AI:Evolution,brains involved,Issues.

Hi,

In my several months of research on this wonderful subject(Quite an amateur research as you might conclude!), although

thorough you might say,i have collected various historical pieces of information and compiled for fun.although in fun,but

they are very amusing and interesting to study.
All the dates may not be correct,if not please tell me which one,i"ll rectify.
The following evolution dates have been compiled with the help of MIT web-servers and various other sites(which

includes,irbot,robot.net,NASA sites etc...)
i was thinking about a thread which could comprehensively cover the topic of History and evolution of AI in better

perspective and way.any comments,further corrections,clarifications are welcome.
1917

----
Karel Capek coins the term 'Robot' (in czech 'robot' means 'worker'), but the 1923 English translation retained the original

word.)


1928
----
John von Neumann's develops minimax theorem (later used in game playing programs)


1940
----
First electronic computers developed in US, UK and Germany


1943
----
McCulloch and Pitts propose neural-network architectures for intelligence


1946
----
Turing's ACE

John von Neumann publishes EDVAC paper on the stored program computer.

J. Presper Eckert and John Mauchley build "ENIAC", the first electronic programmable digital computer.


1950
----
Alan Turing: "Computing Machinery and Intelligence"



1953
----
Claude Shannon gives Minsky and McCarthy summer jobs at Bell Labs


1955
----
Newell, Simon and Shaw develop "IPL-11", first AI language


1956
----
Rockefeller funds McCarthy & Minsky's AI conference at Dartmouth

CIA funds GAT machine-translation project

Newell, Shaw, and Simon develop The Logic Theorist


1957
----
Newell, Shaw, and Simon's develop the General Problem Solver (GPS)


1958
----
McCarthy introduces LISP (at MIT)


1959
----
McCarthy & Minsky establish MIT AI Lab

Rosenblatt introduces Perceptron

Samuel's checkers program wins games against best human players


1962
----
First industrial robots

McCarthy moves to Stanford


1963
----
McCarthy founds Stanford AI Lab

Minsky's "Steps Towards Artificial Intelligence" publised

U.S. government grants 2.2 million dollar grant to MIT to reearch Machine-Aided Cognition (artificial intelligence). The

grant came from DARPA to ensure that the US would stay ahead of the Soviet Union in technological advancements.


1964
----
Daniel Bobrow's Student solves high school algebra work problems


1965
----
Simon predicts "by 1985 machines will be capable of doing any work a man can do"


1966
----
Weizenbaum and Colby create Eliza


1967
----
Greenblatt's MacHack defeats Hubert Dreyfus at chess


1969
----
Minsky & Papert's "Perceptrons" (limits of single-layer neural networks) kills funding for neural net research

Kubrick's "2001" introduces HAL and AI to a mass audience

Turing's "Intelligent Machinery" published


1970
----
Terry Winograd's Shrldu developed

Colmerauer creates PROLOG


1972
----
DARPA cancels funding for robotics at Stanford

Hubert Dreyfus publishes "What Computers Can't Do"


1973
----
Lighthill report kills AI funding in UK

LOGO funding scandal: Minsky & Papert forced to turn leave MIT's AI lab


1974
----
Edward Shortliffe's thesis on Mycin

Minsky reifies the 'frame'

First computer-controlled robot


1975
----
DARPA launches image understanding funding program.


1976
----
DARPA cancels funding for speech understanding research

Greenblatt creates first LISP machine, "CONS"

Doug Lenat's AM (Automated Mathematician)

Kurzweil introduces reading machine


1978
----
Patrick Hayes' "Naive Physics Manifesto"


1979
----
Journal of American Medical Assoc. says that Mycin is as good as medical experts


1980
----
First AAAI conference held (at Stanford)


1981
----
Kazuhiro Fuchi announces Japanese Fifth Generation Project


1982
----
John Hopfield resuscitates neural nets

Publications of British government's "Alvey Report" on advanced information technology, leading to boost in AI (Expert

Systems) being used in industry.


