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  • Psychology of reasoning

    Psychology of reasoning

    The psychology of reasoning (also known as the cognitive science of reasoning) is the study of how people reason, often broadly defined as the process of drawing conclusions to inform how people solve problems and make decisions. It overlaps with psychology, philosophy, linguistics, cognitive science, artificial intelligence, logic, and probability theory. Psychological experiments on how humans and other animals reason have been carried out for over 100 years. An enduring question is whether or not people have the capacity to be rational. Current research in this area addresses various questions about reasoning, rationality, judgments, intelligence, relationships between emotion and reasoning, and development. == Everyday reasoning == One of the most obvious areas in which people employ reasoning is with sentences in everyday language. Most experimentation on deduction has been carried out on hypothetical thought, in particular, examining how people reason about conditionals, e.g., If A then B. Participants in experiments make the modus ponens inference, given the indicative conditional If A then B, and given the premise A, they conclude B. However, given the indicative conditional and the minor premise for the modus tollens inference, not-B, about half of the participants in experiments conclude not-A and the remainder concludes that nothing follows. The ease with which people make conditional inferences is affected by context, as demonstrated in the well-known selection task developed by Peter Wason. Participants are better able to test a conditional in an ecologically relevant context, e.g., if the envelope is sealed then it must have a 50 cent stamp on it compared to one that contains symbolic content, e.g., if the letter is a vowel then the number is even. Background knowledge can also lead to the suppression of even the simple modus ponens inference Participants given the conditional if Lisa has an essay to write then she studies late in the library and the premise Lisa has an essay to write make the modus ponens inference 'she studies late in the library', but the inference is suppressed when they are also given a second conditional if the library stays open then she studies late in the library. Interpretations of the suppression effect are controversial Other investigations of propositional inference examine how people think about disjunctive alternatives, e.g., A or else B, and how they reason about negation, e.g., It is not the case that A and B. Many experiments have been carried out to examine how people make relational inferences, including comparisons, e.g., A is better than B. Such investigations also concern spatial inferences, e.g. A is in front of B and temporal inferences, e.g. A occurs before B. Other common tasks include categorical syllogisms, used to examine how people reason about quantifiers such as All or Some, e.g., Some of the A are not B. For example if all A are B and some B are C, what (if anything) follows? == Theories of reasoning == There are several alternative theories of the cognitive processes that human reasoning is based on. One view is that people rely on a mental logic consisting of formal (abstract or syntactic) inference rules similar to those developed by logicians in the propositional calculus. Another view is that people rely on domain-specific or content-sensitive rules of inference. A third view is that people rely on mental models, that is, mental representations that correspond to imagined possibilities. A fourth view is that people compute probabilities. One controversial theoretical issue is the identification of an appropriate competence model, or a standard against which to compare human reasoning. Initially classical logic was chosen as a competence model. Subsequently, some researchers opted for non-monotonic logic and Bayesian probability. Research on mental models and reasoning has led to the suggestion that people are rational in principle but err in practice. Connectionist approaches towards reasoning have also been proposed. Despite the ongoing debate about the cognitive processes involved in human reasoning, recent research has shown that multiple approaches can be useful in modeling human thinking. For instance, studies have found that people's reasoning is often influenced by their prior beliefs, which can be modeled using Bayesian probability theory. Additionally, research on mental models has shown that people tend to reason about problems by constructing multiple mental representations of the situation, which can help them to identify relevant features and make inferences based on their understanding of the problem. Moreover, connectionist approaches to reasoning have also gained attention, which focus on the neural network models that can learn from data and generalize to new situations. == Development of reasoning == It is an active question in psychology how, why, and when the ability to reason develops from infancy to adulthood. Jean Piaget's theory of cognitive development posited general mechanisms and stages in the development of reasoning from infancy to adulthood. According to the neo-Piagetian theories of cognitive development, changes in reasoning with development come from increasing working memory capacity, increasing speed of processing, and enhanced executive functions and control. Increasing self-awareness is also an important factor. In their book The Enigma of Reason, the cognitive scientists Hugo Mercier and Dan Sperber put forward an "argumentative" theory of reasoning, claiming that humans evolved to reason primarily to justify our beliefs and actions and to convince others in a social environment. Key evidence for their theory includes the errors in reasoning that solitary individuals are prone to when their arguments are not criticized, such as logical fallacies, and how groups become much better at performing cognitive reasoning tasks when they communicate with one another and can evaluate each other's arguments. Sperber and Mercier offer one attempt to resolve the apparent paradox that the confirmation bias is so strong despite the function of reasoning naively appearing to be to come to veridical conclusions about the world. The study of the development of reasoning abilities is an ongoing area of research in psychology, and multiple factors have been proposed to explain how, why, and when reasoning develops from infancy to adulthood. Recent research has suggested that early experiences and social interactions play a critical role in the development of reasoning abilities. For example, studies have shown that infants as young as six months old can engage in basic logical reasoning, such as reasoning about the relationship between objects and their properties. Furthermore, research has highlighted the importance of parental interaction and cognitive stimulation in the development of children's reasoning abilities. Additionally, studies have suggested that cultural factors, such as educational practices and the emphasis on critical thinking, can also influence the development of reasoning skills across different populations. == Different sorts of reasoning == Philip Johnson-Laird trying to taxonomize thought, distinguished between goal-directed thinking and thinking without goal, noting that association was involved in unrelated reading. He argues that goal directed reasoning can be classified based on the problem space involved in a solution, citing Allen Newell and Herbert A. Simon. Inductive reasoning makes broad generalizations from specific cases or observations. In this process of reasoning, general assertions are made based on past specific pieces of evidence. This kind of reasoning allows the conclusion to be false even if the original statement is true. For example, if one observes a college athlete, one makes predictions and assumptions about other college athletes based on that one observation. Scientists use inductive reasoning to create theories and hypotheses. Philip Johnson-Laird distinguished inductive from deductive reasoning, in that the former creates semantic information while the later does not . In opposition, deductive reasoning is a basic form of valid reasoning. In this reasoning process a person starts with a known claim or a general belief and from there asks what follows from these foundations or how will these premises influence other beliefs. In other words, deduction starts with a hypothesis and examines the possibilities to reach a conclusion. Deduction helps people understand why their predictions are wrong and indicates that their prior knowledge or beliefs are off track. An example of deduction can be seen in the scientific method when testing hypotheses and theories. Although the conclusion usually corresponds and therefore proves the hypothesis, there are some cases where the conclusion is logical, but the generalization is not. For example, the argument, "All young girls wear skirts; Julie is a young

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  • Operational historian

    Operational historian

    In manufacturing, an operational historian is a time-series database application that is developed for operational process data. Historian software is often embedded or used in conjunction with standard DCS and PLC control systems to provide enhanced data capture, validation, compression, and aggregation capabilities. Historians have been deployed in almost every industry and contribute to functions such as supervisory control, performance monitoring, quality assurance, and, more recently, machine learning applications which can learn from vast quantities of historical data. These systems were originally developed to capture instrumentation and control data, which led many to use the term "tag" for a stream of process data, referring to the physical "tags" which had been placed on instrumentation for manually capturing data. Raw data may be accessed via OPC HDA, SQL, or REST API interfaces. == Operational Support == Operational historians are typically used within the manufacturing facility by engineers and operators for supervisory functions and analysis. An operational historian will typically capture all instrumentation and control data, whereas an enterprise historian that is deployed to support business functions will capture only a subset of the plant data. Typically, these applications offer data access through dedicated APIs (Application Programming Interfaces) and SDKs (Software Development Kits) which offer high-performance read and write operations. These operate through vendor-specific or custom applications. Front-end tools for trending process data over time are the most common interfaces to these databases. Because these applications are typically deployed next to or near the source of their process data, they are often marketed and sold as 'real-time database systems.' This distinction varies among vendors, who often have to make tradeoffs in performance between data capture and presentation, and application and analysis functionality. The following is a list of typical challenges for operational historians: data collection from instrumentation and controls storage and archiving of very large volumes of data organization of data in the form of "tags" or "points" limiting of monitoring (alarms) and validation aggregation and interpolation manual data entry (MDE) == Data access == As opposed to enterprise historians, the data access layer in the operational historian is designed to offer sophisticated data fetching modes without complex information analysis facilities. The following settings are typically available for data access operations: Data scope (single point or tag, history based on time range, history based on sample count) Request modes (raw data, last-known value, aggregation, interpolation) Sampling (single point, all points without sampling, all points with interval sampling) Data omission (based on the sample quality, based on the sample value, based on the count) Even though the operational historians are rarely relational database management systems, they often offer SQL-based interfaces to query the database. In most of such implementations, the dialect does not follow the SQL standard in order to provide syntax for specifying data access operations parameters.

