artificial life
In plain wordsThe study of life by building lifelike systems — in computer simulations, robots or chemistry — to learn how living things organize themselves, reproduce and evolve, and what other kinds of life could exist.
The interdisciplinary field that investigates life by trying to build it, or things that behave like it — as computer simulations ('soft' ALife), as robots and other hardware ('hard') or in the chemistry laboratory ('wet') — rather than only by dissecting existing organisms, with the aim of understanding 'life as it could be' as well as life as we know it. Classic topics include self-replication, from John von Neumann's self-reproducing automata; cellular automata such as John Conway's Game of Life; artificial chemistries; digital evolution systems such as Tierra and Avida; flocking and swarm behaviorswarm intelligence, n.: The teamwork that appears when crowds of simple creatures or machines, such as ants, bees or small robots, each respond only to what is nearby, with nobody in charge, yet together solve problems.; and open-ended evolution, the search for systems that keep generating novelty. The field overlaps with evolutionary computationevolutionary computation, n.: Problem-solving by simulated evolution, much as breeders improve crops: keep many candidate answers, let the better ones produce slightly altered offspring, drop the worse, and repeat for many rounds. and the study of emergenceemergence, n.: When a whole shows properties or abilities that none of its parts has and that are hard to predict from them — like a traffic jam arising from individual cars, or new skills appearing in larger AI models., and connects to AI through embodied cognitionembodied cognition, n.: The idea that thinking is not something the brain does on its own: the body, its senses and movements, and its surroundings play a real part in how we think and understand., self-organizing agents and, more recently, the use of foundation models to search for lifelike simulations. A standing debate is whether a computational process could literally be alive (strong ALife) or only model life.
Origin
Named by the computer scientist Christopher Langton in 1986; the term appears in his paper 'Studying Artificial Life with Cellular Automata' (Physica D, 1986). He organized the first artificial life workshop at Los Alamos National Laboratory in September 1987; its proceedings (1989) and the journal Artificial Life (from 1993) established the field, which built on John von Neumann's work on self-reproducing automata from the late 1940s.
Related
- emergenceemergence, n.: When a whole shows properties or abilities that none of its parts has and that are hard to predict from them — like a traffic jam arising from individual cars, or new skills appearing in larger AI models.
- evolutionary computationevolutionary computation, n.: Problem-solving by simulated evolution, much as breeders improve crops: keep many candidate answers, let the better ones produce slightly altered offspring, drop the worse, and repeat for many rounds.
- swarm intelligenceswarm intelligence, n.: The teamwork that appears when crowds of simple creatures or machines, such as ants, bees or small robots, each respond only to what is nearby, with nobody in charge, yet together solve problems.
- genetic algorithmgenetic algorithm, n.: A problem-solving method that copies natural evolution: many candidate solutions compete, the best are 'bred' by mixing and randomly tweaking them, and over many generations the solutions improve.
- embodied cognitionembodied cognition, n.: The idea that thinking is not something the brain does on its own: the body, its senses and movements, and its surroundings play a real part in how we think and understand.
- free energy principlefree energy principle, n.: A sweeping theory by the neuroscientist Karl Friston that living things persist by keeping themselves in expected states — in effect, by constantly minimizing surprise about what they sense.
Sources
- Langton, C. G. (ed.) (1989). Artificial Life: Proceedings of an Interdisciplinary Workshop on the Synthesis and Simulation of Living Systems. Addison-Wesley.
Mentioned in
emergenceemergence, n.: When a whole shows properties or abilities that none of its parts has and that are hard to predict from them — like a traffic jam arising from individual cars, or new skills appearing in larger AI models., John von NeumannJohn von Neumann, prop. n.: A Hungarian-American mathematician who shaped the design of modern computers, co-founded game theory, and is the first person known to have spoken of a coming technological 'singularity'., active inferenceactive inference, n.: A theory that living things, and machines built on the same idea, act in order to make the world match their predictions, choosing actions expected to keep them in preferred states and reduce their uncertainty., free energy principlefree energy principle, n.: A sweeping theory by the neuroscientist Karl Friston that living things persist by keeping themselves in expected states — in effect, by constantly minimizing surprise about what they sense., open-endednessopen-endedness, n.: The ability of a system to keep producing new and interesting things without end — as evolution and human culture do — instead of stopping once it solves one fixed task.