I include samplers with audio files. Immediately above, as stated, I work out how to teach the whole programme with judicious use of coursera and EDX product. Yet it is not planned directly to use this material; rather, students who have a signature of completion in these courses will be exempt from separate examination on these subjects. It is a form of credit transfer.

Readers will note that the degree has a biological slant; for example, text linguistics is studied as an instance of bioinformatics. In my opinion, that is the way of the future. A new set of subjects is emerging from the renewed engagement of biology with engineering; the application of these new areas of knowledge to the brain will be the cutting edge of 21st century science.

Similarly, all of the course will be available on-line. This will involve a mixture of video, slides plus audio, weblinks and indeed (e-)books. Much of the computer science material, to take one example, is already available on the web in all of these forms, and it seems pointless to try and duplicate it. Instead, the resources should be put into answering students’ queries and giving them feedback on their performance, whether they are physically or virtually present. In the case that a wholly new lecture is being delivered, or a students is presenting, opportunities for dialogue with her classmates over the internet will be afforded all registered students.

 

 

 

B. Sc. . In Cognitive Science

 

Years 1 and 2; two 12-week semesters, 7 subjects, 14 hours lectures and 14 hours lab per week. (In year 2 one subject becomes optional with consequent lessening of class time).At present, the labs are computing; it could well be the case that students will eventually have the option of doing biology lab as well.

 

Year 3; mainly work experience/study abroad with a project

 

Year 4; Mainly project with roughly half the previous class-time

Focus specialisations are Symbolic Systems and Cognitive neuroscience.

 

 

 

COMMON FIRST YEAR

1 Algorithms:

Analysis of Algorithms:

Basics of the analysis of algorithms, worst-case behavior, order notation, asymptotics, recurrences, summations.

Sorting and Selection:

Comparison-based sorting algorithms (mergesort, quicksort, and heapsort), medians and order statistics, lower bounds on sorting, radix and counting sorts.

Other Algorithms:

Algorithms on graphs, strings, and geometric objects.

NP-completeness:

Complexity classes P and NP, examples of reductions.

 

There are many good video resources on the web, for example;

 

http://video.google.com/videoplay?docid=-2333306016564732003 #

 

http://www.youtube.com/watch?v=KkMDCCdjyW8

 

http://www.catonmat.net/blog/summary-of-mit-introduction-to-algorithms/

 

 

is complemented by

 

http://www.catonmat.net/blog/mit-introduction-to-algorithms-part-one/

http://www.catonmat.net/blog/mit-introduction-to-algorithms-part-two/

 

etc, until part fourteen.

 

2 Cognitive science one:

 

Introduction to methodologies and thrust of the
discipline

It will use my book “The Search for Mind”

3 Biosemiotics one:

 

This will focus on the specifics of human symbolic
behaviour in a classical linguistics course

 

We all use language every day of our lives. Language, regardless of the particular dialect spoken, is the tool we use to express our wants, our needs, and our feelings.
Recently, many experts who study language have become convinced by an idea about this remarkable human trait that was, only a few decades ago, utterly revolutionary. These experts believe that the capacity for spoken language and the rules for its structure are not cultural but universal—a set of rules shared by humans in every culture and that even may be hardwired into our brains. Moreover, these rules apply regardless of which of the world’s 6,000 languages are being spoken. A sample web resource is

http://videolectures.net/mlcs07_monaghan_wip/

 

A more introductory set is

Human Language Series

?The Human Language Series-Part One

?The Human Language Series-Part Two

?The Human Language Series-Part Three

?The Human Language Series-Part Four

4 Mathematics: Introduction to logic and linear systems

Logic

 

Learning the possible worlds account of logical possibility, necessity and contingency, Understanding the difference between sense and reference, and the role of indexicals in natural language

Understanding what a function is and what it is for the connectives of propositional logic to be truth functional, Translating English sentences into the languages of propositional logic and predicate logic

Using truth tables to test for tautologousness, logical equivalence, consistency and validity

