AI Detector And Fixer

AI Detector And Fixer — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

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  Intelligent agent

  In artificial intelligence, an intelligent agent is an entity that perceives its environment, takes actions autonomously to achieve goals, and may improve its performance through machine learning or by acquiring knowledge. AI textbooks define artificial intelligence as the "study and design of intelligent agents," emphasizing that goal-directed behavior is central to intelligence. A specialized subset of intelligent agents, agentic AI (also known as an AI agent or simply agent), expands this concept by proactively pursuing goals, making decisions, and taking actions over extended periods. Intelligent agents can range from simple to highly complex. A basic thermostat or control system is considered an intelligent agent, as is a human being, or any other system that meets the same criteria—such as a firm, a state, or a biome. Intelligent agents operate based on an objective function, which encapsulates their goals. They are designed to create and execute plans that maximize the expected value of this function upon completion. For example, a reinforcement learning agent has a reward function, which allows programmers to shape its desired behavior. Similarly, an evolutionary algorithm's behavior is guided by a fitness function. Intelligent agents in artificial intelligence are closely related to agents in economics, and versions of the intelligent agent paradigm are studied in cognitive science, ethics, and the philosophy of practical reason, as well as in many interdisciplinary socio-cognitive modeling and computer social simulations. Intelligent agents are often described schematically as abstract functional systems similar to computer programs . To distinguish theoretical models from real-world implementations, abstract descriptions of intelligent agents are called abstract intelligent agents. Intelligent agents are also closely related to software agents—autonomous computer programs that carry out tasks on behalf of users. They are also referred to using a term borrowed from economics: a "rational agent". == Intelligent agents as the foundation of AI == The concept of intelligent agents provides a foundational lens through which to define and understand artificial intelligence. For instance, the influential textbook Artificial Intelligence: A Modern Approach (Russell & Norvig) describes: Agent: Anything that perceives its environment (using sensors) and acts upon it (using actuators). E.g., a robot with cameras and wheels, or a software program that reads data and makes recommendations. Rational Agent: An agent that strives to achieve the best possible outcome based on its knowledge and past experiences. "Best" is defined by a performance measure – a way of evaluating how well the agent is doing. Artificial Intelligence (as a field): The study and creation of these rational agents. Other researchers and definitions build upon this foundation. Padgham & Winikoff emphasize that intelligent agents should react to changes in their environment in a timely way, proactively pursue goals, and be flexible and robust (able to handle unexpected situations). Some also suggest that ideal agents should be "rational" in the economic sense (making optimal choices) and capable of complex reasoning, like having beliefs, desires, and intentions (BDI model). Kaplan and Haenlein offer a similar definition, focusing on a system's ability to understand external data, learn from that data, and use what is learned to achieve goals through flexible adaptation. Defining AI in terms of intelligent agents offers several key advantages: Avoids Philosophical Debates: It sidesteps arguments about whether AI is "truly" intelligent or conscious, like those raised by the Turing test or Searle's Chinese Room. It focuses on behavior and goal achievement, not on replicating human thought. Objective Testing: It provides a clear, scientific way to evaluate AI systems. Researchers can compare different approaches by measuring how well they maximize a specific "goal function" (or objective function). This allows for direct comparison and combination of techniques. Interdisciplinary Communication: It creates a common language for AI researchers to collaborate with other fields like mathematical optimization and economics, which also use concepts like "goals" and "rational agents." == Objective function == An objective function (or goal function) specifies the goals of an intelligent agent. An agent is deemed more intelligent if it consistently selects actions that yield outcomes better aligned with its objective function. In effect, the objective function serves as a measure of success. The objective function may be: Simple: For example, in a game of Go, the objective function might assign a value of 1 for a win and 0 for a loss. Complex: It might require the agent to evaluate and learn from past actions, adapting its behavior based on patterns that have proven effective. The objective function encapsulates all of the goals the agent is designed to achieve. For rational agents, it also incorporates the trade-offs between potentially conflicting goals. For instance, a self-driving car's objective function might balance factors such as safety, speed, and passenger comfort. Different terms are used to describe this concept, depending on the context. These include: Utility function: Often used in economics and decision theory, representing the desirability of a state. Objective function: A general term used in optimization. Loss function: Typically used in machine learning, where the goal is to minimize the loss (error). Reward Function: Used in reinforcement learning. Fitness Function: Used in evolutionary systems. Goals, and therefore the objective function, can be: Explicitly defined: Programmed directly into the agent. Induced: Learned or evolved over time. In reinforcement learning, a "reward function" provides feedback, encouraging desired behaviors and discouraging undesirable ones. The agent learns to maximize its cumulative reward. In evolutionary systems, a "fitness function" determines which agents are more likely to reproduce. This is analogous to natural selection, where organisms evolve to maximize their chances of survival and reproduction. Some AI systems, such as nearest-neighbor, reason by analogy rather than being explicitly goal-driven. However, even these systems can have goals implicitly defined within their training data. Such systems can still be benchmarked by framing the non-goal system as one whose "goal" is to accomplish its narrow classification task. Systems not traditionally considered agents, like knowledge-representation systems, are sometimes included in the paradigm by framing them as agents with a goal of, for example, answering questions accurately. Here, the concept of an "action" is extended to encompass the "act" of providing an answer. As a further extension, mimicry-driven systems can be framed as agents optimizing a "goal function" based on how closely the agent mimics the desired behavior. In generative adversarial networks (GANs) of the 2010s, an "encoder"/"generator" component attempts to mimic and improvise human text composition. The generator tries to maximize a function representing how well it can fool an antagonistic "predictor"/"discriminator" component. While symbolic AI systems often use an explicit goal function, the paradigm also applies to neural networks and evolutionary computing. Reinforcement learning can generate intelligent agents that appear to act in ways intended to maximize a "reward function". Sometimes, instead of setting the reward function directly equal to the desired benchmark evaluation function, machine learning programmers use reward shaping to initially give the machine rewards for incremental progress. Yann LeCun stated in 2018, "Most of the learning algorithms that people have come up with essentially consist of minimizing some objective function." AlphaZero chess had a simple objective function: +1 point for each win, and -1 point for each loss. A self-driving car's objective function would be more complex. Evolutionary computing can evolve intelligent agents that appear to act in ways intended to maximize a "fitness function" influencing how many descendants each agent is allowed to leave. The mathematical formalism of AIXI was proposed as a maximally intelligent agent in this paradigm. However, AIXI is uncomputable. In the real world, an intelligent agent is constrained by finite time and hardware resources, and scientists compete to produce algorithms that achieve progressively higher scores on benchmark tests with existing hardware. == Agent function == An intelligent agent's behavior can be described mathematically by an agent function. This function determines what the agent does based on what it has seen. A percept refers to the agent's sensory inputs at a single point in time. For example, a self-driving car's percepts might include camera images, lidar data, GPS coordinates, and speed r

