Math1 Comput. Mode&g, Vol. 14, pp. 1-3, 1990 Printed in Great Britain

Plenary Lectures

0895-7177/90 $3.00 + 0.00 Pergamon Press plc

MODELING: FROM PRIMITIVE BRAINS TO HYPERINTELLIGENCE

George Bugliarello

President, Polytechnic University

333 Jay Street

Brooklyn, NY 11201 U.S.A.

One hundred and thirty-six years ago in his epochal work, An Investieation of the Laws of ThouPht, George Boole wrote: “It would, perha to soeculate here uoon the auestron w K

s, be premature ether the meth-

ods’of abstract science are l;kely at any future day to render service in the investigation of social problems at all commensurate with those which they have rendered in various departments of physical inquiry” (Boole).

Thanks to Boole, we have come a long way. His algebra has helped us in programming computers and using them to translate the methods of abstract science, as he put it, into increasingly practical applications, in the social domain as well as in the physical one.

We may view the creation of computers and computer and mathematical models to help us exper- iment with the translation of abstract thoughts into guides for action, as the latest step in the development of the cognitive abilities of our soecies. It is. however. a step thatwe still do not quite understand in its full ’ implications, and neither, as a society, do we fully trust and put to full use. The public acceptance of mathemat- ical and computer modeling is still ambivalent, in spite of the fact that modeling has made possible significant advances, from engineering to medtcine, from business to defense, from agriculture to ecology and the environ- ment.

Perhaps one of the main reasons for the public’s ambivalence IS the disbelief that has come to be associ- ated with some models of economic or financial systems. Economic models often are only as of the very imperfect science behin j

ood as the theories them, and financial

models have been held responsible --oversimplistically and unfairly -- for the kind of programmed automatic manipulations of the stock market that led to the last Wall Street panic in 1987. In other countries, a models have been seen as the indispensable un d

ain,. erpm-

nings of centralized economic decisions, and thus responsible for the lack of economic progress and flexi- bility.

The Evolution of Our Cognitive Abilities

The evolution of our cognitive abilities has its beginning in some of the simplest forms of life, and will progress beyond what we are and we are able to accom- plish today. The choice of a starting gression is difficult and somewhat ar

oint for that pro- ’ g. ttrary. For our

purposes here, let me consider a unicellular animal like an amoeba, simply to denote a minimal sensor-motor capacity-- a minimal capacity to sense the physical envi- ronment and react to it with appropriate motion.

From such modest beginnings in single cell organisms, the ability to sense and to take corresponding action has evolved through a long progression of “designs.”

The simple sensing and reaction mechanism evolved into one in which an intermediate and crucial step was interposed between sensing and reaction: the comprehension of spatial and temporal organization. In a broad sense, we could say that the beginning of com- prehension is the beginning of modeling.

Comprehension clearly exists at various instinc- tive levels in animals hunting for prey -- and in animals escaping the hunter. However, it made a dramatic advance with the emergence of conceptual awareness in man, an emergence tied to the development of our cere- bral cortex. Conceotual awareness was in turn cruciallv advanced by the debelopment of a symbolic capability,* which eventually, as its most obvious and dramatic consequence, led to language and the ability to clarify and precisely define concepts (e.g., Reynolds).

As our sensor-motor capacity became enhanced by conceptual awareness and by language, a third ele- ment in the development of our co nitive abilities became possible -- the capacity to ormulate hypotheses B and pro ositions, and to test them against other hypoth- eses an B propositions. Without this ability, we would not have science and technology, and neither for that matter would we have the ability to develop systems of values, and hence philosophy or religion.

The formulation and testing of hypotheses against propositions is the crowning intellectual achieve- ment ofour species, one that goes hand in hand, in terms of evolutionary importance, with toolmaking. The achievement has been enormously enhanced by the development of mathematical and computer models, as well as other sorts of models.

From Bio-Social Intellipence to Hwerintellieence

Models, of course, originate in our brain. Our brain made possible what can be called bio_social intelli- gence or the collective intelligence of groups. In nomadic societies these erou~s were limited to the size of the clans or of the tribes (e.g., Dubos) -- perhaps no more than some 500 people, which is also the character- istic size of today’s few remaining primitive groups, such as those in Amazonia. During the last evolutionary plateau of our brain, now 50,000 years olds, our collec- tive intelligence has come to involve the interactions of

2 Proc. 7th Int. Conf on Mathematical and Computer Model@

many more people -- millions. This expanded biosocial intelligence has given us culture and the specialization that has made civilization possible --and it has given us machines.