Using truth trees to test for consistency and validity

Doing natural deduction proofs in propositional logic to test arguments for validity and sentences for tautologousness

Learning about the semantics for propositional and predicate logic

 

Resource example;

 

http://webcast.berkeley.edu/course_details.php?seriesid=1906978469

 

Linear systems

The Geometry of Linear Equations, Elimination with Matrices, Matrix Multiplication and Inverse Matrices, A=LU Factorization, Vector Spaces and Subspaces

 

There are several video resources available, including

 

http://videolectures.net/mit1806s05_strang_lec01/

5 Bioinformatics one:

 

Introduction with
classical data base, search, and signal processing issues introduced as mediated through biological
process. It can follow a classical text like Polanski et al; in conjunction with the algorithms component, the student can begin to get to grips with algorithms like Burrows-Wheeler and Needleman-Wunsch, to be continued in year 2

 

This site catches some of the course;

 

http://videolectures.net/mlss07_gunnar_intbio/

6 Science and society one: .

An overview of contemporary science and its controversies, with an emphasis in ethical and philosophical Issues. It will use my book “Being human” (second edition)

7. Complementary Studies:

 

Students will have the option of taking a language, or studying universal cultural issues mediated through Irish, American (or any such ethnicity/nationality) studies (In the manner of American studies at UC Berkeley, students have to take a module on Irish studies before graduating in Ireland for the course being marketed there; in India, Indian studies etc )


 

 

 

 

COMMON YEAR TWO

1. Mathematics will cover the emerging sciences of chaoplexity as well as develop the first years material. Non-linearity will be introduced

 

http://ocw.mit.edu/OcwWeb/Mathematics/18-03Spring-2006/VideoLectures/index.htm

catches some of the impetus

 

Much of the cutting-edge of applied mathematics in the computational sciences requires good linear systems skills, and a sense of how probability theory can yield itself to algebraic treatment. This course will address these desiderata

2. Cognitive science, likewise, will begin to explore the areas classically
considered as its constituent disciplines : philosophy, psychology,
linguistics, neuroscience, anthropology, Consciousness as described in my “The search for Mind” (Third edition)

3. Biosemiotics History of the area, primitive signalling systems in nature, the roots of
human symbolic behaviour.

See summary and sample

4. Bioinformatics will continue to familiarise the students with the details of molecular, developmental, cell, and integrative biology, while demonstrating the application of computational techniques to these areas and their large overlapping areas, It is expected that by the end, the student will have a deep understanding of processes like singular value decomposition as well as the capacity to implement them computationally

5. Science and society will cover recent innovations. It will focus on controversies in one subject as an exemplar.

See summary and sample
 

 

6 Software engineering will follow up on the themes from the algorithms module with an applied orientation.

 

Introduction to Software Engineering, Overview of Phases, Systems Modeling, Process Modeling, Data Modeling, Production Quality Software, Software Design, Design Patterns, Architectural Design, Software Testing, Structural Programming, Software Metrics and Quality, Verification and Validation, Software Evolution, Software Reuse, Project Time Management and Quality Management Systems.

The material is well-handled in this freely available resource;

http://www.cs.washington.edu/education/courses/csep503/01wi/lectures/

 

http://groups.csail.mit.edu/mac/classes/6.001/abelson-sussman-lectures/

 

is a classic, more theoretical course

 

7. Again, Complementary studies will either be a language or culture mediated through Irish (or Indian, American etc) studies. In this year, it is optional.

COMMON YEAR THREE


This will involve a year studying or working abroad for students who have taken a foreign
language, and other job experience . Students will also do a project, and can take one or more subjects from their final year if they wish while on work placement. If students are already employed in a suitable environment, year 3 can be skipped completely once a suitable project is assessed and judged satisfactory

SPECIALISED YEAR FOUR: SYMBOLIC SYSTEMS
1 Human Computer Interaction: multimodality, enablement, making computers
invisible.