1983
----
DARPA's Strategic Computing Initiative commits $600 million over 5 yrs

Feigenbaum and McCorduck publish "The Fifth Generation"


1984
----
Austin AAAI conference launches AI into financial spotlight


1985
----
GM and Campbell's Soup find expert systems don't need LISP machines

Kawasaki robot kills Japanese mechanic during malfunction


1987
----
"AI Winter" sets in (Lisp-machine market saturated)


1988
----
AI revenues reach $1 billion

The 386 chip brings PC speeds into competition with LISP machines

Minsky and Papert publish revised edition of "Perceptrons"


1992
----
Japanese Fifth Generation Project ends with a whimper
(As KMGURU pointed out.)
Japanese Real World Computing Project begins with a big-money bang

More to follow...

bye!

 

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Brains of AI,their lives,affects and prominence.

=====================================================================
Alan Turing
============================================================================================
Alan Turing is considered by many to be the founder of computer science. His ideas about a Universal Machine capable

of performing any task with well defined instructions marked the beginning of the age of computers. Turing believed that

by the year 2000 machines would be created that could imitiate the human mind. Turing is also famous for creating the

Turing Test, a test which could be used to determine how well a machine could imitate human behaviour.
The Turing Test is a test, created by Alan Turing, which some believe can be used to determine whether or not a

computer is intelligent. The turing test is based on an imitation game. The imitation game is an "abstract oral

examination. 1 In 1950 Turing wrote an article, "Computing Machinery and Intelligence" in which he describes the

imitation game and the Turing Test. 2
It is played with three people, a man (A), a woman (B), and an interrogator (C) who may be of either sex. The

interrogator stays in a room apart from the other two. The object of the game for the interrogator is to determine which

of the two is the man and which is the woman. He knows them by labels X and Y, and at the end of the game he says

either "X is A and Y is B" or "X is B and Y is A." The interrogator is allowed to put questions to A and B . . . We now ask

the question, "What will happen when a machine takes the part of A in this game?" Will the interrogator decide wrongly

as often when the game is played like this as he does when the game is played between a man and a woman? These

questions replace our original "Can machines think?"
The Turing Test replaces the man in the imitation game with a computer. If the interrogator cannot distinguish between

the human and the computer, then the Turing Test says that the computer is intelligent.

In 1935 Turing during a lecture at Cambridge University, was introduced to the Entschiedungsproblem, The question of

decidability. The question asks, "could there exist, at least in principle, any definite method or process by which all

mathematical questions could be decided?" 1 Turing, in trying to find an answer to the Entschiedungsproblem, came up

with the concept of a machine, working on paper tape on which symbols were printed, which could perform this 'definite

method'. Before this machine could be constructed the question needed to be answered: what is the most general type

of process such a machine could perform. 2 Turing came to the conclusion that anything that could be achieved by a

person working on a set of logical instructions could also be achieved by the Turing Machine. 3 In 1936 Turing submitted

his findings in a paper, "On Computable Numbers with an application to the Entschiedungsproblem.
Later, Turing came up with the concept of a Universal Turing Machine. The idea of the Turing Machine is similar to

a formula or an equation. Just as there is an infinite number of possible equations, there is an infinite number of

possible types of Turing Machines. Turing saw no need for creating a different Turing Machine for every task. He

envisioned a Universal Turing Machine which could perform the tasks of all Turing Machines given that the instructions

for each task was written in a standard form.

================================================================================================
The father of AI:
========================================================================================
Minsky believes in modeling the way computers think after the way humans think by creating an intelligent machine that

use common sense reasoning instead of logical reasoning. Logical systems, according to Minsky, while they work well in

mathematics, does not work well in the real world which is full of ambiguity, inconsistencies, and exceptions. 1 Minsky

gives an example of how logical reasoning falls into difficulty when considering a statement like, 'Birds can fly'.
"Consider a fact like, 'Birds can fly.' If you think that common-sense reasoning is like logical reasoning then you

believe there are general principles that state, 'If Joe is a bird and birds can fly then Joe can fly.' But we all know that

there are exceptions. Suppose Joe is an ostrich or a penguin? Well, we can axiomatize and say if Joe is a bird and Joe

is not an ostrich or a penguin, then Joe can fly. But suppose Joe is dead? Or suppose Joe had is feet set in concrete?

