AI News, Artificial Intelligence

Artificial Intelligence: A Creative Player in the Game of Copyright

may be understood to be an entity sufficiently simulating the cognitive aspects

to meet the individual conceptual features of copyrighted works and to gain copyright protection.

and persists on the criterion of a natural person as the author.

place in the world of intellectual property law, and to shift the paradigm

of copyright towards the modern age, the age of AI.

The paper focuses on a rudimentary metanalysis of AI and copyright in mutual

outlining all the intended proposed phases of regulation of AI within the

copyright law and the methodology suitable for such a move.

chapter, AI definition and outcomes for the further operating are presented

represents the critical analysis and demarcation of the main problematic

friction surfaces of copyright and earlier defined AI.

'The rise of the machines is here, but they do not come as conquerors, they

their presence known, at all possible levels, including law.

most touched areas of law is copyright where we can find more and more outcomes

the increasing share of an AI in the creative process, we shall seek the

an easy step but requires a lot of arrangements and preparatory materials

as well as strict methodology and phasing with individual but logically

The author is aware of such problems and he proposes the following structure

to set and identify an applicable definition or model of an AI, for

the research and regulation to be completely effective and transparent as

may serve for the descriptive-analytic study of the real state, where the theoretical

background of defined AI and copyright could be proved or reversed.

These two phases are crucial for the precise improvement and creation

of a functional model of AI's regulation within copyright law. This

third step of restructuring the copyright for the needs of AI shall be driven mostly

as an experimental phase working with AI as an equal object or subject

In the final realization phase with de lege ferenda, the model

Appropriate choice of methodology is one of the key elements for the whole research

provable and replicable research (Urbáníková, Smekal 2017, 26:4, p. 38-41;

basic set of methodological approaches (Smits 2012) to the research itself,

The descriptive and analytical part shall play the key-role at the beginning

then continue with the setting of what the right regulation of AI, as well

with the ideas of 'new' state of law without proper metanalysis and

description of the values could lead to a desirable purpose of the research

This paper focuses on the first part of the process, on a metanalysis of AI and copyright in mutual interactions, because of the

need of AI's definition and understanding for the following work to be acceptable

on presenting the options of AI and the possible ways of its understandings

creativity is the fundamental aspect of copyright and could influence the

authorship or assessment of the conceptual features within the outcomes of

The paper tries to present another view and argumentation line for

the presence of creativity within an AI based on some generally accepted

definitions of this phenomenon and doctrinal understanding of the creative

itself is built on the territoriality premise, the legal system used

for reflection of the options of legal framework shall be the system of

The first part of the paper is focused on an AI per se.

possible understandings of it from the moments of Turing test and its opponents,

through the weak and strong AI division, to the creative aspects of

In this part, it tries to point out that AI's outcomes could be found

creative in the IP way of understanding and the AI could be found to be

creative, based on the alteration of some accepted definitions in this area.

The next part is focused on the general regulatory framework of AI, which is

nowadays still 'unregulated' specifically, just as a part of another general

the role of AI and its creative options, but neither the general

legal framework nor the copyright framework is specifically built for

The third major part of the paper is trying to highlight the crucial friction

areas of copyright and earlier defined AI, focusing on the outcome of

an AI as a copyrighted work, AI as a possible author of such work, and the

needs for making the special regime of AI's outcomes reflecting all the specifics

An AI is continuously developing and its share in the area of potentially copyrightable

of its outcomes expands into a greater number of fields, from purely

technological and industrial areas to areas of art inherently connected

as very well-functioning software, as mentioned below (Rushby 1998).

according to a plethora of criteria, where all the groups may be represented

focus on the examples from the area of art which stands for the main part

new look-a-like work of the Dutch maestro when comparing all his works,

analysing his style, choice of colours and brush strokes and based on

that developing the best matching and presumed portrait (Guadamuz 2017). Another

- or operated - to compare the previously chosen artworks in form of

musicals where the positive impact on the audience was proven.

it has analysed the 'function parts' and modulated the best matching musical

mention the IntelLabs/Stanford project with the production of photos of non-existent places (Chen, Koltun 2017).

