1.
Synthetic life
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Synthetic biology is an interdisciplinary branch of biology and engineering. Descriptions of synthetic biology depend on how the user approaches it, originally seen as a subset of biology, in recent years the role of electrical and chemical engineering has become more important. For example, one description designates synthetic biology as a discipline that uses engineering principles to design. The definition of synthetic biology is debated, not only among natural scientists and engineers but also in the sciences, arts. One popular definition is designing and constructing biological modules, biological systems, however, the functional aspects of this definition are rooted in molecular biology and biotechnology. In general its purpose can be described as the design and construction of novel artificial biological pathways, organisms or devices, the first identifiable use of the term synthetic biology was in Stéphane Leduc’s publication of Théorie physico-chimique de la vie et générations spontanées and his La Biologie Synthétique. A notable advance in synthetic biology occurred in 2000, when two articles in Nature by Michael B, elowitz and Stanislas Leibler discussed the creation of synthetic biological circuit devices of a genetic toggle switch and a biological clock by combining genes within E. coli cells. Engineers view biology as a technology – a given systems biotechnology or its biological engineering, biomolecular engineering includes approaches which aim to create a toolkit of functional units that can be introduced to present new technological functions in living cells. Genetic engineering includes approaches to construct synthetic chromosomes for whole or minimal organisms, biomolecular design refers to the general idea of de novo design and additive combination of biomolecular components. Each of these share a similar task, to develop a more synthetic entity at a higher level of complexity by inventively manipulating a simpler part at the preceding level. Re-writers are synthetic biologists interested in testing the irreducibility of biological systems, re-writers draw inspiration from refactoring, a process sometimes used to improve computer software. Several key enabling technologies are critical to the growth of synthetic biology, the key concepts include standardization of biological parts and hierarchical abstraction to permit using those parts in increasingly complex synthetic systems. Achieving this is greatly aided by basic technologies of reading and writing of DNA, measurements under a variety of conditions are needed for accurate modeling and computer-aided-design. The most used standardized DNA parts are BioBrick plasmids invented by Tom Knight in 2003. In 2007 it was reported that companies were offering the synthesis of genetic sequences up to 2000 bp long, for a price of about $1 per base pair. Additionally, the CRISPR/Cas system has emerged as a technique for gene editing. It was hailed by The Washington Post as the most important innovation in the synthetic biology space in nearly 30 years, while other methods take months or years to edit gene sequences, CRISPR speeds that time up to weeks. However, due to its ease of use and accessibility, it has raised a number of ethical concerns, DNA sequencing is determining the order of the nucleotide bases in a molecule of DNA
2.
Artificial Life (journal)
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Artificial Life is a peer-reviewed scientific journal that covers the study of man-made systems that exhibit the behavioral characteristics of natural living systems. Its articles cover system synthesis in software, hardware, and wetware, Artificial Life was established in 1993 and is the official journal of the International Society of Artificial Life. It is published online and in hard copy by the MIT Press, official website International Society of Artificial Life
3.
System
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A system is a set of interacting or interdependent component parts forming a complex or intricate whole. Every system is delineated by its spatial and temporal boundaries, surrounded and influenced by its environment, described by its structure and purpose and expressed in its functioning. Alternatively, and usually in the context of social systems. The term system comes from the Latin word systēma, in turn from Greek σύστημα systēma, whole compounded of several parts or members, system, according to Marshall McLuhan, System means something to look at. You must have a high visual gradient to have systematization. In philosophy, prior to Descartes, there was no system, in the 19th century the French physicist Nicolas Léonard Sadi Carnot, who studied thermodynamics, pioneered the development of the concept of a system in the natural sciences. In 1824 he studied the system which he called the substance in steam engines. The working substance could be put in contact with either a boiler, in 1850, the German physicist Rudolf Clausius generalized this picture to include the concept of the surroundings and began to use the term working body when referring to the system. The biologist Ludwig von Bertalanffy became one of the pioneers of the systems theory. Norbert Wiener and Ross Ashby, who pioneered the use of mathematics to study systems, in the 1980s John H. Holland, Murray Gell-Mann and others coined the term complex adaptive system at the interdisciplinary Santa Fe Institute. Environment and boundaries Systems theory views the world as a system of interconnected parts. One scopes a system by defining its boundary, this means choosing which entities are inside the system, one can make simplified representations of the system in order to understand it and to predict or impact its future behavior. These models may define the structure and behavior of the system, Natural and human-made systems There are natural and human-made systems. Natural systems may not have an apparent objective but their behavior can be interpreted as purposefull by an observer, human-made systems are made to satisfy an identified and stated need with purposes that are achieved by the delivery of wanted outputs. Their parts must be related, they must be designed to work as a coherent entity – otherwise they would be two or more distinct systems, Theoretical framework An open system exchanges matter and energy with its surroundings. Most systems are open systems, like a car, a coffeemaker, a closed system exchanges energy, but not matter, with its environment, like Earth or the project Biosphere2 or 3. An isolated system exchanges neither matter nor energy with its environment, a theoretical example of such system is the Universe. Inputs are consumed, outputs are produced, the concept of input and output here is very broad
4.
Life
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Various forms of life exist, such as plants, animals, fungi, protists, archaea, and bacteria. The criteria can at times be ambiguous and may or may not define viruses, viroids, biology is the primary science concerned with the study of life, although many other sciences are involved. The definition of life is controversial, the current definition is that organisms maintain homeostasis, are composed of cells, undergo metabolism, can grow, adapt to their environment, respond to stimuli, and reproduce. However, many other definitions have been proposed, and there are some borderline cases. Modern definitions are more complex, with input from a diversity of scientific disciplines, biophysicists have proposed many definitions based on chemical systems, there are also some living systems theories, such as the Gaia hypothesis, the idea that the Earth itself is alive. Another theory is that life is the property of systems, and yet another is elaborated in complex systems biology. Abiogenesis describes the process of life arising from non-living matter. Properties common to all organisms include the need for certain chemical elements to sustain biochemical functions. Life on Earth first appeared as early as 4.28 billion years ago, soon after ocean formation 4.41 billion years ago, Earths current life may have descended from an RNA world, although RNA-based life may not have been the first. The mechanism by which began on Earth is unknown, though many hypotheses have been formulated and are often based on the Miller–Urey experiment. The earliest known forms are microfossils of bacteria. In July 2016, scientists reported identifying a set of 355 genes believed to be present in the last universal ancestor of all living organisms. Since its primordial beginnings, life on Earth has changed its environment on a time scale. To survive in most ecosystems, life must often adapt to a range of conditions. Some microorganisms, called extremophiles, thrive in physically or geochemically extreme environments that are detrimental to most other life on Earth, Aristotle was the first person to classify organisms. Later, Carl Linnaeus introduced his system of nomenclature for the classification of species. Eventually new groups and categories of life were discovered, such as cells and microorganisms, cells are sometimes considered the smallest units and building blocks of life. There are two kinds of cells, prokaryotic and eukaryotic, both of which consist of cytoplasm enclosed within a membrane and contain many such as proteins
5.
Simulation
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Simulation is the imitation of the operation of a real-world process or system over time. The model represents the system itself, whereas the simulation represents the operation of the system over time, Simulation is used in many contexts, such as simulation of technology for performance optimization, safety engineering, testing, training, education, and video games. Often, computer experiments are used to study simulation models, Simulation is also used with scientific modelling of natural systems or human systems to gain insight into their functioning, as in economics. Simulation can be used to show the real effects of alternative conditions. Physical simulation refers to simulation in which objects are substituted for the real thing. These physical objects are chosen because they are smaller or cheaper than the actual object or system. Simulation Fidelity is used to describe the accuracy of a simulation, Fidelity is broadly classified as 1 of 3 categories, low, medium, and high. Simulation in failure analysis refers to simulation in which we create environment/conditions to identify the cause of equipment failure and this was the best and fastest method to identify the failure cause. A computer simulation is an attempt to model a real-life or hypothetical situation on a computer so that it can be studied to see how the system works, by changing variables in the simulation, predictions may be made about the behaviour of the system. It is a tool to investigate the behaviour of the system under study. A good example of the usefulness of using computers to simulate can be found in the field of network traffic simulation, in such simulations, the model behaviour will change each simulation according to the set of initial parameters assumed for the environment. Computer simulation is used as an adjunct to, or substitution for. Several software packages exist for running computer-based simulation modeling that makes all the modeling almost effortless, modern usage of the term computer simulation may encompass virtually any computer-based representation. The computer simulates the subject machine, accordingly, in theoretical computer science the term simulation is a relation between state transition systems, useful in the study of operational semantics. Less theoretically, an application of computer simulation is to simulate computers using computers. For example, simulators have been used to debug a microprogram or sometimes commercial application programs, simulators may also be used to interpret fault trees, or test VLSI logic designs before they are constructed. Symbolic simulation uses variables to stand for unknown values, in the field of optimization, simulations of physical processes are often used in conjunction with evolutionary computation to optimize control strategies. Simulation is extensively used for educational purposes and it is frequently used by way of adaptive hypermedia
6.