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  Glushkov's construction algorithm

  In computer science theory – particularly formal language theory – Glushkov's construction algorithm, invented by Victor Mikhailovich Glushkov, transforms a given regular expression into an equivalent nondeterministic finite automaton (NFA). Thus, it forms a bridge between regular expressions and nondeterministic finite automata: two abstract representations of the same class of formal languages. A regular expression may be used to conveniently describe an advanced search pattern in a "find and replace"–like operation of a text processing utility. Glushkov's algorithm can be used to transform it into an NFA, which furthermore is small by nature, as the number of its states equals the number of symbols of the regular expression, plus one. Subsequently, the NFA can be made deterministic by the powerset construction and then be minimized to get an optimal automaton corresponding to the given regular expression. The latter format is best suited for execution on a computer. From another, more theoretical point of view, Glushkov's algorithm is a part of the proof that NFA and regular expressions both accept exactly the same languages; that is, the regular languages. The converse of Glushkov's algorithm is Kleene's algorithm, which transforms a finite automaton into a regular expression. The automaton obtained by Glushkov's construction is the same as the one obtained by Thompson's construction algorithm, once its ε-transitions are removed. Glushkov's construction algorithm is also called The algorithm of Berry-Sethi, named after Gérard Berry and Ravi Sethi who worked on this construction. == Construction == Given a regular expression e, the Glushkov Construction Algorithm creates a non-deterministic automaton that accepts the language L ( e ) {\displaystyle L(e)} accepted by e. The construction uses four steps: === Step 1 === Linearisation of the expression. Each letter of the alphabet appearing in the expression e is renamed, so that each letter occurs at most once in the new expression e ′ {\displaystyle e'} . Glushkov's construction essentially relies on the fact that e ′ {\displaystyle e'} represents a local language L ( e ′ ) {\displaystyle L(e')} . Let A be the old alphabet and let B be the new one. === Step 2a === Computation of the sets P ( e ′ ) {\displaystyle P(e')} , D ( e ′ ) {\displaystyle D(e')} , and F ( e ′ ) {\displaystyle F(e')} . The first, P ( e ′ ) {\displaystyle P(e')} , is the set of letters which occurs as first letter of a word of L ( e ′ ) {\displaystyle L(e')} . The second, D ( e ′ ) {\displaystyle D(e')} , is the set of letters that can end a word of L ( e ′ ) {\displaystyle L(e')} . The last one, F ( e ′ ) {\displaystyle F(e')} , is the set of letter pairs that can occur in words of L ( e ′ ) {\displaystyle L(e')} , i.e. it is the set of factors of length two of the words of L ( e ′ ) {\displaystyle L(e')} . Those sets are mathematically defined by P ( e ′ ) = { x ∈ B ∣ x B ∗ ∩ L ( e ′ ) ≠ ∅ } {\displaystyle P(e')=\{x\in B\mid xB^{}\cap L(e')\neq \emptyset \}} , D ( e ′ ) = { y ∈ B ∣ B ∗ y ∩ L ( e ′ ) ≠ ∅ } {\displaystyle D(e')=\{y\in B\mid B^{}y\cap L(e')\neq \emptyset \}} , F ( e ′ ) = { u ∈ B 2 ∣ B ∗ u B ∗ ∩ L ( e ′ ) ≠ ∅ } {\displaystyle F(e')=\{u\in B^{2}\mid B^{}uB^{}\cap L(e')\neq \emptyset \}} . They are computed by induction over the structure of the expression, as explained below. === Step 2b === Computation of the set Λ ( e ′ ) {\displaystyle \Lambda (e')} which contains the empty word ε {\displaystyle \varepsilon } if this word belongs to L ( e ′ ) {\displaystyle L(e')} , and is the empty set otherwise. Formally, this is Λ ( e ′ ) = { ε } ∩ L ( e ′ ) {\displaystyle \Lambda (e')=\{\varepsilon \}\cap L(e')} . === Step 3 === Computation of automaton recognizing the local language, as defined by P ( e ′ ) {\displaystyle P(e')} , D ( e ′ ) {\displaystyle D(e')} , F ( e ′ ) {\displaystyle F(e')} , and Λ ( e ′ ) {\displaystyle \Lambda (e')} . By definition, the local language defined by the sets P, D, and F is the set of words which begin with a letter of P, end by a letter of D, and whose factors of length 2 belong to F, optionally also including the empty word; that is, it is the language: L ′ = ( P B ∗ ∩ B ∗ D ) ∖ B ∗ ( B 2 ∖ F ) B ∗ ∪ Λ ( e ′ ) {\displaystyle L'=(PB^{}\cap B^{}D)\setminus B^{}(B^{2}\setminus F)B^{}\cup \Lambda (e')} . Strictly speaking, it is the computation of the automaton for the local language denoted by this linearised expression that is Glushkov's construction. === Step 4 === Remove the linearisation, replacing each indexed letter B by the original letter of A. == Example == Consider the regular expression e = ( a ( a b ) ∗ ) ∗ + ( b a ) ∗ {\displaystyle e=(a(ab)^{})^{}+(ba)^{}} . == Computation of the set of letters == The computation of the sets P, D, F, and Λ is done inductively over the regular expression e ′ {\displaystyle e'} . One must give the values for ∅, ε (the symbols for the empty language and the singleton language containing the empty word), the letters, and the results of the operations + , ⋅ , ∗ {\displaystyle +,\cdot ,^{}} . The most costly operations are the cartesian products of sets for the computation of F. == Properties == The obtained automaton is non-deterministic, and it has as many states as the number of letters of the regular expression, plus one. It has been proven that every Thompson's automaton can be transformed into Glushkov's automaton via a ε-transitions elimination method. == Applications and deterministic expressions == The computation of the automaton by the expression occurs often; it has been systematically used in search functions, in particular by the Unix grep command. Similarly, XML's specification also uses such constructions; for more efficiency, regular expressions of a certain kind, called deterministic expressions, have been studied.

