Semi-automation is a process or procedure that is performed by the combined activities of man and machine with both human and machine steps typically orchestrated by a centralized computer controller. Within manufacturing, production processes may be fully manual, semi-automated, or fully automated. In this case, semi-automation may vary in its degree of manual and automated steps. Semi-automated manufacturing processes are typically orchestrated by a computer controller which sends messages to the worker at the time in which he/she should perform a step. The controller typically waits for feedback that the human performed step has been completed via either a human-machine interface or via electronic sensors distributed within the process. Controllers within semi-automated processes may either directly control machinery or send signals to machinery distributed within the process. Centralized computer controllers within semi-automated processes orchestrate processes by instructing the worker, providing electronic communication and control to process equipment, tools, or machines, as well as perform data management to record and ensure that the process meets established process criteria. Many manufacturers choose not to fully automate a process, and instead implement semi-automation due to the complexity of the task, or the number of products produced is too low to justify the investment in full automation. Other processes may not be fully automated because it may reduce the flexibility to easily adapt the processes to reflect production needs.
Feed forward (control)
A feed forward (sometimes written feedforward) is an element or pathway within a control system that passes a controlling signal from a source in its external environment to a load elsewhere in its external environment. This is often a command signal from an external operator. In control engineering, a feedforward control system is a control system that uses sensors to detect disturbances affecting the system and then applies an additional input to minimize the effect of the disturbance. This requires a mathematical model of the system so that the effect of disturbances can be properly predicted. A control system which has only feed-forward behavior responds to its control signal in a pre-defined way without responding to the way the system reacts; it is in contrast with a system that also has feedback, which adjusts the input to take account of how it affects the system, and how the system itself may vary unpredictably. In a feed-forward system, the control variable adjustment is not error-based. Instead it is based on knowledge about the process in the form of a mathematical model of the process and knowledge about, or measurements of, the process disturbances. Some prerequisites are needed for control scheme to be reliable by pure feed-forward without feedback: the external command or controlling signal must be available, and the effect of the output of the system on the load should be known (that usually means that the load must be predictably unchanging with time). Sometimes pure feed-forward control without feedback is called 'ballistic', because once a control signal has been sent, it cannot be further adjusted; any corrective adjustment must be by way of a new control signal. In contrast, 'cruise control' adjusts the output in response to the load that it encounters, by a feedback mechanism. These systems could relate to control theory, physiology, or computing. == Overview == With feed-forward or feedforward control, the disturbances are measured and accounted for before they have time to affect the system. In the house example, a feed-forward system may measure the fact that the door is opened and automatically turn on the heater before the house can get too cold. The difficulty with feed-forward control is that the effects of the disturbances on the system must be accurately predicted, and there must not be any unmeasured disturbances. For instance, if a window was opened that was not being measured, the feed-forward-controlled thermostat might let the house cool down. The term has specific meaning within the field of CPU-based automatic control. The discipline of feedforward control as it relates to modern, CPU based automatic controls is widely discussed, but is seldom practiced due to the difficulty and expense of developing or providing for the mathematical model required to facilitate this type of control. Open-loop control and feedback control, often based on canned PID control algorithms, are much more widely used. There are three types of control systems: open-loop, feed-forward, and feedback. An example of a pure open-loop control system is manual non-power-assisted steering of a motor car; the steering system does not have access to an auxiliary power source and does not respond to varying resistance to turning of the direction wheels; the driver must make that response without help from the steering system. In comparison, power steering has access to a controlled auxiliary power source, which depends on the engine speed. When the steering wheel is turned, a valve is opened which allows fluid under pressure to turn the wheels. A sensor monitors that pressure so that the valve only opens enough to cause the correct pressure to reach the wheel turning mechanism. This is feed-forward control where the output of the system, the change in direction of travel of the vehicle, plays no part in the system. See Model predictive control. If the driver is included in the system, then they do provide a feedback path by observing the direction of travel and compensating for errors by turning the steering wheel. In that case you have a feedback system, and the block labeled System in Figure(c) is a feed-forward system. In other words, systems of different types can be nested, and the overall system regarded as a black-box. Feedforward control is distinctly different from open-loop control and teleoperator systems. Feedforward control requires a mathematical model of the plant (process and/or machine being controlled) and the plant's relationship to any inputs or feedback the system might receive. Neither open-loop control nor teleoperator systems require the sophistication of a mathematical model of the physical system or plant being controlled. Control based on operator input without integral processing