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  DryvIQ

  DryvIQ is a software application that enables businesses to migrate on-site system files and associated data across storage and content management platforms, as well as create synchronized hybrid storage systems. == History == Before it was DryvIQ, the software SkySync was released in 2013 by Ann Arbor, Michigan based company, Portal Architects, Inc. The company created SkySync, a back-end, administrative application designed to transfer content across storage platforms, after abandoning 18 months of development on a desktop application called SkyBrary in 2011. Between 2014 and 2015, Portal Architects established partnerships with the following companies: Autodesk, Box, Dropbox, Egnyte, EMC, Google, Syncplicity, Huddle, IBM, Microsoft, OpenText, Oracle, Citrix ShareFile, Hightail and Internet2. SkySync (currently DryvIQ) was named a "Cool Vendor in Content Management" by Gartner in 2015. In 2022, SkySync changed its name to DryvIQ, which is now what the company is currently known as. == Overview == DryvIQ is a software application that syncs, migrates or backs up files including their associated properties, metadata, versions, user accounts and permissions across on-premises and Cloud-based storage platforms. The software deploys on a server, virtual machine or within Microsoft Azure, Amazon Web Services or other cloud computing services.

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  Inductive probability

  Inductive probability attempts to give the probability of future events based on past events. It is the basis for inductive reasoning, and gives the mathematical basis for learning and the perception of patterns. It is a source of knowledge about the world. There are three sources of knowledge: inference, communication, and deduction. Communication relays information found using other methods. Deduction establishes new facts based on existing facts. Inference establishes new facts from data. Its basis is Bayes' theorem. Information describing the world is written in a language. For example, a simple mathematical language of propositions may be chosen. Sentences may be written down in this language as strings of characters. But in the computer it is possible to encode these sentences as strings of bits (1s and 0s). Then the language may be encoded so that the most commonly used sentences are the shortest. This internal language implicitly represents probabilities of statements. Occam's razor says the "simplest theory, consistent with the data is most likely to be correct". The "simplest theory" is interpreted as the representation of the theory written in this internal language. The theory with the shortest encoding in this internal language is most likely to be correct. == History == Probability and statistics was focused on probability distributions and tests of significance. Probability was formal, well defined, but limited in scope. In particular its application was limited to situations that could be defined as an experiment or trial, with a well defined population. Bayes's theorem is named after Rev. Thomas Bayes 1701–1761. Bayesian inference broadened the application of probability to many situations where a population was not well defined. But Bayes' theorem always depended on prior probabilities, to generate new probabilities. It was unclear where these prior probabilities should come from. Ray Solomonoff developed algorithmic probability which gave an explanation for what randomness is and how patterns in the data may be represented by computer programs, that give shorter representations of the data circa 1964. Chris Wallace and D. M. Boulton developed minimum message length circa 1968. Later Jorma Rissanen developed the minimum description length circa 1978. These methods allow information theory to be related to probability, in a way that can be compared to the application of Bayes' theorem, but which give a source and explanation for the role of prior probabilities. Marcus Hutter combined decision theory with the work of Ray Solomonoff and Andrey Kolmogorov to give a theory for the Pareto optimal behavior for an Intelligent agent, circa 1998. === Minimum description/message length === The program with the shortest length that matches the data is the most likely to predict future data. This is the thesis behind the minimum message length and minimum description length methods. At first sight Bayes' theorem appears different from the minimimum message/description length principle. At closer inspection it turns out to be the same. Bayes' theorem is about conditional probabilities, and states the probability that event B happens if firstly event A happens: P ( A ∧ B ) = P ( B ) ⋅ P ( A | B ) = P ( A ) ⋅ P ( B | A ) {\displaystyle P(A\land B)=P(B)\cdot P(A|B)=P(A)\cdot P(B|A)} becomes in terms of message length L, L ( A ∧ B ) = L ( B ) + L ( A | B ) = L ( A ) + L ( B | A ) . {\displaystyle L(A\land B)=L(B)+L(A|B)=L(A)+L(B|A).} This means that if all the information is given describing an event then the length of the information may be used to give the raw probability of the event. So if the information describing the occurrence of A is given, along with the information describing B given A, then all the information describing A and B has been given. ==== Overfitting ==== Overfitting occurs when the model matches the random noise and not the pattern in the data. For example, take the situation where a curve is fitted to a set of points. If a polynomial with many terms is fitted then it can more closely represent the data. Then the fit will be better, and the information needed to describe the deviations from the fitted curve will be smaller. Smaller information length means higher probability. However, the information needed to describe the curve must also be considered. The total information for a curve with many terms may be greater than for a curve with fewer terms, that has not as good a fit, but needs less information to describe the polynomial. === Inference based on program complexity === Solomonoff's theory of inductive inference is also inductive inference. A bit string x is observed. Then consider all programs that generate strings starting with x. Cast in the form of inductive inference, the programs are theories that imply the observation of the bit string x. The method used here to give probabilities for inductive inference is based on Solomonoff's theory of inductive inference. ==== Detecting patterns in the data ==== If all the bits are 1, then people infer that there is a bias in the coin and that it is more likely also that the next bit is 1 also. This is described as learning from, or detecting a pattern in the data. Such a pattern may be represented by a computer program. A short computer program may be written that produces a series of bits which are all 1. If the length of the program K is L ( K ) {\displaystyle L(K)} bits then its prior probability is, P ( K ) = 2 − L ( K ) {\displaystyle P(K)=2^{-L(K)}} The length of the shortest program that represents the string of bits is called the Kolmogorov complexity. Kolmogorov complexity is not computable. This is related to the halting problem. When searching for the shortest program some programs may go into an infinite loop. ==== Considering all theories ==== The Greek philosopher Epicurus is quoted as saying "If more than one theory is consistent with the observations, keep all theories". As in a crime novel all theories must be considered in determining the likely murderer, so with inductive probability all programs must be considered in determining the likely future bits arising from the stream of bits. Programs that are already longer than n have no predictive power. The raw (or prior) probability that the pattern of bits is random (has no pattern) is 2 − n {\displaystyle 2^{-n}} . Each program that produces the sequence of bits, but is shorter than the n is a theory/pattern about the bits with a probability of 2 − k {\displaystyle 2^{-k}} where k is the length of the program. The probability of receiving a sequence of bits y after receiving a series of bits x is then the conditional probability of receiving y given x, which is the probability of x with y appended, divided by the probability of x. ==== Universal priors ==== The programming language affects the predictions of the next bit in the string. The language acts as a prior probability. This is particularly a problem where the programming language codes for numbers and other data types. Intuitively we think that 0 and 1 are simple numbers, and that prime numbers are somehow more complex than numbers that may be composite. Using the Kolmogorov complexity gives an unbiased estimate (a universal prior) of the prior probability of a number. As a thought experiment an intelligent agent may be fitted with a data input device giving a series of numbers, after applying some transformation function to the raw numbers. Another agent might have the same input device with a different transformation function. The agents do not see or know about these transformation functions. Then there appears no rational basis for preferring one function over another. A universal prior insures that although two agents may have different initial probability distributions for the data input, the difference will be bounded by a constant. So universal priors do not eliminate an initial bias, but they reduce and limit it. Whenever we describe an event in a language, either using a natural language or other, the language has encoded in it our prior expectations. So some reliance on prior probabilities are inevitable. A problem arises where an intelligent agent's prior expectations interact with the environment to form a self reinforcing feed back loop. This is the problem of bias or prejudice. Universal priors reduce but do not eliminate this problem. === Universal artificial intelligence === The theory of universal artificial intelligence applies decision theory to inductive probabilities. The theory shows how the best actions to optimize a reward function may be chosen. The result is a theoretical model of intelligence. It is a fundamental theory of intelligence, which optimizes the agents behavior in, Exploring the environment; performing actions to get responses that broaden the agents knowledge. Competing or co-operating with another agent; games. Balancing short and long term rewards. In general no agent will always provi

