A social network game (sometimes simply referred to as a social media game, social gaming, or online social game) is a type of online game that is played through social networks or social media. They typically feature gamification systems with multiplayer gameplay mechanics. Social network games were originally implemented as browser games. As mobile gaming took off, the games moved to mobile as well. While they share many aspects of traditional video games, social network games often employ additional ones that make them distinct. Traditionally they are oriented to be social games and casual games. The first cross-platform "Facebook-to-Mobile" social network game was developed in 2011 by a Finnish company Star Arcade. Social network games are amongst the most popular games played in the world, with several products with tens of millions of players. (Lil) Green Patch, Happy Farm, and Mob Wars were some of the first successful games of this genre. FarmVille, Mafia Wars, Kantai Collection, and The Sims Social are more recent examples of popular social network game. Major companies that made or published social network games include Zynga, Wooga and Bigpoint Games. == Demographics == As of 2010, it was reported that 55 percent of the social network gaming demographic in the United States consisted of women while in the United Kingdom, women made up nearly 60 percent of the demographic. In addition, most social gamers were around the 30 to 59 age range, with the average social gamer being 43 years old. Social gaming may appeal more to the older demographic because it is free, easier to advance through in a short period, does not involve as much violence as traditional video games, and is easier to grasp. Other games target certain demographics that use social media, such as Pot Farm creating a community by involving elements of cannabis subculture in its gameplay. == Technology and platforms == A social network video game is a client-server application. The client in the web era was implemented with a mix of web technologies like Flash, HTML5, PHP and JavaScript. When mobile games moved to mobile, social game front ends were developed using mobile platform technologies like Java, Objective-C, Swift and C++. The back end was a mix of programming languages and systems, including PHP, Ruby, C++ and go. Where social network video games diverged from traditional game development was the combination of real-time analytics to continuously optimize game mechanics to drive growth, revenue, and engagement. == Distinct features == The following table outlines common characteristics of social games, mentioned by Björk at the 2010 GCO Games Convention Online: A social network game may employ any of the following features: asynchronous gameplay, which allows rules to be resolved without needing players to play at the same time. gamification, which video game mechanics such as achievements and points are applied to those experienced when playing games in order to motivate and engage users. community, as one of the most distinct features of social video games is in leveraging the player's social network. Quests or game goals may only be possible if a player "shares" with friends connected by the social network hosting the game or gets them to play, as well as "neighbors" or "allies". a lack of victory conditions: there are generally no victory conditions since most developers count on users playing their games often. The game never ends and no one is ever declared winner. Instead, many casual games have "quests" or "missions" for players to complete. This is not true for board game-like social games, such as Scrabble. a virtual currency which players usually must purchase with real-world money. With the in-game currency, players can buy upgrades that would otherwise take much longer to earn through in-game achievements. In many cases, some upgrades are only available with the virtual currency. == Engagement strategies == Since social network games are often less challenging than console games and they have relatively shorter game play, they use different techniques to stretch game play and tools to retain users. Continuous goals: The games assign specific goals for users to achieve. As they advance in the game, the goals become more challenging and time-consuming. They also provide frequent feedback with their performance. Every action will translate towards a certain goal that will be used to attain higher gaming capitals. Gaming capitals: Players are encouraged to earn different badges, trophies, and accolades that indicate their progress and accomplishments. Some achievements are unlocked just by advancing in the game while others may significantly alter the rationale behind the game and require extensive investment from players. The ways of gaining gaming capital are not limited to playing games but the games-related productive activities that are appreciated in the player's social circle too. By accumulating gaming capitals, they provide an intrinsic benefit to gamers as there is an avenue to boost their accomplishment and showcase their expertise of the game. The achievements are visible to their network of friends. Gaming capitals are a way for developers to increase replay value provides extended play time, and players get more value from the game. Motivation for collecting gaming capitals: 1. Legitimization: refers to society's willingness to approve or condone certain behavior. Collecting is about channeling one's