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Movie Title Year Distributor Notes Rev Formats 110% Natural 18 2009 Red Light District Bald 1 DRO Ami Jordan Jacks Off The Cock 2010 Synister O Backroom Casting Couch 1059: Ami 2009 backroomcastingcouch.com Facial Bald Barely Legal It Barely Fits 2010 Hustler Video Facial 1 DRO Barely Legal POV 7 2010 Hustler Video Facial 1 DRO Big Bodacious Knockers 6 2009 Lethal Hardcore Bald 1 DRO Big Natural Breasts 12 2009 New Sensations DVDOnly DRO Big Naturals 11 2009 Reality Kings 1 DRO Blowjob Beginners 4 2012 Spungy Gunk Films O Boobaholics Anonymous 5 2009 Jules Jordan Video Creampie 5 DRO Bounce 2 2009 Vivid Facial Bald 1 DRO Brand New Faces 21 2009 Vivid Facial 2 DRO Breast Worship 2 2009 Jules Jordan Video Facial Squirt Bald 5 DRO Cameltoe Workout 3 2009 Lethal Hardcore Facial Bald 1 DRO Cock Crazy Teens 2009 Lethal Hardcore Facial Bald 1 DRO First Day Jitters 2 2011 Paper Street Media Facial Bald 1 DRO Forever Young 2014 Reality Kings Facial 1 DRO Fresh Outta High School 16 2009 New Sensations Facial Bald 1 DRO I Am Eighteen 2 2009 Porn.com Facial Bald DR I Cum In Peace 3 2010 Synister O Juggernauts 4 2015 Naughty Sinner DRO Natural 2009 realitykings.com O
Naughty Bookworms 15 2009 Pure Play Media 1 DR Naughty Bookworms 3436 2008 naughtyamerica.com Facial Bald Naughty Cheerleaders Club 2 2009 New Sensations Facial Bald 1 DRO No Skinny Bitches 2012 Vouyer Media DRO Only Teen Blowjobs 1 2009 Overboard Video BJOnly Facial DRO Pleasure Principle 2008 realitykings.com Facial Bald DO Pure 18 14 2010 Reality Kings Facial Bald DRO SeXXX in Public 3 2010 Pure Play Media DRO Snort That Cum 4 2010 Immoral Productions DO Suck It (ll) 2009 Zaye Entertainment BJOnly Facial DR Super Naturals 10 2009 New Sensations Bald 1 DRO Teen Sluts: Ami Jordan 2012 Spungy Gunk Films O Teen Sluts: Cami Smalls 2011 Spungy Gunk Films O Teen Trouble 2011 Digital Sin DO Udderly Fantastic 2010 Vouyer Media DRO Who's That Girl 8 2009 Vouyer Media Bald Creampie Artificial life (often abbreviated ALife or A-Life) is a field of study wherein researchers examine systems related to natural life, its processes, and its evolution, through the use of simulations with computer models, robotics, and biochemistry.[1] The discipline was named by Christopher Langton, an American theoretical biologist, in 1986.[2] In 1987 Langton organized the first conference on the field, in Los Alamos, New Mexico.[3] There are three main kinds of alife,[4] named for their approaches: soft,[5] from software; hard,[6] from hardware; and wet, from biochemistry. Artificial life researchers study traditional biology by trying to recreate aspects of biological phenomena.[7][8] A Braitenberg vehicle simulation, programmed in breve, an artificial life simulator

Contents 1 Overview 2 Philosophy 3 Software-based ("soft") 3.1 Techniques 3.2 Notable simulators 3.2.1 Program-based 3.2.2 Module-based 3.2.3 Parameter-based 3.2.4 Neural net–based 3.3 Complex systems modeling 4 Hardware-based ("hard") 5 Biochemical-based ("wet") 6 Open problems 7 Related subjects 8 History 9 Criticism 10 See also 11 References 12 External links Overview Artificial life studies the fundamental processes of living systems in artificial environments in order to gain a deeper understanding of the complex information processing that define such systems. These topics are broad, but often include evolutionary dynamics, emergent properties of collective systems, biomimicry, as well as related issues about the philosophy of the nature of life and the use of lifelike properties in artistic works. Philosophy The modeling philosophy of artificial life strongly differs from traditional modeling by studying not only "life-as-we-know-it" but also "life-as-it-might-be".[9] A traditional model of a biological system will focus on capturing its most important parameters. In contrast, an alife modeling approach will generally seek to decipher the most simple and general principles underlying life and implement them in a simulation. The simulation then offers the possibility to analyse new and different lifelike systems. Vladimir Georgievich Red'ko proposed to generalize this distinction to the modeling of any process, leading to the more general distinction of "processes-as-we-know-them" and "processes-as-they-could-be".[10] At present, the commonly accepted definition of life does not consider any current alife simulations or software to be alive, and they do not constitute part of the evolutionary process of any ecosystem. However, different opinions about artificial life's potential have arisen: The strong alife (cf. Strong AI) position states that "life is a process which can be abstracted away from any particular medium" (John von Neumann)[citation needed]. Notably, Tom Ray declared that his program Tierra is not simulating life in a computer but synthesizing it.[11] The weak alife position denies the possibility of generating a "living process" outside of a chemical solution. Its researchers try instead to simulate life processes to understand the underlying mechanics of biological phenomena. Software-based ("soft") Techniques Cellular automata were used in the early days of artificial life, and are still often used for ease of scalability and parallelization. Alife and cellular automata share a closely tied history. Artificial neural networks are sometimes used to model the brain of an agent. Although traditionally more of an artificial intelligence technique, neural nets can be important for simulating population dynamics of organisms that can learn. The symbiosis between learning and evolution is central to theories about the development of instincts in organisms with higher neurological complexity, as in, for instance, the Baldwin effect. Notable simulators This is a list of artificial life/digital organism simulators, organized by the method of creature