Perpetual war

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

 

Perpetual war, endless war, or a forever war, is a lasting state of war with no clear conditions that would lead to its conclusion These wars are situations of ongoing tension that may escalate at any moment, similar to the Cold War. From the late 20th century, the concepts have been used to critique the United States Armed Forces interventions in foreign nations and the military–industrial complex, or wars with ambiguous enemies such as the War on Terror, War on Poverty and the War on Drugs.

Causes

Poor military planning is one of the major reasons that a forever war can occur. If the territory gained in a war is not occupied or controlled properly, this can allow a deadly insurgency to occur, potentially stretching out a conflict that never ends. Similarly, warfare that is fought irregularly, such as rebellions in Africa, do not have a set of military objectives in mind, usually because these rebel groups intend to commit war crimes against the civilian population. Thus, the lack of actual military goals can in itself be a reason that a forever war can occur. A very large defense budget may also be a factor in the transpiration of a forever war. This allows a country to fight several forever wars. As of 2018, the United States has a high military budget that is larger than their budget for World War II, allowing for inflation, which enables them to fight wars forever in Iraq and other countries. The idea of a forever war can also extend to civil wars. Simply, civil wars can last for a prolonged period of time whenever a military stalemate occurs between both sides.

A lack of democracy in a country can lead to a forever war, as this makes a country more likely to become engulfed in a civil war. Politically, forever wars can occur in order to keep money flowing into institutions, such as the Military-Industrial-Congressional Complex (MICC). Thus, forever wars can serve as domestic political engines. The continuous changes in capitalism in globalized markets influences policy makers. This, in turn, causes the policy makers to promote policies of continuing and expanding wars. As well, forever wars can be used by small armed groups in an attempt to wear down a larger group or country. For example, in the aftermath of the 9/11 attacks and the beginning of the War on Terror, Al-Qaeda attempted to get the United States involved in a prolonged guerrilla war in Afghanistan, or in other words a forever war. The reason for this was to destroy American will to fight such a long war, and ultimately force the United States to not only withdraw from Afghanistan, but from the Middle East as well. Thus, forever wars can be started in an attempt to achieve political goals for armed groups. 

Wars between ethnic or ideological groups can become forever wars, as such wars are harder to end with a negotiated peace deal due to the different interests of the two sides.

Perpetual war can also stem from financial support for a rebellion or country, such as rebel groups selling illegal products or taxing civilians on one side of the conflict. The financial assistance allows rebellion groups to be able to fight longer with more supplies.

In current events

The concept of a perpetual war has been used since opposition to United States involvement in the Vietnam War. James Pinckney Harrison argues in The Endless War: Fifty Years of Struggle in Vietnam (1981) that the Vietnam War was "endless" due to the success of the communist revolution in nationalizing the people. The concept was used by Trần Văn Đôn, a general in the Army of the Republic of Vietnam, in his book Our Endless War: Inside Vietnam (1978).

American historian James Chace argues in his book Endless War: How we got involved in Central America (1983) that US policy in Central America is based upon the assumption that US hegemony is threatened within the region. According to Chace, US involvement in Central America worked towards resisting the domino effect of the spread of a "communist take-over", largely through establishing the credibility of US military. Though these policies were meant to deter conflict, they themselves created the conditions for instability in the region, which furthered a US response. This resulted in a self-perpetuating, or "endless", loop. He additionally argues US investment in pursuing an expanding military presence in Central America reflects an endless preparation for war.

A key argument of Chace is that much this military involvement stems from a logic of US paranoia in reaction to the Cuban Revolution. A similar argument is put forward by David Keen, political economist and Professor of Complex Emergencies at the London School of Economics. His book Endless War? Hidden Function of the 'War on Terror' (2006) argues that the United States' strategies and tactics in the War on Terror use a "militaristic state-cased framework". This framework, though "counterproductive", has an "inner logic" and a "psychological function" of responding to the trauma of September 11 attacks.

Noam Chomsky posits that a state of perpetual war is an aid to (and is promoted by) the powerful members of dominant political and economic classes, helping maintain their positions of economic and political superiority.

British journalist Robert Fisk, a critic of Western policies in the Middle East, argues that recent Western conflicts against the Middle East, after the end of the Cold War, have been part of a new perpetual war. He suggests that former U.S. President George H.W. Bush launched attacks on Iraq, Sudan, and Afghanistan to distract the population from his domestic political problems. In addition, he claims that despite victorious claims after the first Gulf War that Saddam Hussein had been "defanged", he was again the target of Western attacks until his execution in 2006.

Similarly, Ted Koppel described the War on Terror as "Our Children's Children's War". Critics of Western policies have used the term "perpetual war" in reference to non-military "wars", such as the "War on Drugs", "War on Poverty", "War on Cancer", Lou Dobbs's "War on the Middle Class", or the "War on Terrorism", the "War on Women", or Bill O'Reilly's "War on Christmas".

In socioeconomics and politics

The economic make-up of the 5th century BC Athens-led Delian League also bears resemblance to the economic ramifications of preparing for perpetual war. Aspects of any given empire, such as the British Empire and its relation to its domestic businesses that were owned by a wealthy minority of individuals, such as the East India Company, the Hudson's Bay Company, and De Beers, manifest an observed relationship between a minority of individuals influencing Empire or State policy, such as the Child's War in India, the Anglo-Mysore Wars in India, the Anglo-French conflicts on Hudson Bay in Canada, and the Second Boer War in South Africa, follow a pattern where the Empire allocates resources pursuing and sustaining policies that financially profit the Empire's domestic business's owners.