Computer simulation
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Computer simulations reproduce the behavior of a system using a model. Simulation of a system is represented as the running of the systems model and it can be used to explore and gain new insights into new technology and to estimate the performance of systems too complex for analytical solutions. The scale of events being simulated by computer simulations has far exceeded anything possible using traditional paper-and-pencil mathematical modeling, other examples include a 1-billion-atom model of material deformation, a 2. Because of the computational cost of simulation, computer experiments are used to perform such as uncertainty quantification. A computer model is the algorithms and equations used to capture the behavior of the system being modeled, by contrast, computer simulation is the actual running of the program that contains these equations or algorithms. Simulation, therefore, is the process of running a model, thus one would not build a simulation, instead, one would build a model, and then either run the model or equivalently run a simulation. It was a simulation of 12 hard spheres using a Monte Carlo algorithm, Computer simulation is often used as an adjunct to, or substitute for, modeling systems for which simple closed form analytic solutions are not possible. The external data requirements of simulations and models vary widely, for some, the input might be just a few numbers, while others might require terabytes of information. Because of this variety, and because diverse simulation systems have common elements. Systems that accept data from external sources must be careful in knowing what they are receiving. While it is easy for computers to read in values from text or binary files, often they are expressed as error bars, a minimum and maximum deviation from the value range within which the true value lie. Even small errors in the data can accumulate into substantial error later in the simulation. While all computer analysis is subject to the GIGO restriction, this is true of digital simulation. Indeed, observation of this inherent, cumulative error in digital systems was the main catalyst for the development of chaos theory, another way of categorizing models is to look at the underlying data structures. For time-stepped simulations, there are two classes, Simulations which store their data in regular grids and require only next-neighbor access are called stencil codes. Many CFD applications belong to this category, if the underlying graph is not a regular grid, the model may belong to the meshfree method class. Equations define the relationships between elements of the system and attempt to find a state in which the system is in equilibrium. Such models are used in simulating physical systems, as a simpler modeling case before dynamic simulation is attempted
7.
Robotics
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Robotics is the interdisciplinary branch of engineering and science that includes mechanical engineering, electrical engineering, computer science, and others. Robotics deals with the design, construction, operation, and use of robots, as well as systems for their control, sensory feedback. These technologies are used to develop machines that can substitute for humans, Robots can be used in any situation and for any purpose, but today many are used in dangerous environments, manufacturing processes, or where humans cannot survive. Robots can take on any form but some are made to resemble humans in appearance and this is said to help in the acceptance of a robot in certain replicative behaviors usually performed by people. Such robots attempt to replicate walking, lifting, speech, cognition, many of todays robots are inspired by nature, contributing to the field of bio-inspired robotics. Throughout history, it has been assumed that robots will one day be able to mimic human behavior. Many robots are built to do jobs that are hazardous to people such as defusing bombs, finding survivors in unstable ruins, Robotics is also used in STEM as a teaching aid. The word robotics was derived from the robot, which was introduced to the public by Czech writer Karel Čapek in his play R. U. R. which was published in 1920. The word robot comes from the Slavic word robota, which means labour, the play begins in a factory that makes artificial people called robots, creatures who can be mistaken for humans – very similar to the modern ideas of androids. Karel Čapek himself did not coin the word and he wrote a short letter in reference to an etymology in the Oxford English Dictionary in which he named his brother Josef Čapek as its actual originator. According to the Oxford English Dictionary, the word robotics was first used in print by Isaac Asimov, published in May 1941 in Astounding Science Fiction. Asimov was unaware that he was coining the term, since the science and technology of electrical devices is electronics, he assumed robotics already referred to the science and technology of robots. In some of Asimovs other works, he states that the first use of the word robotics was in his short story Runaround, however, the original publication of Liar. Predates that of Runaround by ten months, so the former is generally cited as the words origin, in 1942, the science fiction writer Isaac Asimov created his Three Laws of Robotics. In 1948, Norbert Wiener formulated the principles of cybernetics, the basis of practical robotics, fully autonomous only appeared in the second half of the 20th century. The first digitally operated and programmable robot, the Unimate, was installed in 1961 to lift hot pieces of metal from a die casting machine, commercial and industrial robots are widespread today and used to perform jobs more cheaply, more accurately and more reliably, than humans. They are also employed in jobs which are too dirty, dangerous. For example, a designed to travel across heavy dirt or mud
8.
Biochemistry
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Biochemistry, sometimes called biological chemistry, is the study of chemical processes within and relating to living organisms. By controlling information flow through biochemical signaling and the flow of energy through metabolism. Biochemistry is closely related to biology, the study of the molecular mechanisms by which genetic information encoded in DNA is able to result in the processes of life. Depending on the definition of the terms used, molecular biology can be thought of as a branch of biochemistry, or biochemistry as a tool with which to investigate. The chemistry of the cell depends on the reactions of smaller molecules. These can be inorganic, for water and metal ions, or organic, for example the amino acids. The mechanisms by which cells harness energy from their environment via chemical reactions are known as metabolism, the findings of biochemistry are applied primarily in medicine, nutrition, and agriculture. In medicine, biochemists investigate the causes and cures of diseases, in nutrition, they study how to maintain health and study the effects of nutritional deficiencies. In agriculture, biochemists investigate soil and fertilizers, and try to discover ways to improve crop cultivation, crop storage and pest control. However, biochemistry as a scientific discipline has its beginning sometime in the 19th century, or a little earlier. Gowland Hopkins on enzymes and the nature of biochemistry. The term biochemistry itself is derived from a combination of biology, the German chemist Carl Neuberg however is often cited to have coined the word in 1903, while some credited it to Franz Hofmeister. Then, in 1828, Friedrich Wöhler published a paper on the synthesis of urea and these techniques allowed for the discovery and detailed analysis of many molecules and metabolic pathways of the cell, such as glycolysis and the Krebs cycle. Another significant historic event in biochemistry is the discovery of the gene and this part of biochemistry is often called molecular biology. In the 1950s, James D. Watson, Francis Crick, Rosalind Franklin, in 1958, George Beadle and Edward Tatum received the Nobel Prize for work in fungi showing that one gene produces one enzyme. In 1988, Colin Pitchfork was the first person convicted of murder with DNA evidence, mello received the 2006 Nobel Prize for discovering the role of RNA interference, in the silencing of gene expression. Around two dozen of the 92 naturally occurring elements are essential to various kinds of biological life. Most rare elements on Earth are not needed by life, while a few common ones are not used, most organisms share element needs, but there are a few differences between plants and animals
9.
Christopher Langton
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Christopher Langton is an American computer scientist and one of the founders of the field of artificial life. He coined the term in the late 1980s when he organized the first Workshop on the Synthesis, following his time at Los Alamos, Langton joined the Santa Fe Institute, to continue his research on artificial life. He left SFI in the late 1990s, and abandoned his work on artificial life, Langton is the first-born son of Jane Langton, author of books including the Homer Kelly Mysteries. He has two sons, Gabe and Colin. Langton made numerous contributions to the field of life, both in terms of simulation and computational models of given problems and to philosophical issues. He early identified the problems of information, computation and reproduction as intrinsically connected with complexity and these ideas were also explored simultaneously, albeit with different approximations, by James P. Crutchfield and Per Bak among others. For a 2-state, 1-r neighborhood, 1D cellular automata the value is close to 0.5, for a 2-state, Moore neighborhood, 2D cellular automata, like Conways Life, the value is 0.273. Artificial Life III, Proceedings of the Third Interdisciplinary Workshop on the Synthesis, Life at the Edge of Chaos. in Artificial Life II, Addison-Wesley,1991. Artificial Life II, Proceedings of the Second Interdisciplinary Workshop on the Synthesis, Computation at the edge of chaos. Computation at the edge of Chaos, Phase-Transitions and Emergent Computation, is There a Sharp Phase Transition for Deterministic Cellular Automata. With W. K Wootters, Physica D,45,1990, Artificial Life, Proceedings of an Interdisciplinary Workshop on the Synthesis and Simulation of Living Systems. Studying Artificial Life with Cellular Automata, about Langtons work A. GaJardo, A. Moreira, E. Goles. Origins of Order, Self-Organization and Selection in Evolution, melanie Mitchell, Peter T. Hraber, and James P. Crutchfield. Revisiting the edge of chaos, Evolving cellular automata to perform computations, melanie Mitchell, James P. Crutchfield and Peter T. Hraber. Artificial life Langtons loops Langtons ant Cellular Automata Explanation of Langtons Lambda The Swarm development group
10.