definition. Name Driven By Started Ended ApeSDK (formerly Noble Ape) language/social simulation 1996 ongoing Avida executable DNA 1993 ongoing Biogenesis executable DNA 2006 ongoing Neurokernel Geppetto 2014 ongoing Creatures neural net/simulated biochemistry 1996-2001 Fandom still active to this day, some abortive attempts at new products Critterding neural net 2005 ongoing Darwinbots executable DNA 2003 ongoing DigiHive executable DNA 2006 ongoing DOSE executable DNA 2012 ongoing EcoSim Fuzzy Cognitive Map 2009 ongoing Framsticks executable DNA 1996 ongoing Geb neural net 1997 ongoing OpenWorm Geppetto 2011 ongoing Polyworld neural net 1990 ongoing Primordial Life executable DNA 1994 2003 ScriptBots executable DNA 2010 ongoing TechnoSphere modules 1995 Tierra executable DNA 1991 2004 3D Virtual Creature Evolution neural net 2008 NA Program-based Further information: programming game Program-based simulations contain organisms with a complex DNA language, usually Turing complete. This language is more often in the form of a computer program than actual biological DNA. Assembly derivatives are the most common languages used. An organism "lives" when its code is executed, and there are usually various methods allowing self-replication. Mutations are generally implemented as random changes to the code. Use of cellular automata is common but not required. Another example could be an artificial intelligence and multi-agent system/program. Module-based Individual modules are added to a creature. These modules modify the creature's behaviors and characteristics either directly, by hard coding into the simulation (leg type A increases speed and metabolism), or indirectly, through the emergent interactions between a creature's modules (leg type A moves up and down with a frequency of X, which interacts with other legs to create motion). Generally these are simulators which emphasize user creation and accessibility over mutation and evolution. Parameter-based Organisms are generally constructed with pre-defined and fixed behaviors that are controlled by various parameters that mutate. That is, each organism contains a collection of numbers or other finite parameters. Each parameter controls one or several aspects of an organism in a well-defined way. Neural net–based These simulations have creatures that learn and grow using neural nets or a close derivative. Emphasis is often, although not always, more on learning than on natural selection. Complex systems modeling Mathematical models of complex systems are of three types: black-box (phenomenological), white-box (mechanistic, based on the first principles) and grey-box (mixtures of phenomenological and mechanistic models).[12][13] In black-box models, the individual-based (mechanistic) mechanisms of a complex dynamic system remain hidden. Mathematical models for complex systems Black-box models are completely nonmechanistic. They are phenomenological and ignore a composition and internal structure of a complex system. We cannot investigate interactions of subsystems of such a non-transparent model. A white-box model of complex dynamic system has ‘transparent walls’ and directly shows underlying mechanisms. All events at micro-, meso- and macro-levels of a dynamic system are directly visible at all stages of its white-box model evolution. In most cases mathematical modelers use the heavy black-box mathematical methods, which cannot produce mechanistic models of complex dynamic systems. Grey-box models are intermediate and combine black-box and white-box approaches. Logical deterministic individual-based cellular automata model of single species population growth Creation of a white-box model of complex system is associated with the problem of the necessity of an a priori basic knowledge of the modeling subject. The deterministic logical cellular automata are necessary but not sufficient condition of a white-box model. The second necessary prerequisite of a white-box model is the presence of the physical ontology of the object under study. The white-box modeling represents an automatic hyper-logical inference from the first principles because it is completely based on the deterministic logic and axiomatic theory of the subject. The purpose of the white-box modeling is to derive from the basic axioms a more detailed, more concrete mechanistic knowledge about the dynamics of the object under study. The necessity to formulate an intrinsic axiomatic system of the subject before creating its white-box model distinguishes the cellular automata models of white-box type from cellular automata models based on arbitrary logical rules. If cellular automata rules have not been formulated from the first principles of the subject, then such a model may have a weak relevance to the real problem.[13] Logical deterministic individual-based cellular automata model of interspecific competition for a single limited resource Hardware-based ("hard") Further information: Robot Hardware-based artificial life mainly consist of robots, that is, automatically guided machines able to do tasks on their own. Biochemical-based ("wet") Further information: Synthetic biology Biochemical-based life is studied in the field of synthetic biology. It involves e.g. the creation of synthetic DNA. The term "wet" is an extension of the term "wetware". Efforts toward "wet" artificial life focus on engineering live minimal cells from living bacteria Mycoplasma laboratorium and in building non-living biochemical cell-like systems from scratch. In May 2019, researchers, in a milestone effort, reported the creation of a new synthetic (possibly artificial) form of viable life, a variant of the bacteria Escherichia coli, by reducing the natural number of 64 codons in the bacterial genome to 59 codons instead, in order to encode 20 amino acids.[14][15]

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