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  • Ontology (information science)

    Ontology (information science)

    In information science, an ontology encompasses a representation, formal naming, and definitions of the categories, properties, and relations between the concepts, data, or entities that pertain to one, many, or all domains of discourse. More simply, an ontology is a way of showing the properties of a subject area and how they are related, by defining a set of terms and relational expressions that represent the entities in that subject area. The field which studies ontologies so conceived is sometimes referred to as applied ontology. Every academic discipline or field, in creating its terminology, thereby lays the groundwork for an ontology. Each uses ontological assumptions to frame explicit theories, research and applications. Improved ontologies may improve problem solving within that domain, interoperability of data systems, and discoverability of data. Translating research papers within every field is a problem made easier when experts from different countries maintain a controlled vocabulary of jargon between each of their languages. For instance, the definition and ontology of economics is a primary concern in Marxist economics, but also in other subfields of economics. An example of economics relying on information science occurs in cases where a simulation or model is intended to enable economic decisions, such as determining what capital assets are at risk and by how much (see risk management). What ontologies in both information science and philosophy have in common is the attempt to represent entities, including both objects and events, with all their interdependent properties and relations, according to a system of categories. In both fields, there is considerable work on problems of ontology engineering (e.g., Quine and Kripke in philosophy, Sowa and Guarino in information science), and debates concerning to what extent normative ontology is possible (e.g., foundationalism and coherentism in philosophy, BFO and Cyc in artificial intelligence). Applied ontology is considered by some as a successor to prior work in philosophy. However many current efforts are more concerned with establishing controlled vocabularies of narrow domains than with philosophical first principles, or with questions such as the mode of existence of fixed essences or whether enduring objects (e.g., perdurantism and endurantism) may be ontologically more primary than processes. Artificial intelligence has retained considerable attention regarding applied ontology in subfields like natural language processing within machine translation and knowledge representation, but ontology editors are being used often in a range of fields, including biomedical informatics and industry. Such efforts often use ontology editing tools such as Protégé. == Ontology in philosophy == Ontology is a branch of philosophy and intersects areas such as metaphysics, epistemology, and philosophy of language, as it considers how knowledge, language, and perception relate to the nature of reality. Metaphysics deals with questions like "what exists?" and "what is the nature of reality?". One of five traditional branches of philosophy, metaphysics is concerned with exploring existence through properties, entities and relations such as those between particulars and universals, intrinsic and extrinsic properties, or essence and existence. Metaphysics has been an ongoing topic of discussion since recorded history. == Etymology == The compound word ontology combines onto-, from the Greek ὄν, on (gen. ὄντος, ontos), i.e. "being; that which is", which is the present participle of the verb εἰμί, eimí, i.e. "to be, I am", and -λογία, -logia, i.e. "logical discourse", see classical compounds for this type of word formation. While the etymology is Greek, the oldest extant record of the word itself, the Neo-Latin form ontologia, appeared in 1606 in the work Ogdoas Scholastica by Jacob Lorhard (Lorhardus) and in 1613 in the Lexicon philosophicum by Rudolf Göckel (Goclenius). The first occurrence in English of ontology as recorded by the OED (Oxford English Dictionary, online edition, 2008) came in Archeologia Philosophica Nova or New Principles of Philosophy (1663) by Gideon Harvey. == Formal ontology == Since the mid-1970s, researchers in the field of artificial intelligence (AI) have recognized that knowledge engineering is the key to building large and powerful AI systems. AI researchers argued that they could create new ontologies as computational models that enable certain kinds of automated reasoning, which was only marginally successful. In the 1980s, the AI community began to use the term ontology to refer to both a theory of a modeled world and a component of knowledge-based systems. In particular, David Powers introduced the word ontology to AI to refer to real world or robotic grounding, publishing in 1990 literature reviews emphasizing grounded ontology in association with the call for papers for a AAAI Summer Symposium Machine Learning of Natural Language and Ontology, with an expanded version published in SIGART Bulletin and included as a preface to the proceedings. Some researchers, drawing inspiration from philosophical ontologies, viewed computational ontology as a kind of applied philosophy. In 1993, the widely cited web page and paper "Toward Principles for the Design of Ontologies Used for Knowledge Sharing" by Tom Gruber used ontology as a technical term in computer science closely related to earlier idea of semantic networks and taxonomies. Gruber introduced the term as a specification of a conceptualization: An ontology is a description (like a formal specification of a program) of the concepts and relationships that can formally exist for an agent or a community of agents. This definition is consistent with the usage of ontology as set of concept definitions, but more general. And it is a different sense of the word than its use in philosophy. Attempting to distance ontologies from taxonomies and similar efforts in knowledge modeling that rely on classes and inheritance, Gruber stated (1993): Ontologies are often equated with taxonomic hierarchies of classes, class definitions, and the subsumption relation, but ontologies need not be limited to these forms. Ontologies are also not limited to conservative definitions, that is, definitions in the traditional logic sense that only introduce terminology and do not add any knowledge about the world (Enderton, 1972). To specify a conceptualization, one needs to state axioms that do constrain the possible interpretations for the defined terms. Recent experimental ontology frameworks have also explored resonance-based AI-human co-evolution structures, such as IAMF (Illumination AI Matrix Framework), OntoMotoOS (a meta-operating system concept for ethical and ontological AI–human co-evolution), and PSRT (Phase-Structural Reality Theory across multi-scale ontological layers). Though not yet widely adopted in academic discourse, such models propose phased approaches to ethical harmonization and structural emergence. As refinement of Gruber's definition Feilmayr and Wöß (2016) stated: "An ontology is a formal, explicit specification of a shared conceptualization that is characterized by high semantic expressiveness required for increased complexity." == Formal ontology components == Contemporary ontologies share many structural similarities, regardless of the language in which they are expressed. Most ontologies describe individuals (instances), classes (concepts), attributes and relations. === Types === ==== Domain ontology ==== A domain ontology (or domain-specific ontology) represents concepts which belong to a realm of the world, such as biology or politics. Each domain ontology typically models domain-specific definitions of terms. For example, the word card has many different meanings. An ontology about the domain of poker would model the "playing card" meaning of the word, while an ontology about the domain of computer hardware would model the "punched card" and "video card" meanings. Since domain ontologies are written by different people, they represent concepts in very specific and unique ways, and are often incompatible within the same project. As systems that rely on domain ontologies expand, they often need to merge domain ontologies by hand-tuning each entity or using a combination of software merging and hand-tuning. This presents a challenge to the ontology designer. Different ontologies in the same domain arise due to different languages, different intended usage of the ontologies, and different perceptions of the domain (based on cultural background, education, ideology, etc.). At present, merging ontologies that are not developed from a common upper ontology is a largely manual process and therefore time-consuming and expensive. Domain ontologies that use the same upper ontology to provide a set of basic elements with which to specify the meanings of the domain ontology entities can be merged with less effo

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  • Organizational metacognition

