Organoid intelligence (OI) is an emerging field of study in computer science and biology that develops and studies biological wetware computing using 3D cultures of human brain cells (or brain organoids) and brain-machine interface technologies. Such technologies may be referred to as OIs or the nervous filesystem. Organoid intelligent computer systems can be an example of biohybrid systems. == Differences with non-organic computing == As opposed to traditional non-organic silicon-based approaches, OI seeks to use lab-grown cerebral organoids to serve as "biological hardware". While these structures are still far from being able to think like a regular human brain and do not yet possess strong computing capabilities, OI research currently offers the potential to improve the understanding of brain development, learning and memory, potentially finding treatments for neurological disorders such as dementia. Thomas Hartung, a professor from Johns Hopkins University, argued in 2023 that "while silicon-based computers are certainly better with numbers, brains are better at learning." He noted that transistor density in computer chip may be approaching its limits, whereas brains, being wired differently, are more energy-efficient and can store large amounts of information. Some researchers claim that even though human brains are slower than machines at processing simple information, they are far better at processing complex information as brains can deal with fewer and more uncertain data, perform both sequential and parallel processing, being highly heterogenous, use incomplete datasets, and is said to outperform non-organic machines in decision-making. Training OIs involve the process of biological learning (BL) as opposed to machine learning (ML) for AIs. == Bioinformatics in OI == OI generates complex biological data, necessitating sophisticated methods for processing and analysis. Bioinformatics provides the tools and techniques to decipher raw data, uncovering the patterns and insights. Researchers have developed a platform named Neuroplatform for experimenting remotely with brain organoids via an API. == Intended functions == Brain-inspired computing hardware aims to emulate the structure and working principles of the brain and could be used to address current limitations in AI technologies. However, brain-inspired silicon chips are still limited in their ability to fully mimic brain function, as most examples are built on digital electronic principles. One study performed OI computation (which they termed Brainoware) by sending and receiving information from the brain organoid using a high-density multielectrode array. By applying spatiotemporal electrical stimulation, nonlinear dynamics, and fading memory properties, as well as unsupervised learning from training data by reshaping the organoid functional connectivity, the study showed the potential of this technology by using it for speech recognition and nonlinear equation prediction in a reservoir computing framework. == Ethical concerns == While researchers are hoping to use OI and biological computing to complement traditional silicon-based computing, there are also questions about the ethics of such an approach. Concerns include the possibility that an organoid could develop sentience or consciousness, and the question of the relationship between a stem cell donor (for growing the organoid) and the respective OI system.
Lexalytics
Lexalytics, Inc. provides sentiment and intent analysis to an array of companies using SaaS and cloud based technology. Salience 6, the engine behind Lexalytics, was built as an on-premises, multi-lingual text analysis engine. It is leased to other companies who use it to power filtering and reputation management programs. In July, 2015 Lexalytics acquired Semantria to be used as a cloud option for its technology. In September, 2021 Lexalytics was acquired by CX company InMoment. == History == Lexalytics spun into existence in January 2003 out of a content management startup called Lightspeed. Lightspeed consolidated on America's West Coast. Jeff Catlin, a Lightspeed General Manager, and Mike Marshall, a Lighstpeed Principal Engineer, convinced investors to give them the East Coast company so as to avoid shutdown costs. Catlin and Marshall renamed the operation Lexalytics. Catlin took on the role of chief executive officer with Marshall working as Chief Technology Officer. Lexalytics opted to not accept venture cash. Instead, the company initially shared sales and marketing expenses with U.K. based document management company Infonic. The partner companies soon formed a joint venture in July 2008, which was later dissolved. Since then, Lexalytics has worked with many other companies, like Bottlenose, Salesforce, Thomson Reuters, Oracle and DataSift. Relationships with social media monitoring companies like Datasift tend to find Lexalytics’ Salience engine baked into the product itself. Lexalytics is used similarly to monitor sentiment as it relates to stock trading. In December 2014, Lexalytics announced the latest iteration to its sentiment analysis engine, Salience 6. Earlier that year Lexalytics acquired Semantria in a bid to appeal to a wider variety of business models. Created by former Lexalytics Marketing Director Oleg Rogynskyy, Semantria is a SaaS text mining service offered as an API and Excel based plugin that measures sentiment. The goal of the acquisition, which cost Lexalytics less than US$10 million, was to expand the customer base both within the United States and abroad with multilingual support. The engine that powers Semantria, Salience, is grounded in its deep learning ability. An example of this is its concept matrix, which allows Salience an understanding of concepts and relationship between concepts based on a detailed reading of the entire repository of Wikipedia. This matrix allows Salience to use Wikipedia for automatic categorization. Along with features like the concept matrix, Salience supports 16 international languages. The engine has earned Lexalytics a spot on EContent's “Top 100 Companies in the Digital Content Industry” List for 2014–2015. In September 2018, Lexalytics launched document data extraction market using natural language processing (NLP).