whole picture divided into zones, a database of the street photos from dashboards

of cars and a special algorithm, the AI was able to combine and modify

them resulting in the photos of places almost unrecognisable from real-world

where the rap verses are created based on pre-set pairing algorithms

for creating songs sounding like the songs of any popular band. Regardless

of how much the creative process is dependent on some form of machine

learning, unlike in the case of AI as specialised software, the role

part of the creative process and their uploaded data are fundamental for

substrate of the creative process and may play the key role in determining

Just considering the above-mentioned options and projects, some protection should

should be chosen to avoid undesirable consequences of non-existence

lead to mishandling such products and ad hoc solutions possibly

Of course, the public domain scheme could be applied in here, but such a solution could

thing to realise is that AI can't be identified with pure objects used

hand, AI is not so independent and autonomous as humans and their cognitive

for general objects nor the framework determined for human creators

focusing on research of non-human intelligence and its cognitive

as an object basically helping people to understand the operations

and cognitive aspects of the human brain (Luber 2011, p.

463-518) or argument of the Chinese room (Searle, 1980, 3:3, p.

'AI is that activity devoted to making machines intelligent, and intelligence

in the case of field of informatics, because it - among others - doesn't reflect

its software character or its ability to create, and it is just one of

the definitions of an AI as the field of activities to create something intelligent

More research is therefore needed for it to be caught up perfectly

When talking about an AI and its extent (in the sense of ability to make some

moves within or beyond the limits), some basic division is needed.

opinion generally recognises two basic extents of an AI, the strong (full) AI and the weak (narrow) one.

56, 260), the strong AI shall be able - despite its own

problematic definition - to move beyond such constraints (Preston, Bishop 2002).

that this test can provide only an answer for the distinction of weak

and strong AI on the one side and non-AI on the second side (for the problems

The answer lies with the Chinese room argument as a counterpart to the Turing test .

In the case of weak AI, there is no doubt about the existence of the computer

However, the Chinese room argument is questioning this thesis through the idea of

the intelligent answer of non-thinking AI on the one side and intelligent

human is theoretically able (based on a question in Chinese) to provide the

answer only with the ability to search and orient in the text materials (without

The argument tries to establish that the essence of the

Turing test, the ability to respond meaningfully to the asked questions,

the entity is answering or to be creative in the answer (Dennett 1991, p.

relevant in the case of copyright law when we realise the ability

of humans to create works worthy of some copyright protection. There

is no need for authors to create purposely, not to mention the possibility

of minors or people limited in - or just without - legal capacity

aspect of the black box paradox (Star 1992, 5:4, p.

to a plethora of debates on all kinds of levels (Black, William 2010).

consist in the degree of human control and knowledge of internal processes

this paper as well as the connected research let's leave this AI aside and

type of AI is based on a finite number of algorithms and clear assumptions

The extent of weak AI is very broad but despite that, the individual

When looking under the hood of the projects mentioned earlier and when talking

about a creative ability of an AI, it is helpful to be inspired by the

Newell, Shaw and Simon's view of computational creativity (Newell, Shaw, Simon 1958) while focusing on the assessment

we can easily reject the ideas of immediate failure of copyright

There are theseinfinite monkey theorem (Borel 1913, 3:1, p.

189-196) or total library theory (Borges 2007) pointing to the ethereal randomness

the framework of time or probability and since creating the works within

the AI's outcomes could be found creative in the IP way of understanding

of inputs as well as moves and functions resulting necessarily in a limited

needed to bear in mind is that this set of criteria is set for providing the

creative (not pre-set) answer to the input question, generally speaking.

In the case of copyright law, we must specify these criteria a little

bit narrower while preserving their meaning on the one hand and preserving

the ideas and role of copyright law on the other hand.

about granting the copyright protection to some outcomes, it is needed

to analyse if such an outcome would meet the conceptual features of the

novelty and usefulness are not the criteria of copyright law; they

it must be modified to the originality or uniqueness (based on the

national legal systems or the system of the European Union, as stated below)

The criterion resulting from intense motivation and persistence then needs to be slightly

matter if the 'work' is created intentionally or accidentally, in an

final outcome would unjustifiably exclude a large number of creations from

be understood within the copyright law as enriching the cultural fund. Even

within the copyright law (for the more thorough analysis of conceptual

outcome results especially from intense motivation and persistence, the

of inputs as well as moves and functions resulting necessarily in a limited

needed to bear in mind is that this set of criteria is set for providing the

creative (not pre-set) answer to the input question, generally speaking.

