The term digital citizen is used with different meanings. According to the definition provided by Karen Mossberger, one of the authors of Digital Citizenship: The Internet, Society, and Participation, digital citizens are "those who use the internet regularly and effectively". In this sense, a digital citizen is a person who uses information technology (IT) to engage in society, politics, and government. More recent elaborations of the concept define digital citizenship as the self-enactment of people’s role in society through the use of digital technologies, stressing the empowering and democratizing characteristics of the citizenship idea. These theories aim at taking into account the ever-increasing datafication of contemporary societies (symbolically linked to the Snowden leaks), which has called into question the meaning of “being (digital) citizens in a datafied society”. This condition is also referred to as the “algorithmic society”, characterised by the increasing datafication of social life and the pervasive presence of surveillance practices – see surveillance and surveillance capitalism, the use of artificial intelligence, and Big Data. Datafication presents crucial challenges for the very notion of citizenship, so that data collection can no longer be seen as an issue of privacy alone so that:We cannot simply assume that being a citizen online already means something (whether it is the ability to participate or the ability to stay safe) and then look for those whose conduct conforms to this meaning Instead, the idea of digital citizenship shall reflect the idea that we are no longer mere “users” of technologies since they shape our agency both as individuals and as citizens. Digital citizenship refers to the responsible and respectful use of technology to engage online, evaluate information, and protect human rights. It encompasses skills for communication, collaboration, empathy, privacy protection, and security to prevent data breaches and identity theft. == Digital citizenship in the "algorithmic society" == In the context of the algorithmic society, the question of digital citizenship "becomes one of the extents to which subjects are able to challenge, avoid or mediate their data double in this datafied society”. These reflections put the emphasis on the idea of the digital space (or cyberspace) as a political space where the respect of fundamental rights of the individual shall be granted (with reference both to the traditional ones as well as to new specific rights of the internet [see “digital constitutionalism”]) and where the agency and the identity of the individuals as citizens is at stake. This idea of digital citizenship is thought to be not only active but also performative, in the sense that “in societies that are increasingly mediated through digital technologies, digital acts become important means through which citizens create, enact and perform their role in society.” In particular, for Isin and Ruppert this points towards an active meaning of (digital) citizenship based on the idea that we constitute ourselves as digital citizen by claiming rights on the internet, either by saying or by doing something. == Types of digital participation == People who characterize themselves as digital citizens often use IT extensively—creating blogs, using social networks, and participating in online journalism. Although digital citizenship begins when any child, teen, or adult signs up for an email address, posts pictures online, uses e-commerce to buy merchandise online, and/or participates in any electronic function that is B2B or B2C, the process of becoming a digital citizen goes beyond simple internet activity. According to Thomas Humphrey Marshall, a British sociologist known for his work on social citizenship, a primary framework of citizenship comprises three different traditions: liberalism, republicanism, and ascriptive hierarchy. Within this framework, the digital citizen needs to exist in order to promote equal economic opportunities and increase political participation. In this way, digital technology helps to lower the barriers to entry for participation as a citizen within a society. They also have a comprehensive understanding of digital citizenship, which is the appropriate and responsible behavior when using technology. Since digital citizenship evaluates the quality of an individual's response to membership in a digital community, it often requires the participation of all community members, both visible and those who are less visible. A large part in being a responsible digital citizen encompasses digital literacy, etiquette, online safety, and an acknowledgement of private versus public information. The development of digital citizen participation can be divided into two main stages. The first stage is through information dissemination, which includes subcategories of its own: static information dissemination, characterized largely by citizens who use read-only websites where they take control of data from credible sources in order to formulate judgments or facts. Many of these websites where credible information may be found are provided by the government. dynamic information dissemination, which is more interactive and involves citizens as well as public servants. Both questions and answers can be communicated, and citizens have the opportunity to engage in question-and-answer dialogues through two-way communication platforms The second stage of digital citizen participation is citizen deliberation, which evaluates what type of participation and role that they play when attempting to ignite some sort