Conway's Game of Life

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https://en.wikipedia.org/wiki/Conway%27s_Game_of_Life 

A single Gosper's glider gun creating gliders

 

A screenshot of a puffer-type breeder (red) that leaves glider guns (green) in its wake, which in turn create gliders (blue) (animation)

The Game of Life, also known simply as Life, is a cellular automaton devised by the British mathematician John Horton Conway in 1970. It is a zero-player game, meaning that its evolution is determined by its initial state, requiring no further input. One interacts with the Game of Life by creating an initial configuration and observing how it evolves. It is Turing complete and can simulate a universal constructor or any other Turing machine.

Rules

The universe of the Game of Life is an infinite, two-dimensional orthogonal grid of square cells, each of which is in one of two possible states, live or dead (or populated and unpopulated, respectively). Every cell interacts with its eight neighbours, which are the cells that are horizontally, vertically, or diagonally adjacent. At each step in time, the following transitions occur:

1. Any live cell with fewer than two live neighbours dies, as if by underpopulation.
2. Any live cell with two or three live neighbours lives on to the next generation.
3. Any live cell with more than three live neighbours dies, as if by overpopulation.
4. Any dead cell with exactly three live neighbours becomes a live cell, as if by reproduction.

These rules, which compare the behavior of the automaton to real life, can be condensed into the following:

1. Any live cell with two or three live neighbours survives.
2. Any dead cell with three live neighbours becomes a live cell.
3. All other live cells die in the next generation. Similarly, all other dead cells stay dead.

The initial pattern constitutes the seed of the system. The first generation is created by applying the above rules simultaneously to every cell in the seed, live or dead; births and deaths occur simultaneously, and the discrete moment at which this happens is sometimes called a tick. Each generation is a pure function of the preceding one. The rules continue to be applied repeatedly to create further generations.

Origins

Stanislaw Ulam, while working at the Los Alamos National Laboratory in the 1940s, studied the growth of crystals, using a simple lattice network as his model. At the same time, John von Neumann, Ulam's colleague at Los Alamos, was working on the problem of self-replicating systems. Von Neumann's initial design was founded upon the notion of one robot building another robot. This design is known as the kinematic model. As he developed this design, von Neumann came to realize the great difficulty of building a self-replicating robot, and of the great cost in providing the robot with a "sea of parts" from which to build its replicant. Neumann wrote a paper entitled "The general and logical theory of automata" for the Hixon Symposium in 1948. Ulam was the one who suggested using a discrete system for creating a reductionist model of self-replication. Ulam and von Neumann created a method for calculating liquid motion in the late 1950s. The driving concept of the method was to consider a liquid as a group of discrete units and calculate the motion of each based on its neighbors' behaviors. Thus was born the first system of cellular automata. Like Ulam's lattice network, von Neumann's cellular automata are two-dimensional, with his self-replicator implemented algorithmically. The result was a universal copier and constructor working within a cellular automaton with a small neighborhood (only those cells that touch are neighbors; for von Neumann's cellular automata, only orthogonal cells), and with 29 states per cell. Von Neumann gave an existence proof that a particular pattern would make endless copies of itself within the given cellular universe by designing a 200,000 cell configuration that could do so. This design is known as the tessellation model, and is called a von Neumann universal constructor.

Motivated by questions in mathematical logic and in part by work on simulation games by Ulam, among others, John Conway began doing experiments in 1968 with a variety of different two-dimensional cellular automaton rules. Conway's initial goal was to define an interesting and unpredictable cellular automaton. According to Martin Gardner, Conway experimented with different rules, aiming for rules that would allow for patterns to "apparently" grow without limit, while keeping it difficult to prove that any given pattern would do so. Moreover, some "simple initial patterns" should "grow and change for a considerable period of time" before settling into a static configuration or a repeating loop. Conway later wrote that the basic motivation for Life was to create a "universal" cellular automaton.

The game made its first public appearance in the October 1970 issue of Scientific American, in Martin Gardner's "Mathematical Games" column, which was based on personal conversations with Conway. Theoretically, the Game of Life has the power of a universal Turing machine: anything that can be computed algorithmically can be computed within the Game of Life. Gardner wrote, "Because of Life's analogies with the rise, fall and alterations of a society of living organisms, it belongs to a growing class of what are called 'simulation games' (games that resemble real-life processes)."

Since its publication, the Game of Life has attracted much interest because of the surprising ways in which the patterns can evolve. It provides an example of emergence and self-organization. A version of Life that incorporates random fluctuations has been used in physics to study phase transitions and nonequilibrium dynamics. The game can also serve as a didactic analogy, used to convey the somewhat counter-intuitive notion that design and organization can spontaneously emerge in the absence of a designer. For example, philosopher Daniel Dennett has used the analogy of the Game of Life "universe" extensively to illustrate the possible evolution of complex philosophical constructs, such as consciousness and free will, from the relatively simple set of deterministic physical laws which might govern our universe.

The popularity of the Game of Life was helped by its coming into being at the same time as increasingly inexpensive computer access. The game could be run for hours on these machines, which would otherwise have remained unused at night. In this respect, it foreshadowed the later popularity of computer-generated fractals. For many, the Game of Life was simply a programming challenge: a fun way to use otherwise wasted CPU cycles. For some, however, the Game of Life had more philosophical connotations. It developed a cult following through the 1970s and beyond; current developments have gone so far as to create theoretic emulations of computer systems within the confines of a Game of Life board.

Examples of patterns

Many different types of patterns occur in the Game of Life, which are classified according to their behaviour. Common pattern types include: still lifes, which do not change from one generation to the next; oscillators, which return to their initial state after a finite number of generations; and spaceships, which translate themselves across the grid.

The earliest interesting patterns in the Game of Life were discovered without the use of computers. The simplest still lifes and oscillators were discovered while tracking the fates of various small starting configurations using graph paper, blackboards, and physical game boards, such as those used in Go. During this early research, Conway discovered that the R-pentomino failed to stabilize in a small number of generations. In fact, it takes 1103 generations to stabilize, by which time it has a population of 116 and has generated six escaping gliders; these were the first spaceships ever discovered.

Frequently occurring examples (in that they emerge frequently from a random starting configuration of cells) of the three aforementioned pattern types are shown below, with live cells shown in black and dead cells in white. Period refers to the number of ticks a pattern must iterate through before returning to its initial configuration.

Still lifes Block Bee- hive Loaf Boat Tub

Oscillators Blinker (period 2) Toad (period 2) Beacon (period 2) Pulsar (period 3) Penta- decathlon (period 15)

Spaceships Glider Light- weight spaceship (LWSS) Middle- weight spaceship (MWSS) Heavy- weight spaceship (HWSS)

The pulsar is the most common period-3 oscillator. The great majority of naturally occurring oscillators have a period of 2, like the blinker and the toad, but oscillators of many periods are known to exist, and oscillators of periods 4, 8, 14, 15, 30, and a few others have been seen to arise from random initial conditions. Patterns which evolve for long periods before stabilizing are called Methuselahs, the first-discovered of which was the R-pentomino. Diehard is a pattern that eventually disappears, rather than stabilizing, after 130 generations, which is conjectured to be maximal for patterns with seven or fewer cells. Acorn takes 5206 generations to generate 633 cells, including 13 escaped gliders.

The R-pentomino

Diehard

Acorn

Conway originally conjectured that no pattern can grow indefinitely—i.e. that for any initial configuration with a finite number of living cells, the population cannot grow beyond some finite upper limit. In the game's original appearance in "Mathematical Games", Conway offered a prize of fifty dollars to the first person who could prove or disprove the conjecture before the end of 1970. The prize was won in November by a team from the Massachusetts Institute of Technology, led by Bill Gosper; the "Gosper glider gun" produces its first glider on the 15th generation, and another glider every 30th generation from then on. For many years, this glider gun was the smallest one known. In 2015, a gun called the "Simkin glider gun", which releases a glider every 120th generation, was discovered that has fewer live cells but which is spread out across a larger bounding box at its extremities.

