Brain & Computer--Dynamic Modification

[ Feedback & Cybernetics | Levels of Abstraction | Dynamic Modification by Learning | Genetic Algorithms | General Learning Systems & Domains ]

dynamic Modification means the ability to keep changing as circumstances change.   Adaptability, in other words, is a sign of intelligence because it requires both acquiring information and deciding on a course of action based on the input.  As more and more information is required to make better decisions, the concept of learning is introduced into the field of AI.  First of all, the story behind dynamic modification begins with feedback systems and cybernetics.

Feedback & Cybernetics

The notion of feedback has been a long-standing idea in biology and in mechanics.  For example, temperature regulation is a feedback process whereby a person maintains his 98.7 degrees Fahrenheit internal body temperature by having the brain constantly check on the current temperature of the body, and either slows down the rate at which cells metabolizes and release less heat or vice versa.  Similar in function, a thermostat either turns on or turns off the air conditioner based on the temperature of the room.  In both cases, it is the control mechanism(temperature) that determines how the system reacts which modifies the control mechanism and the new control feeds back to the system to stop acting.

Mathematician and engineer Norbert of MIT was one of the first Americans to link how the mind and machines can be similar.  Applying the Feedback Theory, proposed that any system that reacts to a control mechanism can be considered an information processor.  In a sense, the thermostat decides that the room is too warm and acts accordingly while the brain does the same with the body temperature.   However, the more complex brain also has feedback mechanisms to regulate glucose levels in the blood, bone mass, and other physiological factors.  Creating the field called cybernetics--the science of control-- even went as far as to speculate that all intelligent behavior is attributed to highly complex feedback mechanisms.  In other words, intelligence stems from information processing.(Crevier 28)

[ Top ]

Levels of Abstraction

Since how one learns is dependent on how one's knowledge base is arranged, one way to look at learning is by examining how the brain processes information.  Basic learning can be depicted as taking in data from the physical world, translating the data into some comprehensible form, and fitting the information in an appropriate place within the knowledge structure.  If one assumes the brain's memory to be like a semantic net, then the information may provide ways to relate the new fact to existing facts in memory.   So a deeper step in learning would be forming new knowledge from new facts.

As mentioned previously in the Inference section, humans have the ability to summarize facts collectively into generalizations.  There could be many generalizations for the same group of facts--men are generally more aggressive than women, men usually make more money than women, and women tend to take care of the children more than men--are all generalized knowledge taken from factual observations about the sexes.  What is learned in generalization is the collective relationships between a certain amount of facts--another level of abstraction.

In the third level of abstraction, learning not only relates to the forming of knowledge between facts, but forming knowledge between generalizations.  Whether it is by conscious or unconscious thought, the brain can form more knowledge between a group of facts by perhaps finding similar structures between two unrelated generalizations.   Most people can determine how the play West Side Story relates to Romeo and Juliet relate to one another because the similarities in the plot--two lovers(Fact #1) are kept apart(Fact #2) because their affiliated groups are feuding(Fact #3)--despite the fact that the languages in the plays are literally significant--the former is written in modern English while the latter is written in old English.  In this case, one has generalized the actions and the dialogue in each play into their significance, then generalized the meanings in each section together into a summery the plot or theme, and then compared the plots and/or themes for similarity.  If doing that does not establish a clear relationship between the two plays, the meanings comprising the plots may show may match up many times which would lead one to form the overall(top level) generalized conclusion that West Side Story and Romeo and Juliet are very similar--a new fact plus the similarity itself is a relation(i.e. knowledge)!

The power of the human brain for generating new knowledge for learning is a compelling suggestion that the methods designed to replicate intelligence in machines must be as flexible and robust as a real brain in fact-manipulation.  There have been several ways in which this goal has been approached by various people.

[ Top

Dynamic Modification by Learning

Learning is inextricably linked to intelligence because it is both the means and and end of cognition.  The existing structures in the brain allow one to learn in certain ways.  As one gains information through learning, it is the very storage and arrangement of the knowledge in the brain that redefine how one functions--including how one learns in the future.  Quite simply, knowledge and learning affect one another in a feedback loop.  For example, a person may find that he has better reading comprehension when he verbalizes the words he reads.  First of all, he has learned that how to read more effectively.  With this new knowledge, he can learn more when he reads from now on.  Thus in a broader sense, learning is not just the taking in of information, but it is the means to change how one thinks and learns later.

[ Top ]

Genetic Algorithms

As a subset of computer science, genetic algorithms are programs that can improve themselves by modifying how they function.  The computer doesn't necessary modify the lines of code in it's program per se, but the program can be robust enough to change how it works.  For example, in the Traveling Salesman Problem, a genetic algorithm can be used to find out the shortest route a salesman should take to travel through a certain number of cities.  The program randomly tries out different paths and notes how long it takes(usually based on the number of computer clock cycles) to reach a certain destination.  Taking the path that is the most efficient to the first city to be traveled, the program starts on the next city and the process starts over again.  At the end of this trial and error process, the computer will merge all the best routes into a single path that would be the shortest route for the salesman to travel.[cite]

The previous example is a perfect illustration of the ways genetic algorithms deal with a more general set of problems.  The huge combination of ways to travel through all the cities can be tamed by the evolution of the solution as the genetic algorithm constantly takes the best mini-solutions that takes it closer to the answer and "breeds" them to produce a better answer.  Thus, genetic algorithms are another avenue in which computers can learn and improve.  However, one of the most notable examples of genetic algorithms and computer learning is Douglas Lenat's Eurisko.

[ Top ]

General Learning Systems and Domains

Obviously, the script and frame approaches further illustrate how domain-specific a computer is.  A computer could know how to make any French dish, but would not know who the famous French cook François Mitterrand is.  In the script-method, knowledge could be swapped, but a computer can not have the kind of general knowledge humans possess.  However, in a sense, people are not total general learning systems.   In society, people usually have specialized jobs that require knowledge specific to their field of work.  Firemen know how to put out flames in the safest way, doctors know what medications to prescribe their patients, and architects are aware of the latest architectural styles.  Even within a specific field, such as a software company like Microsoft, there are different people with specific knowledge in specific positions like secretaries, executives, technicians, programmers, etc.  One can not expect a policeman to diagnose a disease in a person nor would one hope to have a doctor direct traffic.