Software
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Computer software, or simply software, is that part of a computer system that consists of data or computer instructions, in contrast to the physical hardware from which the system is built. In computer science and software engineering, computer software is all information processed by computer systems, programs, computer software includes computer programs, libraries and related non-executable data, such as online documentation or digital media. Computer hardware and software require each other and neither can be used on its own. At the lowest level, executable code consists of machine language instructions specific to an individual processor—typically a central processing unit, a machine language consists of groups of binary values signifying processor instructions that change the state of the computer from its preceding state. For example, an instruction may change the value stored in a storage location in the computer—an effect that is not directly observable to the user. An instruction may also cause something to appear on a display of the computer system—a state change which should be visible to the user. The processor carries out the instructions in the order they are provided, unless it is instructed to jump to a different instruction, the majority of software is written in high-level programming languages that are easier and more efficient for programmers, meaning closer to a natural language. High-level languages are translated into machine language using a compiler or an interpreter or a combination of the two, an outline for what would have been the first piece of software was written by Ada Lovelace in the 19th century, for the planned Analytical Engine. However, neither the Analytical Engine nor any software for it were ever created, the first theory about software—prior to creation of computers as we know them today—was proposed by Alan Turing in his 1935 essay Computable numbers with an application to the Entscheidungsproblem. This eventually led to the creation of the academic fields of computer science and software engineering. Computer science is more theoretical, whereas software engineering focuses on practical concerns. However, prior to 1946, software as we now understand it—programs stored in the memory of stored-program digital computers—did not yet exist, the first electronic computing devices were instead rewired in order to reprogram them. On virtually all platforms, software can be grouped into a few broad categories. There are many different types of software, because the range of tasks that can be performed with a modern computer is so large—see list of software. System software includes, Operating systems, which are collections of software that manage resources and provides common services for other software that runs on top of them. Supervisory programs, boot loaders, shells and window systems are parts of operating systems. In practice, an operating system bundled with additional software so that a user can potentially do some work with a computer that only has an operating system. Device drivers, which operate or control a particular type of device that is attached to a computer, utilities, which are computer programs designed to assist users in the maintenance and care of their computers
11.
Computer hardware
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Computer hardware is the collection of physical components that constitute a computer system. By contrast, software is instructions that can be stored and run by hardware, hardware is directed by the software to execute any command or instruction. A combination of hardware and software forms a usable computing system, the template for all modern computers is the Von Neumann architecture, detailed in a 1945 paper by Hungarian mathematician John von Neumann. The meaning of the term has evolved to mean a stored-program computer in which an instruction fetch and this is referred to as the Von Neumann bottleneck and often limits the performance of the system. For the third year, U. S. business-to-business channel sales increased. The impressive growth was the fastest sales increase since the end of the recession, sales growth accelerated in the second half of the year peaking in fourth quarter with a 6.9 percent increase over the fourth quarter of 2012. There are a number of different types of system in use today. The personal computer, also known as the PC, is one of the most common types of computer due to its versatility, laptops are generally very similar, although they may use lower-power or reduced size components, thus lower performance. The computer case is a plastic or metal enclosure that houses most of the components, a case can be either big or small, but the form factor of motherboard for which it is designed matters more. A power supply unit converts alternating current electric power to low-voltage DC power for the components of the computer. Laptops are capable of running from a battery, normally for a period of hours. The motherboard is the component of a computer. It is usually cooled by a heatsink and fan, or water-cooling system, most newer CPUs include an on-die Graphics Processing Unit. The clock speed of CPUs governs how fast it executes instructions, many modern computers have the option to overclock the CPU which enhances performance at the expense of greater thermal output and thus a need for improved cooling. The chipset, which includes the bridge, mediates communication between the CPU and the other components of the system, including main memory. Random-Access Memory, which stores the code and data that are being accessed by the CPU. For example, when a web browser is opened on the computer it takes up memory, RAM usually comes on DIMMs in the sizes 2GB, 4GB, and 8GB, but can be much larger. Read-Only Memory, which stores the BIOS that runs when the computer is powered on or otherwise begins execution, the BIOS includes boot firmware and power management firmware
12.
Synthetic biology
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Synthetic biology is an interdisciplinary branch of biology and engineering. Synthetic biology applies these disciplines to build biological systems for research, engineering. Descriptions of synthetic biology depend on how the user approaches it, originally seen as a subset of biology, in recent years the role of electrical and chemical engineering has become more important. For example, one description designates synthetic biology as a discipline that uses engineering principles to design. The definition of synthetic biology is debated, not only among natural scientists and engineers but also in the sciences, arts. One popular definition is designing and constructing biological modules, biological systems, however, the functional aspects of this definition are rooted in molecular biology and biotechnology. In general its purpose can be described as the design and construction of novel artificial biological pathways, organisms or devices, the first identifiable use of the term synthetic biology was in Stéphane Leduc’s publication of Théorie physico-chimique de la vie et générations spontanées and his La Biologie Synthétique. A notable advance in synthetic biology occurred in 2000, when two articles in Nature by Michael B, elowitz and Stanislas Leibler discussed the creation of synthetic biological circuit devices of a genetic toggle switch and a biological clock by combining genes within E. coli cells. Engineers view biology as a technology – a given systems biotechnology or its biological engineering, biomolecular engineering includes approaches which aim to create a toolkit of functional units that can be introduced to present new technological functions in living cells. Genetic engineering includes approaches to construct synthetic chromosomes for whole or minimal organisms, biomolecular design refers to the general idea of de novo design and additive combination of biomolecular components. Each of these share a similar task, to develop a more synthetic entity at a higher level of complexity by inventively manipulating a simpler part at the preceding level. Re-writers are synthetic biologists interested in testing the irreducibility of biological systems, re-writers draw inspiration from refactoring, a process sometimes used to improve computer software. Several key enabling technologies are critical to the growth of synthetic biology, the key concepts include standardization of biological parts and hierarchical abstraction to permit using those parts in increasingly complex synthetic systems. Achieving this is greatly aided by basic technologies of reading and writing of DNA, measurements under a variety of conditions are needed for accurate modeling and computer-aided-design. The most used standardized DNA parts are BioBrick plasmids invented by Tom Knight in 2003. In 2007 it was reported that companies were offering the synthesis of genetic sequences up to 2000 bp long, for a price of about $1 per base pair. Additionally, the CRISPR/Cas system has emerged as a technique for gene editing. It was hailed by The Washington Post as the most important innovation in the synthetic biology space in nearly 30 years, while other methods take months or years to edit gene sequences, CRISPR speeds that time up to weeks
13.
Biology
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Biology is a natural science concerned with the study of life and living organisms, including their structure, function, growth, evolution, distribution, identification and taxonomy. Modern biology is a vast and eclectic field, composed of branches and subdisciplines. However, despite the broad scope of biology, there are certain unifying concepts within it that consolidate it into single, coherent field. In general, biology recognizes the cell as the unit of life, genes as the basic unit of heredity. It is also understood today that all organisms survive by consuming and transforming energy and by regulating their internal environment to maintain a stable, the term biology is derived from the Greek word βίος, bios, life and the suffix -λογία, -logia, study of. The Latin-language form of the term first appeared in 1736 when Swedish scientist Carl Linnaeus used biologi in his Bibliotheca botanica, the first German use, Biologie, was in a 1771 translation of Linnaeus work. In 1797, Theodor Georg August Roose used the term in the preface of a book, karl Friedrich Burdach used the term in 1800 in a more restricted sense of the study of human beings from a morphological, physiological and psychological perspective. The science that concerns itself with these objects we will indicate by the biology or the doctrine of life. Although modern biology is a recent development, sciences related to. Natural philosophy was studied as early as the ancient civilizations of Mesopotamia, Egypt, the Indian subcontinent, however, the origins of modern biology and its approach to the study of nature are most often traced back to ancient Greece. While the formal study of medicine back to Hippocrates, it was Aristotle who contributed most extensively to the development of biology. Especially important are his History of Animals and other works where he showed naturalist leanings, and later more empirical works that focused on biological causation and the diversity of life. Aristotles successor at the Lyceum, Theophrastus, wrote a series of books on botany that survived as the most important contribution of antiquity to the plant sciences, even into the Middle Ages. Scholars of the medieval Islamic world who wrote on biology included al-Jahiz, Al-Dīnawarī, who wrote on botany, biology began to quickly develop and grow with Anton van Leeuwenhoeks dramatic improvement of the microscope. It was then that scholars discovered spermatozoa, bacteria, infusoria, investigations by Jan Swammerdam led to new interest in entomology and helped to develop the basic techniques of microscopic dissection and staining. Advances in microscopy also had a impact on biological thinking. In the early 19th century, a number of biologists pointed to the importance of the cell. Thanks to the work of Robert Remak and Rudolf Virchow, however, meanwhile, taxonomy and classification became the focus of natural historians
14.