Military–industrial complex

The concept of a military–industrial complex was first suggested by U.S. President Dwight D. Eisenhower and the idea that military action can be seen as a form of market-creation goes back at least as far as speeches beginning in 1930 prior to the publication of War Is a Racket in 1935. On January 16, 1961, President Eisenhower delivered his farewell speech expressing great concern for the direction of the newfound armaments industry post-WWII. While recognizing the boom in economic growth after the war, he reminded the people of United States that this was a way of profiting off warfare and that if not regulated enough it could lead to the "grave" expansion of the armaments industry. For his warning of the thirst to profit from warfare through weapon production, Eisenhower coined the term "military industrial complex". He said, "The potential for the disastrous rise of misplaced power exists and will persist." Eisenhower feared that the military-industrial complex could lead to a state of perpetual war as the big armament industry will continue to profit from warfare. Additionally, NSC 68 can be used as a reference to understand U.S. President Harry S. Truman's reasoning for the continued build up the United States' nuclear arsenal and how this contributed to the Cold War. This concept is still present in today's policies as William D. Hartung states in his article "The Doctrine of Armed Exceptionalism".

Cold War

The Cold War was a time of extreme tensions between the Soviet Union's interest of expansion of Communism and the NATO countries which operated on a dominantly capitalist economy. The Soviet Union was viewed as a threat to the American national government as well as its citizens. When the Soviet military reached Afghanistan, the United States took action in training the people of the Middle Eastern nations to combat the Soviet Army. During the Soviet–Afghan War under the Carter administration, the CIA gave a lot of aid and training to the Islamic Jihadists and helped fund Wahhabi Universities in Afghanistan, Pakistan as well as Iraq. In 1979, Osama Bin Laden was assigned to the CIA and received U.S. military training. In 1985, President Reagan met with Islamic Jihadists at the White House. Under Reagan's presidency, these Islamic Jihadists were known as "freedom fighters", but were later relabeled as "Islamic terrorists" under President George W. Bush's administration. It should also be noted that non-Jihadist Islamist resistance fighters were also aided by the CIA during the Soviet-Afghan War, and these groups became the Northern Alliance, but since the support was funnelled through Pakistan's ISI, Massoud received less support than the more radical factions.

The War on Terror

Traditionally, the term War referred to the physical and conventional act of engaging in armed conflict. However, the implications of what war entails has evolved over time. The War on Terror has often been cited as a perpetual war, being a war with “no specific battlefield and the enemy isn’t an army”. The War on Terror has been directed at countless 'enemies' as it has no clear target. Georgetown University Historian Bruce Hoffman describes traditional war as a war that "ends with the vanquishing of an opponent, with some form or armistice or truce- some kind of surrender instrument or document". In contrast, The War on Terror continues to rage on, with no end in sight.

The War on Terror was declared in 2001 by President George W. Bush, following the September 11 attacks, but as early as 1996, Osama bin Laden of Al-Qaeda made a threat to the United States, by making a declaration of war. The growing tensions of the Middle East are suggested by Laurence Andrew Dobrot to be very wide cultural misunderstandings and faults the West for not making peace with the Middle East. As the Deputy Director for the Missile Defense Agency's Airborne Laser Program, Dobrot examines the hostility which has been continuous not only since 2001, but since the birth of Wahhabism.

Dobrot proposed that the U.S should recognize the cultural hostility of the tribal Arabs in order to make the first steps towards progression of making peace.

The Crusades arose as European expansion was growing at the peak of unified Islamic dominance. Some say that the so-called war on terror is a continuation of the Crusades. On September 16, 2001, in a speech, President Bush referred to the War on terror as a crusade. He said:

No one could have conceivably imagined suicide bombers burrowing into our society and then emerging all in the same day to fly their aircraft - fly U.S. aircraft into buildings full of innocent people - and show no remorse. This is a new kind of -- a new kind of evil. And we understand. And the American people are beginning to understand. This crusade, this war on terrorism is going to take a while. And the American people must be patient. I'm going to be patient.

Andrew Bacevich described Bush's naming of the war on terror as a crusade as something which does not make the war separate, rather something that shows that it is part of an "eternal war."

War on Drugs

The 1960s gave birth to a rebellious movement that popularized drug use. "Hippies" sought to expand their minds with the use of hallucinogens like LSD, whilst many soldiers returned from the Vietnam War with heroin habits. Demand for drugs skyrocketed in the 1960s.

The War on Drugs was declared by President Nixon in June 1971. It was later picked up by the Reagan administration as First Lady Nancy Reagan spread the message with her slogan "Just Say No" to drugs. Though coined by Ronald Reagan, the policies which his administration implemented existed stretching back to Woodrow Wilson's presidency. Security measures were taken under Reagan to restrict drugs. The Comprehensive Drug Abuse Prevention and Control Act of 1970 was passed so that pharmaceutical companies may keep track of the distributions and maintain restrictions on certain types of drugs. In 1988 the Office of National Drug Control Policy was set to pass more regulations and restrictions on drug policies, though the media labeled the agency directors as "drug czars." The average annual funding for eradication and interdiction programs increased from $437 million during Carter's presidency to $1.4 billion during Reagan's first term. Under George Bush's administration, a significant increase of actions were taken toward the War on Drugs, including militant force, student drug testing, and drug raids.

The War on Drugs received criticism from political figures, such as President Barack Obama and Pat Robertson. Robertson said that the War on Drugs must come to an end as there is a mass incarceration of drug users, who did not commit any violent acts, serving time. He says, "We here in America make up 5 percent of the world's population, but we make up 25 percent of jailed prisoners", in reference to the War on Drugs.

With the advent of perpetual war, communities have begun to construct War Memorials with names of the dead while the wars are ongoing. See the Northwood Community Park's memorial which has space for 8,000 names (approximately 4,500 used at time of construction) and plans to update it yearly.