Gosper glider gun

Simkin glider gun

Smaller patterns were later found that also exhibit infinite growth. All three of the patterns shown below grow indefinitely. The first two create a single block-laying switch engine: a configuration that leaves behind two-by-two still life blocks as it translates itself across the game's universe. The third configuration creates two such patterns. The first has only ten live cells, which has been proven to be minimal. The second fits in a five-by-five square, and the third is only one cell high.

Later discoveries included other guns, which are stationary, and which produce gliders or other spaceships; puffer trains, which move along leaving behind a trail of debris; and rakes, which move and emit spaceships. Gosper also constructed the first pattern with an asymptotically optimal quadratic growth rate, called a breeder or lobster, which worked by leaving behind a trail of guns.

It is possible for gliders to interact with other objects in interesting ways. For example, if two gliders are shot at a block in a specific position, the block will move closer to the source of the gliders. If three gliders are shot in just the right way, the block will move farther away. This sliding block memory can be used to simulate a counter. It is possible to construct logic gates such as AND, OR, and NOT using gliders. It is possible to build a pattern that acts like a finite-state machine connected to two counters. This has the same computational power as a universal Turing machine, so the Game of Life is theoretically as powerful as any computer with unlimited memory and no time constraints; it is Turing complete. In fact, several different programmable computer architectures have been implemented in the Game of Life, including a pattern that simulates Tetris.

Furthermore, a pattern can contain a collection of guns that fire gliders in such a way as to construct new objects, including copies of the original pattern. A universal constructor can be built which contains a Turing complete computer, and which can build many types of complex objects, including more copies of itself.

In 2018, the first truly elementary knightship, Sir Robin, was discovered by Adam P. Goucher. A knightship is a spaceship that moves two squares left for every one square it moves down (like a knight in chess), as opposed to moving orthogonally or along a 45° diagonal. This is the first new spaceship movement pattern for an elementary spaceship found in forty-eight years. "Elementary" means that it cannot be decomposed into smaller interacting patterns such as gliders and still lifes.

Undecidability

Many patterns in the Game of Life eventually become a combination of still lifes, oscillators, and spaceships; other patterns may be called chaotic. A pattern may stay chaotic for a very long time until it eventually settles to such a combination.

The Game of Life is undecidable, which means that given an initial pattern and a later pattern, no algorithm exists that can tell whether the later pattern is ever going to appear. This is a corollary of the halting problem: the problem of determining whether a given program will finish running or continue to run forever from an initial input.

Indeed, since the Game of Life includes a pattern that is equivalent to a universal Turing machine (UTM), this deciding algorithm, if it existed, could be used to solve the halting problem by taking the initial pattern as the one corresponding to a UTM plus an input, and the later pattern as the one corresponding to a halting state of the UTM. It also follows that some patterns exist that remain chaotic forever. If this were not the case, one could progress the game sequentially until a non-chaotic pattern emerged, then compute whether a later pattern was going to appear.

Self-replication

On May 18, 2010, Andrew J. Wade announced a self-constructing pattern, dubbed "Gemini", that creates a copy of itself while destroying its parent. This pattern replicates in 34 million generations, and uses an instruction tape made of gliders oscillating between two stable configurations made of Chapman–Greene construction arms. These, in turn, create new copies of the pattern, and destroy the previous copy. Gemini is also a spaceship, and is the first spaceship constructed in the Game of Life that is an oblique spaceship, which is a spaceship that is neither orthogonal nor purely diagonal. In December 2015, diagonal versions of the Gemini were built.

On November 23, 2013, Dave Greene built the first replicator in the Game of Life that creates a complete copy of itself, including the instruction tape.

In October 2018, Adam P. Goucher finished his construction of the 0E0P metacell, a metacell capable of self-replication. This differed from previous metacells, such as the OTCA metapixel by Brice Due, which only worked with already constructed copies near them. The 0E0P metacell works by using construction arms to create copies that simulate the programmed rule. The actual simulation of the Game of Life or other Moore neighbourhood rules is done by simulating an equivalent rule using the von Neumann neighbourhood with more states. The name 0E0P is short for "Zero Encoded by Zero Population", which indicates that instead of a metacell being in an "off" state simulating empty space, the 0E0P metacell removes itself when the cell enters that state, leaving a blank space.

Iteration

From most random initial patterns of living cells on the grid, observers will find the population constantly changing as the generations tick by. The patterns that emerge from the simple rules may be considered a form of mathematical beauty. Small isolated subpatterns with no initial symmetry tend to become symmetrical. Once this happens, the symmetry may increase in richness, but it cannot be lost unless a nearby subpattern comes close enough to disturb it. In a very few cases, the society eventually dies out, with all living cells vanishing, though this may not happen for a great many generations. Most initial patterns eventually burn out, producing either stable figures or patterns that oscillate forever between two or more states; many also produce one or more gliders or spaceships that travel indefinitely away from the initial location. Because of the nearest-neighbour based rules, no information can travel through the grid at a greater rate than one cell per unit time, so this velocity is said to be the cellular automaton speed of light and denoted c.

Algorithms

Early patterns with unknown futures, such as the R-pentomino, led computer programmers to write programs to track the evolution of patterns in the Game of Life. Most of the early algorithms were similar: they represented the patterns as two-dimensional arrays in computer memory. Typically, two arrays are used: one to hold the current generation, and one to calculate its successor. Often 0 and 1 represent dead and live cells, respectively. A nested for loop considers each element of the current array in turn, counting the live neighbours of each cell to decide whether the corresponding element of the successor array should be 0 or 1. The successor array is displayed. For the next iteration, the arrays may swap roles so that the successor array in the last iteration becomes the current array in the next iteration, or one may copy the values of the second array into the first array then update the second array from the first array again.

A variety of minor enhancements to this basic scheme are possible, and there are many ways to save unnecessary computation. A cell that did not change at the last time step, and none of whose neighbours changed, is guaranteed not to change at the current time step as well, so a program that keeps track of which areas are active can save time by not updating inactive zones.

The Game of Life on the surface of a trefoil knot

To avoid decisions and branches in the counting loop, the rules can be rearranged from an egocentric approach of the inner field regarding its neighbours to a scientific observer's viewpoint: if the sum of all nine fields in a given neighbourhood is three, the inner field state for the next generation will be life; if the all-field sum is four, the inner field retains its current state; and every other sum sets the inner field to death.

To save memory, the storage can be reduced to one array plus two line buffers. One line buffer is used to calculate the successor state for a line, then the second line buffer is used to calculate the successor state for the next line. The first buffer is then written to its line and freed to hold the successor state for the third line. If a toroidal array is used, a third buffer is needed so that the original state of the first line in the array can be saved until the last line is computed.

Glider gun within a toroidal array. The stream of gliders eventually wraps around and destroys the gun.

 

Red glider on the square lattice with periodic boundary conditions

In principle, the Game of Life field is infinite, but computers have finite memory. This leads to problems when the active area encroaches on the border of the array. Programmers have used several strategies to address these problems. The simplest strategy is to assume that every cell outside the array is dead. This is easy to program but leads to inaccurate results when the active area crosses the boundary. A more sophisticated trick is to consider the left and right edges of the field to be stitched together, and the top and bottom edges also, yielding a toroidal array. The result is that active areas that move across a field edge reappear at the opposite edge. Inaccuracy can still result if the pattern grows too large, but there are no pathological edge effects. Techniques of dynamic storage allocation may also be used, creating ever-larger arrays to hold growing patterns. The Game of Life on a finite field is sometimes explicitly studied; some implementations, such as Golly, support a choice of the standard infinite field, a field infinite only in one dimension, or a finite field, with a choice of topologies such as a cylinder, a torus, or a Möbius strip.

Alternatively, programmers may abandon the notion of representing the Game of Life field with a two-dimensional array, and use a different data structure, such as a vector of coordinate pairs representing live cells. This allows the pattern to move about the field unhindered, as long as the population does not exceed the size of the live-coordinate array. The drawback is that counting live neighbours becomes a hash-table lookup or search operation, slowing down simulation speed. With more sophisticated data structures this problem can also be largely solved.