materialistic desires into more meaningful pursuits. Game achievements serve a similar purpose, allowing players to justify the hours spent playing the game. 2. Self-extension: Gathering and controlling meaningful objects or experiences can work to gain one an improved sense of self. The collector's goal to complete a collection is symbolically about completing the self too. Events timed to real world: Popular games such as Dragon City and Wild Ones require users to wait a certain time period before their "energy bars" replenish. Without energy, they are unable to conduct any form of action. Gamers are forced to wait and return after their energy replenishes to continue playing. == Monetization == Social network games frequently monetize based on virtual good transactions, but other games are emerging that utilize newer economic models. === Virtual goods === Gamers will be able to purchase in game items like power-ups, avatar accessories, or decorative items users purchase within the game itself. This is realized by monetize products that do not technically exist. Virtual goods account for over 90% of all revenue generated by the world's top social game developers. Designers optimize user experience through additional gameplay, missions, and quests, without having to worry about overhead or unused stock. == Advertising == The following are common ways of advertising in social network games: === Banner advertisements === As banner ads within social networks tend to be where ad response is low, they tend to be priced at bottom-of-the-barrel CPMs of around $2. However, because social games generate so many page views, they are the biggest part of advertising revenue for the social gaming industry. === Video ads === Videos are the ad format with the most revenue per view. They tend to be higher-priced, either by CPMs ($35+ CPM in social games) or cost-per-completed-view. According to studies, video ads result in highest brand recall thus a good return on investment for advertisers. Video ads are shown either in in-game interstitials (e.g. when the game is loading a new screen) or through incentive-based advertising, i.e. you will get either an in-game reward or Facebook credits for watching an advertisement. === Product placement === A brand or product will be injected in a game in some way. Due to the variety of ways in which product placement can be accomplished in any media, and because the category is nascent, this category is not standardized at all, but some examples include branded in-game goods or even in-game quests. For example, in a game where you run a restaurant, you might be asked to collect ingredients to make a Starbucks Frappuccino, and receive in-game rewards for doing so. As these product placement deals are non-standard, they are largely charged with a production fee, which can be $350,000 to $750,000 depending on the type of placement and the popularity of the game. === Lead generation offers === Another form of advertising that is prevalent in many social games are lead generation offers. In this form of advertising, companies, usually from different industries, aim to convince players to sign up for their goods or services and in exchange, players will receive virtual gifts or advance in the game as a reward. === Sponsorship === ==== White label games ==== Applications that are built once, then individualized and licensed again and again. Developer can create a quality app focused on fun while leaving the edge
A Logical Calculus of the Ideas Immanent in Nervous Activity
"A Logical Calculus of the Ideas Immanent in Nervous Activity" is a 1943 paper written by Warren Sturgis McCulloch and Walter Pitts, published in the journal The Bulletin of Mathematical Biophysics. The paper proposed a mathematical model of the nervous system as a network of simple logical elements, later known as artificial neurons, or McCulloch–Pitts neurons. These neurons receive inputs, perform a weighted sum, and fire an output signal based on a threshold function. By connecting these units in various configurations, McCulloch and Pitts demonstrated that their model could perform all logical functions. It is a seminal work in cognitive science, computational neuroscience, computer science, and artificial intelligence. It was a foundational result in automata theory. John von Neumann cited it as a significant result. == Mathematics == The artificial neuron used in the original paper is slightly different from the modern version. They considered neural networks that operate in discrete steps of time t = 0 , 1 , … {\displaystyle t=0,1,\dots } . The neural network contains a number of neurons. Let the state of a neuron i {\displaystyle i} at time t {\displaystyle t} be N i ( t ) {\displaystyle N_{i}(t)} . The state of a neuron can either be 0 or 1, standing for "not firing" and "firing". Each neuron also has a firing threshold θ {\displaystyle \theta } , such that it fires if the total input exceeds the threshold. Each neuron can connect to any other neuron (including itself) with positive synapses (excitatory) or negative synapses (inhibitory). That is, each neuron can connect to another neuron with a weight w {\displaystyle w} taking an integer value. A peripheral afferent is a neuron with no incoming synapses. We can regard each neural network as a directed graph, with the nodes being the neurons, and the directed edges being the synapses. A neural network has a circle or a circuit if there exists a directed circle