and interpretation through a mathematical model of the system is a teleoperator system and is not considered feedforward control. == History == Historically, the use of the term feedforward is found in works by Harold S. Black in US patent 1686792 (invented 17 March 1923) and D. M. MacKay as early as 1956. While MacKay's work is in the field of biological control theory, he speaks only of feedforward systems. MacKay does not mention feedforward control or allude to the discipline of feedforward controls. MacKay and other early writers who use the term feedforward are generally writing about theories of how human or animal brains work. Black also has US patent 2102671 invented 2 August 1927 on the technique of feedback applied to electronic systems. The discipline of feedforward controls was largely developed by professors and graduate students at Georgia Tech, MIT, Stanford and Carnegie Mellon. Feedforward is not typically hyphenated in scholarly publications. Meckl and Seering of MIT and Book and Dickerson of Georgia Tech began the development of the concepts of Feedforward Control in the mid-1970s. The discipline of Feedforward Controls was well defined in many scholarly papers, articles and books by the late 1980s. == Benefits == The benefits of feedforward control are significant and can often justify the extra cost, time and effort required to implement the technology. Control accuracy can often be improved by as much as an order of magnitude if the mathematical model is of sufficient quality and implementation of the feedforward control law is well thought out. Energy consumption by the feedforward control system and its driver is typically substantially lower than with other controls. Stability is enhanced such that the controlled device can be built of lower cost, lighter weight, springier materials while still being highly accurate and able to operate at high speeds. Other benefits of feedforward control include reduced wear and tear on equipment, lower maintenance costs, higher reliability and a substantial reduction in hysteresis. Feedforward control is often combined with feedback control to optimize performance. == Model == The mathematical model of the plant (machine, process or organism) used by the feedforward control system may be created and input by a control engineer or it may be learned by the control system. Control systems capable of learning and/or adapting their mathematical model have become more practical as microprocessor speeds have increased. The discipline of modern feedforward control was itself made possible by the invention of microprocessors. Feedforward control requires integration of the mathematical model into the control algorithm such that it is used to determine the control actions based on what is known about the state of the system being controlled. In the case of control for a lightweight, flexible robotic arm, this could be as simple as compensating between when the robot arm is carrying a payload and when it is not. The target joint angles are adjusted to place the payload in the desired position based on knowing the deflections in the arm from the mathematical model's interpretation of the disturbance caused by the payload. Systems that plan actions and then pass the plan to a different system for execution do not satisfy the above definition of feedforward control. Unless the system includes a means to detect a disturbance or receive an input and process that input through the mathematical model to determine the required modification to the control action, it is not true feedforward control. === Open system === In control theory, an open system is a feed forward system that does not have any feedback loop to control its output. In contrast, a closed system uses on a feedback loop to control the operation of the system. In an open system, the output of the system is not fed back into the input to the system for control or operation. == Applications == === Physiological feed-forward system === In physiology, feed-forward control is exemplified by the normal anticipatory regulation of heartbeat in advance of actual physical exertion by the central autonomic network. Feed-forward
Archetype (information science)
In the field of informatics, an archetype is a formal re-usable model of a domain concept. Traditionally, the term archetype is used in psychology to mean an idealized model of a person, personality or behaviour (see Archetype). The usage of the term in informatics is derived from this traditional meaning, but applied to domain modelling instead. An archetype is defined by the OpenEHR Foundation (for health informatics) as follows: An archetype is a computable expression of a domain content model in the form of structured constraint statements, based on some reference model. openEHR archetypes are based on the openEHR reference model. Archetypes are all expressed in the same formalism. In general, they are defined for wide re-use, however, they can be specialized to include local particularities. They can accommodate any number of natural languages and terminologies. == Formal specifications == The modern archetype formalism is specified and maintained by the openEHR Foundation, and although originally developed for the health IT domain, is completely domain-independent, and has been used in geospatial modelling, telecommunications, and defence. The archetype formalism consists of a number of specifications including: 'ADL 1.4': original release of Archetype Definition Language (ADL) and Archetype Object Model (AOM); widely implemented in health IT domain; 'ADL 2': modern release of Archetype Definition Language (ADL), Archetype Object Model (AOM), Archetype Identification specification and Archetype Technology Overview. The Archetype Technology Overview provides a short technical overview of the archetype formalism useful for new users. The ADL/AOM 1.4 specifications were provided to ISO TC 215 in 2008 by the openEHR Foundation and became the ISO 13606-2 standard, extant until 2019. ISO TC 215 accepted the AOM 2 specification as the basis for a revision of this standard, which was issued in 2019. In late 2015, the Object Management Group (OMG) accepted an RfP entitled 'Archetype Modeling Language (AML)' as a new candidate standard. This specification is a form of ADL re-engineered as a UML profile so as to enable archetype modelling to be supported within UML tools. == Tools == A number of tools area available for working with archetypes. Most are listed on the openEHR modelling tools page. They include: ADL Designer, a modern AOM2-based web editing application Archetype Editor, an original desktop clinical modelling tool Template Designer, an original desktop clinical templating tool LinkEHR, an archetype and data integration tool ADL Workbench, reference compiler and visualiser tool == Example ==
