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  Right to explanation

  In the regulation of algorithms, particularly artificial intelligence and its subfield of machine learning, a right to [an] explanation is a right to be given an explanation for an output of the algorithm. Such rights primarily refer to individual rights to be given an explanation for decisions that significantly affect an individual, particularly legally or financially. For example, a person who applies for a loan and is denied may ask for an explanation, which could be "Credit bureau X reports that you declared bankruptcy last year; this is the main factor in considering you too likely to default, and thus we will not give you the loan you applied for." Some such legal rights already exist, while the scope of a general "right to explanation" is a matter of ongoing debate. There have been arguments made that a "social right to explanation" is a crucial foundation for an information society, particularly as the institutions of that society will need to use digital technologies, artificial intelligence, machine learning. In other words, that the related automated decision making systems that use explainability would be more trustworthy and transparent. Without this right, which could be constituted both legally and through professional standards, the public will be left without much recourse to challenge the decisions of automated systems. == Examples == === Credit scoring in the United States === Under the Equal Credit Opportunity Act (Regulation B of the Code of Federal Regulations), Title 12, Chapter X, Part 1002, §1002.9, creditors are required to notify applicants who are denied credit with specific reasons for the detail. As detailed in §1002.9(b)(2): (2) Statement of specific reasons. The statement of reasons for adverse action required by paragraph (a)(2)(i) of this section must be specific and indicate the principal reason(s) for the adverse action. Statements that the adverse action was based on the creditor's internal standards or policies or that the applicant, joint applicant, or similar party failed to achieve a qualifying score on the creditor's credit scoring system are insufficient. The official interpretation of this section details what types of statements are acceptable. Creditors comply with this regulation by providing a list of reasons (generally at most 4, per interpretation of regulations), consisting of a numeric reason code (as identifier) and an associated explanation, identifying the main factors affecting a credit score. An example might be: 32: Balances on bankcard or revolving accounts too high compared to credit limits === European Union === The European Union General Data Protection Regulation (GDPR, enacted 2016, taking effect 2018) extends the automated decision-making rights in the 1995 Data Protection Directive to provide a legally disputed form of a right to an explanation, stated as such in Recital 71: "[the data subject should have] the right ... to obtain an explanation of the decision reached". In full: The data subject should have the right not to be subject to a decision, which may include a measure, evaluating personal aspects relating to him or her which is based solely on automated processing and which produces legal effects concerning him or her or similarly significantly affects him or her, such as automatic refusal of an online credit application or e-recruiting practices without any human intervention. ... In any case, such processing should be subject to suitable safeguards, which should include specific information to the data subject and the right to obtain human intervention, to express his or her point of view, to obtain an explanation of the decision reached after such assessment and to challenge the decision. However, the extent to which the regulations themselves provide a "right to explanation" is heavily debated. There are two main strands of criticism. There are significant legal issues with the right as found in Article 22 — as recitals are not binding, and the right to an explanation is not mentioned in the binding articles of the text, having been removed during the legislative process. In addition, there are significant restrictions on the types of automated decisions that are covered — which must be both "solely" based on automated processing, and have legal or similarly significant effects — which significantly limits the range of automated systems and decisions to which the right would apply. In particular, the right is unlikely to apply in many of the cases of algorithmic controversy that have been picked up in the media. The UK has also recently amended its implementation of Article 22. A second potential source of such a right has been pointed to in Article 15, the "right of access by the data subject". This restates a similar provision from the 1995 Data Protection Directive, allowing the data subject access to "meaningful information about the logic involved" in the same significant, solely automated decision-making, found in Article 22. Yet this too suffers from alleged challenges that relate to the timing of when this right can be drawn upon, as well as practical challenges that mean it may not be binding in many cases of public concern. Other EU legislative instruments contain explanation rights. The European Union's Artificial Intelligence Act provides in Article 86 a "[r]ight to explanation of individual decision-making" of certain high risk systems which produce significant, adverse effects to an individual's health, safety or fundamental rights. The right provides for "clear and meaningful explanations of the role of the AI system in the decision-making procedure and the main elements of the decision taken", although only applies to the extent other law does not provide such a right. The Digital Services Act in Article 27, and the Platform to Business Regulation in Article 5, both contain rights to have the main parameters of certain recommender systems to be made clear, although these provisions have been criticised as not matching the way that such systems work. The Platform Work Directive, which provides for regulation of automation in gig economy work as an extension of data protection law, further contains explanation provisions in Article 11, using the specific language of "explanation" in a binding article rather than a recital as is the case in the GDPR. Scholars note that remains uncertainty as to whether these provisions imply sufficiently tailored explanation in practice which will need to be resolved by courts. === France === In France the 2016 Loi pour une République numérique (Digital Republic Act or loi numérique) amends the country's administrative code to introduce a new provision for the explanation of decisions made by public sector bodies about individuals. It notes that where there is "a decision taken on the basis of an algorithmic treatment", the rules that define that treatment and its "principal characteristics" must be communicated to the citizen upon request, where there is not an exclusion (e.g. for national security or defence). These should include the following: the degree and the mode of contribution of the algorithmic processing to the decision- making; the data processed and its source; the treatment parameters, and where appropriate, their weighting, applied to the situation of the person concerned; the operations carried out by the treatment. Scholars have noted that this right, while limited to administrative decisions, goes beyond the GDPR right to explicitly apply to decision support rather than decisions "solely" based on automated processing, as well as provides a framework for explaining specific decisions. Indeed, the GDPR automated decision-making rights in the European Union, one of the places a "right to an explanation" has been sought within, find their origins in French law in the late 1970s. == Criticism == Some argue that a "right to explanation" is at best unnecessary, at worst harmful, and threatens to stifle innovation. Specific criticisms include: favoring human decisions over machine decisions, being redundant with existing laws, and focusing on process over outcome. Authors of study "Slave to the Algorithm? Why a 'Right to an Explanation' Is Probably Not the Remedy You Are Looking For" Lilian Edwards and Michael Veale argue that a right to explanation is not the solution to harms caused to stakeholders by algorithmic decisions. They also state that the right of explanation in the GDPR is narrowly defined, and is not compatible with how modern machine learning technologies are being developed. With these limitations, defining transparency within the context of algorithmic accountability remains a problem. For example, providing the source code of algorithms may not be sufficient and may create other problems in terms of privacy disclosures and the gaming of technical systems. To mitigate this issue, Edwards and Veale argue that an auditing system could be more effective, to allow auditors to loo