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  Dan Klein

  Daniel Klein (born c. 1976) is an American computer scientist and professor of computer science at the University of California, Berkeley. His research focuses on natural language processing and artificial intelligence. He was educated at Mt. Lebanon High School in Mt. Lebanon Township, Pennsylvania and earned a B.A. in mathematics, computer science, and linguistics from Cornell University (1998), a MSt in linguistics by Oxford University (1999) and a Ph.D. from Stanford University (2004), under Christopher D. Manning. He attended Oxford on a Marshall Scholarship. In addition to the Marshall scholarship, he has been awarded the ACM's Grace Murray Hopper Award, the Sloan Research Fellowship, the NSF CAREER Award, and the Microsoft New Faculty Fellowship.

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  Is an AI Sales Assistant Worth It in 2026?

  Shopping for the best AI sales assistant? An AI sales assistant is software that uses machine learning to help you get more done — it keeps getting smarter as the underlying models improve. Pricing, accuracy, and the size of the model behind the tool are the three factors that most affect daily usefulness. Whether you are a beginner or a pro, the right AI sales assistant slots into your workflow and pays for itself fast. Below we compare features, pricing, and real output so you can choose with confidence.

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  IT operations analytics

  In the fields of information technology (IT) and systems management, IT operations analytics (ITOA) is an approach or method to retrieve, analyze, and report data for IT operations. ITOA may apply big data analytics to large datasets to produce business insights. In 2014, Gartner predicted its use might increase revenue or reduce costs. By 2017, it predicted that 15% of enterprises will use IT operations analytics technologies. == Definition == IT operations analytics (ITOA) (also known as advanced operational analytics, or IT data analytics) technologies are primarily used to discover complex patterns in high volumes of often "noisy" IT system availability and performance data. Forrester Research defined IT analytics as "The use of mathematical algorithms and other innovations to extract meaningful information from the sea of raw data collected by management and monitoring technologies." Note, ITOA is different than AIOps, which focuses on applying artificial intelligence and machine learning to the applications of ITOA. == History == Operations research as a discipline emerged from the Second World War to improve military efficiency and decision-making on the battlefield. However, only with the emergence of machine learning tech in the early 2000s could an artificially intelligent operational analytics platform actually begin to engage in the high-level pattern recognition that could adequately serve business needs. A critical catalyst towards ITOA development was the rise of Google, which pioneered a predictive analytics model that represented the first attempt to read into patterns of human behavior on the Internet. IT specialists then applied predictive analytics to the IT Industry, coming forward with platforms that can sift through data to generate insights without the need for human intervention. Due to the mainstream embrace of cloud computing and the increasing desire for businesses to adopt more big data practices, the ITOA industry has grown significantly since 2010. A 2016 ExtraHop survey of large and mid-size corporations indicates that 65 percent of the businesses surveyed will seek to integrate their data silos either this year or the next. The current goals of ITOA platforms are to improve the accuracy of their APM services, facilitate better integration with the data, and to enhance their predictive analytics capabilities. == Applications == ITOA systems tend to be used by IT operations teams, and Gartner describes seven applications of ITOA systems: Root cause analysis: The models, structures and pattern descriptions of IT infrastructure or application stack being monitored can help users pinpoint fine-grained and previously unknown root causes of overall system behavior pathologies. Proactive control of service performance and availability: Predicts future system states and the impact of those states on performance. Problem assignment: Determines how problems may be resolved or, at least, direct the results of inferences to the most appropriate individuals, or communities in the enterprise for problem resolution. Service impact analysis: When multiple root causes are known, the analytics system's output is used to determine and rank the relative impact, so that resources can be devoted to correcting the fault in the most timely and cost-effective way possible. Complement best-of-breed technology: The models, structures and pattern descriptions of IT infrastructure or application stack being monitored are used to correct or extend the outputs of other discovery-oriented tools to improve the fidelity of information used in operational tasks (e.g., service dependency maps, application runtime architecture topologies, network topologies). Real time application behavior learning: Learns & correlates the behavior of Application based on user pattern and underlying Infrastructure on various application patterns, create metrics of such correlated patterns and store it for further analysis. Dynamically baselines threshold: Learns behavior of Infrastructure on various application user patterns and determines the Optimal behavior of the Infra and technological components, bench marks and baselines the low and high water mark for the specific environments and dynamically changes the bench mark baselines with the changing infra and user patterns without any manual intervention. == Types == In their Data Growth Demands a Single, Architected IT Operations Analytics Platform, Gartner Research describes five types of analytics technologies: Log analysis Unstructured text indexing, search and inference (UTISI) Topological analysis (TA) Multidimensional database search and analysis (MDSA) Complex operations event processing (COEP) Statistical pattern discovery and recognition (SPDR) == Tools and ITOA platforms == A number of vendors operate in the ITOA space:

