What is Creature Labs' CyberLife?

By Steve Grand, former Technology Director at CyberLife Technology.

CyberLife is Creature Labs' proprietary A-Life technology based on the application of biological metaphors to software-complexity problems. As software becomes increasingly complex we start to face problems of how to manage and understand the systems we build. However, the levels of complexity of these systems are trivial in comparison to those of even the most modest biological systems. Why then with all our genius, logic, and organisational abilities do we find it so difficult to build complex systems? After years of research it seems the reason and the problem all lie within the way we think of and approach complex systems.

From mechanistic to systemic

Traditionally, science was all about breaking down systems into their constituent parts. These parts would then be analysed to reveal their structure and the functions they perform. This was the prominent endeavour of the 19th century, and was very useful as a method of gaining understanding about many things including simple biology, medicine and physics. During the 20th century, our endeavours focused on building systems, from the industrial revolution through to the digital revolution. However, somewhere along the way we had a paradigm shift and decided that the way to build or model complex systems was to consider the behavior required and try to capture this in high level constructs. Massive rule bases were developed in order to capture the intelligence and subtlety of human and animal behavior. Needless to say, these systems failed.

Adaptive life

The route of the problem seems to be that the abstracted knowledge has no grounding - there is no actual physical meaning to any of the concepts. Therefore, if the programmer of the system had not considered a possible situation, then the response of the system may turn out to be erratic, wrong or non-existent. Natural systems are rarely this brittle. All animals learn from experience and generalise. An animal will never be in the exact same situation twice, however it has the innate ability to reason about the similarities between its current situation and those it has experienced in the past. The animal will then usually perform some action that was profitable to it in the similar situations of its past. If this is a bad thing for the animal to do, it will learn from its mistakes and try out some other behavior if faced with a similar situation in the future. Why then don't we base our artificial systems on biological systems?

Modeled systems

Well, that is exactly what we are doing with CyberLife. If we want a system that behaves like a small creature, then we build a small creature. We model large numbers of cells in the brain (neurones), and connect them up and send signals between them, in a way similar to natural cells. We model blood-streams and chemical reactions. We model a world for the creature to inhabit, and objects for the creature to interact with. Finally we model diseases, hunger, emotions, needs and the ability for the creature to grow, breed and evolve. Only then do you get a system that behaves like a creature.

The first results of this philosophy can be seen in Creatures. Take a look, interact with them. Decide for yourself.

A-life is not AI

We believe that true intelligence is an emergent property of lifelike systems. Conventional approaches to artificial intelligence do not lead to true intelligence, just "smartness." This is because they attempt to create intelligent behaviour without regard to the structures that give rise to such behaviour in the real world (i.e., organisms). In the space of all possible machines, there may be many regions that show intelligence, but we only know where one of those regions lies - the region occupied by living creatures. Approaching AI without regard to Biology is just thrashing around in the dark.

CyberLife approaches the problem through simulation. We argue that certain types of simulation can become instances of the thing being simulated. By simulating suitable brain-like structures, we create brains, and (given suitable inputs and outputs) those brains will be intelligent and have minds of their own. By simulating biological organisms in the correct way, we create biological organisms. Artificial intelligence is not achieved by trying to simulate intelligent behaviour, but by simulating populations of dumb objects, whose aggregate behaviour emerges as intelligent.

Origins of Artificial Life

Though A-Life and artificial intelligence approach a common problem from radically divergent perspectives, historically they are closely related, both evolving from the work and research of Alan Turing and John Von Neumann. Turing's wartime effort cracking code in 1930's Britain initiated computer science in general and set off a wealth of scientific and philosophical discussions into the viability of a thinking machine. The Turing machine is a theoretically defined computing system with an infinite tape, capable of performing any possible computation.

Von Neumann's design for a digital computer in the 1940's was inspired in part by his research into computational neuroscience and theoretical research into cellular automata and self-reproducing systems. Studying the "logic" of reproduction, Von Neumann defined a universal replicator, a computational system capable of reproducing any system and realised that the system must function as both instruction and as data. He also remarked that errors in copying self-description could lead to evolution, which could be studied computationally.

Despite this pioneering research, computer science neglected A-Life for many years, focussing instead on AI and cybernetics. Computational evolution eventually developed once genetic algorithms were formally defined by John Holland in the 1960s. Still, the field of A-Life had to wait until the late 1980's to achieve unity and visibility.

In September of 1987, the first workshop on Artificial Life was held at the Los Alamos National Laboratory in the United States. One hundred and sixty computer scientists, biologists, anthropologists and other researchers were brought together and the term A-Life was officially coined. The organiser of the conference, Christopher Langton, there presented a paper which is now largely regarded as the manifesto defining A-Life's agenda.