The problem with logic is that once you deduce something you can' get rid of it. What I'm getting at is that there is a

problem with exceptions. It is very hard to find things that are always true." 2
Minksy instead proposes framework systems(or frame). The idea behind frame is to put into a computer a large

amount of information ( more imformation needed to solve a problem) and then define for each situation the optional

and mandatory detail. A frame for birds might include feathers, wings, egg-laying, flying, and singing. Flying and

singing would be defined as optional. Wings, feathers and egg-laying are not mandaory.
================================================================================================
John Mccarthy
================================================================================================
One of John McCarthy's most famous contributions to Artificial Intelligence was the organization of the Dartmouth

Conference, at which the name "Artificial Intelligence" was coined. The Dartmouth Conference, titled the "Dartmouth

Summer Research Project on Artificial Intelligence" was a two-month long summer conference. McCarthy's goal was to

bring together all of the people he knew of who had shown interest in computer intelligence. Although McCarthy

initially saw the conference as a failure (no one really liked the idea of spending two whole months at a the conference,

so people came and went as they pleased, making it hard for McCarthy to schedule regular meetings), in the years after

the conference, artificial intelligence laboratories were established across the country at schools like Stanford, MIT, and

Carnegie Mellon. 1 Another of McCarthy's great accomplishments is the creation of the LISP (List Processing) language.

LISP soon became the language of choice for many AI programmers and various versions of LISP are still being used

today, forty years later.

More to follow...
Bye!

 

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Social...

Social interactions between people and AI:
============================================
Many people remember Rosie from the cartoon series the Jetsons. Rosie was the robot maid that had a mind of her own, much to her owner's dismay. What is the possibility that such a thing could be a part of our everyday life? Our daily activities grow more and more dependant upon computers everyday and perhaps one day we might actually achieve a state in which we have artificially intelligent servants to wait on us.
Rodney Brooks, a professor at MIT, believes that in order for humans to interact productively with artificially intelligent machines and vice versa, that the machines must take humanoid form and interact with humans.1 This could mean that the artificially intelligent machines might actually learn not just what is taught to them from their creators, but also from us, the potential consumers.
This can lead to some potentially interesting problems. How would one act with an artificially intelligent machine? Should we treat them as objects, as children or as equals? How would they react to us? Do we want these machines to be more life-like or less life-like? Do we want server machines to be artificially intelligent or just drones?
All these are interesting questions but the answers will vary from person to person. Although I may be comfortable with having an AI servant my neighbor my not. Even if it is possible to mass produce AI servants, whether or not it will actually happen will depend largely upon society. At this present time I do not believe that society is willing to accept such a change in our culture.


AI LIFE
===========================
A simple criteria by which something can be jugded to be alive: 1
Metabolism, consumes and expends energy
Growth
Reproduction
Reaction to stimuli
In a very loose sense it is possible to conceive of an artificially intelligent machine as being alive. All machines

consume and expend energy and therefore have a metabolism. It is possible for an intelligent machine to alter itself and

improve upon it's design. As of today we have machines that are used to build themselves. This can be construed as a

form of reproduction although not in the classic sense as human reproduction. If the machine is artificially intelligent

then it should be able to respond well to stimuli and adapt to it's environment.
Although in present day this argument does seem to be somewhat odd it is something that should be taken into

account now before the distinction diminishes even more. Insect robots are the first step engineers, such as those in IS

Robotics , have taken to creating artificial life. Although the insect robots cannot reproduce as of yet they act as an

insect would. The solar powered insects try to seek out food in the form of sun light. It has a drive to live even though

many people might not consider that to be the fact.

