AI Assistant Vs AI Agent

AI Assistant Vs AI Agent — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

Acoustic model

An acoustic model is used in automatic speech recognition to represent the relationship between an audio signal and the phonemes or other linguistic units that make up speech. The model is learned from a set of audio recordings and their corresponding transcripts. It is created by taking audio recordings of speech, and their text transcriptions, and using software to create statistical representations of the sounds that make up each word. == Background == Modern speech recognition systems use both an acoustic model and a language model to represent the statistical properties of speech. The acoustic model models the relationship between the audio signal and the phonetic units in the language. The language model is responsible for modeling the word sequences in the language. These two models are combined to get the top-ranked word sequences corresponding to a given audio segment. Most modern speech recognition systems operate on the audio in small chunks known as frames with an approximate duration of 10ms per frame. The raw audio signal from each frame can be transformed by applying the mel-frequency cepstrum. The coefficients from this transformation are commonly known as mel-frequency cepstral coefficients (MFCCs) and are used as an input to the acoustic model along with other features. Recently, the use of convolutional neural networks has led to major improvements in acoustic modeling. == Speech audio characteristics == Audio can be encoded at different sampling rates (i.e. samples per second – the most common being: 8, 16, 32, 44.1, 48, and 96 kHz), and different bits per sample (the most common being: 8-bits, 16-bits, 24-bits or 32-bits). Speech recognition engines work best if the acoustic model they use was trained with speech audio which was recorded at the same sampling rate/bits per sample as the speech being recognized. == Telephony-based speech recognition == The limiting factor for telephony based speech recognition is the bandwidth at which speech can be transmitted. For example, a standard land-line telephone only has a bandwidth of 64 kbit/s at a sampling rate of 8 kHz and 8-bits per sample (8000 samples per second 8-bits per sample = 64000 bit/s). Therefore, for telephony based speech recognition, acoustic models should be trained with 8 kHz/8-bit speech audio files. In the case of voice over IP, the codec determines the sampling rate/bits per sample of speech transmission. Codecs with a higher sampling rate/bits per sample for speech transmission (which improve the sound quality) necessitate acoustic models trained with audio data that matches that sampling rate/bits per sample. == Desktop-based speech recognition == For speech recognition on a standard desktop PC, the limiting factor is the sound card. Most sound cards today can record at sampling rates of between 16–48 kHz of audio, with bit rates of 8- to 16-bits per sample, and playback at up to 96 kHz. As a general rule, a speech recognition engine works better with acoustic models trained with speech audio data recorded at higher sampling rates/bits per sample. But using audio with too high a sampling rate/bits per sample can slow the recognition engine down. A compromise is needed. Thus for desktop speech recognition, the current standard is acoustic models trained with speech audio data recorded at sampling rates of 16 kHz/16 bits per sample.
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HTK (software)

HTK (Hidden Markov Model Toolkit) is a proprietary software toolkit for handling HMMs. It is mainly intended for speech recognition, but has been used in many other pattern recognition applications that employ HMMs, including speech synthesis, character recognition and DNA sequencing. Originally developed at the Machine Intelligence Laboratory (formerly known as the Speech Vision and Robotics Group) of the Cambridge University Engineering Department (CUED), HTK is now being widely used among researchers who are working on HMMs.
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Sriram Krishnan

