Artificial life

Artificial Life, also called ALife, is a special area of research where scientists study living things and how they change over time. They do this by using computer programs, robots, and tiny parts of living material. This field was started by a computer scientist named Christopher Langton in 1986. He held the first meeting about it in Los Alamos, New Mexico, in 1987.

There are three main ways scientists create artificial life. One way uses computer software, called "soft" artificial life. Another uses real machines and tools, called "hard" artificial life. The third uses experiments with biological materials, called "wet" artificial life. By creating these models, scientists can learn more about how real living things work and develop.

Overview

Artificial life is a special kind of study. Scientists use computers and robots to create fake versions of living things. This helps us learn more about real life and how it changes. Researchers study how groups of living things work together. They also copy nature in new ways and ask big questions about what life means. They even use these ideas to make interesting art.

Philosophy

The philosophy behind artificial life is different from regular ways we study life. Instead of just looking at life as we know it, this field explores what life could be like in other ways.

Traditional models of living things focus on the most important details. But artificial life tries to find the simplest and most general ideas about life and put them into a computer program. This lets scientists study new and different kinds of life.

One big goal in artificial life is to create Open-Ended Evolution (OEE). This means making a system that can always create new, complex behaviors or creatures, without stopping or reaching a final point. Right now, most artificial life programs are not considered truly alive, but scientists have different ideas about what artificial life could become.

Software-based ("soft")

Techniques

Cellular automata have been used in artificial life from the start because they are easy to grow and run at the same time. Artificial neural networks sometimes help model an agent's brain. Though they are part of artificial intelligence, neural networks can show how groups of organisms change over time, especially ones that can learn. Learning and evolution help us understand how more complex instincts grow in organisms.

Program-based simulations use a "genome" language, often as a computer program, instead of real biological DNA. These organisms "live" when their code runs, and they can copy themselves. Changes, or mutations, happen when the code changes randomly. Cellular automata are often used but not always needed. Another example includes artificial intelligence and multi-agent systems.

In module-based simulations, individual parts are added to a creature, changing its actions and traits either directly or through how the parts work together. These simulations focus more on letting users build and use the simulation rather than on mutation and evolution.

Parameter-based simulations use pre-set actions controlled by numbers or other fixed values that can change. Each organism has a group of numbers that control different parts of its behavior.

Neural net-based simulations use neural networks to help creatures learn and grow. The focus is usually on learning rather than natural selection.

Complex systems modeling

Mathematical models of complex systems come in three types: black-box, white-box, and grey-box. Black-box models do not show the inner workings of a system, so you cannot see how smaller parts work together. White-box models show all the inner mechanisms clearly. Grey-box models are a mix of the two.

Creating a white-box model needs a good understanding of the subject before you start. Cellular automata are needed but not enough on their own. A white-box model is built using logical rules and basic ideas to create detailed knowledge about how the system works.

Notable simulators

This is a list of artificial life and digital organism simulators:

Hardware-based ("hard")

Further information: Robot

Hardware-based artificial life includes robots. Robots are machines that can move and do tasks by themselves. They do not need someone to tell them what to do at every step. These robots work automatically, like guided machines that can operate on their own.

Biochemical-based ("wet")

Further information: Artificial cell, Synthetic biology, and Xenobiology

Biochemical-based life is studied in synthetic biology. This research includes creating synthetic DNA. The word "wet" comes from "wetware". Scientists try to build simple living cells from bacteria called Mycoplasma laboratorium. They also work on making cell-like systems from non-living materials.

In May 2019, scientists changed a type of bacteria called Escherichia coli. They reduced the number of building blocks in its genome from 64 to 59. This helped them study how life works at a tiny level.

In 2020, researchers made a biological robot with help from artificial intelligence. In 2021, they made the first biological robots that could make copies of themselves. These robots gather cells to build new versions of themselves.

Open problems

Scientists who study artificial life have many big questions they are still trying to answer. One big question is how life can start from things that aren’t alive. They want to make simple living-like structures in labs and on computers. They also want to see if new kinds of living things are possible. They aim to simulate a single-celled organism and learn how living things turn actions into rules and symbols.

Another question is about what can and cannot happen with living systems. Researchers want to know what will always happen as life evolves, and what the simplest conditions are for big changes in evolution. They aim to create a system that can organize itself at many different sizes. They also want to predict what will happen when we change organisms or ecosystems, and develop ideas about how information works and changes in evolving systems.

Finally, they wonder how life connects to thinking, machines, and culture. They want to show how intelligence can arise in artificial living systems. They also want to study how machines might affect the future of life’s evolution, and create models showing how culture and biology affect each other. They aim to create ethical rules for working with artificial life.

Related subjects

Agent-based modeling helps us study how complicated actions can come from simple systems. Artificial intelligence starts with big ideas, but artificial life looks at how simple rules can grow into more complex actions.

Artificial chemistry is a way to copy how chemicals act in artificial life. Evolutionary algorithms are tools that use artificial life to solve problems, like finding the best way to do something. These tools include things like ant colony optimization, bacterial colony optimization, genetic algorithms, genetic programming, and swarm intelligence. Multi-agent systems are computer programs made of many smart parts that work together. Ideas from artificial life are also used in evolutionary art and evolutionary music to make new kinds of art and music. Studying how life began can use artificial life methods, too. Quantum artificial life uses quantum computing to look at life-like systems.

History

Main article: History of artificial life

Artificial Life, also called ALife, is a field where scientists study how living things work. They use computers, robots, and special chemistry to make models that act like real life. The idea began with a computer expert named Christopher Langton from the United States in 1986. In 1987, Langton held the first meeting about this topic in Los Alamos, New Mexico.

Criticism

Some people have questioned the ideas behind artificial life. In 1994, a scientist named John Maynard Smith said that some work in this area was not based on real facts. Another thinker, Mario Bunge, argued that some beliefs about artificial life mix up the difference between a computer model and the real processes it tries to copy.