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  Structural risk minimization

  Structural risk minimization (SRM) is an inductive principle of use in machine learning. Commonly in machine learning, a generalized model must be selected from a finite data set, with the consequent problem of overfitting – the model becoming too strongly tailored to the particularities of the training set and generalizing poorly to new data. The SRM principle addresses this problem by balancing the model's complexity against its success at fitting the training data. This principle was first set out in a 1974 book by Vladimir Vapnik and Alexey Chervonenkis and uses the VC dimension. In practical terms, Structural Risk Minimization is implemented by minimizing E t r a i n + β H ( W ) {\displaystyle E_{train}+\beta H(W)} , where E t r a i n {\displaystyle E_{train}} is the train error, the function H ( W ) {\displaystyle H(W)} is called a regularization function, and β {\displaystyle \beta } is a constant. H ( W ) {\displaystyle H(W)} is chosen such that it takes large values on parameters W {\displaystyle W} that belong to high-capacity subsets of the parameter space. Minimizing H ( W ) {\displaystyle H(W)} in effect limits the capacity of the accessible subsets of the parameter space, thereby controlling the trade-off between minimizing the training error and minimizing the expected gap between the training error and test error. The SRM problem can be formulated in terms of data. Given n data points consisting of data x and labels y, the objective J ( θ ) {\displaystyle J(\theta )} is often expressed in the following manner: J ( θ ) = 1 2 n ∑ i = 1 n ( h θ ( x i ) − y i ) 2 + λ 2 ∑ j = 1 d θ j 2 {\displaystyle J(\theta )={\frac {1}{2n}}\sum _{i=1}^{n}(h_{\theta }(x^{i})-y^{i})^{2}+{\frac {\lambda }{2}}\sum _{j=1}^{d}\theta _{j}^{2}} The first term is the mean squared error (MSE) term between the value of the learned model, h θ {\displaystyle h_{\theta }} , and the given labels y {\displaystyle y} . This term is the training error, E t r a i n {\displaystyle E_{train}} , that was discussed earlier. The second term, places a prior over the weights, to favor sparsity and penalize larger weights. The trade-off coefficient, λ {\displaystyle \lambda } , is a hyperparameter that places more or less importance on the regularization term. Larger λ {\displaystyle \lambda } encourages sparser weights at the expense of a more optimal MSE, and smaller λ {\displaystyle \lambda } relaxes regularization allowing the model to fit to data. Note that as λ → ∞ {\displaystyle \lambda \to \infty } the weights become zero, and as λ → 0 {\displaystyle \lambda \to 0} , the model typically suffers from overfitting.

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  Coupled pattern learner

  Coupled Pattern Learner (CPL) is a machine learning algorithm which couples the semi-supervised learning of categories and relations to forestall the problem of semantic drift associated with boot-strap learning methods. == Coupled Pattern Learner == Semi-supervised learning approaches using a small number of labeled examples with many unlabeled examples are usually unreliable as they produce an internally consistent, but incorrect set of extractions. CPL solves this problem by simultaneously learning classifiers for many different categories and relations in the presence of an ontology defining constraints that couple the training of these classifiers. It was introduced by Andrew Carlson, Justin Betteridge, Estevam R. Hruschka Jr. and Tom M. Mitchell in 2009. == CPL overview == CPL is an approach to semi-supervised learning that yields more accurate results by coupling the training of many information extractors. Basic idea behind CPL is that semi-supervised training of a single type of extractor such as ‘coach’ is much more difficult than simultaneously training many extractors that cover a variety of inter-related entity and relation types. Using prior knowledge about the relationships between these different entities and relations CPL makes unlabeled data as a useful constraint during training. For e.g., ‘coach(x)’ implies ‘person(x)’ and ‘not sport(x)’. == CPL description == === Coupling of predicates === CPL primarily relies on the notion of coupling the learning of multiple functions so as to constrain the semi-supervised learning problem. CPL constrains the learned function in two ways. Sharing among same-arity predicates according to logical relations Relation argument type-checking === Sharing among same-arity predicates === Each predicate P in the ontology has a list of other same-arity predicates with which P is mutually exclusive. If A is mutually exclusive with predicate B, A’s positive instances and patterns become negative instances and negative patterns for B. For example, if ‘city’, having an instance ‘Boston’ and a pattern ‘mayor of arg1’, is mutually exclusive with ‘scientist’, then ‘Boston’ and ‘mayor of arg1’ will become a negative instance and a negative pattern respectively for ‘scientist.’ Further, Some categories are declared to be a subset of another category. For e.g., ‘athlete’ is a subset of ‘person’. === Relation argument type-checking === This is a type checking information used to couple the learning of relations and categories. For example, the arguments of the ‘ceoOf’ relation are declared to be of the categories ‘person’ and ‘company’. CPL does not promote a pair of noun phrases as an instance of a relation unless the two noun phrases are classified as belonging to the correct argument types. === Algorithm description === Following is a quick summary of the CPL algorithm. Input: An ontology O, and a text corpus C Output: Trusted instances/patterns for each predicate for i=1,2,...,∞ do foreach predicate p in O do EXTRACT candidate instances/contextual patterns using recently promoted patterns/instances; FILTER candidates that violate coupling; RANK candidate instances/patterns; PROMOTE top candidates; end end ==== Inputs ==== A large corpus of Part-Of-Speech tagged sentences and an initial ontology with predefined categories, relations, mutually exclusive relationships between same-arity predicates, subset relationships between some categories, seed instances for all predicates, and seed patterns for the categories. ==== Candidate extraction ==== CPL finds new candidate instances by using newly promoted patterns to extract the noun phrases that co-occur with those patterns in the text corpus. CPL extracts, Category Instances Category Patterns Relation Instances Relation Patterns ==== Candidate filtering ==== Candidate instances and patterns are filtered to maintain high precision, and to avoid extremely specific patterns. An instance is only considered for assessment if it co-occurs with at least two promoted patterns in the text corpus, and if its co-occurrence count with all promoted patterns is at least three times greater than its co-occurrence count with negative patterns. ==== Candidate ranking ==== CPL ranks candidate instances using the number of promoted patterns that they co-occur with so that candidates that occur with more patterns are ranked higher. Patterns are ranked using an estimate of the precision of each pattern. ==== Candidate promotion ==== CPL ranks the candidates according to their assessment scores and promotes at most 100 instances and 5 patterns for each predicate. Instances and patterns are only promoted if they co-occur with at least two promoted patterns or instances, respectively. == Meta-Bootstrap Learner == Meta-Bootstrap Learner (MBL) was also proposed by the authors of CPL. Meta-Bootstrap learner couples the training of multiple extraction techniques with a multi-view constraint, which requires the extractors to agree. It makes addition of coupling constraints on top of existing extraction algorithms, while treating them as black boxes, feasible. MBL assumes that the errors made by different extraction techniques are independent. Following is a quick summary of MBL. Input: An ontology O, a set of extractors ε Output: Trusted instances for each predicate for i=1,2,...,∞ do foreach predicate p in O do foreach extractor e in ε do Extract new candidates for p using e with recently promoted instances; end FILTER candidates that violate mutual-exclusion or type-checking constraints; PROMOTE candidates that were extracted by all extractors; end end Subordinate algorithms used with MBL do not promote any instance on their own, they report the evidence about each candidate to MBL and MBL is responsible for promoting instances. == Applications == In their paper authors have presented results showing the potential of CPL to contribute new facts to existing repository of semantic knowledge, Freebase