In the case of copyright law, we must specify these criteria a little

bit narrower while preserving their meaning on the one hand and preserving

to analyse if such an outcome would meet the conceptual features of the

The criterion resulting from intense motivation and persistence then needs to be slightly

matter if the 'work' is created intentionally or accidentally, in an

final outcome would unjustifiably exclude a large number of creations from

be understood within the copyright law as enriching the cultural fund. Even

within the copyright law (for the more thorough analysis of conceptual

The research of an AI and copyright in mutual interactions then need the systematic

calculations puts the values together in a pre-defined order and

objects in an otherwise familiar whole (Boden 1998, p.

Just as humans are led to behave analogically to the behavioural models accepted

on a set of inputs and set of processes with the task to analyse (map,

general process framework is created which is then used for creating a completely

there could be recognised (ii) group of presumed outcomes and (iii)

group of possible outcomes, while these groups are subsets to each other

While within the first group it is not possible to find the AI to be creative because the individual operations and instructions

for creative freedom, within the second and third group it could be possible

looser relations with the help of the multiplicity of answers under specific

the final outcome may exist within the second (presumed) or the third

relations can lead finally into moving of AI beyond the limits of presumed

case of chaining the algorithms the resulting combination of 'choices' may

the result of creative activity as understood by copyright law, as it could

To regulate an uncertain phenomenon with no strict terminology and understanding

effort is particularly significant considering basic fundaments of

Horáček 2017), the onlysemi-regulation material is the Industry 4.0 initiative report of Ministry of Industry and Trade, where

problems in job vacancies, its role as a discipline (sic!) in theIndustry 4.0 is emphasised (Iniciativa průmysl 4.0, p.

the needs of quality research for another study to be based on something

AI is obviously counted within the question of its impact on economics and

inteligence: Sektory a jejich potenciál 2018), but until AI has a strict and steady definition for the following regulation

particularly in the data access area, supply of skills improvement

Besides the European Civil Law Rules in Robotics (2016) talking about what general considerations and ethical problems must be

field of AI and appeal to an appropriate ethical and legal framework.

neither any international treaty nor declaration dealing with AI itself nor

are for sure some reactions of copyright to modern technologies (like the

apply the reverse scheme and start from the international level to conclude

The international level of copyright is short of AI in the question of its regulation.

none of them is dealing neither with AI per se nor the outcomes of an

protection to the software whatever the mode or form of their expression

question of authorship and only the natural persons operating with it may

the current regulatory efforts, including copyright, and that AI itself is

we must mention what the crucial friction areas of copyright and earlier

divided into three logically connected levels: 1) outcome of an AI as a copyrighted

work, 2) AI as an author of such work, and 3) the special regime

An outcome of an AI as a 'work' means to describe and assess meeting the conceptual

'a literary work or any other work of art or scientific work, which is a

unique outcome of the creative activity of the author and is expressed

The first conceptual feature, literary, artistic or scientific character depends on the nature of

the creative freedom (see below) - new in relation to already existing

and at the same time unrepeatable in relation to pro futuro possibly existing works (Knap 1998, p.

abovementioned AI examples and concerning the form of already existing creations,

distinctive creation and reflection of the author's personality in the sense

of a unique combination of internal elements such as fantasy, talent, experience

conceptual feature is (unlike the character of the creation) connected to

be able to prove the creativity within the AI, AI itself can't be currently

within the personal structure of the creative process, because such subjects

The last feature, objectively perceivable manner is not needed to specifically

is primarily focused on inherent nature of human authors but could be understood

problem could lie within the authorship per se.