Braitenberg vehicle
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A Braitenberg vehicle is a concept conceived in a thought experiment by the Italian-Austrian cyberneticist Valentino Braitenberg. The motion of the vehicle is controlled by some sensors. Yet the resulting behaviour may appear complex or even intelligent, a Braitenberg vehicle is an agent that can autonomously move around based on its sensor inputs. It has primitive sensors that measure some stimulus at a point, in the simplest configuration, a sensor is directly connected to an effector, so that a sensed signal immediately produces a movement of the wheel. Depending on how sensors and wheels are connected, the vehicle exhibits different behaviors and this means that, depending on the sensor-motor wiring, it appears to strive to achieve certain situations and to avoid others, changing course when the situation changes. The following examples are some of Braitenbergs simplest vehicles, a first agent has one sensor that directly stimulates its single wheel in an excitatory way. The vehicle can standstill or move forward, the behaviour of this vehicle can be interpreted as, More light produces faster movement. This behavior can be understood as a creature that is afraid of the light and its goal is to find a dark spot to hide. A slightly more complex agent has two symmetric sensors each stimulating a wheel on the side of the body. This vehicle represent a model of negative animal tropotaxis and it obeys the following rule, More light right → right wheel turns faster → turns towards the left, away from the light. This is more efficient as a behavior to escape from the source, since the creature can move in different directions. In another variation, the connections are negative or inhibitory, more light → slower movement, in this case, the agents move away from the dark and towards the light. The agent has the two symmetric sensors, but each one stimulating a wheel on the other side of the body. It obeys the rule, More light left → right wheel turns faster → turns towards the left. As a result, the robot follows the light, it moves to be closer to the light, in a complex environment with several sources of stimulus, Braitenberg vehicles will exhibit complex and dynamic behavior. This behavior is undoubtedly goal-directed, flexible and adaptive, and might appear to be intelligent. Yet, the functioning of the agent is purely mechanical, without any information processing or other cognitive processes. Often, BEAM robotics implements these sorts of behaviors
15.
Emergence
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Emergence is central in theories of integrative levels and of complex systems. For instance, the phenomenon of life as studied in biology is an emergent property of chemistry, in philosophy, theories that emphasize emergent properties have been called emergentism. Almost all accounts of emergentism include a form of epistemic or ontological irreducibility to the lower levels, in philosophy, emergence is often understood to be a claim about the etiology of a systems properties. An emergent property of a system, in context, is one that is not a property of any component of that system. Nicolai Hartmann, one of the first modern philosophers to write on emergence and this idea of emergence has been around since at least the time of Aristotle. John Stuart Mill and Julian Huxley are two of many scientists and philosophers who have written on the concept, further, every resultant is clearly traceable in its components, because these are homogeneous and commensurable. The emergent is unlike its components insofar as these are incommensurable, economist Jeffrey Goldstein provided a current definition of emergence in the journal Emergence. Goldstein initially defined emergence as, the arising of novel and coherent structures, patterns, Systems scientist Peter Corning also says that living systems cannot be reduced to underlying laws of physics, Rules, or laws, have no causal efficacy, they do not in fact “generate” anything. They serve merely to describe regularities and consistent relationships in nature and these patterns may be very illuminating and important, but the underlying causal agencies must be separately specified. But that aside, the game of chess illustrates, why any laws or rules of emergence and evolution are insufficient. Even in a game, you cannot use the rules to predict “history” – i. e. the course of any given game. Indeed, you cannot even reliably predict the move in a chess game. Because the “system” involves more than the rules of the game and it also includes the players and their unfolding, moment-by-moment decisions among a very large number of available options at each choice point. The game of chess is inescapably historical, even though it is constrained and shaped by a set of rules. Moreover, and this is a key point, the game of chess is also shaped by teleonomic, cybernetic and it is not simply a self-ordered process, it involves an organized, “purposeful” activity. Usage of the notion emergence may generally be subdivided into two perspectives, that of weak emergence and strong emergence, in terms of physical systems, weak emergence is a type of emergence in which the emergent property is amenable to computer simulation. This is opposed to the notion of strong emergence, in which the emergent property cannot be simulated by a computer. Some common points between the two notions are that emergence concerns new properties produced as the system grows, which is to say ones which are not shared with its components or prior states, also, it is assumed that the properties are supervenient rather than metaphysically primitive
16.
Biomimetics
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Biomimetics or biomimicry is the imitation of the models, systems, and elements of nature for the purpose of solving complex human problems. The terms biomimetics and biomimicry derive from Ancient Greek, βίος, life, a closely related field is bionics. Living organisms have evolved well-adapted structures and materials over time through natural selection. Biomimetics has given rise to new technologies inspired by biological solutions at macro, humans have looked at nature for answers to problems throughout our existence. Nature has solved engineering problems such as self-healing abilities, environmental exposure tolerance and resistance, hydrophobicity, self-assembly, one of the early examples of biomimicry was the study of birds to enable human flight. The Wright Brothers, who succeeded in flying the first heavier-than-air aircraft in 1903, Biomimetics was coined by the American biophysicist and polymath Otto Schmitt during the 1950s. It was during his research that he developed the Schmitt trigger by studying the nerves in squid. He continued to focus on devices that mimic natural systems and by 1957 he had perceived a converse to the view of biophysics at that time. Biophysics is not so much a matter as it is a point of view. It is an approach to problems of biological science utilizing the theory, conversely, biophysics is also a biologists approach to problems of physical science and engineering, although this aspect has largely been neglected. A similar term, bionics was coined by Jack E. Steele in 1960 at Wright-Patterson Air Force Base in Dayton, Steele defined bionics as the science of systems which have some function copied from nature, or which represent characteristics of natural systems or their analogues. The term bionic then became associated with the use of electronically operated artificial body parts, because the term bionic took on the implication of supernatural strength, the scientific community in English speaking countries largely abandoned it. The term biomimicry appeared as early as 1982, Biomimicry was popularized by scientist and author Janine Benyus in her 1997 book Biomimicry, Innovation Inspired by Nature. Biomimicry is defined in the book as a new science that studies natures models and then imitates or takes inspiration from these designs, Benyus suggests looking to Nature as a Model, Measure, and Mentor and emphasizes sustainability as an objective of biomimicry. Biomorphic mineralization is a technique that produces materials with morphologies and structures resembling those of living organisms by using bio-structures as templates for mineralization. Compared to other methods of production, biomorphic mineralization is facile, environmentally benign. Morpho butterfly wings contain microstructures that create its coloring effect through structural coloration rather than pigmentation, incident light waves are reflected at specific wavelengths to create vibrant colors due to multilayer interference, diffraction, thin film interference, and scattering properties. The scales of these butterflies consist of such as ridges, cross-ribs, ridge-lamellae
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Ecosystem
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An ecosystem is a community of living organisms in conjunction with the nonliving components of their environment, interacting as a system. These biotic and abiotic components are regarded as linked together through nutrient cycles, as ecosystems are defined by the network of interactions among organisms, and between organisms and their environment, they can be of any size but usually encompass specific, limited spaces. Energy, water, nitrogen and soil minerals are other essential components of an ecosystem. The energy that flows through ecosystems is obtained primarily from the sun and it generally enters the system through photosynthesis, a process that also captures carbon from the atmosphere. By feeding on plants and on one another, animals play an important role in the movement of matter and they also influence the quantity of plant and microbial biomass present. Ecosystems are controlled both by external and internal factors, other external factors include time and potential biota. Ecosystems are dynamic entities—invariably, they are subject to disturbances and are in the process of recovering from some past disturbance. Ecosystems in similar environments that are located in different parts of the world can have different characteristics simply because they contain different species. The introduction of species can cause substantial shifts in ecosystem function. Internal factors not only control ecosystem processes but are controlled by them and are often subject to feedback loops. Other internal factors include disturbance, succession and the types of species present, although humans exist and operate within ecosystems, their cumulative effects are large enough to influence external factors like climate. Biodiversity affects ecosystem function, as do the processes of disturbance, classifying ecosystems into ecologically homogeneous units is an important step towards effective ecosystem management, but there is no single, agreed-upon way to do this. The term ecosystem was first used in 1935 in a publication by British ecologist Arthur Tansley, Tansley devised the concept to draw attention to the importance of transfers of materials between organisms and their environment. He later refined the term, describing it as The whole system, including not only the organism-complex, but also the whole complex of physical factors forming what we call the environment. Tansley regarded ecosystems not simply as natural units, but as mental isolates, Tansley later defined the spatial extent of ecosystems using the term ecotope. G. Raymond Lindeman took these ideas one step further to suggest that the flow of energy through a lake was the driver of the ecosystem. Most mineral nutrients, on the hand, are recycled within ecosystems. Ecosystems are controlled both by external and internal factors, external factors, also called state factors, control the overall structure of an ecosystem and the way things work within it, but are not themselves influenced by the ecosystem
18.
Philosophy of artificial intelligence
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The philosophy of artificial intelligence attempts to answer such questions as follows, Can a machine act intelligently. Can it solve any problem that a person would solve by thinking, are human intelligence and machine intelligence the same. Is the human brain essentially a computer, can a machine have a mind, mental states, and consciousness in the same way that a human being can. Can it feel how things are and these three questions reflect the divergent interests of AI researchers, Linguists, cognitive scientists and philosophers respectively. The scientific answers to these questions depend on the definition of intelligence and consciousness, important propositions in the philosophy of AI include, Turings polite convention, If a machine behaves as intelligently as a human being, then it is as intelligent as a human being. The Dartmouth proposal, Every aspect of learning or any feature of intelligence can be so precisely described that a machine can be made to simulate it. Newell and Simons physical symbol system hypothesis, A physical symbol system has the necessary, Searles strong AI hypothesis, The appropriately programmed computer with the right inputs and outputs would thereby have a mind in exactly the same sense human beings have minds. Hobbes mechanism, Reason is nothing but reckoning, is it possible to create a machine that can solve all the problems humans solve using their intelligence. This question defines the scope of what machines will be able to do in the future, arguments in favor of the basic premise must show that such a system is possible. The first step to answering the question is to define intelligence. Alan Turing, in a famous and seminal 1950 paper, reduced the problem of defining intelligence to a question about conversation. He suggests that, if a machine can answer any question put to it, using the words that an ordinary person would. A modern version of his design would use an online chat room. The program passes the test if no one can tell which of the two participants is human, Turing notes that no one ever asks the question can people think. He writes instead of arguing continually over this point, it is usual to have a convention that everyone thinks. Turings test extends this polite convention to machines, If a machine acts as intelligently as human being, one criticism of the Turing test is that it is explicitly anthropomorphic. If our ultimate goal is to create machines that are more intelligent than people, Russell and Norvig write that aeronautical engineering texts do not define the goal of their field as making machines that fly so exactly like pigeons that they can fool other pigeons. Recent A. I. research defines intelligence in terms of intelligent agents, an agent is something which perceives and acts in an environment
19.