Sriram Krishnan (born 1984) is a tech executive and White House official, currently serving as the Senior White House Policy Advisor on Artificial Intelligence. Krishnan was named a Time Person of the Year in 2025 as an "Architect of Artificial Intelligence." He was described in Time as providing the "wake-up call that we needed" to the other AI builders, leading to "a multiyear, $500 billion initiative dubbed Stargate" to push American-made AI, as well as numerous other AI initiatives. Also in December 2025, President Trump said of Krishnan, "without him, things on AI would not function well" and cited Krishnan as the leading figure behind the American executive order on AI. As the leader of the United States' policy team regarding artificial intelligence, Krishnan plays "a significant role in shaping the administration’s approach to AI and driving measures to advance federal adoption of AI." The role calls for removing barriers to AI adoption within the government, driving vendors toward solutions suitable for federal needs, designing sensible regulation of private-sector AI, and conducting "AI diplomacy". He has stated a policy goal of "reinvigorating US dominance in emerging technologies," including AI. He also represents the United States' interests in AI abroad, such as at the Paris AI Summit. He is one of the authors of the American "AI Action Plan" released in July, 2025, which he contends is necessary to win the "existential race with China" for AI supremacy. Krishnan, a U.S. citizen born in India, is also a venture capitalist, podcaster, product manager and author. Early in his career, he led product teams at Microsoft, Twitter, Yahoo!, Facebook, and Snap. In addition to his work as an investor and technologist, he and his wife, Aarthi Ramamurthy, rose to additional prominence in 2021 as podcast hosts. He served as a general partner at the venture capital firm Andreessen Horowitz and led its London office. In 2022, Krishnan announced that he was working with Elon Musk on the rebuilding of Twitter following Musk's acquisition of the company. On December 22, 2024, US president-elect Donald Trump announced that Krishnan would be Senior White House Policy Advisor on Artificial Intelligence in his incoming administration; in 2026 he joined the National Economic Council. == Early life and education == Krishnan was born in Chennai, India. He earned his Bachelor of Technology in Information Technology from SRM University (2001–2005), moved to the United States in 2007 to join Microsoft, and became a naturalized U.S. citizen in 2016. == Career == === Early career === In 2007, he began working at Microsoft where he served as a program manager for Visual Studio. At Facebook, Krishnan built the Facebook Audience Network, a competitive platform to Google's ad technologies. At Twitter, he led product and core user experience, driving a 20% annual user growth rate and launching a redesigned home page and events experience. === Andreessen Horowitz === Krishnan was appointed a general partner of American venture capital firm Andreessen Horowitz ("a16z") in February 2021. He was anticipated to serve consumer and social markets, however he has also theorized on the impact of "deep tech" on society. In 2023 he was appointed to lead the firm's London office, its first non-US location. The office is expected to serve Web3 investments as well as AI and other fields. Krishnan announced that he would leave the firm at the end of 2024. === Social media and AI === In 2022, various news media reported that Krishnan was assisting Elon Musk in the revamp of Twitter following Musk's takeover of the company. Additional reports named Krishnan as the leading candidate for the role of CEO of the newly private company. Krishnan penned a 2023 New York Times opinion column regarding social media, AI, and related fields. He predicted a rise in the number and diversity of online spaces due to decentralization and platforms like Farcaster, Bluesky and Mastodon. === Public office === In 2024, the Financial Times reported that Krishnan was active in international affairs, reintroducing Boris Johnson to Elon Musk, following Musk's nomination to the proposed Department of Government Efficiency. Krishnan was also reported as potentially leaving a16z at the end of the year to "be jumping into something I've wanted to spend [his] energy on," which was widely reported as being related to Musk's and Vivek Ramaswamy's work at DOGE. Others reported to be involved include Joe Lonsdale, Marc Andreesen, Bill Ackman, and Travis Kalanick. On December 22, 2024, US president-elect Donald Trump announced that he would be Senior White House Policy Advisor on Artificial Intelligence in his incoming administration. On February 6, 2025, Reuters reported that Krishnan would be accompanying Vice President Vance to the Paris AI Summit, a "major artificial intelligence" event later that month. Other members of the White House Office of Science and Technology Policy would also be joining the event with around 100 other countries to "focus on AI's potential." Krishnan joined a U.S. technology policy delegation to the Middle East in advance of President Trump's visit in May 2025. Conducting "AI diplomacy," Krishnan negotiated the spread of U.S. AI technologies with Crown Prince Mohammed bin Salman of Saudi Arabia, as well as other means to strengthen bilateral trade in artificial intelligence technologies. He explained that the goal of the diplomatic mission was that "we want American A.I. to spread." Krishnan, along with David Sacks and Michael Kratsios, were credited as authors of the American AI Action Plan released in July 2025. The plan is "the administration’s most significant policy directive" regarding artificial intelligence; it calls for financing to support the global spread of American AI models and a policy to enforce neutrality in models. The Washington Post referred to the plan as a "bold action to ensure that American AI remains at the cutting edge." The AI Action Plan is a continuation of prior efforts to reduce barriers to U.S. production of AI systems and the removal of rules that were considered to hinder such growth. Later in 2025, at the POLITICO AI & Tech Summit, Krishnan called national AI development "an existential race with China." He suggested that private companies are best positioned to create new models, quipping "let them cook." He further suggested that state-by-state regulation of AI technologies may hinder national AI competitiveness. Also in 2025, at the Axios AI+ Summit, Krishnan stated that the United States and China are in a race for AI supremacy, in which the winner will be judged by market share. Winning the race is a "business strategy" to Krishnan. Krishnan was named in the 2025 Time Person of the Year article as an "AI Architect". === The Aarthi and Sriram Show and other media === In early 2021, Krishnan and his wife, Aarthi Ramamurthy, launched a Clubhouse talk show that "focuses on organic conversations on anything from startups to venture capitalism and cryptocurrencies." An early appearance by Elon Musk on the Good Time Show was described as the first show that "broke Clubhouse" by rapidly exceeding the limit of 5,000 simultaneous users. The desire to interact with a larger community led to a variety of later innovations to allow streaming and replaying of Clubhouse chats. On that episode, Elon Musk grilled Robinhood CEO Vlad Tenev regarding the GameStop trading controversy. As of December 2021, the show had over 187,000 subscribers, plus 735,000 subscribers between Krishnan and Ramamurthy's personal Clubhouse accounts. Other guests have included Facebook CEO Mark Zuckerberg, Diane von Fürstenberg, Tony Hawk, MrBeast, and A.R. Rahman. In 2022, the Good Time Show moved to YouTube. It then evolved to a podcasting format under the name The Aarthi and Sriram Show, with both audio and video content. The Hollywood Reporter reported that the podcast had received more than 1 million downloads by early 2023. == Personal life == Krishnan is married to Aarthi Ramamurthy, co-host of The Aarthi and Sriram Show (formerly the Good Time Show) and a serial entrepreneur. They met in college in 2003 through a Yahoo! chat room related to a coding project and began dating in 2006 and eloped in 2010. == Awards == Time Person of the Year - 2025
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Blockhead (thought experiment)