The second step then is the question of AI as an author of such work or outcome determining the authorship in

the idea of ​​the objective authorship, it is necessary to declare

Kurki, Pietrzykowski 2017) as well as it is important to find the rational arguments for such granting

such a move, an individual analysis is needed with finding the solution to

all the questions raised regarding AI as the subject of legal relations.

we may find three major groups of such subjects, 1) authors of an AI,

2) users using the AI and 3) authors of the works used for 'training' the

creative activity may be focused on the individual outcome, the problem

created based on scanning and analysing already existing sources (e.g. baroque

general ideal model is created which allows users to modify their data into

they are so fundamental for the whole creative process, their works can be

(a simili to creating the classic work with no participation of an

Thirdly, what are - or supposed to be - the specifics of protection granted to

natural persons, lifetime duration and protection granted for some period

of time after their death as well as financial compensation for their

regime of computer-generated works or some special category of works

subjects of copyright law while preserving the existing form of copyright

the introduction of the possible regulation analysis scheme, the paper

tried to point out that AI itself could be found creative based on the

In the following chapter, the regulatory framework of AI was presented with the

The last chapter then presented the main problematic copyright questions of AI

outcomes within the current legislation setting with a focus on the law of

CR, it outlined necessary arising questions and tried to provide possible

divided into three logically connected levels: 1) outcome of an AI as a

copyrighted work, 2) AI as an author of such work, and 3) the special regime

an AI within the authorship claims is still the unclear situation with a few

possible solutions and that to prepare the right regime for the outcomes

then may result in creating a new functional model of copyright operating

critical review and feedback when writing this paper, and to Abbe Brown for her unceasing help during the editing process. Special

z Česka udělat rozvojovou zemi, varuje professor, který

1896 lastly revised on 24 July 1971 [online].

Is Artificial Intelligence the ‘Killer App’ for Data Governance?

that pushes slow adopters and those disinclined against the technology to finally adopt it.  If all my friends and family are text messaging and I am the only one left out, I probably need to get a mobile phone and start texting –

Then along came artificial intelligence (AI) in all its many forms and practices: machine learning, deep learning, artificial neural networks, reinforcement learning, generative adversarial networks, predictive analytics, recommendation systems, natural language processing and the list goes on.

AI promises game-changing advancements in business models, customer experience, personalization, preventive maintenance, automation, efficiency and many other areas.  A September 2018 report from McKinsey and Company predicts that artificial intelligence (AI) will boost the global economy by $13 trillion by 2030, adding roughly 1.2% to global GDP a year.

If we are building a recommendation system and we use the wrong product code for customer purchases, we could end up recommending the wrong products to our customers.  Recommending the wrong products or inappropriate products can be more damaging to customer relationships than recommending no products at all.

If we are building a system to predict HR attrition and we don’t have good data about compensation, overtime, work-life balance, job satisfaction, promotions, etc then we could wind up losing good people that otherwise could have been retained if we had better data for our predictive model.

If we are building an AI system to predict when machines will need maintenance and we are automating the dispatch of the field service technician based on the predictions from the AI model, we could waste a good deal of money sending technicians to investigate healthy machines.

Artificial Intelligence Part 1: The Basics for Utilities

In Part 1 of our series on how utilities are using artificial intelligence, we look at the basics —

Defined by Merriam-Webster as “a branch of computer science dealing with the simulation of intelligent behavior,” the term dates back to 1956 when a team of researchers and scholars teamed up for a two-month study of the topic at Dartmouth College in Hanover, New Hampshire.

“An attempt will be made to find how to make machines use language, form abstractions and concepts, solve kinds of problems now reserved for humans, and improve themselves,” the researchers wrote in their study proposal.

“We think that a significant advance can be made in one or more of these problems if a carefully selected group of scientists work on it together for a summer.” However, once the AI ball was rolling, it took more than a summer to refine the concept.

AI also turns one language into another via Google translations, calculates your fare on ride-sharing platforms like Lyft or Uber, answers your online questions via chatbots and probably even handles some of the stock trades in your investment portfolio.

“Intelligent robotic process automation will emerge as business critical, as companies will require the high automation level necessary to become intelligent enterprises in 2019,” said SAP’s Markus Noga, SVP of machine learning, in a recent Forbes article.

For example, according to the article, the Nest learning thermostat and a legion like it has been around for years, and some of those same ideas are now being used with water heaters, electric vehicle charging, and HVAC systems.

Artificial neural network

Artificial neural networks (ANN) or connectionist systems are computing systems that are inspired by, but not necessarily identical to, the biological neural networks that constitute animal brains.

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.

An ANN is based on a collection of connected units or nodes called artificial neurons, which loosely model the neurons in a biological brain.

An artificial neuron that receives a signal can process it and then signal additional artificial neurons connected to it.