in the graph. Let w i j ( t ) {\displaystyle w_{ij}(t)} be the connection weight from neuron j {\displaystyle j} to neuron i {\displaystyle i} at time t {\displaystyle t} , then its next state is N i ( t + 1 ) = H ( ∑ j = 1 n w i j ( t ) N j ( t ) − θ i ( t ) ) , {\displaystyle N_{i}(t+1)=H\left(\sum _{j=1}^{n}w_{ij}(t)N_{j}(t)-\theta _{i}(t)\right),} where H {\displaystyle H} is the Heaviside step function (outputting 1 if the input is greater than or equal to 0, and 0 otherwise). === Symbolic logic === The paper used, as a logical language for describing neural networks, "Language II" from The Logical Syntax of Language by Rudolf Carnap with some notations taken from Principia Mathematica by Alfred North Whitehead and Bertrand Russell. Language II covers substantial parts of classical mathematics, including real analysis and portions of set theory. To describe a neural network with peripheral afferents N 1 , N 2 , … , N p {\displaystyle N_{1},N_{2},\dots ,N_{p}} and non-peripheral afferents N p + 1 , N p + 2 , … , N n {\displaystyle N_{p+1},N_{p+2},\dots ,N_{n}} they considered logical predicate of form P r ( N 1 , N 2 , … , N p , t ) {\displaystyle Pr(N_{1},N_{2},\dots ,N_{p},t)} where P r {\displaystyle Pr} is a first-order logic predicate function (a function that outputs a boolean), N 1 , … , N p {\displaystyle N_{1},\dots ,N_{p}} are predicates that take t {\displaystyle t} as an argument, and t {\displaystyle t} is the only free variable in the predicate. Intuitively speaking, N 1 , … , N p {\displaystyle N_{1},\dots ,N_{p}} specifies the binary input patterns going into the neural network over all time, and P r ( N 1 , N 2 , … , N n , t ) {\displaystyle Pr(N_{1},N_{2},\dots ,N_{n},t)} is a function that takes some binary input patterns, and constructs an output binary pattern P r ( N 1 , N 2 , … , N n , 0 ) , P r ( N 1 , N 2 , … , N n , 1 ) , … {\displaystyle Pr(N_{1},N_{2},\dots ,N_{n},0),Pr(N_{1},N_{2},\dots ,N_{n},1),\dots } . A logical sentence P r ( N 1 , N 2 , … , N n , t ) {\displaystyle Pr(N_{1},N_{2},\dots ,N_{n},t)} is realized by a neural network iff there exists a time-delay T ≥ 0 {\displaystyle T\geq 0} , a neuron i {\displaystyle i} in the network, and an initial state for the non-peripheral neurons N p + 1 ( 0 ) , … , N n ( 0 ) {\displaystyle N_{p+1}(0),\dots ,N_{n}(0)} , such that for any time t {\displaystyle t} , the truth-value of the logical sentence is equal to the state of the neuron i {\displaystyle i} at time t + T {\displaystyle t+T} . That is, ∀ t = 0 , 1 , 2 , … , P r ( N 1 , N 2 , … , N p , t ) = N i ( t + T ) {\displaystyle \forall t=0,1,2,\dots ,\quad Pr(N_{1},N_{2},\dots ,N_{p},t)=N_{i}(t+T)} === Equivalence === In the paper, they considered some alternative definitions of artificial neural networks, and have shown them to be equivalent, that is, neural networks under one definition realizes precisely the same logical sentences as neural networks under another definition. They considered three forms of inhibition: relative inhibition, absolute inhibition, and extinction. The definition above is relative inhibition. By "absolute inhibition" they meant that if any negative synapse fires, then the neuron will not fire. By "extinction" they meant that if at time t {\displaystyle t} , any inhibitory synapse fires on a neuron i {\displaystyle i} , then θ i ( t + j ) = θ i ( 0 ) + b j {\displaystyle \theta _{i}(t+j)=\theta _{i}(0)+b_{j}} for j = 1 , 2 , 3 , … {\displaystyle j=1,2,3,\dots } , until the next time an inhibitory synapse fires on i {\displaystyle i} . It is required that b j = 0 {\displaystyle b_{j}=0} for all large j {\displaystyle j} . Theorem 4 and 5 state that these are equivalent. They considered three forms of excitation: spatial summation, temporal summation, and facilitation. The definition above is spatial summation (which they pictured as having multiple synapses placed close together, so that the effect of their firing sums up). By "temporal summation" they meant that the total incoming signal is ∑ τ = 0 T ∑ j = 1 n w i j ( t ) N j ( t − τ ) {\displaystyle \sum _{\tau =0}^{T}\sum _{j=1}^{n}w_{ij}(t)N_{j}(t-\tau )} for some T ≥ 1 {\displaystyle T\geq 1} . By "facilitation" they meant the same as extinction, except that b j ≤ 0 {\displaystyle b_{j}\leq 0} . Theorem 6 states that these are equivalent. They considered neural networks that do not change, and those that change by Hebbian learning. That is, they assume that at t = 0 {\displaystyle t=0} , some excitatory synaptic connections are not active. If at any t {\displaystyle t} , both N i ( t ) = 1 , N j ( t ) = 1 {\displaystyle N_{i}(t)=1,N_{j}(t)=1} , then any latent excitatory synapse between i , j {\displaystyle i,j} becomes active. Theorem 7 states that these are equivalent. === Logical expressivity === They considered "temporal propositional expressions" (TPE), which are propositional formulas with one free variable t {\displaystyle t} . For example, N 1 ( t ) ∨ N 2 ( t ) ∧ ¬ N 3 ( t ) {\displaystyle N_{1}(t)\vee N_{2}(t)\wedge \neg N_{3}(t)} is such an expression. Theorem 1 and 2 together showed that neural nets without circles are equivalent to TPE. For neural nets with loops, they noted that "realizable P r {\displaystyle Pr} may involve reference to past events of an indefinite degree of remoteness". These then encodes for sentences like "There was some x such that x was a ψ" or ( ∃ x ) ( ψ x ) {\displaystyle (\exists x)(\psi x)} . Theorems 8 to 10 showed that neural nets with loops can encode