For exploring large patterns at great time depths, sophisticated algorithms such as Hashlife may be useful. There is also a method for implementation of the Game of Life and other cellular automata using arbitrary asynchronous updates whilst still exactly emulating the behaviour of the synchronous game. Source code examples that implement the basic Game of Life scenario in various programming languages, including C, C++, Java and Python can be found at Rosetta Code.

Variations

Main article: Life-like cellular automaton

Since the Game of Life's inception, new, similar cellular automata have been developed. The standard Game of Life is symbolized as B3/S23. A cell is born if it has exactly three neighbours, survives if it has two or three living neighbours, and dies otherwise. The first number, or list of numbers, is what is required for a dead cell to be born. The second set is the requirement for a live cell to survive to the next generation. Hence B6/S16 means "a cell is born if there are six neighbours, and lives on if there are either one or six neighbours". Cellular automata on a two-dimensional grid that can be described in this way are known as Life-like cellular automata. Another common Life-like automaton, Highlife, is described by the rule B36/S23, because having six neighbours, in addition to the original game's B3/S23 rule, causes a birth. HighLife is best known for its frequently occurring replicators.

Additional Life-like cellular automata exist. The vast majority of these 2¹⁸ different rules produce universes that are either too chaotic or too desolate to be of interest, but a large subset do display interesting behavior. A further generalization produces the isotropic rulespace, with 2¹⁰² possible cellular automaton rules (the Game of Life again being one of them). These are rules that use the same square grid as the Life-like rules and the same eight-cell neighbourhood, and are likewise invariant under rotation and reflection. However, in isotropic rules, the positions of neighbour cells relative to each other may be taken into account in determining a cell's future state—not just the total number of those neighbours.

A sample of a 48-step oscillator along with a 2-step oscillator and a 4-step oscillator from a two-dimensional hexagonal Game of Life (rule H:B2/S34)

Some variations on the Game of Life modify the geometry of the universe as well as the rule. The above variations can be thought of as a two-dimensional square, because the world is two-dimensional and laid out in a square grid. One-dimensional square variations, known as elementary cellular automata, and three-dimensional square variations have been developed, as have two-dimensional hexagonal and triangular variations. A variant using aperiodic tiling grids has also been made.

Conway's rules may also be generalized such that instead of two states, live and dead, there are three or more. State transitions are then determined either by a weighting system or by a table specifying separate transition rules for each state; for example, Mirek's Cellebration's multi-coloured Rules Table and Weighted Life rule families each include sample rules equivalent to the Game of Life.

Patterns relating to fractals and fractal systems may also be observed in certain Life-like variations. For example, the automaton B1/S12 generates four very close approximations to the Sierpinski triangle when applied to a single live cell. The Sierpinski triangle can also be observed in the Game of Life by examining the long-term growth of an infinitely long single-cell-thick line of live cells, as well as in Highlife, Seeds (B2/S), and Wolfram's Rule 90.

Immigration is a variation that is very similar to the Game of Life, except that there are two on states, often expressed as two different colours. Whenever a new cell is born, it takes on the on state that is the majority in the three cells that gave it birth. This feature can be used to examine interactions between spaceships and other objects within the game. Another similar variation, called QuadLife, involves four different on states. When a new cell is born from three different on neighbours, it takes the fourth value, and otherwise, like Immigration, it takes the majority value. Except for the variation among on cells, both of these variations act identically to the Game of Life.

Music

Various musical composition techniques use the Game of Life, especially in MIDI sequencing. A variety of programs exist for creating sound from patterns generated in the Game of Life.

Notable programs

The 6366548773467669985195496000th (6×10²⁷) generation of a Turing machine, made in the game of Life, computed in less than 30 seconds on an Intel Core Duo 2 GHz CPU using Golly in Hashlife mode

Computers have been used to follow Game of Life configurations since it was first publicized. When John Conway was first investigating how various starting configurations developed, he tracked them by hand using a go board with its black and white stones. This was tedious and prone to errors. The first interactive Game of Life program was written in an early version of ALGOL 68C for the PDP-7 by M. J. T. Guy and S. R. Bourne. The results were published in the October 1970 issue of Scientific American, along with the statement: "Without its help, some discoveries about the game would have been difficult to make."

A color version of the Game of Life was written by Ed Hall in 1976 for Cromemco microcomputers, and a display from that program filled the cover of the June 1976 issue of Byte. The advent of microcomputer-based color graphics from Cromemco has been credited with a revival of interest in the game.

Two early implementations of the Game of Life on home computers were by Malcolm Banthorpe written in BBC BASIC. The first was in the January 1984 issue of Acorn User magazine, and Banthorpe followed this with a three-dimensional version in the May 1984 issue. Susan Stepney, Professor of Computer Science at the University of York, followed this up in 1988 with Life on the Line, a program that generated one-dimensional cellular automata.

There are now thousands of Game of Life programs online, so a full list will not be provided here. The following is a small selection of programs with some special claim to notability, such as popularity or unusual features. Most of these programs incorporate a graphical user interface for pattern editing and simulation, the capability for simulating multiple rules including the Game of Life, and a large library of interesting patterns in the Game of Life and other cellular automaton rules.

• Golly is a cross-platform (Windows, Macintosh, Linux, iOS, and Android) open-source simulation system for the Game of Life and other cellular automata (including all Life-like cellular automata, the Generations family of cellular automata from Mirek's Cellebration, and John von Neumann's 29-state cellular automaton) by Andrew Trevorrow and Tomas Rokicki. It includes the Hashlife algorithm for extremely fast generation, and Lua or Python scriptability for both editing and simulation.
• Mirek's Cellebration is a freeware one- and two-dimensional cellular automata viewer, explorer, and editor for Windows. It includes powerful facilities for simulating and viewing a wide variety of cellular automaton rules, including the Game of Life, and a scriptable editor.
• Xlife is a cellular-automaton laboratory by Jon Bennett. The standard UNIX X11 Game of Life simulation application for a long time, it has also been ported to Windows. It can handle cellular automaton rules with the same neighbourhood as the Game of Life, and up to eight possible states per cell.

• Dr. Blob's Organism is a Shoot 'em up based on Conway's Life. In the game, Life continually generates on a group of cells within a "petri dish". The patterns formed are smoothed and rounded to look like a growing amoeba spewing smaller ones (actually gliders). Special "probes" zap the "blob" to keep it from overflowing the dish while destroying its nucleus.

Google implemented an easter egg of the Game of Life in 2012. Users who search for the term are shown an implementation of the game in the search results page.

Why I Am Pro-Abortion, Not Just Pro-Choice

Valerie Tarico

From: Volume 36, No. 5
August / September 2016

Original Link:  https://secularhumanism.org/2016/07/cont-why-i-am-pro-abortion-not-just-pro-choice/?fbclid=IwAR1uk1ixZh7ObRBXs0ObDkt2TnEoAJ_LhrTEMcQIEsSEIo4uI8HDaNIKR98

I believe that abortion care is a positive social good—and I think it’s time people said so.

Not long ago, the Daily Kos published an article titled “I Am Pro-Choice, Not Pro-Abortion.” “Has anyone ever truly been pro-abortion?” one commenter asked.

Uh. Yes. Me. That would be me.

I am pro-abortion like I’m pro–knee replacement and pro-chemotherapy and pro–cataract surgery. As the last protection against ill-conceived childbearing when all else fails, abortion is part of a set of tools that help women and men to form the families of their choosing. I believe that abortion care is a positive social good. And I suspect that a lot of other people secretly believe the same thing. I think it’s time we said so.

Note: I’m also pro-choice. Choice is about who gets to make the decision. The question of whether and when we bring a new life into the world is, to my mind, one of the most important decisions a person can make. It is too big a decision for us to make for each other, especially for perfect strangers.