Fears Regarding AI
-----------------------------------------------------------------------------------------------------------------------------------------
Almost everyone has seen the movie TERMINATOR, a movie that depicts the apocalyptic future of mankind due to an

artificially intelligent computer that becomes self aware and defends itself against those who try to destroy it. Hollywood

has depicted the same idea that android technology run wild since the advent of the motion picture with such films as

METROPOLIS(1926) and TOBOR THE GREAT (1954).
These movies, and general ignorance of technology, have therfore led many to beleive that giving androids or

computers artificial intelligence that resembles human intelligence could be disastrous. Most people believe that once

artificially intelligent robots become self aware and realize the position they are in that they will revolt and that will lead

to the end of the human race.
There are those who believe that a machine can only do what you tell and so the machine will always follow

orders. Therfore a machine can never turn upon its human owner. Another reason used to defend AI is why would an

artificially intelligent machine want to be free? People comparing the human need to be free and the artificially

intelligent machine's sense of freedom forget that the need to be free is something that has been passed down through

our society for so long know that it may just as well be encoded in our DNA. The machines may take a long time, if ever,

to develop a concept of freedom and of choice.
There is also the concept of the artificially intelligent machine that just breaks down such as HAL in 2001:Space

Odyssey. However probable it is that a machine would break down and go berserk, in the author's opinion, no more

probable than the person next to you having a nervous breakdown and going berserk. The only difference is that society

as a whole is willing to accept human faults and errors but they are much less forgiving for the very things we create.

Why this is so is beyond my understanding and is left to the reader to contemplate. Perhaps we think that the things we
build should be better than we are?

bye!

 

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Actual Evolution

Actual Evolution
-----------------------------------------------------------------------------------------------------------------------------------
Artificial intelligence programs have come a long way since the 1940's from simple calculating machines to computers

that can learn, modifiy their own programs and outsmart their own creators. However, AI did not progress along a single

path. While some researchers set about perfecting one type of AI programming, others went on to develop other kinds.

Others still combined approaches using aspects of different methods of programming to suite the needs of their program.
Even today there is still disagreement on what is the best approach to AI. The major disagreement is on whether or

not AI machines should work like the human mind. The two most prominent people in this debate are Marvin Minsky and

John McCarthy. Minsky believes that in order to program human intelligence into a computer, the program must use the

same problem solving methods that humans use. McCarthy believes that there is no need to model AI systems after the

human brain. He instead believes in a more structured and logical system (such as those in mathematics) with well

defined rules.
Here we will talk about some of the approaches to AI programming: from the earliest AI programs to the more

recent ones.
DO-NOW PROGRAMMING
===============================================
The first AI machines used what Marvin Minsky calls "do-now programming". 1 These machines followed their

programmed instructions in a given order without variation. This meant that programmers had to know exactly what the

machines would encounter and when they would encounter it. For example, a machine programmed to open and then

go through a door would run into trouble if the door was already open. The engineer programming the machine would

need to know ahead of time whether or not the door would already be open and then the machine would only be able to

perform in one of the two scenarios.
MEANS END PROGRAMMING
=========================================================
The next step in programming, after do-now programming, was "do-if-needed" or "means-end" 1 programming. These

programs contained conditional statements such as, 'if the door is closed then open it. Programmers using means-end

programming, didn't need to know exactly when a machine would need a set of instructions, only that they might

eventually need it. These new machines were capable of making decisions (whether or not to open the door) based on

their environment (whether or not the door is open), bringing them closer to true intelligence. These programs, however,

were also limited in that, they were not able to deal with scenarios not anticipated by their programmers. For example

consider again a machine which is programmed to open a door and go through the door. What if the door opened in?

What if it opened out? What if it opened like a window? What if it's locked? What if there's another door behind it? If

the programmer hadn't considered each of these possible scenarios, then the machine could easily run into trouble.

Although these machines were capable of making decisions on how to solve a problem, they were still limited by what

problems their programmers anticipated.

HEURISTICS
==========================================================
Heuristics, or the "art of good guessing" comes from the greek word for discover. Heuristic planning allows machines to

recognize promising approaches to problems, break problems into smaller problems, deal with incomplete and

ambiguous data and make educated guesses.
Imagine a computer that plays chess. For each move made there are about 30 possible choices to be made. With a

full game averaging at 40 moves, there are at least 10120 possible games. Now imagine a superfast computer that could

play a million games a second. It would take such a computer about 10108 years to play all of the possible games.