Blockhead is a theoretical computer system invented as part of a thought experiment by philosopher Ned Block, which appeared in a paper titled "Psychologism and Behaviorism". Block did not personally name the computer in the paper. == Overview == In "Psychologism and Behaviorism", Block argues that the internal mechanism of a system is important in determining whether that system is intelligent and claims to show that a non-intelligent system could pass the Turing test. Block asks the reader to imagine a conversation lasting any given amount of time. He states that given the nature of language, there are a finite number of syntactically and grammatically correct sentences that can be used to start a conversation. Consequently, there is a limit to how many "sensible" responses can be made to the first sentence, then to the second sentence, and so on until the conversation ends. Block then asks the reader to imagine a computer which had been programmed with all the sentences in theory, if not in practice. Block argues that such a machine could continue a conversation with a person on any topic because the computer would be programmed with every sentence that it was possible to use so the computer would be able to pass the Turing test despite the fact that—according to Block—it was not intelligent. Block says that this does not show that there is only one correct internal structure for generating intelligence but simply that some internal structures do not generate intelligence. The argument is related to John Searle's Chinese room.
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TigerGraph

TigerGraph is a private company headquartered in Redwood City, California. It provides graph database and graph analytics software. == History == TigerGraph was founded in 2012 by programmer Yu, Ruoming, Li, Like and Mingxi, under the name GraphSQL. In September 2017, the company came out of stealth mode under the name TigerGraph with $33 million in funding. It raised an additional $32 million in funding in September 2019 and another $105 million in a series C round in February 2021. Cumulative funding as of March 2021 is $170 million. == Products == TigerGraph's hybrid transactional/analytical processing database and analytics software can scale to hundreds of terabytes of data with trillions of edges, and is used for data intensive applications such as fraud detection, customer data analysis (customer 360), IoT, artificial intelligence and machine learning. It is available using the cloud computing delivery model. The analytics uses C++ based software and a parallel processing engine to process algorithms and queries. It has its own graph query language that is similar to SQL. TigerGraph also provides a software development kit for creating graphs and visual representations. As of Mar 2024, TigerGraph version is up to version 4.2.0 TigerGraph offers free Community Edition for developers, researchers, and educators. It can be obtained from https://dl.tigergraph.com/ == Query Language == GSQL , designed by Mingxi Wu and Alin Deutsch in 2015, is a SQL-like Turing complete query language. GSQL includes additions to make it compliant with the Graph Query Language standard.
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Thomas Bolander

Thomas Bolander is a Danish professor at DTU Compute, Technical University of Denmark, where he studies logic and artificial intelligence. Most of his studies focus on the social aspect of artificial intelligence, and how we can make future AI able to navigate in social interactions. Thomas Bolander also sits in different commissions, expert panels and boards, among these he is a member of the Siri Commission, the TeckDK Commission, a member of the editorial board of the journal Studia Logica and co-organizer of Science and Cocktails. Bolander is known for his dissemination of science. In 2019 he was awarded the H. C. Ørsted Medal. Which he was the first to achieve after a break of three years.
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Botler AI

Botler AI is a Montreal-based Canadian Artificial Intelligence company that helps users navigate the legal system. Launched in 2017 by Amir Morv and Ritika Dutt, Botler offers a free online tool which provides users who are unaware of their legal rights with information and guidance. Botler is known for its role in unveiling misconduct in the Government of Canada's procurement practices. Botler's findings have prompted numerous investigations, including by the Royal Canadian Mounted Police. == History == Botler's first AI was trained on over 300,000 U.S. and Canadian legal documents to help individuals identify and enforce their legal rights, without fear of judgment. Launched during the height of the #MeToo movement, the tool initially focused on sexual harassment with a goal of creating "a general artificial intelligence that would help the average person with any legal issue." === Department of Justice Canada === In 2020, Botler launched an expanded misconduct detection system in the form of an anonymous chatbot which provided users with an explanation of the law and relevant resources. In March 2021, the Minister of Justice and Attorney General of Canada announced the Government of Canada's support for Botler AI to assist complainants of sexual harassment in the workplace. The initiative, entitled Botler for Citizens and implemented with the support of the Department of Justice Canada, established an Artificial Intelligence-powered hybrid legal service delivery model. == Notable cases == On October 4, 2023, the RCMP confirmed to The Globe and Mail that they "are investigating a file referred from the CBSA (Canada Border Services Agency) that is based on allegations brought to their attention by Botler". In 2019, GCStrategies's managing partner, Kristian Firth, reached out to Botler on behalf of his client, the CBSA, to solicit their misconduct detection chatbot. After interactions with GCStrategies, Dalian Enterprises and Coradix Technology Consulting, the three main contractors involved in developing the controversial ArriveCAN app, Dutt and Morv alerted the CBSA to questionable contracting practices in federal government procurement in September, 2021, and again in November, 2022. In response to Botler's November 2022 report, the CBSA launched an internal review and referred the matter to the RCMP. During testimony before a parliamentary committee, the CBSA's President stated that the CBSA investigation to date has raised some concerns and shows "that there was a pattern of persistent collaboration between certain officials and GCStrategies... to circumvent or ignore certain established processes and roles and responsibilities". The Auditor General of Canada, which extended its study into ArriveCAN following the Botler revelations, found that GCStrategies was directly involved in setting narrow terms for a request for proposal for a $25-million government contract it ultimately won. The firm, which has just two employees, charges the government a commission of between 15 per cent and 30 per cent of each contract's value. The Office of the Procurement Ombudsman of Canada found "numerous examples" where GCStrategies "had simply copied and pasted" the required work experience to meet contracting requirements. To date, more than a dozen probes have been launched into the matter, including by the government, parliamentary committees, independent watchdogs and law-enforcement agencies. On April 17, 2024, GCStrategies' Firth was the first person summoned in over a century to answer questions before Members of Parliament in the House of Commons. During his appearance, Firth testified that the RCMP had raided "my property to obtain electronic goods surrounding the Botler allegations". === Government of Canada Reforms === One day after The Globe reported that the RCMP is investigating allegations of misconduct, the federal government responded by announcing new guidelines from the Treasury Board of Canada aimed at cutting back on the use of private consultants and that outsourcing contracts were under examination. Public Services and Procurement Canada (PSPC) invalidated and replaced all master level user agreements with government client departments in November 2023. The agreements set out the conditions for access to select Professional Services methods of supply which are used for outsourcing. In March 2024, PSPC announced its suspension of the respective security statuses of GCStrategies, Dalian and Coradix, barring them from participating in all federal procurements. Records show that the total value of contracts awarded to the three companies amounts to more than $1 Billion.
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Semantic analysis (knowledge representation)