In common ANN implementations, the signal at a connection between artificial neurons is a real number, and the output of each artificial neuron is computed by some non-linear function of the sum of its inputs.

Signals travel from the first layer (the input layer), to the last layer (the output layer), possibly after traversing the layers multiple times.

Artificial neural networks have been used on a variety of tasks, including computer vision, speech recognition, machine translation, social network filtering, playing board and video games and medical diagnosis.

(1943) created a computational model for neural networks based on mathematics and algorithms called threshold logic.

One approach focused on biological processes in the brain while the other focused on the application of neural networks to artificial intelligence.

With mathematical notation, Rosenblatt described circuitry not in the basic perceptron, such as the exclusive-or circuit that could not be processed by neural networks at the time.[7]

In 1959, a biological model proposed by Nobel laureates Hubel and Wiesel was based on their discovery of two types of cells in the primary visual cortex: simple cells and complex cells.[8]

The second was that computers didn't have enough processing power to effectively handle the work required by large neural networks.

Much of artificial intelligence had focused on high-level (symbolic) models that are processed by using algorithms, characterized for example by expert systems with knowledge embodied in if-then rules, until in the late 1980s research expanded to low-level (sub-symbolic) machine learning, characterized by knowledge embodied in the parameters of a cognitive model.[citation needed]

key trigger for renewed interest in neural networks and learning was Werbos's (1975) backpropagation algorithm that made the training of multi-layer networks feasible and efficient.

Support vector machines and other, much simpler methods such as linear classifiers gradually overtook neural networks in machine learning popularity.

The vanishing gradient problem affects many-layered feedforward networks that used backpropagation and also recurrent neural networks (RNNs).[20][21]

As errors propagate from layer to layer, they shrink exponentially with the number of layers, impeding the tuning of neuron weights that is based on those errors, particularly affecting deep networks.

To overcome this problem, Schmidhuber adopted a multi-level hierarchy of networks (1992) pre-trained one level at a time by unsupervised learning and fine-tuned by backpropagation.[22]

(2006) proposed learning a high-level representation using successive layers of binary or real-valued latent variables with a restricted Boltzmann machine[24]

Once sufficiently many layers have been learned, the deep architecture may be used as a generative model by reproducing the data when sampling down the model (an 'ancestral pass') from the top level feature activations.[25][26]

In 2012, Ng and Dean created a network that learned to recognize higher-level concepts, such as cats, only from watching unlabeled images taken from YouTube videos.[27]

Earlier challenges in training deep neural networks were successfully addressed with methods such as unsupervised pre-training, while available computing power increased through the use of GPUs and distributed computing.

for very large scale principal components analyses and convolution may create a new class of neural computing because they are fundamentally analog rather than digital (even though the first implementations may use digital devices).[29]

in Schmidhuber's group showed that despite the vanishing gradient problem, GPUs make back-propagation feasible for many-layered feedforward neural networks.

Between 2009 and 2012, recurrent neural networks and deep feedforward neural networks developed in Schmidhuber's research group won eight international competitions in pattern recognition and machine learning.[31][32]

won three competitions in connected handwriting recognition at the 2009 International Conference on Document Analysis and Recognition (ICDAR), without any prior knowledge about the three languages to be learned.[35][34]

Researchers demonstrated (2010) that deep neural networks interfaced to a hidden Markov model with context-dependent states that define the neural network output layer can drastically reduce errors in large-vocabulary speech recognition tasks such as voice search.

A team from his lab won a 2012 contest sponsored by Merck to design software to help find molecules that might identify new drugs.[45]

As of 2011[update], the state of the art in deep learning feedforward networks alternated between convolutional layers and max-pooling layers,[40][46]

Artificial neural networks were able to guarantee shift invariance to deal with small and large natural objects in large cluttered scenes, only when invariance extended beyond shift, to all ANN-learned concepts, such as location, type (object class label), scale, lighting and others.

An artificial neural network is a network of simple elements called artificial neurons, which receive input, change their internal state (activation) according to that input, and produce output depending on the input and activation.

An artificial neuron mimics the working of a biophysical neuron with inputs and outputs, but is not a biological neuron model.

The network forms by connecting the output of certain neurons to the input of other neurons forming a directed, weighted graph.