Operational historian
In manufacturing, an operational historian is a time-series database application that is developed for operational process data. Historian software is often embedded or used in conjunction with standard DCS and PLC control systems to provide enhanced data capture, validation, compression, and aggregation capabilities. Historians have been deployed in almost every industry and contribute to functions such as supervisory control, performance monitoring, quality assurance, and, more recently, machine learning applications which can learn from vast quantities of historical data. These systems were originally developed to capture instrumentation and control data, which led many to use the term "tag" for a stream of process data, referring to the physical "tags" which had been placed on instrumentation for manually capturing data. Raw data may be accessed via OPC HDA, SQL, or REST API interfaces. == Operational Support == Operational historians are typically used within the manufacturing facility by engineers and operators for supervisory functions and analysis. An operational historian will typically capture all instrumentation and control data, whereas an enterprise historian that is deployed to support business functions will capture only a subset of the plant data. Typically, these applications offer data access through dedicated APIs (Application Programming Interfaces) and SDKs (Software Development Kits) which offer high-performance read and write operations. These operate through vendor-specific or custom applications. Front-end tools for trending process data over time are the most common interfaces to these databases. Because these applications are typically deployed next to or near the source of their process data, they are often marketed and sold as 'real-time database systems.' This distinction varies among vendors, who often have to make tradeoffs in performance between data capture and presentation, and application and analysis functionality. The following is a list of typical challenges for operational historians: data collection from instrumentation and controls storage and archiving of very large volumes of data organization of data in the form of "tags" or "points" limiting of monitoring (alarms) and validation aggregation and interpolation manual data entry (MDE) == Data access == As opposed to enterprise historians, the data access layer in the operational historian is designed to offer sophisticated data fetching modes without complex information analysis facilities. The following settings are typically available for data access operations: Data scope (single point or tag, history based on time range, history based on sample count) Request modes (raw data, last-known value, aggregation, interpolation) Sampling (single point, all points without sampling, all points with interval sampling) Data omission (based on the sample quality, based on the sample value, based on the count) Even though the operational historians are rarely relational database management systems, they often offer SQL-based interfaces to query the database. In most of such implementations, the dialect does not follow the SQL standard in order to provide syntax for specifying data access operations parameters.