Non-personal data
Non-Personal Data (NPD) is electronic data that does not contain any information that can be used to identify a natural person. Thus, it can either be data that has no personal information to begin with (such as weather data, stock prices, data from anonymous IoT sensors); or it is data that had personal data that was subsequently pseudoanonymized (for example, identifiable strings substituted with random strings) or anonymized (such as by irreversibly removing all personal data). NPD is part of the overall Data Governance Strategy of a region or country. While personal data are covered by Data Protection Legislation such as GDPR, other kinds of data would fall under the scope of NPD Regulation. == Importance of non-personal data == It has been pointed out that the future is data-driven. What this means is that much of the present innovation taking place in domains such as Machine Learning and Artificial Intelligence is fueled by data, which is needed for calibrating the complex models (comprising neural network-based as well as other kinds). The larger the volume, diversity and quality of the data, the higher is the quality of the model, leading to better predictions and explanations. However, there is a flip-side to data availability. The newly-emerging awareness of privacy and the consequent need for powerful Data Protection Regulations (such as GDPR) makes it increasingly difficult or impossible to obtain data in the quantities required. This is a contradiction, and the only way out would be to remove all personal data from data sets (either by Data anonymization or Pseudonymization coupled with noise injection, at which point it becomes NPD. Therefore, many innovation-friendly countries are coming out with regulatory regimes that would ensure that personal data is protected, while, at the same time, non-personal data can be extracted from personal data so that innovation is fostered. In other words, NPD 'unlocks' value that was locked away in data sets that have personally-identifiable information. It is expected that multiple NPD data sets will begin to be available on free or commercial basis from different providers once the regulations are in place. == Emerging regulatory frameworks == Non-Personal Data has significant uses that may be economic, social, political or security-related. Several countries and regions are in the process of regulating the use of NPD. In May 2019, the European Union operationalized its Regulation of the Free Flow of NPD. India announced a nine-member expert committee to make recommendations on the regulation of NPD in 2019, which published its first report in mid-2020. The report was opened for public comments, after which it was revised and published in December 2020. == Proposed NPD regulatory framework in India == The following were the objectives of the proposed Indian regulation as per the revised report: Sovereignty: India has rights over the data of India, its people and organisations. Benefit India: Benefits of data must accrue to India and its people. Benefits the world: Innovation, new models and algorithms for the world. Privacy: Misuse, reidentification and harms must be prevented. Simplicity: The regulations should be simple, digital and unambiguous. Innovation and entrepreneurship: The data should be freely available for innovation and entrepreneurship in India. == Concerns == The major concern in the use of NPD is if there are techniques (statistical or AI-based) by which multiple data sets can be used to extract personally-identifiable data.
Causal AI
Causal AI is a technique in artificial intelligence that builds a causal model and can thereby make inferences using causality rather than just correlation. One practical use for causal AI is for organisations to explain decision-making and the causes for a decision. Systems based on causal AI, by identifying the underlying web of causality for a behaviour or event, provide insights that solely predictive AI models might fail to extract from historical data. An analysis of causality may be used to supplement human decisions in situations where understanding the causes behind an outcome is necessary, such as quantifying the impact of different interventions, policy decisions or performing scenario planning. A 2024 paper from Google DeepMind demonstrated mathematically that "Any agent capable of adapting to a sufficiently large set of distributional shifts must have learned a causal model". The paper offers the interpretation that learning to generalise beyond the original training set requires learning a causal model, concluding that causal AI is necessary for artificial general intelligence. == History == The concept of causal AI and the limits of machine learning were raised by Judea Pearl, the Turing Award-winning computer scientist and philosopher, in 2018's The Book of Why: The New Science of Cause and Effect. Pearl asserted: “Machines' lack of understanding of causal relations is perhaps the biggest roadblock to giving them human-level intelligence.” In 2020, Columbia University established a Causal AI Lab under Director Elias Bareinboim. Professor Bareinboim's research focuses on causal and counterfactual inference and their applications to data-driven fields in the health and social sciences as well as artificial intelligence and machine learning. Technological research and consulting firm Gartner for the first time included causal AI in its 2022 Hype Cycle report, citing it as one of five critical technologies in accelerated AI automation. Causal AI is closely related to but distinct from fields such as causal inference, explainable AI and causal reasoning. While causal inference focuses on estimating cause-effect relationships (often from observational data), causal AI emphasises the integration of those causal models into AI systems for prediction, planning and adaptation.