The weights as well as the functions that compute the activation can be modified by a process called learning which is governed by a learning rule.[50]

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Sometimes a bias term is added to the total weighted sum of inputs to serve as a threshold to shift the activation function.[51]

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The learning rule is a rule or an algorithm which modifies the parameters of the neural network, in order for a given input to the network to produce a favored output.

A common use of the phrase 'ANN model' is really the definition of a class of such functions (where members of the class are obtained by varying parameters, connection weights, or specifics of the architecture such as the number of neurons or their connectivity).

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(commonly referred to as the activation function[53]) is some predefined function, such as the hyperbolic tangent, sigmoid function, softmax function, or rectifier function.

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is an important concept in learning, as it is a measure of how far away a particular solution is from an optimal solution to the problem to be solved.

For applications where the solution is data dependent, the cost must necessarily be a function of the observations, otherwise the model would not relate to the data.

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In neural network methods, some form of online machine learning is frequently used for finite datasets.

While it is possible to define an ad hoc cost function, frequently a particular cost function is used, either because it has desirable properties (such as convexity) or because it arises naturally from a particular formulation of the problem (e.g., in a probabilistic formulation the posterior probability of the model can be used as an inverse cost).

Backpropagation is a method to calculate the gradient of the loss function (produces the cost associated with a given state) with respect to the weights in an ANN.

In 1970, Linnainmaa finally published the general method for automatic differentiation (AD) of discrete connected networks of nested differentiable functions.[62][63]

In 1986, Rumelhart, Hinton and Williams noted that this method can generate useful internal representations of incoming data in hidden layers of neural networks.[69]

The choice of the cost function depends on factors such as the learning type (supervised, unsupervised, reinforcement, etc.) and the activation function.

For example, when performing supervised learning on a multiclass classification problem, common choices for the activation function and cost function are the softmax function and cross entropy function, respectively.

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The model consists of multiple layers, each of which has a rectified linear unit as its activation function for non-linear transformation.

The network is trained to minimize L2 error for predicting the mask ranging over the entire training set containing bounding boxes represented as masks.

the cost function is related to the mismatch between our mapping and the data and it implicitly contains prior knowledge about the problem domain.[77]

commonly used cost is the mean-squared error, which tries to minimize the average squared error between the network's output,

Minimizing this cost using gradient descent for the class of neural networks called multilayer perceptrons (MLP), produces the backpropagation algorithm for training neural networks.

Tasks that fall within the paradigm of supervised learning are pattern recognition (also known as classification) and regression (also known as function approximation).

The supervised learning paradigm 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.

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).

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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).

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The aim is to discover a policy for selecting actions that minimizes some measure of a long-term cost, e.g., the expected cumulative cost.

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because of the ability of Artificial neural networks to mitigate losses of accuracy even when reducing the discretization grid density for numerically approximating the solution of the original control problems.

Tasks that fall within the paradigm of reinforcement learning are control problems, games and other sequential decision making tasks.

Training a neural network model essentially means selecting one model from the set of allowed models (or, in a Bayesian framework, determining a distribution over the set of allowed models) that minimizes the cost.

This is done by simply taking the derivative of the cost function with respect to the network parameters and then changing those parameters in a gradient-related direction.

When an input vector is presented to the network, it is propagated forward through the network, layer by layer, until it reaches the output layer.

The error values are then propagated from the output back through the network, until each neuron has an associated error value that reflects its contribution to the original output.

In the second phase, this gradient is fed to the optimization method, which in turn uses it to update the weights, in an attempt to minimize the loss function.

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The following is pseudocode for a stochastic gradient descent algorithm for training a three-layer network (only one hidden layer):

The lines labeled 'backward pass' can be implemented using the backpropagation algorithm, which calculates the gradient of the error of the network regarding the network's modifiable weights.[92]

is important, since a high value can cause too strong a change, causing the minimum to be missed, while a too low learning rate slows the training unnecessarily.

In order to avoid oscillation inside the network such as alternating connection weights, and to improve the rate of convergence, refinements of this algorithm use an adaptive learning rate.[93]

Similar to a ball rolling down a mountain, whose current speed is determined not only by the current slope of the mountain but also by its own inertia, inertia can be added:

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depend both on the current gradient of the error function (slope of the mountain, 1st summand), as well as on the weight change from the previous point in time (inertia, 2nd summand).