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  Hidden Markov model

  A hidden Markov model (HMM) is a Markov model in which the observations are dependent on a latent (or hidden) Markov process (referred to as X {\displaystyle X} ). An HMM requires that there be an observable process Y {\displaystyle Y} whose outcomes depend on the outcomes of X {\displaystyle X} in a known way. Since X {\displaystyle X} cannot be observed directly, the goal is to learn about state of X {\displaystyle X} by observing Y {\displaystyle Y} . By definition of being a Markov model, an HMM has an additional requirement that the outcome of Y {\displaystyle Y} at time t = t 0 {\displaystyle t=t_{0}} must be "influenced" exclusively by the outcome of X {\displaystyle X} at t = t 0 {\displaystyle t=t_{0}} and that the outcomes of X {\displaystyle X} and Y {\displaystyle Y} at t < t 0 {\displaystyle t

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  Kurt Keutzer

  Kurt Keutzer (born November 9, 1955) is an American computer scientist. == Early life and education == Kurt Keutzer grew up in Indianapolis, Indiana. He earned a bachelor's degree in mathematics from Maharishi University of Management (formerly Mararishi International University) in 1978, and a PhD in computer science from Indiana University Bloomington in 1984. == Career == Keutzer joined Bell Labs in 1984, where he worked on logic synthesis. In 1991, he joined the electronic design automation company Synopsys, where he was promoted to chief technology officer. He subsequently joined the University of California, Berkeley as a professor in 1998. His research at Berkeley has focused on the intersection of high performance computing and machine learning. Working with a number of graduate students at Berkeley, Keutzer developed FireCaffe, which scaled the training of deep neural networks to over 100 GPUs. Later, with LARS and LAMB optimizers, they scaled it to over 1000 servers. Keutzer and his students also developed deep neural networks such as SqueezeNet, SqueezeDet, and SqueezeSeg, which can run efficiently on mobile devices. Keutzer co-founded DeepScale with his PhD student Forrest Iandola in 2015, and Keutzer served as the company's chief strategy officer. The firm was focused on developing deep neural networks for advanced driver assistance systems in passenger cars. On October 1, 2019, electric vehicle manufacturer Tesla, Inc. purchased DeepScale to augment and accelerate its self-driving vehicle work. == Honors and awards == Keutzer was named a Fellow of the IEEE in 1996. Recipient of DAC Most Influential Paper (MIP) award (24th DAC, 1987) for his "Dagon: technology binding and local optimization by DAG matching” publication. == Books by Keutzer == 1988. Dwight Hill, Don Shugard, John Fishburn, and Kurt Keutzer. Algorithms and Techniques for VLSI Layout Synthesis. Springer. 1994. Srinivas Devadas, Abhijit Ghosh, and Kurt Keutzer. Logic Synthesis. McGraw-Hill. 2002. David Chinnery and Kurt Keutzer. Closing the Gap Between ASIC & Custom: Tools and Techniques for High-Performance ASIC Design. Springer. (2nd edition appeared in 2007.) 2004. Pinhong Chen, Desmond A. Kirkpatrick, and Kurt Keutzer. Static Crosstalk-Noise Analysis: For Deep Sub-Micron Digital Designs. Springer. 2005. Matthias Gries and Kurt Keutzer. Building ASIPs: The Mescal Methodology. Springer.

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  The Best Free AI Headshot Generator for Beginners

  Shopping for the best AI headshot generator? An AI headshot generator is software that uses machine learning to help you get more done — it keeps getting smarter as the underlying models improve. Pricing, accuracy, and the size of the model behind the tool are the three factors that most affect daily usefulness. Whether you are a beginner or a pro, the right AI headshot generator slots into your workflow and pays for itself fast. Below we compare features, pricing, and real output so you can choose with confidence.