Outline of artificial intelligence
The following outline is provided as an overview of and topical guide to artificial intelligence: Artificial intelligence (AI) is intelligence exhibited by machines or software. It is also the name of the scientific field which studies how to create computers and computer software that are capable of intelligent behavior. == AI terminology == Glossary of artificial intelligence == Goals and applications == === General intelligence === Artificial general intelligence AI-complete === Reasoning and problem solving === Automated reasoning Mathematics Automated theorem prover Computer-assisted proof – Computer algebra General Problem Solver Expert system – Decision support system – Clinical decision support system – === Knowledge representation === Knowledge representation Knowledge management Cyc === Planning === Automated planning and scheduling Strategic planning Sussman anomaly – === Learning === Machine learning – Constrained Conditional Models – Deep learning – Neural modeling fields – Supervised learning – Weak supervision (semi-supervised learning) – Unsupervised learning – === Natural language processing === Natural language processing (outline) – Chatterbots – Language identification – Large language model – Retrieval-augmented generation – Natural language user interface – Natural language understanding – Machine translation – Statistical semantics – Question answering – Semantic translation – Concept mining – Data mining – Text mining – Process mining – E-mail spam filtering – Information extraction – Named-entity extraction – Coreference resolution – Named-entity recognition – Relationship extraction – Terminology extraction – === Perception === Machine perception Pattern recognition – Computer Audition – Speech recognition – Speaker recognition – Computer vision (outline) – Image processing Intelligent word recognition – Object recognition – Optical mark recognition – Handwriting recognition – Optical character recognition – Automatic number plate recognition – Information extraction – Image retrieval – Automatic image annotation – Facial recognition systems – Silent speech interface – Activity recognition – Percept (artificial intelligence) === Robotics === Robotics – Behavior-based robotics – Cognitive – Cybernetics – Developmental robotics – Evolutionary robotics – === Control === Intelligent control Self-management (computer science) – Autonomic Computing – Autonomic Networking – === Social intelligence === Affective computing Kismet === Game playing === Game artificial intelligence – Computer game bot – computer replacement for human players. Video game AI – Computer chess – Computer Go – General game playing – General video game playing – === Creativity, art and entertainment === Artificial creativity Artificial life Artificial intelligence art AI anthropomorphism AI agent AI web browser AI boom AI slop Creative computing Generative artificial intelligence Generative pre trained transformer Uncanny valley Music and artificial intelligence Computational humor Chatbot === Integrated AI systems === AIBO – Sony's robot dog. It integrates vision, hearing and motorskills. Asimo (2000 to present) – humanoid robot developed by Honda, capable of walking, running, negotiating through pedestrian traffic, climbing and descending stairs, recognizing speech commands and the faces of specific individuals, among a growing set of capabilities. MIRAGE – A.I. embodied humanoid in an augmented reality environment. Cog – M.I.T. humanoid robot project under the direction of Rodney Brooks. QRIO – Sony's version of a humanoid robot. TOPIO, TOSY's humanoid robot that can play ping-pong with humans. Watson (2011) – computer developed by IBM that played and won the game show Jeopardy! It is now being used to guide nurses in medical procedures. Purpose: Open domain question answering Technologies employed: Natural language processing Information retrieval Knowledge representation Automated reasoning Machine learning Project Debater (2018) – artificially intelligent computer system, designed to make coherent arguments, developed at IBM's lab in Haifa, Israel. === Intelligent personal assistants === Intelligent personal assistant – Amazon Alexa – Assistant – Braina – Cortana – Google Assistant – Google Now – Mycroft – Siri – Viv – === Other applications === Artificial life – simulation of natural life through the means of computers, robotics, or biochemistry. Automatic target recognition – Diagnosis (artificial intelligence) – Speech generating device – Vehicle infrastructure integration – Virtual Intelligence – == History == History of artificial intelligence Progress in artificial intelligence Timeline of artificial intelligence AI effect – as soon as AI successfully solves a problem, the problem is no longer considered by the public to be a part of AI. This phenomenon has occurred in relation to every AI application produced, so far, throughout the history of development of AI. AI winter – a period of disappointment and funding reductions occurring after a wave of high expectations and funding in AI. Such funding cuts occurred in the 1970s, for instance. Moore's law === History by period === 2017 in artificial intelligence 2018 in artificial intelligence 2019 in artificial intelligence 2020 in artificial intelligence 2021 in artificial intelligence 2022 in artificial intelligence 2023 in artificial intelligence 2024 in artificial intelligence 2025 in artificial intelligence 2026 in artificial intelligence 2027 in artificial intelligence 2028 in artificial intelligence 2029 in artificial intelligence === History by subject === History of logic (formal reasoning is an important precursor of AI) History of machine learning (timeline) History of machine translation (timeline) History of natural language processing History of optical character recognition (timeline) == AI algorithms and techniques == === Search === Discrete search algorithms Uninformed search Brute force search – Problem-solving technique