Since, for example, the gradient of the error function becomes very small in flat plateaus, a plateau would immediately lead to a 'deceleration' of the gradient descent.

Stochastic learning introduces 'noise' into the gradient descent process, using the local gradient calculated from one data point;

However, batch learning typically yields a faster, more stable descent to a local minimum, since each update is performed in the direction of the average error of the batch.

A common compromise choice is to use 'mini-batches', meaning small batches and with samples in each batch selected stochastically from the entire data set.

convolutional neural network (CNN) is a class of deep, feed-forward networks, composed of one or more convolutional layers with fully connected layers (matching those in typical Artificial neural networks) on top.

recent development has been that of Capsule Neural Network (CapsNet), the idea behind which is to add structures called capsules to a CNN and to reuse output from several of those capsules to form more stable (with respect to various perturbations) representations for higher order capsules.[103]

can find an RNN weight matrix that maximizes the probability of the label sequences in a training set, given the corresponding input sequences.

provide a framework for efficiently trained models for hierarchical processing of temporal data, while enabling the investigation of the inherent role of RNN layered composition.[clarification needed]

This is particularly helpful when training data are limited, because poorly initialized weights can significantly hinder model performance.

that integrate the various and usually different filters (preprocessing functions) into its many layers and to dynamically rank the significance of the various layers and functions relative to a given learning task.

This grossly imitates biological learning which integrates various preprocessors (cochlea, retina, etc.) and cortexes (auditory, visual, etc.) and their various regions.

Its deep learning capability is further enhanced by using inhibition, correlation and its ability to cope with incomplete data, or 'lost' neurons or layers even amidst a task.

The link-weights allow dynamic determination of innovation and redundancy, and facilitate the ranking of layers, of filters or of individual neurons relative to a task.

LAMSTAR had a much faster learning speed and somewhat lower error rate than a CNN based on ReLU-function filters and max pooling, in 20 comparative studies.[140]

These applications demonstrate delving into aspects of the data that are hidden from shallow learning networks and the human senses, such as in the cases of predicting onset of sleep apnea events,[132]

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The whole process of auto encoding is to compare this reconstructed input to the original and try to minimize the error to make the reconstructed value as close as possible to the original.

with a specific approach to good representation, a good representation is one that can be obtained robustly from a corrupted input and that will be useful for recovering the corresponding clean input.

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of the first denoising auto encoder is learned and used to uncorrupt the input (corrupted input), the second level can be trained.[146]

Once the stacked auto encoder is trained, its output can be used as the input to a supervised learning algorithm such as support vector machine classifier or a multi-class logistic regression.[146]

It formulates the learning as a convex optimization problem with a closed-form solution, emphasizing the mechanism's similarity to stacked generalization.[150]

Each block estimates the same final label class y, and its estimate is concatenated with original input X to form the expanded input for the next block.

Thus, the input to the first block contains the original data only, while downstream blocks' input adds the output of preceding blocks.

It offers two important improvements: it uses higher-order information from covariance statistics, and it transforms the non-convex problem of a lower-layer to a convex sub-problem of an upper-layer.[152]

TDSNs use covariance statistics in a bilinear mapping from each of two distinct sets of hidden units in the same layer to predictions, via a third-order tensor.

The need for deep learning with real-valued inputs, as in Gaussian restricted Boltzmann machines, led to the spike-and-slab RBM (ssRBM), which models continuous-valued inputs with strictly binary latent variables.[156]

One of these terms enables the model to form a conditional distribution of the spike variables by marginalizing out the slab variables given an observation.

However, these architectures are poor at learning novel classes with few examples, because all network units are involved in representing the input (a distributed representation) and must be adjusted together (high degree of freedom).

It is a full generative model, generalized from abstract concepts flowing through the layers of the model, which is able to synthesize new examples in novel classes that look 'reasonably' natural.

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deep predictive coding network (DPCN) is a predictive coding scheme that uses top-down information to empirically adjust the priors needed for a bottom-up inference procedure by means of a deep, locally connected, generative model.