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  Data remanence

  Data remanence is the residual representation of digital data that remains even after attempts have been made to remove or erase the data. This residue may result from data being left intact by a nominal file deletion operation, by reformatting of storage media that does not remove data previously written to the media, or through physical properties of the storage media that allow previously written data to be recovered. Data remanence may make inadvertent disclosure of sensitive information possible should the storage media be released into an uncontrolled environment (e.g., thrown in refuse containers or lost). Various techniques have been developed to counter data remanence. These techniques are classified as clearing, purging/sanitizing, or destruction. Specific methods include overwriting, degaussing, encryption, and media destruction. Effective application of countermeasures can be complicated by several factors, including media that are inaccessible, media that cannot effectively be erased, advanced storage systems that maintain histories of data throughout the data's life cycle, and persistence of data in memory that is typically considered volatile. Several standards exist for the secure removal of data and the elimination of data remanence. == Causes == Many operating systems, file managers, and other software provide a facility where a file is not immediately deleted when the user requests that action. Instead, the file is moved to a holding area (i.e. the "trash"), making it easy for the user to undo a mistake. Similarly, many software products automatically create backup copies of files that are being edited, to allow the user to restore the original version, or to recover from a possible crash (autosave feature). Even when an explicit deleted file retention facility is not provided or when the user does not use it, operating systems do not actually remove the contents of a file when it is deleted unless they are aware that explicit erasure commands are required, like on a solid-state drive. (In such cases, the operating system will issue the Serial ATA TRIM command or the SCSI UNMAP command to let the drive know to no longer maintain the deleted data.) Instead, they simply remove the file's entry from the file system directory because this requires less work and is therefore faster, and the contents of the file—the actual data—remain on the storage medium. The data will remain there until the operating system reuses the space for new data. In some systems, enough filesystem metadata are also left behind to enable easy undeletion by commonly available utility software. Even when undelete has become impossible, the data, until it has been overwritten, can be read by software that reads disk sectors directly. Computer forensics often employs such software. Likewise, reformatting, repartitioning, or reimaging a system is unlikely to write to every area of the disk, though all will cause the disk to appear empty or, in the case of reimaging, empty except for the files present in the image, to most software. Finally, even when the storage media is overwritten, physical properties of the media may permit recovery of the previous contents. In most cases however, this recovery is not possible by just reading from the storage device in the usual way, but requires using laboratory techniques such as disassembling the device and directly accessing/reading from its components. § Complications below gives further explanations for causes of data remanence. == Countermeasures == There are three levels commonly recognized for eliminating remnant data: === Clearing === Clearing is the removal of sensitive data from storage devices in such a way that there is assurance that the data may not be reconstructed using normal system functions or software file/data recovery utilities. The data may still be recoverable, but not without special laboratory techniques. Clearing is typically an administrative protection against accidental disclosure within an organization. For example, before a hard drive is re-used within an organization, its contents may be cleared to prevent their accidental disclosure to the next user. === Purging === Purging or sanitizing is the physical rewrite of sensitive data from a system or storage device done with the specific intent of rendering the data unrecoverable at a later time. Purging, proportional to the sensitivity of the data, is generally done before releasing media beyond control, such as before discarding old media, or moving media to a computer with different security requirements. === Destruction === The storage media is made unusable for conventional equipment. Effectiveness of destroying the media varies by medium and method. Depending on recording density of the media, and/or the destruction technique, this may leave data recoverable by laboratory methods. Conversely, destruction using appropriate techniques is the most secure method of preventing retrieval. == Specific methods == === Overwriting === A common method used to counter data remanence is to overwrite the storage media with new data. This is often called wiping or shredding a disk or file, by analogy to common methods of destroying print media, although the mechanism bears no similarity to these. Because such a method can often be implemented in software alone, and may be able to selectively target only part of the media, it is a popular, low-cost option for some applications. Overwriting is generally an acceptable method of clearing, as long as the media is writable and not damaged. The simplest overwrite technique writes the same data everywhere—often just a pattern of all zeros. At a minimum, this will prevent the data from being retrieved simply by reading from the media again using standard system functions. The UEFI in modern machines may offer an ATA class disk erase function as well. The ATA-6 standard governs secure erases specifications. Bitlocker is whole disk encryption and illegible without the key. Writing a fresh GPT allows a new file system to be established. Blocks will set empty but LBA read is illegible. New data will be unaffected and work fine. In an attempt to counter more advanced data recovery techniques, specific overwrite patterns and multiple passes have often been prescribed. These may be generic patterns intended to eradicate any trace signatures; an example is the seven-pass pattern 0xF6, 0x00, 0xFF, , 0x00, 0xFF, , sometimes erroneously attributed to US standard DOD 5220.22-M. One challenge with overwriting is that some areas of the disk may be inaccessible, due to media degradation or other errors. Software overwrite may also be problematic in high-security environments, which require stronger controls on data commingling than can be provided by the software in use. The use of advanced storage technologies may also make file-based overwrite ineffective (see the related discussion below under § Complications). There are specialized machines and software that are capable of doing overwriting. The software can sometimes be a standalone operating system specifically designed for data destruction. There are also machines specifically designed to wipe hard drives to the department of defense specifications DOD 5220.22-M. Writing zero to each block on hard disks and SSDs has the advantage of affording the firmware to deploy spare blocks when bad blocks are identified. Bitlocker has the advantage that data is illegible without the key. Seatools and other tools can erase disks with zero which is typical to revive old consumer class disks but they can wipe server disks albeit slowly. Modern 28TB and larger disks have an enormous number of LBA48 blocks. 40TB and 60TB disks will take proportionately longer times to wipe. ==== Feasibility of recovering overwritten data ==== Peter Gutmann investigated data recovery from nominally overwritten media in the mid-1990s. He suggested magnetic force microscopy may be able to recover such data, and developed specific patterns, for specific drive technologies, designed to counter such. These patterns have come to be known as the Gutmann method. Gutmann's belief in the possibility of data recovery is based on many questionable assumptions and factual errors that indicate a low level of understanding of how hard drives work. Daniel Feenberg, an economist at the private National Bureau of Economic Research, claims that the chances of overwritten data being recovered from a modern hard drive amount to "urban legend". He also points to the "18+1⁄2-minute gap" Rose Mary Woods created on a tape of Richard Nixon discussing the Watergate break-in. Erased information in the gap has not been recovered, and Feenberg claims doing so would be an easy task compared to recovery of a modern high density digital signal. As of November 2007, the United States Department of Defense considers overwriting acceptable for clearing magnetic media within the same security area/

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  How to Choose an AI Logo Maker

  Trying to pick the best AI logo maker? An AI logo maker is software that uses machine learning to help you get more done — it scales effortlessly from a single task to thousands. The best picks balance beginner-friendly simplicity with the depth power users need, and they ship updates often. Whether you are a beginner or a pro, the right AI logo maker slots into your workflow and pays for itself fast. This guide breaks down the top picks, their pros and cons, and who each one is best for.