John von Neumann
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John von Neumann was a Hungarian-American mathematician, physicist, inventor, computer scientist, and polymath. He made major contributions to a number of fields, including mathematics, physics, economics, computing, and statistics. He published over 150 papers in his life, about 60 in pure mathematics,20 in physics, and 60 in applied mathematics and his last work, an unfinished manuscript written while in the hospital, was later published in book form as The Computer and the Brain. His analysis of the structure of self-replication preceded the discovery of the structure of DNA, also, my work on various forms of operator theory, Berlin 1930 and Princeton 1935–1939, on the ergodic theorem, Princeton, 1931–1932. During World War II he worked on the Manhattan Project, developing the mathematical models behind the lenses used in the implosion-type nuclear weapon. After the war, he served on the General Advisory Committee of the United States Atomic Energy Commission, along with theoretical physicist Edward Teller, mathematician Stanislaw Ulam, and others, he worked out key steps in the nuclear physics involved in thermonuclear reactions and the hydrogen bomb. Von Neumann was born Neumann János Lajos to a wealthy, acculturated, Von Neumanns place of birth was Budapest in the Kingdom of Hungary which was then part of the Austro-Hungarian Empire. He was the eldest of three children and he had two younger brothers, Michael, born in 1907, and Nicholas, who was born in 1911. His father, Neumann Miksa was a banker, who held a doctorate in law and he had moved to Budapest from Pécs at the end of the 1880s. Miksas father and grandfather were both born in Ond, Zemplén County, northern Hungary, johns mother was Kann Margit, her parents were Jakab Kann and Katalin Meisels. Three generations of the Kann family lived in apartments above the Kann-Heller offices in Budapest. In 1913, his father was elevated to the nobility for his service to the Austro-Hungarian Empire by Emperor Franz Joseph, the Neumann family thus acquired the hereditary appellation Margittai, meaning of Marghita. The family had no connection with the town, the appellation was chosen in reference to Margaret, Neumann János became Margittai Neumann János, which he later changed to the German Johann von Neumann. Von Neumann was a child prodigy, as a 6 year old, he could multiply and divide two 8-digit numbers in his head, and could converse in Ancient Greek. When he once caught his mother staring aimlessly, the 6 year old von Neumann asked her, formal schooling did not start in Hungary until the age of ten. Instead, governesses taught von Neumann, his brothers and his cousins, Max believed that knowledge of languages other than Hungarian was essential, so the children were tutored in English, French, German and Italian. A copy was contained in a private library Max purchased, One of the rooms in the apartment was converted into a library and reading room, with bookshelves from ceiling to floor. Von Neumann entered the Lutheran Fasori Evangelikus Gimnázium in 1911 and this was one of the best schools in Budapest, part of a brilliant education system designed for the elite
20.
Thomas S. Ray
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Thomas S. Ray is an ecologist who created and developed the Tierra project, a computer simulation of artificial life. In 1975, he and Donald R, the thesis was expanded into his Ph. D. thesis at Harvard University. He is currently Professor of Zoology and Adjunct Professor of Computer Science at the University of Oklahoma in Norman, Tom Ray is also a former member of the International Core War Society. It is specified that the one responsible for it was Tom Ray, strong, Donald R. Ray, Thomas S. Host Tree Location Behavior of a Tropical Vine by Skototropism, Thomas S. Rays web site Kurzweils Turing Fallacy Mediamatic. net
21.
Tierra (computer simulation)
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Tierra is a computer simulation developed by ecologist Thomas S. Ray in the early 1990s in which computer programs compete for time and space. In this context, the programs in Tierra are considered to be evolvable and can mutate, self-replicate. Tierras virtual machine is written in C and it operates on a custom instruction set designed to facilitate code changes and reordering, including features such as jump to template. The basic Tierra model has been used to explore in silico the basic processes of evolutionary. Processes such as the dynamics of punctuated equilibrium, host-parasite co-evolution, often in such models there is the notion of a function being optimized, in the case of Tierra, the fitness function is endogenous, there is simply survival and death. The issue of how true open-ended evolution can be implemented in a system is still an open question in the field of artificial life. Mark Bedau and Norman Packard developed a method of classifying evolutionary systems and in 1997. Russell K. Standish has measured the informational complexity of Tierran organisms, the creation of more detailed models in which more realistic dynamics of biological systems and organisms are incorporated is now an active research field. Previously published in Great Britain in 2001 by Headline Book Publishing, Ray, T. S.1991, Evolution and optimization of digital organisms, in Billingsley K. R. et al. Scientific Excellence in Supercomputing, The IBM1990 Contest Prize Papers, Athens, GA,30602, The Baldwin Press, publication date, December 1991, pp. 489–531. New York ISBN 0-471-12308-0 Tierra home page
22.
Cellular automaton
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A cellular automaton is a discrete model studied in computability theory, mathematics, physics, complexity science, theoretical biology and microstructure modeling. Cellular automata are also called cellular spaces, tessellation automata, homogeneous structures, cellular structures, tessellation structures, a cellular automaton consists of a regular grid of cells, each in one of a finite number of states, such as on and off. The grid can be in any number of dimensions. For each cell, a set of cells called its neighborhood is defined relative to the specified cell, an initial state is selected by assigning a state for each cell. A new generation is created, according to some fixed rule that determines the new state of cell in terms of the current state of the cell. The concept was discovered in the 1940s by Stanislaw Ulam. While studied by some throughout the 1950s and 1960s, it was not until the 1970s and Conways Game of Life, a cellular automaton. Wolfram published A New Kind of Science in 2002, claiming that cellular automata have applications in fields of science. These include computer processors and cryptography, the primary classifications of cellular automata, as outlined by Wolfram, are numbered one to four. This last class are thought to be universal, or capable of simulating a Turing machine. Cellular automata can simulate a variety of systems, including biological and chemical ones. One way to simulate a two-dimensional cellular automaton is with a sheet of graph paper along with a set of rules for the cells to follow. Each square is called a cell and each cell has two states, black and white. The neighborhood of a cell is the nearby, usually adjacent, the two most common types of neighborhoods are the von Neumann neighborhood and the Moore neighborhood. The former, named after the cellular automaton theorist, consists of the four orthogonally adjacent cells. The latter includes the von Neumann neighborhood as well as the four remaining cells surrounding the cell whose state is to be calculated, for such a cell and its Moore neighborhood, there are 512 possible patterns. For each of the 512 possible patterns, the table would state whether the center cell will be black or white on the next time interval. Conways Game of Life is a version of this model
23.
Parallelization
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Parallel computing is a type of computation in which many calculations or the execution of processes are carried out simultaneously. Large problems can often be divided into smaller ones, which can then be solved at the same time, there are several different forms of parallel computing, bit-level, instruction-level, data, and task parallelism. Parallelism has been employed for years, mainly in high-performance computing. As power consumption by computers has become a concern in recent years, parallel computing has become the dominant paradigm in computer architecture, specialized parallel computer architectures are sometimes used alongside traditional processors, for accelerating specific tasks. Communication and synchronization between the different subtasks are typically some of the greatest obstacles to getting good parallel program performance, a theoretical upper bound on the speed-up of a single program as a result of parallelization is given by Amdahls law. Traditionally, computer software has been written for serial computation, to solve a problem, an algorithm is constructed and implemented as a serial stream of instructions. These instructions are executed on a central processing unit on one computer, only one instruction may execute at a time—after that instruction is finished, the next one is executed. Parallel computing, on the hand, uses multiple processing elements simultaneously to solve a problem. This is accomplished by breaking the problem into independent parts so that each processing element can execute its part of the algorithm simultaneously with the others. The processing elements can be diverse and include such as a single computer with multiple processors, several networked computers, specialized hardware. Frequency scaling was the dominant reason for improvements in performance from the mid-1980s until 2004. The runtime of a program is equal to the number of instructions multiplied by the time per instruction. Maintaining everything else constant, increasing the frequency decreases the average time it takes to execute an instruction. An increase in frequency thus decreases runtime for all compute-bound programs. However, power consumption P by a chip is given by the equation P = C × V2 × F, where C is the capacitance being switched per clock cycle, V is voltage, increases in frequency increase the amount of power used in a processor. Moores law is the observation that the number of transistors in a microprocessor doubles every 18 to 24 months. Despite power consumption issues, and repeated predictions of its end, with the end of frequency scaling, these additional transistors can be used to add extra hardware for parallel computing. Optimally, the speedup from parallelization would be linear—doubling the number of processing elements should halve the runtime, however, very few parallel algorithms achieve optimal speedup
24.