all first-order logic with equality and conversely, any looped neural networks is equivalent to a sentence in first-order logic with equality, thus showing that they are equivalent in logical expressiveness. As a remark, they noted that a neural network, if furnished with a tape, scanners, and write-heads, is equivalent to a Turing machine, and conversely, every Turing machine is equivalent to some such neural network. Thus, these neural networks are equivalent to Turing computability and Church's lambda-definability. == Context == === Previous work === The paper built upon several previous strands of work. In the symbolic logic side, it built on the previous work by Carnap, Whitehead, and Russell. This was contributed by Walter Pitts, who had a strong proficiency with symbolic logic. Pitts provided mathematical and logical rigor to McCulloch’s vague ideas on psychons (atoms of psychological events) and circular causality. In the neuroscience side, it built on previous work by the mathematical biology research group centered around Nicolas Rashevsky, of which McCulloch was a member. The paper was published in the Bulletin of Mathematical Biophysics, which was founded by Rashevsky in 1939. During the late 1930s, Rashevsky's research group was producing papers that had difficulty publishing in other journals at the time, so Rashevsky decided to found a new journal exclusively devoted to mathematical biophysics. Also in the Rashevsky's group was Alston Scott Householder, who in 1941 published an abstract model
Intelligent automation
Intelligent automation (IA), or intelligent process automation, is a software term that refers to a combination of artificial intelligence (AI) and robotic process automation (RPA). Companies use intelligent automation to cut costs and streamline tasks by using artificial-intelligence-powered robotic software to mitigate repetitive tasks. As it accumulates data, the system learns in an effort to improve its efficiency. Intelligent automation applications consist of, but are not limited to, pattern analysis, data assembly, and classification. The term is similar to hyperautomation, a concept identified by research group Gartner as being one of the top technology trends of 2020. == Technology == Intelligent automation applies the assembly line concept of breaking tasks into repetitive steps to improve business processes. Rather than having humans perform each step, intelligent automation can replace steps with an intelligent software robot, improving efficiency. Intelligent automation integrates robotic process automation (RPA) with artificial intelligence techniques (such as machine learning, natural-language processing, and computer vision) enabling systems to interpret data, make decisions, and adapt to changing inputs. Modern platforms use a layered architecture combining workflow orchestration, low-code tools, integration middleware, and AI services to coordinate bots and data pipelines across organisational systems. == Applications == Intelligent automation is used to process unstructured content. Common real-world applications include self-driving cars, self-checkouts at grocery stores, smart home assistants, and appliances. Businesses can apply data and machine learning to build predictive analytics that react to consumer behavior changes, or to implement RPA to improve manufacturing floor operations. For example, the technology has also been used to automate the workflow behind distributing COVID-19 vaccines. Data provided by hospital systems’ electronic health records can be processed to identify and educate patients, and schedule vaccinations. Intelligent automation can provide real-time insights on profitability and efficiency. However, in an April 2022 survey by Alchemmy, despite three quarters of businesses acknowledging the importance of Artificial Intelligence to their future development, just a quarter of business leaders (25%) considered Intelligent Automation a “game changer” in understanding current performance. 42% of CTOs see “shortage of talent” as the main obstacle to implementing Intelligent Automation in their business, while 36% of CEOs see ‘upskilling and professional development of existing workforce’ as the most significant adoption barrier. IA is becoming increasingly accessible for firms of all sizes. With this in mind, it is expected to continue to grow rapidly in all industries. This technology has the potential to change the workforce. As it advances, it will be able to perform increasingly complex and difficult tasks. In addition, this may expose certain workforce issues as well as change how tasks are allocated. Tools such as Semrush's AI Visibility Toolkit and Enterprise AIO reflect these developments by analysing how entities are referenced and represented within responses produced by large-language-model-based systems. == Benefits == Streamline processes: Repetitive manual tasks can put a strain on the workforce. However, with AI agents, these tasks can be automated to allow teams to focus on more important matters that require human cognition. Intelligent automation can also be used to mitigate tasks with human error which in turn increases proficiency. This allows the opportunity for firms to scale production without the traditional negative consequences such as reduced quality or increased risk. Customer service improvement: Customer service can be significantly improved, providing the firm with a competitive advantage. IA utilizing chat