Until recently, machines were only tools to modify our natural environment and create other machines. But with the computer, machines have become much more. They have become complements to our brain. Today, with the diffusion of computers - - particularly personal computers -- and their growing interconnections via powerful telecommunication net- works, a new evolutionary step is emerging in our cogni- tive capacity.

biolo This is a step that moves our species beyond

ical and biosocial intelli ence to what can be calle $ bio-social-machine inte 11 Pg ence -- an intelli ence in which the machine is an integral part of our abt ity to .f sense, feel, memorize, abstract, recognize patterns, make logical decisions. We cannot fully fathom yet the power and the implications of this new step, but it is clear that it will present an immense new challenge to those engaged in modelin This is a challenge so fun f

and to our entire society. amental that, if imaginatively

responded to, will lead to what properly may be called hyperintelligence (Bugliarello). (I use here the prefix “hyper” not only in the normal sense of great magnifica- tion, but specifically to denote the synergism of some- what comparable capacities in the biological, social and machine domain.)

The foundation of this new evolutionary step, whether we call it bio-social-machine intelligence or hyperintelligence, is our increasing ability tolink tens and, soon, probably hundreds of millions of machines all over the world. This enables us to create a global net- work in which everv node is a human being. with his billions of neurons; interactin millions and soon hundreds o B

with a com$ter and its millions of memory and

logical elements, and interconnected to every other nodal point on the globe, across frontiers, across cul- tures, across institutions.

In a ent world, t 1.

eaceful and, to be hoped, increasingly afflu- IS global network will continue to grow

rapidly acquiring, possibly by the first quarter of the next century? dimensrons somewhat comparable to those of our bram. This will enable us collectively to achieve hyper feats of intelli logical decisions an d

ence, memory, pattern recognition, so on, that will expand by a quan-

turn jump the intellectual capabilities of our civilization.

Hvperintellieence and Modeling

The cognitive abilities of our brain have iven us increasingly sophisticated models of reality that % ave made posstble our biolo ical survival. We can harbor

-- this is, the co nitive moral potentra of the 3

global network we see emerging, will improve our models and simulations and. throueh them. the func- tioning of our society and the chanzes of survival of our species. In the harsh reality of nature, that survival, as we know, is by no means assured, so that any new tool we can use to improve our chances gives us further hope.

It is inevitable that our models. as anvthine oro- duced by humans, and indeed as our own biology:\;ould fail us from time to time. Hyperintelligence gives us the ho

.P e not only to enhance ourreach, btit to reduce those

fai ures. Todav, when our models fail us. thev do so for a variet is aske dy

of causes. They may fail us because’too much of them, or because of too naive or limited a

view of the phenomena to be modeled -- for instance for lack of sufficient inputs or of sufficient skills in idealiz-

ing a phenomenon. At times some failures occur for lack of sufficientlv uowerful machines to carrv out a model. Most fn&;atingl of all, some failures occur not because of any intrinsic dy efects of a model, but simply because a model may not be directly connected to action -- primaril that woul d

for lack of confidence of the policymaker use it.

These failure are probably more often encoun- tered in the modeling of social phenomena -- for instance the the spread o B

reat uncertainties still present in modeling AIDS (actually a bio-social problem), or in

predicting economic upturns or recession. But the fail- ures also exist in the physical models are primary examples have been hampered by lack of suf lites notwithstanding --and lack of machines adequate to the task -- the current supercomputer notwithstanding. As a result, we still do not have sufficiently accurate prediction of local weathers or of storms.

The lack of confidence of the policymaker in a model is both instinctive and complex. Often a major part of it is lack of understandin a model must approach that oft !?

that the complexity of e object being

modelled. Nature certainly knows this. As biological organisms we could not hope to begin to model some aspect of reality if we did not have m our brain -- in the hardware of our models -- a mechanism of high com- plexity, with tens of billions of interconnections. AS a result of lack of confidence of the policymaker, a model is often starved of resources and support and thus handi- CaDDCd a n&xi. This is narticularlv true in the social domain, where complexfty and the”‘fuzziness” of inputs are a multiple of those encountered in dealing with physical phenomena.