Artificial neural network
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Each neural unit is connected with many others, and links can enhance or inhibit the activation state of adjoining neural units. Each individual neural unit computes using summation function, There may be a threshold function or limiting function on each connection and on the unit itself, such that the signal must surpass the limit before propagating to other neurons. These systems are self-learning and trained, rather than explicitly programmed, Neural networks typically consist of multiple layers or a cube design, and the signal path traverses from the first, to the last layer of neural units. Back propagation is the use of stimulation to reset weights on the front neural units. More modern networks are a bit more free flowing in terms of stimulation and inhibition with connections interacting in a more chaotic. Dynamic neural networks are the most advanced, in that they dynamically can, based on rules, form new connections, the goal of the neural network is to solve problems in the same way that the human brain would, although several neural networks are more abstract. New brain research often stimulates new patterns in neural networks, one new approach is using connections which span much further and link processing layers rather than always being localized to adjacent neurons. Neural networks are based on numbers, with the value of the core. An interesting facet of these systems is that they are unpredictable in their success with self-learning, after training, some become great problem solvers and others dont perform as well. In order to them, several thousand cycles of interaction typically occur. Warren McCulloch and Walter Pitts created a model for neural networks based on mathematics. This model paved the way for neural network research to split into two distinct approaches, one approach focused on biological processes in the brain and the other focused on the application of neural networks to artificial intelligence. This work led to the paper by Kleene on nerve networks. “Representation of events in nerve nets and finite automata. ”In, Automata Studies, ed. by C. E. Shannon, annals of Mathematics Studies, no.34. Princeton University Press, Princeton, N. J.1956, in the late 1940s psychologist Donald Hebb created a hypothesis of learning based on the mechanism of neural plasticity that is now known as Hebbian learning. Hebbian learning is considered to be a typical unsupervised learning rule, researchers started applying these ideas to computational models in 1948 with Turings B-type machines. Farley and Wesley A. Clark first used computational machines, then called calculators, other neural network computational machines were created by Rochester, Holland, Habit, and Duda. Frank Rosenblatt created the perceptron, an algorithm for pattern recognition based on a computer learning network using simple addition and subtraction
25.
Artificial intelligence
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Artificial intelligence is intelligence exhibited by machines. Colloquially, the artificial intelligence is applied when a machine mimics cognitive functions that humans associate with other human minds, such as learning. As machines become increasingly capable, mental facilities once thought to require intelligence are removed from the definition, for instance, optical character recognition is no longer perceived as an example of artificial intelligence, having become a routine technology. AI research is divided into subfields that focus on specific problems or on specific approaches or on the use of a tool or towards satisfying particular applications. The central problems of AI research include reasoning, knowledge, planning, learning, natural language processing, perception, general intelligence is among the fields long-term goals. Approaches include statistical methods, computational intelligence, and traditional symbolic AI, Many tools are used in AI, including versions of search and mathematical optimization, logic, methods based on probability and economics. The AI field draws upon computer science, mathematics, psychology, linguistics, philosophy, neuroscience, the field was founded on the claim that human intelligence can be so precisely described that a machine can be made to simulate it. Some people also consider AI a danger to humanity if it progresses unabatedly, while thought-capable artificial beings appeared as storytelling devices in antiquity, the idea of actually trying to build a machine to perform useful reasoning may have begun with Ramon Llull. With his Calculus ratiocinator, Gottfried Leibniz extended the concept of the calculating machine, since the 19th century, artificial beings are common in fiction, as in Mary Shelleys Frankenstein or Karel Čapeks R. U. R. The study of mechanical or formal reasoning began with philosophers and mathematicians in antiquity, in the 19th century, George Boole refined those ideas into propositional logic and Gottlob Frege developed a notational system for mechanical reasoning. Around the 1940s, Alan Turings theory of computation suggested that a machine, by shuffling symbols as simple as 0 and 1 and this insight, that digital computers can simulate any process of formal reasoning, is known as the Church–Turing thesis. Along with concurrent discoveries in neurology, information theory and cybernetics, the first work that is now generally recognized as AI was McCullouch and Pitts 1943 formal design for Turing-complete artificial neurons. The field of AI research was born at a conference at Dartmouth College in 1956, attendees Allen Newell, Herbert Simon, John McCarthy, Marvin Minsky and Arthur Samuel became the founders and leaders of AI research. At the conference, Newell and Simon, together with programmer J. C, shaw, presented the first true artificial intelligence program, the Logic Theorist. This spurred tremendous research in the domain, computers were winning at checkers, solving problems in algebra, proving logical theorems. By the middle of the 1960s, research in the U. S. was heavily funded by the Department of Defense and laboratories had been established around the world. AIs founders were optimistic about the future, Herbert Simon predicted, machines will be capable, within twenty years, Marvin Minsky agreed, writing, within a generation. The problem of creating artificial intelligence will substantially be solved and they failed to recognize the difficulty of some of the remaining tasks
26.
Population dynamics
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Population dynamics is the branch of life sciences that studies the size and age composition of populations as dynamical systems, and the biological and environmental processes driving them. Example scenarios are ageing populations, population growth, or population decline, the first principle of population dynamics is widely regarded as the exponential law of Malthus, as modeled by the Malthusian growth model. The Lotka–Volterra predator-prey equations are another example, as well as the alternative Arditi-Ginzburg equations. The computer game SimCity and the MMORPG Ultima Online, among others, in the past 30 years, population dynamics has been complemented by evolutionary game theory, developed first by John Maynard Smith. Under these dynamics, evolutionary biology concepts may take a deterministic mathematical form, Population dynamics overlap with another active area of research in mathematical biology, mathematical epidemiology, the study of infectious disease affecting populations. Various models of viral spread have been proposed and analyzed, the rate at which a population increases in size if there are no density-dependent forces regulating the population is known as the intrinsic rate of increase. D N d t 1 N = r Where d N / d t is the rate of increase of the population, N is the population size and this is therefore the theoretical maximum rate of increase of a population per individual. The concept is used in insect population biology to determine how environmental factors affect the rate at which pest populations increase. See also exponential population growth and logistic population growth and it is very unusual to see this in nature. In the last 100 years, human population growth has appeared to be exponential, in the long run, however, it is not likely. Paul Ehrlich and Thomas Malthus believed that population growth would lead to overpopulation and starvation due to scarcity of resources. They believed that population would grow at rate in which they exceed the ability at which humans can find food. In the future, humans would be unable to feed large populations, the biological assumptions of exponential growth is that the per capita growth rate is constant. Growth is not limited by resource scarcity or predation, N t +1 = λ N t where λ is the discrete-time per capita growth rate. At λ =1, we get a line and a discrete-time per capita growth rate of zero. At λ <1, we get a decrease in per capita growth rate, at λ >1, we get an increase in per capita growth rate. At λ =0, we get extinction of the species, D N d T = r N where d N d T is the rate of population growth per unit time, r is the maximum per capita growth rate, and N is the population size. At r >0, there is an increase in per capita growth rate, at r =0, the per capita growth rate is zero
27.