Semantic analysis is a method for eliciting and representing knowledge about organisations. Initially the problem must be defined by domain experts and passed to the project analyst(s). The next step is the generation of candidate affordances. This step will generate a list of semantic units that may be included in the schema. The candidate grouping follows where some of the semantic units that will appear in the schema are placed in simple groups. Finally the groups will be integrated together into an ontology chart. Semantic analysis always starts from the problem definition which if not clear, require the analyst to employ relevant literature, interviews with the stakeholders and other techniques towards collecting supplementary information. All assumptions made must be genuine and not limiting the system.
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Language-Theoretic Security

Language-theoretic security, or LangSec, is an approach to software security that focuses on input handling, complexity, and program design as strategies to improve the verifiability of computer programs. It was introduced in 2005 by Robert J. Hansen and Meredith L. Patterson at BlackHat and in 2011 by Len Sassaman and Patterson. It aims to create a formal description of which software is likely to have security vulnerabilities of particular classes, and why. It considers programs to have an inherent parser component, whether or not explicit, composed of that part of the program which operates on external input before that input is fully parsed. A central hypothesis of language-theoretic security is that vulnerabilities in software increase according to the computational power of the notional input-accepting automaton equivalent to this parser, using the definitions of automata theory. The lower bound on this computational power is the input language complexity of the program. The extent to which reducing this complexity is possible is a function of the specification of the communication protocol or file format the program takes as input. == Parsing as a security mechanism == The behaviour of a program is defined with reference to its expected input. Unexpected input being used by a program is a factor in numerous security bugs, including the so-called Android master key vulnerability (CVE-2013-4787), because accepting unexpected input renders the program's specification ambiguous. In that instance, the unexpected ambiguity came in the form of a ZIP file with duplicate filenames. If a program fully parses its input and only acts on input that unambiguously meets the specification, it follows that the program will avoid these types of vulnerabilities. This is an intentional inversion of the Postel principle. Accepting only unambiguous and valid input is a more formal requirement than input validation or sanitization, and narrows the number of possible but unanticipated program states that can be induced in an application via user input. Conversely, failure to do this is associated with security vulnerabilities. Input sanitization in particular is held to be an inadequate approach to avoiding malicious input because it inherently ignores context-sensitive properties of the input; it can therefore result in paradoxical effects, such as sanitization code activating otherwise inert cross-site scripting payloads in browsers. === Parser differentials === If the language of accepted program input is sufficiently simple, it is possible to verify that two implementations parse the same input language consistently. This is advantageous because it shows no parser differential exists between the two implementations. The requisite level of simplicity is theoretically that for which there is a solution to the equivalence problem. If the two parsers involved in CVE-2013-4787 were equivalent - that is, if they rendered the same output state given the same input state - the vulnerability could not have existed. One strategy for doing this is to publish machine-readable specifications of a format or protocol, and then use a parser generator to generate the parser code. An example of a parser generator built for this purpose is DaeDaLus. The combination of Lex with any of GNU Bison, ANTLR, or Yacc also accomplishes this. However, many parser generators allow the mixing of general purpose code with the parsing definitions, which weakens the guarantees provided by parsing. === Analysis of injection attacks === Injection attacks are generally the result of differences between the serializer (or "unparser") and the corresponding parser at a layer boundary in a system; therefore, they are a special case of parser differentials. In a SQL injection attack, for example, an attacker is able to cause the application with which they are interacting to serialize a SQL query that has different semantics than intended. In the simplest case where the payload ends a string and adds new code, the payload has crossed the code-data boundary in SQL. In language-theoretic security, this is treated as a bug in the serializer of the SQL query, which should instead be written in a way that constrains its possible outputs to those within the scope of the intended query. === Parser combinators === If a parser generator is not used, it is still possible to avoid implementation bugs by using parser combinator such as Nom to implement the parser code. This has the drawback of relying on a programmer correctly translating the specification into the language of the parser generator library, though this task is still less error-prone than hand-coding a parser. == Input format complexity == Complexity in computer programs is associated with security vulnerabilities. Within the domain of language-theoretic security, complexity is described with reference to the computational power of the abstract machine necessary to implement the program, or more particularly, to implement the parser for its input language. This complexity describes whether it is possible to show that there is no unintended or undesired functionality in the program which might be exploitable by an attacker. To be bounded in complexity, the program's input must be well-defined both in terms of form and of semantics. === Weird machines === A weird machine is a model of computation in a program that exists in parallel with, but is distinct from, the intended abstract model of computation in that program. Some classes of weird machine arise from the multi-layered nature of computer programs, or the context in which the programs run; others result from the unanticipated functionality a program has due to its complexity or to software bugs. The more complex the computation model of a program, the more likely it is to implement a weird machine. Depending on context, the weird machine may or may not be concretely useful for an attacker. Since the space of weird machines in the context of some program is the universe of all possible states that are not within the program's intended states, many exploited states including remote code execution and injection attacks belong to the domain of weird machines. A reduction in weird machines is therefore a likely correlate with reduced program vulnerability. === SafeDocs project === SafeDocs is a DARPA project undertaken in 2018 to take existing file formats, create safer subsets of them, and develop programming tools to work for the safer formats. The initial test case for this was PDF. The purpose of creating safer subsets in this case is to lower the minimum bound on parser complexity so that it becomes possible to create tools that will generate correct, normative parsers for them. == Relation to programming languages == The analytic framework of language-theoretic security assumes programs to be virtual machines that execute their input. A document that is read by an application is in this sense a form of machine code, in a generalization of the data as code idea, following the automata theory description of parsers. === Type-safe programming languages === Parsing input and serializing output are operations that consume one data type and emit another. A programming language can therefore check that data is correctly parsed and contains the expected structure by checking data types, and correct serializing (or unparsing) can be implemented as operations on the data types that are relevant to the program's output. This approach can be used to show that the recognizer and unparser patterns have been implemented. It is also possible to implement type checking across a distributed system to enforce parsing and unparsing of the expected structures and to verify that the assumptions made in designing the compositional properties of a distributed system have been followed. === Memory-safe programming languages === In the general case, spatial memory correctness is undecidable. If any proof of spatial memory correctness is to be made, it is therefore necessary to bound the complexity of the code. Interpreted languages such as Java and Python effectively accomplish this via runtime bounds checking, and frameworks for runtime bounds checking also exist for C. The effect of these strategies for spatial memory correctness are to create a halt state in place of a spatial memory correctness violation; therefore, it can be shown that the program will not violate spatial memory correctness, but in exchange, it cannot be shown in the general case that programs will not have runtime bounds checking exceptions. Some programming languages, such as Rust, accomplish this using borrow checking. The borrow checker acts to assure spatial memory correctness by compile-time reference counting. Code for which spatial memory correctness cannot be shown to not be violated therefore does not compile, inherently limiting the complexity of the spatial memory correctness of the program to what is decidable. Thi
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ProVisual Engine

