2. Tom Ray's Tierra Artificial Life and World -less graphical but no less revealing
3. Conway's Game of Life-cellular automata, Life created on a Grid
Until a few decades ago, most people thought they knew what it meant to say that something was alive. Breathing, eating, the ability to reproduce, having a shape, existing in a time-space dimension etc were some of the characteristics which defined Life. Until the advent of computers and programmers who wrote code that could also perform most of the things which living things do. And that has led people [like me] to re-evaluate the meaning of saying that you are a living thing. It was John von Neumann, who first thought of self-reproducing cellular automata, and inspired John Conway, Chris Langton, Tom Ray, and Stephen Wolfram, John Holland and countless others to institute the study and creation of entities that exhibited life-like behavior, including, its most vital functions-REPRODUCTION and THINKING! It was easier to make the 'creatures' reproduce , than to make them think. In living creatures, thinking is the activation of neurons in a mass of cells called the brain, by electrical-chemical activities. Soon, basic artificial neural networks called Perceptrons were invented to show some primitive forms of learning and memory. Combinations of new types and combinations of Artificial Neural Networks, Genetic Algorithms and Fuzzy Logic are now so sophisticated that they can perform complex almost human-like functions of reasoning, pattern-recognistion, memory recall, decision-making etc Not only the ability to acquire data through sensors and use the information, but also the ability to adapt, respond, physicallly transform or adapt to their environment based on analysis of information, meant that these entities have the characteristics of Life as we usually associate it with. In it's pure form, the Life created were merely strings of code with rules for interaction with other strings of code, and the environment. But over time, the rules became more and more complex and realistic- not merely yes/no, threshold rules, but rules which took into account many variables and their interaction in non-linear ways, and rules which calculated ahead the probability of the effect of the rules on other parties as well as their retaliation/response, counter response etc. But because most humans find it difficult to visualize Life just by looking at strings of mathematical code, GUIs [graphical user interfaces] were created. to make it easier to understand. And today's Artificial Life and Artificial Intelligence enthusiasts are spoiled for choice for colorful, user-friendly, and strikingly life-like [oops! wrong choice of words] software with which they can play God and create their own forms of 'Life'
*** Important Note: Though there are Genetic Algorithms and Pattern Recognition algorithms that can compose music, AI and AL environments even when simulated over thousands of computer generations have not shown any affinity or propensity to create forms, structures or ideas resembling what we know as art, music, or religion. [Although the existence of parasitic relationships, homosexual behaviour, herding and mutual cooperation, and periods of mass self-destruction and renewal, critical thresholds which produced accelerated evolution, genocide, cannibalism etc were sometimes exhibited]
To me, Life is not defined by the ability to move, to breath or the ability to reproduce. Nor can it be completely defined by the ability to see, hear, smell, touch and taste. Or by its morphology [shape] To me, something is alive when it shows signs of neural-type activities, and so death is most aptly defined by the absence of neural-type activities [brain-dead]. At the very basic level, the pre-requisites for Life are an ‘environment’ and some entities capable of generating neural-type activities; and the interaction among and between these entities and the environment. Life is defined as the following processes 1 to 12 described below. Frogs, lions, fish, flowers, birds, humans are alive because they all do 1 to 12. Bacteria, Fungi, Viruses, and strings of computer code also do 1 to 12, consciously or unconsciously, explicitly or implicitly, sophisticatedly or primitively. Computer-generated Artificial Life and Artificial Intelligence and their behavior in simulated environments have shown that they do steps 1 to 11 too, sometimes generating strikingly similar 'effects' on the evolution of themselves and the environment,'
State t=1 to n where t= a given point in time-space. Kt=state of knowledge (K) at time (t)
1. Sensing and gathering data from its constantly changing environment with its sensors: e.g. vision, touch, smell, taste. [Man-made sensors replace human sensors for AI and AL
2. Pre-processing the data to reduce complexity: e.g. filtering, compressing, de-noising, averaging, analysis of trends etc
3. Identifying patterns and inter-relationships in the data to convert to knowledge
4. Learning: Comparing with accumulated knowledge and synthesizing new knowledge
5. Storing the new knowledge content
6. Setting out objectives to be achieved: which includes maintaining its existence and extending its existence by reproduction [the passing on of genes],
7.Prioritizing, sequencing, consideration of resources and situation [also called Planning] for achieving the objectives
8.Forecasting ( in some way or other from estimation of probabilities to setting out of possible alternative scenarios)
9. Optimization: attempting to obtain best results with least efforts.
10. Competition to gain advantage or dominate over other entities in the environment
11. Measuring Results and making decision on response to changed environment
12. Respond [adaptation] to changed environment by changing behavior, it's morphological [if any] characteristics, mutation etc
Repeat steps 1 to 11 to Kt=n in a continuous feedback loop where n is dynamic and a ‘moving target.
*** Steps 1 to 12 may take place in milli- seconds [just watch a Lion fish in the act of catching and swallowing its prey], or take a few days, months etc as when a man is courting a woman with the ultimate objective of marriage (and the passing on of his genes, which is a desire that has been hardwired into him).
Epilogue: The AI and AL things we discussed above, have been partly adapted and applied in complex Financial Markets programmed trades, which has affected the character of modern financial markets, by emphasisingthe dynamics rather than the fundamentals of the market.
Cellular Automata are a good introduction to Artificial Life, as they are simple to understand and to create. Start by reading Stephen Wolfram's " A New Kind of Science" Not only will you discover how organisms [whether real or digital] behave, but you will also discover the four Classes of Cellular Automata each exhibiting different behavior and evolution over time " and why, the best life is to be found on the edge between Order and Chaos" the type of characteristics exhibited by a Class 4 cellular automata. If you want to know more about Class 4 Cellular Automata, go to this link : http://everything2.com/e2node/Wolfram%2527s%2520classes%2520of%2520cellular%2520automata. On Neural Networks and how they learn, remember and recall the stored memory, on how they can recognize incomplete patterns. On self-organizing maps, unsupervised learning, and predictions from unseen data, read one of the numerous books on introduction to neural networks that you can find on Amazon.