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  Evolutionary computation

  Evolutionary computation (EC) from computer science is a family of algorithms for global optimization inspired by biological evolution, and a subfield of computational intelligence and soft computing studying these algorithms. In technical terms, they are a family of population-based trial and error problem solvers with a metaheuristic or stochastic optimization character. In evolutionary computation, an initial set of candidate solutions is generated and iteratively updated. Each new generation is produced by stochastically removing less desired solutions, and introducing small random changes as well as, depending on the method, mixing parental information. In biological terminology, a population of solutions is subjected to natural selection (or artificial selection), mutation and possibly recombination. These biological functions serve as role models for the genetic operators - mutation, crossover, and selection - used in the EC procedures. As a result, the population will gradually evolve to increase in fitness, in this case the chosen fitness function of the algorithm. Evolutionary computation techniques can produce highly optimized solutions in a wide range of problem settings, making them popular in computer science. Many variants and extensions exist, suited to more specific families of problems and data structures. Evolutionary computation is also sometimes used in evolutionary biology as an in silico experimental procedure to study common aspects of general evolutionary processes. == History == The concept of mimicking evolutionary processes to solve problems originates before the advent of computers, such as when Alan Turing proposed a method of genetic search in 1948 . Turing's B-type u-machines resemble primitive neural networks, and connections between neurons were learnt via a sort of genetic algorithm. His P-type u-machines resemble a method for reinforcement learning, where pleasure and pain signals direct the machine to learn certain behaviors. However, Turing's paper went unpublished until 1968, and he died in 1954, so this early work had little to no effect on the field of evolutionary computation that was to develop. Evolutionary computing as a field began in earnest in the 1950s and 1960s. There were several independent attempts to use the process of evolution in computing at this time, which developed separately for roughly 15 years. Three branches emerged in different places to attain this goal: evolution strategies, evolutionary programming, and genetic algorithms. A fourth branch, genetic programming, eventually emerged in the early 1990s. These approaches differ in the method of selection, the permitted mutations, and the representation of genetic data. By the 1990s, the distinctions between the historic branches had begun to blur, and the term 'evolutionary computing' was coined in 1991 to denote a field that exists over all four paradigms. In 1962, Lawrence J. Fogel initiated the research of Evolutionary Programming in the United States, which was considered an artificial intelligence endeavor. In this system, finite state machines are used to solve a prediction problem: these machines would be mutated (adding or deleting states, or changing the state transition rules), and the best of these mutated machines would be evolved further in future generations. The final finite state machine may be used to generate predictions when needed. The evolutionary programming method was successfully applied to prediction problems, system identification, and automatic control. It was eventually extended to handle time series data and to model the evolution of gaming strategies. In 1964, Ingo Rechenberg and Hans-Paul Schwefel introduce the paradigm of evolution strategies in Germany. Since traditional gradient descent techniques produce results that may get stuck in local minima, Rechenberg and Schwefel proposed that random mutations (applied to all parameters of some solution vector) may be used to escape these minima. Child solutions were generated from parent solutions, and the more successful of the two was kept for future generations. This technique was first used by the two to successfully solve optimization problems in fluid dynamics. Initially, this optimization technique was performed without computers, instead relying on dice to determine random mutations. By 1965, the calculations were performed wholly by machine. John Henry Holland introduced genetic algorithms in the 1960s, and it was further developed at the University of Michigan in the 1970s. While the other approaches were focused on solving problems, Holland primarily aimed to use genetic algorithms to study adaptation and determine how it may be simulated. Populations of chromosomes, represented as bit strings, were transformed by an artificial selection process, selecting for specific 'allele' bits in the bit string. Among other mutation methods, interactions between chromosomes were used to simulate the recombination of DNA between different organisms. While previous methods only tracked a single optimal organism at a time (having children compete with parents), Holland's genetic algorithms tracked large populations (having many organisms compete each generation). By the 1990s, a new approach to evolutionary computation that came to be called genetic programming emerged, advocated for by John Koza among others. In this class of algorithms, the subject of evolution was itself a program written in a high-level programming language (there had been some previous attempts as early as 1958 to use machine code, but they met with little success). For Koza, the programs were Lisp S-expressions, which can be thought of as trees of sub-expressions. This representation permits programs to swap subtrees, representing a sort of genetic mixing. Programs are scored based on how well they complete a certain task, and the score is used for artificial selection. Sequence induction, pattern recognition, and planning were all successful applications of the genetic programming paradigm. Many other figures played a role in the history of evolutionary computing, although their work did not always fit into one of the major historical branches of the field. The earliest computational simulations of evolution using evolutionary algorithms and artificial life techniques were performed by Nils Aall Barricelli in 1953, with first results published in 1954. Another pioneer in the 1950s was Alex Fraser, who published a series of papers on simulation of artificial selection. As academic interest grew, dramatic increases in the power of computers allowed practical applications, including the automatic evolution of computer programs. Evolutionary algorithms are now used to solve multi-dimensional problems more efficiently than software produced by human designers, and also to optimize the design of systems. == Techniques == Evolutionary computing techniques mostly involve metaheuristic optimization algorithms. Broadly speaking, the field includes: Agent-based modeling Ant colony optimization Particle swarm optimization Swarm intelligence Artificial immune systems Artificial life Digital organism Cultural algorithms Differential evolution Dual-phase evolution Estimation of distribution algorithm Evolutionary algorithm Genetic algorithm Evolutionary programming Genetic programming Gene expression programming Grammatical evolution Evolution strategy Learnable evolution model Learning classifier system Memetic algorithms Neuroevolution Self-organization such as self-organizing maps, competitive learning Over recent years many dubious algorithms have been proposed, that are often just copies of existing algorithms (frequently Particle Swarm Optimization), where only the metaphor changed, but the algorithm itself is not new at all. A thorough catalogue with many of these dubious algorithms has been published in the Evolutionary Computation Bestiary. It is also important to note that many of these dubiously 'novel' algorithms have poor experimental validation. == Evolutionary algorithms == Evolutionary algorithms form a subset of evolutionary computation in that they generally only involve techniques implementing mechanisms inspired by biological evolution such as reproduction, mutation, recombination and natural selection. Candidate solutions to the optimization problem play the role of individuals in a population, and the cost function determines the environment within which the solutions "live" (see also fitness function). Evolution of the population then takes place after the repeated application of the above operators. In this process, there are two main forces that form the basis of evolutionary systems: Recombination (e.g. crossover) and mutation create the necessary diversity and thereby facilitate novelty, while selection acts as a force increasing quality. Many aspects of such an evolutionary process are stochastic. Changed pieces of information due to recombination and mutati

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  Artificial intelligence engineering

  Artificial intelligence engineering (AI engineering) is a technical discipline that focuses on the design, development, and deployment of AI systems. AI engineering involves applying engineering principles and methodologies to create scalable, efficient, and reliable AI-based solutions. It merges aspects of data engineering and software engineering to create real-world applications in diverse domains such as healthcare, finance, autonomous systems, and industrial automation. == Terminology ambiguity == According to Chip Huyen's book AI Engineering: Building Applications with Foundation Models, the term AI engineering refers to the process of building applications that use foundation models, which are typically models developed by a small number of research laboratories and made available as a service. Huyen distinguishes this from machine learning (ML) engineering, which involves building and deploying models developed in-house. She notes that most practical AI systems combine both approaches. For example, a customer-support chatbot may use a generative model to produce responses while also incorporating locally built components such as request classifiers or scoring mechanisms to assess response quality. As a result, the terms AI engineering and ML engineering are often used together or interchangeably in practice. The distinction and broader usage of the term have been discussed in industry publications and interviews, where AI engineering has been described as an emerging discipline focused on productionizing applications built with foundation models. == Key components == AI engineering integrates a variety of technical domains and practices, all of which are essential to building scalable, reliable, and ethical AI systems. === Data engineering and infrastructure === Data serves as the cornerstone of AI systems, necessitating careful engineering to ensure premium quality, wide spread availability, and usability. AI engineers gather large, diverse datasets from multiple sources such as databases, APIs, and real-time streams. This data undergoes cleaning, normalization, and preprocessing, often facilitated by automated data pipelines that manage extraction, transformation, and loading (ETL) processes. Efficient storage solutions, such as SQL (or NoSQL) databases and data lakes, must be selected based on data characteristics and use cases. Security measures, including encryption and access controls, are critical for protecting sensitive information and ensuring compliance with regulations like GDPR. Scalability is essential, frequently involving cloud services and distributed computing frameworks to handle growing data volumes effectively. === Algorithm selection and optimization === Selecting the appropriate algorithm is crucial for the success of any AI system. Engineers evaluate the problem (which could be classification or regression, for example) to determine the most suitable machine learning algorithm, including deep learning paradigms. Once an algorithm is chosen, optimizing it through hyperparameter tuning is essential to enhance efficiency and accuracy. Techniques such as grid search or Bayesian optimization are employed, and engineers often utilize parallelization to expedite training processes, particularly for large models and datasets. For existing models, techniques like transfer learning can be applied to adapt pre-trained models for specific tasks, reducing the time and resources needed for training. === Deep learning engineering === Deep learning is particularly important for tasks involving large and complex datasets. Engineers design neural network architectures tailored to specific applications, such as convolutional neural networks for visual tasks or recurrent neural networks for sequence-based tasks. Transfer learning, where pre-trained models are fine-tuned for specific use cases, helps streamline development and often enhances performance. Optimization for deployment in resource-constrained environments, such as mobile devices, involves techniques like pruning and quantization to minimize model size while maintaining performance. Engineers also mitigate data imbalance through augmentation and synthetic data generation, ensuring robust model performance across various classes. === Natural language processing === Natural language processing (NLP) is a crucial component of AI engineering, focused on enabling machines to understand and generate human language. The process begins with text preprocessing to prepare data for machine learning models. Recent advancements, particularly transformer-based models like BERT and GPT, have greatly improved the ability to understand context in language. AI engineers work on various NLP tasks, including sentiment analysis, machine translation, and information extraction. These tasks require sophisticated models that utilize attention mechanisms to enhance accuracy. Applications range from virtual assistants and chatbots to more specialized tasks like named-entity recognition (NER) and Part of speech (POS) tagging. === Reasoning and decision-making systems === Developing systems capable of reasoning and decision-making is a significant aspect of AI engineering. Whether starting from scratch or building on existing frameworks, engineers create solutions that operate on data or logical rules. Symbolic AI employs formal logic and predefined rules for inference, while probabilistic reasoning techniques like Bayesian networks help address uncertainty. These models are essential for applications in dynamic environments, such as autonomous vehicles, where real-time decision-making is critical. === Security === Security is a critical consideration in AI engineering, particularly as AI systems become increasingly integrated into sensitive and mission-critical applications. AI engineers implement robust security measures to protect models from adversarial attacks, such as evasion and poisoning, which can compromise system integrity and performance. Techniques such as adversarial training, where models are exposed to malicious inputs during development, help harden systems against these attacks. Additionally, securing the data used to train AI models is of paramount importance. Encryption, secure data storage, and access control mechanisms are employed to safeguard sensitive information from unauthorized access and breaches. AI systems also require constant monitoring to detect and mitigate vulnerabilities that may arise post-deployment. In high-stakes environments like autonomous systems and healthcare, engineers incorporate redundancy and fail-safe mechanisms to ensure that AI models continue to function correctly in the presence of security threats. === Ethics and compliance === As AI systems increasingly influence societal aspects, ethics and compliance are vital components of AI engineering. Engineers design models to mitigate risks such as data poisoning and ensure that AI systems adhere to legal frameworks, such as data protection regulations like GDPR. Privacy-preserving techniques, including data anonymization and differential privacy, are employed to safeguard personal information and ensure compliance with international standards. Ethical considerations focus on reducing bias in AI systems, preventing discrimination based on race, gender, or other protected characteristics. By developing fair and accountable AI solutions, engineers contribute to the creation of technologies that are both technically sound and socially responsible. == Workload == An AI engineer's workload revolves around the AI system's life cycle, which is a complex, multi-stage process. This process may involve building models from scratch or using pre-existing models through transfer learning, depending on the project's requirements. Each approach presents unique challenges and influences the time, resources, and technical decisions involved. === Problem definition and requirements analysis === Regardless of whether a model is built from scratch or based on a pre-existing model, the work begins with a clear understanding of the problem. The engineer must define the scope, understand the business context, and identify specific AI objectives that align with strategic goals. This stage includes consulting with stakeholders to establish key performance indicators (KPIs) and operational requirements. When developing a model from scratch, the engineer must also decide which algorithms are most suitable for the task. Conversely, when using a pre-trained model, the workload shifts toward evaluating existing models and selecting the one most aligned with the task. The use of pre-trained models often allows for a more targeted focus on fine-tuning, as opposed to designing an entirely new model architecture. === Data acquisition and preparation === Data acquisition and preparation are critical stages regardless of the development method chosen, as the performance of any AI system relies heavily on high-quality, re