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  Corpus language

  A corpus language is a language that has no living speakers but for which numerous records produced by its native speakers survive. Examples of corpus languages are Ancient Greek, Latin, the Egyptian language, Old English, Old Norse, Elamite, and Sanskrit. Some corpus languages, such as Ancient Greek and Latin, left very large corpora and therefore can be fully reconstructed, even though some details of pronunciation may be unclear. Such languages can be used even today, as is the case with Sanskrit and Latin. Other languages have such limited corpora that some important words—e.g., some pronouns—are lacking in the corpora. Examples of these are Ugaritic and Gothic. Languages attested only by a few words, often names, and a few phrases, are called Trümmersprache (literally "rubble languages") in German linguistics. These can be reconstructed only in a very limited way, and often their genetic relationship to other languages remains unclear. Examples are Dalmatian, Etruscan, also known as Rasenna, Dadanitic, a Semitic language that may be close to classical Arabic, Lombardic, Burgundian, Vandalic, and Oscan, Umbrian, and Faliscan, all Italic languages that were related to Latin. Corpus languages are studied using the methods of corpus linguistics, but corpus linguistics can also be used (and is commonly used) for the study of the writings and other records of living languages. Not all extinct languages are corpus languages, since there are many extinct languages in which few or no writings or other records survive, as is the case in the vast majority of languages that have ever existed.

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  Best AI Paraphrasing Tools in 2026

  Curious about the best AI paraphrasing tool? An AI paraphrasing tool is software that uses machine learning to help you get more done — it combines speed, accuracy, and an interface that just works. Hands-on testing shows real-world results vary, so a short free trial is the smartest way to decide. Whether you are a beginner or a pro, the right AI paraphrasing tool slots into your workflow and pays for itself fast. Read on for hands-on impressions, pricing tiers, and the standout features that matter.