Facebook Messenger
Messenger (formerly known as Facebook Messenger) is an American proprietary instant messaging service developed by Meta Platforms, the company that operates Facebook. Originally developed as Facebook Chat in 2008, the client application of Messenger is currently available on iOS and Android mobile platforms, Windows and macOS desktop platforms, through the Messenger.com web application, and on the standalone Meta Portal hardware. Messenger is used to send messages and exchange photos, videos, stickers, audio, and files, and also react to other users' messages and interact with bots. The service also supports voice and video calling. The standalone apps support using multiple accounts, conversations with end-to-end encryption, and playing games. There are also group chats where you can connect with multiple people at once in a private space such as Panama Chat. With a monthly userbase of over 1 billion people, it is among the largest social media platforms. == History == Following tests of a new instant messaging platform on Facebook in March 2008, the feature, then-titled "Facebook Chat", was gradually released to users in April 2008. Facebook revamped its messaging platform in November 2010, and subsequently acquired group messaging service Beluga in March 2011, which the company used to launch its standalone iOS and Android mobile apps on August 9, 2011. Facebook later launched a BlackBerry version in October 2011. An app for Windows Phone, though lacking features including voice messaging and chat heads, was released in March 2014. In April 2014, Facebook announced that the messaging feature would be removed from the main Facebook app and users will be required to download the separate Messenger app. An iPad-optimized version of the iOS app was released in July 2014. On April 8, 2015, Facebook launched a website interface for Messenger. A Tizen app was released on July 13, 2015. Facebook launched Messenger for Windows 10 in April 2016. In October 2016, Facebook released Messenger Lite, a stripped-down version of Messenger with a reduced feature set. The app is aimed primarily at old Android phones and regions where high-speed Internet is not widely available. In April 2017, Messenger Lite was expanded to 132 more countries. In May 2017, Facebook revamped the design for Messenger on Android and iOS, bringing a new home screen with tabs and categorization of content and interactive media, red dots indicating new activity, and relocated sections. Facebook announced a Messenger program for Windows 7 in a limited beta test in November 2011. The following month, Israeli blog TechIT leaked a download link for the program, with Facebook subsequently confirming and officially releasing the program. The program was eventually discontinued in March 2014. A Firefox web browser add-on was released in December 2012, but was also discontinued in March 2014. In December 2017, Facebook announced Messenger Kids, a new app aimed for persons under 13 years of age. The app comes with some differences compared to the standard version. In 2019, Messenger announced to be the 2nd most downloaded mobile app of the decade, from 2011 to 2019. In December 2019, Messenger dropped support for users to sign in using only a mobile number, meaning that users must sign in to a Facebook account in order to use the service. In March 2020, Facebook started to ship its dedicated Messenger for macOS app through the Mac App Store. The app is currently live in regions including France, Australia, Mexico, Poland, and many others. In April 2020, Facebook began rolling out a new feature called Messenger Rooms, a video chat feature that allows users to chat with up to 50 people at a time. The feature rivals Zoom, an application that gained a lot of popularity during the COVID-19 pandemic. Privacy concerns arose since the feature uses the same data collection policies as mainstream Facebook. In July 2020, Facebook added a new feature in Messenger that lets iOS users to use Apple's Face ID or Touch ID to lock their chats. The feature is called App Lock and is a part of several changes in Messenger regarding privacy and security. The option to view only "Unread Threads" was removed from the inbox, requiring the account holder to scroll through the entire inbox to be certain every unread message has been seen. On October 13, 2020, the Messenger application introduced cross-app messaging with Instagram, which was launched in September 2021. In addition to the integrated messaging, the application announced the introduction of a new logo, which should be an amalgamation of the Messenger and Instagram logo. The desktop app of Messenger was shut down on December 15, 2025. Messaging services were moved to the Facebook website or Messenger's site for those without an account on the former. The Messenger site was discontinued on April 16, 2026. Messaging services were moved to the Facebook website on the morning of April 17, 2026 without an Messenger account on the former to use Facebook account. == Features == The following is a table of features available in Messenger, as