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  Vagueness

  In linguistics and philosophy, a vague predicate is one which gives rise to borderline cases. For example, the English adjective "tall" is vague since it is not clearly true or false for someone of middling height. By contrast, the word "prime" is not vague since every number is definitively either prime or not. Vagueness is commonly diagnosed by a predicate's ability to give rise to the sorites paradox. Vagueness is separate from ambiguity, in which an expression has multiple denotations. For instance the word "bank" is ambiguous since it can refer either to a river bank or to a financial institution, but there are no borderline cases between both interpretations. Vagueness is a major topic of research in philosophical logic, where it serves as a potential challenge to classical logic. Work in formal semantics has sought to provide a compositional semantics for vague expressions in natural language. Work in philosophy of language has addressed implications of vagueness for the theory of meaning, while metaphysicists have considered whether reality itself is vague. == Importance == The concept of vagueness has philosophical importance. Suppose one wants to come up with a definition of "right" in the moral sense. One wants a definition to cover actions that are clearly right and exclude actions that are clearly wrong, but what does one do with the borderline cases? Surely, there are such cases. Some philosophers say that one should try to come up with a definition that is itself unclear on just those cases. Others say that one has an interest in making his or her definitions more precise than ordinary language, or his or her ordinary concepts, themselves allow; they recommend one advances precising definitions. === In law === Vagueness is also a problem which arises in law, and in some cases, judges have to arbitrate regarding whether a borderline case does, or does not, satisfy a given vague concept. Examples include disability (how much loss of vision is required before one is legally blind?), human life (at what point from conception to birth is one a legal human being, protected for instance by laws against murder?), adulthood (most familiarly reflected in legal ages for driving, drinking, voting, consensual sex, etc.), race (how to classify someone of mixed racial heritage), etc. Even such apparently unambiguous concepts such as biological sex can be subject to vagueness problems, not just from transsexuals' gender transitions but also from certain genetic conditions which can give an individual mixed male and female biological traits (see intersex). In the common law system, vagueness is a possible legal defence against by-laws and other regulations. The legal principle is that delegated power cannot be used more broadly than the delegator intended. Therefore, a regulation may not be so vague as to regulate areas beyond what the law allows. Any such regulation would be "void for vagueness" and unenforceable. This principle is sometimes used to strike down municipal by-laws that forbid "explicit" or "objectionable" contents from being sold in a certain city; courts often find such expressions to be too vague, giving municipal inspectors discretion beyond what the law allows. In the US this is known as the vagueness doctrine and in Europe as the principle of legal certainty. === In science === Many scientific concepts are of necessity vague, for instance species in biology cannot be precisely defined, owing to unclear cases such as ring species. Nonetheless, the concept of species can be clearly applied in the vast majority of cases. As this example illustrates, to say that a definition is "vague" is not necessarily a criticism. Consider those animals in Alaska that are the result of breeding huskies and wolves: are they dogs? It is not clear: they are borderline cases of dogs. This means one's ordinary concept of doghood is not clear enough to let us rule conclusively in this case. == Approaches == The philosophical question of what the best theoretical treatment of vagueness is—which is closely related to the problem of the paradox of the heap, a.k.a. sorites paradox—has been the subject of much philosophical debate. === Fuzzy logic === One theoretical approach is that of fuzzy logic, developed by American mathematician Lotfi Zadeh. Fuzzy logic proposes a gradual transition between "perfect falsity", for example, the statement "Bill Clinton is bald", to "perfect truth", for, say, "Patrick Stewart is bald". In ordinary logics, there are only two truth-values: "true" and "false". The fuzzy perspective differs by introducing an infinite number of truth-values along a spectrum between perfect truth and perfect falsity. Perfect truth may be represented by "1", and perfect falsity by "0". Borderline cases are thought of as having a "truth-value" anywhere between 0 and 1 (for example, 0.6). Advocates of the fuzzy logic approach have included K. F. Machina (1976) and Dorothy Edgington (1993). === Supervaluationism === Another theoretical approach is known as "supervaluationism". This approach has been defended by Kit Fine and Rosanna Keefe. Fine argues that borderline applications of vague predicates are neither true nor false, but rather are instances of "truth value gaps". He defends an interesting and sophisticated system of vague semantics, based on the notion that a vague predicate might be "made precise" in many alternative ways. This system has the consequence that borderline cases of vague terms yield statements that are neither true, nor false. Given a supervaluationist semantics, one can define the predicate "supertrue" as meaning "true on all precisifications". This predicate will not change the semantics of atomic statements (e.g. "Frank is bald", where Frank is a borderline case of baldness), but does have consequences for logically complex statements. In particular, the tautologies of sentential logic, such as "Frank is bald or Frank is not bald", will turn out to be supertrue, since on any precisification of baldness, either "Frank is bald" or "Frank is not bald" will be true. Since the presence of borderline cases seems to threaten principles like this one (excluded middle), the fact that supervaluationism can "rescue" them is seen as a virtue. === Subvaluationism === Subvaluationism is the logical dual of supervaluationism, and has been defended by Dominic Hyde (2008) and Pablo Cobreros (2011). Whereas the supervaluationist characterises truth as 'supertruth', the subvaluationist characterises truth as 'subtruth', or "true on at least some precisifications". Subvaluationism proposes that borderline applications of vague terms are both true and false. It thus has "truth-value gluts". According to this theory, a vague statement is true if it is true on at least one precisification and false if it is false under at least one precisification. If a vague statement comes out true under one precisification and false under another, it is both true and false. Subvaluationism ultimately amounts to the claim that vagueness is a truly contradictory phenomenon. Of a borderline case of "bald man" it would be both true and false to say that he is bald, and both true and false to say that he is not bald. === Epistemicist view === A fourth approach, known as "the epistemicist view", has been defended by Timothy Williamson (1994), R. A. Sorensen (1988) and (2001), and Nicholas Rescher (2009). They maintain that vague predicates do, in fact, draw sharp boundaries, but that one cannot know where these boundaries lie. One's confusion about whether some vague word does or does not apply in a borderline case is due to one's ignorance. For example, in the epistemicist view, there is a fact of the matter, for every person, about whether that person is old or not old; some people are ignorant of this fact. === As a property of objects === One possibility is that one's words and concepts are perfectly precise, but that objects themselves are vague. Consider Peter Unger's example of a cloud (from his famous 1980 paper, "The Problem of the Many"): it is not clear where the boundary of a cloud lies; for any given bit of water vapor, one can ask whether it is part of the cloud or not, and for many such bits, one will not know how to answer. Hence, perhaps such a term as 'cloud' is not itself vague, but rather precisely denotes a vague object. This strategy has occasionally been poorly received; most notably, in Gareth Evans' short paper "Can There Be Vague Objects?" (1978), wherein an argument is examined which appears to show that vague identity-statements are impossible (i.e., result in logical incoherence). David Lewis explains that the reader is intended to conclude, with Evans, that—since there clearly are, in fact, meaningful vague identities—any purported proof to the contrary cannot be right; and as the proof relies upon the premise that vague terms precisely denote vague objects, but fails under the view that vague terms reflect a merel

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  Computational intelligence