The ProVisual Engine is an AI-powered imaging system developed by Samsung Electronics for mobile devices. It was introduced in 2024 with the Galaxy S24 series as a component of Samsung's Galaxy AI ecosystem, providing advanced image processing to enhance image quality in photography and videography. == Overview == The ProVisual Engine processes images using adaptive scene recognition, real-time optimization, and advanced image processing. It adjusts color accuracy, dynamic range, and noise levels, providing both automated and manual controls to accommodate various user preferences. == Features == The ProVisual Engine encompasses several features. === Quad Tele System === The Quad Tele System features 2x, 3x, 5x, and 10x optical zoom, supported by digital processing to enhance zoom clarity and detail. It incorporates Image Signal Processing (ISP) to refine detail retention, reduce noise, and enhance image clarity at different zoom levels while minimizing distortion. === Nightography === Nightography utilizes noise reduction techniques and advanced sensor technology to enhance low-light photography. By adjusting exposure and minimizing motion blur, the system helps produce more precise and more detailed images in dark environments for both photos and videos. === Generative Edit === Generative Edit allows for object removal, background expansion, and intelligent resizing. It reconstructs missing areas by filling backgrounds and completing cut-off objects, adjusting composition while preserving image integrity and refinement. === Expert RAW === Expert RAW allows users to capture RAW images directly from the camera app for advanced shooting and editing. It includes HDR (High Dynamic Range) support to enhance detail and dynamic range. The ProVisual Engine utilizes multi-frame processing to generate RAW images with increased clarity and depth for post-processing. === Enhance-X and Camera Shift === Enhance-X is an AI-based image processing tool that applies upscaling, noise reduction, and sharpening. Its Camera Shift feature adjusts the perceived camera height by modifying framing and proportions. A recent update extended support to human and pet images. == Compatible devices == As of 2025, the ProVisual Engine is available on the following devices: === Galaxy S series === Galaxy S26 Series (Galaxy S26, S26+. S26 Ultra) Galaxy S25 Series (Galaxy S25, S25+, S25 Edge, S25 Ultra, S25 FE) Galaxy S24 Series (Galaxy S24, S24+, S24 Ultra) === Galaxy Z series === Galaxy Z Fold 7 Galaxy Z Flip 7, Z Flip 7 FE Galaxy Z Fold 6 Galaxy Z Flip 6 === Galaxy Tab S series === Galaxy Tab S10 series (Tab S10+, Tab S10 Ultra) Galaxy Tab S9 series (Tab S9, Tab S9+, Tab S9 Ultra) === Galaxy Z series === Galaxy Z Fold 7, Z Flip 7, Z Flip 7 FE Galaxy Z Fold 6, Z Flip 6 === Galaxy Tab S series === Galaxy Tab S10 series (Tab S10+, Tab S10 Ultra) Galaxy Tab S9 series (Tab S9, Tab S9+, Tab S9 Ultra) Note: Quad Tele System refers to the multi-telephoto setup (2×, 3×, 5×, 10×) available only on the Ultra models (S24 Ultra and S25 Ultra). Note: On Galaxy Tab models, only Enhance-X editing features are supported; the Expert RAW camera app is not available.
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Resilience (mathematics)

