Copyright infringementCopyright infringement (at times referred to as piracy) is the use of works protected by copyright without permission for a usage where such permission is required, thereby infringing certain exclusive rights granted to the copyright holder, such as the right to reproduce, distribute, display or perform the protected work, or to make derivative works. The copyright holder is typically the work's creator, or a publisher or other business to whom copyright has been assigned.
CopyrightA copyright is a type of intellectual property that gives its owner the exclusive right to copy, distribute, adapt, display, and perform a creative work, usually for a limited time. The creative work may be in a literary, artistic, educational, or musical form. Copyright is intended to protect the original expression of an idea in the form of a creative work, but not the idea itself. A copyright is subject to limitations based on public interest considerations, such as the fair use doctrine in the United States.
Copyright law of the United KingdomUnder the law of the United Kingdom, a copyright is an intangible property right subsisting in certain qualifying subject matter. Copyright law is governed by the Copyright, Designs and Patents Act 1988 (the 1988 Act), as amended from time to time. As a result of increasing legal integration and harmonisation throughout the European Union a complete picture of the law can only be acquired through recourse to EU jurisprudence, On 12 September 2018, the European Parliament approved new copyright rules to help secure the rights of writers and musicians.
Text-to-image modelA text-to-image model is a machine learning model which takes an input natural language description and produces an image matching that description. Such models began to be developed in the mid-2010s, as a result of advances in deep neural networks. In 2022, the output of state of the art text-to-image models, such as OpenAI's DALL-E 2, Google Brain's , StabilityAI's Stable Diffusion, and Midjourney began to approach the quality of real photographs and human-drawn art.
Diffusion modelIn machine learning, diffusion models, also known as diffusion probabilistic models or score-based generative models, are a class of latent variable models. They are Markov chains trained using variational inference. The goal of diffusion models is to learn the latent structure of a dataset by modeling the way in which data points diffuse through the latent space. In computer vision, this means that a neural network is trained to denoise images blurred with Gaussian noise by learning to reverse the diffusion process.
Criticism of copyrightCriticism of copyright, or anti-copyright sentiment, is a dissenting view of the current state of copyright law or copyright as a concept. Critics often discuss philosophical, economical, or social rationales of such laws and the laws' implementations, the benefits of which they claim do not justify the policy's costs to society. They advocate for changing the current system, though different groups have different ideas of what that change should be.
Contributory copyright infringementContributory copyright infringement is a way of imposing secondary liability for infringement of a copyright. It is a means by which a person may be held liable for copyright infringement even though he or she did not directly engage in the infringing activity. In the United States, the Copyright Act does not itself impose liability for contributory infringement expressly. It is one of the two forms of secondary liability apart from vicarious liability.
Copyright noticeIn United States copyright law, a copyright notice is a notice of statutorily prescribed form that informs users of the underlying claim to copyright ownership in a published work. Copyright is a form of protection provided by US law to authors of "original works of authorship". When a work is published under the authority of the copyright owner, a notice of copyright may be placed on all publicly distributed copies or phonorecords. The use of the notice is the responsibility of the copyright owner and does not require permission from, or registration with, the Copyright Office.
Copyright law of CanadaThe copyright law of Canada governs the legally enforceable rights to creative and artistic works under the laws of Canada. Canada passed its first colonial copyright statute in 1832 but was subject to imperial copyright law established by Britain until 1921. Current copyright law was established by the Copyright Act of Canada which was first passed in 1921 and substantially amended in 1988, 1997, and 2012. All powers to legislate copyright law are in the jurisdiction of the Parliament of Canada by virtue of section 91(23) of the Constitution Act, 1867.
Copyright symbolThe copyright symbol, or copyright sign, (a circled capital letter C for copyright), is the symbol used in copyright notices for works other than sound recordings. The use of the symbol is described by the Universal Copyright Convention. The symbol is widely recognized but, under the Berne Convention, is no longer required in most nations to assert a new copyright. In the United States, the Berne Convention Implementation Act of 1988, effective March 1, 1989, removed the requirement for the copyright symbol from U.