  In computer science, computational intelligence (CI) refers to concepts, paradigms, algorithms and implementations of systems that are designed to show "intelligent" behavior in complex and changing environments. These systems are aimed at mastering complex tasks in a wide variety of technical or commercial areas and offer solutions that recognize and interpret patterns, control processes, support decision-making or autonomously manoeuvre vehicles or robots in unknown environments, among other things. These concepts and paradigms are characterized by the ability to learn or adapt to new situations, to generalize, to abstract, to discover and associate. Nature-analog or nature-inspired methods play a key role in this. CI approaches primarily address those complex real-world problems for which traditional or mathematical modeling is not appropriate for various reasons: the processes cannot be described exactly with complete knowledge, the processes are too complex for mathematical reasoning, they contain some uncertainties during the process, such as unforeseen changes in the environment or in the process itself, or the processes are simply stochastic in nature. Thus, CI techniques are properly aimed at processes that are ill-defined, complex, nonlinear, time-varying and/or stochastic. A recent definition of the IEEE Computational Intelligence Societey describes CI as the theory, design, application and development of biologically and linguistically motivated computational paradigms. Traditionally the three main pillars of CI have been Neural Networks, Fuzzy Systems and Evolutionary Computation. ... CI is an evolving field and at present in addition to the three main constituents, it encompasses computing paradigms like ambient intelligence, artificial life, cultural learning, artificial endocrine networks, social reasoning, and artificial hormone networks. ... Over the last few years there has been an explosion of research on Deep Learning, in particular deep convolutional neural networks. Nowadays, deep learning has become the core method for artificial intelligence. In fact, some of the most successful AI systems are based on CI. However, as CI is an emerging and developing field there is no final definition of CI, especially in terms of the list of concepts and paradigms that belong to it. The general requirements for the development of an “intelligent system” are ultimately always the same, namely the simulation of intelligent thinking and action in a specific area of application. To do this, the knowledge about this area must be represented in a model so that it can be processed. The quality of the resulting system depends largely on how well the model was chosen in the development process. Sometimes data-driven methods are suitable for finding a good model and sometimes logic-based knowledge representations deliver better results. Hybrid models are usually used in real applications. According to actual textbooks, the following methods and paradigms, which largely complement each other, can be regarded as parts of CI: Fuzzy systems Neural networks and, in particular, convolutional neural networks Evolutionary computation and, in particular, multi-objective evolutionary optimization Swarm intelligence Bayesian networks Artificial immune systems Learning theory Probabilistic methods == Relationship between hard and soft computing and artificial and computational intelligence == Artificial intelligence (AI) is used in the media, but also by some of the scientists involved, as a kind of umbrella term for the various techniques associated with it or with CI. Craenen and Eiben state that attempts to define or at least describe CI can usually be assigned to one or more of the following groups: "Relative definition” comparing CI to AI Conceptual treatment of key notions and their roles in CI Listing of the (established) areas that belong to it The relationship between CI and AI has been a frequently discussed topic during the development of CI. While the above list implies that they are synonyms, the vast majority of AI/CI researchers working on the subject consider them to be distinct fields, where either CI is an alternative to AI AI includes CI CI includes AI The view of the first of the above three points goes back to Zadeh, the founder of the fuzzy set theory, who differentiated machine intelligence into hard and soft computing techniques, which are used in artificial intelligence on the one hand and computational intelligence on the other. In hard computing (HC) and traditional AI (e.g. expert systems), inaccuracy and uncertainty are undesirable characteristics of a system, while soft computing (SC) and thus CI focus on dealing with these characteristics. The adjacent figure illustrates this view and lists the most important CI techniques. Another frequently mentioned distinguishing feature is the representation of information in symbolic form in AI and in sub-symbolic form in CI techniques. Hard computing is a conventional computing method based on the principles of certainty and accuracy and it is deterministic. It requires a precisely stated analytical model of the task to be processed and a prewritten program, i.e. a fixed set of instructions. The models used are based on Boolean logic (also called crisp logic), where e.g. an element can be either a member of a set or not and there is nothing in between. When applied to real-world tasks, systems based on HC result in specific control actions defined by a mathematical model or algorithm. If an unforeseen situation occurs that is not included in the model or algorithm used, the action will most likely fail. Soft computing, on the other hand, is based on the fact that the human mind is capable of storing information and processing it in a goal-oriented way, even if it is imprecise and lacks certainty. SC is based on the model of the human brain with probabilistic thinking, fuzzy logic and multi-valued logic. Soft computing can process a wealth of data and perform a large number of computations, which may not be exact, in parallel. For hard problems for which no satisfying exact solutions based on HC are available, SC methods can be applied successfully. SC methods are usually stochastic in nature i.e., they are a randomly defined processes that can be analyzed statistically but not with precision. Up to now, the results of some CI methods, such as deep learning, cannot be verified and it is also not clear what they are based on. This problem represents an important scientific issue for the future. AI and CI are catchy terms, but they are also so similar that they can be confused. The meaning of both terms has developed and changed over a long period of time, with AI being used first. Bezdek describes this impressively and concludes that such buzzwords are frequently used and hyped by the scientific community, science management and (science) journalism. Not least because AI and biological intelligence are emotionally charged terms and it is still difficult to find a generally accepted definition for the basic term intelligence. == History == In 1950, Alan Turing, one of the founding fathers of computer science, developed a test for computer intelligence known as the Turing test. In this test, a person can ask questions via a keyboard and a monitor without knowing whether his counterpart is a human or a computer. A computer is considered intelligent if the interrogator cannot distinguish the computer from a human. This illustrates the discussion about intelligent computers at the beginning of the computer age. The term Computational Intelligence was first used as the title of the journal of the same name in 1985 and later by the IEEE Neural Networks Council (NNC), which was founded 1989 by a group of researchers interested in the development of biological and artificial neural networks. On November 21, 2001, the NNC became the IEEE Neural Networks Society, to become the IEEE Computational Intelligence Society two years later by including new areas of interest such as fuzzy systems and evolutionary computation. The NNC helped organize the first IEEE World Congress on Computational Intelligence in Orlando, Florida in 1994. On this conference the first clear definition of Computational Intelligence was introduced by Bezdek: A system is computationally intelligent when it: deals with only numerical (low-level) data, has pattern-recognition components, does not use knowledge in the AI sense; and additionally when it (begins to) exhibit (1) computational adaptivity; (2) computational fault tolerance; (3) speed approaching human-like turnaround and (4) error rates that approximate human performance. Today, with machine learning and deep learning in particular utilizing a breadth of supervised, unsupervised, and reinforcement learning approaches, the CI landscape has been greatly enhanced, with novell intelligent approaches. == The main algorithmic approaches of CI and their applicati