features allows for instant curated responses to customers. In addition, it can give updates to customers, make appointments, manage calls, and personalize campaigns. Flexibility: Due to the wide range of applications, IA is useful across a variety of fields, technologies, projects and industries. In addition, IA can be integrated with current automated systems in place. This allows for optimized systems unique to each firm to best fit their individual needs. == Capabilities == Cognitive automation: Employs AI techniques to assist humans in decision-making and task completion Natural language processing: Allows computers to automate knowledge work Business process management: Enhances the consistency and agility of corporate operations Process mining: Applies data mining methods to discover, analyze, and improve business processes Intelligent document processing: Utilizes OCR and other advanced technologies to extract data from documents and convert it into structured, usable data Computer vision: Allows computers to extract information from digital images, videos, and other visual inputs Integration automation: Establishes a unified platform with automated workflows that integrate data, applications, and devices.
Automation in construction
Automation in construction is the combination of methods, processes, and systems that allow for greater machine autonomy in construction activities. Construction automation may have multiple goals, including but not limited to, reducing jobsite injuries, decreasing activity completion times, and assisting with quality control and quality assurance. Some systems may be fielded as a direct response to increasing skilled labor shortages in some countries. Opponents claim that increased automation may lead to less construction jobs and that software leaves heavy equipment vulnerable to hackers. Research insights on this subject are today published in several journals such as Automation in Construction by Elsevier. == Uses of automation in construction == Equipment control and management: Automation can be used to control and monitor construction equipment, such as cranes, excavators, and bulldozers. Material handling: Automated systems can be used to handle, transport, and place materials such as concrete, bricks, and stones. Surveying: Automated survey equipment and drones can be used to collect and analyze data on construction sites. Quality control: Automated systems can be used to monitor and control the quality of materials and construction processes. Safety management: Automated systems can be used to monitor and control safety conditions on construction sites. Scheduling and planning: Automated systems can be used to manage schedules, resources, and costs. Waste management: Automated systems can be used to manage and dispose of waste materials generated during construction. 3D printing: Automated 3D printing can be used to create prototypes, models, and even full-scale building components. == Autonomous heavy equipment == Advances in sensors, machine learning, and autonomous vehicle technology have led to the development of self-operating construction equipment and retrofit systems designed to automate excavators, bulldozers, tracked loaders, skid steer loaders, and haul trucks, allowing them to perform tasks with limited human supervision. Since 2017, tech companies have developed autonomous or semi-autonomous retrofit kits that can be installed on existing construction machinery. Examples include Bedrock Robotics, Built Robotics, and SafeAI, which develop sensor and software systems that enable excavators and other earthmoving machines to operate with varying degrees of autonomy. Major equipment manufacturers have also introduced autonomous capabilities: Caterpillar and John Deere have developed autonomous or semi-autonomous systems for construction and mining equipment, including haul trucks and earthmoving machines. == Transportation сonstruction == Kratos Defense & Security Solutions fielded the world’s first Autonomous Truck-Mounted Attenuator (ATMA) in 2017, in conjunction with Royal Truck & Equipment. == Benefits of automation in construction == The use of automation in construction has become increasingly prevalent in recent years due to its numerous benefits. Automation in construction refers to the use of machinery, software, and other technologies to perform tasks that were previously done manually by workers. One of the most significant benefits of automation in construction is increased productivity. Automation can help speed up construction processes, reduce project completion times, and improve overall efficiency. For example, using automated machinery for tasks such as concrete pouring, bricklaying, and welding can significantly increase the speed and accuracy of these tasks, allowing for more work to be completed in a shorter amount of time. Another benefit of automation in construction is improved safety. By automating tasks that are hazardous to workers, such as demolition or working at height, companies can reduce the risk of accidents and injuries on site. Automation can also help to reduce worker fatigue, which can be a significant factor in accidents and mistakes. Overall, the use of automation in construction can improve productivity, reduce costs, increase safety, and improve the quality of construction projects. As technology continues to advance, the use of automation is likely to become even more prevalent in the construction industry.