well as their geographical coverage and what devices they are available on. In addition there is a vanishing message feature. In addition there is an audio recording feature which allows audio recordings of up to one minute which may or may not be vanishing: === Messenger Rooms === It is a video conferencing feature of Messenger. It allows users to add up to 50 people at a time. Messenger Rooms does not require a Facebook account. Messenger Rooms competes with other services such as Zoom. Back in 2014, Facebook introduced an unrelated, stand-alone application named Rooms, letting users create places for users with similar interests, with users being anonymous to others. This was shut down in December 2015. In April 2020, during the COVID-19 pandemic, Facebook revealed video conferencing features for Messenger called Messenger Rooms. This was seen as a response to the popularity of other video conferencing platforms such as Zoom and Skype in the midst of the COVID-19 pandemic. Messenger Rooms allows users to add up to 50 people per room, without restrictions on time. It does not require a Facebook account or a separate app from Messenger. When used, it only prompts the user for basic information. Users can add 360° virtual backgrounds, mood lighting, and other AR effects as well as share screens. To prevent unwanted participants from joining, users can lock rooms and remove participants. Some have voiced concerns in regards to Messenger Room's privacy and how its parent, Facebook, handles data. Messenger Rooms, unlike some of its competitors, does not use end-to-end encryption. In addition, there have been concerns over how Messenger Rooms collects user data. == Monetization == In January 2017, Facebook announced that it was testing showing advertisements in Messenger's home feed. At the time, the testing was limited to a "small number of users in Australia and Thailand", with the ad format being swipe-based carousel ads. In July, the company announced that they were expanding the testing to a global audience. Stan Chudnovsky, head of Messenger, told VentureBeat that "We'll start slow ... When the average user can be sure to see them we truly don't know because we're just going to be very data-driven and user feedback-driven on making that decision". Facebook told TechCrunch that the advertisements' placement in the inbox depends on factors such as thread count, phone screen size, and pixel density. In a TechCrunch editorial by Devin Coldewey, he described the ads as "huge" in the space they occupy, "intolerable" in the way they appear in the user interface, and "irrelevant" due to the lack of context. Coldewey finished by writing "Advertising is how things get paid for on the internet, including TechCrunch, so I'm not an advocate of eliminating it or blocking it altogether. But bad advertising experiences can spoil a perfectly good app like (for the purposes of argument) Messenger. Messaging is a personal, purposeful use case and these ads are a bad way to monetize it." == Reception == In November 2014, the Electronic Frontier Foundation (EFF) listed Messenger (Facebook chat) on its Secure Messaging Scorecard. It received a score of 2 out of 7 points on the scorecard. It received points for having communications encrypted in transit and for having recently completed an independent security audit. It missed points because the communications were not encrypted with keys the provider didn't have access to, users could not verify contacts' identities, past messages were not secure if the encryption keys were stolen, the source code was not open to independent review, and the security design was not properly documented. As stated by Facebook in its Help Center, there is no way to log out of the Messenger application. Instead, users can choose between different availability statuses, including "Appear as inactive", "S
Novell File Reporter
Novell File Reporter (NFR) is software that allows network administrators to identify files stored on the network and generates reports regarding the size of individual files, file type, when files were last accessed, and where duplicates exist. Additionally, the File Reporter tracks storage volume capacity and usage. It is a component of the Novell File Management Suite. == How it works == Novell File Reporter examines and reports on terabytes of data via a central reporting engine (NFR Engine) and distributed agents (NFR Agents). The NFR Engine schedules the scans of file instances conducted by NFR Agents, processes and compiles the scans for reporting purposes, and provides report information to the user interface. In addition to the standard reports it can generate, the NFR Engine can also produce "trigger reports" in response to specific events (a server volume crossing a capacity threshold, for example). Accordingly, the NFR Engine monitors the data gathered by the NFR Agents in order to identify these "triggers." The NFR Engine when working in either eDirectory or Active Directory connects to the directory via a Directory Services Interface (DSI) and thus can monitor and check file permissions.