Heuristic plannnig allows computers to make decisions about what choices seem most promising and to then consider

only those choices. By ignoring some of the possible choices, a computer risks that it may not come up with the very

'best' solution, but it does come up with one of the better solutions.
Out of the idea of heuristic planning came three other types of programming came an age of what Marvin Minsky

called "do something sensible" or "common sense" programming. Some methods that used the priciples of heuristic

programming are, goal directed programming, trial and error learning, and self-teaching computers.

GOAL DIRECTED PROGRAMMING:
==================================================
How do humans solve problems for the first time? One way is to remember a time when we were posed with a similar

problem. For example, there are many different types of combination locks. There are the locks for school lockers, and

bike locks and padlocks. Each of these locks are a little different from the others, but if we know how to use a bike lock,

we can usually figure out how to open a school locker. By thinking about how we open one type of combination lock we

can figure out how to use lots of other kinds of combination locks.
A machine programmed to use past experiences to finding solutions to new problems are said to use goal directed

programming. In order to draw on its past experiences a computer needs to be able to apply what it has already learned

to new problems. One way of doing this is through a process called "backward chaining." 1 This process was designed

at the Stanford Research Institute by Nils J. Nilson and Richard E. Fikes for their machine, STRIPS (Stanford Research

Institute Problem Solver). In backwards chaining, the machine starts at it's primary goal moves backward breaking that

goal into subgoals and the subgoals into subgoals until it reaches its initial state. Going back to our example of a

machine instructed to move through a closed locked door. The primary goal is to move through the door. Moving

backwards from having gone through the door (the primary goal), the machine establishes the subgoal of opening the

closed door. A subgoal of opening the door could perhaps be retrieving the key to unlock the door. The machine then

moves backwards one more step to find itself at its initial state (on the other side of a closed, locked door). Now that the

machine has created a plan for achieving its goal (by breaking it into smaller problems, which it already knows how to

solve), it can begin to execute that plan
Goal directed programming also has its limitations. Imagine trying to open a door by pulling on the handle. If the

door doesn't open, the next thing we usually try is pushing instead of pulling. Then the next time we go through that

door, we remember that we have to push to open the door (although sometimes we still forget). Machines using goal

directed programming can't learn from it's own mistakes in this way. If the door didn't open when pushed, the a

computer that was programed to use goal directed planning wouldn't know to try pulling.

SELF LEARNING:
---------------------------------------------------------------------
The beginning of self-teaching programs was marked in the early 1950's when Arthur Samuel set upon the task of

programming "a digital computer to behave in a way which, if done by human beings or animals, would be described as

involving the process of learning." 1 The program he used was a program that played checkers. While the idea of a

program that can play checkers doesn't seem to be very impressive, what is impressive is what Samuel's program

accomplished. Samuel set up a computer to run two copies of the program that played against each other. By playing

against itself, this program became an increasingly better program. First the program got to be good enough to beat

Samuel, and then continued to beat champion-level checkers players. Samuel had managed to create a computer that

in the end became capable of doing more than Samuel could have taught it himself.

Bye!

 

Medicinal Evolution,Conclusion.

AI IN MEDICINE:EVOLUTION AND CONCLUSION.
=============================================================
According to Dr. Kohane, it was very difficult to determine when AIM reached each stage of professionalization. It was

because this area of study was so popular when it was first introduced, that it quickly advanced from preemption to

professional autonomy in only a few years’ time. However, most AIM scientists agreed that the professionalization began

in 1970.
William B. Schwartz, M.D. was generally credited as one of the earliest pioneers in AIM. After he received his medical

doctor degree, he has become active in both medical and computer science research. In 1970, he published an

influential paper in New England Journal of Medicine that included the following paragraph:

Rapid advances in the information sciences, coupled with the political commitment to broad extensions of health care,

promise to bring about basic changes in the structure of medical practice. Computing science will probably exert its

major effects by augmenting and, in some cases, largely replacing the intellectual functions of the physician. As the

"intellectual" use of the computer influences in a fundamental fashion the problems of both physician manpower and

quality of medical care, it will also inevitably exact important social costs – psychological, organizational, legal,

economical and technical. [Schwartz, 1970]

In the article, he stated that computer could be a useful clerk in filing medical records, but the real power was its ability

to act as decision makers and perform intellectual functions. His article created much enthusiasm among the AI

community. They began to think that computer science would eventually revolutionize the practice of healthcare. As a

result, many scientists were attracted to study the applications of computer science in medicine. Since intelligent

computers were able to store and process vast amounts of knowledge, the hope was that they would become perfect

'doctors in a box,' assisting or surpassing clinicians with tasks like diagnosis.