A sample resource;

2 Artificial Intelligence ;classical Symbolic approaches versus subsymbolic and statistical
approaches. Resources include;

 

http://www.youtube.com/view_play_list?p=6EE0CD02910E57B8

A New Marriage of Brain and Computer (Sep 21, 2007)

Artificial General Intelligence: Now Is the Time (Ben Goertzel) (May 30, 2007)

3. Consciousness studies — Philosophy of mind: Classical approaches to mind
and brain, recent developments
Introduction, sample lecture, Executive Summary

 

4. Computational linguistics ; exemplified in practical approaches to machine
translation and speech processing.

 

This area is again hotting up, and Google inter alia is very interested;

 

?Computers versus Common Sense (Douglas Lenat) (May 30, 2006)

 

?Wordmaking: What it take to succeed in hacking English and invent a new word (Erin McKean) (July 17, 2007)

 

5. Final year project for entire last semester

 

 

 

SPECIALISED YEAR FOUR: COGNITIVE NEUROSCIENCE

1 Neural networks: Bep, Art, Sigma pi, etc. again, my “search for mind” covers this thoroughly

 

http://www.learnerstv.com/lectures.php?course=ltv064&cat=Engineering

2 Cognitive neuroscience; EEG, Fmri, problems with temporal and spatial
discrimination; much from “search for mind”

 

http://ocw.mit.edu/OcwWeb/hs/icn/VideoLectures/embed01/embed01.htm

 

3. Consciousness studies — Philosophy of mind: Classical approaches to mind
and brain, recent developments
Introduction, sample lecture, Executive Summary


4. Clinical work: It is intended to get psychology certification for the
course that will motivate practical work from “test bashing ” and
elementary first aid to preliminary diagnosis

5 Final year project for entire last semester

 

 

Specifics of the techniques to be learned

 

Cog Sci studies mind as an informational system. The area of classical Cog Sci is well covered in such books as “The search for Mind”; what this final section involves is an unpacking of the contextual material that will ensure that graduates are ready for technical challenges for several decades, both in their professional work and as engaged citizens seeking to understand he effects of science on society.

The mathematics used clearly should use logic, with grounding in set theory. While this introduces the student to the world explored in the 2oth century by many greats, it also gives them a deeper sense of the Boolean operations central to computation, the concept of what a proof is that will be vital for programming, and the concept of a rule that will be critical for grammar in linguistics.

Secondly, there is a mathematical consensus on how to go from analog to digital, and how to analyze the elements of a time series with a view to prediction. Matrix theory opened the door to the recent breakthroughs in sparse coding that has seen a vast range in applications from the study of perception through to the engineering-based analysis of signals. Given that this branch of math deals with vector bases, it is inevitable that the linear systems courses will need to be thorough.

Secondly, the Fourier transform led the way for analog to digital conversion, and is now complemented by more precise methods like the Hilbert transform. Knowing this area will assist the graduates if they decide later in their careers to become electronic engineers. A grounding in probability and statistics will help no matter what thy decide to do.

Thirdly, it is increasingly clear that the perceptual space in which we function has non-Euclidean characteristics; it is also plausible that our symbolic functioning exploits the same mechanisms as our spatial functioning. Students should be familiar with formalisms like Lie groups that can handle non-Euclidean spaces, while also giving them an exciting entrée into the techniques used in quantum mechanics

Likewise, they may decide to become programmers. That requires not only the five-finger exercises in algorithms like searching and sorting and the later For close to two generations, there has been an aesthetically pleasing approach based on the LISP language, and culminating in current subsets of Lisp like Scheme and pedagogical tools like BYOB that introduce even high school students to the notion that data and programs can have the same form – like DNA.

It is vital that students learn central biological mechanisms like gene expression, epigenetics, and micro-evolution. It is also desirable that they have a sense of such theses as that molecular biology is a reduction of biology to physics and chemistry, and that they be able to make up their minds as to the degree of success of this reduction.