Artificial Life and Synthetic Realities
Simulating Life: Clockwork Bodies
Background on the Golem Legends Kay E. Vandergrift
It is not my aim to surprise or shock you -- but the simplest way I can summarize is to say that there are now in the world machines that can think, that can learn and that can create. Moreover, their ability to do these things is going to increase rapidly until -- in a visible future -- the range of problems they can handle will be coextensive with the range to which the human mind has been applied.
Herbert Simon
History of computer development
Communication Theory
Also see Racter -- often touted as the first literature-creating computer program.
Introduction
The desire to create life artificially has long underpinned Western cultures. Fear of the consequences has been with us nearly as long. One of the Biblical Apocrypha relates that when God created Adam from clay he also created a mate, Lilith, from the same clay. Lilith, however, was her own woman and she wasn't as ready as Adam to do what she was told. For her independence she was banished from Eden and exists still as an elemental demon, ever ready to bring men to grief. The story of Lilith is almost a template for all our subsequent stories about creating life from inanimate matter. From Golem to Frankenstein's monster to Hal -- it always ends in tears.
Even those engaged in the search for the secrets of artificial life often fear the consequences of their success. A story about Tom Ray, noted aLife researcher, tells that when he was coding the first version of Tierra, a self-replicating computer code designed to model adaptive and evolutionary biological concepts, he smashed the serial ports off his computer with a hammer to make sure none of his code entities escaped into the 'real' world. This wasn't quite true -- actually, he planned a 'containment facility' to prevent the code entities access to disk drives and ports.
The entities that Ray coded compete for computer space and time. Computer viruses, worms and other assorted beasties are very similar in concept (and sometimes in coding) to Ray's entities We are all well aware of the damage and financial cost the release of a virus like the 'Love Bug' can cause. It wasn't just science fiction-fuelled paranoia that led Ray to worry. It seems that perhaps the doomsayers might have a point...
But whatever the real or imagined dangers, people keep trying to create synthetic lifeforms and today it has even become big business. Sony, for example, sold out its first run of robot puppies within hours of their appearance on-line. ALife and AI (artificial intelligence), however, have mostly been the province of a variety of researchers and scientists investigating (amongst other things) the dynamics of biological systems, evolution and chaos theory. So why is it of importance to multimedia and other 'creatives'? Expatriate artist and theorist, Simon Penny has explored this question in depth:
Scientific ideas have been a powerful influence in shaping western culture. In many cases, the power of influence that the hard sciences have had, has encouraged social sciences and humanistic disciplines to become more 'scientific' (and therefore, by definition, more rigorous, more respectable) by the adoption of scientific tropes. The theory of relativity and quantum theory are examples which have been ludicrously misapplied in the popular science and the social sciences. It is arguable that the modernist tradition in art itself is a highly scientized world view, privileging as it does ideas of experiment and progress...Now interactive media and artificial life offer a quite new type of mimesis, one which combines the trajectory of technological mimesis with ideas influenced by the 'systems art' of the 70s. The representationalism here tends to be not so much optical as systematic. The dynamics of biological systems are modeled more than their appearance. These works exhibit a new order of mimesis in which nature as a generative system, not an appearance, is being represented. This change of order is akin to the move from harnessing the products of biodiversity to harnessing the mechanism of biodiversity which I discussed earlier. Numerous works employ 'nature' not as a representation but in the structure of the systems: biological growth algorithms, simulated ecosystems or communities, genetic algorithms, neural networks.
The Darwin Machine: Artificial Life and Art, Simon PennyEarly History
We can trace interest in creating intelligent machines/lifeforms back to ancient Egypt, but with the development of the electronic computer in 1941, the technology finally became available to create machine intelligence. Although the computer provided the technology necessary for AI, it was not until the early 1950's that the link between human intelligence and machines was really observed. Norbert Wiener was one of the first to make observations on the principle of feedback theory. What was so important about his research into feedback loops was that Wiener theorized that all intelligent behavior was the result of feedback mechanisms; mechanisms that could possibly be simulated by machines. This discovery influenced much of early development of AI.
In late 1955, Newell and Simon developed The Logic Theorist, considered by many to be the first AI program. The program, representing each problem as a tree model, would attempt to solve it by selecting the branch that would most likely result in the correct conclusion. The impact that the logic theorist made on both the public and the field of AI has made it a crucial stepping stone in developing the AI field.
Example of a branching 'tree' model
Graphic 'borrowed' fromAn Introduction to the Science of Artificial IntelligenceThe term 'artificial intelligence' was first coined in 1956, at the Dartmouth conference organised by John McCarthy, regarded as the father of AI, to draw the talent and expertise of others interested in machine intelligence for a month of brainstorming. Although not a huge success, the Dartmouth conference did bring together the founders in AI, and served to lay the groundwork for the future of AI research.