Views of influential writers

Thomas Hobbes

Political Philosopher Thomas Hobbes succinctly wrote in 1651 that a hypothetical State of nature was a condition of perpetual war. The following quotation from chapter 13 of his book Leviathan explores the causes and effects of perpetual war:

So that in the nature of man we find three principal causes of quarrel. First, competition; secondly, diffidence; thirdly, glory.
The first maketh man invade for gain; the second, for safety; and the third, for reputation. They first use violence, to make themselves masters of other men's persons, wives, children, and cattle; the second, to defend them; the third, for trifles, as a word, a smile, a different opinion, and any other sign of undervalue, either direct in their persons or by reflection in their kindred, their friends, their nation, their profession, or their name.
Hereby it is manifest that, during the time men live without a common power to keep them all in awe, they are in that condition which is called war, and such a war as is of every man against every man. For 'war' consisteth not in battle only or the act of fighting, but in a tract of time wherein the will to contend by battle is sufficiently known, and therefore the notion of 'time' is to be considered in the nature of war, as it is in the nature of weather. ...
Whatsoever therefore is consequent to a time or war where every man is enemy to every man, the same is consequent to the time wherein men live without other security than what their own strength and their own invention shall furnish them withal. In such condition there is no place for industry, because the fruit thereof is uncertain, and consequently no culture of the earth, no navigation nor use of the commodities that may be imported by sea, no commodious building, no instruments of moving and removing such things as require much force, no knowledge of the face of the earth; no account of time, no arts, no letters, no society, and, which is worst of all, continual fear and danger of violent death, and the life of man solitary, poor, nasty, brutish, and short.

Sun Tzu

Ancient war advisor Sun Tzu expressed views in the 6th century BC about perpetual war. The following quotation from chapter 2, Waging War, of his book The Art of War suggests the negative impacts of prolonged war:

Sun Tzŭ said: ... When you engage in actual fighting, if victory is long in coming, the men's weapons will grow dull and their ardour will be damped. If you lay siege to a town, you will exhaust your strength ... There is no instance of a country having benefited from prolonged warfare ... In war, then, let your great object be victory, not lengthy campaigns.

Alexis de Tocqueville

Historian Alexis de Tocqueville made predictions in 1840 concerning perpetual war in democratic countries. The following is from Volume 2, chapter 22, "Why Democratic Nations Naturally Desire Peace and Democratic Armies, War", 18th paragraph, in his book, Democracy in America:

No protracted war can fail to endanger the freedom of a democratic country. Not indeed that after every victory it is to be apprehended that the victorious generals will possess themselves by force of the supreme power, after the manner of Sulla and Caesar; the danger is of another kind. War does not always give over democratic communities to military government, but it must invariably and immeasurably increase the powers of civil government; it must almost compulsorily concentrate the direction of all men and the management of all things in the hands of the administration. If it does not lead to despotism by sudden violence, it prepares men for it more gently by their habits. All those who seek to destroy the liberties of a democratic nation ought to know that war is the surest and the shortest means to accomplish it. This is the first axiom of the science.

Relationship with the democratic republic

The development of a relationship network between people who wield political and economic power as well as those who own capital in companies that financially profit from warfare have a relationship to records influencing public opinion of war through the influence of mass media outlets. These may also include the presentation for the causes of war, the effects of war, and the Censorship of war. The following authors, have suggested that entering a state of perpetual war becomes progressively easier in a modern democratic republic, such as the United States:

• The Iron Triangle: Inside the Secret World of the Carlyle Group (2004), by Dan Briody.
• The Pentagon Labyrinth: 10 Short Essays to Help You Through It (2011) an anthology by nine authors who are Pierre M. Sprey, George Wilson, Franklin C. Spinney, Bruce I. Gudmundsson, Col. G. I. Wilson, Col. Chet Richards, Andrew Cockburn, Thomas Christie, and Winslow T. Wheeler.
• Prophets of War: Lockheed Martin and the Making of the Military-Industrial Complex (2010), by William D. Hartung.
• Media Control, Second Edition: The Spectacular Achievements of Propaganda (2002), by Noam Chomsky.
• Manufacturing Consent: The Political Economy of the Mass Media (2002), by Edward S. Herman and Noam Chomsky. The hypothesized relationship of networking between people wielding such power is known as the military–industrial complex and was briefly described by President Dwight D. Eisenhower on 17 January 1961.