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  Text Retrieval Conference

  The Text REtrieval Conference (TREC) is an ongoing series of workshops focusing on a list of different information retrieval (IR) research areas, or tracks. It is co-sponsored by the National Institute of Standards and Technology (NIST) and the Intelligence Advanced Research Projects Activity (part of the office of the Director of National Intelligence), and began in 1992 as part of the TIPSTER Text program. Its purpose is to support and encourage research within the information retrieval community by providing the infrastructure necessary for large-scale evaluation of text retrieval methodologies and to increase the speed of lab-to-product transfer of technology. TREC's evaluation protocols have improved many search technologies. A 2010 study estimated that "without TREC, U.S. Internet users would have spent up to 3.15 billion additional hours using web search engines between 1999 and 2009." Hal Varian the Chief Economist at Google wrote that "The TREC data revitalized research on information retrieval. Having a standard, widely available, and carefully constructed set of data laid the groundwork for further innovation in this field." Each track has a challenge wherein NIST provides participating groups with data sets and test problems. Depending on track, test problems might be questions, topics, or target extractable features. Uniform scoring is performed so the systems can be fairly evaluated. After evaluation of the results, a workshop provides a place for participants to collect together thoughts and ideas and present current and future research work.Text Retrieval Conference started in 1992, funded by DARPA (US Defense Advanced Research Project) and run by NIST. Its purpose was to support research within the information retrieval community by providing the infrastructure necessary for large-scale evaluation of text retrieval methodologies. == Goals == Encourage retrieval search based on large text collections Increase communication among industry, academia, and government by creating an open forum for the exchange of research ideas Speed the transfer of technology from research labs into commercial products by demonstrating substantial improvements retrieval methodologies on real world problems To increase the availability of appropriate evaluation techniques for use by industry and academia including development of new evaluation techniques more applicable to current systems TREC is overseen by a program committee consisting of representatives from government, industry, and academia. For each TREC, NIST provide a set of documents and questions. Participants run their own retrieval system on the data and return to NIST a list of retrieved top-ranked documents. NIST pools the individual result judges the retrieved documents for correctness and evaluates the results. The TREC cycle ends with a workshop that is a forum for participants to share their experiences. == Relevance judgments in TREC == TREC defines relevance as: "If you were writing a report on the subject of the topic and would use the information contained in the document in the report, then the document is relevant." Most TREC retrieval tasks use binary relevance: a document is either relevant or not relevant. Some TREC tasks use graded relevance, capturing multiple degrees of relevance. Most TREC collections are too large to perform complete relevance assessment; for these collections it is impossible to calculate the absolute recall for each query. To decide which documents to assess, TREC usually uses a method call pooling. In this method, the top-ranked n documents from each contributing run are aggregated, and the resulting document set is judged completely. == Various TRECs == In 1992 TREC-1 was held at NIST. The first conference attracted 28 groups of researchers from academia and industry. It demonstrated a wide range of different approaches to the retrieval of text from large document collections .Finally TREC1 revealed the facts that automatic construction of queries from natural language query statements seems to work. Techniques based on natural language processing were no better no worse than those based on vector or probabilistic approach. TREC2 Took place in August 1993. 31 group of researchers participated in this. Two types of retrieval were examined. Retrieval using an ‘ad hoc’ query and retrieval using a ‘routing' query In TREC-3 a small group experiments worked with Spanish language collection and others dealt with interactive query formulation in multiple databases TREC-4 they made even shorter to investigate the problems with very short user statements TREC-5 includes both short and long versions of the topics with the goal of carrying out deeper investigation into which types of techniques work well on various lengths of topics In TREC-6 Three new tracks speech, cross language, high precision information retrieval were introduced. The goal of cross language information retrieval is to facilitate research on system that are able to retrieve relevant document regardless of language of the source document TREC-7 contained seven tracks out of which two were new Query track and very large corpus track. The goal of the query track was to create a large query collection TREC-8 contain seven tracks out of which two –question answering and web tracks were new. The objective of QA query is to explore the possibilities of providing answers to specific natural language queries TREC-9 Includes seven tracks In TREC-10 Video tracks introduced Video tracks design to promote research in content based retrieval from digital video In TREC-11 Novelty tracks introduced. The goal of novelty track is to investigate systems abilities to locate relevant and new information within the ranked set of documents returned by a traditional document retrieval system TREC-12 held in 2003 added three new tracks; Genome track, robust retrieval track, HARD (Highly Accurate Retrieval from Documents) == Tracks == === Current tracks === New tracks are added as new research needs are identified, this list is current for TREC 2018. CENTRE Track – Goal: run in parallel CLEF 2018, NTCIR-14, TREC 2018 to develop and tune an IR reproducibility evaluation protocol (new track for 2018). Common Core Track – Goal: an ad hoc search task over news documents. Complex Answer Retrieval (CAR) – Goal: to develop systems capable of answering complex information needs by collating information from an entire corpus. Incident Streams Track – Goal: to research technologies to automatically process social media streams during emergency situations (new track for TREC 2018). The News Track – Goal: partnership with The Washington Post to develop test collections in news environment (new for 2018). Precision Medicine Track – Goal: a specialization of the Clinical Decision Support track to focus on linking oncology patient data to clinical trials. Real-Time Summarization Track (RTS) – Goal: to explore techniques for real-time update summaries from social media streams. === Past tracks === Chemical Track – Goal: to develop and evaluate technology for large scale search in chemistry-related documents, including academic papers and patents, to better meet the needs of professional searchers, and specifically patent searchers and chemists. Clinical Decision Support Track – Goal: to investigate techniques for linking medical cases to information relevant for patient care Contextual Suggestion Track – Goal: to investigate search techniques for complex information needs that are highly dependent on context and user interests. Crowdsourcing Track – Goal: to provide a collaborative venue for exploring crowdsourcing methods both for evaluating search and for performing search tasks. Genomics Track – Goal: to study the retrieval of genomic data, not just gene sequences but also supporting documentation such as research papers, lab reports, etc. Last ran on TREC 2007. Dynamic Domain Track – Goal: to investigate domain-specific search algorithms that adapt to the dynamic information needs of professional users as they explore in complex domains. Enterprise Track – Goal: to study search over the data of an organization to complete some task. Last ran on TREC 2008. Entity Track – Goal: to perform entity-related search on Web data. These search tasks (such as finding entities and properties of entities) address common information needs that are not that well modeled as ad hoc document search. Cross-Language Track – Goal: to investigate the ability of retrieval systems to find documents topically regardless of source language. After 1999, this track spun off into CLEF. FedWeb Track – Goal: to select best resources to forward a query to, and merge the results so that most relevant are on the top. Federated Web Search Track – Goal: to investigate techniques for the selection and combination of search results from a large number of real on-line web search services. Filtering Track – Goal: to binarily decide retrieval of new