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  Computer appliance

  A computer appliance is a computer system with a combination of hardware, software, or firmware that is specifically designed to provide a particular computing resource. Such devices became known as appliances because of the similarity in role or management to a home appliance, which are generally closed and sealed, and are not serviceable by the user or owner. The hardware and software are delivered as an integrated product and may even be pre-configured before delivery to a customer, to provide a turn-key solution for a particular application. Unlike general purpose computers, appliances are generally not designed to allow the customers to change the software and the underlying operating system, or to flexibly reconfigure the hardware. Another form of appliance is the virtual appliance, which has similar functionality to a dedicated hardware appliance, but is distributed as a software virtual machine image for a hypervisor-equipped device. == Overview == Traditionally, software applications run on top of a general-purpose operating system, which uses the hardware resources of the computer (primarily memory, disk storage, processing power, and networking bandwidth) to meet the computing needs of the user. The main issue with the traditional model is related to complexity. It is complex to integrate the operating system and applications with a hardware platform, and complex to support it afterwards. By tightly constraining the variations of the hardware and software, the appliance becomes easily deployable, and can be used without nearly as wide (or deep) IT knowledge. Additionally, when problems and errors appear, the supporting staff very rarely needs to explore them deeply to understand the matter thoroughly. The staff needs merely training on the appliance management software to be able to resolve most of problems. In all forms of the computer appliance model, customers benefit from easy operations. The appliance has exactly one combination of hardware and operating system and application software, which has been pre-installed at the factory. This prevents customers from needing to perform complex integration work, and dramatically simplifies troubleshooting. In fact, this "turnkey operation" characteristic is the driving benefit that customers seek when purchasing appliances. To be considered an appliance, the (hardware) device needs to be integrated with software, and both are supplied as a package. This distinguishes appliances from "home grown" solutions, or solutions requiring complex implementations by integrators or value-added resellers (VARs). The appliance approach helps to decouple the various systems and applications, for example in the data center. Once a resource is decoupled, in theory it can be also centralized to become shared among many systems, centrally managed and optimized, all without requiring changes to any other system. == Tradeoffs of the computer appliance approach == The major disadvantage of deploying a computer appliance is that since they are designed to supply a specific resource, they most often include a customized operating system running over specialized hardware, neither of which are likely to be compatible with the other systems previously deployed. Customers lose flexibility. One may believe that a proprietary embedded operating system, or operating system within an application, can make the appliance much more secure from common cyber attacks. However, the opposite is true. Security by obscurity is a poor security decision, and appliances are often plagued by security issues as evidenced by the proliferation of IoT devices. == Types of appliances == The variety of computer appliances reflects the wide range of computing resources they provide to applications. Some examples: Storage appliances provide large amounts of storage, often available to many machines on the network. See Network-attached storage and Storage area network. Network appliances are general purpose routers which may also provide firewall protection, Transport Layer Security (TLS), messaging, access to specialized networking protocols (like the ebXML Message Service) and bandwidth multiplexing for the multiple systems they front-end. Backup and disaster recovery appliances computer appliances that are integrated backup software and backup targets, sometimes with hypervisors to support local DR of protected servers. They are often a gateway to a full DRaaS solution. Firewall and Security appliances Dedicated network appliances that are designed to protect computer networks from unwanted traffic. IIoT and MES Gateway appliances Computer appliances that are designed to translate data bidirectionally between control systems and enterprise systems. Proprietary, embedded, firmware applications running on the appliance use point-to-point connections to translate data between field devices in their native automation protocols and MES systems through their APIs, ODBC, or RESTful interfaces. Anti-spam appliances for e-mail spam Software appliances A single application server appliance, with just enough operating system (JeOS) for it to run. Virtual machine appliances consist of a "hypervisor style" embedded operating system running on appliance hardware. The hypervisor layer is matched to the hardware of the appliance, and cannot be varied by the customer, but the customer may load other operating systems and applications onto the appliance in the form of virtual machines. == Consumer appliances == Aside from its deployment within data centers, many computer appliances are directly used by the general public. These include: Digital video recorder Residential gateway Network-attached storage (NAS) Video game console Consumer uses stress the need for an appliance to have easy installation, configuration, and operation, with little or no technical knowledge being necessary. == Appliances in industrial automation == The world of industrial automation has been rich in appliances. These appliances have been hardened to withstand temperature and vibration extremes. These appliances are also highly configurable, enabling customization to meet a wide variety of applications. The key benefits of an appliance in automation are: Reduced downtime - a failed appliance is typically replaced with a COTS replacement and its task is quickly and easily reloaded from a backup. Highly scalable - appliances are typically targeted solutions for an area of a plant or process. As the requirements change, scalability is achieved through the installation of another appliance. Automation concepts are easily replicated throughout the enterprise by standardizing on appliances to perform the needed tasks, as opposed to the development of custom automation schemes for each task. Low TCO (total cost of ownership) - appliances are developed, tested and supported by automation product vendors and undergo a much broader level of quality testing than custom designed automation solutions. The use of appliances in automation reduce the level of testing needed in each individual application. Reduced design time - appliances perform specific functions and although they are highly configurable, they are typically self documenting. This enables appliance based solutions to be transferred from engineer to engineer with minimal need for training and documentation. Types of automation appliances: PLC (programmable logic controller) - Programmable logic controllers are appliances that are typically used for discrete control and offer a wide range of Input and Output options. They are configured through standardized programming languages such as IEC-1131. PID (proportional–integral–derivative controller) - PID controllers are appliances that monitor a process variable and, based on an error term, effect change on a control output (manipulated variable) to drive the process variable to a setpoint. PAC (programmable automation controller) - Programmable automation controllers are appliances that embody properties of both PLCs and PID controllers enabling the integration of both analog and discrete control. Universal gateway - A universal gateway appliance has the ability to communicate with a variety of devices through their respective communication protocols, and will affect data transactions between them. This in increasingly important as manufacturing strives to improve agility, quality, production rates, production costs and reduce downtime through enhanced M2M (machine to machine) communications. EATMs (Enterprise Appliance Transaction Modules) - Enterprise appliance transaction modules are appliances that affect data transactions from plant floor automation systems to enterprise business systems. They communicate to plant floor equipment through various vendor automation protocols, and communicate to business systems through database communication protocols such as JMS (Java Message Service) and SQL (Structured Query Language). == Internal structure == There are several