    Organizational metacognition

    Organizational metacognition is knowing what an organization knows, a concept related to metacognition, organizational learning, the learning organization and sensemaking. It is used to describe how organizations and teams develop an awareness of their own thinking, learning how to learn, where awareness of ignorance can motivate learning. The organizational deutero-learning concept identified by Argyris and Schon defines when organizations learn how to carry out single-loop and double-loop learning. It has also been described as learning how to learn through a process of collaborative inquiry and reflection (evaluative inquiry). "When an organization engages in deutero-learning its members learn about the previous context for learning. They reflect on and inquire into previous episodes of organizational learning, or failure to learn. They discover what they did that facilitated or inhibited learning, they invent new strategies for learning, they produce these strategies, and they evaluate and generalize what they have produced" Learning what facilitates and inhibits learning enables organizations to develop new strategies to develop their knowledge. For example, identification of a gap between perceived performance (such as satisfaction) and actual performance (outcomes) creates an awareness that makes the organization understand that learning needs to occur, driving appropriate changes to the environment and processes. == Learning prototypes == Wijnhoven (2001) grouped four learning prototypes that best meet learning needs, the match between these needs and learning norms dictating an organization's learning capabilities; deutero-learning is the acquisition of these capabilities. knowledge gap analysis classification of problems to select operationally required knowledge and skills coping with organizational tremors and jolts by anticipation, response and adjustments of behavioural repertoires decisional uncertainty measurement == Terminological ambiguities == Organizational metacognition and organizational deutero-learning have both been described as the concept or phenomenon where organizations learn how to learn. Argyris and Schon (1978) place deutero-learning into their cognitive theory of action framework, neglecting aspects of adaptive behaviour and context core to Bateson's (1972) original definitions. In order to resolve terminological ambiguities, Visser (2007) reviewed and reformulated the concept of deutero-learning as, "the behavioral adaptation to patterns of conditioning in relationships in organizational contexts, distinguishing it from meta-learning and planned learning" (pg. 659). == Significance == Organizational metacognition is considered a key norm to the prescriptive concept of the learning organization. Its significance has been recognized by industry, the military and in disaster response. == Examples in practice == Examples of poor metacognition (deutero-learning) have been described in knowledge network environments, "Knowledge networking is important to most competitive enterprises today. Enterprise knowledge is becoming ever more specialized in nature, so no single person or organization can know everything in detail. Hence addressing complex, multidisciplinary problems requires developing and accessing a network of knowledgeable people and organizations. The problem is, many otherwise knowledgeable people and organizations are not fully aware of their knowledge networks, and even more problematic, they are not aware that they are not aware. This focuses our attention toward organizational metacognition."

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  • Intelligent automation

    Intelligent automation

    Intelligent automation (IA), or intelligent process automation, is a software term that refers to a combination of artificial intelligence (AI) and robotic process automation (RPA). Companies use intelligent automation to cut costs and streamline tasks by using artificial-intelligence-powered robotic software to mitigate repetitive tasks. As it accumulates data, the system learns in an effort to improve its efficiency. Intelligent automation applications consist of, but are not limited to, pattern analysis, data assembly, and classification. The term is similar to hyperautomation, a concept identified by research group Gartner as being one of the top technology trends of 2020. == Technology == Intelligent automation applies the assembly line concept of breaking tasks into repetitive steps to improve business processes. Rather than having humans perform each step, intelligent automation can replace steps with an intelligent software robot, improving efficiency. Intelligent automation integrates robotic process automation (RPA) with artificial intelligence techniques (such as machine learning, natural-language processing, and computer vision) enabling systems to interpret data, make decisions, and adapt to changing inputs. Modern platforms use a layered architecture combining workflow orchestration, low-code tools, integration middleware, and AI services to coordinate bots and data pipelines across organisational systems. == Applications == Intelligent automation is used to process unstructured content. Common real-world applications include self-driving cars, self-checkouts at grocery stores, smart home assistants, and appliances. Businesses can apply data and machine learning to build predictive analytics that react to consumer behavior changes, or to implement RPA to improve manufacturing floor operations. For example, the technology has also been used to automate the workflow behind distributing COVID-19 vaccines. Data provided by hospital systems’ electronic health records can be processed to identify and educate patients, and schedule vaccinations. Intelligent automation can provide real-time insights on profitability and efficiency. However, in an April 2022 survey by Alchemmy, despite three quarters of businesses acknowledging the importance of Artificial Intelligence to their future development, just a quarter of business leaders (25%) considered Intelligent Automation a “game changer” in understanding current performance. 42% of CTOs see “shortage of talent” as the main obstacle to implementing Intelligent Automation in their business, while 36% of CEOs see ‘upskilling and professional development of existing workforce’ as the most significant adoption barrier. IA is becoming increasingly accessible for firms of all sizes. With this in mind, it is expected to continue to grow rapidly in all industries. This technology has the potential to change the workforce. As it advances, it will be able to perform increasingly complex and difficult tasks. In addition, this may expose certain workforce issues as well as change how tasks are allocated. Tools such as Semrush's AI Visibility Toolkit and Enterprise AIO reflect these developments by analysing how entities are referenced and represented within responses produced by large-language-model-based systems. == Benefits == Streamline processes: Repetitive manual tasks can put a strain on the workforce. However, with AI agents, these tasks can be automated to allow teams to focus on more important matters that require human cognition. Intelligent automation can also be used to mitigate tasks with human error which in turn increases proficiency. This allows the opportunity for firms to scale production without the traditional negative consequences such as reduced quality or increased risk. Customer service improvement: Customer service can be significantly improved, providing the firm with a competitive advantage. IA utilizing chat features allows for instant curated responses to customers. In addition, it can give updates to customers, make appointments, manage calls, and personalize campaigns. Flexibility: Due to the wide range of applications, IA is useful across a variety of fields, technologies, projects and industries. In addition, IA can be integrated with current automated systems in place. This allows for optimized systems unique to each firm to best fit their individual needs. == Capabilities == Cognitive automation: Employs AI techniques to assist humans in decision-making and task completion Natural language processing: Allows computers to automate knowledge work Business process management: Enhances the consistency and agility of corporate operations Process mining: Applies data mining methods to discover, analyze, and improve business processes Intelligent document processing: Utilizes OCR and other advanced technologies to extract data from documents and convert it into structured, usable data Computer vision: Allows computers to extract information from digital images, videos, and other visual inputs Integration automation: Establishes a unified platform with automated workflows that integrate data, applications, and devices.

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  • Sparse identification of non-linear dynamics