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  TCEC Season 14

  The 14th season of the Top Chess Engine Championship took place between 17 November 2018 and 24 February 2019. Stockfish was the defending champion, having defeated Komodo in the previous season's superfinal. The season is notable for two things: the emergence of two strong, new engines, the Komodo variant Komodo Monte Carlo tree search (MCTS) and the neural network engine Leela Chess Zero, and the dramatic superfinal. Komodo MCTS and Leela fought their way from Division 4 and Division 3 respectively to the Premier Division, with Leela further qualifying for the superfinal against Stockfish. The superfinal was a topsy-turvy affair with the lead changing hands several times. It finished as the closest superfinal TCEC has ever seen, with Stockfish winning by a single game, 50.5–49.5 (+10 =81 -9). == Overview == === Structure === The season comprised five divisions: from the lowest Division 4 to the Premier Division. The top two engines of each division promote to the division above, while the bottom two engines relegate. The top two engines of the Premier Division contest a 100-game superfinal. The lengths of the opening books used increases as the divisions progress. The superfinal itself used a custom opening book designed by Jeroen Noomen. === Rules === The TCEC draw and win rules were slightly modified for Season 14. The game is now adjudicated as drawn if, after move 30, both engines have evals ±0.08 for five consecutive moves, and there are neither pawn moves nor a capture. Win adjudication now occurs if both engines have an eval of ±10 for five consecutive moves. Following the controversy over DeusX's participation last season, the uniqueness rule for neural networks was modified such that at least two of the following three hallmarks must be unique: The code for training the neural network The neural network (and weights file) itself The engine that executes this network This change meant DeusX did not meet the uniqueness criteria and therefore did not participate. Aside from this change, the season used the standard rules of the TCEC. == Results == === Division 4 === New entrant Komodo MCTS dominated Division 4, winning by a clear four points, although it did lose a game to second-place finisher rofChade. Fellow new entrant Scorpio NN performed badly and finished last, drawing only one game and losing the rest. === Division 3 === The neural network engine Leela Chess Zero had just missed promotion to Division 2 in the previous season. Since its relatively weak performance last season was partly due to hardware problems, and since it had shown a lot of improvement in strength, it was the hot favourite in this division. Leela lived up to its billing by comprehensively defeating everyone else. In a portent of future divisions however, Leela surprisingly dropped a game to third-place Arasan. Komodo MCTS was also improving quickly, and an updated version finished second behind Leela. The gap between second and third was 6.5 points, illustrating the gulf in class. === Division 2 === Although Division 2 engines are significantly stronger than Division 3, Leela and Komodo MCTS continued to dominate the competition, and again finished first and second. Komodo MCTS only lost one game to Leela, while Leela's tendency to occasionally lose to weaker engines saw her losing a game to 4th-placed Booot. Third place finisher Xiphos gave Leela and Komodo MCTS a run for their money, and was in the running up until the final rounds when it lost a crucial game to Leela. This loss left it one point behind Komodo MCTS in the final standings. === Division 1 === Leela and Komodo MCTS's rampage through the lower divisions continued, and they again finished first and second. In a demonstration of how much it had improved, Leela scored 20/28 in this division, the same score it had achieved in Division 2. This was also a TCEC points record for this division. However, Leela dropped a game against fourth-place finisher Chiron. Komodo MCTS, which had yet to lose a game in the lower divisions except to Leela, also conceded its first loss to third-place Fizbo. At the other end of the table, former champions Jonny and Fritz, which had not been updated, found themselves outclassed and finished second-last and last respectively; however with fellow competitor Ginkgo crashing five times (and therefore being disqualified), Jonny managed to stay in the division. The penultimate game for this division set a new TCEC moves record for a decisive game: 308 moves before Leela defeated Fritz. === Premier division === This was the strongest premier division ever, with multiple-time champions Stockfish, Komodo, and Houdini in the mix. Right from the start it became clear that Stockfish was in a league of its own, and it dominated the division, scoring wins against every other engine without losing a game. Second place however was a hotly-contested affair, with Leela, Komodo and Houdini neck-and-neck for most of the division. Houdini took the early lead, but Komodo gained second after winning two games by forfeit when its sibling Komodo MCTS crashed. This led to murmurs of a "Konspiracy". However, when both Komodo and Houdini failed to score more wins against the lower half of the field, Leela was able to take the lead. Halfway through the division the race was upended again when Leela went through a bad streak, losing three games in a row to Stockfish, Komodo, and Fire. This led to Komodo regaining second place, only for Komodo MCTS to crash yet again. By TCEC rules this meant Komodo MCTS was disqualified and all its scores were zeroed out, which put Leela back in second place. With three games left, Leela missed a win against Andscacs, which would've more or less secured her a place in the superfinal. Meanwhile, Komodo kept the division interesting by winning two of its last three games. Because Komodo had superior tiebreakers to Leela, this meant Komodo would qualify for the superfinal unless Leela managed to hold Stockfish to a draw with Black in the last game of the division. In a tense final game, Stockfish came close to winning, but missed the winning line. Leela managed to draw and qualified for the superfinal. At the other end of the table, it was quickly apparent that Ethereal and Andscacs were the weakest engines and would likely relegate. However, when Komodo MCTS was disqualified (and therefore relegated), it threw both engines a lifeline, since they could now stay in the division by beating the other. Andscacs was able to score a head-to-head win against Ethereal, but was crushed by Stockfish (+0 =2 -4) and Leela (+0 =3 -3). Ethereal didn't manage to score a win in the entire division, but did manage to score more draws than Andscacs, condemning Andscacs to relegation. === Superfinal === Going into the superfinal expectations were high for Leela: she had received a new network and had just won her first major competition when she defeated Houdini in the second TCEC cup. However, she had won the tournament without having played Stockfish (who had been surprisingly eliminated by Houdini in the semifinals). That, plus the fact that Stockfish dominated Premier Division and had never lost a match to Leela, left it unclear which engine was superior, although most spectators favored Stockfish. The superfinal turned out to be a roller-coaster. It began with Stockfish drawing first blood in game 7, and then scoring another win in game 10. Leela hit back with wins in game 11 and 13, but then lost games 20, 21, and 22. This gave Stockfish a 3-point lead. However, in the next 30 games, Leela was the only one to score wins: it first equalized by winning games 25, 27, and 29, and then took the lead by winning games 49 and 53. Stockfish won game 56, but Leela won game 63, maintaining her lead. There followed two dramatic games. In game 65, Leela built up a winning position. Stockfish showed a +153 evaluation, indicating that it had found a forced line leading to an endgame tablebase win; indeed analysis with 7-piece tablebases showed that Leela's position was winning. Under previous seasons' rules, the game would have been adjudicated as a win because Leela's evaluation was above 6.5. However under the new rules, Leela's +8.92 evaluation was not enough to adjudicate. It turned out that Leela could not see the winning line, and shuffled her pieces aimlessly, leading to a 50-move draw. In game 66, Stockfish was given a substantial advantage by the opening, but failed to make the most of it. The evaluations were leveling out to zero when the internet connection to the GPU servers was cut off. By tournament rules, this meant the game was replayed from scratch. After a further internet disconnection and restart, Stockfish handled the opening better and won, leaving Leela with a 1-point lead. In the last third of the superfinal, there followed more drama as Leela often built up strong advantages, but Stockfish showed great resourcefulness in defending inferior positions. Meanwh

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  Torment: Tides of Numenera

  Torment: Tides of Numenera is a 2017 role-playing video game developed by inXile Entertainment and published by Techland Publishing for Microsoft Windows, macOS, Linux, PlayStation 4 and Xbox One. It is a spiritual successor to 1999's Planescape: Torment. The game takes place in The Ninth World, a science fantasy campaign setting written by Monte Cook for his tabletop RPG Numenera. Torment: Tides of Numenera, like its predecessor, is primarily story-driven while placing greater emphasis on interaction with the world and characters, with combat and item accumulation taking a secondary role. The game was crowd-funded through Kickstarter in March 2013. At the campaign's conclusion, Torment: Tides of Numenera had set the record for highest-funded video game on Kickstarter with over US$4 million pledged. The release date was initially set for December 2014, but was pushed back to February 2017. == Gameplay == Torment: Tides of Numenera uses the Unity engine to display the pre-rendered 2.5D isometric perspective environments. The tabletop ruleset of Monte Cook's Numenera has been adapted to serve as the game's rule mechanic, and its Ninth World setting is where the events of Torment: Tides of Numenera take place. The player experiences the game from the point of view of the Last Castoff, a human host that was once inhabited by a powerful being, but was suddenly abandoned without memory of prior events. As with its spiritual predecessor, Planescape: Torment, the gameplay of Torment: Tides of Numenera places a large emphasis on storytelling, which unfolds through a "rich, personal narrative", and complex character interaction through the familiar dialog tree system. The player is able to select the gender of the protagonist, who will otherwise start the game as a "blank slate", and may develop his or her skills and personality from their interactions with the world. The Numenera setting provides three base character classes: Glaive (warrior), Nano (wizard) and Jack (rogue). These classes can be further customized with a number of descriptors (such as "Tough" or "Mystical") and foci, which allow the character to excel in a certain role or combat style. Instead of a classic alignment system acting as a character's ethical and moral compass, Torment: Tides of Numenera uses "Tides" to represent the reactions a person inspires in their peers. Each Tide has a specific color and embodies a number of nuanced concepts that are associated with it. The composition of Tides a character has manipulated the most determines their Legacy, which roughly describes the way they have taken in life. Different Legacies may affect what bonuses and powers certain weapons and relics provide, as well as give a character special abilities and enhance certain skills. == Synopsis == === Setting === Tides of Numenera has a science fantasy setting. In the far future (one billion years), the rise and fall of countless civilizations have left Earth in a roughly medieval state, with most of humanity living in simple settlements, surrounded by technological relics of the mysterious past. The current age is called the "Ninth World" by its scholars, who believe that eight great ages existed and were destroyed, disappeared or left the Earth for unknown reasons before the present day, leaving ruins and various oddities and artifacts behind. These artifacts are known as the "numenera" and represent what is left of the science and technology of these past civilizations. Many of them are irreparably broken, but some are still able to function in ways that are beyond the level of understanding of most humans, who believe these objects to be magical in nature. === Characters === Character complexity and dialogue depth were identified among the primary elements of the Planescape: Torment legacy to be preserved and refined by the developers of Torment: Tides of Numenera. The tormented nature of the game's protagonist, the Last Castoff, attracts other, similarly affected people. They will play a significant role in his or her story as friends and companions, or as powerful enemies. The game contains seven companions in total: Aligern, Callistege, Erritis, Matkina, Oom, Tybir, and Rhin. === Plot === The protagonist of the story, known as the Last Castoff, is the final vessel for the consciousness of an ancient man, who managed to find a way to leave his physical body and be reborn in a new one, thus achieving a kind of immortality by means of the relics. The actions of this man, known as the Changing God to some, attracted the enmity of "The Sorrow" (renamed from "The Angel of Entropy" to reduce the potential to imply a religious role), who now seeks to destroy him and his creations. The Last Castoff, being one such "creation", is also targeted by the Sorrow, and must find their master before both are undone. To do so, the protagonist must explore the Ninth World, discovering other castoffs, making friends and enemies along the way. One means of such exploration are the "Meres" – artifacts that let their user gain control over the lives of other castoffs, and experience different worlds or dimensions through them. Through these travels the Last Castoff will leave their mark on the world – their Legacy – and will find an answer to the fundamental question of the story: What does one life matter? While the overall story varies wildly depending on personal preferences and specific interactions, the central storyline follows the Last Castoff as they search for a way to defeat or escape the Sorrow. They explore Sagus Cliffs after falling from a great height into a domed structure, destroying an artifact known as a resonance chamber that is believed to be capable saving the Last Castoff from the Sorrow. Finding another castoff, Matkina, The Last uses a Mere, a repository of memory to locate the entrance to Sanctuary. Using the Mere also alters the past, allowing Matkina to be healed of her mental damage. The Last finds Sanctuary, which the Changing God created as a hiding place from the Sorrow, where the Last finds a number of castoffs who represent both sides of the Eternal War: a conflict between followers of the Changing God, and followers of the First Castoff, who believe the God is selfish and malevolent. The Sorrow breaches Sanctuary after the Last is told that the resonance chamber will "defeat" the Sorrow by destroying every castoff in existence. After escaping the Sorrow through a portal to the Bloom, an apparition appears claiming to be the actual Changing God and attempts to possess the Last by force of will. == Development == In a 2007 interview, designers Chris Avellone and Colin McComb, who had worked on Planescape: Torment, stated that although a direct sequel was not considered because the game's story was over, they were open to the idea of a similar-themed Planescape game if they could gather most of the original development team and find an "understanding set of investors". This combination was deemed infeasible at the time. Talks about creating a sequel with the help of a crowd funding platform resumed in 2012, but attempts to acquire a Planescape license from Wizards of the Coast failed. Later that year, Colin McComb joined inXile, which was at the time working on its successfully crowd funded Wasteland 2 project. The studio gained the rights to the Torment title shortly thereafter. In January 2013, inXile's CEO Brian Fargo announced that the spiritual successor to Planescape: Torment was in pre-production and would be set in the Numenera RPG universe created by Monte Cook. Cook acted as one of the designers of the Planescape setting, and Fargo saw the Numenera setting as the natural place to continue the themes of the previous Torment title. Although the connections to its predecessor will not be relatively overt, due to licensing issues, it was noted that certain traditional RPG elements are relatively hard to copyright, and some elements of Planescape: Torment may make a reappearance. Development of the game began shortly after the acquisition of the Torment license, and various inXile staff will transition over to the Numenera team as production on Wasteland 2 winds down. In late January 2013, inXile confirmed the game's title as Torment: Tides of Numenera, and announced that Planescape: Torment composer Mark Morgan would create the soundtrack. The pre-production period was initially expected to continue until October 2013. During this phase, team composition for the project was to be finalised and development would focus on production planning, game design and dialog writing. With the Wasteland 2 project facing delays in 2014, full production of Torment: Tides of Numenera was rescheduled to a later date. A Kickstarter campaign to crowd fund Torment: Tides of Numenera was launched on March 6, 2013 with a US$900,000 goal. Project director Kevin Saunders explained this choice of a funding source by stating that the traditional publisher-based funding model is flawed