and algorithmic paradigmPages displaying short descriptions of redirect targets Search tree – Data structure in tree form sorted for fast lookup Breadth-first search – Algorithm to search the nodes of a graph Depth-first search – Algorithm to search the nodes of a graph State space search – Class of search algorithmsPages displaying short descriptions of redirect targets Informed search Best-first search – Graph exploring search algorithm A search algorithm – Algorithm used for pathfinding and graph traversal Heuristics – Problem-solving methodPages displaying short descriptions of redirect targets Pruning (algorithm) – Data compression techniquePages displaying short descriptions of redirect targets Adversarial search Minmax algorithm – Decision rule used for minimizing the possible loss for a worst-case scenarioPages displaying short descriptions of redirect targets Logic as search Production system (computer science) – Computer program used to provide artificial intelligence Rule based system – Type of computer systemPages displaying short descriptions of redirect targets Production rule – Computer program used to provide artificial intelligence Inference rule – Method of deriving conclusionsPages displaying short descriptions of redirect targets Horn clause – Type of logical formula Forward chaining – Inference engine in an expert system Backward chaining – Method of forming inferences Planning as search State space search – Class of search algorithmsPages displaying short descriptions of redirect targets Means–ends analysis – Problem solving technique === Optimization search === Optimization (mathematics) algorithms Hill climbing – Optimization algorithm Simulated annealing – Probabilistic optimization technique and metaheuristic Beam search – Heuristic search algorithm Random optimization – Optimization technique in mathematics Evolutionary computation Genetic algorithms – Competitive algorithm for searching a problem spacePages displaying short descriptions of redirect targets Gene expression programming – Evolutionary algorithm Genetic programming – Evolving computer programs with techniques analogous to natural genetic processes Differential evolution – Method of mathematical optimization Society based learning algorithms. Swarm intelligence – Collective behavior of decentralized, self-organized systems Particle swarm optimization – Iterative simulation method Ant colony optimization – Optimization algorithmPages displaying short descriptions of redirect targets Metaheuristic – Optimization technique === Logic === Logic and automated reasoning Programming using logic Logic programming – Programming paradigm based on formal logic See "Logic as search" above. Forms of Logic Propositional logic First-order logic First-order logic with equality Constraint satisfaction – Process in artificial intelligence and operations research Fuzzy logic Fuzzy set theory – Sets whose elements have degrees of membershipPages displaying short descriptions
Data (word)
The word data is most often used as a singular collective mass noun in educated everyday usage. However, due to the history and etymology of the word, considerable controversy has existed on whether it should be considered a mass noun used with verbs conjugated in the singular, or should be treated as the plural of the now-rarely-used datum. == Usage in English == In one sense, data is the plural form of datum. Datum actually can also be a count noun with the plural datums (see usage in datum article) that can be used with cardinal numbers (e.g., "80 datums"); data (originally a Latin plural) is not used like a normal count noun with cardinal numbers and can be plural with plural determiners such as these and many, or it can be used as a mass noun with a verb in the singular form. Even when a very small quantity of data is referenced (one number, for example), the phrase piece of data is often used, as opposed to datum. The debate over appropriate usage continues, but "data" as a singular form is far more common. In English, the word datum is still used in the general sense of "an item given". In cartography, geography, nuclear magnetic resonance and technical drawing, it is often used to refer to a single specific reference datum from which distances to all other data are measured. Any measurement or result is a datum, though data point is now far more common. Data is indeed most often used as a singular mass noun in educated everyday usage. Some major newspapers, such as The New York Times, use it either in the singular or plural. In The New York Times, the phrases "the survey data are still being analyzed" and "the first year for which data is available" have appeared within one day. The Wall Street Journal explicitly allows this usage in its style guide. The Associated Press style guide classifies data as a collective noun that takes the singular when treated as a unit but the plural when referring to individual items (e.g., "The data is sound" and "The data have been carefully collected"). In scientific writing, data is often treated as a plural, as in These data do not support the conclusions, but the word is also used as a singular mass entity like information (e.g., in computing and related disciplines). British usage now widely accepts treating data as singular in standard English, including everyday newspaper usage at least in non-scientific use. UK scientific publishing still prefers treating it as a plural. Some UK university style guides recommend using data for both singular and plural use, and others recommend treating it only as a singular in connection with computers. The IEEE Computer Society allows usage of data as either a mass noun or plural based on author preference, while IEEE in the editorial style manual indicates to always use the plural form. Some professional organizations and style guides require that authors treat data as a plural noun. For example, the Air Force Flight Test Center once stated that the word data is always plural, never singular.