    Sparse identification of non-linear dynamics

    Sparse identification of nonlinear dynamics (SINDy) is a data-driven algorithm for obtaining dynamical systems from data. Given a series of snapshots of a dynamical system and its corresponding time derivatives, SINDy performs a sparsity-promoting regression (such as LASSO and sparse Bayesian inference) on a library of nonlinear candidate functions of the snapshots against the derivatives to find the governing equations. This procedure relies on the assumption that most physical systems only have a few dominant terms which dictate the dynamics, given an appropriately selected coordinate system and quality training data. It has been applied to identify the dynamics of fluids, based on proper orthogonal decomposition, as well as other complex dynamical systems, such as biological networks. == Mathematical Overview == First, consider a dynamical system of the form x ˙ = d d t x ( t ) = f ( x ( t ) ) , {\displaystyle {\dot {\textbf {x}}}={\frac {d}{dt}}{\textbf {x}}(t)={\textbf {f}}({\textbf {x}}(t)),} where x ( t ) ∈ R n {\displaystyle {\textbf {x}}(t)\in \mathbb {R} ^{n}} is a state vector (snapshot) of the system at time t {\displaystyle t} and the function f ( x ( t ) ) {\displaystyle {\textbf {f}}({\textbf {x}}(t))} defines the equations of motion and constraints of the system. The time derivative may be either prescribed or numerically approximated from the snapshots. With x {\displaystyle {\textbf {x}}} and x ˙ {\displaystyle {\dot {\textbf {x}}}} sampled at m {\displaystyle m} equidistant points in time ( t 1 , t 2 , ⋯ , t m {\displaystyle t_{1},t_{2},\cdots ,t_{m}} ), these can be arranged into matrices of the form X = [ x T ( t 1 ) x T ( t 2 ) ⋮ x T ( t m ) ] = [ x 1 ( t 1 ) x 2 ( t 1 ) ⋯ x n ( t 1 ) x 1 ( t 2 ) x 2 ( t 2 ) ⋯ x n ( t 2 ) ⋮ ⋮ ⋱ ⋮ x 1 ( t m ) x 2 ( t m ) ⋯ x n ( t m ) ] , {\displaystyle {\bf {{X}={\begin{bmatrix}\mathbf {x} ^{\mathsf {T}}(t_{1})\\\mathbf {x} ^{\mathsf {T}}(t_{2})\\\vdots \\\mathbf {x} ^{\mathsf {T}}(t_{m})\end{bmatrix}}={\begin{bmatrix}x_{1}(t_{1})&x_{2}(t_{1})&\cdots &x_{n}(t_{1})\\x_{1}(t_{2})&x_{2}(t_{2})&\cdots &x_{n}(t_{2})\\\vdots &\vdots &\ddots &\vdots \\x_{1}(t_{m})&x_{2}(t_{m})&\cdots &x_{n}(t_{m})\end{bmatrix}},}}} and similarly for X ˙ {\displaystyle {\dot {\mathbf {X} }}} . Next, a library Θ ( X ) {\displaystyle \mathbf {\Theta } (\mathbf {X} )} of nonlinear candidate functions of the columns of X {\displaystyle {\textbf {X}}} is constructed, which may be constant, polynomial, or more exotic functions (like trigonometric and rational terms, and so on): Θ ( X ) = [ | | | | | | 1 X X 2 X 3 ⋯ sin ⁡ ( X ) cos ⁡ ( X ) ⋯ | | | | | | ] {\displaystyle \ \ \ {\bf {{\Theta }({\bf {{X})={\begin{bmatrix}\vline &\vline &\vline &\vline &&\vline &\vline &\\1&{\bf {X}}&{\bf {{X}^{2}}}&{\bf {{X}^{3}}}&\cdots &\sin({\bf {{X})}}&\cos({\bf {{X})}}&\cdots \\\vline &\vline &\vline &\vline &&\vline &\vline &\end{bmatrix}}}}}}} The number of possible model structures from this library is combinatorially high. f ( x ( t ) ) {\displaystyle {\textbf {f}}({\textbf {x}}(t))} is then substituted by Θ ( X ) {\displaystyle {\bf {{\Theta }({\textbf {X}})}}} and a vector of coefficients Ξ = [ ξ 1 ξ 2 ⋯ ξ n ] {\displaystyle {\bf {{\Xi }=\left[{\bf {{\xi }_{1}{\bf {{\xi }_{2}\cdots {\bf {{\xi }_{n}}}}}}}\right]}}} determining the active terms in f ( x ( t ) ) {\displaystyle {\textbf {f}}({\textbf {x}}(t))} : X ˙ = Θ ( X ) Ξ {\displaystyle {\dot {\bf {X}}}={\bf {{\Theta }({\bf {{X}){\bf {\Xi }}}}}}} Because only a few terms are expected to be active at each point in time, an assumption is made that f ( x ( t ) ) {\displaystyle {\textbf {f}}({\textbf {x}}(t))} admits a sparse representation in Θ ( X ) {\displaystyle {\bf {{\Theta }({\textbf {X}})}}} . This then becomes an optimization problem in finding a sparse Ξ {\displaystyle {\bf {\Xi }}} which optimally embeds X ˙ {\displaystyle {\dot {\textbf {X}}}} . In other words, a parsimonious model is obtained by performing least squares regression on the system (4) with sparsity-promoting ( L 1 {\displaystyle L_{1}} ) regularization ξ k = arg ⁡ min ξ k ′ | | X ˙ k − Θ ( X ) ξ k ′ | | 2 + λ | | ξ k ′ | | 1 , {\displaystyle {\bf {{\xi }_{k}={\underset {\bf {{\xi }'_{k}}}{\arg \min }}\left|\left|{\dot {\bf {X}}}_{k}-{\bf {{\Theta }({\bf {{X}){\bf {{\xi }'_{k}}}}}}}\right|\right|_{2}+\lambda \left|\left|{\bf {{\xi }'_{k}}}\right|\right|_{1},}}} where λ {\displaystyle \lambda } is a regularization parameter. Finally, the sparse set of ξ k {\displaystyle {\bf {{\xi }_{k}}}} can be used to reconstruct the dynamical system: x ˙ k = Θ ( x ) ξ k {\displaystyle {\dot {x}}_{k}={\bf {{\Theta }({\bf {{x}){\bf {{\xi }_{k}}}}}}}}

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  • Chinese speech synthesis

    Chinese speech synthesis

    Chinese speech synthesis is the application of speech synthesis to the Chinese language (usually Standard Chinese). It poses additional difficulties due to Chinese characters frequently having different pronunciations in different contexts and the complex prosody, which is essential to convey the meaning of words, and sometimes the difficulty in obtaining agreement among native speakers concerning what the correct pronunciation is of certain phonemes. == Concatenation (Ekho and KeyTip) == Recordings can be concatenated in any desired combination, but the joins sound forced (as is usual for simple concatenation-based speech synthesis) and this can severely affect prosody; these synthesizers are also inflexible in terms of speed and expression. However, because these synthesizers do not rely on a corpus, there is no noticeable degradation in performance when they are given more unusual or awkward phrases. Ekho is an open source TTS which simply concatenates sampled syllables. It currently supports Cantonese, Mandarin, and experimentally Korean. Some of the Mandarin syllables have been pitched-normalised in Praat. A modified version of these is used in Gradint's "synthesis from partials". cjkware.com used to ship a product called KeyTip Putonghua Reader which worked similarly; it contained 120 Megabytes of sound recordings (GSM-compressed to 40 Megabytes in the evaluation version), comprising 10,000 multi-syllable dictionary words plus single-syllable recordings in 6 different prosodies (4 tones, neutral tone, and an extra third-tone recording for use at the end of a phrase). == Lightweight synthesizers (eSpeak and Yuet) == The lightweight open-source speech project eSpeak, which has its own approach to synthesis, has experimented with Mandarin and Cantonese. eSpeak was used by Google Translate from May 2010 until December 2010. The commercial product "Yuet" is also lightweight (it is intended to be suitable for resource-constrained environments like embedded systems); it was written from scratch in ANSI C starting from 2013. Yuet claims a built-in NLP model that does not require a separate dictionary; the speech synthesised by the engine claims clear word boundaries and emphasis on appropriate words. Communication with its author is required to obtain a copy. Both eSpeak and Yuet can synthesis speech for Cantonese and Mandarin from the same input text, and can output the corresponding romanisation (for Cantonese, Yuet uses Yale and eSpeak uses Jyutping; both use Pinyin for Mandarin). eSpeak does not concern itself with word boundaries when these don't change the question of which syllable should be spoken. == Corpus-based == A "corpus-based" approach can sound very natural in most cases but can err in dealing with unusual phrases if they can't be matched with the corpus. The synthesiser engine is typically very large (hundreds or even thousands of megabytes) due to the size of the corpus. === iFlyTek === Anhui USTC iFlyTek Co., Ltd (iFlyTek) published a W3C paper in which they adapted Speech Synthesis Markup Language to produce a mark-up language called Chinese Speech Synthesis Markup Language (CSSML) which can include additional markup to clarify the pronunciation of characters and to add some prosody information. The amount of data involved is not disclosed by iFlyTek but can be seen from the commercial products that iFlyTek have licensed their technology to; for example, Bider's SpeechPlus is a 1.3 Gigabyte download, 1.2 Gigabytes of which is used for the highly compressed data for a single Chinese voice. iFlyTek's synthesiser can also synthesise mixed Chinese and English text with the same voice (e.g. Chinese sentences containing some English words); they claim their English synthesis to be "average". The iFlyTek corpus appears to be heavily dependent on Chinese characters, and it is not possible to synthesize from pinyin alone. It is sometimes possible by means of CSSML to add pinyin to the characters to disambiguate between multiple possible pronunciations, but this does not always work. === NeoSpeech === There is an online interactive demonstration for NeoSpeech speech synthesis, which accepts Chinese characters and also pinyin if it's enclosed in their proprietary "VTML" markup. === Mac OS === Mac OS had Chinese speech synthesizers available up to version 9. This was removed in 10.0 and reinstated in 10.7 (Lion). === Historical corpus-based synthesizers (no longer available) === A corpus-based approach was taken by Tsinghua University in SinoSonic, with the Harbin dialect voice data taking 800 Megabytes. This was planned to be offered as a download but the link was never activated. Nowadays, only references to it can be found on Internet Archive. Bell Labs' approach, which was demonstrated online in 1997 but subsequently removed, was described in a monograph "Multilingual Text-to-Speech Synthesis: The Bell Labs Approach" (Springer, October 31, 1997, ISBN 978-0-7923-8027-6), and the former employee who was responsible for the project, Chilin Shih (who subsequently worked at the University of Illinois) put some notes about her methods on her website.