The lack of direct connection between model and action is thus a difficult topic. Direct connections characterize our own physiology, actuate robots, control nuclear power plants and telephone networks, and trig- er

s automatic buymg and sellmg programs m Wall

treet. Some very direct connectrons also occur in some sectors of defense -- being for instance key to the con- cept of a strategic defense initiative. However, recent episodes of shooting down the wrong target have put somewhat of a damper on the enthusiasm for direct connection, and so did the Wall Street panic of 1987. Yet, without the direct connection to a model, action, if taken at all, is too often late or inade for instance the case of the Exxon Va 1

uate. Consider dez, where a great

deal of time was wasted before effective action was taken. The slowness in responding to natural disasters of that kind is only matched by the slowness in taking action to respond-to future natural disasters that models predict, such as those due to overpopulation and damage to the environment. Hence the tra

f edy, in spite

of all our considerable skills in modeling, o the destruc- tions of the Brazilian forest, of human hunger. or of the persisting death toll in floods.

The emer thesis of biosocta and machme intelligence gives us the 3

ence of hperintelligence as the syn-

possibility to greatly improve this situation. The interac- tion of millions of human-machine nodes, often particu- larized for specific affinity groups and specific purpose, can give us, intrinsically, a great abundance of sensors and m imme c!

ut points, enormous distributed computer power, lacy of feedback and also immediacy of connec-

tions between model and action. At the same time, the emergence of hyperintelligence will present our society, and particularly the modeling community with a set of formidable challenges, some easily perceivable today, other ones still difficult to fathom.

Proc. 7th Int. Conf. on Mathematical and Computer kiodelling 3

The Challenees to the Modeline Community

The fundamental challenge to the modelinn community is to develop models%f hyperintelligence and of the global network that constitutes it, and also to model the rmpacts of hyperintelligence on our society. First of all, we need models of how the global network will function as it develops probably as a coalescing of specialized or regional networks -- models, that is of the overall network organization, of the interconnections of distributed memones, of the protocols for communica- tion, and of how to balance local and global activities. This is far from a trivial task if one thmks of today’s

f reat difficulties in modeling ‘ust tele hone networks.

L .P. n the case of a global nehvor , the dl faculties are com- pounded by thekeed to provide each user or grou of users with an a

.P propriate filter -- and hence a mo g el--

to make posslb e, for example, the sampling of meteoro- logical information about a given region from thousands of inputs, or of world opinion from millions of inputs.

Next we need models of the development of hyperintelli ence as it is made

f. P ossible by the global

network. T IS involves basical y two sets of interactin tasks: modeling the development of fundamental inte f - ligence capacities, such as %sociation, logics or pattern recognition. and modeling the develooment of other capaiities associated with-intelligence’ -- such as playing, art, emotion. To consider this second task, for instance, what kind of model could we envision for network art -- that is. art to which all interested members of the net- work-hould contribute -- or for network “Olympics” involving real time mathematical skills competitions among r&lions of people?

It is evident that these modeling tasks associated with hyperintelligence involve a merger of modeling, design and operation, reinforcing the need discussed earher to connect model and action. It is also evident that hyperintelligence itself is a conceptual model to describe the new bio-social-machine reality -- a reality in part designed and in part having a momentum of its own.

However, modeling the emergence of this new phenomena is onlv one Dart of the challenge. There are ko other issues &at should involve centrally the model- ing community. The first is how to take advantage of

hyperintelligence and the more powerful models in t !il

lobal network to develop e sciences and in engineer-

ing, utilizing the synergism of interconnected communi- ties of humans and machines working in the same field, or having common interests.

The other challenee is how to model the imoact of a hyperintelligence cafability on our institutions: our values, and on how we do things. Clearly that impact is bound to be enormous -- on our schools, on our political and social views, on how we do science and engineering, on how we deliver health care and other social services, and on how we do business.

Modeling is central to the new fusion of the biological, the social and the machine domains -- to makin cessfu k

this new evolutionary step of our species suc- . Since the time we emerged from the trees it is

indeed this growing ability to model not only what it is, but also what will or could be, that is emancipating us ever more from the strictures of our animal ori in and from the fatal consequences of the harsh law o B the survival of the fittest. Modeling has enabled us to anti- cipate the consequences of bad designs and decisions, rather than suffer them.

The jury is still out on our species, but our ho K

es for survival and for further enhancement of our reac on our planet and beyond, are closely tied to the pow& and vision of our models, and, now, to the promise of hyperintelligence.

References

Boole, George. The Laws of Thought. Dover, New York, 1953.

&IO edge. C eatlon. Diffusion.% Bugl$rello, George. “Toward H erintelligence,”

No. 1, Septkmier 1988,67-89. ilization, Vol. 10,

Dubos, Rene. Beast or Angel? Scribner, New York, 1974.

Reyonlds, V. The Biology of Human Action. Freeman, Reading, 1976.