Deadbot
A deadbot, deathbot, or griefbot is a digital avatar, created with artificial intelligence, which resembles a person who is dead. Griefbots employ natural language processing and machine-learning techniques to approximate the style and personality of a deceased person. They may appear as chatbots, voice assistants, or animated avatars, and are often trained on an individual's digital remains. == History == Among the earliest researchers, Muhammad Aurangzeb Ahmad of the University of Washington, developed the Grandpa Bot project, a conversational simulation of his late father designed for his children to interact with. Other efforts include journalist James Vlahos's Dadbot, which evolved into the commercial platform HereAfter AI. Hossein Rahnama's Augmented Eternity research at MIT Media Lab and Toronto Metropolitan University, and game designer Jason Rohrer's "Project December", have enabled users to converse with language-model representations of loved ones. Early commercial projects such as Eternime, founded by Marius Ursache, also popularized the notion of interactive digital immortality. == Cultural and societal impact == Scholars have proposed frameworks and critiques addressing the ethics of these technologies. Tomasz Hollanek and Katarzyna Nowaczyk-Basińska developed a design-ethics taxonomy distinguishing the data donor, data recipient, and interactant. Edina Harbinja and Lilian Edwards formalized the concept of post-mortem privacy, and Carl J. Öhman at the Oxford Internet Institute studied the management of large-scale digital remains. Cultural acceptance varies: while some view them as expressions of remembrance, others regard them as unsettling or ethically problematic. Concerns have been raised about deadbots' potential for creating psychological harm. Griefbots are considered part of the phenomenon of artificial intimacy.
Pattern playback
The pattern playback is an early talking device that was built by Dr. Franklin S. Cooper and his colleagues, including John M. Borst and Caryl Haskins, at Haskins Laboratories in the late 1940s and completed in 1950. There were several different versions of this hardware device. Only one currently survives. The machine converts pictures of the acoustic patterns of speech in the form of a spectrogram back into sound. Using this device, Alvin Liberman, Frank Cooper, and Pierre Delattre (later joined by Katherine Safford Harris, Leigh Lisker, and others) were able to discover acoustic cues for the perception of phonetic segments (consonants and vowels). This research was fundamental to the development of modern techniques of speech synthesis, reading machines for the blind, the study of speech perception and speech recognition, and the development of the motor theory of speech perception. To create sound, the pattern playback machine uses an arc light source which is directed against a rotating disk with 50 concentric tracks whose transparencies vary systematically in order to produce 50 harmonics of a fundamental frequency. The light is further projected against a spectrogram, whose reflectance corresponds to the sound pressure level of the partial of the signal, and is then directed towards a photovoltaic cell by which the light variation is converted into sound pressure variations. The pattern playback was last used in an experimental study by Robert Remez in 1976. The pattern playback now resides in the Museum at Haskins Laboratories in New Haven, Connecticut. The technique of pattern playback also now refers, more generally, to algorithms or techniques for converting spectrograms, cochleagrams, and correlograms from pictures back into sounds. A demonstration is in the TV show Adventure. Pioneering technology in psycholinguistics (CBS Television. 1953). == Digital pattern playback == In the 1970s, digital pattern playbacks began to supplant the earlier version. An early prototype was developed by Patrick Nye, Philip Rubin, and colleagues at Haskins Laboratories. It combined a "Ubiquitous Spectrum Analyzer"[1] for automatic spectral analysis, along with a VAX GT-40 display processor for graphic manipulation of the displayed spectrogram, a form of "synthesis by art", and subsequent re-synthesis using a 40 channel filter bank. This hybrid hardware/software digital pattern playback was eventually replaced at Haskins Laboratories by the HADES analysis and display system, designed by Philip Rubin, and implemented in Fortran on the VAX family of computers. A more modern version has been described by Arai and colleagues [2]. An on-line demonstration is available [3].