DPCNs predict the representation of the layer, by using a top-down approach using the information in upper layer and temporal dependencies from previous states.[174]

For example, in sparse distributed memory or hierarchical temporal memory, the patterns encoded by neural networks are used as addresses for content-addressable memory, with 'neurons' essentially serving as address encoders and decoders.

Preliminary results demonstrate that neural Turing machines can infer simple algorithms such as copying, sorting and associative recall from input and output examples.

Approaches that represent previous experiences directly and use a similar experience to form a local model are often called nearest neighbour or k-nearest neighbors methods.[189]

Unlike sparse distributed memory that operates on 1000-bit addresses, semantic hashing works on 32 or 64-bit addresses found in a conventional computer architecture.

These models have been applied in the context of question answering (QA) where the long-term memory effectively acts as a (dynamic) knowledge base and the output is a textual response.[194]

A team of electrical and computer engineers from UCLA Samueli School of Engineering has created a physical artificial neural network that can analyze large volumes of data and identify objects at the actual speed of light.[195]

While training extremely deep (e.g., 1 million layers) neural networks might not be practical, CPU-like architectures such as pointer networks[196]

overcome this limitation by using external random-access memory and other components that typically belong to a computer architecture such as registers, ALU and pointers.

The key characteristic of these models is that their depth, the size of their short-term memory, and the number of parameters can be altered independently – unlike models like LSTM, whose number of parameters grows quadratically with memory size.

In that work, an LSTM RNN or CNN was used as an encoder to summarize a source sentence, and the summary was decoded using a conditional RNN language model to produce the translation.[201]

Multilayer kernel machines (MKM) are a way of learning highly nonlinear functions by iterative application of weakly nonlinear kernels.

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For the sake of dimensionality reduction of the updated representation in each layer, a supervised strategy selects the best informative features among features extracted by KPCA.

The main idea is to use a kernel machine to approximate a shallow neural net with an infinite number of hidden units, then use stacking to splice the output of the kernel machine and the raw input in building the next, higher level of the kernel machine.

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.[205]

Because of their ability to reproduce and model nonlinear processes, Artificial neural networks have found many applications in a wide range of disciplines.

object recognition and more), sequence recognition (gesture, speech, handwritten and printed text recognition), medical diagnosis, finance[211]

and to distinguish highly invasive cancer cell lines from less invasive lines using only cell shape information.[215][216]

models of how the dynamics of neural circuitry arise from interactions between individual neurons and finally to models of how behavior can arise from abstract neural modules that represent complete subsystems.

These include models of the long-term, and short-term plasticity, of neural systems and their relations to learning and memory from the individual neuron to the system level.

specific recurrent architecture with rational valued weights (as opposed to full precision real number-valued weights) has the full power of a universal Turing machine,[236]

The first is to use cross-validation and similar techniques to check for the presence of over-training and optimally select hyperparameters to minimize the generalization error.

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.

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.

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 neural network) for categorical target variables, the outputs can be interpreted as posterior probabilities.

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 and by grouping examples in so-called mini-batches.

For example, by introducing a recursive least squares algorithm for CMAC neural network, the training process only takes one step to converge.[90]

Back propagation is a critical part of most artificial neural networks, although no such mechanism exists in biological neural networks.[238]

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.[239]

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.

The motivation behind artificial neural networks is not necessarily to strictly replicate neural function, but to use biological neural networks as an inspiration.

A central claim of artificial neural networks is therefore that it embodies some new and powerful general principle for processing information.

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.

argued that the brain self-wires largely according to signal statistics and therefore, a serial cascade cannot catch all major statistical dependencies.

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 compel a neural network designer to fill many millions of database rows for its connections – 

Schmidhuber notes 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.[244]

Neuromorphic engineering addresses the hardware difficulty directly, by constructing non-von-Neumann chips to directly implement neural networks in circuitry.

Arguments against Dewdney's position are that neural networks have been successfully used to solve many complex and diverse tasks, ranging from autonomously flying aircraft[247]

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.

Although it is true that analyzing what has been learned by an artificial neural network is difficult, it is much easier to do so 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.

Advocates of hybrid models (combining neural networks and symbolic approaches), claim that such a mixture can better capture the mechanisms of the human mind.[250][251]

The simplest, static types have one or more static components, including number of units, number of layers, unit weights and topology.

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