of policy change. static citizen participants can play a role by engaging in online polls as well as through complaints and recommendations sent up, mainly toward the government who can create changes in policy decisions. dynamic citizen participants can deliberate amongst others on their thoughts and recommendations in town hall meetings or various media sites. One potential advantage of online participation through digital citizenship is increased social inclusion. In a report on civic engagement, citizen-powered democracy can be initiated either through information shared through the web, direct communication signals made by the state toward the public, and social media tactics from both private and public companies. In fact, it was found that the community-based nature of social media platforms allow individuals to feel more socially included and informed about political issues that peers have also been found to engage with, otherwise known as a "second-order effect." Understanding strategic marketing on social media would further explain social media customers’ participation. Two types of opportunities rise as a result, the first being the ability to lower barriers that can make exchanges much easier. In addition, they have the chance to participate in transformative disruption, giving people who have a historically lower political engagement to mobilize in a much easier and convenient fashion. Nonetheless, there are several challenges that face the presence of digital technologies in political participation. Both current as well as potential challenges can create significant risks for democratic processes. Not only is digital technology still seen as relatively ambiguous, it was also seen to have "less inclusivity in democratic life." Demographic groups differ considerably in the use of technology, and thus, one group could potentially be more represented than another as a result of digital participation. Another primary challenge consists in the ideology of a "filter bubble" effect. Alongside a tremendous spread of false information, internet users could reinforce existing prejudices and assist in polarizing disagreements in the public sphere. This can lead to misinformed voting and decisions based on exposure rather than on pure knowledge. A communication technology director, Van Dijk, stated, "Computerized information campaigns and mass public information systems have to be designed and supported in such a way that they help to narrow the gap between the 'information rich' and 'information poor' otherwise the spontaneous development of ICT will widen it." Access and equivalent amounts of knowledge behind digital technology must be equivalent in order for a fair system to put into place. Alongside a lack of evidenced support for technology that can be proven to be safe for citizens, the OECD has identified five struggles for the online engagement of citizens: Scale: To what extent can a society allow every individual's voice to be heard, but also not be lost in the mass debate? This can be extremely challenging for the government, which may not effectively know how to listen and respond to each individual contribution. Capacity: How can digital technology offer citizens more information on public policy-making? The opportunity for citizens to debate with one another is lacking for acti
Douglas Parkhill
Douglas F. Parkhill is a Canadian technologist and former research minister, best known for his pioneering work on what is now called cloud computing, and his work on Canada's Telidon videotex project. He started working at the Canadian ministry of Communications (now part of the Department of Trade and Industry) in 1969, having previously worked at the Mitre Corporation. He was responsible for many activities in communications satellites, computer communications, command and control systems and telecommunications. He was winner of the Treasury Board of Canada Secretariat's Outstanding Achievement award in 1982, the Conestoga shield for services to government and industry in computer communications research and development, the Touche Ross award for Telidon development. He was an author of several publications including the 1966 book, The Challenge of the Computer Utility. In the book, Parkhill thoroughly explored many of the modern-day characteristics of cloud computing (elastic provisioning through a utility service) as well as the comparison to the electricity industry and the use of public, private, government and community forms. The book won the McKinsey Foundation award for distinguished contributions to management literature. He worked with Dave Godfrey, the Canadian writer and novelist on a later book Gutenberg two about the social and political meaning of computer technology. He was in charge of research at the Federal Department of Communications at the time when the department was funding development of the Telidon videotext system, was heavily involved in promoting the system, and had overall control of the program. In a radio broadcast in 1980, he outlined some of the potential of the system, from financial information, to theatre reservations, with the ability to pay and print out tickets from the system. He later documented the history of the Telidon project, and the history of videotext in general. == Publications == The Challenge of the Computer Utility, Addison-Wesley, 1966, ISBN 0-201-05720-4 edited with Dave Godfrey, Gutenberg Two: The New Electronics and Social Change, Press Porcepic, 1979, ISBN 0-88878-191-1 The Beginning of a Beginning. Ottawa; Department of Communications, 1987. A history of the Telidon project.