Lessac Technologies
Lessac Technologies, Inc. (LTI) is an American firm which develops voice synthesis software, licenses technology and sells synthesized novels as MP3 files. The firm currently has seven patents granted and three more pending for its automated methods of converting digital text into human-sounding speech, more accurately recognizing human speech and outputting the text representing the words and phrases of said speech, along with recognizing the speaker's emotional state. The LTI technology is partly based on the work of the late Arthur Lessac, a Professor of Theater at the State University of New York and the creator of Lessac Kinesensic Training, and LTI has licensed exclusive rights to exploit Arthur Lessac's copyrighted works in the fields of speech synthesis and speech recognition. Based on the view that music is speech and speech is music, Lessac's work and books focused on body and speech energies and how they go together. Arthur Lessac's textual annotation system, which was originally developed to assist actors, singers, and orators in marking up scripts to prepare for performance, is adapted in LTI's speech synthesis system as the basic representation of the speech to be synthesized (Lessemes), in contrast to many other systems which use a phonetic representation. LTI's software has two major components: (1) a linguistic front-end that converts plain text to a sequence of prosodic and phonosensory graphic symbols (Lessemes) based on Arthur Lessac's annotation system, which specify the speech units to be synthesized; (2) a signal-processing back-end that takes the Lessemes as acoustic data and produces human-sounding synthesized speech as output, using unit selection and concatenation. LTI's text-to-speech system came in second in the world-wide Blizzard Challenge 2011 and 2012. The first-place team in 2011 also employed LTI's "front-end" technology, but with its own back-end. The Blizzard Challenge, conducted by the Language Technologies Institute of Carnegie Mellon University, was devised as a way to evaluate speech synthesis techniques by having different research groups build voices from the same voice-actor recordings, and comparing the results through listening tests. LTI was founded in 2000 by H. Donald Wilson (chairman), a lawyer, LexisNexis entrepreneur and business associate of Arthur Lessac; and Gary A. Marple (chief inventor), after Marple suggested that Arthur Lessac's kinesensic voice training might be applicable to computational linguistics. After Wilson's death in 2006, his nephew John Reichenbach became the firm's CEO.
Weak stability boundary
Weak stability boundary (WSB), including low-energy transfer, is a concept introduced by Edward Belbruno in 1987. The concept explained how a spacecraft could change orbits using very little fuel. Weak stability boundary is defined for the three-body problem. This problem considers the motion of a particle P of negligible mass moving with respect to two larger bodies, P1, P2, modeled as point masses, where these bodies move in circular or elliptical orbits with respect to each other, and P2 is smaller than P1. The force between the three bodies is the classical Newtonian gravitational force. For example, P1 is the Earth, P2 is the Moon and P is a spacecraft; or P1 is the Sun, P2 is Jupiter and P is a comet, etc. This model is called the restricted three-body problem. The weak stability boundary defines a region about P2 where P is temporarily captured. This region is in position-velocity space. Capture means that the Kepler energy between P and P2 is negative. This is also called weak capture. == Background == This boundary was defined for the first time by Edward Belbruno of Princeton University in 1987. He described a Low-energy transfer which would allow a spacecraft to change orbits using very little fuel. It was for motion about Moon (P2) with P1 = Earth. It is defined algorithmically by monitoring cycling motion of P about the Moon and finding the region where cycling motion transitions between stable and unstable after one cycle. Stable motion means P can completely cycle about the Moon for one cycle relative to a reference section, starting in weak capture. P needs to return to the reference section with negative Kepler energy. Otherwise, the motion is called unstable, where P does not return to the reference section within one cycle or if it returns, it has non-negative Kepler energy. The set of all transition points about the Moon comprises the weak stability boundary, W. The motion of P is sensitive or chaotic as it moves about the Moon within W. A mathematical proof that the motion within