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  Vidby

  Vidby AG (stylized in lower-case) is a start-up based in Rotkreuz, Switzerland specializing in AI language translation for videos. Founded by Alexander Konovalov (uk:Олександр Коновалов) and Eugen von Rubinberg in September 2021, the company has especially garnered attention for its use in translating speeches given by President Volodymyr Zelenskyy during the Russian invasion of Ukraine. == History == Vidby AG was founded by Alexander Konovalov and Eugen von Rubinberg. Konovalov is a native of Ukraine and retains Ukrainian citizenship; Rubinberg came to Switzerland from Germany and holds German citizenship. Both are residents of Switzerland. The latter founded his first business, a trading company, at age 16. In 2013, the business partners launched a consumer-oriented video-call translation service called DROTR (Droid Translator) AG, utilizing a Konovalov-created AI-powered language translation technology enabling simultaneous translation of messages, voice and video calls in 104 languages (written), with 44 available in spoken form. This was the world's first video calling app with translation. The technology was pronounced a competitor of Skype and Viber by Forbes and claimed first prize at the "Innovative Breakthrough 2013" Competition. In 2021, with a new business-oriented focus, DROTR became Vidby, with the former Google technology partners Konovalov and Rubinberg remaining at the helm, each with the title Co-CEO. While headquartered in Switzerland, Vidby's development team is, according to the company's founders, based in Ukraine. The technology behind Vidby has an accuracy level variously reported as up to 99 percent or 99 to 100 percent, equalling the highest level of human translation. Additionally, the technology is capable of removing the original language while maintaining ambient sounds. Currently, some 70 languages plus 60 dialects are possible with the algorithm-based technology. == Notable use == In addition to its use with speeches delivered by Pope Francis, the technology has been provided to Ukrainian authorities and embassies during the ongoing military conflict with Russia free of remuneration. By July, 2022, some 70 speeches given by President Zelenskyy totalling 650 minutes had been translated into 30 languages, for a total of over 10,000 minutes of video material. Of its use in translating Zelenskyy's wartime speeches, Konovalov has said, "Like any citizen, I want to help defend my country." Notable corporate clients of Vidby include Samsung, Siemens, Cisco, Kärcher, Generali and McDonald's Corporation; an academic client is Harvard University. Google Cloud Technology Partner status of Vidby was confirmed officially after a six-month audit in December 2022. Denys Krasnikov, a Vidby co-founder, is responsible for cooperation with Google, YouTube, Microsoft, and other key partners. After the launch of multilingual YouTube channels, Vidby started AI translating and dubbing creators' videos for this new type of channel at the end of February 2023. == Accolades == Vidby headed a list of the five best video translation services as named by TechRadar Deutschland in September, 2022. In the same month, Tech Times named Vidby #1 in their list of the five best such services. It similarly topped a list of the five best content translation technologies as judged by European Business Review in October, 2022. Prior to these lead-position rankings (August, 2022), it was featured as Business Insider's special start-up recommendation (German: "Unser Lesetipp auf Gründerszene"). In 2023, YouTube recognized Vidby as its recommended vendor.

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  Crucible (software)

  Crucible is a collaborative code review application by Australian software company Atlassian. Like other Atlassian products, Crucible is a Web-based application primarily aimed at enterprise, and certain features that enable peer review of a codebase may be considered enterprise social software. Crucible is particularly tailored to remote workers, and facilitates asynchronous review and commenting on code. Crucible also integrates with popular source control tools, such as Git and Subversion. Crucible is not open source, but customers are allowed to view and modify the code for their own use.

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  Argument mining

  Argument mining, or argumentation mining, is a research area within the natural language processing field. The goal of argument mining is the automatic extraction and identification of argumentative structures from natural language text with the aid of computer programs. Such argumentative structures include the premise, conclusions, the argument scheme and the relationship between the main and subsidiary argument, or the main and counter-argument within discourse. The Argument Mining workshop series is the main research forum for argument mining related research. == Applications == Argument mining has been applied in many different genres including the qualitative assessment of social media content (e.g. Twitter, Facebook), where it provides a powerful tool for policy-makers and researchers in social and political sciences. Other domains include legal documents, product reviews, scientific articles, online debates, newspaper articles and dialogical domains. Transfer learning approaches have been successfully used to combine the different domains into a domain agnostic argumentation model. Argument mining has been used to provide students individual writing support by accessing and visualizing the argumentation discourse in their texts. The application of argument mining in a user-centered learning tool helped students to improve their argumentation skills significantly compared to traditional argumentation learning applications. == Challenges == Given the wide variety of text genres and the different research perspectives and approaches, it has been difficult to reach a common and objective evaluation scheme. Many annotated data sets have been proposed, with some gaining popularity, but a consensual data set is yet to be found. Annotating argumentative structures is a highly demanding task. There have been successful attempts to delegate such annotation tasks to the crowd but the process still requires a lot of effort and carries significant cost. Initial attempts to bypass this hurdle were made using the weak supervision approach.

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  Mars Plus

  Mars Plus is a 1994 science fiction novel by American writer Frederik Pohl and Thomas T. Thomas. It is the sequel to Pohl's 1976 novel Man Plus, which is about a cyborg, Roger Torraway, who is designed to operate in the harsh Martian environment, so that humans can start to colonize Mars. Mars Plus is set fifty years after the first novel. Young Demeter Coghlan travels to Mars, now settled by humans and cyborgs, and finds herself amidst a rebellion by the colonists. == Plot == In Man Plus, set in the not-too-distant future, with threat of the Cold War becoming a fighting war, people plan for the colonization of Mars to escape the seemingly-inevitable Armageddon. The American government begins a cyborg program to create a being capable of surviving the harsh Martian environment: a "Man Plus" called Roger Torraway who is converted from man to cyborg. While his cyborg body is adapted to Mars, he feels strange at first. As more nations develop cyborgs, the computer networks of Earth become sentient. Mars Plus is set fifty years after the first novel, when Mars is settled by humans and cyborgs. The cyborg Torroway is in the novel, but he is not the main character. The protagonist is Demeter Coghlan, a young woman from Earth who travels to Mars. Demeter is seeking information about a canyon that she believes may be significant if the colonists begin to convert Mars to an Earth-like planet. Amidst a backdrop of spies and newly dispatched Earth diplomats, the inexperienced Demeter senses that tensions are rising on the planet. She is further disoriented due to recovering from an accident. Despite the risks in the region, Demeter has intense sexual encounters with some of the local colonists. When the locals rebel against the surveillance set up by the computer network, Demeter is kidnapped by the computer network. == Reception == The reviewer from SFBook Reviews criticizes the book, saying "nothing really happens" and stating that there is no linkage to Man Plus apart from the presence of the cyborg Torraway; moreover, the reviewer states that the questions posed in the first novel are not answered. SF Reviews calls Mars Plus "...not as good as Man Plus but...not bad", and it is praised for "...some nice touches: Demeter continuously forgetting to think about geology; her careless dictation to the computer and her irresistible urges for wild sex." SF Reviews criticizes the writing in Mars Plus for being "...a little careless in places" and in need of more "...more crafting and pruning."