Digital organism
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A digital organism is a self-replicating computer program that mutates and evolves. Digital organisms are used as a tool to study the dynamics of Darwinian evolution, the study of digital organisms is closely related to the area of artificial life. Digital organisms can be traced back to the game Darwin, developed in 1961 at Bell Labs, a similar implementation that followed this was the game Core War. In Core War, it turned out one of the winning strategies was to replicate as fast as possible. Programs in the Core War game were able to mutate themselves. This allowed competing programs to embed damaging instructions in each other that caused errors, enslaved processes, or even change strategies mid-game and heal themselves. Steen Rasmussen at Los Alamos National Laboratory took the idea from Core War one step further in his world system by introducing a genetic algorithm that automatically wrote programs. However, Rasmussen did not observe the evolution of complex and stable programs and it turned out that the programming language in which core world programs were written was very brittle, and more often than not mutations would completely destroy the functionality of a program. The first to solve the issue of program brittleness was Thomas S. Ray with his Tierra system, Ray made some key changes to the programming language such that mutations were much less likely to destroy a program. With these modifications, he observed for the first time computer programs that did indeed evolve in a meaningful, later, Chris Adami, Titus Brown, and Charles Ofria started developing their Avida system, which was inspired by Tierra but again had some crucial differences. In Tierra, all lived in the same address space. In Avida, on the hand, each program lives in its own address space. Because of this modification, experiments with Avida became much cleaner and easier to interpret than those with Tierra, with Avida, digital organism research has begun to be accepted as a valid contribution to evolutionary biology by a growing number of evolutionary biologists. Evolutionary biologist Richard Lenski of Michigan State University has used Avida extensively in his work, Lenski, Adami, and their colleagues have published in journals such as Nature and the Proceedings of the National Academy of Sciences. In 1996, Andy Pargellis created a Tierra-like system called Amoeba that evolved self-replication from a randomly seeded initial condition, artificial life Evolutionary computation Genetic algorithms Combinatorial optimization Cellular automaton List of digital organism simulators Evolution@Home Polyworld ONeill, B. Trends in Ecology and Evolution 17, 528-532, the spontaneous generation of digital Life. Physica D91 86-96 Misevic, Dusan & Ofria, Charles & Lenski, Richard E. Sexual reproduction reshapes the genetic architecture of digital organisms Proc Biol Sci.2006 February 22,273, 457–464
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Avida
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Avida is an artificial life software platform to study the evolutionary biology of self-replicating and evolving computer programs. The software was inspired by the Tierra system. Tierra simulated an evolutionary system by introducing computer programs competed for computer resources, specifically processor time. In this respect it was similar to Core Wars, but differed in that the programs being run in the simulation were able to modify themselves, tierras programs were artificial life organisms. Unlike Tierra, Avida assigns every digital organism its own protected region of memory, by default, other digital organisms cannot access this memory space, neither for reading nor for writing, and cannot execute code that is not in their own memory space. Adami and Ofria, in collaboration with others, have used Avida to conduct research in digital evolution, the 2003 paper The Evolutionary Origin of Complex Features describes the evolution of a mathematical equals operation from simpler bitwise operations. Testing Darwin, Discover Magazine, February 2005, Avida Software - GitHub Avida-ED Project - Robert T. Pennock An Avida Developers Site MSU Devolab website C. Brown, Evolutionary Learning in the 2D Artificial Life Systems Avida, in, R. Brooks, P. Maes, Artificial Life IV, MIT Press, Cambridge, MA, p. 377-381. Genome Complexity, Robustness, and Genetic Interactions in Digital Organisms, evolution of Digital Organisms at High Mutation Rate Leads To Survival of the Flattest. The Evolutionary Origin of Complex Features, adaptive Radiation from Resource Competition in Digital Organisms. Natural selection fails to optimize mutation rates for long-term adaptation on rugged fitness landscapes, pdf Benjamin E. Beckmann, Philip K. McKinley, Charles Ofria. Evolution of an adaptive response in digital organisms
29.
Creatures (video game series)
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Gameplay focuses on raising alien creatures known as Norns, teaching them to survive, helping them explore their world, defending them against other species, and breeding them. Words can be taught to creatures by a computer or by repeating the name of the object while the creature is looking at it. After a creature understands language, the player can instruct their creature by typing in instructions, a complete life cycle is modelled for the creatures - childhood, adolescence, adulthood, and senescence, each with their own particular needs. The gameplay is designed to foster an emotional bond between the player and their creatures, rather than taking a scripted approach, Creatures series games were driven by detailed biological and neurological simulation and their unexpected results. There were six major Creatures releases from Creature Labs, between 1996 and 2001, there were three principal games released, the Docking Station add-on and two childrens games, and there were three games created for console systems. A sequel named Creatures Online was in development, with the artificial life technology from Creatures 3, the program was significant as it was one of the first commercial titles to code alife organisms from the genetic level upwards using a sophisticated biochemistry and neural network brains. This meant that the Norns and their DNA could develop and evolve in increasingly diverse ways, by breeding certain Norns with others, some traits could be passed on to following generations. Most interestingly, the Norns turned out to behave similarly to living creatures and this was seen as an important insight into how real world organisms may function and evolve. The norns possess simulated biological drives which give punishment when they are raised, the model for norns decision-making process is Behaviorist and based on norns learning how to reduce their drives. Mutations in the genome also occur, allowing new characteristics to appear in the population, faulty genomes can also occur - for example, norns which cannot see or hear, or immortal norns due to a hyper-efficient digestion system. Creatures used the emergent approach seen in animats, but with the goal of more complex behaviours than locomotion, Grand describes the psychological model of the creatures as being inspired by rats. In 2000, Steve Grand described the level of norns as being like ants. Margaret Boden, in 2003, rejected Creatures as being a form of life as the simulated metabolism is concerned with controlling the norns behaviour. In 2011, Steve Grand stated that while the norns in Creatures could learn, generalise from past experiences to novel experiences, earlier a-life programs had worked by giving their organisms a limited set of commands and parameters, and seeing whether the way the subjects behaved was realistic. The genetics in Creatures are somewhat different from human genetics, they are haploid, there is no concept of dominant gene and recessive gene, much less recombination between loci. Nevertheless, the complexity of the simulated biochemistry meant that Norn behaviour was highly unpredictable, among the fans of Creatures were the Oxford zoologist Richard Dawkins, who called it a quantum leap in the development of artificial life, and author Douglas Adams. Creatures inspired some players to take up careers in the sciences, originally conceived as a desktop pet with a brain, Grand later incorporated inspiration from The Planiverse to pitch a little computer ewoks game, like the game Little Computer People. Creatures was developed as a product by Millennium, and was released by Mindscape in 1996
30.
EcoSim
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EcoSim is an individual-based predator-prey ecosystem simulation in which agents can evolve. It has been designed to investigate several broad ecological questions, as well as long-term evolutionary patterns, EcoSim has been designed by Robin Gras at the University of Windsor in 2009 and it is still currently used for research in his Bioinformatics and Ecosystem Simulation Lab. The agents have a model which allows the evolutionary process to modify the behaviors of the predators. Furthermore, there is a mechanism which allows to study global patterns as well as species-specific patterns. In EcoSim, an individuals genomic data codes for its model and is represented by a fuzzy cognitive map. The FCM contains sensory concepts such as foodClose or predatorClose, internal states such as fear or hunger, the FCM is represented as an array of floating-point values which represent the extent to which one concept influences another. For example, it would be expected that the sensory concept predatorClose would positively affect the internal concept fear and these relationships among concepts evolve over time, sometimes giving a new meaning to a concept. Furthermore, the FCM is heritable, meaning that a new agent is given an FCM which is a combination of that of its parents with possible mutations, EcoSim subscribes to the “genotypic cluster” definition of a species. Since EcoSim has the capacity to allow speciation events to occur, it is possible to track speciation events throughout a run of the simulation, energy is provided to individuals by the resources they find in their environment. An agent consumes some energy each time it performs an action, if an individual uses all its energy, it dies. A typical run lasts several tens of thousands of time steps, in addition, a food chain consisting of three levels, primary producers, predators and preys, has been implemented allowing complex interactions between agents and co-evolution to occur. All events, the state and action of every agent, are saved for every time step of every run. Several studies have already been done using EcoSim, for example, Devaurs and Gras have analyzed the species abundance patterns observed in the communities generated by EcoSim, based on Fishers log series. In other studies, the behavior of the system with multi-fractal properties has been proven in as it also has been observed for real ecosystems. Mashayekhi and Gras investigated the effect of spatial distribution and spatiotemporal information on speciation, in more recent research, Golestani et al. Ecosims webpage A page with videos of EcoSim
31.