Upper ontology
In information science, an upper ontology (also known as a top-level ontology, upper model, or foundation ontology) is an ontology (in the sense used in information science) that consists of very general terms (such as "object", "property", "relation") that are common across all domains. An important function of an upper ontology is to support broad semantic interoperability among a large number of domain-specific ontologies by providing a common starting point for the formulation of definitions. Terms in the domain ontology are ranked under the terms in the upper ontology, e.g., the upper ontology classes are superclasses or supersets of all the classes in the domain ontologies. A number of upper ontologies have been proposed, each with its own proponents. Library classification systems predate upper ontology systems. Though library classifications organize and categorize knowledge using general concepts that are the same across all knowledge domains, neither system is a replacement for the other. == Development == Any standard foundational ontology is likely to be contested among different groups, each with its own idea of "what exists". One factor exacerbating the failure to arrive at a common approach has been the lack of open-source applications that would permit the testing of different ontologies in the same computational environment. The differences have thus been debated largely on theoretical grounds, or are merely the result of personal preferences. Foundational ontologies can however be compared on the basis of adoption for the purposes of supporting interoperability across domain ontologies. No particular upper ontology has yet gained widespread acceptance as a de facto standard. Different organizations have attempted to define standards for specific domains. The 'Process Specification Language' (PSL) created by the National Institute of Standards and Technology (NIST) is one example. Another important factor leading to the absence of wide adoption of any existing upper ontology is the complexity. Some upper ontologies—Cyc is often cited as an example in this regard—are very large, ranging up to thousands of elements (classes, relations), with complex interactions among them and with a complexity similar to that of a human natural language, and the learning process can be even longer than for a natural language because of the unfamiliar format and logical rules. The motivation to overcome this learning barrier is largely absent because of the paucity of publicly accessible examples of use. As a result, those building domain ontologies for local applications tend to create the simplest possible domain-specific ontology, not related to any upper ontology. Such domain ontologies may function adequately for the local purpose, but they are very time-consuming to relate accurately to other domain ontologies. To solve this problem, some genuinely top level ontologies have been developed, which are deliberately designed to have minimal overlap with any domain ontologies. Examples are Basic Formal Ontology and the DOLCE (see below). === Arguments for the infeasibility of an upper ontology === Historically, many attempts in many societies have been made to impose or define a single set of concepts as more primal, basic, foundational, authoritative, true or rational than all others. A common objection to such attempts points out that humans lack the sort of transcendent perspective — or God's eye view — that would be required to achieve this goal. Humans are bound by language or culture, and so lack the sort of objective perspective from which to observe the whole terrain of concepts and derive any one standard. Thomasson, under the headline "1.5 Skepticism about Category Systems", wrote: "category systems, at least as traditionally presented, seem to presuppose that there is a unique true answer to the question of what categories of entity there are – indeed the discovery of this answer is the goal of most such inquiries into ontological categories. [...] But actual category systems offered vary so much that even a short survey of past category systems like that above can undermine the belief that such a unique, true and complete system of categories may be found. Given such a diversity of answers to the question of what the ontological categories are, by what criteria could we possibly choose among them to determine which is uniquely correct?" Another objection is the problem of formulating definitions. Top level ontologies are designed to maximize support for interoperability across a large number of terms. Such ontologies must therefore consist of terms expressing very general concepts, but such concepts are so basic to our understanding that there is no way in which they can be defined, since the very process of definition implies that a less basic (and less well understood) concept is defined in terms of concepts that are more basic and so (ideally) more well understood. Very general concepts can often only be elucidated, for example by means of examples, or paraphrase. There is no self-evident way of dividing the world up into concepts, and certainly no non-controversial one There is no neutral ground that can serve as a means of translating between specialized (or "lower" or "application-specific") ontologies Human language itself is already an arbitrary approximation of just one among many possible conceptual maps. To draw any necessary correlation between English words and any number of intellectual concepts, that we might like to represent in our ontologies, is just asking for trouble. (WordNet, for instance, is successful and useful, precisely because