But independent of who owns the decision, I’m pro on the procedure. I’ve decided that it’s time, for once and for all, to count it out on my ten fingers.

1. I’m pro-abortion because being able to delay and limit childbearing is fundamental to female empowerment and equality. A woman who lacks the means to manage her fertility lacks the means to manage her life. Any plans, dreams, aspirations, responsibilities or commitments—no matter how important—have a great big contingency clause built-in: “… until or unless I get pregnant, in which case all bets are off.” Think of any professional woman you know. She wouldn’t be in that role if she hadn’t been able to time and limit her childbearing. Think of any girl you know who imagines becoming a professional woman. She won’t get there unless she has effective, reliable means to manage her fertility. In generations past, nursing care was provided by nuns and teachers who were spinsters, because avoiding sexual intimacy was the only way women could avoid unpredictable childbearing and so be freed up to serve their communities in other capacities. But if you think that abstinence should be our model for modern fertility management, consider the little graves that get found every so often under old nunneries and Catholic homes for unwed mothers.
2. I’m pro-abortion because well-timed pregnancies give children a healthier start in life. We now have ample evidence that babies do best when women are able to space their pregnancies and get both prenatal and preconception care. The specific nutrients we ingest in the weeks before we get pregnant can have a lifelong effect on the well-being of our offspring. Rapid repeat pregnancies increase the risk of low birth-weight babies and other complications. Wanted babies are more likely to get their toes kissed, to be welcomed into families that are financially and emotionally ready to receive them, to get preventive medical care during childhood, and to receive the kinds of loving engagement that helps young brains to develop.
3. I’m pro-abortion because I take motherhood seriously. Most female bodies can incubate a baby; thanks to antibiotics, cesareans, and anti-hemorrhage drugs, most of us are able to survive pushing a baby out into the world. But parenting is a lot of work, and doing it well takes twenty dedicated years of focus, attention, patience, persistence, social support, mental health, money, and a whole lot more. This is the biggest, most life-transforming thing most of us will ever do. The idea that women should simply go with it when they find themselves pregnant after a one-night stand, or a rape, or a broken condom completely trivializes motherhood.
4. I’m pro-abortion because intentional childbearing helps couples, families, and communities to get out of poverty. Decades of research in countries ranging from the United States to Bangladesh show that reproductive policy is economic policy. It is no coincidence that the American middle class rose along with the ability of couples to plan their families, starting at the beginning of the last century. Having two or three kids instead of eight or ten was critical to prospering in the modern industrial economy. Early, unsought childbearing nukes economic opportunity and contributes to multigenerational poverty. Today in the United States, unsought pregnancy and childbearing is declining for everyone but the poorest families and communities, contributing to what some call a growing “caste system” in America. Strong, determined girls and women sometimes beat the odds, but their stories inspire us precisely because they are the exceptions to the rule. Justice dictates that the full range of fertility management tools—including the best state-of-the-art contraceptive technologies and, when that fails, abortion care—be equally available to all, not just a privileged few.
5. I’m pro-abortion because reproduction is a highly imperfect process. Genetic recombination is a complicated progression with flaws and false starts at every step along the way. To compensate, in every known species including humans, reproduction operates as a big funnel. Many more eggs and sperm are produced than will ever meet; more combine into embryos than will ever implant; more implant than will grow into babies; and more babies are born than will grow up to have babies of their own. This systematic culling makes God or nature the world’s biggest abortion provider: nature’s way of producing healthy kids essentially requires every woman to have an abortion mill built into her own body. In humans, an estimated 60 to 80 percent of fertilized eggs self-destruct before becoming babies, which is why the people who kill the most embryos are those like the Duggars who try to maximize their number of pregnancies. But the weeding-out process is also highly imperfect. Sometimes perfectly viable combinations boot themselves out; sometimes horrible defects slip through. A woman’s body may be less fertile when she is stressed or ill or malnourished, but as pictures of skeletal moms and babies show, some women conceive even under devastating circumstances. Like any other medical procedure, therapeutic contraception and abortion complement natural processes designed to help us survive and thrive.
6. I’m pro-abortion because I think morality is about the well-being of sentient beings. I believe that morality is about the lived experience of sentient beings—beings who can feel pleasure and pain, preference and intention and who at their most complex can live in relation to other beings, love and be loved, and value their own existence. What are they capable of wanting? What are they capable of feeling? These are the questions my husband and I explored with our children when they were figuring out their responsibility to their chickens and guinea pigs. It was a lesson that turned expensive when the girls stopped drinking milk from cows that didn’t get to see the light of day or eat grass, but it’s not one I regret. Do unto others as they want you to do unto them. It’s called the “Platinum Rule.” In this moral universe, real people count more than potential people, hypothetical people, or corporate people.
7. I’m pro-abortion because contraceptives are imperfect, and people are too. The pill is 1960s technology, now half a century old. For decades, women were told that the pill was 99 percent effective, and they blamed themselves when they got pregnant anyway. But that 99 percent is a “perfect use” statistic. In the real world, where most of us live, people aren’t perfect. In the real world, one in eleven women relying on the pill gets pregnant each year. For a couple relying on condoms, that’s one in six. Young and poor women—those whose lives are least predictable and most vulnerable to being thrown off course—are also those who have the most difficulty taking pills consistently. Pill technology most fails those who need it most, which makes abortion access a matter not only of compassion but of justice. State-of-the-art IUDs and implants radically change this equation, largely because they take human error out of the picture for years on end, or until a woman wants a baby. And despite the deliberate misinformation being spread by opponents, these methods are genuine contraceptives, not abortifacients. Depending on the method chosen, they disable sperm or block their path, or prevent an egg from being released. Once settled into place, an IUD or implant drops the annual pregnancy rate below one in five hundred. And guess what? Teen pregnancies and abortions plummet—which makes me happy, because even though I’m pro-abortion, I’d love the need for abortion to go away. Why mitigate harm when you can prevent it?
8. I’m pro-abortion because I believe in mercy, grace, compassion, and the power of fresh starts. Many years ago, my friend Chip was driving his family on vacation when his kids started squabbling. His wife, Marla, undid her seatbelt to help them, and, as Chip looked over at her, their top-heavy minivan veered onto the shoulder and then rolled, and Marla died. Sometimes people make mistakes or have accidents that they pay for the rest of their lives. But I myself have swerved onto the shoulder and simply swerved back. The price we pay for a lapse in attention or judgment or an accident of any kind isn’t proportional to the error we made. Who among us hasn’t had unprotected sex when the time or situation or partnership wasn’t quite right for bringing a new life into the world? Most of the time we get lucky; sometimes we don’t. And in those situations we rely on the mercy, compassion, and generosity of others. In this regard, an unsought pregnancy is like any other accident. I can walk today only because surgeons reassembled my lower leg after it was crushed between the front of a car and a bicycle frame when I was a teen. And I can walk today (and run and jump) because another team of surgeons reassembled my knee joint after I fell off a ladder. And I can walk today (and bicycle with my family) because a third team of surgeons repaired my other knee after I pulled a whirring brush mower onto myself, cutting clear through bone. Three accidents, all my own doing, and three knee surgeries. Some women have three abortions.
9. I’m pro-abortion because the future is always in motion, and we have the power and responsibility to shape it well. As a college student, I read a Ray Bradbury story about a man who travels back into prehistory on a “time safari.” The tourists have been coached about the importance of not disturbing anything lest they change the flow of history. When they return to the present, they realize that the outcome of an election has changed, and they discover that the protagonist, who had gone off the trail, has a crushed butterfly on the bottom of his shoe. In baby-making, as in Bradbury’s story, the future is always in motion, and every little thing we do has consequences we have no way to predict. Any small change means that a different child comes into the world. Which nights your mother had headaches, the sexual position of your parents when they conceived you, whether or not your mother rolled over in bed afterward—if any of these things had been different, someone else would be here instead of you. Every day, men and women make small choices and potential people wink into and out of existence. We move, and our movements ripple through time in ways that are incomprehensible, and we can never know what the alternate futures might have been. But some things we can know or predict, at least at the level of probability, and I think this knowledge provides a basis for guiding wise reproductive decisions. My friend Judy says that parenting begins before conception. I agree. How and when we choose to carry forward a new life can stack the odds in favor of our children or against them, and to me that is a sacred trust.
10. I’m pro-abortion because I love my daughter. I first wrote the story of my own abortion when Dr. George Tiller was murdered, and I couldn’t bear the thought of abortion providers standing in the crosshairs alone. “My Abortion Baby” was about my daughter, Brynn, who exists only because a kind doctor such as George Tiller gave me and my husband the gift of a fresh start when we learned that our wanted pregnancy was unhealthy. Brynn literally embodies the ever-changing flow of the future, because she could not exist in an alternate universe in which I would have carried that first pregnancy to term. She was conceived while I would still have been pregnant with a child we had begun to imagine but who never came to be. My husband and I felt very clear that carrying forward that pregnancy would have been a violation of our values, and neither of us ever second-guessed our decision. Even so, I grieved. Even when I got pregnant again a few months later, I remember feeling petulant and thinking, I want that baby, not this one. And then Brynn came out into the world, and I looked into her eyes, fell in love, and never looked back.