Fiction

• In the landmark George Orwell novel Nineteen Eighty Four, the three superstates of the world, Eurasia, Oceania and Eastasia, are said to be in a perpetual state of war with each other. The attacks are in the form of rocket attacks (similar to the V2 Attacks on London in WW2) although it is implied in the book that the attacks could be launched by the home Government against their own people in order to perpetuate fear and hatred of the enemy. Therefore, perpetual war may in fact secretly be a strategy used by the state to continuously promote its own political agenda. However, the military attacks are limited to the non-aligned areas (North and Central Africa, India etc.), an example of this is The Malabar Front (India) where Oceania won a victory against Eurasia.
• In the Doctor Who series Genesis of the Daleks, the Kaleds and the Thals are in a perpetual state of war and have been for 1000 years. This state of war finally results in both sides occupying one city each on either side of mountains, and leads to both sides supplies being so completely ravaged by the war that both sides have a collection of black powder weapons, modern and futuristic weapons and armour. It is out of this war that the Daleks are created by Davros.
• Also in the Doctor Who series, the Sontarans and the Rutans have been in a perpetual state of war for over 50,000 years. There appears to be no end in sight, with each side continually attempting to completely obliterate the other. This has resulted in either side constantly gaining and losing territory (including the Milky Way galaxy, which is known in Doctor Who as the "Mutter's Spiral").
• And again in Doctor Who in Destiny of the Daleks, the Daleks and the Movellans have basically been drawn into an Endless War, due to their battle computers basically giving a logical set of orders only to have them countered by the other Battle computer with an equally logical set of orders. As both sides are using logical instructions neither side could win as both sides would be able to counter the advances of the other as both were using logic. The Daleks returned to Skaro to find Davros to see if he could give them an advantage. The Movellans tried to get the Doctor to give them the same advantage.
• In the 2000AD series Rogue Trooper, the North (Norts) and South (Southers) of the Planet Nu-Earth, for hundreds of years, have been in a perpetual state of war against each other using conventional, biological, chemical, and nuclear weapons. The length of the war as well as the weapons involved have turned the planet uninhabitable without protective suits. It was for this reason that the Southers created the GIs or Genetic Infantry which would be able to survive in the environment.
• Joe Haldeman's The Forever War is about a war that is made perpetual due to the Einsteinian Time Dilation effects due to Space Travel. The novel is said to have been shaped by Haldeman's experience in the Vietnam War as the book contains references to the war paired up with sci-fi concepts. A quote by Haldeman shows great influence from Hobbes concept of perpetual war, "Life begins in a bloody mess and sometimes it ends the same way, and only odd people seek out blood between those times, maybe crazy people."
• In the original Star Trek episode "A Taste of Armageddon", the neighboring planets of Eminiar and Vendikar have been at war for 500 years. To avoid the physical devastation of an actual war, the belligerents agreed to conduct only computer-simulated attacks as long as the resulting "victims" voluntarily kill themselves in "disintegration stations".
• The 2006 film Children of Men displays themes of perpetual war by exploring the wars on Terror and Poverty. The movie is set in a dystopia suffering universal infertility. The social and political world has become chaotic as few people exercise social power from their wealthy positions. Meanwhile, there is constant conflict all around the world, which specifically the oppressed group suffers. Manohla Dargis of The New York Times takes notice to the norm of bombs casually exploding in public places, such as a cafe. Dargis writes, "It imagines the unthinkable: What if instead of containing Iraq, the world has become Iraq, a universal battleground of military control, security zones, refugee camps and warring tribal identities?"
• The 2013 science fiction film Snowpiercer illustrates tensions between socioeconomic classes, environmentalism, and usage of militarism. The Earth is rendered uninhabitable due to human destruction and carelessness. What remains of humanity must live in a self-sustaining biosphere on a train ruled by a tyrannical government. The working class are oppressed by the elite. The film displays a strong message on class structure and war between socioeconomic classes.
• The Danganronpa videogame series centers around an ongoing crisis known as "The Biggest, Most Awful, Most Tragic Event in Human History," or just The Tragedy. Originally beginning as a student protest against Hope's Peak Academy, it gradually escalated into an uprising of the poor against the elite, and a state of global social unrest, violence, and warfare happening not for any sort of political or ideological purpose, but just for the sake of causing death and destruction.
• Perpetual war is also associated with Warhammer 40,000 and other Warhammer titles. This concept is seen throughout these universes with the common tagline: "In the grim darkness of the far future, there is only war" and is essential to the "Grimdark" setting.
• In 2045, a economic disaster known as Simultaneous Global Default triggered the never-ending economic war, called the "Sustainable War", taking place at the 2020 animated internet web series Ghost in the Shell: SAC 2045, which was released on Netflix in April 2020.

Artificial neural network

From Wikipedia, the free encyclopedia

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

 

An artificial neural network is an interconnected group of nodes, inspired by a simplification of neurons in a brain. Here, each circular node represents an artificial neuron and an arrow represents a connection from the output of one artificial neuron to the input of another.

Artificial neural networks (ANN) or connectionist systems are computing systems vaguely inspired by the biological neural networks that constitute animal brains. Such systems "learn" to perform tasks by considering examples, generally without being programmed with task-specific rules. For example, in image recognition, they might learn to identify images that contain cats by analyzing example images that have been manually labeled as "cat" or "no cat" and using the results to identify cats in other images. They do this without any prior knowledge of cats, for example, that they have fur, tails, whiskers and cat-like faces. Instead, they automatically generate identifying characteristics from the examples that they process.

An ANN is based on a collection of connected units or nodes called artificial neurons, which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit a signal to other neurons. An artificial neuron that receives a signal then processes it and can signal neurons connected to it. 

In ANN implementations, the "signal" at a connection is a real number, and the output of each neuron is computed by some non-linear function of the sum of its inputs. The connections are called edges. Neurons and edges typically have a weight that adjusts as learning proceeds. The weight increases or decreases the strength of the signal at a connection. Neurons may have a threshold such that a signal is sent only if the aggregate signal crosses that threshold. Typically, neurons are aggregated into layers. Different layers may perform different transformations on their inputs. Signals travel from the first layer (the input layer), to the last layer (the output layer), possibly after traversing the layers multiple times. 

The original goal of the ANN approach was to solve problems in the same way that a human brain would. But over time, attention moved to performing specific tasks, leading to deviations from biology. ANNs have been used on a variety of tasks, including computer vision, speech recognition, machine translation, social network filtering, playing board and video games, medical diagnosis, and even in activities that have traditionally been considered as reserved to humans, like painting.

History

Warren McCulloch and Walter Pitts (1943) opened the subject by creating a computational model for neural networks. In the late 1940s, D. O. Hebb created a learning hypothesis based on the mechanism of neural plasticity that became known as Hebbian learning. Farley and Wesley A. Clark (1954) first used computational machines, then called "calculators", to simulate a Hebbian network. Rosenblatt (1958) created the perceptron. The first functional networks with many layers were published by Ivakhnenko and Lapa in 1965, as the Group Method of Data Handling. The basics of continuous backpropagation were derived in the context of control theory by Kelley in 1960 and by Bryson in 1961, using principles of dynamic programming.