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  Data preprocessing

  Data preprocessing can refer to manipulation, filtration or augmentation of data before it is analyzed, and is often an important step in the data mining process. Data collection methods are often loosely controlled, resulting in out-of-range values, impossible data combinations, and missing values, amongst other issues. Preprocessing is the process by which unstructured data is transformed into intelligible representations suitable for machine-learning models. This phase of model deals with noise in order to arrive at better and improved results from the original data set which was noisy. This dataset also has some level of missing value present in it. The preprocessing pipeline used can often have large effects on the conclusions drawn from the downstream analysis. Thus, representation and quality of data is necessary before running any analysis. If there is a high proportion of irrelevant and redundant information present or noisy and unreliable data, then knowledge discovery during the training phase may be more difficult. Data preparation and filtering steps can take a considerable amount of processing time. Examples of methods used in data preprocessing include cleaning, instance selection, normalization, one-hot encoding, data transformation, feature extraction and feature selection. == Applications == === Data mining === Data preprocessing allows for the removal of unwanted data with the use of data cleaning, this allows the user to have a dataset to contain more valuable information after the preprocessing stage for data manipulation later in the data mining process. Editing such dataset to either correct data corruption or human error is a crucial step to get accurate quantifiers like true positives, true negatives, false positives and false negatives found in a confusion matrix that are commonly used for a medical diagnosis. Users are able to join data files together and use preprocessing to filter any unnecessary noise from the data which can allow for higher accuracy. Users use Python programming scripts accompanied by the pandas library which gives them the ability to import data from a comma-separated values as a data-frame. The data-frame is then used to manipulate data that can be challenging otherwise to do in Excel. Pandas (software) which is a powerful tool that allows for data analysis and manipulation; which makes data visualizations, statistical operations and much more, a lot easier. Many also use the R programming language to do such tasks as well. The reason why a user transforms existing files into a new one is because of many reasons. Aspects of data preprocessing may include imputing missing values, aggregating numerical quantities and transforming continuous data into categories (data binning). More advanced techniques like principal component analysis and feature selection are working with statistical formulas and are applied to complex datasets which are recorded by GPS trackers and motion capture devices. === Semantic data preprocessing === Semantic data mining is a subset of data mining that specifically seeks to incorporate domain knowledge, such as formal semantics, into the data mining process. Domain knowledge is the knowledge of the environment the data was processed in. Domain knowledge can have a positive influence on many aspects of data mining, such as filtering out redundant or inconsistent data during the preprocessing phase. Domain knowledge also works as constraint. It does this by using working as set of prior knowledge to reduce the space required for searching and acting as a guide to the data. Simply put, semantic preprocessing seeks to filter data using the original environment of said data more correctly and efficiently. There are increasingly complex problems which are asking to be solved by more elaborate techniques to better analyze existing information. Instead of creating a simple script for aggregating different numerical values into a single value, it make sense to focus on semantic based data preprocessing. The idea is to build a dedicated ontology, which explains on a higher level what the problem is about. In regards to semantic data mining and semantic pre-processing, ontologies are a way to conceptualize and formally define semantic knowledge and data. The Protégé (software) is the standard tool for constructing an ontology. In general, the use of ontologies bridges the gaps between data, applications, algorithms, and results that occur from semantic mismatches. As a result, semantic data mining combined with ontology has many applications where semantic ambiguity can impact the usefulness and efficiency of data systems. Applications include the medical field, language processing, banking, and even tutoring, among many more. There are various strengths to using a semantic data mining and ontological based approach. As previously mentioned, these tools can help during the per-processing phase by filtering out non-desirable data from the data set. Additionally, well-structured formal semantics integrated into well designed ontologies can return powerful data that can be easily read and processed by machines. A specifically useful example of this exists in the medical use of semantic data processing. As an example, a patient is having a medical emergency and is being rushed to hospital. The emergency responders are trying to figure out the best medicine to administer to help the patient. Under normal data processing, scouring all the patient’s medical data to ensure they are getting the best treatment could take too long and risk the patients’ health or even life. However, using semantically processed ontologies, the first responders could save the patient’s life. Tools like a semantic reasoner can use ontology to infer the what best medicine to administer to the patient is based on their medical history, such as if they have a certain cancer or other conditions, simply by examining the natural language used in the patient's medical records. This would allow the first responders to quickly and efficiently search for medicine without having worry about the patient’s medical history themselves, as the semantic reasoner would already have analyzed this data and found solutions. In general, this illustrates the incredible strength of using semantic data mining and ontologies. They allow for quicker and more efficient data extraction on the user side, as the user has fewer variables to account for, since the semantically pre-processed data and ontology built for the data have already accounted for many of these variables. However, there are some drawbacks to this approach. Namely, it requires a high amount of computational power and complexity, even with relatively small data sets. This could result in higher costs and increased difficulties in building and maintaining semantic data processing systems. This can be mitigated somewhat if the data set is already well organized and formatted, but even then, the complexity is still higher when compared to standard data processing. Below is a simple a diagram combining some of the processes, in particular semantic data mining and their use in ontology. The diagram depicts a data set being broken up into two parts: the characteristics of its domain, or domain knowledge, and then the actual acquired data. The domain characteristics are then processed to become user understood domain knowledge that can be applied to the data. Meanwhile, the data set is processed and stored so that the domain knowledge can applied to it, so that the process may continue. This application forms the ontology. From there, the ontology can be used to analyze data and process results. Fuzzy preprocessing is another, more advanced technique for solving complex problems. Fuzzy preprocessing and fuzzy data mining make use of fuzzy sets. These data sets are composed of two elements: a set and a membership function for the set which comprises 0 and 1. Fuzzy preprocessing uses this fuzzy data set to ground numerical values with linguistic information. Raw data is then transformed into natural language. Ultimately, fuzzy data mining's goal is to help deal with inexact information, such as an incomplete database. Currently fuzzy preprocessing, as well as other fuzzy based data mining techniques see frequent use with neural networks and artificial intelligence.

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  Artificial intelligence

  Artificial intelligence (AI) is the capability of computational systems to perform tasks typically associated with human intelligence, such as learning, reasoning, problem-solving, perception, and decision-making. It is a field of research in engineering, mathematics and computer science that develops and studies methods and software that enable machines to perceive their environment and use learning and intelligence to take actions that maximize their chances of achieving defined goals. High-profile applications of AI include advanced web search engines, chatbots, virtual assistants, autonomous vehicles, and play and analysis in strategy games (e.g., chess and Go). Since the 2020s, generative AI has become widely available to generate images, audio, and videos from text prompts. The traditional goals of AI research include learning, reasoning, knowledge representation, planning, natural language processing, and perception, as well as support for robotics. To reach these goals, AI researchers have used techniques including state space search and mathematical optimization, formal logic, artificial neural networks, and methods based on statistics, operations research, and economics. AI also draws upon psychology, linguistics, philosophy, neuroscience, and other fields. Some companies, such as OpenAI, Google DeepMind and Meta, aim to create artificial general intelligence (AGI) – AI that can complete virtually any cognitive task at least as well as a human. Artificial intelligence was founded as an academic discipline in 1956, and the field went through multiple cycles of optimism throughout its history, followed by periods of disappointment and loss of funding, known as AI winters. Funding and interest increased substantially after 2012, when graphics processing units began being used to accelerate neural networks, and deep learning outperformed previous AI techniques. This growth accelerated further after 2017 with the transformer architecture. In the 2020s, an AI boom has coincided with advances in generative AI, which allowed for the creation and modification of media. In addition to AI safety and unintended consequences and harms from the use of AI, ethical concerns, AI's long-term effects, and potential existential risks have prompted discussions of AI regulation. == Goals == The general problem of simulating (or creating) intelligence has been broken into subproblems. These consist of particular traits or capabilities that researchers expect an intelligent system to display. The traits described below have received the most attention and cover the scope of AI research. === Reasoning and problem-solving === Early researchers developed algorithms that imitated step-by-step reasoning that humans use when they solve puzzles or make logical deductions. By the late 1980s and 1990s, methods were developed for dealing with uncertain or incomplete information, employing concepts from probability and economics. Many of these algorithms are insufficient for solving large reasoning problems because they experience a "combinatorial explosion": They become exponentially slower as the problems grow. Even humans rarely use the step-by-step deduction that early AI research could model. They solve most of their problems using fast, intuitive judgments. Accurate and efficient reasoning is an unsolved problem. === Knowledge representation === Knowledge representation and knowledge engineering allow AI programs to answer questions intelligently and make deductions about real-world facts. Formal knowledge representations are used in content-based indexing and retrieval, scene interpretation, clinical decision support, knowledge discovery (mining "interesting" and actionable inferences from large databases), and other areas. A knowledge base is a body of knowledge represented in a form that can be used by a program. An ontology is the set of objects, relations, concepts, and properties used by a particular domain of knowledge. Knowledge bases need to represent things such as objects, properties, categories, and relations between objects; situations, events, states, and time; causes and effects; knowledge about knowledge (what we know about what other people know); default reasoning (things that humans assume are true until they are told differently and will remain true even when other facts are changing); and many other aspects and domains of knowledge. Among the most difficult problems in knowledge representation are the breadth of commonsense knowledge (the set of atomic facts that the average person knows is enormous); and the sub-symbolic form of most commonsense knowledge (much of what people know is not represented as "facts" or "statements" that they could express verbally). There is also the difficulty of knowledge acquisition, the problem of obtaining knowledge for AI applications. === Planning and decision-making === An "agent" is any entity (artificial or not) that perceives and takes actions in the world. A rational agent has goals or preferences and takes actions to make them happen. In automated planning, the agent has a specific goal. In automated decision-making, the agent has preferences—there are some situations it would prefer to be in, and some situations it is trying to avoid. The decision-making agent assigns a number to each situation (called the "utility") that measures how much the agent prefers it. For each possible action, it can calculate the "expected utility": the utility of all possible outcomes of the action, weighted by the probability that the outcome will occur. It can then choose the action with the maximum expected utility. In classical planning, the agent knows exactly what the effect of any action will be. In most real-world problems, however, the agent may not be certain about the situation they are in (it is "unknown" or "unobservable") and it may not know for certain what will happen after each possible action (it is not "deterministic"). It must choose an action by making a probabilistic guess and then reassess the situation to see if the action worked. Alongside thorough testing and improvement based on previous decisions, having an explanation for why the agent took certain decisions is a way to build trust, especially when the decisions have to be relied upon. In some problems, the agent's preferences may be uncertain, especially if there are other agents or humans involved. These can be learned (e.g., with inverse reinforcement learning), or the agent can seek information to improve its preferences. Information value theory can be used to weigh the value of exploratory or experimental actions. The space of possible future actions and situations is typically intractably large, so the agents must take actions and evaluate situations while being uncertain of what the outcome will be. A Markov decision process has a transition model that describes the probability that a particular action will change the state in a particular way and a reward function that supplies the utility of each state and the cost of each action. A policy associates a decision with each possible state. The policy could be calculated (e.g., by iteration), be heuristic, or it can be learned. Game theory describes the rational behavior of multiple interacting agents and is used in AI programs that make decisions that involve other agents. === Learning === Machine learning is the study of programs that can improve their performance on a given task automatically. It has been a part of AI from the beginning. There are several kinds of machine learning. Unsupervised learning analyzes a stream of data and finds patterns and makes predictions without any other guidance. Supervised learning requires labeling the training data with the expected answers, and comes in two main varieties: classification (where the program must learn to predict what category the input belongs in) and regression (where the program must deduce a numeric function based on numeric input). In reinforcement learning, the agent is rewarded for good responses and punished for bad ones. The agent learns to choose responses that are classified as "good". Transfer learning is when the knowledge gained from one problem is applied to a new problem. Deep learning is a type of machine learning that runs inputs through biologically inspired artificial neural networks for all of these types of learning. Computational learning theory can assess learners by computational complexity, by sample complexity (how much data is required), or by other notions of optimization. === Natural language processing === Natural language processing (NLP) allows programs to read, write and communicate in human languages. Specific problems include speech recognition, speech synthesis, machine translation, information extraction, information retrieval and question answering. Early work, based on Noam Chomsky's generative grammar and semantic networks, had difficulty with word-sense disambiguation unless