In mathematical modeling, resilience refers to the ability of a dynamical system to recover from perturbations and return to its original stable steady state. It is a measure of the stability and robustness of a system in the face of changes or disturbances. If a system is not resilient enough, it is more susceptible to perturbations and can more easily undergo a critical transition. A common analogy used to explain the concept of resilience of an equilibrium is one of a ball in a valley. A resilient steady state corresponds to a ball in a deep valley, so any push or perturbation will very quickly lead the ball to return to the resting point where it started. On the other hand, a less resilient steady state corresponds to a ball in a shallow valley, so the ball will take a much longer time to return to the equilibrium after a perturbation. The concept of resilience is particularly useful in systems that exhibit tipping points, whose study has a long history that can be traced back to catastrophe theory. While this theory was initially overhyped and fell out of favor, its mathematical foundation remains strong and is now recognized as relevant to many different systems. == History == In 1973, Canadian ecologist C. S. Holling proposed a definition of resilience in the context of ecological systems. According to Holling, resilience is "a measure of the persistence of systems and of their ability to absorb change and disturbance and still maintain the same relationships between populations or state variables". Holling distinguished two types of resilience: engineering resilience and ecological resilience. Engineering resilience refers to the ability of a system to return to its original state after a disturbance, such as a bridge that can be repaired after an earthquake. Ecological resilience, on the other hand, refers to the ability of a system to maintain its identity and function despite a disturbance, such as a forest that can regenerate after a wildfire while maintaining its biodiversity and ecosystem services. With time, the once well-defined and unambiguous concept of resilience has experienced a gradual erosion of its clarity, becoming more vague and closer to an umbrella term than a specific concrete measure. == Definition == Mathematically, resilience can be approximated by the inverse of the return time to an equilibrium given by resilience ≡ − Re ( λ 1 ( A ) ) {\displaystyle {\text{resilience}}\equiv -{\text{Re}}(\lambda _{1}({\textbf {A}}))} where λ 1 {\textstyle \lambda _{1}} is the maximum eigenvalue of matrix A {\textstyle {\textbf {A}}} . The largest this value is, the faster a system returns to the original stable steady state, or in other words, the faster the perturbations decay. == Applications and examples == In ecology, resilience might refer to the ability of the ecosystem to recover from disturbances such as fires, droughts, or the introduction of invasive species. A resilient ecosystem would be one that is able to adapt to these changes and continue functioning, while a less resilient ecosystem might experience irreversible damage or collapse. The exact definition of resilience has remained vague for practical matters, which has led to a slow and proper application of its insights for management of ecosystems. In epidemiology, resilience may refer to the ability of a healthy community to recover from the introduction of infected individuals. That is, a resilient system is more likely to remain at the disease-free equilibrium after the invasion of a new infection. Some stable systems exhibit critical slowing down where, as they approach a basic reproduction number of 1, their resilience decreases, hence taking a longer time to return to the disease-free steady state. Resilience is an important concept in the study of complex systems, where there are many interacting components that can affect each other in unpredictable ways. Mathematical models can be used to explore the resilience of such systems and to identify strategies for improving their resilience in the face of environmental or other changes. For example, when modelling networks it is often important to be able to quantify network resilience, or network robustness, to the loss of nodes. Scale-free networks are particularly resilient since most of their nodes have few links. This means that if some nodes are randomly removed, it is more likely that the nodes with fewer connections are taken out, thus preserving the key properties of the network.
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Tim Houlne

Tim Houlne is an American business executive, entrepreneur, and author known for his work in outsourcing and homeshoring, remote working, and artificial intelligence (AI) in customer service. He is the founder and CEO of Humach, a company that uses human agents and AI in customer experience solutions. Previously, he was co-founder and CEO of Working Solutions, a virtual contact center company in the United States. == Early life and education == Houlne graduated from Missouri Western State University (MWSU) in 1986 with a bachelor's degree in business administration and from the University of Texas in Dallas with an MBA. In 2024, MWSU and North Central Missouri College renamed the Convergent Technology Alliance Center to the Houlne Center for Convergent Technology. The 20,000 square-foot learning laboratory provides training and applied education experiences in industries such as AI, cybersecurity, manufacturing and construction, and service technologies. == Career == In 1998, Houlne co-founded Working Solutions, a Plano, Texas-based U.S. outsourcing company that provides customer service using remote, home-based agents. As CEO, he oversaw the development of a virtual workforce model that routes service calls to either domestic or offshore agents, according to client needs and service requirements. In 2015, Houlne founded Humach, a customer experience outsourcing provider that uses human service agents with AI-based digital agents. The company derives its name from the combination of services provided by humans and machines. Its clients include Amazon, Carfax and McDonald's. The company acquired InfiniteAI in 2020, and Markets EQ in 2025. In 2013, Houlne was named a finalist for the Ernst & Young Entrepreneur of the Year Award (Southwest Region).He is the co-author of several books focused on the evolution of work, the gig economy, and the influence of AI in customer-facing roles. == Works == The New World of Work: From the Cube to the Cloud (2013) ISBN 0982562276 OCLC 813933360 The New World of Work, Second Edition: The Cube, the Cloud and What's Next (2023) ISBN 9781642258318 OCLC 1389815847 The Intelligent Workforce: How Humans & Machines Will Co-Create a Better Future (2024) ISBN 9798887501604 OCLC 1439598569
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Application-release automation