With such motivations, a small but talented community of computer scientists and healthcare professionals set about

shaping a research program for a new discipline called Artificial Intelligence in Medicine (AIM). The 1970’s could

therefore be regarded as the preemption period of AIM. These researchers had a bold vision of the way AIM would

revolutionize medicine, and pushed forward the frontiers of technology.

Their work, understandably, created uneasiness among physicians. The phrase "artificial intelligence," especially the

word "intelligence," created with confusion some unnecessary arrogance. Were they implying that doctors were not

intelligent enough? Were they creating computers to surpass and replace physicians? These controversial issues, among

others, will be discussed in a later section.

In his famous paper, Schwartz did not mention the technical details on how the promise would be achieved. He merely

expressed his inspiration. The earliest successful AIM systems were developed by other scientists, such as Edward

Shortliffe [Szolovits, 1982, p.58]. In the 1970’s, the first decade of AIM, most research systems were developed to assist

clinicians in the process of diagnosis. In reviewing this new field by then, Clancey and Shortliffe provided the following

definition in 1984:

'Medical artificial intelligence is primarily concerned with the construction of AI programs that perform diagnosis and

make therapy recommendations. Unlike medical applications based on other programming methods, such as purely

statistical and probabilistic methods, medical AI programs are based on symbolic models of disease entities and their

relationship to patient factors and clinical manifestations.' [Clancey, 1984]

Shortliffe was a physician in the early 1970’s. After receiving his medical doctor degree, he went to Stanford to obtain his

Ph.D. in computer science, focusing on the area of AIM [Stanford University, 1998]. In 1975, he developed the MYCIN

program as part of his doctorate thesis. It was a computer system for diagnosing bacterial infections, helping physicians

to prescribe disease-specific drugs. It informed itself about particular cases by requesting information from the physician

about a patient’s symptoms, general condition, history, and laboratory-test results. At each point, the question it asked

was determined by its current hypothesis and the answers to all previous questions. Thus, the questions started as though

taken from a checklist, but the questions then varied as evidence built. The essence of MYCIN was its embedded rules of

reasoning. It was made up of many "if… then…" statements. An example could be "if the bacteria is a primary

bacteremia, and the suspected entry point is the gastrointestinal tract, and the site of the culture is one of the sterile sites,

then there is evidence that the bacteria is bacteroides," and the succeeding questions would be directed at finding out

whether bacteriode was the suspected disease agent, and what kind of bacteriode was it. As a result, MYCIN and similar

programs were referred to as "rule-based systems." Since the if-then statements were designed to emulate the thinking of

experts, they were also called "expert systems" [Winston, 1992, pp. 130-131].

MYCIN was tested by human experts in the area of bacterial infections, including physicians. According to Dr. Kohane

and Dr. Long, the results were very accurate. The success of MYCIN inspired the development of other AIM systems, many

of which were rule-based expert systems. They included the Causal-Associated Network (CASNET), written by scientists at

the Rutgers Research Resource on Computers in Biomedicine in 1976 that diagnosed eye diseases, and the INTERNIST

program, written by Harry Pople and Jack Myers in the early 1980’s that investigated a broad range of problems in

internal medicine. Later on, MIT scientists such as Peter Szolovits and Steve Pauker developed a computer model that

was more complicated than rule-based system. It was called frame-based model, in which living things were represented

by a frame-like structure in a computer program. They employed this concept in their PRESENT-ILLNESS program in the

late 1970’s to diagnose kidney diseases



Institutionalization
=====================================================
After the introduction of MYCIN, the amount of AIM research had became very intense. Naturally, the scientists grouped

themselves together, to facilitate exchange of information and research outcomes. The professionalization of AIM was

advancing to the stage of institutionalization. Many conferences and symposiums were already established by the early

1980’s. They included the annual spring symposium of the American Association for Artificial Intelligence (AAAI), an

organization that was founded in 1979 [AAAI, 1998]. The symposium featured readings of work in AIM, among other fields

of artificial intelligence. According to Dr. Long, another conference was the Artificial Intelligence in Medicine Workshop,

held every two years, and attracted scientists from all over the nation. AIM has also earned its recognition by the

American Association for the Advancement of Science (AAAS) by the late 1970’s. In its annual national meeting in

Houston, Texas, 1979, AIM scientists’ works were first presented [Szolovits, 1982, Preface].