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  Label noise

  Label noise refers to errors or inaccuracies in the class labels of data instances. This is a widespread issue in machine learning datasets, arising from human annotator mistakes, unclear labeling instructions, automated labeling methods, or adversarial attacks in supervised learning. Label noise can be roughly divided into random noise, where labels are flipped independently of input features, and systematic noise, where mislabeling is dependent on certain patterns or biases in the data. Label noise can be damaging to model performance, especially for complex models that may overfit to noisy labels rather than generalizable patterns. Many approaches have been proposed to deal with the effects of label noise, including robust loss functions, noise-tolerant algorithms, data cleaning methods, and semi-supervised learning approaches. To reduce the impact of wrong labels during training, techniques like label smoothing, sample reweighting and using trusted validation sets are used. The role of noise-robust training paradigms and curriculum learning strategies to improve resilience against mislabeled data is also explored in recent research.

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  Thunderspy

  Thunderspy is a type of security vulnerability, based on the Intel Thunderbolt 3 port, first reported publicly on 10 May 2020, that can result in an evil maid (i.e., attacker of an unattended device) attack gaining full access to a computer's information in about five minutes, and may affect millions of Apple, Linux and Windows computers, as well as any computers manufactured before 2019, and some after that. According to Björn Ruytenberg, the discoverer of the vulnerability, "All the evil maid needs to do is unscrew the backplate, attach a device momentarily, reprogram the firmware, reattach the backplate, and the evil maid gets full access to the laptop. All of this can be done in under five minutes." The malicious firmware is used to clone device identities which makes classical DMA attack possible. == History == The Thunderspy security vulnerabilities were first publicly reported by Björn Ruytenberg of Eindhoven University of Technology in the Netherlands on 10 May 2020. Thunderspy is similar to Thunderclap, another security vulnerability, reported in 2019, that also involves access to computer files through the Thunderbolt port. == Impact == The security vulnerability affects millions of Apple, Linux and Windows computers, as well as all computers manufactured before 2019, and some after that. However, this impact is restricted mainly to how precise a bad actor would have to be to execute the attack. Physical access to a machine with a vulnerable Thunderbolt controller is necessary, as well as a writable ROM chip for the Thunderbolt controller's firmware. Additionally, part of Thunderspy, specifically the portion involving re-writing the firmware of the controller, requires the device to be in sleep, or at least in some sort of powered-on state, to be effective. Machines that force power-off when the case is open may assist in resisting this attack to the extent that the feature (switch) itself resists tampering. Due to the nature of attacks that require extended physical access to hardware, it's unlikely the attack will affect users outside of a business or government environment. == Mitigation == The researchers claim there is no easy software solution, and may only be mitigated by disabling the Thunderbolt port altogether. However, the impacts of this attack (reading kernel level memory without the machine needing to be powered off) are largely mitigated by anti-intrusion features provided by many business machines. Intel claims enabling such features would substantially restrict the effectiveness of the attack. Microsoft's official security recommendations recommend disabling sleep mode while using BitLocker. Using hibernation in place of sleep mode turns the device off, mitigating potential risks of attack on encrypted data.

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  Automated negotiation

  Automated negotiation is a form of interaction in systems that are composed of multiple autonomous agents, in which the aim is to reach agreements through an iterative process of making offers. Automated negotiation can be employed for many tasks human negotiators regularly engage in, such as bargaining and joint decision making. The main topics in automated negotiation revolve around the design of protocols and negotiating strategies. == History == Through digitization, the beginning of the 21st century has seen a growing interest in the automation of negotiation and e-negotiation systems, for example in the setting of e-commerce. This interest is fueled by the promise of automated agents being able to negotiate on behalf of human negotiators, and to find better outcomes than human negotiators. == Examples == Examples of automated negotiation include: Online dispute resolution, in which disagreements between parties are settled. Sponsored search auction, where bids are placed on advertisement keywords. Content negotiation, in which user agents negotiate over HTTP about how to best represent a web resource. Negotiation support systems, in which negotiation decision-making activities are supported by an information system.