All around us living, breathing, and loving are the chosen children of mothers who waited, who ended an ill-timed or unhealthy pregnancy and then later chose to carry forward a new life. “I was only going to have two children,” my friend Jane said as her daughters raced, screeching joyfully, across my lawn. Jane followed them with her eyes. “My abortions let me have these two when the time was right, with someone I loved.”

Those who see abortion as an unmitigated evil often talk about the “millions of missing people” who were not born into this world because a pregnant woman decided “Not now.” But they never talk about the millions of children and adults who are here today only because their mothers had abortions—real people who exist in this version of the future, people who are living out their lives all around us—loving, laughing, suffering, struggling, dancing, dreaming, and having babies of their own.

When those who oppose abortion lament the “missing people,” I hear an echo of my own petulant thought: I want that person, not this one. And I wish that they could simply experience what I did, that they could look into the beautiful eyes of the people in front of them and fall in love.

Streaming media

From Wikipedia, the free encyclopedia

https://en.wikipedia.org/wiki/Streaming_media 

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Streaming media is multimedia that is delivered and consumed in a continuous manner from a source, with little or no intermediate storage in network elements. Streaming refers to the delivery method of content, rather than the content itself.

Distinguishing delivery method from the media applies specifically to telecommunications networks, as most of the traditional media delivery systems are either inherently streaming (e.g. radio, television) or inherently non-streaming (e.g. books, videotape, audio CDs). There are challenges with streaming content on the Internet. For example, users whose Internet connection lacks sufficient bandwidth may experience stops, lags, or poor buffering of the content, and users lacking compatible hardware or software systems may be unable to stream certain content. With the use of buffering of the content for just a few seconds in advance of playback, the quality can be much improved.

Livestreaming is the real-time delivery of content during production, much as live television broadcasts content via television channels. Livestreaming requires a form of source media (e.g. a video camera, an audio interface, screen capture software), an encoder to digitize the content, a media publisher, and a content delivery network to distribute and deliver the content.

Streaming is an alternative to file downloading, a process in which the end-user obtains the entire file for the content before watching or listening to it. Through streaming, an end-user can use their media player to start playing digital video or digital audio content before the entire file has been transmitted. The term "streaming media" can apply to media other than video and audio, such as live closed captioning, ticker tape, and real-time text, which are all considered "streaming text".

Streaming is most prevalent in video on demand and streaming television services. Other services stream music. Video game live streaming uses streaming for online gaming.

Etymology

The term "streaming" was first used for tape drives manufactured by Data Electronics Inc. that were meant to slowly ramp up and run for the entire track; slower ramp times lowered drive costs. "Streaming" was applied in the early 1990s as a better description for video on demand and later live video on IP networks. It was first done by Starlight Networks for video streaming and Real Networks for audio streaming. Such video had previously been referred to by the misnomer "store and forward video."

Precursors

Beginning in 1881, Théâtrophone enabled subscribers to listen to opera and theatre performances over telephone lines. This operated until 1932. The concept of media streaming eventually came to America.

In the early 1920s, George O. Squier was granted patents for a system for the transmission and distribution of signals over electrical lines, which was the technical basis for what later became Muzak, a technology streaming continuous music to commercial customers without the use of radio.

The Telephone Music Service, a live jukebox service, began in 1929 and continued until 1997. The clientele eventually included 120 bars and restaurants in the Pittsburgh area. A tavern customer would deposit money in the jukebox, use a telephone on top of the jukebox, and ask the operator to play a song. The operator would find the record in the studio library of more than 100,000 records, put it on a turntable, and the music would be piped over the telephone line to play in the tavern. The music media began as 78s, 33s and 45s, played on the six turntables they monitored. CDs and tapes were incorporated in later years.

The business had a succession of owners, notably Bill Purse, his daughter Helen Reutzel, and finally, Dotti White. The revenue stream of each quarter was split 60% to the music service and 40% to the tavern owner. This business model eventually became unsustainable due to city permits and the cost of setting up these telephone lines.

History

Early development

See also: Timeline of online video

Attempts to display media on computers date back to the earliest days of computing in the mid-20th century. However, little progress was made for several decades, primarily due to the high cost and limited capabilities of computer hardware. From the late 1980s through the 1990s, consumer-grade personal computers became powerful enough to display various media. The primary technical issues related to streaming were having enough CPU and bus bandwidth to support the required data rates, achieving real-time computing performance required to prevent buffer underrun and enable smooth streaming of the content. However, computer networks were still limited in the mid-1990s, and audio and video media were usually delivered over non-streaming channels, such as playback from a local hard disk drive or CD-ROMs on the end user's computer.

In 1990 the first commercial Ethernet switch was introduced by Kalpana, which enabled the more powerful computer networks that led to the first streaming video solutions used by schools and corporations.

Practical streaming media was only made possible with advances in data compression, due to the impractically high bandwidth requirements of uncompressed media. Raw digital audio encoded with pulse-code modulation (PCM) requires a bandwidth of 1.4 Mbit/s for uncompressed CD audio, while raw digital video requires a bandwidth of 168 Mbit/s for SD video and over 1000 Mbit/s for FHD video.

Late 1990s to early 2000s

See also: Original net animation

During the late 1990s and early 2000s, users had increased access to computer networks, especially the Internet. During the early 2000s, users had access to increased network bandwidth, especially in the last mile. These technological improvements facilitated the streaming of audio and video content to computer users in their homes and workplaces. There was also an increasing use of standard protocols and formats, such as TCP/IP, HTTP, HTML as the Internet became increasingly commercialized, which led to an infusion of investment into the sector.

The band Severe Tire Damage was the first group to perform live on the Internet. On June 24, 1993, the band was playing a gig at Xerox PARC while elsewhere in the building, scientists were discussing new technology (the Mbone) for broadcasting on the Internet using multicasting. As proof of PARC's technology, the band's performance was broadcast and could be seen live in Australia and elsewhere. In a March 2017 interview, band member Russ Haines stated that the band had used approximately "half of the total bandwidth of the internet" to stream the performance, which was a 152 × 76 pixel video, updated eight to twelve times per second, with audio quality that was, "at best, a bad telephone connection."

RealNetworks pioneered the broadcast of a baseball game between the New York Yankees and the Seattle Mariners over the Internet in 1995. The first symphonic concert on the Internet—a collaboration between the Seattle Symphony and guest musicians Slash, Matt Cameron, and Barrett Martin—took place at the Paramount Theater in Seattle, Washington, on November 10, 1995.

In 1996, Marc Scarpa produced the first large-scale, online, live broadcast, the Adam Yauch-led Tibetan Freedom Concert, an event that would define the format of social change broadcasts. Scarpa continued to pioneer in the streaming media world with projects such as Woodstock '99, Townhall with President Clinton, and more recently Covered CA's campaign "Tell a Friend Get Covered" which was live streamed on YouTube.