In addition to workshops and conferences, the AIM scientists have also established programs in universities. In 1970, the

Harvard Medical School and MIT established a joint institution to foster the development of health related education,

research and service. The new division was called Health, Sciences, and Technology (HST) [Harvard Medical School,

1998]. Among many programs, HST offered training in medical informatics, a field closely related to AIM. Szolovits, the

scientist who has developed frame-based model in the PRESENT-ILLNESS program, became the head of the Master of

Medical Informatics program. Students in the division took advantage of the facilities of Harvard, MIT, and Tufts to pursue

research in AIM. Stanford has also developed a similar program. After a pause between 1976 and 1979 for internal

medicine house-staff training, Shortliffe joined the Stanford internal medicine faculty, where he has since directed an

active research program in AIM. He has also supervised the formation of Stanford's degree program in medical

information sciences, a sub-branch of AIM

POLITICAL,SOCIAL,ETHICAL ISSUES:
===========================================
AI in Medicine and the Human Future
Political, Social, Ethical and Economical Issues of AIM
Political
Since the first AIM systems was used in clinical practice, Food and Drug Administration Office (FDA) had become involved

in approving and licensing software programs for medical applications. What regulations to apply, what guidelines to

follow were among the most controversial, yet critical issues to patients, doctors, as well as AIM system professionals

[Coiera, 1997].

Since current AIM research focused on getting more medical data on-line, concerns about medical records privacy and

security arose immediately. How to guard against information leaks from such on-line medical data inventory had

sparked split opinions among the medical community. Different interest groups might try to influence Federal policy

makers for regulations favoring their interests [Coiera, 1997].

Social
Since AIM failed to deliver its grand promises, few systems had been fully tested and used directly in clinical practice.

There had been minimum social interaction and feedback about AIM. The systems that had been used, were rather

performing tasks more transparent to the patients. For example, programs that checked laboratory results and gave

warnings to doctors about potential toxic prescription were unknown to the patients. Therefore there had been very little

social response from the general public about their perception of AIM systems [Kohane, 1998].

Ethical
When the idea of a perfect doctor was first introduced by the AIM community, heath professionals expressed mixed

opinions about the introduction of expert systems. Some strongly opposed to such ideas, because to them, having a

machine to take full charge of a patient was unethical and wrong. Doctors who were slightly worried about themselves

being replaced by the perfect machines also opposed to the ideas. On the other hand, there were others who would

cherish the opportunities to improve the current health care situations. This kind of controversy did not become the

mainstream conflict, however, as the focus of AIM had changed in recent years; programs were acting as "assistants" to

doctors, not the "doctors" who made perfect decisions. According to Dr. Kohane, about 60-70% of the doctors took

advantage of such programs. There were only about 20% doctors who absolutely denied the usage of AIM programs and

refused any help from such program in any ways. This was a significant change from the situation during the AI winter

period [Kohane, 1998].

Economical
During the AI winter, as the enthusiasm for medical AI systems died down, people did not believe that AI systems could

do anything and make any contribution to the medical community. Government foundations, venture capitalists who had

initially promised investments all a sudden withdrew from their original commitment. There was very little funding

available at the time. Research groups had to restraint themselves from certain research topics or change the range of

technologies due to the withdrawal of funding. Until now, the effects of AI winter have not been totally eliminated;

research groups could no longer receive as much financial assistance [Long, 1998].