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  Decision tree pruning

  Pruning is a data compression technique in machine learning and search algorithms that reduces the size of decision trees by removing sections of the tree that are non-critical and redundant to classify instances. Pruning reduces the complexity of the final classifier, and hence improves predictive accuracy by the reduction of overfitting. One of the questions that arises in a decision tree algorithm is the optimal size of the final tree. A tree that is too large risks overfitting the training data and poorly generalizing to new samples. A small tree might not capture important structural information about the sample space. However, it is hard to tell when a tree algorithm should stop because it is impossible to tell if the addition of a single extra node will dramatically decrease error. This problem is known as the horizon effect. A common strategy is to grow the tree until each node contains a small number of instances then use pruning to remove nodes that do not provide additional information. Pruning should reduce the size of a learning tree without reducing predictive accuracy as measured by a cross-validation set. There are many techniques for tree pruning that differ in the measurement that is used to optimize performance. == Techniques == Pruning processes can be divided into two types (pre- and post-pruning). Pre-pruning procedures prevent a complete induction of the training set by replacing a stop () criterion in the induction algorithm (e.g. max. Tree depth or information gain (Attr)> minGain). Pre-pruning methods are considered to be more efficient because they do not induce an entire set, but rather trees remain small from the start. Prepruning methods share a common problem, the horizon effect. This is to be understood as the undesired premature termination of the induction by the stop () criterion. Post-pruning (or just pruning) is the most common way of simplifying trees. Here, nodes and subtrees are replaced with leaves to reduce complexity. Pruning can not only significantly reduce the size but also improve the classification accuracy of unseen objects. It may be the case that the accuracy of the assignment on the train set deteriorates, but the accuracy of the classification properties of the tree increases overall. The procedures are differentiated on the basis of their approach in the tree (top-down or bottom-up). === Bottom-up pruning === These procedures start at the last node in the tree (the lowest point). Following recursively upwards, they determine the relevance of each individual node. If the relevance for the classification is not given, the node is dropped or replaced by a leaf. The advantage is that no relevant sub-trees can be lost with this method. These methods include Reduced Error Pruning (REP), Minimum Cost Complexity Pruning (MCCP), or Minimum Error Pruning (MEP). === Top-down pruning === In contrast to the bottom-up method, this method starts at the root of the tree. Following the structure below, a relevance check is carried out which decides whether a node is relevant for the classification of all n items or not. By pruning the tree at an inner node, it can happen that an entire sub-tree (regardless of its relevance) is dropped. One of these representatives is pessimistic error pruning (PEP), which brings quite good results with unseen items. == Pruning algorithms == === Reduced error pruning === One of the simplest forms of pruning is reduced error pruning. Starting at the leaves, each node is replaced with its most popular class. If the prediction accuracy is not affected then the change is kept. While somewhat naive, reduced error pruning has the advantage of simplicity and speed. === Cost complexity pruning === Cost complexity pruning generates a series of trees ⁠ T 0 … T m {\displaystyle T_{0}\dots T_{m}} ⁠ where ⁠ T 0 {\displaystyle T_{0}} ⁠ is the initial tree and ⁠ T m {\displaystyle T_{m}} ⁠ is the root alone. At step ⁠ i {\displaystyle i} ⁠, the tree is created by removing a subtree from tree ⁠ i − 1 {\displaystyle i-1} ⁠ and replacing it with a leaf node with value chosen as in the tree building algorithm. The subtree that is removed is chosen as follows: Define the error rate of tree ⁠ T {\displaystyle T} ⁠ over data set ⁠ S {\displaystyle S} ⁠ as ⁠ err ⁡ ( T , S ) {\displaystyle \operatorname {err} (T,S)} ⁠. The subtree t {\displaystyle t} that minimizes err ⁡ ( prune ⁡ ( T , t ) , S ) − err ⁡ ( T , S ) | leaves ⁡ ( T ) | − | leaves ⁡ ( prune ⁡ ( T , t ) ) | {\displaystyle {\frac {\operatorname {err} (\operatorname {prune} (T,t),S)-\operatorname {err} (T,S)}{\left\vert \operatorname {leaves} (T)\right\vert -\left\vert \operatorname {leaves} (\operatorname {prune} (T,t))\right\vert }}} is chosen for removal. The function ⁠ prune ⁡ ( T , t ) {\displaystyle \operatorname {prune} (T,t)} ⁠ defines the tree obtained by pruning the subtrees ⁠ t {\displaystyle t} ⁠ from the tree ⁠ T {\displaystyle T} ⁠. Once the series of trees has been created, the best tree is chosen by generalized accuracy as measured by a training set or cross-validation. == Examples == Pruning could be applied in a compression scheme of a learning algorithm to remove the redundant details without compromising the model's performances. In neural networks, pruning removes entire neurons or layers of neurons.

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  Instance (computer science)

  In computer science, an instance or token (from metalogic and metamathematics) is a specific occurrence of a software element that is based on a type definition. When created, an occurrence is said to have been instantiated, and both the creation process and the result of creation are called instantiation. == Examples == Chat AI instance In chat-based AI systems, an assistant can be invoked across many independent conversation sessions (often called a thread), each with its own message history. A specific execution of the assistant over that session may be represented as a run (an execution on a thread). Class instance In object-oriented programming, an object created from a class type. Each instance of a class shares the class-defined structure and behavior but has its own identity and state. Procedural instance In some contexts (including Simula), each procedure call can be viewed as an instance of that procedure—an activation with its own parameters and local variables. Computer instance In cloud computing and virtualization, an instance commonly refers to a provisioned virtual machine or virtual server with an allocated combination of compute, memory, network, and storage resources. Polygonal model In computer graphics, a model may be instanced so it can be drawn multiple times with different transforms and parameters, improving performance by reusing shared geometry data. Program instance In a POSIX-oriented operating system, a running process is an instance of a program. It can be instantiated via system calls such as fork() and exec(). Each executing process is an instance of a program it has been instantiated from.

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  Comparison of color models in computer graphics

  This article provides introductory information about the RGB, HSV, and HSL color models from a computer graphics (web pages, images) perspective. An introduction to colors is also provided to support the main discussion. == Basics of color == === Primary colors and hue === First, "color" refers to the human brain's subjective interpretation of combinations of a narrow band of wavelengths of light. For this reason, the definition of "color" is not based on a strict set of physical phenomena. Therefore, even basic concepts like "primary colors" are not clearly defined. For example, traditional "Painter's Colors" use red, blue, and yellow as the primary colors, "Printer's Colors" use cyan, yellow, and magenta, and "Light Colors" use red, green, and blue. "Light colors", more formally known as additive colors, are formed by combining red, green, and blue light. This article refers to additive colors and refers to red, green, and blue as the primary colors. Hue is a term describing a pure color, that is, a color not modified by tinting or shading (see below). In additive colors, hues are formed by combining two primary colors. When two primary colors are combined in equal intensities, the result is a "secondary color". === Color wheel === A color wheel is a tool that provides a visual representation of the relationships between all possible hues. The primary colors are arranged around a circle at equal (120 degree) intervals. (Warning: Color wheels frequently depict "Painter's Colors" primary colors, which leads to a different set of hues than additive colors.) The illustration shows a simple color wheel based on the additive colors. Note that the position (top, right) of the starting color, typically red, is arbitrary, as is the order of green and blue (clockwise, counter-clockwise). The illustration also shows the secondary colors, yellow, cyan, and magenta, located halfway between (60 degrees) the primary colors. == Complementary color == The complement of a hue is the hue that is opposite it (180 degrees) on the color wheel. Using additive colors, mixing a hue and its complement in equal amounts produces white. === Tints and shades === The following discussion uses an illustration involving three projectors pointing to the same spot on a screen. Each projector is capable of generating one hue. The "intensities" of each projector are "matched" and can be equally adjusted from zero to full. (Note: "Intensity" is used here in the same sense as the RGB color model. The subject of matching, or "gamma correction", is beyond the level of this article.) A shade is produced by "dimming" a maximum chroma color. Painters refer to this as "adding black". In our illustration, one projector is set to full intensity, a second is set to some intensity between zero and full, and third is set to zero. "Dimming" is accomplished by decreasing each projector's intensity setting to the same fraction of its start setting. In the shade example, with any fully shaded hue, that all three projectors are set to zero intensity, resulting in black. A tint is produced by "lightening" a maximum chroma color. Painters refer to this as "adding white". In our illustration, one projector is set to full intensity, a second is set to some intensity between zero and full, and third is set to zero. "Lightening" is accomplished by increasing each projector's intensity setting by the same fraction from its start setting to full. In the tinting example, note that the third projector is now contributing. When the hue is fully lightened, all three projectors are each at full intensity, and the result is white. Note an attribute of the total intensity in the additive model. If full intensity for one projector is 1, then a primary color has a combined intensity of 1. A secondary color has a total intensity of 2. White has a total intensity of 3. Tinting, or "adding white", increases the total intensity of the hue. While this is simply a fact, the HSL model will take this fact into account in its design. === Tones === Tone is a general term, typically used by painters, to refer to the effects of reducing the "colorfulness" of a maximum chroma color; painters refer to it as "adding gray". Note that gray is not a color or even a single concept but refers to all the range of values between black and white where all three primary colors are equally represented. The general term is provided as more specific terms have conflicting definitions in different color models. Thus, shading takes a hue toward black, tinting takes a hue towards white, and tones cover the range between. == Choosing a color model == No one color model is necessarily "better" than another. Typically, the choice of a color model is dictated by external factors, such as a graphics tool or the need to specify colors according to the CSS2 or CSS3 standard. The following discussion only describes how the models function, centered on the concepts of hue, shade, tint, and tone. === RGB === The RGB model's approach to colors is important because: It directly reflects the physical properties of "Truecolor" displays As of 2011, most graphic cards define pixel values in terms of the colors red, green, and blue. The typical range of intensity values for each color, 0–255, is based on taking a binary number with 32 bits and breaking it up into four bytes of 8 bits each. 8 bits can hold a value from 0 to 255. The fourth byte is used to specify the "alpha", or the opacity, of the color. Opacity comes into play when layers with different colors are stacked. If the color in the top layer is less than fully opaque (alpha < 255), the color from underlying layers "shows through". In the RGB model, hues are represented by specifying one color as full intensity (255), a second color with a variable intensity, and the third color with no intensity (0). The following provides some examples using red as the full-intensity and green as the partial-intensity colors; blue is always zero: Shades are created by multiplying the intensity of each primary color by 1 minus the shade factor, in the range 0 to 1. A shade factor of 0 does nothing to the hue, a shade factor of 1 produces black: new intensity = current intensity (1 – shade factor) The following provides examples using orange: Tints are created by modifying each primary color as follows: the intensity is increased so that the difference between the intensity and full intensity (255) is decreased by the tint factor, in the range 0 to 1. A tint factor of 0 does nothing, a tint factor of 1 produces white: new intensity = current intensity + (255 – current intensity) tint factor The following provides examples using orange: Tones are created by applying both a shade and a tint. The order in which the two operations are performed does not matter, with the following restriction: when a tint operation is performed on a shade, the intensity of the dominant color becomes the "full intensity"; that is, the intensity value of the dominant color must be used in place of 255. The following provides examples using orange: === HSV === The HSV, or HSB, model describes colors in terms of hue, saturation, and value (brightness). Note that the range of values for each attribute is arbitrarily defined by various tools or standards. Be sure to determine the value ranges before attempting to interpret a value. Hue corresponds directly to the concept of hue in the Color Basics section. The advantages of using hue are The angular relationship between tones around the color circle is easily identified Shades, tints, and tones can be generated easily without affecting the hue Saturation corresponds directly to the concept of tint in the Color Basics section, except that full saturation produces no tint, while zero saturation produces white, a shade of gray, or black. Value corresponds directly to the concept of intensity in the Color Basics section. Pure colors are produced by specifying a hue with full saturation and value Shades are produced by specifying a hue with full saturation and less than full value Tints are produced by specifying a hue with less than full saturation and full value Tones are produced by specifying a hue and both less than full saturation and value White is produced by specifying zero saturation and full value, regardless of hue Black is produced by specifying zero value, regardless of hue or saturation Shades of gray are produced by specifying zero saturation and between zero and full value The advantage of HSV is that each of its attributes corresponds directly to the basic color concepts, which makes it conceptually simple. The perceived disadvantage of HSV is that the saturation attribute corresponds to tinting, so desaturated colors have increasing total intensity. For this reason, the CSS3 standard plans to support RGB and HSL but not HSV. === HSL === The HSL model describes colors in terms of hue, saturation, and lightness (also called luminance). (Note: the definition of sa