Business developments

The first commercial streaming product appeared in late 1992 and was named StarWorks. StarWorks enabled on-demand MPEG-1 full-motion videos to be randomly accessed on corporate Ethernet networks. Starworks was from Starlight Networks, who also pioneered live video streaming on Ethernet and via Internet Protocol over satellites with Hughes Network Systems. Other early companies that created streaming media technology include RealNetworks (originally known as Progressive Networks) and Protocomm both prior to widespread World Wide Web usage. Once the web became popular in the late 90s, streaming video on the internet blossomed from startups such as VDOnet (later acquired by RealNetworks) and Precept (later acquired by Cisco).

Microsoft developed a media player known as ActiveMovie in 1995 that supported streaming media and included a proprietary streaming format, which was the precursor to the streaming feature later in Windows Media Player 6.4 in 1999. In June 1999 Apple also introduced a streaming media format in its QuickTime 4 application. It was later also widely adopted on websites along with RealPlayer and Windows Media streaming formats. The competing formats on websites required each user to download the respective applications for streaming and resulted in many users having to have all three applications on their computer for general compatibility.

In 2000 Industryview.com launched its "world's largest streaming video archive" website to help businesses promote themselves. Webcasting became an emerging tool for business marketing and advertising that combined the immersive nature of television with the interactivity of the Web. The ability to collect data and feedback from potential customers caused this technology to gain momentum quickly.

Around 2002, the interest in a single, unified, streaming format and the widespread adoption of Adobe Flash prompted the development of a video streaming format through Flash, which was the format used in Flash-based players on video hosting sites. The first popular video streaming site, YouTube, was founded by Steve Chen, Chad Hurley and Jawed Karim in 2005. It initially used a Flash-based player, which played MPEG-4 AVC video and AAC audio, but now defaults to HTML5 video. Increasing consumer demand for live streaming prompted YouTube to implement a new live streaming service to users. The company currently also offers a (secured) link returning the available connection speed of the user.

The Recording Industry Association of America (RIAA) revealed through its 2015 earnings report that streaming services were responsible for 34.3 percent of the year's total music industry's revenue, growing 29 percent from the previous year and becoming the largest source of income, pulling in around $2.4 billion. US streaming revenue grew 57 percent to $1.6 billion in the first half of 2016 and accounted for almost half of industry sales.

Streaming wars

See also: List of streaming media services

The term streaming wars was coined to discuss the new era of competition between video streaming services such as Netflix, Amazon Prime Video, Hulu, HBO Max, Disney+, Paramount+, Apple TV+, and Peacock, among others.

Competition among online platforms has forced them to find ways to differentiate themselves. One key way they have done this is by offering exclusive content, often self-produced and created specifically for a market. This approach to streaming competition, can have disadvantages for consumers and for the industry as a whole. Once content is made available online, the corresponding piracy searches decrease. Competition or legal availability across multiple platforms effectively deters online piracy and more exclusivity does not necessarily translate into higher average investment in content, as investment decisions are also dependent on the level and type of competition in online markets.

This competition, combined with the current COVID-19 crisis, the decline of the popularity of mainstream films, and the rise of independent films winning major film awards within the last six years, may have resulted in Apple TV+ becoming the first streaming service to produce an Academy Award winner for Best Picture, 2021’s CODA, a film about the only hearing member of a deaf family struggling for fame.

Use by the general public

Live streaming service at zoo by Niconico

Advances in computer networking, combined with powerful home computers and operating systems made streaming media practical and affordable for the public. Stand-alone Internet radio devices emerged to offer listeners a non-technical option for listening to audio streams. These audio streaming services became increasingly popular; streaming music reached 118.1 billion streams in 2013.

"Streaming creates the illusion—greatly magnified by headphone use, which is another matter—that music is a utility you can turn on and off; the water metaphor is intrinsic to how it works. It dematerializes music, denies it a crucial measure of autonomy, reality, and power. It makes music seem disposable, impermanent. Hence it intensifies the ebb and flow of pop fashion, the way musical 'memes' rise up for a week or a month and are then forgotten. And it renders our experience of individual artists/groups shallower."

—Robert Christgau, 2018

In general, multimedia content is data intensive, so media storage and transmission costs are still significant. Media is generally compressed for transport and storage. Increasing consumer demand for streaming of high-definition (HD) content has led the industry to develop technologies such as WirelessHD and G.hn, which are optimized for streaming HD content. Many developers have introduced HD streaming apps that work on smaller devices such as tablets and smartphones for everyday purposes.

A media stream can be streamed either live or on demand. Live streams are generally provided by a means called true streaming. True streaming sends the information straight to the computer or device without saving to a local file. On-demand streaming is provided by a means called progressive download. Progressive download saves the received information to a local file and then is played from that location. On-demand streams are often saved to files for extended amounts of time; while the live streams are only available at one time only (e.g. during the football game).

Streaming media is increasingly being coupled with use of social media. For example, sites such as YouTube encourage social interaction in webcasts through features such as live chat, online surveys, user posting of comments online and more. Furthermore, streaming media is increasingly being used for social business and e-learning.

The Horowitz Research State of Pay TV, OTT and SVOD 2017 report said that 70 percent of those viewing content did so through a streaming service and that 40 percent of TV viewing was done this way, twice the number from five years earlier. Millennials, the report said, streamed 60 percent of content.

Transition from DVD

One of the movie streaming industry's largest impacts was on the DVD industry, which effectively met its demise with the mass popularization of online content. The rise of media streaming caused the downfall of many DVD rental companies such as Blockbuster. In July 2015, The New York Times published an article about Netflix's DVD services. It stated that Netflix was continuing their DVD services with 5.3 million subscribers, which was a significant drop from the previous year. On the other hand, their streaming services had 65 million members.

Napster

Music streaming is one of the most popular ways in which consumers interact with streaming media. In the age of digitization, the private consumption of music transformed into a public good largely due to one player in the market: Napster.

Napster, a peer-to-peer (P2P) file-sharing network where users could upload and download MP3 files freely, broke all music industry conventions when it launched in early 1999 in Hull, Massachusetts. The platform was developed by Shawn and John Fanning as well as Sean Parker. In an interview from 2009, Shawn Fanning explained that Napster "was something that came to me as a result of seeing a sort of an unmet need and the passion people had for being able to find all this music, particularly a lot of the obscure stuff which wouldn't be something you go to a record store and purchase, so it felt like a problem worth solving."

Not only did this development disrupt the music industry by making songs that previously required payment to acquire freely accessible to any Napster user, but it also demonstrated the power of P2P networks in turning any digital file into a public, shareable good. For the brief period of time that Napster existed, mp3 files fundamentally changed as a type of good. Songs were no longer financially excludable – barring access to a computer with internet access – and they were not rival, meaning if one person downloaded a song it did not diminish another user from doing the same. Napster, like most other providers of public goods, faced the free-rider problem. Every user benefits when an individual uploads an mp3 file, but there is no requirement or mechanism that forces all users to share their music. Thus, Napster users were incentivized to let others upload music without sharing any of their own files.

This structure revolutionized the consumer's perception of ownership over digital goods – it made music freely replicable. Napster quickly garnered millions of users, growing faster than any other business in history. At the peak of its existence, Napster boasted about 80 million users globally. The site gained so much traffic that many college campuses had to block access to Napster because it created network congestion from so many students sharing music files.

The advent of Napster sparked the creation of numerous other P2P sites including LimeWire (2000), BitTorrent (2001), and the Pirate Bay (2003). The reign of P2P networks was short-lived. The first to fall was Napster in 2001. Numerous lawsuits were filed against Napster by various record labels, all of which were subsidiaries of Universal Music Group, Sony Music Entertainment, Warner Music Group, or EMI. In addition to this, the Recording Industry Association of America (RIAA) also filed a lawsuit against Napster on the grounds of unauthorized distribution of copyrighted material, which ultimately led Napster to shut down in 2001. In an interview with the New York Times, Gary Stiffelman, who represents Eminem, Aerosmith, and TLC, explained, "I’m not an opponent of artists’ music being included in these services, I'm just an opponent of their revenue not being shared."