it does not pretend to be a general-purpose upper ontology; rather, it is a tool for semantic / syntactic / linguistic disambiguation, which is richly embedded in the particulars and peculiarities of the English language.) Any hierarchical or topological representation of concepts must begin from some ontological, epistemological, linguistic, cultural, and ultimately pragmatic perspective. Such pragmatism does not allow for the exclusion of politics between persons or groups, indeed it requires they be considered as perhaps more basic primitives than any that are represented. Those who doubt the feasibility of general purpose ontologies are more inclined to ask "what specific purpose do we have in mind for this conceptual map of entities and what practical difference will this ontology make?" This pragmatic philosophical position surrenders all hope of devising the encoded ontology version of "The world is everything that is the case." (Wittgenstein, Tractatus Logico-Philosophicus). Finally, there are objections similar to those against artificial intelligence. Technically, the complex concept acquisition and the social / linguistic interactions of human beings suggest any axiomatic foundation of "most basic" concepts must be cognitive biological or otherwise difficult to characterize since we don't have axioms for such systems. Ethically, any general-purpose ontology could quickly become an actual tyranny by recruiting adherents into a political program designed to propagate it and its funding means, and possibly defend it by violence. Historically, inconsistent and irrational belief systems have proven capable of commanding obedience to the detriment or harm of persons both inside and outside a society that accepts them. How much more harmful would a consistent rational one be, were it to contain even one or two basic assumptions incompatible with human life? === Arguments for the feasibility of an upper ontology === Many of those who doubt the possibility of developing wide agreement on a common upper ontology fall into one of two traps: they assert that there is no possibility of universal agreement on any conceptual scheme; but they argue that a practical common ontology does not need to have universal agreement, it only needs a large enough user community (as is the case for human languages) to make it profitable for developers to use it as a means to general interoperability, and for third-party developer to develop utilities to make it easier to use; and they point out that developers of data schemes find different representations congenial for their local purposes; but they do not demonstrate that these different representations are in fact logically inconsistent. In fact, different representations of assertions about the real world (though not philosophical models), if they accurately reflect the world, must be logically consistent, even if they focus on different aspects of the same physical object or phenomenon. If any two assertions about the real world are logically inconsistent, one or both must be wrong, and that is a topic for experimental investigation, not for ontological representation. In practice, representations of the real world are created as and known to be approximations to the basic reality, and their use is circumscribed by the limits of e
Vinelink.com
Vinelink.com (VINE) is a national website in the United States that allows victims of crime, and the general public, to track the movements of prisoners held by the various states and territories. The first four letters in the websites name, "vine", are an acronym for "Victim Information and Notification Everyday". Vinelink.com displays information, based on the information provided by the various states' departments of correction and other law enforcement agencies, on whether an inmate is in custody, has been released, has been granted parole or probation, or has escaped from custody. In some cases, the website will reveal whether a defendant has been granted parole or probation, but then subsequently violated conditions of their release and become a fugitive. Information provided on Vinelink.com represents metadata, in that the website lists a defendant's custody status; but does not list what the individual is charged with, their criminal history, or the amount of their bail, if applicable. Internet users accessing the Vinelink.com website choose from a map of states and provinces within the United States where they wish to perform a search for an inmate. The user may then search for an individual using the inmate's or parolee's name, or by entering the inmate's specific department of corrections inmate number, if known. When the inmate's custody status changes, users who have registered to be notified of such changes will be notified via email, phone or both. This information is currently released upon request, without the website requesting reasons for the users search or requiring payment, as public records available to the general public. Inmate information is available for most states, and for Puerto Rico, on the website. The states of Arizona, Georgia, Massachusetts, Montana, New Hampshire and West Virginia provide very limited information on the site. In March of 2025, The Maine Sheriff's Association entered into a contract to pilot the use of the VINE system in three counties in the state as well as a regional jail, therefore making South Dakota the only state that does not participate in the VINE system to any degree. The website does not provide data on prisoners detained by the Federal Bureau of Prisons which has its own inmate locator web site nor for inmates of the U.S. military prisons.