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  AI Overviews

  AI Overviews is an artificial intelligence (AI) feature integrated into Google Search that produces AI-generated summaries of search results. The feature has been criticized for its inaccuracy and for reducing website traffic. == History and development == AI Overviews were first introduced as part of Google's Search Generative Experience (SGE), which was unveiled at the Google I/O conference in May 2023. In May 2024 at Google I/O 2024, the feature was rebranded as AI Overviews and launched in the United States. The introduction of AI Overviews was seen as a strategic move to compete with other generative AI advancements, including OpenAI's ChatGPT. By August 2024, AI Overviews was rolled out to several other countries, including the United Kingdom, India, Japan, Brazil, Mexico, and Indonesia, with support for multiple languages. In October 2024, Google expanded the feature globally, making it available in over 100 countries. In December 2024, Botify x Demandsphere released findings stating that when AI Overviews and featured snippets appear together on the search engine results page, they take up approximately 67.1% of the screen on desktop and 75.7% on mobile. Even if content is ranking in the #1 position, it may not be visible to consumers if other visual elements on the results page are more prominent. In March 2025, Google started testing an "AI Mode", where the search results page is AI-generated. The company was also considering adding advertisements to the AI Mode, as they already exist in AI Overviews. As of May 2025, AI Overviews are available in over 200 countries and territories and in more than 40 languages. As of March 2026, Google AI Overviews appear on more than 48% of total Google Search queries, compared to just 6.49% in the previous year (58% year-over-year growth). == Functionality == The AI Overviews feature uses large language models to generate summaries from web content. The overviews are designed to be concise, providing a snapshot of relevant information about the queried topic. Google allows users to adjust the language complexity in summaries, offering both simplified and detailed options. The overviews also include links to sources. According to a June 2025 study by Semrush, the most cited source is Quora, followed by Reddit. == Reception == The feature has faced criticism for inaccuracies, including instances where erroneous or nonsensical content was generated. Depending on what is searched for, the overview may also consist of hallucinated content, such as when searching for idioms that do not exist. In May 2024, Google temporarily restricted the AI tool after it provided suggestions that were seen as nonsensical and harmful, such as telling users to eat rocks or apply glue on pizza. Concerns were also raised by content publishers, who feared a decline in web traffic as users relied on the summaries instead of visiting source websites. A Google patent from 2026 raised the concern of webmasters that Google could entirely replace the landing page of websites by an AI optimized copy of the website in its results. There is also apprehension about the ethical implications of AI-driven content aggregation, including its impact on intellectual property rights and the visibility of smaller content providers. The European Commission announced in December 2025 that they were investigating whether AI Overviews breached European competition law. In response, Google has stated its commitment to improve content validation and refine the algorithms used to filter unreliable information. Google implemented measures to prioritize link placement within AI Overviews, aiming to balance user convenience with the needs of content creators. In January 2026, Google restricted AI Overviews on certain health-related searches following an investigation by The Guardian. == Lawsuits == On February 24, 2025, Chegg sued Alphabet over the AI Overviews feature, claiming that it was leading to students preferring "low-quality, unverified AI summaries", thus violating antitrust law. Chegg also said it was considering either a sale or a take-private transaction. In September 2025, Penske Media Corporation, the publisher of Rolling Stone and The Hollywood Reporter, sued Google, claiming that AI Overviews illegally regurgitate content from their websites and drive off potential site visitors by always appearing on top of the search results while leaving little incentive to see the linked sources. The company stated that "the future of digital media and [...] its integrity [...] is threatened by Google's current actions", alleging that 20% of searches that link to Penske-owned websites show AI Overviews and that the figure is expected to rise. Google spokesperson José Castañeda called the claims "meritless" and stated that "AI Overviews send traffic to a greater diversity of sites." In 2026, Canadian musician Ashley MacIsaac filed a lawsuit against Google claiming that the AI Overview feature had wrongly stated that MacIsaac had been convicted of numerous criminal offences and was on the sex offender registry. He claims this incorrect information led to the cancellation of a December 2025 gig organized by the Sipekne'katik First Nation.