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  • Enumeration algorithm

    Enumeration algorithm

    In computer science, an enumeration algorithm is an algorithm that enumerates the answers to a computational problem. Formally, such an algorithm applies to problems that take an input and produce a list of solutions, similarly to function problems. For each input, the enumeration algorithm must produce the list of all solutions, without duplicates, and then halt. The performance of an enumeration algorithm is measured in terms of the time required to produce the solutions, either in terms of the total time required to produce all solutions, or in terms of the maximal delay between two consecutive solutions and in terms of a preprocessing time, counted as the time before outputting the first solution. This complexity can be expressed in terms of the size of the input, the size of each individual output, or the total size of the set of all outputs, similarly to what is done with output-sensitive algorithms. == Formal definitions == An enumeration problem P {\displaystyle P} is defined as a relation R {\displaystyle R} over strings of an arbitrary alphabet Σ {\displaystyle \Sigma } : R ⊆ Σ ∗ × Σ ∗ {\displaystyle R\subseteq \Sigma ^{}\times \Sigma ^{}} An algorithm solves P {\displaystyle P} if for every input x {\displaystyle x} the algorithm produces the (possibly infinite) sequence y {\displaystyle y} such that y {\displaystyle y} has no duplicate and z ∈ y {\displaystyle z\in y} if and only if ( x , z ) ∈ R {\displaystyle (x,z)\in R} . The algorithm should halt if the sequence y {\displaystyle y} is finite. == Common complexity classes == Enumeration problems have been studied in the context of computational complexity theory, and several complexity classes have been introduced for such problems. A very general such class is EnumP, the class of problems for which the correctness of a possible output can be checked in polynomial time in the input and output. Formally, for such a problem, there must exist an algorithm A which takes as input the problem input x, the candidate output y, and solves the decision problem of whether y is a correct output for the input x, in polynomial time in x and y. For instance, this class contains all problems that amount to enumerating the witnesses of a problem in the class NP. Other classes that have been defined include the following. In the case of problems that are also in EnumP, these problems are ordered from least to most specific: Output polynomial, the class of problems whose complete output can be computed in polynomial time. Incremental polynomial time, the class of problems where, for all i, the i-th output can be produced in polynomial time in the input size and in the number i. Polynomial delay, the class of problems where the delay between two consecutive outputs is polynomial in the input (and independent from the output). Strongly polynomial delay, the class of problems where the delay before each output is polynomial in the size of this specific output (and independent from the input or from the other outputs). The preprocessing is generally assumed to be polynomial. Constant delay, the class of problems where the delay before each output is constant, i.e., independent from the input and output. The preprocessing phase is generally assumed to be polynomial in the input. == Common techniques == Backtracking: The simplest way to enumerate all solutions is by systematically exploring the space of possible results (partitioning it at each successive step). However, performing this may not give good guarantees on the delay, i.e., a backtracking algorithm may spend a long time exploring parts of the space of possible results that do not give rise to a full solution. Flashlight search: This technique improves on backtracking by exploring the space of all possible solutions but solving at each step the problem of whether the current partial solution can be extended to a partial solution. If the answer is no, then the algorithm can immediately backtrack and avoid wasting time, which makes it easier to show guarantees on the delay between any two complete solutions. In particular, this technique applies well to self-reducible problems. Closure under set operations: If we wish to enumerate the disjoint union of two sets, then we can solve the problem by enumerating the first set and then the second set. If the union is non disjoint but the sets can be enumerated in sorted order, then the enumeration can be performed in parallel on both sets while eliminating duplicates on the fly. If the union is not disjoint and both sets are not sorted then duplicates can be eliminated at the expense of a higher memory usage, e.g., using a hash table. Likewise, the cartesian product of two sets can be enumerated efficiently by enumerating one set and joining each result with all results obtained when enumerating the second step. == Examples of enumeration problems == The vertex enumeration problem, where we are given a polytope described as a system of linear inequalities and we must enumerate the vertices of the polytope. Enumerating the minimal transversals of a hypergraph. This problem is related to monotone dualization and is connected to many applications in database theory and graph theory. Enumerating the answers to a database query, for instance a conjunctive query or a query expressed in monadic second-order. There have been characterizations in database theory of which conjunctive queries could be enumerated with linear preprocessing and constant delay. The problem of enumerating maximal cliques in an input graph, e.g., with the Bron–Kerbosch algorithm Listing all elements of structures such as matroids and greedoids Several problems on graphs, e.g., enumerating independent sets, paths, cuts, etc. Enumerating the satisfying assignments of representations of Boolean functions, e.g., a Boolean formula written in conjunctive normal form or disjunctive normal form, a binary decision diagram such as an OBDD, or a Boolean circuit in restricted classes studied in knowledge compilation, e.g., NNF. == Connection to computability theory == The notion of enumeration algorithms is also used in the field of computability theory to define some high complexity classes such as RE, the class of all recursively enumerable problems. This is the class of sets for which there exist an enumeration algorithm that will produce all elements of the set: the algorithm may run forever if the set is infinite, but each solution must be produced by the algorithm after a finite time.

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  • Score bug

    Score bug

    A score bug is a digital on-screen graphic which is displayed in a broadcast of a sporting event, displaying the current score and other statistics. It is similar in function to a scoreboard, and is usually placed at either the top or lower third of the television screen. == History == The concept of a persistent score bug was devised by Sky Sports head David Hill, who was dissatisfied over having to wait to see what the score was after tuning into a football match in-progress. The score bug was introduced when Sky launched its coverage of the then newly-formed English Premier League in August 1992. Hill's boss repeatedly demanded that the graphic be removed, describing it as the "stupidest thing [he] had ever seen". Hill defied the boss's demands and kept the graphic in place. ITV introduced a score bug at the start of the 1993–94 football season, and the BBC introduced a score bug towards the end of 1993. The concept was introduced to the United States by ABC Sports and ESPN during coverage of the 1994 FIFA World Cup. Their justification for the graphic was to provide a location for a rotating series of sponsor logos, in order to allow matches to air without commercial interruption. With the acquisition of rights to the National Football League (NFL) by BSkyB's American sibling Fox (a fellow venture of Rupert Murdoch), Hill became the first president of Fox Sports. Under Hill's leadership, Fox introduced a version of the score bug branded as the "Fox Box", which was part of its inaugural season of NFL coverage in 1994. Variety criticized it as an "annoying see-through clock and score graphic" and expressed concern for people "who actually watched the beginning of the game and would rather have their screen clear of graphics". Hill even received a death threat from an irate viewer, with a specific emphasis on him being a "foreigner", but the score bug soon became a ubiquitous feature for American football broadcasts, along with almost all American sports broadcasts in the years that followed. Dick Ebersol of NBC Sports initially opposed the idea of a score bug, as he thought that fans would dislike seeing more graphics on the screen and would change the channel from blowout games if the score was constantly being displayed. Since the 2010s, the on-air design and positioning of some score bugs have been influenced by the needs of Internet video (especially when viewing an event on devices with smaller screens), including bugs noticeably larger than prior iterations designed with television viewing in mind, or designs primarily kept towards the bottom-center of the screen (easing the ability for the bug to remain visible when highlights are cropped for square videos posted on social media). == Details == Score bugs used in team sports typically include the names of both teams, an abbreviation of the team's name, and/or the team's logo; for individual sports, they include the names of individual competitors. In sports where a game clock or playing periods are used, those are generally also displayed as part of the score bug. Some broadcasts also include teams' win-loss records. In 2024, ESPN experimented with adding a persistent win probability meter to its bug in Major League Baseball, which was based on input from its statisticians. === Variations === In addition to the above information, score bugs in some sports include additional information: In baseball, score bugs display the current inning, number of outs, the pitch clock if applicable, and a graphic displaying which bases are occupied; and usually include names of the current pitcher and batter, the pitcher's pitch count, and the number of balls and strikes accrued by the batter. In basketball, score bugs generally include the shot clock, the number of fouls accrued by each team, and whether a team is in the bonus. In cricket, score bugs often take the form of larger dashboards across the bottom of the screen, displaying the current team up and their number of runs, wickets, and overs, a display showing the runs scored and number of balls faced by the current batting partnership, and statistics for the opposing team's bowler (including the number of wickets scored and runs given up). In American football, score bugs usually include the play clock and the down and distance of the current play; they also incorporate graphics indicating when a penalty flag has been thrown. In ice hockey, score bugs display when a penalty or power play is in effect, and often include the number of shots on goal accrued by each team. In golf, Fox popularized the display of a persistent leaderboard graphic in the bottom-right of the screen, usually displaying the top 5. ==== Racing ==== Telecasts of automobile races often include a score bug with the current positions of participants, statistics such as distance behind the leader, and the remaining distance or number of laps. In the mid-2010s, NASCAR broadcasters such as Fox began to transition from horizontal tickers to vertical leaderboards (also referred to as "pylons", in reference to the physical scoring pylons at). The CW differentiated itself by using a horizontal display that divides the field into multiple columns along the bottom of the screen.