Noble Ape
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Noble Ape is an artificial life development project launched in June,1996 by Tom Barbalet. It was designed to be a forum for a diversity of contributors to work towards a coherent cognitive simulation development environment, the central software of the project is the Noble Ape Simulation, based on the ideas outlined in Noble Ape Philosophic. There are two primary aims of the Simulation, to simulate the biological environment the Noble Apes inhabit, the Noble Apes move around and react to external and internal events. Noble Ape integrates many concepts in the areas of philosophy, biology and it runs under Mac, Windows, and Unix platforms. The graphics engine in the Noble Ape Simulation, codenamed Ocelot, is a non-polygonal engine, the Noble Ape Simulation also contains the programming language, ApeScript. Noble Ape also features a podcast, Ape Reality, where aspects of the programming, biology, philosophy
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TechnoSphere (virtual environment)
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TechnoSphere was an online digital environment launched on September 1,1995 and hosted on a computer at a UK university. This online program was one of many digital artificial life simulations that evolved as the World Wide Web began to grow, many museums and classrooms found the tool to be a valuable complement to learning material on natural selection and ecosystems. The experiment operated online until 2002 and was unavailable until January 15,2007 when it was launched again, TechnoSphere was a real-time, 3D simulation of an environment that was populated by virtual creatures. Users across the globe had the capability to create their own creatures through a website, the program was capable of modeling such concepts as simple evolution and carrying capacity. Despite limited available creature designs, no two would behave in the same way, due to chance interactions with its environment. Physically, the landscape of TechnoSphere consisted of 16 km² of terrain. It was capable of supporting approximately 4,000 creatures, though sources suggest that as many as 20,000 creatures typically would coexist in the virtual environment at one time. After the relaunch, it was stated that the software limited the number of creatures at 200,000. Because each creatures behavior was unique, no single event could have been predicted, for example, even though there was no explicit flocking algorithm written into the program, creatures could be found organizing themselves into groups, most likely impelled by urges to mate and eat. The programs that supported the website were scalable, and could be modified to support a larger or smaller community of creatures, users accessing the site were able to create their own artificial life forms, building carnivores or herbivores from a select few component parts. Their digital DNA was linked to each component and the completed creatures attributes was determined by the combination of each features strengths, once a creature design was finished, users would name their digital creature, tag it with their e-mail address, and enter it into the digital environment. There they chased or evaded each other, ate, grew and they also produced offspring, which were variants of the parents, sometimes incorporating aspects of both parents and other times favoring one parent creatures attributes over the others. General behavior patterns had emerged, but it was difficult to predict what was going to happen based solely on a creatures design, the one thing all TechnoSphere creatures did have in common was that they would all eventually die. There was only one gender in TechnoSphere, so the creature that initiated mating was the parent that ended up carrying and caring for the offspring, Creature behavior was directed by a set of algorithms called Creature Comforts, designed by Julian Saunderson. It dictated, for example, that behavior could only be initiated if both creatures hunger was at least 50% satiated. When significant events occurred in the TechnoSphere, a users creature would send brief email messages home, users were also able to visit the website and view 2D snapshots of their creature, check family trees, world statistics, and search for other creatures and their users. One report described the popularity by citing that the online version had attracted over a 100,000 users who had created 3,286,148. Over the years in which the website was operating, the growing popularity facilitated necessary updates to the software and hardware, causing website downtime
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3D Virtual Creature Evolution
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3D Virtual Creature Evolution, abbreviated to 3DVCE, is an artificial evolution simulation program created by Lee Graham. The program was inspired by Karl Sims’1994 artificial evolution program, the program is run through volunteers who download the program from the home website and return information from completed simulations. It is currently available on Windows and in some cases Linux. 3DVCE uses evolutionary algorithms to simulate evolution, the user sets the body plan restrictions and whether fitness score is scaled in relation to size. Limb interpenetration is also an option, crossover rate determines what percentage of an individual is created via crossover of parents and mutation. Mutation rate in body and brain is then determined, specific mathematical operations and values can be attributed to the creature’s brain as well. Artificial organisms’ fitness score is determined by how well they achieve their fitness goal within their evaluation time, fitness functions include distance traveled, maximum height, average height, “TOG”, and “Sphere”. These goals are not individualized and can be set to specific strengths to determine the fitness goal, what generations the fitness function applies to can also be set. The environment, or “Terrain”, is then determined and this includes a flat plain, bumpy terrain, water, and “spheres”. Everything in the simulation is viewed from a first person viewpoint, after settings are determined, the first generation is generated from randomly created individuals. All creatures appear at the same spawning point and are made of segments or rectangular prisms connected to others at joints, colors are assigned to segment types randomly. Segment type is determined by the size and joints a segment has, colors indicate nothing else than that. These first generation creatures move randomly, with no influence from the fitness goal, Creatures with the largest fitness value reproduce and the following generation is based on this reproduction. Eventually, patterns in the form and fitness increases even further. Fitness function can be changed during the simulation to simulate environmental changes, 3DVCE is not only for evolutionary research. Objects can also be spawned for graphics and simulated physics tests and this includes pre-installed blocks, spheres, grenades, and structures that can either be thrown from camera or generated at a spawning point. Artificial gravity can also be manipulated, random and archived creatures can also be re-spawned to manipulate or view. Lee Graham has also included a TARDIS in the simulation, which moved into can teleport the camera back to the original spawning point
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Self-replication
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Self-replication is any behavior of a dynamical system that yields construction of an identical copy of itself. Biological cells, given suitable environments, reproduce by cell division, during cell division, DNA is replicated and can be transmitted to offspring during reproduction. Biological viruses can replicate, but only by commandeering the reproductive machinery of cells through a process of infection, harmful prion proteins can replicate by converting normal proteins into rogue forms. Computer viruses reproduce using the hardware and software already present on computers, self-replication in robotics has been an area of research and a subject of interest in science fiction. Any self-replicating mechanism which does not make a copy will experience genetic variation. These variants will be subject to selection, since some will be better at surviving in their current environment than others. For example, scientists have come close to constructing RNA that copies itself in an environment that is a solution of RNA monomers, in this case, the body is the genome, and the specialized copy mechanisms are external. However, the simplest possible case is only a genome exists. Without some specification of the steps, a genome-only system is probably better characterized as something like a crystal. Recent research has begun to categorize replicators, often based on the amount of support they require, natural replicators have all or most of their design from nonhuman sources. Such systems include natural life forms, autotrophic replicators can reproduce themselves in the wild. It is conjectured that non-biological autotrophic replicators could be designed by humans, self-reproductive systems are conjectured systems which would produce copies of themselves from industrial feedstocks such as metal bar and wire. Self-assembling systems assemble copies of themselves from finished, delivered parts, simple examples of such systems have been demonstrated at the macro scale. The design space for machine replicators is very broad, in computer science a self-reproducing computer program is a computer program that, when executed, outputs its own code. This is also called a quine, in this case the program is treated as both executable code, and as data to be manipulated. This approach is common in most self-replicating systems, including biological life, in many programming languages an empty program is still a legal program, which executes without producing errors or any other output. The output is thus the same as the code, so the program is trivially self-reproducing. In geometry a self-replicating tiling is a pattern in which several congruent tiles may be joined together to form a larger tile that is similar to the original
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Multi-agent system
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A multi-agent system is a computerized system composed of multiple interacting intelligent agents within an environment. Multi-agent systems can be used to solve problems that are difficult or impossible for an agent or a monolithic system to solve. Intelligence may include some methodic, functional, procedural approach, algorithmic search or reinforcement learning, although there is considerable overlap, a multi-agent system is not always the same as an agent-based model. The terminology of ABM tends to be used often in the sciences. Topics where multi-agent systems research may deliver an appropriate approach include online trading, disaster response, multi-agent systems consist of agents and their environment. Typically multi-agent systems research refers to software agents, however, the agents in a multi-agent system could equally well be robots, humans or human teams. A multi-agent system may contain combined human-agent teams, Agents can be divided into different types ranging from simple to complex. Agent actions in the environment are typically mediated via an appropriate middleware and this middleware offers a first-class design abstraction for multi-agent systems, providing means to govern resource access and agent coordination. When agents can share knowledge using any agreed language, within the constraints of the communication protocol. Example languages are Knowledge Query Manipulation Language or FIPAs Agent Communication Language, many M. A. systems are implemented in computer simulations, stepping the system through discrete time steps. Only the relevant components respond, I can, at this price, another paradigm commonly used with MAS systems is the pheromone, where components leave information for other components next in line or in the vicinity. These pheromones may evaporate with time, that is their values may decrease with time, M. A. systems, also referred to as self-organized systems, tend to find the best solution for their problems without intervention. There is high similarity here to physical phenomena, such as energy minimizing, for example, many of the cars entering a metropolis in the morning will be available for leaving that same metropolis in the evening. The systems also tend to prevent propagation of faults, self-recover and be fault tolerant, the study of multi-agent systems is concerned with the development and analysis of sophisticated AI problem-solving and control architectures for both single-agent and multiple-agent systems. These frameworks save developers time and also aid in the standardization of MAS development, one such developmental framework for robotics is given in. See also Comparison of agent-based modeling software, multi-agent systems are applied in the real world to graphical applications such as computer games. Agent systems have used in films. They are also used for coordinated defence systems, other applications include transportation, logistics, graphics, GIS as well as in many other fields
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Black box
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In science, computing, and engineering, a black box is a device, system or object which can be viewed in terms of its inputs and outputs, without any knowledge of its internal workings. Almost anything might be referred to as a box, a transistor. To analyse something, as a system, with a typical black box approach, only the behavior of the stimulus/response will be accounted for. The usual representation of black box system is a data flow diagram centered in the box. The opposite of a box is a system where the inner components or logic are available for inspection. The modern term black box seems to have entered the English language around 1945, although Cauer did not himself use the term, others who followed him certainly did describe the method as black-box analysis. In cybernetics, a treatment was given by Ross Ashby in 1956. A black box was described by Norbert Wiener in 1961 as a system that was to be identified using the techniques of system identification. He saw the first step in self-organization as being to be able to copy the output behavior of a black box, many other engineers, scientists and epistemologists, as Mario Bunge, used and perfected the black box theory in the 1960s. An observer makes observations over time, from this there follows the fundamental deduction that all knowledge obtainable from a Black Box is such as can be obtained by re-coding the protocol, all that, and nothing more. If the observer also controls input, the investigation turns into an experiment, when the experimenter is also motivated to control the box, there is an active feedback in the box/observer relation, promoting what in control theory is called a feed forward architecture. The modeling process is the construction of a mathematical model. A developed black box model is a model when black-box testing methods ensures that it is. With backtesting, inputs for past events are entered into the model to see how well the output matches the known results, Black box theories are things defined only in terms of their function. The observer is assumed ignorant in the first instance as the majority of data is held in an inner situation away from facile investigations. The black box theory of states that the mind is fully understood once the inputs and outputs are well-defined. In computer programming and software engineering, black box testing is used to check that the output of a program is as expected, the term black box is used because the actual program being executed is not examined. Also in computing, a black box refers to a piece of equipment provided by a vendor, for the purpose of using that vendors product and it is often the case that the vendor maintains and supports this equipment, and the company receiving the black box typically is hands-off
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