Application-release automation (ARA) refers to the process of packaging and deploying an application or update of an application from development, across various environments, and ultimately to production. ARA solutions must combine the capabilities of deployment automation, environment management and modeling, and release coordination. == Relationship with DevOps == ARA tools help cultivate DevOps best practices by providing a combination of automation, environment modeling and workflow-management capabilities. These practices help teams deliver software rapidly, reliably and responsibly. ARA tools achieve a key DevOps goal of implementing continuous delivery with a large quantity of releases quickly. == Relationship with deployment == ARA is more than just software-deployment automation – it deploys applications using structured release-automation techniques that allow for an increase in visibility for the whole team. It combines workload automation and release-management tools as they relate to release packages, as well as movement through different environments within the DevOps pipeline. ARA tools help regulate deployments, how environments are created and deployed, and how and when releases are deployed. == ARA Solutions == All ARA solutions must include capabilities in automation, environment modeling, and release coordination. Additionally, the solution must provide this functionality without reliance on other tools.
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Semantic data model

A semantic data model (SDM) is a high-level semantics-based database description and structuring formalism (database model) for databases. This database model is designed to capture more of the meaning of an application environment than is possible with contemporary database models. An SDM specification describes a database in terms of the kinds of entities that exist in the application environment, the classifications and groupings of those entities, and the structural interconnections among them. SDM provides a collection of high-level modeling primitives to capture the semantics of an application environment. By accommodating derived information in a database structural specification, SDM allows the same information to be viewed in several ways; this makes it possible to directly accommodate the variety of needs and processing requirements typically present in database applications. The design of the present SDM is based on our experience in using a preliminary version of it. SDM is designed to enhance the effectiveness and usability of database systems. An SDM database description can serve as a formal specification and documentation tool for a database; it can provide a basis for supporting a variety of powerful user interface facilities, it can serve as a conceptual database model in the database design process; and, it can be used as the database model for a new kind of database management system. == In software engineering == A semantic data model in software engineering has various meanings: It is a conceptual data model in which semantic information is included. This means that the model describes the meaning of its instances. Such a semantic data model is an abstraction that defines how the stored symbols (the instance data) relate to the real world. It is a conceptual data model that includes the capability to express and exchange information which enables parties to interpret meaning (semantics) from the instances, without the need to know the meta-model. Such semantic models are fact-oriented (as opposed to object-oriented). Facts are typically expressed by binary relations between data elements, whereas higher order relations are expressed as collections of binary relations. Typically binary relations have the form of triples: Object-RelationType-Object. For example: the Eiffel Tower Paris. Typically the instance data of semantic data models explicitly include the kinds of relationships between the various data elements, such as . To interpret the meaning of the facts from the instances, it is required that the meaning of the kinds of relations (relation types) be known. Therefore, semantic data models typically standardize such relation types. This means that the second kind of semantic data models enables that the instances express facts that include their own meanings. The second kind of semantic data models are usually meant to create semantic databases. The ability to include meaning in semantic databases facilitates building distributed databases that enable applications to interpret the meaning from the content. This implies that semantic databases can be integrated when they use the same (standard) relation types. This also implies that in general they have a wider applicability than relational or object-oriented databases. == Overview == The logical data structure of a database management system (DBMS), whether hierarchical, network, or relational, cannot totally satisfy the requirements for a conceptual definition of data, because it is limited in scope and biased toward the implementation strategy employed by the DBMS. Therefore, the need to define data from a conceptual view has led to the development of semantic data modeling techniques. That is, techniques to define the meaning of data within the context of its interrelationships with other data, as illustrated in the figure. The real world, in terms of resources, ideas, events, etc., are symbolically defined within physical data stores. A semantic data model is an abstraction which defines how the stored symbols relate to the real world. Thus, the model must be a true representation of the real world. According to Klas and Schrefl (1995), the "overall goal of semantic data models is to capture more meaning of data by integrating relational concepts with more powerful abstraction concepts known from the Artificial Intelligence field. The idea is to provide high level modeling primitives as an integral part of a data model in order to facilitate the representation of real world situations". == History == The need for semantic data models was first recognized by the U.S. Air Force in the mid-1970s as a result of the Integrated Computer-Aided Manufacturing (ICAM) Program. The objective of this program was to increase manufacturing productivity through the systematic application of computer technology. The ICAM Program identified a need for better analysis and communication techniques for people involved in improving manufacturing productivity. As a result, the ICAM Program developed a series of techniques known as the IDEF (ICAM Definition) Methods which included the following: IDEF0 used to produce a “function model” which is a structured representation of the activities or processes within the environment or system. IDEF1 used to produce an “information model” which represents the structure and semantics of information within the environment or system. IDEF1X a semantic data modeling technique used to produce a graphical information model which represents the structure and semantics of information within an environment or system. Use of this standard permits the construction of semantic data models which may serve to support the management of data as a resource, the integration of information systems, and the building of computer databases. IDEF2 used to produce a “dynamics model” which represents the time varying behavioral characteristics of the environment or system. During the 1990s, the application of semantic modelling techniques resulted in the semantic data models of the second kind. An example of such is the semantic data model that is standardised as ISO 15926-2 (2002), which is further developed into the semantic modelling language Gellish (2005). The definition of the Gellish language is documented in the form of a semantic data model. Gellish itself is a semantic modelling language, that can be used to create other semantic models. Those semantic models can be stored in Gellish Databases, being semantic databases. == Applications == A semantic data model can be used to serve many purposes. Some key objectives include: Planning of data resources: A preliminary data model can be used to provide an overall view of the data required to run an enterprise. The model can then be analyzed to identify and scope projects to build shared data resources. Building of shareable databases: A fully developed model can be used to define an application independent view of data which can be validated by users and then transformed into a physical database design for any of the various DBMS technologies. In addition to generating databases which are consistent and shareable, development costs can be drastically reduced through data modeling. Evaluation of vendor software: Since a data model actually represents the infrastructure of an organization, vendor software can be evaluated against a company’s data model in order to identify possible inconsistencies between the infrastructure implied by the software and the way the company actually does business. Integration of existing databases: By defining the contents of existing databases with semantic data models, an integrated data definition can be derived. With the proper technology, the resulting conceptual schema can be used to control transaction processing in a distributed database environment. The U.S. Air Force Integrated Information Support System (I2S2) is an experimental development and demonstration of this kind of technology, applied to a heterogeneous type of DBMS environments.
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Mira Murati