Divide-and-conquer algorithm
In computer science, divide and conquer is an algorithm design paradigm. A divide-and-conquer algorithm recursively breaks down a problem into two or more sub-problems of the same or related type, until these become simple enough to be solved directly. The solutions to the sub-problems are then combined to give a solution to the original problem. The divide-and-conquer technique is the basis of efficient algorithms for many problems, such as sorting (e.g., quicksort, merge sort), multiplying large numbers (e.g., the Karatsuba algorithm), finding the closest pair of points, syntactic analysis (e.g., top-down parsers), SAT solving, and computing the discrete Fourier transform (FFT). Designing efficient divide-and-conquer algorithms can be difficult. As in mathematical induction, it is often necessary to generalize the problem to make it amenable to a recursive solution. The correctness of a divide-and-conquer algorithm is usually proved by mathematical induction, and its computational cost is often determined by solving recurrence relations. == Divide and conquer == The divide-and-conquer paradigm is often used to find an optimal solution of a problem. Its basic idea is to decompose a given problem into two or more similar, but simpler, subproblems, to solve them in turn, and to compose their solutions to solve the given problem. Problems of sufficient simplicity are solved directly. For example, to sort a given list of n natural numbers, split it into two lists of about n/2 numbers each, sort each of them in turn, and interleave both results appropriately to obtain the sorted version of the given list (see the picture). This approach is known as the merge sort algorithm. The name "divide and conquer" is sometimes applied to algorithms that reduce each problem to only one sub-problem, such as the binary search algorithm for finding a record in a sorted list (or its analogue in numerical computing, the bisection algorithm for root finding). These algorithms can be implemented more efficiently than general divide-and-conquer algorithms; in particular, if they use tail recursion, they can be converted into simple loops. Under this broad definition, however, every algorithm that uses recursion or loops could be regarded as a "divide-and-conquer algorithm". Therefore, some authors consider that the name "divide and conquer" should be used only when each problem may generate two or more subproblems. The name decrease and conquer has been proposed instead for the single-subproblem class. An important application of divide and conquer is in optimization, where if the search space is reduced ("pruned") by a constant factor at each step, the overall algorithm has the same asymptotic complexity as the pruning step, with the constant depending on the pruning factor (by summing the geometric series); this is known as prune and search. == Early historical examples == Early examples of these algorithms are primarily decrease and conquer – the original problem is successively broken down into single subproblems, and indeed can be solved iteratively. Binary search, a decrease-and-conquer algorithm where the subproblems are of roughly half the original size, has a long history. While a clear description of the algorithm on computers appeared in 1946 in an article by John Mauchly, the idea of using a sorted list of items to facilitate searching dates back at least as far as Babylonia in 200 BC. Another ancient decrease-and-conquer algorithm is the Euclidean algorithm to compute the greatest common divisor of two numbers by reducing the numbers to smaller and smaller equivalent subproblems, which dates to several centuries BC. An early example of a divide-and-conquer algorithm with multiple subproblems is Gauss's 1805 description of what is now called the Cooley–Tukey fast Fourier transform (FFT) algorithm, although he did not analyze its operation count quantitatively, and FFTs did not become widespread until they were rediscovered over a century later. An early two-subproblem D&C algorithm that was specifically developed for computers and properly analyzed is the merge sort algorithm, invented by John von Neumann in 1945. Another notable example is the algorithm invented by Anatolii A. Karatsuba in 1960 that could multiply two n-digit numbers in O ( n log 2 3 ) {\displaystyle O(n^{\log _{2}3})} operations (in Big O notation). This algorithm disproved Andrey Kolmogorov's 1956 conjecture that Ω ( n 2 ) {\displaystyle \Omega (n^{2})} operations would be required for that task. As another example of a divide-and-conquer algorithm that did not originally involve computers, Donald Knuth gives the method a post office typically uses to route mail: letters are sorted into separate bags for different geographical areas, each of these bags is itself sorted into batches for smaller sub-regions, and so on until they are delivered. This is related to a radix sort, described for punch-card sorting machines as early as 1929. == Advantages == === Solving difficult problems === Divide and conquer is a powerful tool for solving conceptually