In 1970, Seppo Linnainmaa published the general method for automatic differentiation (AD) of discrete connected networks of nested differentiable functions. In 1973, Dreyfus used backpropagation to adapt parameters of controllers in proportion to error gradients. Werbos's (1975) backpropagation algorithm enabled practical training of multi-layer networks. In 1982, he applied Linnainmaa's AD method to neural networks in the way that became widely used. Thereafter research stagnated following Minsky and Papert (1969), who discovered that basic perceptrons were incapable of processing the exclusive-or circuit and that computers lacked sufficient power to process useful neural networks.

Increasing transistor count in digital electronics provided more processing power that enabled the development of practical artificial neural networks in the 1980s. 

In 1992, max-pooling was introduced to help with least-shift invariance and tolerance to deformation to aid 3D object recognition. Schmidhuber adopted a multi-level hierarchy of networks (1992) pre-trained one level at a time by unsupervised learning and fine-tuned by backpropagation.

Geoffrey Hinton et al. (2006) proposed learning a high-level representation using successive layers of binary or real-valued latent variables with a restricted Boltzmann machine to model each layer. In 2012, Ng and Dean created a network that learned to recognize higher-level concepts, such as cats, only from watching unlabeled images. Unsupervised pre-training and increased computing power from GPUs and distributed computing allowed the use of larger networks, particularly in image and visual recognition problems, which became known as "deep learning".

Ciresan and colleagues (2010) showed that despite the vanishing gradient problem, GPUs make backpropagation feasible for many-layered feedforward neural networks. Between 2009 and 2012, ANNs began winning prizes in ANN contests, approaching human level performance on various tasks, initially in pattern recognition and machine learning.  for example, the bi-directional and multi-dimensional long short-term memory (LSTM) of Graves et al. won three competitions in connected handwriting recognition in 2009 without any prior knowledge about the three languages to be learned.

Ciresan and colleagues built the first pattern recognizers to achieve human-competitive/superhuman performance on benchmarks such as traffic sign recognition (IJCNN 2012).

Models

Neuron and myelinated axon, with signal flow from inputs at dendrites to outputs at axon terminals

 

ANNs began as an attempt to exploit the architecture of the human brain to perform tasks that conventional algorithms had little success with. They soon reoriented towards improving empirical results, mostly abandoning attempts to remain true to their biological precursors. Neurons are connected to each other in various patterns, to allow the output of some neurons to become the input of others. The network forms a directed, weighted graph.

An artificial neural network consists of a collection of simulated neurons. Each neuron is a node which is connected to other nodes via links that correspond to biological axon-synapse-dendrite connections. Each link has a weight, which determines the strength of one node's influence on another.

Components of ANNs

Neurons

ANNs are composed of artificial neurons which retain the biological concept of neurons, which receive input, combine the input with their internal state (activation) and an optional threshold using an activation function, and produce output using an output function. The initial inputs are external data, such as images and documents. The ultimate outputs accomplish the task, such as recognizing an object in an image. The important characteristic of the activation function is that it provides a smooth, differentiable transition as input values change, i.e. a small change in input produces a small change in output.

Connections and weights

The network consists of connections, each connection providing the output of one neuron as an input to another neuron. Each connection is assigned a weight that represents its relative importance. A given neuron can have multiple input and output connections.

Propagation function

The propagation function computes the input to a neuron from the outputs of its predecessor neurons and their connections as a weighted sum. A bias term can be added to the result of the propagation.

Organization

The neurons are typically organized into multiple layers, especially in deep learning. Neurons of one layer connect only to neurons of the immediately preceding and immediately following layers. The layer that receives external data is the input layer. The layer that produces the ultimate result is the output layer. In between them are zero or more hidden layers. Single layer and unlayered networks are also used. Between two layers, multiple connection patterns are possible. They can be fully connected, with every neuron in one layer connecting to every neuron in the next layer. They can be pooling, where a group of neurons in one layer connect to a single neuron in the next layer, thereby reducing the number of neurons in that layer. Neurons with only such connections form a directed acyclic graph and are known as feedforward networks. Alternatively, networks that allow connections between neurons in the same or previous layers are known as recurrent networks.

Hyperparameter

A hyperparameter is a constant parameter whose value is set before the learning process begins. The values of parameters are derived via learning. Examples of hyperparameters include learning rate, the number of hidden layers and batch size. The values of some hyperparameters can be dependent on those of other hyperparameters. For example, the size of some layers can depend on the overall number of layers.

Learning

Learning is the adaptation of the network to better handle a task by considering sample observations. Learning involves adjusting the weights (and optional thresholds) of the network to improve the accuracy of the result. This is done by minimizing the observed errors. Learning is complete when examining additional observations does not usefully reduce the error rate. Even after learning, the error rate typically does not reach 0. If after learning, the error rate is too high, the network typically must be redesigned. Practically this is done by defining a cost function that is evaluated periodically during learning. As long as its output continues to decline, learning continues. The cost is frequently defined as a statistic whose value can only be approximated. The outputs are actually numbers, so when the error is low, the difference between the output (almost certainly a cat) and the correct answer (cat) is small. Learning attempts to reduce the total of the differences across the observations. Most learning models can be viewed as a straightforward application of optimization theory and statistical estimation.

Learning rate

The learning rate defines the size of the corrective steps that the model takes to adjust for errors in each observation. A high learning rate shortens the training time, but with lower ultimate accuracy, while a lower learning rate takes longer, but with the potential for greater accuracy. Optimizations such as Quickprop are primarily aimed at speeding up error minimization, while other improvements mainly try to increase reliability. In order to avoid oscillation inside the network such as alternating connection weights, and to improve the rate of convergence, refinements use an adaptive learning rate that increases or decreases as appropriate. The concept of momentum allows the balance between the gradient and the previous change to be weighted such that the weight adjustment depends to some degree on the previous change. A momentum close to 0 emphasizes the gradient, while a value close to 1 emphasizes the last change.