Ermira "Mira" Murati (born 16 December 1988) is an Albanian-American business executive. She launched an AI startup called Thinking Machines Lab in February 2025. Previously she was the chief technology officer of OpenAI, and a senior product manager at Tesla. == Early life and education == Murati was born on 16 December 1988 in Vlorë, Albania. She is fluent in Italian. At age 16, she won a United World Colleges (UWC) scholarship to study at Pearson College on Vancouver Island in Canada, from which she graduated in 2007 with an International Baccalaureate. After Pearson, she went to the United States to pursue further studies through a dual-degree program, earning a Bachelor of Arts from Colby College in 2011, and a Bachelor of Engineering degree from Dartmouth College's Thayer School of Engineering in 2012. == Career == === Early career === Murati interned in 2011 as a summer analyst at Goldman Sachs in Tokyo, Japan. She then briefly worked for Zodiac Aerospace as an intern before joining the electric car company Tesla in 2013 as a product manager on the Model X. From 2016 to 2018, she worked for the augmented reality start-up Leap Motion (now Ultraleap). === OpenAI === In 2018, she joined OpenAI as the VP of Applied AI and partnerships. She became chief technology officer (CTO) in May 2022. She led OpenAI's work on ChatGPT, Dall-E, Codex and Sora, while overseeing its research, product and safety teams. She oversaw technical advancements and direction of OpenAI's various projects, including the development of advanced AI models and tools. Murati worked on several of OpenAI's notable products, such as the Generative Pretrained Transformer (GPT) series of language models. Commenting about the potential loss of creative jobs to AI, Murati said that "maybe [the jobs] shouldn’t have been there in the first place". In October 2023, Murati was ranked 57th on Fortune's list of "The 100 Most Powerful Women in Business of 2023". In November 2023, Murati became interim chief executive officer of OpenAI following the removal of Sam Altman from the job. She had collaborated with Ilya Sutskever, whose 52-page memo outlining concerns about Altman relied heavily on screenshots and information she provided, which contributed to the board's decision to oust him. Murati was replaced by Emmett Shear three days later, who left when Altman was reinstated five days later. Following these events, Murati returned to her role as CTO. In June 2024, Dartmouth College awarded Murati an honorary Doctor of Science for having "democratized technology and advanced a better, safer world for us all". In September 2024, Murati announced that she was stepping down as CTO to allow her the opportunity to "do my own exploration". This move came amid a wider executive exodus as OpenAI chief research officer Bob McGrew and a vice president of research, Barret Zoph, also announced their departures soon after. === Thinking Machines Lab === In February 2025, Murati launched Thinking Machines Lab, a new public benefit corporation aiming "to make AI systems more widely understood, customizable, and generally capable". She was reported to have hired "a team of about 30 leading researchers and engineers from competitors including Meta, Mistral, and OpenAI." People involved with the startup include OpenAI cofounder John Schulman, and advisors Alec Radford and Bob McGrew. The following month, Bloomberg reported that the company had reached an estimated valuation of $9 billion, with an "average founder stake value" of $1.4 billion. In April 2025, Thinking Machines Lab reportedly aimed for a $2 billion seed round (requiring a minimum investment of $50 million). The round was led by Andreessen Horowitz and included participation from the government of Albania, valuing the company at $12 billion. Thinking Machines Lab follows a governance structure wherein Mira Murati holds a deciding vote on board matters, weighted to provide her with a majority decision-making capability. In October 2025, Thinking Machines Lab announced its first product, Tinker, a tool used to create custom frontier AI models. == Publications == Murati, Ermira (Spring 2022). "Language & Coding Creativity". Daedalus. 151 (2). Cambridge, MA: American Academy of Arts and Sciences (AAAS): 156–167. doi:10.1162/daed_a_01907. Retrieved 25 September 2024.
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