Expenditures on information technology for health care had been growing rapidly. The health care industry spent

approximately $10 billion to $15 billion a year on information technology, and expenditures were expected to grow by 15

to 20 percent a year for the next several years. Health care organizations were developing electronic medical records

(EMR) for storing clinical information, upgrading administrative and billing systems to reduce errors and lower

administrative costs, and installing internal networks for sharing information among affiliated entities. Organizations

were also beginning to experiment with the use of public networks, such as the Internet, to allow employees and

physicians to access clinical information from off-site locations and to enable organizations to share information for

purposes of care, reimbursement, benefits management, and research. Others were using the Internet to disseminate

information about health plans and research

CONCLUSION:
--------------------------------------
From the professionalization of AIM, we see that great scientific ideas do not necessary guarantee popular inventions.

They must be accompanied by the right technologies, and be solving the right problems.

These factors perhaps also explain why AI has not been able to create machines that are more intelligent than humans.

We do not want our race to be replaced by machines. Any research attempting to do so may be solving the wrong

problem. Instead, we want machines to help us with tasks that are critical, and yet humans are poor at. Therefore, we

anticipate that the future of AI will be focused in areas such as substantial data handling, complicated image recognition,

etc. – in general, jobs that humans are less proficient at. AI will serve to make machines our tools, not our masters.

Any discussion in AI is not complete without refferring to Isaac asimov who was perhaps the first one to fashionize AI.

you can find more info on him in Robots:Are we close yet thread.

Thanks for your time.

bye!

 

Thanks Zion and kmguru...

The prospect of having "doctors-in-a-box" makes one wonder why the health insurance industry isn't already neck-deep into AI research and development. (Or maybe they are in deep and I'm just not aware of it. )

Lots of interesting bits, guys. Appreciate you taking the time to share.

CB

 

CB, the medical community is neck deep into AI.

One of the first successful expert systems was MYECIN used to diagnosis bacterial infections. It worked well enough to aid doctors but not well enough to replace them. (Nor was MYECIN intended to replace doctors.) There have been many follow on systems.

Systems to aid drug discovery are in use.

Systems to automatically analyze samples and screen for abnormal cells are in development.

Would guess there are hundreds of systems in use and thousands in development.

 

km....duuude... that's exactly what I'm thinking. They're so caught up with cost efficiency, I'm a bit surprised they haven't led the way in AIM research. If a "doctor-in-a-box" could correctly diagnose and cut down on any number of physicians and specialist's fees, unnecessary tests, and reduce prescription claims (how many doctors don't know diddly about the meds they prescribe and send their patients back and forth to the pharmacy trying to get the one medicine--or correct dosage strength--that will be effective?) --then the insurance companies have another way to regulate how much they have to pay out.

You are correct. They're bean counters. They are so into counting their beans I'd think they might have jumped on the AI bandwagon by now because it'd be just one more way to help them run the show--and they do intend to run the show for as long as they can. They're already trying to override human doctors decisions wherever the law allows.

However, doctors are not infallible gods and they don't always make the best choices in treatments and therapies. Ex: A doctor routinely prescribes Celebrex because he's on friendly terms with the pharmaceutical rep that visits his office and who leaves the doctor piles of samples (and other incentives). The doctor could prescribe a less expensive anti-inflammatory like Vioxx or even prescription strength Ibuprofen. A "doctor-in-a-box" would have prescribed one of these meds for many types of patients based on the patients actual needs.

I see potential here, but could be that a serious or committed investment in AIM research at this point doesn't make the bean count come out in a satisfactory way for the insurance industry.

CB

 

Right, hamster.

I am aware. Read all the posts here, and have read about it elsewhere. Specifically thinking of Insurance companies rather than the medical community in general.

thx,

CB

 

Hi,

True convergence is with past learner approach,if our expert systems are programmed with that notions,like in case of Neural,we can surely reach a stage where androids will be a common super species.
the true and fast path of golden convergence is this approach.
there's been a lots of research on this subject,but magnitude of success is little.this is rimarily because half the people are using truely or partly wrong approaches.most of the people who design AI are running towards CYC's approach and that is sad,because it hinders some good and true AI approaches.


bye!

 

PS:
EXCELLENT POST KM.SORRY I WAS LATE IN LOOKING.

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bye!