Regularization (mathematics)In mathematics, statistics, finance, computer science, particularly in machine learning and inverse problems, regularization is a process that changes the result answer to be "simpler". It is often used to obtain results for ill-posed problems or to prevent overfitting. Although regularization procedures can be divided in many ways, the following delineation is particularly helpful: Explicit regularization is regularization whenever one explicitly adds a term to the optimization problem.
Artificial intelligenceArtificial intelligence (AI) is the intelligence of machines or software, as opposed to the intelligence of human beings or animals. AI applications include advanced web search engines (e.g., Google Search), recommendation systems (used by YouTube, Amazon, and Netflix), understanding human speech (such as Siri and Alexa), self-driving cars (e.g., Waymo), generative or creative tools (ChatGPT and AI art), and competing at the highest level in strategic games (such as chess and Go).
Iterative methodIn computational mathematics, an iterative method is a mathematical procedure that uses an initial value to generate a sequence of improving approximate solutions for a class of problems, in which the n-th approximation is derived from the previous ones. A specific implementation with termination criteria for a given iterative method like gradient descent, hill climbing, Newton's method, or quasi-Newton methods like BFGS, is an algorithm of the iterative method.
AutoencoderAn autoencoder is a type of artificial neural network used to learn efficient codings of unlabeled data (unsupervised learning). An autoencoder learns two functions: an encoding function that transforms the input data, and a decoding function that recreates the input data from the encoded representation. The autoencoder learns an efficient representation (encoding) for a set of data, typically for dimensionality reduction. Variants exist, aiming to force the learned representations to assume useful properties.
Euler methodIn mathematics and computational science, the Euler method (also called the forward Euler method) is a first-order numerical procedure for solving ordinary differential equations (ODEs) with a given initial value. It is the most basic explicit method for numerical integration of ordinary differential equations and is the simplest Runge–Kutta method. The Euler method is named after Leonhard Euler, who first proposed it in his book Institutionum calculi integralis (published 1768–1870).
Complexity classIn computational complexity theory, a complexity class is a set of computational problems "of related resource-based complexity". The two most commonly analyzed resources are time and memory. In general, a complexity class is defined in terms of a type of computational problem, a model of computation, and a bounded resource like time or memory. In particular, most complexity classes consist of decision problems that are solvable with a Turing machine, and are differentiated by their time or space (memory) requirements.
Generative modelIn statistical classification, two main approaches are called the generative approach and the discriminative approach. These compute classifiers by different approaches, differing in the degree of statistical modelling. Terminology is inconsistent, but three major types can be distinguished, following : A generative model is a statistical model of the joint probability distribution on given observable variable X and target variable Y; A discriminative model is a model of the conditional probability of the target Y, given an observation x; and Classifiers computed without using a probability model are also referred to loosely as "discriminative".
Adversarial machine learningAdversarial machine learning is the study of the attacks on machine learning algorithms, and of the defenses against such attacks. A survey from May 2020 exposes the fact that practitioners report a dire need for better protecting machine learning systems in industrial applications. To understand, note that most machine learning techniques are mostly designed to work on specific problem sets, under the assumption that the training and test data are generated from the same statistical distribution (IID).
Fair dealingFair dealing is a limitation and exception to the exclusive rights granted by copyright law to the author of a creative work. Fair dealing is found in many of the common law jurisdictions of the Commonwealth of Nations. Fair dealing is an enumerated set of possible defences against an action for infringement of an exclusive right of copyright. Unlike the related United States doctrine of fair use, fair dealing cannot apply to any act which does not fall within one of these categories, although common law courts in some jurisdictions are less stringent than others in this regard.
Parameterized complexityIn computer science, parameterized complexity is a branch of computational complexity theory that focuses on classifying computational problems according to their inherent difficulty with respect to multiple parameters of the input or output. The complexity of a problem is then measured as a function of those parameters. This allows the classification of NP-hard problems on a finer scale than in the classical setting, where the complexity of a problem is only measured as a function of the number of bits in the input.