Cost function

While it is possible to define a cost function ad hoc, frequently the choice is determined by the functions desirable properties (such as convexity) or because it arises from the model (e.g., in a probabilistic model the model's posterior probability can be used as an inverse cost).

Backpropagation

Backpropagation is a method to adjust the connection weights to compensate for each error found during learning. The error amount is effectively divided among the connections. Technically, backprop calculates the gradient (the derivative) of the cost function associated with a given state with respect to the weights. The weight updates can be done via stochastic gradient descent or other methods, such as Extreme Learning Machines, "No-prop" networks, training without backtracking, "weightless" networks, and non-connectionist neural networks.

Learning paradigms

The three major learning paradigms are supervised learning, unsupervised learning and reinforcement learning. They each correspond to a particular learning task

Supervised learning

Supervised learning uses a set of paired inputs and desired outputs. The learning task is to produce the desired output for each input. In this case the cost function is related to eliminating incorrect deductions. A commonly used cost is the mean-squared error, which tries to minimize the average squared error between the network's output and the desired output. Tasks suited for supervised learning are pattern recognition (also known as classification) and regression (also known as function approximation). Supervised learning is also applicable to sequential data (e.g., for hand writing, speech and gesture recognition). This can be thought of as learning with a "teacher", in the form of a function that provides continuous feedback on the quality of solutions obtained thus far.

Unsupervised learning

In unsupervised learning, input data is given along with the cost function, some function of the data ${\displaystyle \textstyle x}$ and the network's output. The cost function is dependent on the task (the model domain) and any a priori assumptions (the implicit properties of the model, its parameters and the observed variables). As a trivial example, consider the model ${\displaystyle \textstyle f(x)=a}$ where ${\displaystyle \textstyle a}$ is a constant and the cost ${\displaystyle \textstyle C=E[(x-f(x))^{2}]}$. Minimizing this cost produces a value of ${\displaystyle \textstyle a}$ that is equal to the mean of the data. The cost function can be much more complicated. Its form depends on the application: for example, in compression it could be related to the mutual information between ${\displaystyle \textstyle x}$ and ${\displaystyle \textstyle f(x)}$, whereas in statistical modeling, it could be related to the posterior probability of the model given the data (note that in both of those examples those quantities would be maximized rather than minimized). Tasks that fall within the paradigm of unsupervised learning are in general estimation problems; the applications include clustering, the estimation of statistical distributions, compression and filtering.

Reinforcement learning

In applications such as playing video games, an actor takes a string of actions, receiving a generally unpredictable response from the environment after each one. The goal is to win the game, i.e., generate the most positive (lowest cost) responses. In reinforcement learning, the aim is to weight the network (devise a policy) to perform actions that minimize long-term (expected cumulative) cost. At each point in time the agent performs an action and the environment generates an observation and an instantaneous cost, according to some (usually unknown) rules. The rules and the long-term cost usually only can be estimated. At any juncture, the agent decides whether to explore new actions to uncover their costs or to exploit prior learning to proceed more quickly.

Formally the environment is modeled as a Markov decision process (MDP) with states ${\displaystyle \textstyle {s_{1},...,s_{n}}\in S}$ and actions ${\displaystyle \textstyle {a_{1},...,a_{m}}\in A}$. Because the state transitions are not known, probability distributions are used instead: the instantaneous cost distribution ${\displaystyle \textstyle P(c_{t}|s_{t})}$, the observation distribution ${\displaystyle \textstyle P(x_{t}|s_{t})}$ and the transition distribution ${\displaystyle \textstyle P(s_{t+1}|s_{t},a_{t})}$, while a policy is defined as the conditional distribution over actions given the observations. Taken together, the two define a Markov chain (MC). The aim is to discover the lowest-cost MC.

ANNs serve as the learning component in such applications. Dynamic programming coupled with ANNs (giving neurodynamic programming) has been applied to problems such as those involved in vehicle routing, video games, natural resource management and medicine because of ANNs ability to mitigate losses of accuracy even when reducing the discretization grid density for numerically approximating the solution of control problems. Tasks that fall within the paradigm of reinforcement learning are control problems, games and other sequential decision making tasks.

Self learning

Self learning in neural networks was introduced in 1982 along with a neural network capable of self-learning named Crossbar Adaptive Array (CAA). It is a system with only one input, situation s, and only one output, action (or behavior) a. It has neither external advice input nor external reinforcement input from the environment. The CAA computes, in a crossbar fashion, both decisions about actions and emotions (feelings) about encountered situations. The system is driven by the interaction between cognition and emotion. Given memory matrix W =||w(a,s)||, the crossbar self learning algorithm in each iteration performs the following computation:

  In situation s perform action a;
  Receive consequence situation s’;
  Compute emotion of being in consequence situation v(s’);
  Update crossbar memory  w’(a,s) = w(a,s) + v(s’).

The backpropagated value (secondary reinforcement) is the emotion toward the consequence situation. The CAA exists in two environments, one is behavioral environment where it behaves, and the other is genetic environment, where from it initially and only once receives initial emotions about to be encountered situations in the behavioral environment. Having received the genome vector (species vector) from the genetic environment, the CAA will learn a goal-seeking behavior, in the behavioral environment that contains both desirable and undesirable situations.

Other

In a Bayesian framework, a distribution over the set of allowed models is chosen to minimize the cost. Evolutionary methods, gene expression programming, simulated annealing, expectation-maximization, non-parametric methods and particle swarm optimization are other learning algorithms. Convergent recursion is a learning algorithm for cerebellar model articulation controller (CMAC) neural networks.