In the seven years after the conference, AI began to pick up momentum. Although the field was still undefined, ideas formed at the conference were re-examined, and built upon. Centers for AI research began forming at Carnegie Mellon and MIT, and a new challenges were faced: further research was placed upon creating systems that could efficiently solve problems, by limiting the search, such as the Logic Theorist. And second, making systems that could learn by themselves. In 1957, the first version of a new program by Newell and Simon, The General Problem Solver (GPs), was tested. The GPs was an extension of Wiener's feedback principle, and was capable of solving a greater extent of common sense problems.
A couple of years after the GPs, IBM contracted a team to research artificial intelligence. Herbert Gelerneter spent 3 years working on a program for solving geometry theorems. While more programs were being produced, McCarthy was busy developing a major breakthrough in AI history. In 1958 McCarthy announced his new development; the LISP language, which is still used today. LISP stands for LISt Processing, and was soon adopted as the language of choice among most AI developers.
In 1963 MIT received a 2.2 million dollar grant from the United States government to be used in researching Machine-Aided Cognition (artificial intelligence). The grant by the Department of Defense's Advanced research projects Agency (ARPA), to ensure that the US would stay ahead of the Soviet Union in technological advancements. The project served to increase the pace of development in AI research, by drawing computer scientists from around the world, and continues funding. The MIT researchers headed by Marvin Minsky, demonstrated that when confined to a small subject matter, computer programs could solve spatial problems and logic problems. Other programs which appeared during the late 1960's were STUDENT, which could solve algebra story problems, and SIR which could understand simple English sentences. The result of these programs was a refinement in language comprehension and logic .
One of the first steps toward creating 'a genuine language understanding program' was the program Joseph Weizenbaum created for ELIZA at MIT in 1964. This program and a later variant called DOCTOR mimicked human interaction and created the illusion of being 'a wise, all knowing computerised psychiatrist. The program encouraged people to talk to the machine by playing the user's thoughts back to them. Weizenbaum was surprised that even sophisticated people were drawn into conversations with the machine about their lives and protested vehemently about the 'bond' that people, (even his secretary whom he caught crying into the keyboard during a session), developed with it.
The following passage shows Eliza talking to a teenage girl. The teenager speaks first, and Eliza's answers are in blue.
Men are all alike.
IN WHAT WAY
They're always bugging us about something or other.
CAN YOU THINK OF A SPECIFIC EXAMPLE
Well, my boyfriend made me come here.
YOUR BOYFRIEND MADE YOU COME HERE
He says that I am depressed all the time.
I AM SORRY TO HERE YOU ARE DEPRESSED
It's true that I am unhappy
DO YOU THINK COMING HERE WILL HELP YOU NOT TO BE UNHAPPY
I need help, that seems certain.
WHAT WOULD IT MEAN IF YOU GOT SOME HELP
Perhaps I could learn to get along with my mother.
TELL ME MORE ABOUT YOUR FAMILY
My mother takes care of me.
WHO ELSE IN YOUR FAMILY TAKES CARE OF YOU
My father.
YOUR FATHER
You are like my father in some ways.
WHAT RESEMBLENCE DO YOU SEE
You are not very aggressive but I think you don't want me to notice that.
WHAT MAKES YOU THINK THAT I AM NOT VERY AGGRESSIVE
You don't argue with me.
WHY DON'T YOU THINK I ARGUE WITH YOU
You are afraid of me.
DOES IT PLEASE YOU TO BELIEVE I AM AFRAID OF YOU
My father is afraid of everybody.
WHAT ELSE COMES TO MIND WHEN YOU THINK OF YOUR FATHER
Bullies.
DOES THAT HAVE ANYTHING TO DO WITH THE FACT THAT YOUR BOYFRIEND MADE YOU COME HERE
Some Dazzling Programs, An Introduction to the Science of Artificial Intelligence
During the 1970's many new methods in the development of AI were tested, notably Minsky's frames theory. Also David Marr proposed new theories about machine vision, for example, how it would be possible to distinguish an image based on the shading of an image, basic information on shapes, color, edges, and texture. With analysis of this information, frames of what an image might be could then be referenced. Another development during this time was the PROLOGUE language. Another advancement in the 1970's was the advent of the expert system. Expert systems predict the probability of a solution under set conditions. For example: * Because of the large storage capacity of computers at the time, expert systems had the potential to interpret statistics, to formulate rules. And the applications in the market place were extensive, and over the course of ten years, expert systems had been introduced to forecast the stock market, aiding doctors with the ability to diagnose disease, and instruct miners to promising mineral locations.