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  Algorithmic inference

  Algorithmic inference gathers new developments in the statistical inference methods made feasible by the powerful computing devices widely available to any data analyst. Cornerstones in this field are computational learning theory, granular computing, bioinformatics, and, long ago, structural probability (Fraser 1966). The main focus is on the algorithms which compute statistics rooting the study of a random phenomenon, along with the amount of data they must feed on to produce reliable results. This shifts the interest of mathematicians from the study of the distribution laws to the functional properties of the statistics, and the interest of computer scientists from the algorithms for processing data to the information they process. == The Fisher parametric inference problem == Concerning the identification of the parameters of a distribution law, the mature reader may recall lengthy disputes in the mid 20th century about the interpretation of their variability in terms of fiducial distribution (Fisher 1956), structural probabilities (Fraser 1966), priors/posteriors (Ramsey 1925), and so on. From an epistemology viewpoint, this entailed a companion dispute as to the nature of probability: is it a physical feature of phenomena to be described through random variables or a way of synthesizing data about a phenomenon? Opting for the latter, Fisher defines a fiducial distribution law of parameters of a given random variable that he deduces from a sample of its specifications. With this law he computes, for instance "the probability that μ (mean of a Gaussian variable – omeur note) is less than any assigned value, or the probability that it lies between any assigned values, or, in short, its probability distribution, in the light of the sample observed". == The classic solution == Fisher fought hard to defend the difference and superiority of his notion of parameter distribution in comparison to analogous notions, such as Bayes' posterior distribution, Fraser's constructive probability and Neyman's confidence intervals. For half a century, Neyman's confidence intervals won out for all practical purposes, crediting the phenomenological nature of probability. With this perspective, when you deal with a Gaussian variable, its mean μ is fixed by the physical features of the phenomenon you are observing, where the observations are random operators, hence the observed values are specifications of a random sample. Because of their randomness, you may compute from the sample specific intervals containing the fixed μ with a given probability that you denote confidence. === Example === Let X be a Gaussian variable with parameters μ {\displaystyle \mu } and σ 2 {\displaystyle \sigma ^{2}} and { X 1 , … , X m } {\displaystyle \{X_{1},\ldots ,X_{m}\}} a sample drawn from it. Working with statistics S μ = ∑ i = 1 m X i {\displaystyle S_{\mu }=\sum _{i=1}^{m}X_{i}} and S σ 2 = ∑ i = 1 m ( X i − X ¯ ) 2 , where X ¯ = S μ m {\displaystyle S_{\sigma ^{2}}=\sum _{i=1}^{m}(X_{i}-{\overline {X}})^{2},{\text{ where }}{\overline {X}}={\frac {S_{\mu }}{m}}} is the sample mean, we recognize that T = S μ − m μ S σ 2 m − 1 m = X ¯ − μ S σ 2 / ( m ( m − 1 ) ) {\displaystyle T={\frac {S_{\mu }-m\mu }{\sqrt {S_{\sigma ^{2}}}}}{\sqrt {\frac {m-1}{m}}}={\frac {{\overline {X}}-\mu }{\sqrt {S_{\sigma ^{2}}/(m(m-1))}}}} follows a Student's t distribution (Wilks 1962) with parameter (degrees of freedom) m − 1, so that f T ( t ) = Γ ( m / 2 ) Γ ( ( m − 1 ) / 2 ) 1 π ( m − 1 ) ( 1 + t 2 m − 1 ) m / 2 . {\displaystyle f_{T}(t)={\frac {\Gamma (m/2)}{\Gamma ((m-1)/2)}}{\frac {1}{\sqrt {\pi (m-1)}}}\left(1+{\frac {t^{2}}{m-1}}\right)^{m/2}.} Gauging T between two quantiles and inverting its expression as a function of μ {\displaystyle \mu } you obtain confidence intervals for μ {\displaystyle \mu } . With the sample specification: x = { 7.14 , 6.3 , 3.9 , 6.46 , 0.2 , 2.94 , 4.14 , 4.69 , 6.02 , 1.58 } {\displaystyle \mathbf {x} =\{7.14,6.3,3.9,6.46,0.2,2.94,4.14,4.69,6.02,1.58\}} having size m = 10, you compute the statistics s μ = 43.37 {\displaystyle s_{\mu }=43.37} and s σ 2 = 46.07 {\displaystyle s_{\sigma ^{2}}=46.07} , and obtain a 0.90 confidence interval for μ {\displaystyle \mu } with extremes (3.03, 5.65). == Inferring functions with the help of a computer == From a modeling perspective the entire dispute looks like a chicken-egg dilemma: either fixed data by first and probability distribution of their properties as a consequence, or fixed properties by first and probability distribution of the observed data as a corollary. The classic solution has one benefit and one drawback. The former was appreciated particularly back when people still did computations with sheet and pencil. Per se, the task of computing a Neyman confidence interval for the fixed parameter θ is hard: you do not know θ, but you look for disposing around it an interval with a possibly very low probability of failing. The analytical solution is allowed for a very limited number of theoretical cases. Vice versa a large variety of instances may be quickly solved in an approximate way via the central limit theorem in terms of confidence interval around a Gaussian distribution – that's the benefit. The drawback is that the central limit theorem is applicable when the sample size is sufficiently large. Therefore, it is less and less applicable with the sample involved in modern inference instances. The fault is not in the sample size on its own part. Rather, this size is not sufficiently large because of the complexity of the inference problem. With the availability of large computing facilities, scientists refocused from isolated parameters inference to complex functions inference, i.e. re sets of highly nested parameters identifying functions. In these cases we speak about learning of functions (in terms for instance of regression, neuro-fuzzy system or computational learning) on the basis of highly informative samples. A first effect of having a complex structure linking data is the reduction of the number of sample degrees of freedom, i.e. the burning of a part of sample points, so that the effective sample size to be considered in the central limit theorem is too small. Focusing on the sample size ensuring a limited learning error with a given confidence level, the consequence is that the lower bound on this size grows with complexity indices such as VC dimension or detail of a class to which the function we want to learn belongs. === Example === A sample of 1,000 independent bits is enough to ensure an absolute error of at most 0.081 on the estimation of the parameter p of the underlying Bernoulli variable with a confidence of at least 0.99. The same size cannot guarantee a threshold less than 0.088 with the same confidence 0.99 when the error is identified with the probability that a 20-year-old man living in New York does not fit the ranges of height, weight and waistline observed on 1,000 Big Apple inhabitants. The accuracy shortage occurs because both the VC dimension and the detail of the class of parallelepipeds, among which the one observed from the 1,000 inhabitants' ranges falls, are equal to 6. == The general inversion problem solving the Fisher question == With insufficiently large samples, the approach: fixed sample – random properties suggests inference procedures in three steps: === Definition === For a random variable and a sample drawn from it a compatible distribution is a distribution having the same sampling mechanism M X = ( Z , g θ ) {\displaystyle {\mathcal {M}}_{X}=(Z,g_{\boldsymbol {\theta }})} of X with a value θ {\displaystyle {\boldsymbol {\theta }}} of the random parameter Θ {\displaystyle \mathbf {\Theta } } derived from a master equation rooted on a well-behaved statistic s. === Example === You may find the distribution law of the Pareto parameters A and K as an implementation example of the population bootstrap method as in the figure on the left. Implementing the twisting argument method, you get the distribution law F M ( μ ) {\displaystyle F_{M}(\mu )} of the mean M of a Gaussian variable X on the basis of the statistic s M = ∑ i = 1 m x i {\textstyle s_{M}=\sum _{i=1}^{m}x_{i}} when Σ 2 {\displaystyle \Sigma ^{2}} is known to be equal to σ 2 {\displaystyle \sigma ^{2}} (Apolloni, Malchiodi & Gaito 2006). Its expression is: F M ( μ ) = Φ ( m μ − s M σ m ) , {\displaystyle F_{M}(\mu )=\Phi {\left({\frac {m\mu -s_{M}}{\sigma {\sqrt {m}}}}\right)},} shown in the figure on the right, where Φ {\displaystyle \Phi } is the cumulative distribution function of a standard normal distribution. Computing a confidence interval for M given its distribution function is straightforward: we need only find two quantiles (for instance δ / 2 {\displaystyle \delta /2} and 1 − δ / 2 {\displaystyle 1-\delta /2} quantiles in case we are interested in a confidence interval of level δ symmetric in the tail's probabilities) as indicated on the left in the diagram showing the behavior of

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