Modes

Two modes of learning are available: stochastic and batch. In stochastic learning, each input creates a weight adjustment. In batch learning weights are adjusted based on a batch of inputs, accumulating errors over the batch. Stochastic learning introduces "noise" into the process, using the local gradient calculated from one data point; this reduces the chance of the network getting stuck in local minima. However, batch learning typically yields a faster, more stable descent to a local minimum, since each update is performed in the direction of the batch's average error. A common compromise is to use "mini-batches", small batches with samples in each batch selected stochastically from the entire data set.

Types

ANNs have evolved into a broad family of techniques that have advanced the state of the art across multiple domains. The simplest types have one or more static components, including number of units, number of layers, unit weights and topology. Dynamic types allow one or more of these to evolve via learning. The latter are much more complicated, but can shorten learning periods and produce better results. Some types allow/require learning to be "supervised" by the operator, while others operate independently. Some types operate purely in hardware, while others are purely software and run on general purpose computers. 

Some of the main breakthroughs include: convolutional neural networks that have proven particularly successful in processing visual and other two-dimensional data; long short-term memory avoid the vanishing gradient problem and can handle signals that have a mix of low and high frequency components aiding large-vocabulary speech recognition, text-to-speech synthesis, and photo-real talking heads; competitive networks such as generative adversarial networks in which multiple networks (of varying structure) compete with each other, on tasks such as winning a game or on deceiving the opponent about the authenticity of an input.

Network design

Neural architecture search (NAS) uses machine learning to automate ANN design. Various approaches to NAS have designed networks that compare well with hand-designed systems. The basic search algorithm is to propose a candidate model, evaluate it against a dataset and use the results as feedback to teach the NAS network. Available systems include AutoML and AutoKeras.

Design issues include deciding the number, type and connectedness of network layers, as well as the size of each and the connection type (full, pooling, ...). 

Hyperparameters must also be defined as part of the design (they are not learned), governing matters such as how many neurons are in each layer, learning rate, step, stride, depth, receptive field and padding (for CNNs), etc.

Use

Using Artificial neural networks requires an understanding of their characteristics.

• Choice of model: This depends on the data representation and the application. Overly complex models slow learning.
• Learning algorithm: Numerous trade-offs exist between learning algorithms. Almost any algorithm will work well with the correct hyperparameters for training on a particular data set. However, selecting and tuning an algorithm for training on unseen data requires significant experimentation.
• Robustness: If the model, cost function and learning algorithm are selected appropriately, the resulting ANN can become robust.

ANN capabilities fall within the following broad categories:

• Function approximation, or regression analysis, including time series prediction, fitness approximation and modeling.
• Classification, including pattern and sequence recognition, novelty detection and sequential decision making.
• Data processing, including filtering, clustering, blind source separation and compression.
• Robotics, including directing manipulators and prostheses.
• Control, including computer numerical control.

Applications

Because of their ability to reproduce and model nonlinear processes, Artificial neural networks have found applications in many disciplines. Application areas include system identification and control (vehicle control, trajectory prediction, process control, natural resource management), quantum chemistry, general game playing, pattern recognition (radar systems, face identification, signal classification, 3D reconstruction, object recognition and more), sequence recognition (gesture, speech, handwritten and printed text recognition), medical diagnosis, finance (e.g. automated trading systems), data mining, visualization, machine translation, social network filtering and e-mail spam filtering. ANNs have been used to diagnose cancers, including lung cancer, prostate cancer, colorectal cancer and to distinguish highly invasive cancer cell lines from less invasive lines using only cell shape information.

ANNs have been used to accelerate reliability analysis of infrastructures subject to natural disasters and to predict foundation settlements. ANNs have also been used for building black-box models in geoscience: hydrology, ocean modelling and coastal engineering, and geomorphology. ANNs have been employed in cybersecurity, with the objective to discriminate between legitimate activities and malicious ones. For example, machine learning has been used for classifying Android malware, for identifying domains belonging to threat actors and for detecting URLs posing a security risk. Research is underway on ANN systems designed for penetration testing, for detecting botnets, credit cards frauds and network intrusions.

ANNs have been proposed as a tool to simulate the properties of many-body open quantum systems. In brain research ANNs have studied short-term behavior of individual neurons, the dynamics of neural circuitry arise from interactions between individual neurons and how behavior can arise from abstract neural modules that represent complete subsystems. Studies considered long-and short-term plasticity of neural systems and their relation to learning and memory from the individual neuron to the system level.

Theoretical properties

Computational power

The multilayer perceptron is a universal function approximator, as proven by the universal approximation theorem. However, the proof is not constructive regarding the number of neurons required, the network topology, the weights and the learning parameters.

A specific recurrent architecture with rational-valued weights (as opposed to full precision real number-valued weights) has the power of a universal Turing machine, using a finite number of neurons and standard linear connections. Further, the use of irrational values for weights results in a machine with super-Turing power.

Capacity

A model's "capacity" property corresponds to its ability to model any given function. It is related to the amount of information that can be stored in the network and to the notion of complexity. Two notions of capacity are known by the community. The information capacity and the VC Dimension. The information capacity of a perceptron is intensively discussed in Sir David MacKay's book which summarizes work by Thomas Cover. The capacity of a network of standard neurons (not convolutional) can be derived by four rules  that derive from understanding a neuron as an electrical element. The information capacity captures the functions modelable by the network given any data as input. The second notion, is the VC dimension. VC Dimension uses the principles of measure theory and finds the maximum capacity under the best possible circumstances. This is, given input data in a specific form. As noted in , the VC Dimension for arbitrary inputs is half the information capacity of a Perceptron. The VC Dimension for arbitrary points is sometimes referred to as Memory Capacity.