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  • Xulvi-Brunet–Sokolov algorithm

    Xulvi-Brunet–Sokolov algorithm

    Xulvi-Brunet and Sokolov's algorithm generates networks with chosen degree correlations. This method is based on link rewiring, in which the desired degree is governed by parameter ρ. By varying this single parameter it is possible to generate networks from random (when ρ = 0) to perfectly assortative or disassortative (when ρ = 1). This algorithm allows to keep network's degree distribution unchanged when changing the value of ρ. == Assortative model == In assortative networks, well-connected nodes are likely to be connected to other highly connected nodes. Social networks are examples of assortative networks. This means that an assortative network has the property that almost all nodes with the same degree are linked only between themselves. The Xulvi-Brunet–Sokolov algorithm for this type of networks is the following. In a given network, two links connecting four different nodes are chosen randomly. These nodes are ordered by their degrees. Then, with probability ρ, the links are randomly rewired in such a way that one link connects the two nodes with the smaller degrees and the other connects the two nodes with the larger degrees. If one or both of these links already existed in the network, the step is discarded and is repeated again. Thus, there will be no self-connected nodes or multiple links connecting the same two nodes. Different degrees of assortativity of a network can be achieved by changing the parameter ρ. Assortative networks are characterized by highly connected groups of nodes with similar degree. As assortativity grows, the average path length and clustering coefficient increase. == Disassortative model == In disassortative networks, highly connected nodes tend to connect to less-well-connected nodes with larger probability than in uncorrelated networks. Examples of such networks include biological networks. The Xulvi-Brunet and Sokolov's algorithm for this type of networks is similar to the one for assortative networks with one minor change. As before, two links of four nodes are randomly chosen and the nodes are ordered with respect to their degrees. However, in this case, the links are rewired (with probability p) such that one link connects the highest connected node with the node with the lowest degree and the other link connects the two remaining nodes randomly with probability 1 − ρ. Similarly, if the new links already existed, the previous step is repeated. This algorithm does not change the degree of nodes and thus the degree distribution of the network.

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  • Lancichinetti–Fortunato–Radicchi benchmark

    Lancichinetti–Fortunato–Radicchi benchmark

    Lancichinetti–Fortunato–Radicchi benchmark is an algorithm that generates benchmark networks (artificial networks that resemble real-world networks). They have a priori known communities and are used to compare different community detection methods. The advantage of the benchmark over other methods is that it accounts for the heterogeneity in the distributions of node degrees and of community sizes. == The algorithm == The node degrees and the community sizes are distributed according to a power law, with different exponents. The benchmark assumes that both the degree and the community size have power law distributions with different exponents, γ {\displaystyle \gamma } and β {\displaystyle \beta } , respectively. N {\displaystyle N} is the number of nodes and the average degree is ⟨ k ⟩ {\displaystyle \langle k\rangle } . There is a mixing parameter μ {\displaystyle \mu } , which is the average fraction of neighboring nodes of a node that do not belong to any community that the benchmark node belongs to. This parameter controls the fraction of edges that are between communities. Thus, it reflects the amount of noise in the network. At the extremes, when μ = 0 {\displaystyle \mu =0} all links are within community links, if μ = 1 {\displaystyle \mu =1} all links are between nodes belonging to different communities. One can generate the benchmark network using the following steps. Step 1: Generate a network with nodes following a power law distribution with exponent γ {\displaystyle \gamma } and choose extremes of the distribution k min {\displaystyle k_{\min }} and k max {\displaystyle k_{\max }} to get desired average degree is ⟨ k ⟩ {\displaystyle \langle k\rangle } . Step 2: ( 1 − μ ) {\displaystyle (1-\mu )} fraction of links of every node is with nodes of the same community, while fraction μ {\displaystyle \mu } is with the other nodes. Step 3: Generate community sizes from a power law distribution with exponent β {\displaystyle \beta } . The sum of all sizes must be equal to N {\displaystyle N} . The minimal and maximal community sizes s min {\displaystyle s_{\min }} and s max {\displaystyle s_{\max }} must satisfy the definition of community so that every non-isolated node is in at least in one community: s min > k min {\displaystyle s_{\min }>k_{\min }} s max > k max {\displaystyle s_{\max }>k_{\max }} Step 4: Initially, no nodes are assigned to communities. Then, each node is randomly assigned to a community. As long as the number of neighboring nodes within the community does not exceed the community size a new node is added to the community, otherwise stays out. In the following iterations the “homeless” node is randomly assigned to some community. If that community is complete, i.e. the size is exhausted, a randomly selected node of that community must be unlinked. Stop the iteration when all the communities are complete and all the nodes belong to at least one community. Step 5: Implement rewiring of nodes keeping the same node degrees but only affecting the fraction of internal and external links such that the number of links outside the community for each node is approximately equal to the mixing parameter μ {\displaystyle \mu } . == Testing == Consider a partition into communities that do not overlap. The communities of randomly chosen nodes in each iteration follow a p ( C ) {\displaystyle p(C)} distribution that represents the probability that a randomly picked node is from the community C {\displaystyle C} . Consider a partition of the same network that was predicted by some community finding algorithm and has p ( C 2 ) {\displaystyle p(C_{2})} distribution. The benchmark partition has p ( C 1 ) {\displaystyle p(C_{1})} distribution. The joint distribution is p ( C 1 , C 2 ) {\displaystyle p(C_{1},C_{2})} . The similarity of these two partitions is captured by the normalized mutual information. I n = ∑ C 1 , C 2 p ( C 1 , C 2 ) log 2 ⁡ p ( C 1 , C 2 ) p ( C 1 ) p ( C 2 ) 1 2 H ( { p ( C 1 ) } ) + 1 2 H ( { p ( C 2 ) } ) {\displaystyle I_{n}={\frac {\sum _{C_{1},C_{2}}p(C_{1},C_{2})\log _{2}{\frac {p(C_{1},C_{2})}{p(C_{1})p(C_{2})}}}{{\frac {1}{2}}H(\{p(C_{1})\})+{\frac {1}{2}}H(\{p(C_{2})\})}}} If I n = 1 {\displaystyle I_{n}=1} the benchmark and the detected partitions are identical, and if I n = 0 {\displaystyle I_{n}=0} then they are independent of each other.