Feature (machine learning)
In machine learning and pattern recognition, a feature is an individual measurable property or characteristic of a data set. Choosing informative, discriminating, and independent features is crucial to producing effective algorithms for pattern recognition, classification, and regression tasks. Features are usually numeric, but other types such as strings and graphs are used in syntactic pattern recognition, after some pre-processing step such as one-hot encoding. The concept of "features" is related to that of explanatory variables used in statistical techniques such as linear regression. == Feature types == In feature engineering, two types of features are commonly used: numerical and categorical. Numerical features are continuous values that can be measured on a scale. Examples of numerical features include age, height, weight, and income. Numerical features can be used in machine learning algorithms directly. Categorical features are discrete values that can be grouped into categories. Examples of categorical features include gender, color, and zip code. Categorical features typically need to be converted to numerical features before they can be used in machine learning algorithms. This can be done using a variety of techniques, such as one-hot encoding, label encoding, and ordinal encoding. The type of feature that is used in feature engineering depends on the specific machine learning algorithm that is being used. Some machine learning algorithms, such as decision trees, can handle both numerical and categorical features. Other machine learning algorithms, such as linear regression, can only handle numerical features. == Classification == A numeric feature can be conveniently described by a feature vector. One way to achieve binary classification is using a linear predictor function (related to the perceptron) with a feature vector as input. The method consists of calculating the scalar product between the feature vector and a vector of weights, qualifying those observations whose result exceeds a threshold. Algorithms for classification from a feature vector include nearest neighbor classification, neural networks, and statistical techniques such as Bayesian approaches. == Examples == In character recognition, features may include histograms counting the number of black pixels along horizontal and vertical directions, number of internal holes, stroke detection and many others. In speech recognition, features for recognizing phonemes can include noise ratios, length of sounds, relative power, filter matches, logarithmic Mel-scale spectral vectors and Mel-frequency cepstral coefficients, which represent the frequency characteristics of audio signals. In spam detection algorithms, features may include the presence or absence of certain email headers, the email structure, the language, the frequency of specific terms, the grammatical correctness of the text. In computer vision, there are a large number of possible features, such as edges and objects. == Feature vectors == In pattern recognition and machine learning, a feature vector is an n-dimensional vector of numerical features that represent some object. Many algorithms in machine learning require a numerical representation of objects, since such representations facilitate processing and statistical analysis. When representing images, the feature values might correspond to the pixels of an image, while when representing texts the features might be the frequencies of occurrence of textual terms. Feature vectors are equivalent to the vectors of explanatory variables used in statistical procedures such as linear regression. Feature vectors are often combined with weights using a dot product in order to construct a linear predictor function that is used to determine a score for making a prediction. The vector space associated with these vectors is often called the feature space. In order to reduce the dimensionality of the feature space, a number of dimensionality reduction techniques can be employed. Higher-level features can be obtained from already available features and added to the feature vector; for example, for the study of diseases the feature 'Age' is useful and is defined as Age = 'Year of death' minus 'Year of birth' . This process is referred to as feature construction. Feature construction is the application of a set of constructive operators to a set of existing features resulting in construction of new features. Examples of such constructive operators include checking for the equality conditions {=, ≠}, the arithmetic operators {+,−,×, /}, the array operators {max(S), min(S), average(S)} as well as other more sophisticated operators, for example count(S, C) that counts the number of features in the feature vector S satisfying some condition C or, for example, distances to other recognition classes generalized by some accepting device. Feature construction has long been considered a powerful tool for increasing both accuracy and understanding of structure, particularly in high-dimensional problems. Applications include studies of disease and emotion recognition from speech. == Selection and extraction == The initial set of raw features can be redundant and large enough that estimation and optimization is made difficult or ineffective. Therefore, a preliminary step in many applications of machine learning and pattern recognition consists of selecting a subset of features, or constructing a new and reduced set of features to facilitate learning, and to improve generalization and interpretability. Extracting or selecting features is a combination of art and science; developing systems to do so is known as feature engineering. It requires the experimentation of multiple possibilities and the combination of automated techniques with the intuition and knowledge of the domain expert. Automating this process is feature learning, where a machine not only uses features for learning, but learns the features itself.