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  Mark I Perceptron

  The Mark I Perceptron was a pioneering supervised image classification learning system developed by Frank Rosenblatt in 1958. It was the first implementation of an artificial intelligence (AI) machine. It differs from the Perceptron which is a software architecture proposed in 1943 by Warren McCulloch and Walter Pitts, which was also employed in Mark I, and enhancements of which have continued to be an integral part of cutting edge AI technologies like the Transformer. == Architecture == The Mark I Perceptron was organized into three layers: A set of sensory units which receive optical input A set of association units, each of which fire based on input from multiple sensory units A set of response units, which fire based on input from multiple association units The connection between sensory units and association units were random. The working of association units was very similar to the response units. Different versions of the Mark I used different numbers of units in each of the layers. == Capabilities == In his 1957 proposal for funding for development of the "Cornell Photoperceptron", Rosenblatt claimed:"Devices of this sort are expected ultimately to be capable of concept formation, language translation, collation of military intelligence, and the solution of problems through inductive logic."With the first version of the Mark I Perceptron as early as 1958, Rosenblatt demonstrated a simple binary classification experiment, namely distinguishing between sheets of paper marked on the right versus those marked on the left side. One of the later experiments distinguished a square from a circle printed on paper. The shapes were perfect and their sizes fixed; the only variation was in their position and orientation. The Mark I Perceptron achieved 99.8% accuracy on a test dataset with 500 neurons in a single layer. The size of the training dataset was 10,000 example images. It took 3 seconds for the training pipeline to go through a single image. Higher accuracy was observed with thick outline figures compared to solid figures, likely because outline figures reduced overfitting. Another experiment distinguished between a square and a diamond for which 100% accuracy was achieved with only 60 training images, with a Perceptron having 1,000 neurons in a single layer. The time taken to process each training input for this larger perceptron was 15 seconds. The only variation was in position of the image, since rotation would have been ambiguous. In that same experiment, it could distinguish between the letters X and E with 100% accuracy when trained with only 20 images (10 images of each letter). Variations in the images included both position and rotation by up to 30 degrees. When variation in rotation was increased to any angle (both in training and test datasets), the accuracy reduced to 90% with 60 training images (30 images of each letter). For distinguishing between the letters E and F, a more challenging problem due to their similarity, the same 1,000 neuron perceptron achieved an accuracy of more than 80% with 60 training images. Variation was only in the position of the image, with no rotation.

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  Human-in-the-loop

  Human-in-the-loop (HITL) is used in multiple contexts. It can be defined as a model requiring human interaction. HITL is associated with modeling and simulation (M&S) in the live, virtual, and constructive taxonomy. HITL, along with the related human-on-the-loop, are also used in relation to lethal autonomous weapons. Further, HITL is used in the context of machine learning.It is also used in conversational AI to manage complex interactions that require human empathy. == Machine learning == In machine learning, HITL is used in the sense of humans aiding the computer in making the correct decisions in building a model. HITL improves machine learning over random sampling by selecting the most critical data needed to refine the model. == Simulation == In simulation, HITL models may conform to human factors requirements as in the case of a mockup. In this type of simulation, a human is always part of the simulation and consequently influences the outcome in such a way that is difficult if not impossible to reproduce exactly. HITL also readily allows for the identification of problems and requirements that may not be easily identified by other means of simulation. HITL is often referred to as an interactive simulation, which is a special kind of physical simulation in which physical simulations include human operators, such as in a flight or a driving simulator. === Benefits === Human-in-the-loop allows the user to change the outcome of an event or process. The immersion effectively contributes to a positive transfer of acquired skills into the real world. This can be demonstrated by trainees utilizing flight simulators in preparation to become pilots. HITL also allows for the acquisition of knowledge regarding how a new process may affect a particular event. Utilizing HITL allows participants to interact with realistic models and attempt to perform as they would in an actual scenario. HITL simulations bring to the surface issues that would not otherwise be apparent until after a new process has been deployed. A real-world example of HITL simulation as an evaluation tool is its usage by the Federal Aviation Administration (FAA) to allow air traffic controllers to test new automation procedures by directing the activities of simulated air traffic while monitoring the effect of the newly implemented procedures. As with most processes, there is always the possibility of human error, which can only be reproduced using HITL simulation. Although much can be done to automate systems, humans typically still need to take the information provided by a system to determine the next course of action based on their judgment and experience. Intelligent systems can only go so far in certain circumstances to automate a process; only humans in the simulation can accurately judge the final design. Tabletop simulation may be useful in the very early stages of project development for the purpose of collecting data to set broad parameters, but the important decisions require human-in-the-loop simulation. HITL reflects scenarios where human input remains essential despite advances in automation. === Within the virtual simulation taxonomy === Virtual simulations inject HITL in a central role by exercising motor control skills (e.g. flying an airplane), decision making skills (e.g. committing fire control resources to action), or communication skills (e.g. as members of a C4I team). === Examples === Flight simulators Driving simulators Marine simulators Video games Supply chain management simulators Digital puppetry === Misconceptions === Although human-in-the-loop simulation can include a computer simulation in the form of a synthetic environment, computer simulation is not necessarily a form of human-in-the-loop simulation, and is often considered as human-out-of-the loop simulation. In this particular case, a computer model’s behavior is modified according to a set of initial parameters. The results of the model differ from the results stemming from a true human-in-the-loop simulation because the results can easily be replicated time and time again, by simply providing identical parameters. == Weapons == === Taxonomy === Three classifications of the degree of human control of autonomous weapon systems were laid out by Bonnie Docherty in a 2012 Human Rights Watch report. human-in-the-loop: a human must instigate the action of the weapon (in other words not fully autonomous) human-on-the-loop: a human may abort an action human-out-of-the-loop: no human action is involved === Positive human action === In discussions of autonomous weapons and nuclear command and control, the phrase positive human action has been used alongside "human-in-the-loop" to emphasize that a human operator must affirmatively authorize the use of force. Descriptions of the United States Navy's Aegis Combat System have used the phrase in characterizing a requirement for affirmative human action to initiate live firing. A survey of autonomous weapons systems described the Aegis "Auto SM" mode as one in which "the system fully develops the engagement process however engagement requires positive human action". The phrase entered United States federal law in the National Defense Authorization Act for Fiscal Year 2025, which stipulates that artificial intelligence systems not compromise "the principle of requiring positive human actions in execution of decisions by the President with respect to the employment of nuclear weapons".