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  • Information school

    Information school

    Information school (sometimes abbreviated I-school or iSchool) is a university-level institution committed to understanding the role of information in nature and human endeavors. Synonyms include school of information, department of information studies, or information department. Information schools faculty conduct research into the fundamental aspects of information and related technologies. In addition to granting academic degrees, information schools educate information professionals, researchers, and scholars for an increasingly information-driven world. Information school can also refer, in a more restricted sense, to the members of the iSchools organization (formerly the "iSchools Project"), as governed by the iCaucus. Members of this group share a fundamental interest in the relationships between people, information, technology, and science. These schools, colleges, and departments have been either newly established or have evolved from programs focused on information systems, library science, informatics, computer science, library and information science and information science. Information schools promote an interdisciplinary approach to understanding the opportunities and challenges of information management, with a core commitment to concepts like universal access and user-centered organization of information. The field is concerned broadly with questions of design and preservation across information spaces, from digital and virtual spaces like online communities, the World Wide Web, and databases to physical spaces such as libraries, museums, archives, and other repositories. Information school degree programs include course offerings in areas such as data science, information architecture, design, economics, policy, retrieval, security, and telecommunications; knowledge management, user experience design, and usability; conservation and preservation, including digital preservation; librarianship and library administration; the sociology of information; and human–computer interaction.

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  • Visual Turing Test

    Visual Turing Test

    The Visual Turing Test is “an operator-assisted device that produces a stochastic sequence of binary questions from a given test image”. The query engine produces a sequence of questions that have unpredictable answers given the history of questions. The test is only about vision and does not require any natural language processing. The job of the human operator is to provide the correct answer to the question or reject it as ambiguous. The query generator produces questions such that they follow a “natural story line”, similar to what humans do when they look at a picture. == History == Research in computer vision dates back to the 1960s when Seymour Papert first attempted to solve the problem. This unsuccessful attempt was referred to as the Summer Vision Project. The reason why it was not successful was because computer vision is more complicated than what people think. The complexity is in alignment with the human visual system. Roughly 50% of the human brain is devoted in processing vision, which indicates that it is a difficult problem. Later there were attempts to solve the problems with models inspired by the human brain. Perceptrons by Frank Rosenblatt, which is a form of the neural networks, was one of the first such approaches. These simple neural networks could not live up to their expectations and had certain limitations due to which they were not considered in future research. Later with the availability of the hardware and some processing power the research shifted to image processing which involves pixel-level operations, like finding edges, de-noising images or applying filters to name a few. There was some great progress in this field but the problem of vision which was to make the machines understand the images was still not being addressed. During this time the neural networks also resurfaced as it was shown that the limitations of the perceptrons can be overcome by Multi-layer perceptrons. Also in the early 1990s convolutional neural networks were born which showed great results on digit recognition but did not scale up well on harder problems. The late 1990s and early 2000s saw the birth of modern computer vision. One of the reasons this happened was due to the availability of key, feature extraction and representation algorithms. Features along with the already present machine learning algorithms were used to detect, localise and segment objects in Images. While all these advancements were being made, the community felt the need to have standardised datasets and evaluation metrics so the performances can be compared. This led to the emergence of challenges like the Pascal VOC challenge and the ImageNet challenge. The availability of standard evaluation metrics and the open challenges gave directions to the research. Better algorithms were introduced for specific tasks like object detection and classification. Visual Turing Test aims to give a new direction to the computer vision research which would lead to the introduction of systems that will be one step closer to understanding images the way humans do. == Current evaluation practices == A large number of datasets have been annotated and generalised to benchmark performances of difference classes of algorithms to assess different vision tasks (e.g., object detection/recognition) on some image domain (e.g., scene images). One of the most famous datasets in computer vision is ImageNet which is used to assess the problem of object level Image classification. ImageNet is one of the largest annotated datasets available and has over one million images. The other important vision task is object detection and localisation which refers to detecting the object instance in the image and providing the bounding box coordinates around the object instance or segmenting the object. The most popular dataset for this task is the Pascal dataset. Similarly there are other datasets for specific tasks like the H3D dataset for human pose detection, Core dataset to evaluate the quality of detected object attributes such as colour, orientation, and activity. Having these standard datasets has helped the vision community to come up with well performing algorithms for all these tasks. The next logical step is to create a larger task encompassing of these smaller subtasks. Having such a task would lead to building systems that would understand images, as understanding images would inherently involve detecting objects, localising them and segmenting them. == Details == The Visual Turing Test (VTT) unlike the Turing test has a query engine system which interrogates a computer vision system in the presence of a human co-ordinator. It is a system that generates a random sequence of binary questions specific to the test image, such that the answer to any question k is unpredictable given the true answers to the previous k − 1 questions (also known as history of questions). The test happens in the presence of a human operator who serves two main purposes: removing the ambiguous questions and providing the correct answers to the unambiguous questions. Given an Image infinite possible binary questions can be asked and a lot of them are bound to be ambiguous. These questions if generated by the query engine are removed by the human moderator and instead the query engine generates another question such that the answer to it is unpredictable given the history of the questions. The aim of the Visual Turing Test is to evaluate the Image understanding of a computer system, and an important part of image understanding is the story line of the image. When humans look at an image, they do not think that there is a car at ‘x’ pixels from the left and ‘y’ pixels from the top, but instead they look at it as a story, for e.g. they might think that there is a car parked on the road, a person is exiting the car and heading towards a building. The most important elements of the story line are the objects and so to extract any story line from an image the first and the most important task is to instantiate the objects in it, and that is what the query engine does. === Query engine === The query engine is the core of the Visual Turing Test and it comprises two main parts : Vocabulary and Questions ==== Vocabulary ==== Vocabulary is a set of words that represent the elements of the images. This vocabulary when used with appropriate grammar leads to a set of questions. The grammar is defined in the next section in a way that it leads to a space of binary questions. The vocabulary V {\displaystyle {\mathcal {V}}} consist of three components: Types of Objects T {\displaystyle {\mathcal {T}}} Type-dependent attributes of objects A ( t ) {\displaystyle {\mathcal {A}}(t)} Type-dependent relationships between two objects R ( t , t ′ ) {\displaystyle {\mathcal {R}}(t,t')} For Images of urban street scenes the types of objects include people, vehicle and buildings. Attributes refer to the properties of these objects, for e.g. female, child, wearing a hat or carrying something, for people and moving, parked, stopped, one tire visible or two tires visible for vehicles. Relationships between each pair of object classes can be either “ordered” or “unordered”. The unordered relationships may include talking, walking together and the ordered relationships include taller, closer to the camera, occluding, being occluded etc. Additionally all of this vocabulary is used in context of rectangular image regions w \in W which allow for the localisation of objects in the image. An extremely large number of such regions are possible and this complicates the problem, so for this test, regions at specific scales are only used which include 1/16 the size of image, 1/4 the size of image, 1/2 the size of image or larger. ==== Questions ==== The question space is composed of four types of questions: Existence questions: The aim of the existence questions is to find new objects in the image that have not been uniquely identified previously. They are of the form : Qexist = 'Is there an instance of an object of type t with attributes A partially visible in region w that was not previously instantiated?' Uniqueness questions: A uniqueness question tries to uniquely identify an object to instantiate it. Quniq = 'Is there a unique instance of an object of type t with attributes A partially visible in region w that was not previously instantiated?' The uniqueness questions along with the existence questions form the instantiation questions. As mentioned earlier instantiating objects leads to other interesting questions and eventually a story line. Uniqueness questions follow the existence questions and a positive answer to it leads to instantiation of an object. Attribute questions: An attribute question tries to find more about the object once it has been instantiated. Such questions can query about a single attribute, conjunction of two attributes or disjunction of two attributes. Qatt(ot) = {'Does object ot have attribute a?' , 'Does object

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  • Artificial intelligence industry in Canada