difficult problems: all it requires is a way of breaking the problem into sub-problems, of solving the trivial cases, and of combining sub-problems to the original problem. Similarly, decrease and conquer only requires reducing the problem to a single smaller problem, such as the classic Tower of Hanoi puzzle, which reduces moving a tower of height n {\displaystyle n} to move a tower of height n − 1 {\displaystyle n-1} . === Algorithm efficiency === The divide-and-conquer paradigm often helps in the discovery of efficient algorithms. It was the key, for example, to Karatsuba's fast multiplication method, the quicksort and mergesort algorithms, the Strassen algorithm for matrix multiplication, and fast Fourier transforms. In all these examples, the D&C approach led to an improvement in the asymptotic cost of the solution. For example, if (a) the base cases have constant-bounded size, the work of splitting the problem and combining the partial solutions is proportional to the problem's size n {\displaystyle n} , and (b) there is a bounded number p {\displaystyle p} of sub-problems of size ~ n p {\displaystyle {\frac {n}{p}}} at each stage, then the cost of the divide-and-conquer algorithm will be O ( n log p n ) {\displaystyle O(n\log _{p}n)} . For other types of divide-and-conquer approaches, running times can also be generalized. For example, when a) the work of splitting the problem and combining the partial solutions take c n {\displaystyle cn} time, where n {\displaystyle n} is the input size and c {\displaystyle c} is some constant; b) when n < 2 {\displaystyle n<2} , the algorithm takes time upper-bounded by c {\displaystyle c} , and c) there are q {\displaystyle q} subproblems where each subproblem has size ~ n 2 {\displaystyle {\frac {n}{2}}} . Then, the running times are as follows: if the number of subproblems q > 2 {\displaystyle q>2} , then the divide-and-conquer algorithm's running time is bounded by O ( n log 2 q ) {\displaystyle O(n^{\log _{2}q})} . if the number of subproblems is exactly one, then the divide-and-conquer algorithm's running time is bounded by O ( n ) {\displaystyle O(n)} . If, instead, the work of splitting the problem and combining the partial solutions take c n 2 {\displaystyle cn^{2}} time, and there are 2 subproblems where each has size n 2 {\displaystyle {\frac {n}{2}}} , then the running time of the divide-and-conquer algorithm is bounded by O ( n 2 ) {\displaystyle O(n^{2})} . === Parallelism === Divide-and-conquer algorithms are naturally adapted for execution in multi-processor machines, especially shared-memory systems where the communication of data between processors does not need to be planned in advance because distinct sub-problems can be executed on different processors. === Memory access === Divide-and-conquer algorithms naturally tend to make efficient use of memory caches. The reason is that once a sub-problem is small enough, it and all its sub-problems can, in principle, be solved within the cache, without accessing the slower main memory. An algorithm designed to exploit the cache in this way is called cache-oblivious, because it does not contain the cache size as an explicit parameter. Moreover, D&C algorithms can be designed for important algorithms (e.g., sorting, FFTs, and matrix multiplication) to be optimal cache-oblivious algorithms–they use the cache in a probably optimal way, in an asymptotic sense, regardless of the cache size. In contrast, the traditional approach to exploiting the cache is blocking, as in loop nest optimization, where the problem is explicitly divided into chunks of the appropriate size—this can also use the cache optimally, but only when the algorithm is tuned for the specific cache sizes of a particular machine. The same advantage exists with regards to other hierarchical storage systems, such as NUMA or virtual memory, as well as for multip
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
Artificial intelligence industry in Italy
The artificial intelligence industry in Italy is growing and supports industrial development. In 2024 it reached a new record, reaching 1.2 billion euros with a growth of +58% compared to 2023. While in 2025, the growth of artificial intelligence in the industrial application was even greater than in 2024 both in terms of value and application to industrial sectors. == History == The roots of AI research in Italy extend back to the 1970s, when Italian scholars began exploring automated reasoning, programming language semantics, and pattern recognition. Researchers such as those involved in early projects at the National Research Council and various universities laid the groundwork for subsequent academic and industrial developments in the field. During this period, the focus was predominantly on developing algorithms for automated theorem proving and building systems to reason about complex mathematical problems. This era witnessed the birth of methodologies that would later influence numerous AI subfields, from natural language processing (NLP) to robotics. === Institutional milestones and academic contributions === A turning point in the Italian AI landscape was the formation of the Italian Association for Artificial Intelligence (AIxIA) in 1988. Founded by academics, including Luigia Carlucci Aiello, the association established a platform for collaboration between universities, research centers, and industry. Led by Aiello, AIIA played a role in promoting research, organizing national conferences, and fostering international partnerships that connected Italy's AI community to global networks. At the same time, professors such as Roberto Navigli and numerous practitioners contributed to the advancement of AI in Italy. Navigli has worked in multilingual NLP, including the creation of BabelNet, and led the Minerva project. === Industrial AI === Over recent decades, numerous national and European initiatives supported by funding from programs such as the National Recovery and Resilience Plan (PNRR) have spurred the transition from theoretical research to practical applications. Industrial sectors including manufacturing, banking, and healthcare increasingly embraced AI-driven automation, while research institutions collaborated with industrial partners to deploy cutting-edge solutions. In recent years, Italy has also seen the establishment of specialized research centers and institutes aimed at bridging the gap between academic innovation and industrial application. These initiatives indicate a broader national commitment to integrating AI into the fabric of Italian industry. == Recent developments == === Emergence of generative AI === A landmark in Italy's modern AI evolution is the development of Minerva AI. Developed by the Sapienza NLP research group at Sapienza University of Rome and led by Professor Roberto Navigli, Minerva represents the first family of large language models (LLMs) trained from scratch with a primary focus on the Italian language. ==== Minerva 7B ==== The latest iteration, Minerva 7B, has 7 billion parameters and has been trained on an extensive corpus of over 1.5 trillion words. By using advanced instruction tuning techniques, Minerva 7B is able to produce highly accurate, coherent, and contextually sensitive responses addressing common issues such as hallucinations and inappropriate content generation. This breakthrough sets a benchmark for transparent, open-source AI development in the country. Minerva's development, carried out within the FAIR (Future Artificial Intelligence Research) project in collaboration with CINECA and supported by supercomputing resources like the Leonardo (supercomputer), aligns closely with Italy's cultural and linguistic heritage. === Establishment of AI4I === The recent establishment of the Istituto Italiano per l’Intelligenza Artificiale (AI4I) is part of Italy's strategy to improve its industrial competitiveness in AI. This dedicated institute aims to bridge the gap between research institutions and industrial enterprises; promote training and R&D support to nurture the next generation of Italian AI experts; and enhance national competitiveness. This initiative is expected to serve as a hub for applied AI research, driving innovations that are tailored to the specific needs of Italian industry and public administration. === Benefits of InvestAI === Italy's AI industry stands to benefit from the European InvestAI initiative, a plan unveiled at the recent AI Action Summit in Paris. InvestAI is an effort by the European Commission to mobilize €200 billion for AI investments, with a dedicated €20 billion fund earmarked for building AI gigafactories. These gigafactories are planned as large-scale hubs for training advanced, complex AI models using approximately 100,000 last-generation AI chips. For Italy, this investment presents several major opportunities: Access to State-of-the-Art Infrastructure: Italian companies, research institutions, and start-ups can leverage the gigafactories’ immense computational resources, enabling them to train highly sophisticated language models and other AI systems. Enhanced Competitiveness and Collaboration: With InvestAI's layered funding model where EU funds help de-risk private investments Italian firms can access capital more readily. This will bolster public–private partnerships and create a more dynamic AI ecosystem that spans from academic research to industrial applications. Alignment with National and Regional Initiatives: The Istituto Italiano per l’Intelligenza Artificiale (AI4I), based in Turin, is already recognized as a strategic asset by both Italy and the European Union. As the main recipient of InvestAI funds in Italy, AI4I will play a pivotal role in implementing these investments locally, fostering innovation in sectors like manufacturing, healthcare and aerospace. Commission President Ursula von der Leyen emphasized that InvestAI is designed to democratize AI innovation throughout Europe by ensuring that even smaller companies have access to high-performance computing power. For Italy, this means not only keeping pace with global leaders but also harnessing European-scale investments to transform its AI industry and drive economic growth.