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  17776

  17776 (also known as What Football Will Look Like in the Future) is a serialized speculative fiction multimedia narrative by Jon Bois, published online through SB Nation. Set in the distant future in which all humans have become immortal and infertile, the series follows three sapient space probes that watch humanity play an evolved form of American football in which games can be played for millennia over distances of thousands of miles. The series debuted on July 5, 2017, and new chapters were published daily until the series concluded with its twenty-fifth chapter on July 15, 2017. Bois began developing 17776 in 2016. Because the story incorporates text, animated GIFs, still images, and videos hosted on YouTube, new tools were developed to allow it to be hosted efficiently on the SB Nation website. The work explores themes of consciousness, hope, despair, and why humans play sports. 17776 was well received by critics, who praised it for its innovative use of its medium and for the depth of emotion it evoked. In 2018, the story won a National Magazine Award for Digital Innovation and was longlisted for both the Hugo Awards for Best Novella and Best Graphic Story. It is followed by a sequel series: 20020, released from September to October 2020. The sequel series follows a 111-team game of college football on fields spanning 130,000 miles (210,000 km) across the United States. Bois originally intended to follow up with a further series entitled 20021; however, it was postponed indefinitely. In May 2025, Bois announced that the series would be continued with a novel titled 50007: An American Football Odyssey. == Premise == The story takes place on a future Earth where humans stopped dying, aging, and being born on April 7, 2026. All social ills were subsequently eliminated, and technology preventing humans from any injury was developed. In the United States, American football evolved to include new rules, including those that allow fields thousands of miles long, hundreds of in-game players, and games millennia long. Over time, computers gained sentience due to constant exposure to broadcast human data. By the year 17776, the space probe Pioneer 9 (called Nine) has gained sentience and made contact with Pioneer 10 (called Ten) and the Jupiter Icy Moons Explorer (called Juice). As Nine adjusts to a world radically different from that of the 20th century, the three space probes watch multiple football games occurring across the United States: a game using the entirety of Nebraska as a field in which the next point scored wins the game; a game in which players strive to possess every existing football autographed by obscure NFL player Koy Detmer; a game played between the Canadian border and the Mexican border deadlocked for 13,000 years at the bottom of a gorge in Arizona; an NFL regulation game between the Denver Broncos and the Pittsburgh Steelers that changed over 15,000 years into 58 playing teams owning and capitalizing upon portions of Sports Authority Field at Mile High while the ball is lost; a 500 game that results in the destruction of the Centennial Light; and a game in which the possessing player is attempting to score an automatic win by hiding in his team's end zone for 10,000 years. == Format == 17776 is read by scrolling through web pages occupied by large GIF images and colored dialogue text, interspersed with occasional YouTube videos. The story is divided into chapters, which were originally published in daily installments between July 5 and 15, 2017. Much of the GIF and video content of the series uses Google Earth satellite imagery, 3D buildings, and other tools within Google Earth to create animations and visual effects. == Development == Bois wrote and illustrated 17776 for Vox Media's sports news website SB Nation, of which he is creative director. Aside from 17776, Bois produces two other recurring, humorous video essay programs for the site: Pretty Good, which focuses on unusual sports topics and stories, and Chart Party, which focuses on statistics and has an emphasis on Bois' use of visual art in his journalism and storytelling. Bois is also known for the Breaking Madden series, in which he attempted unusual scenarios in the Madden NFL series of video games. In early 2016, Bois began developing an "anti-sci fi" project as a possible sequel to The Tim Tebow CFL Chronicles, an earlier work for SB Nation, and set the story in a year far enough in the future that "nobody ever thinks about it." Although he liked the concept and the visuals, he believed the project would not connect with readers and shelved it. Later, he realized that the story needed a centering character; he wrote one in the form of a small town, AM radio talk show host before coming up with the characters of the probes. Development renewed in May 2016, and the project solidified after SB Nation published its article "The Future of Football." Bois described it as the biggest project he ever attempted. The series was developed by Graham MacAree, who used a Vox Media tool that creates custom packages from standard article sets to give Bois creative leeway and to accommodate the series' weight on the SB Nation website. MacAree found that there were few resources online for achieving the desired effects. == Themes == Bois has stated that he had "conceived [17776] to give the reader a good time," asserting that this "was literally the whole point." William Hughes writing for The A.V. Club described 17776 as concerned with why humans play sports: "That is, given the massive resources, time, and information at our disposal (not to mention those available to our descendants), why does communal game-playing still hold such an important place in society?" He also listed consciousness, hope, and despair as among the work's themes. Beth Elderkin of io9 described it as "a deep thought experiment into what we consider humanly possible". She also felt that Ten and Juice take on the role of angel and devil, and she suggested the two may be unreliable narrators. Ian Crouch of The New Yorker felt that the work had a "tonal echo" of Don DeLillo's 1972 novel End Zone due to thematic similarities "with the way that the order and logic of football might act as a counterbalance to the chaos of the real world". == Reception == According to the communications director at Vox Media, 17776 garnered over 2.3 million pageviews by July 10. Two days later, it had received more than 2.9 million pageviews. Average engagement time was over nine minutes, and 43 percent of readers finished each installment of the series published by July 7. On July 19, Bois claimed that 17776 received 700,000 unique visitors and 4 million total pageviews, with an average engagement time of 11 minutes. Thu-Huong Ha for Quartz described 17776 as "part Italo Calvino, part Peter Heller [author of The Dog Stars], with humor seemingly from within the depths of Reddit," saying that the story would appeal to fans of both sports and literature. Tor.com described the first chapter as full of tension and felt that receiving answers is a "surprisingly heartbreaking" experience "lessened by a gleeful bouncing immaturity" one would not expect from the characters. Beth Elderkin at io9 said the series is "akin to Homestuck" and described it as "weird, complex, and pretty spectacular". William Hughes writing for The A.V. Club felt that 17776 is a "truly innovative piece of work". After reading the first three chapters, Agatha French of the Los Angeles Times stated that she was "impressed and excited by the innovation" of what she saw, and that she was intrigued despite not knowing what the work is or is saying. She felt the work took full advantage of its online medium and suggested that it "may also be a glimpse into the future of reading on the Internet". Ian Crouch of The New Yorker described the series as, "despite its seemingly meagre parts, a thing of startling beauty". Of the chapters published by July 12, he felt "the most striking chapter" to be one that used audio of Verne Lundquist calling the end of a 2013 game between the University of Alabama and Auburn University over a video panning over Earth. He also noted that the series was compared to Homestuck and relayed additional comparisons to Thomas Pynchon novels and "a Reddit thread hijacked by robot trolls". The series won the inaugural National Magazine Award for Digital Innovation from the American Society of Magazine Editors; this was the first National Magazine Award nomination and win for SB Nation. It was described by the judges as "an extraordinary combination of art, fiction and technology, an online acid trip that had to be experienced to be believed." It was also longlisted for the Hugo Awards for Best Novella and Best Graphic Story in 2018, ultimately finishing in 11th place in both categories. == Sequel series == On September 28, 2020, a sequel titled 20020 was launched on Secret Base, a branch of SB Nation; on October 13, it was revea

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  Sourcegraph

  Sourcegraph Inc. is a company developing code search and code intelligence tools that semantically index and analyze large codebases so that they can be searched across commercial, open-source, local, and cloud-based repositories. The company has two core products: Code Search and Amp. A previous core product, Cody, retains limited legacy support for existing customers. Code Search was initially released in 2013 under the name Sourcegraph, but was rebranded to Code Search when the company unveiled Cody in 2023. As of 2021, the platform has around 800,000 developers and has indexed around 54 billion lines of code. In July 2025, new accounts for Cody were discontinued, and a new AI coding project, Amp, was released. In December 2025, Amp was spun-off to become a separate company. == History == Sourcegraph Inc. was founded by Stanford graduates Quinn Slack and Beyang Liu to drive the development of a code search and code intelligence tool, formerly called Sourcegraph. It was first released in 2013 but was rebranded to Code Search in 2023. It was partly inspired by Liu's experience using Google Code Search while he was a Google intern, It was designed to "tackle the big code problem" by enabling developers to manage large codebases that span multiple repositories, programming languages, file formats, and projects. Code Search was initially self-hosted by each customer on their own infrastructure. Early customers included Uber, Dropbox, and Lyft. In 2016, Code Search was criticized for being provided with a Fair Source License with the developers explaining that "all of Sourcegraph's source code is publicly available and hackable" and was intended to "help open sourcers strike a balance between getting paid and preserving their values". In 2018, Code Search was licensed under the Apache License 2.0, and Sourcegraph OSS has since been released under the Apache License 2.0. The commercial version, Code Search Enterprise, has been released under its own license. In 2023, Code Search was criticized for dropping the Apache license for most of its code, leaving it public but only available under its Enterprise license. In 2024, the main repository was made completely private. In 2019, Code Search was integrated into the GitLab codebase, giving GitLab users access to a browser-based developer platform. In 2021, a browser-based portal became available, allowing users to browse open-source projects and personal private code for free. In 2022, Sourcegraph Cloud, a commercial single-tenant cloud solution for organizations with more than 100 developers, was launched. Sourcegraph has raised a total of $223 million in financing to date. Its most recent $125 million Series D investment in 2021 valued the company at $2.625 billion, a 300% growth from its previous valuation in 2020. In 2023 Sourcegraph Inc. unveiled their new product Cody, and rebranded Sourcegraph to Code Search. In 2025, Sourcegraph announced the discontinuation of Cody Free, Pro, and Enterprise Starter plans, effective July 23, 2025, and launched Amp, a new AI coding agent. == Products == The company has three major products: Code Search, Amp, and Cody. === Sourcegraph Code Search === Code Search tool is used to search and summarize code. It supports over 30 programming languages and integrates with GitHub and GitLab for code hosting, Codecov for code coverage, and Jira Software for project management. Sourcegraph's Code Search uses a variant of Google's PageRank algorithm to rank results by relevance. While it was originally launched under the Apache License, on June 13, 2023, it was relicensed to the non-open-source "Sourcegraph Enterprise" license. Then, on August 22, 2024, the source code was moved to a private repository, and thus no longer source-available. === Sourcegraph Amp === Launched in 2025, Amp can generate code, generate documentation, write tests, and perform refactoring operations on projects. The tool operates on a credit-based pricing model and is available through web interfaces, command-line tools, and IDE extensions. In December 2025, Sourcegraph announced that Amp would be spun-off to become a separate company. === Sourcegraph Cody === Cody is an AI coding application for writing and maintaining code. Cody was released in December 2023 and was available for Microsoft Visual Studio Code and most JetBrains IDEs. As of July 2025, Cody Free, Pro, and Enterprise Starter plans have been discontinued, with only Cody Enterprise remaining available for existing enterprise customers.

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