    Artificial intelligence industry in Canada

    The artificial intelligence industry in Canada is a rapidly expanding sector. Although Canada held a pioneering role in the early development of artificial intelligence, transforming research excellence into broad commercial adoption has proven challenging. Despite globally recognized scientific achievements and a deep pool of skilled experts, by June 2024, Canada recorded the lowest rate of AI integration among OECD countries, with only 12% of firms implementing AI in their products or services. However, AI adoption has shown significant momentum—doubling from mid-2024 to mid-2025, rising from 6.1% to 12.2%. As of September 2025, Statistics Canada indicated that while about one-third of Canadian businesses had no plans to adopt artificial intelligence in the next year, 14.5% reported intentions to begin using AI for producing goods or delivering services. The primary reasons for not moving forward with AI were lack of relevance, insufficient knowledge, and privacy concerns. According to Public Works Canada (PwC), the pace of AI adoption in Canada is roughly three-quarters of the United States rate, highlighting a notable gap between the two countries in business integration of this technology. British-Canadian computer scientist Geoffrey Hinton stated in 2025 that Canadian companies are adopting artificial intelligence at a slower pace, which may result in the loss of the country's early advantages in the field. At the "All In AI" conference held in Montreal in September 2025, the Minister of Artificial Intelligence and Digital Innovation Evan Solomon, described "Building digital sovereignty" as the most pressing democratic issue of the time. He introduced a 26-person task force focused on updating Canada's AI strategy. In their 2024 report " "Learning Together for Responsible Artificial Intelligence" report, the Innovation, Science, and Economic Development Canada stressed that public awareness, trust, and AI literacy are essential for the responsible adoption and governance of AI in Canada. Montreal workshops in 2021 expanded the OECD's 2019 definition of AI as "the set of computer techniques that enable a machine (e.g., a computer or telephone) to perform tasks that typically require intelligence, such as reasoning or learning. It is also referred to as the automation of intelligent tasks. Scientific developments in AI, such as deep-learning techniques, have made it possible to design access to huge amounts of data and ever-increasing computing power. These new techniques have been rapidly deployed on a large scale in all areas of social life, in transport, education, culture and health." == Federal investments and policy == The 2025 federal budget allocates over $1 billion over the next five years to bolster Canada's artificial intelligence and quantum computing ecosystem. == Industry landscape or research hubs == AlexNet, an influential deep convolutional neural network developed at the University of Toronto by Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton, marked a pivotal turning point in modern artificial intelligence. In 2012, it achieved a dramatic reduction in error rates for the ImageNet Large Scale Visual Recognition Challenge (ILSVRC), showcasing the practical power of deep learning and GPU acceleration. The success of AlexNet helped cement Canada’s reputation for AI leadership and inspired rapid adoption of deep learning across the technology sector, with ongoing impact in both academic and commercial domains. In healthcare, AlexNet has been adapted for medical imaging to assist with analyzing radiographs, mammograms, and other scans, including identifying abnormalities and supporting clinical diagnosis. In 2015, the Ottawa-based start-up Advanced Symbolics Inc. (ASI) began developing Polly, an artificial intelligence system designed to analyze and anticipate how target audiences behave—enabling more effective communication strategies and advertising campaigns. Polly was named after its first assignment analyzing the politics of Brexit. The AI gained widespread attention in 2016 for accurately forecasting both the Brexit referendum and the 2016 U.S. presidential election won by Donald Trump. The company states that Polly is used by organizations in diverse sectors—including healthcare, politics, entertainment, and mental health research—to support decision-making based on predictive analytics. Chartwatch, an AI tool developed in Canada, has been shown to reduce unexpected hospital deaths by 26% according to a 2024 study. The system analyzes patient data to detect subtle signs of deterioration, supporting healthcare teams in providing timely interventions. === Notable figures in AI in Canada === Geoffrey Hinton's decades-long work eventually formed the foundation of artificial intelligence, which earned him the Nobel Prize for physics in 2024. Yoshua Bengio, who won the Turing Award in 2018 for his pioneering work in deep learning, founded what would become Mila in 1993. Mila, is currently a collaboration between four Montreal-based academic partners.—the Pan-Canadian Artificial Intelligence Strategy includes Alberta's Amii, Toronto's Vector Institute, and Mila. Fakhreddine Karray's work on operational AI has had tangible impact across several Canadian-relevant sectors, notably intelligent transportation systems, virtual healthcare, and driver safety. === AI in the oil and gas industry === According to a 2020 Ernst & Young report the oil and gas industry in Canada is using AI in automating routine, repetitive, and dangerous tasks with technologies like robotic process automation and machine learning; optimizing production and processing; enhancing transportation logistics; improving equipment operation and monitoring; and enabling preventative maintenance. AI is also deployed for data analysis to improve prediction and decision-making, and is expected to automate up to 50% of job competencies in upstream oil and gas by 2040. Oilsands giant Suncor Energy operates a large fleet of autonomous trucks and has started using AI in its dispatch system at the Mildred Lake mine. As of 2024, AI manages routine tasks such as allocating trucks to dump stations and sending them to refuelling locations. === Indigenous and Inuit Innovation in AI === Indigenous organizations have been working on the creation of new technologies for language revitalization in partnership with National Research Council of Canada since the mid-2010s. In 2025, Inuit researchers and technology partners launched an AI-powered initiative to support the revitalization and preservation of Inuktitut, demonstrating how artificial intelligence can be adapted for Indigenous language and cultural priorities. A 2025 CBC article notes that, while AI can help revitalize Inuktitut, Inuit leaders emphasize concerns about data sovereignty, information ownership, and the need for Indigenous leadership to ensure transparency, privacy, and accountability in AI development. == Regulation == Canada's Artificial Intelligence and Data Act (AIDA) was proposed in November 2022, as part of the Digital Charter Implementation Act (Bill C-27). As well voluntary codes, such as the September 2023 Code of Conduct for Generative AI, and landmark investments in advanced computing infrastructure and the Canadian Artificial Intelligence Safety Institute (CAISI) reflect Canada's commitment to both safety and global competitiveness. == AI infrastructure == Canada has undertaken efforts to expand its AI computing infrastructure at both provincial and federal levels. The federal government's Canadian Sovereign AI Compute Strategy, allocated up to C$2 billion in Budget 2024, aims to enhance computing capacity to support domestic AI industry growth and AI adoption across the economy, with up to C$700 million designated to mobilize private sector investment in new or expanded data centres. Alberta has introduced an AI Data Centres Strategy to position itself as a leading North American destination for data centre investment, targeting C$100 billion worth of AI data centres under development by 2030. One major project under Alberta's strategy is the Wonder Valley AI Data Centre Park near Grande Prairie, which was exempted from provincial environmental impact assessment in April 2026 but still requires permits demonstrating safe construction and operation. According to Statista, as of April 2026, Canada has 287 data centres.

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  • Enterprise information system

    Enterprise information system

    An Enterprise Information System (EIS) is any kind of information system which improves the functions of enterprise business processes through integration. This means typically offering high quality service, dealing with large volumes of data and capable of supporting some large and possibly complex organization or enterprise. An EIS must be able to be used by all parts and all levels of an enterprise. The word enterprise can have various connotations. Frequently the term is used only to refer to very large organizations such as multi-national companies or public-sector organizations. However, the term may be used to mean virtually anything, by virtue of it having become a corporate-speak buzzword. == Purpose == Enterprise information systems provide a technology platform that enables organizations to integrate and coordinate their business processes on a robust foundation. An EIS is currently used in conjunction with customer relationship management and supply chain management to automate business processes. An enterprise information system provides a single system that is central to the organization that ensuring information can be shared across all functional levels and management hierarchies. An EIS can be used to increase business productivity and reduce service cycles, product development cycles and marketing life cycles. It may be used to amalgamate existing applications. Other outcomes include higher operational efficiency and cost savings. Financial value is not usually a direct outcome from the implementation of an enterprise information system. == Design stage == At the design stage the main characteristic of EIS efficiency evaluation is the probability of timely delivery of various messages such as command, service, etc. == Information systems == Enterprise systems create a standard data structure and are invaluable in eliminating the problem of information fragmentation caused by multiple information systems within an organization. An EIS differentiates itself from legacy systems in that it is self-transactional, self-helping and adaptable to general and specialist conditions. Unlike an enterprise information system, legacy systems are limited to department-wide communications. A typical enterprise information system would be housed in one or more data centers, would run enterprise software, and could include applications that typically cross organizational borders such as content management systems.

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