Convergence

Models may not consistently converge on a single solution, firstly because local minima may exist, depending on the cost function and the model. Secondly, the optimization method used might not guarantee to converge when it begins far from any local minimum. Thirdly, for sufficiently large data or parameters, some methods become impractical.

The convergence behavior of certain types of ANN architectures are more understood than others. When the width of network approaches to infinity, the ANN is well described by its first order Taylor expansion throughout training, and so inherits the convergence behavior of affine models. Another example is when parameters are small, it is observed that ANNs often fits target functions from low to high frequencies. This phenomenon is the opposite to the behavior of some well studied iterative numerical schemes such as Jacobi method.

Generalization and statistics

Applications whose goal is to create a system that generalizes well to unseen examples, face the possibility of over-training. This arises in convoluted or over-specified systems when the network capacity significantly exceeds the needed free parameters. Two approaches address over-training. The first is to use cross-validation and similar techniques to check for the presence of over-training and to select hyperparameters to minimize the generalization error.

The second is to use some form of regularization. This concept emerges in a probabilistic (Bayesian) framework, where regularization can be performed by selecting a larger prior probability over simpler models; but also in statistical learning theory, where the goal is to minimize over two quantities: the 'empirical risk' and the 'structural risk', which roughly corresponds to the error over the training set and the predicted error in unseen data due to overfitting.

Confidence analysis of a neural network

Supervised neural networks that use a mean squared error (MSE) cost function can use formal statistical methods to determine the confidence of the trained model. The MSE on a validation set can be used as an estimate for variance. This value can then be used to calculate the confidence interval of network output, assuming a normal distribution. A confidence analysis made this way is statistically valid as long as the output probability distribution stays the same and the network is not modified.

By assigning a softmax activation function, a generalization of the logistic function, on the output layer of the neural network (or a softmax component in a component-based network) for categorical target variables, the outputs can be interpreted as posterior probabilities. This is useful in classification as it gives a certainty measure on classifications. 

The softmax activation function is:

${\displaystyle y_{i}={\frac {e^{x_{i}}}{\sum _{j=1}^{c}e^{x_{j}}}}}$

Criticism

Training

A common criticism of neural networks, particularly in robotics, is that they require too much training for real-world operation. Potential solutions include randomly shuffling training examples, by using a numerical optimization algorithm that does not take too large steps when changing the network connections following an example, grouping examples in so-called mini-batches and/or introducing a recursive least squares algorithm for CMAC.

Theory

A fundamental objection is that ANNs do not sufficiently reflect neuronal function. Backpropagation is a critical step, although no such mechanism exists in biological neural networks. How information is coded by real neurons is not known. Sensor neurons fire action potentials more frequently with sensor activation and muscle cells pull more strongly when their associated motor neurons receive action potentials more frequently. Other than the case of relaying information from a sensor neuron to a motor neuron, almost nothing of the principles of how information is handled by biological neural networks is known.

A central claim of ANNs is that they embody new and powerful general principles for processing information. Unfortunately, these principles are ill-defined. It is often claimed that they are emergent from the network itself. This allows simple statistical association (the basic function of artificial neural networks) to be described as learning or recognition. Alexander Dewdney commented that, as a result, artificial neural networks have a "something-for-nothing quality, one that imparts a peculiar aura of laziness and a distinct lack of curiosity about just how good these computing systems are. No human hand (or mind) intervenes; solutions are found as if by magic; and no one, it seems, has learned anything". One response to Dewdney is that neural networks handle many complex and diverse tasks, ranging from autonomously flying aircraft to detecting credit card fraud to mastering the game of Go.

Technology writer Roger Bridgman commented:

Neural networks, for instance, are in the dock not only because they have been hyped to high heaven, (what hasn't?) but also because you could create a successful net without understanding how it worked: the bunch of numbers that captures its behaviour would in all probability be "an opaque, unreadable table...valueless as a scientific resource".
In spite of his emphatic declaration that science is not technology, Dewdney seems here to pillory neural nets as bad science when most of those devising them are just trying to be good engineers. An unreadable table that a useful machine could read would still be well worth having.

Biological brains use both shallow and deep circuits as reported by brain anatomy, displaying a wide variety of invariance. Weng argued that the brain self-wires largely according to signal statistics and therefore, a serial cascade cannot catch all major statistical dependencies.

Hardware

Large and effective neural networks require considerable computing resources. While the brain has hardware tailored to the task of processing signals through a graph of neurons, simulating even a simplified neuron on von Neumann architecture may consume vast amounts of memory and storage. Furthermore, the designer often needs to transmit signals through many of these connections and their associated neurons – which require enormous CPU power and time.

Schmidhuber noted that the resurgence of neural networks in the twenty-first century is largely attributable to advances in hardware: from 1991 to 2015, computing power, especially as delivered by GPGPUs (on GPUs), has increased around a million-fold, making the standard backpropagation algorithm feasible for training networks that are several layers deeper than before. The use of accelerators such as FPGAs and GPUs can reduce training times from months to days.

Neuromorphic engineering addresses the hardware difficulty directly, by constructing non-von-Neumann chips to directly implement neural networks in circuitry. Another type of chip optimized for neural network processing is called a Tensor Processing Unit, or TPU.

Practical counterexamples

Analyzing what has been learned by an ANN, is much easier than to analyze what has been learned by a biological neural network. Furthermore, researchers involved in exploring learning algorithms for neural networks are gradually uncovering general principles that allow a learning machine to be successful. For example, local vs. non-local learning and shallow vs. deep architecture.

Hybrid approaches

Advocates of hybrid models (combining neural networks and symbolic approaches), claim that such a mixture can better capture the mechanisms of the human mind.