The fight for intellectual property rights: A&M Records, Inc. v. Napster, Inc.

The lawsuit A&M Records, Inc. v. Napster, Inc. fundamentally changed the way consumers interact with music streaming. It was argued on 2 October 2000 and was decided on 12 February 2001. The Court of Appeals for the Ninth Circuit ruled that a P2P file sharing service could be held liable for contributory and vicarious infringement of copyright, serving as a landmark decision for Intellectual property law.

The first issue that the Court addressed was "fair use," which says that otherwise infringing activities are permissible so long as it is for purposes "such as criticism, comment, news reporting, teaching [...] scholarship, or research." Judge Beezer, the judge for this case, noted that Napster claimed that its services fit "three specific alleged fair uses: sampling, where users make temporary copies of a work before purchasing; space-shifting, where users access a sound recording through the Napster system that they already own in audio CD format; and permissive distribution of recordings by both new and established artists." Judge Beezer found that Napster did not fit these criteria, instead enabling their users to repeatedly copy music, which would affect the market value of the copyrighted good.

The second claim by the plaintiffs was that Napster was actively contributing to copyright infringement since it had knowledge of widespread file sharing on their platform. Since Napster took no action to reduce infringement and financially benefited from repeated use, the Court ruled against the P2P site. The court found that "as much as eighty-seven percent of the files available on Napster may be copyrighted and more than seventy percent may be owned or administered by plaintiffs."

The injunction ordered against Napster ended the brief period in which music streaming was a public good – non-rival and non-excludable in nature. Other P2P networks had some success at sharing MP3s, though they all met a similar fate in court. The ruling set the precedent that copyrighted digital content cannot be freely replicated and shared unless given consent by the owner, thereby strengthening the property rights of artists and record labels alike.

As music streaming platforms have become more prevalent in the U.S., music piracy rates have fallen. Piracy rates are calculated as a function of U.S. total population. This data was sourced from the Digital Media Association's (DiMA) annual report from March 2018.

Music streaming platforms

Although music streaming is no longer a freely replicable public good, streaming platforms such as Spotify, Deezer, Apple Music, SoundCloud, and Amazon Music have shifted music streaming to a club-type good. While some platforms, most notably Spotify, give customers access to a freemium service that enables the use of limited features for exposure to advertisements, most companies operate under a premium subscription model. Under such circumstances, music streaming is financially excludable, requiring that customers pay a monthly fee for access to a music library, but non-rival, since one customer's use does not impair another's.

Music streaming platforms have grown rapidly in popularity in recent years. Spotify has over 207 million users, as of 1 January 2019, in 78 countries, Apple Music has about 60 million, and SoundCloud has 175 million. All platforms provide varying degrees of accessibility. Apple Music and Prime Music only offer their services for paid subscribers, whereas Spotify and SoundCloud offer freemium and premium services. Napster, owned by Rhapsody since 2011, has resurfaced as a music streaming platform offering subscription based services to over 4.5 million users as of January 2017. As music streaming providers have proliferated and competition has pushed the price of subscriptions down, music piracy rates have also fallen (see chart to the right).

The music industry's response to music streaming was initially negative. Along with music piracy, streaming services disrupted the market and contributed to the fall in revenue from $14.6 billion in revenue in 1999 to $6.3 billion in 2009 for the U.S. CD's and single-track downloads were not selling because content was freely available on the Internet. The result was that record labels invested more in artists that were "safe" – chart music became more appealing to producers than bands with unique sounds. In 2018, however, music streaming revenue exceeded that of traditional revenue streams (e.g. record sales, album sales, downloads). 2017 alone saw a 41.1% increase in streaming revenue alone and an 8.1% increase in overall revenue. Streaming revenue is one of the largest driving forces behind the growth in the music industry. In an interview, Jonathan Dworkin, a senior vice president of strategy and business development at Universal, said that "we cannot be afraid of perpetual change, because that dynamism is driving growth."

COVID-19 pandemic

By August 2020, the COVID-19 pandemic had streaming services busier than ever. In the UK alone, twelve million people joined a new streaming service that they had not previously had.

An impact analysis of 2020 data by the International Confederation of Societies of Authors and Composers (CISAC) indicated that remuneration from digital streaming of music increased with a strong rise in digital royalty collection (up 16.6% to EUR 2.4 billion), but it would not compensate the overall loss of income of authors from concerts, public performance and broadcast.  The International Federation of the Phonographic Industry (IFPI) recompiled the music industry initiatives around the world related to the COVID-19. In its State of the Industry report, it recorded that the global recorded music market grew by 7.4% in 2022, the 6th consecutive year of growth. This growth was driven by streaming, mostly from paid subscription streaming revenues which increased by 18.5%, fueled by 443 million users of subscription accounts by the end of 2020.

The COVID-19 pandemic has also driven an increase in misinformation and disinformation, particularly on streaming platforms like YouTube and podcasts.

Technologies

Bandwidth

A broadband speed of 2 Mbit/s or more is recommended for streaming standard-definition video without experiencing buffering or skips, especially live video, for example to a Roku, Apple TV, Google TV or a Sony TV Blu-ray Disc Player. 5 Mbit/s is recommended for High Definition content and 9 Mbit/s for Ultra-High Definition content. Streaming media storage size is calculated from the streaming bandwidth and length of the media using the following formula (for a single user and file): storage size in megabytes is equal to length (in seconds) × bit rate (in bit/s) / (8 × 1024 × 1024). For example, one hour of digital video encoded at 300 kbit/s (this was a typical broadband video in 2005 and it was usually encoded in a 320 × 240 pixels window size) will be: (3,600 s × 300,000 bit/s) / (8×1024×1024) requires around 128 MB of storage.

If the file is stored on a server for on-demand streaming and this stream is viewed by 1,000 people at the same time using a Unicast protocol, the requirement is 300 kbit/s × 1,000 = 300,000 kbit/s = 300 Mbit/s of bandwidth. This is equivalent to around 135 GB per hour. Using a multicast protocol the server sends out only a single stream that is common to all users. Therefore, such a stream would only use 300 kbit/s of serving bandwidth. See below for more information on these protocols. The calculation for live streaming is similar. Assuming that the speed at the encoder is 500 kbit/s and if the show lasts for 3 hours with 3,000 viewers, then the calculation is number of MBs transferred = encoder speed (in bit/s) × number of seconds × number of viewers / (8 × 1024 × 1024). The results of this calculation are as follows: number of MBs transferred = 500 x 1024 (bit/s) × 3 × 3,600 ( = 3 hours) × 3,000 (number of viewers) / (8 × 1024 × 1024) = 1,977,539 MB.

In 2018 video was more than 60% of data traffic worldwide and accounted for 80% of growth in data usage.

Protocols

Unicast connections require multiple connections from the same streaming server even when it streams the same content.

The audio stream is compressed to make the file size smaller using an audio coding format such as MP3, Vorbis, AAC or Opus. The video stream is compressed using a video coding format to make the file size smaller. Video coding formats include H.264, HEVC, VP8 or VP9. Encoded audio and video streams are assembled in a container "bitstream" such as MP4, FLV, WebM, ASF or ISMA. The bitstream is delivered from a streaming server to a streaming client (e.g., the computer user with their Internet-connected laptop) using a transport protocol, such as Adobe's RTMP or RTP. In the 2010s, technologies such as Apple's HLS, Microsoft's Smooth Streaming, Adobe's HDS and non-proprietary formats such as MPEG-DASH have emerged to enable adaptive bitrate streaming over HTTP as an alternative to using proprietary transport protocols. Often, a streaming transport protocol is used to send video from an event venue to a "cloud" transcoding service and CDN, which then uses HTTP-based transport protocols to distribute the video to individual homes and users. The streaming client (the end user) may interact with the streaming server using a control protocol, such as MMS or RTSP.