W is chaotic was given in 2004. This is accomplished by showing that the set W about an arbitrary body P2 in the restricted three-body problem contains a hyperbolic invariant set of fractional dimension consisting of the infinitely many intersections Hyperbolic manifolds. The weak stability boundary was originally referred to as the fuzzy boundary. This term was used since the transition between capture and escape defined in the algorithm is not well defined and limited by the numerical accuracy. This defines a "fuzzy" location for the transition points. It is also due the inherent chaos in the motion of P near the transition points. It can be thought of as a fuzzy chaos region. As is described in an article in Discover magazine, the WSB can be roughly viewed as the fuzzy edge of a region, referred to as a gravity well, about a body (the Moon), where its force of gravity becomes small enough to be dominated by force of gravity of another body (the Earth) and the motion there is chaotic. A much more general algorithm defining W was given in 2007. It defines W relative to n-cycles, where n = 1,2,3,..., yielding boundaries of order n. This gives a much more complex region consisting of the union of all the weak stability boundaries of order n. This definition was explored further in 2010. The results suggested that W consists, in part, of the hyperbolic network of invariant manifolds associated to the Lyapunov orbits about the L1, L2 Lagrange points near P2. The explicit determination of the set W about P2 = Jupiter, where P1 is the Sun, is described in "Computation of Weak Stability Boundaries: Sun-Jupiter Case". It turns out that a weak stability region can also be defined relative to the larger mass point, P1. A proof of the existence of the weak stability boundary about P1 was given in 2012, but a different definition is used. The chaos of the motion is analytically proven in "Geometry of Weak Stability Boundaries". The boundary is studied in "Applicability and Dynamical Characterization of the Associated Sets of the Algorithmic Weak Stability Boundary in the Lunar Sphere of Influence". == Applications == There are a number of important applications for the weak stability boundary (WSB). Since the WSB defines a region of temporary capture, it can be used, for example, to find transfer trajectories from the Earth to the Moon that arrive at the Moon within the WSB region in weak capture, which is called ballistic capture for a spacecraft. No fuel is required for capture in this case. This was numerically demonstrated in 1987. This is the first reference for ballistic capture for spacecraft and definition of the weak stability boundary. The boundary was operationally demonstrated to exist in 1991 when it was used to find a ballistic capture transfer to the Moon for Japan's Hiten spacecraft. Other missions have used the same transfer type as Hiten, including Grail, Capstone, Danuri, Hakuto-R Mission 1 and SLIM. The WSB for Mars is studied in "Earth-Mars Transfers with Ballistic Capture" and ballistic capture transfers to Mars are computed. The BepiColombo mission of ESA should achieve ballistic capture at the WSB of Mercury in November 2026. The WSB region can be used in the field of Astrophysics. It can be defined for stars within open star clusters. This is done in "Chaotic Exchange of Solid Material Between Planetary Systems: Implications for the Lithopanspermia Hypothesis" to analyze the capture of solid material that may have arrived on the Earth early in the age of the Solar System to study the validity of the lithopanspermia hypothesis. Numerical explorations of trajectories for P starting in the WSB region about P2 show that after the particle P escapes P2 at the end of weak capture, it moves about the primary body, P1, in a near resonant orbit, in resonance with P2 about P1. This property was used to study comets that move in orbits about the Sun in orbital resonance with Jupiter, which change resonance orbits by becoming weakly captured by Jupiter. An example of such a comet is 39P/Oterma. This property of change of resonance of orbits about P1 when P is weakly captured by the WSB of P2 has an interesting application to the field of quantum mechanics to the motion of an electron about the proton in a hydrogen atom. The transition motion of an electron about the proton between different energy states described by the Schrödinger equation is shown to be equivalent to the change of resonance of P about P1 via weak capture by P2 for a family of transitioning resonance orbits. This gives a classical model using chaotic dynamics with Newtonian gravity for the motion of an electron.