During the 1980's AI was moving at a faster pace, and further into the corporate sector. In 1986, US sales of AI-related hardware and software surged to $425 million. Expert systems in particular demand because of their efficiency. Companies such as Digital Electronics were using XCON, an expert system designed to program the large VAX computers. DuPont, General Motors, and Boeing relied heavily on expert systems Indeed to keep up with the demand for the computer experts, companies such as Teknowledge and Intellicorp specializing in creating software to aid in producing expert systems formed.
The military put AI based hardware to the test of war during Desert Storm. AI-based technologies were used in missile systems, heads-up-displays, and other advancements. AI has also made the transition to the home. With the popularity of the AI computer growing, the interest of the public has also grown. Applications for the Apple Macintosh and IBM compatible computer, such as voice and character recognition have become available. Also AI technology has made steadying camcorders simple using fuzzy logic. With a greater demand for AI-related technology, new advancements are becoming available. Inevitably Artificial Intelligence has, and will continue to affect our lives.
The late 80's saw the appearance of aLife as a distinct discipline. Christopher Langton coined the term 'artificial life' for the first time in the seminal conference 'Evolution, Games and Learning: Models for Adaptation in Machines and Nature'. Since then the momentum has steadily grown. As this happened two other events also had a seminal role, one in 1987 named 'Artificial Life: an Interdisciplinary Workshop on the Synthesis and Simulation of Living Systems', and another, held in 1989 on 'Emergent Computation: Self-organizing, Collective and Cooperative Phenomena in Natural and Artificial Computing Networks'. But it was 'Alife-II', the second Artificial Life Workshop, held at the Santa Fe Institute (USA) - the mecca of artificial life - in 1990, that really marked the consolidation of the field.
So, how are aLife and AI related? There is a strong connection between the two fields in both methodology and research. As we have seen, AI is much older (arguably born in antiquity), whilst aLife really appeared in the late 1980s, when people in many disciplines recognized similarities in the work they were doing. AI methodologies play a large part in aLife work, partly because of the recognizable similarities in the two disciplines: AI studying intelligence, aLife studying life, both with an eye to usefulness and reproducibility. In recent years 'traditional' AI researchers have focussed on aLife techniques for autonomous learning, among other things. In spite of these similarities, there are several dissimilarities. aLife is grounded in biology, physics, chemistry and mathematics, while AI is pursued mainly by computer scientists, engineers, and psychologists. Also, the general philosophy of researchers in the fields seems to approach similar problems from different sides; aLife from the ground up, in an attempt to study synthesis, AI from the top down, focussing on results and not implementation. Simon Penny provides a useful working overview of the overlaps between AI, CA and robotics:
1. Computational biologists. Until now, natural selection, the mechanism of evolution, has been limited to the organic. The realization of evolving, reproducing digital species in silicon using genetic algorithms prompts the question: "Is it alive?" This question divides Alifers into two groups:
- 1a. Hard Alifers hold that self replicating digital organisms are alive in every sense, and that biology must include the study of possible life, and must arrive at some universal laws concerning wet life and digital life.
- 1b. Soft Alifers claim only that genetic and evolutionary simulations are useful in understanding biological dynamics, but remain simply simulations. Around this central group cluster several others:
2. Builders of procedural systems, like Craig Reynolds' Boids and Jessica Hodgin's robot flocks. More recently, these systems are self evolving, such as Karl Sim's work on evolving 3D morphology and behavior by competition, and Jeff Ventralla's evolving animated characters.
3. Subsumption and 'bottom up' roboticists, such as Brooks, who utilise ethological analogies to create bottom up emergent behavior in mobile machines.
4. Builders of autonomous digital agents to do work in the digital realm.
5. Wet Alifers: molecular biologists who are breeding or constructing replicating or behaving groupings of proteins, enzymes and nucleic acids. the instrumentalization of natural selection carries not only for the digital Alifers, but equally for the Wet-Alifers, the closeness in attitude between Alife and the new genetics and reproductive technologies, and nano-technology, should not be elided.
(Catagories defined by Simon Penny) The Darwin Machine: Artificial Life and Art, Simon Penny
Robotics ->
Artificial Life: Cellular Automata and Alife ->
See also Methods used in Creating Intelligence
Thinkquestsee also Simon Penny's 'Paradigm-busters' --
ideas that have changed how we see the world
References include:
An Introduction to the Science of Artificial Intelligence
MESHWORKS, HIERARCHIES AND INTERFACES, Manuel De Landa
The Darwin Machine: Artificial Life and Art, Simon Penny
Artificial Intelligence, Dr. Steve Rapp
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Graphic 'borrowed' from An Introduction to the Science of Artificial Intelligence