The quality of the interaction between servers and users is based on the workload of the streaming service; as more users attempt to access a service, the more quality is affected unless there is enough bandwidth or the host is using enough proxy networks. Deploying clusters of streaming servers is one such method where there are regional servers spread across the network, managed by a singular, central server containing copies of all the media files as well as the IP addresses of the regional servers. This central server then uses load balancing and scheduling algorithms to redirect users to nearby regional servers capable of accommodating them. This approach also allows the central server to provide streaming data to both users as well as regional servers using FFMpeg libraries if required, thus demanding the central server to have powerful data-processing and immense storage capabilities. In return, workloads on the streaming backbone network are balanced and alleviated, allowing for optimal streaming quality.

Designing a network protocol to support streaming media raises many problems. Datagram protocols, such as the User Datagram Protocol (UDP), send the media stream as a series of small packets. This is simple and efficient; however, there is no mechanism within the protocol to guarantee delivery. It is up to the receiving application to detect loss or corruption and recover data using error correction techniques. If data is lost, the stream may suffer a dropout. The Real-time Streaming Protocol (RTSP), Real-time Transport Protocol (RTP) and the Real-time Transport Control Protocol (RTCP) were specifically designed to stream media over networks. RTSP runs over a variety of transport protocols, while the latter two are built on top of UDP.

Another approach that seems to incorporate both the advantages of using a standard web protocol and the ability to be used for streaming even live content is adaptive bitrate streaming. HTTP adaptive bitrate streaming is based on HTTP progressive download, but contrary to the previous approach, here the files are very small, so that they can be compared to the streaming of packets, much like the case of using RTSP and RTP. Reliable protocols, such as the Transmission Control Protocol (TCP), guarantee correct delivery of each bit in the media stream. However, they accomplish this with a system of timeouts and retries, which makes them more complex to implement. It also means that when there is data loss on the network, the media stream stalls while the protocol handlers detect the loss and retransmit the missing data. Clients can minimize this effect by buffering data for display. While delay due to buffering is acceptable in video on demand scenarios, users of interactive applications such as video conferencing will experience a loss of fidelity if the delay caused by buffering exceeds 200 ms.

Multicasting broadcasts the same copy of the multimedia over the entire network to a group of clients

Unicast protocols send a separate copy of the media stream from the server to each recipient. Unicast is the norm for most Internet connections, but does not scale well when many users want to view the same television program concurrently. Multicast protocols were developed to reduce the server/network loads resulting from duplicate data streams that occur when many recipients receive unicast content streams independently. These protocols send a single stream from the source to a group of recipients. Depending on the network infrastructure and type, multicast transmission may or may not be feasible. One potential disadvantage of multicasting is the loss of video on demand functionality. Continuous streaming of radio or television material usually precludes the recipient's ability to control playback. However, this problem can be mitigated by elements such as caching servers, digital set-top boxes, and buffered media players.

IP Multicast provides a means to send a single media stream to a group of recipients on a computer network. A multicast protocol, usually Internet Group Management Protocol, is used to manage delivery of multicast streams to the groups of recipients on a LAN. One of the challenges in deploying IP multicast is that routers and firewalls between LANs must allow the passage of packets destined to multicast groups. If the organization that is serving the content has control over the network between server and recipients (i.e., educational, government, and corporate intranets), then routing protocols such as Protocol Independent Multicast can be used to deliver stream content to multiple Local Area Network segments. Peer-to-peer (P2P) protocols arrange for prerecorded streams to be sent between computers. This prevents the server and its network connections from becoming a bottleneck. However, it raises technical, performance, security, quality, and business issues.

Recording

Media that is livestreamed can be recorded through certain media players such as VLC player, or through the use of a screen recorder. Live-streaming platforms such as Twitch may also incorporate a video on demand system that allows automatic recording of live broadcasts so that they can be watched later. The popular site, YouTube also has recordings of live broadcasts, including television shows aired on major networks. These streams have the potential to be recorded by anyone who has access to them, whether legally or otherwise.

Applications and marketing

Useful – and typical – applications of streaming are, for example, long video lectures performed online. An advantage of this presentation is that these lectures can be very long, although they can always be interrupted or repeated at arbitrary places. There are also new marketing concepts. For example, the Berlin Philharmonic Orchestra sells Internet live streams of whole concerts, instead of several CDs or similar fixed media, by their Digital Concert Hall using YouTube for trailers. These online concerts are also spread over a lot of different places – cinemas – at various places on the globe. A similar concept is used by the Metropolitan Opera in New York. There also is a livestream from the International Space Station. In video entertainment, video streaming platforms like Netflix, Hulu, and Disney+ are mainstream elements of the media industry.

Marketers have found many opportunities offered by streaming media and the platforms that offer them, especially in light of the significant increase in the use of streaming media during COVID lockdowns from 2020 onwards. While revenue and placement traditional advertising continues to decrease, digital marketing increased in 15% in 2021, with digital media and search representing 65% of the expenditures.

A case study commissioned by the WIPO indicates that streaming services attract advertising budgets with the opportunities provided with interactivity and the use of data from users, resulting in personalization on a mass scale with content marketing. Targeted marketing is expanding with the use of artificial intelligence, in particular programmatic advertisement, a tool that helps advertisers decide their campaign parameters, and whether they are interested in buying advertising space online or not. One example of advertising space acquisition is Real-Time Bidding (RTB).

Challenges

Copyright issues

See also: Copyright aspects of downloading and streaming

The availability of large bandwidth internet enabled the audiovisual streaming services to attract large number of users around the world.  For OTT platforms, original content represents a critical variable in order to capture more subscribers. This generated a number of effects related to the copyright over the audiovisual content and its international exploitation through streaming such as contractual practices, international exploitation of rights, widespread use of standards and metadata in digital files. The WIPO has indicated the several basic copyright issues arising for those pursuing to work in the film and music industry in the era of streaming.

Streaming copyrighted content can involve making infringing copies of the works in question. The recording and distribution of streamed content is also an issue for many companies that rely on revenue based on views or attendance.

Greenhouse gas emissions

The net greenhouse gas emissions from streaming music were estimated at between 0.2 and 0.35 million metric tons CO2eq (between 200,000 and 340,000 long tons; 220,000 and 390,000 short tons) per year in the United States, by a 2019 study. This was an increase from emissions in the pre-digital music period, which were estimated at "0.14 million metric tons (140,000 long tons; 150,000 short tons) in 1977, 0.136 million (134,000 long tons; 150,000 short tons) in 1988, and 0.157 million (155,000 long tons; 173,000 short tons) in 2000." However this is far less than other everyday activities such as eating, for example greenhouse gas emissions in the United States from beef cattle (burping of ruminants only - not including their manure) were 129 million metric tons (127 million long tons; 142 million short tons) in 2019.

A 2021 study claimed that one hour of streaming or videoconferencing "emits 150–1,000 grams (5–35 oz) of carbon dioxide ... requires 2–12 liters (0.4–2.6 imp gal; 0.5–3.2 U.S. gal) of water and demands a land area adding up to about the size of an iPad Mini." The study suggests that turning the camera off during video calls can reduce the greenhouse gas and water use footprints by 96%, and that an 86% reduction is possible by using standard definition rather than high definition when streaming content with apps such as Netflix or Hulu. However another study estimated a relatively low amount of 36 grams per hour (1.3 ounces per hour), and concluded that watching a Netflix video for half an hour emitted only the same as driving a gasoline fuelled car for about 100 meters (330 ft), so not a significant amount.

One way to decrease greenhouse gas emissions associated with streaming music is making data centers carbon neutral, by converting to electricity produced from renewable sources. On an individual level, purchase of a physical CD may be more environmentally friendly if it is to be played more than 27 times. Another option for reducing energy use can be downloading the music for offline listening, to reduce the need for streaming over distance. The Spotify service has a built-in local cache to reduce the necessity of repeating song streams.