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  Jun'ichi Tsujii

  Jun'ichi Tsujii (辻井 潤一, Tsujii Jun'ichi; born 7 February 1949) is a Japanese computer scientist specializing in natural language processing and text mining, particularly in the field of biology and bioinformatics. == Education == Tsujii received his Bachelor of Engineering, Master of Engineering and PhD degrees in electrical engineering from Kyoto University in 1971, 1973, and 1978 respectively. He was Assistant Professor and Associate Professor at Kyoto University, before accepting a position as Professor of Computational Linguistics at the University of Manchester Institute of Science and Technology (UMIST) in 1988. He was President of the Association for Computational Linguistics (ACL) in 2006, and has been a permanent member of the International Committee on Computational Linguistics (ICCL) since 1992, and the chair of the committee since 2014. == Research == Since May 2015, Tsujii has been the director of the Artificial Intelligence Research Center at the National Institute of Advanced Industrial Science and Technology, Japan. Tsujii was previously a Principal Researcher at Microsoft Research Asia (MSRA). Before joining MSRA, he was a professor at the University of Tokyo, where he belonged to both the School of Inter-faculty Initiative on Informatics and the Graduate School of Information Science and Technology. Tsujii is also a Visiting Professor and Scientific Advisor at the National Centre for Text Mining (NaCTeM) at the University of Manchester in the United Kingdom. == Awards == On 14 May 2010, Tsujii was awarded the Medals of Honor with Purple Ribbon, one of Japan's highest awards, presented to influential contributors in the fields of art, academics or sports. In September 2014, Tsujii was awarded the FUNAI Achievement Award at the Forum on Information Technology (FIT), which took place at the University of Tsukuba. The award is presented to distinguished individuals engaged in research or related business activities in the field of Information Technology who have produced excellent achievements in the field, are still active in leading positions and have strong impact on young students and researchers. In December 2014, Tsujii was named as an ACL Fellow, in recognition of his significant contributions to MT, parsing by unification-based grammar and text mining for biology. In March 2016, Tsujii was awarded Okawa Prize for his contribution to the field of Natural Language Processing, Machine Translation and Text Mining, together with Professor Jaime Carbonnel of CMU. In August 2021, Tsujii received ACL Lifetime Achievement Award, which is considered the most prestigious award in the field of Computational Linguistics and Natural Language Processing. In May 2022, Tsujii received the Order of the Sacred Treasure, Gold Rays and Neck Ribbon, from the Japanese government. In October 2024, Tsujii was designated a Person of Cultural Merit. == Selected publications == Oiwa, Hidekazu; Tsujii, Jun'ichi (2014). Common Space Embedding of Primal-Dual Relation Semantic Spaces. COLING 2014. Dublin. pp. 1579–1590. Taura, K.; Matsuzaki, T.; Miwa, M.; Kamoshida, Y.; Yokoyama, D.; Dun, N.; Shibata, T.; Jun, C. S.; Tsujii, J. (2013). "Design and implementation of GXP make – A workflow system based on make". Future Generation Computer Systems. 29 (2): 662–672. doi:10.1016/j.future.2011.05.026. S2CID 31627886. Sun, X.; Zhang, Y.; Matsuzaki, T.; Tsuruoka, Y.; Tsujii, J. (2013). "Probabilistic Chinese word segmentation with non-local information and stochastic training". Information Processing & Management. 49 (3): 626–636. doi:10.1016/j.ipm.2012.12.003. Mu, T.; Goulermas, J. Y.; Tsujii, J.; Ananiadou, S. (2012). "Proximity-Based Frameworks for Generating Embeddings from Multi-Output Data". IEEE Transactions on Pattern Analysis and Machine Intelligence. 34 (11): 2216–2232. Bibcode:2012ITPAM..34.2216M. doi:10.1109/TPAMI.2012.20. PMID 23289130. S2CID 711467. Miwa, M.; Sætre, R.; Kim, J. D.; Tsujii, J. (2010). "Event Extraction with Complex Event Classification Using Rich Features". Journal of Bioinformatics and Computational Biology. 08 (1): 131–146. doi:10.1142/S0219720010004586. PMID 20183879. Kim, J. D.; Ohta, T.; Tsujii, J. (2008). "Corpus annotation for mining biomedical events from literature". BMC Bioinformatics. 9 10. doi:10.1186/1471-2105-9-10. PMC 2267702. PMID 18182099. Miyao, Y.; Tsujii, J. (2008). "Feature Forest Models for Probabilistic HPSG Parsing". Computational Linguistics. 34: 35–80. doi:10.1162/coli.2008.34.1.35. S2CID 885002. Sagae, Kenji; Tsujii, Jun'ichi (2007). Dependency Parsing and Domain Adaptation with LR Models and Parser Ensembles. EMNLP-CoNLL. pp. 1044–1050. Ananiadou, S; Pyysalo, S; Tsujii, J; Kell, D. B. (2010). "Event extraction for systems biology by text mining the literature". Trends in Biotechnology. 28 (7): 381–90. doi:10.1016/j.tibtech.2010.04.005. PMID 20570001. Tsuruoka, Y.; Tateishi, Y.; Kim, J. D.; Ohta, T.; McNaught, J.; Ananiadou, S.; Tsujii, J. (2005). "Developing a Robust Part-of-Speech Tagger for Biomedical Text". Advances in Informatics. Lecture Notes in Computer Science. Vol. 3746. p. 382. doi:10.1007/11573036_36. ISBN 978-3-540-29673-7. S2CID 206592413. Tsuruoka, Y.; Tsujii, J. (2005). Bidirectional inference with the easiest-first strategy for tagging sequence data. Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing - HLT '05. pp. 467–474. doi:10.3115/1220575.1220634. Tsujii, J.; Ananiadou, S. (2005). "Thesaurus or Logical Ontology, Which One Do We Need for Text Mining?". Language Resources and Evaluation. 39: 77–90. doi:10.1007/s10579-005-2697-0. S2CID 3204827. Kazama, J. I.; Tsujii, J. I. (2005). "Maximum Entropy Models with Inequality Constraints: A Case Study on Text Categorization". Machine Learning. 60 (1–3): 159–194. doi:10.1007/s10994-005-0911-3. hdl:10119/3305. Matsuzaki, T.; Miyao, Y.; Tsujii, J. I. (2005). Probabilistic CFG with latent annotations. 43rd Annual Meeting on Association for Computational Linguistics - ACL '05. p. 75. doi:10.3115/1219840.1219850. Kim, J. -D.; Ohta, T.; Tateisi, Y.; Tsujii, J. (2003). "GENIA corpus--a semantically annotated corpus for bio-textmining". Bioinformatics. 19: i180–i182. doi:10.1093/bioinformatics/btg1023. PMID 12855455. Hirschman, L.; Park, J. C.; Tsujii, J.; Wong, L.; Wu, C. H. (2002). "Accomplishments and challenges in literature data mining for biology". Bioinformatics. 18 (12): 1553–1561. doi:10.1093/bioinformatics/18.12.1553. PMID 12490438. Torisawa, K.; Tsujii, J. I. (1996). Computing phrasal-signs in HPSG prior to parsing. 16th conference on Computational linguistics -. Vol. 2. p. 949. doi:10.3115/993268.993332.

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  Ginger Software

  Ginger Software is an American and Israeli start-up specialized in natural language processing and AI. The main products are tools aiming to improve written communications, develop English speaking skills and boost productivity. The company was founded in 2008 by Yael Karov and Avner Zangvil. Ginger Software uses the context of complete sentences to suggest corrections. In December 2011, Ginger Software was one of nine projects approved by the Board of Governors of the Israel-U.S. Binational Industrial Research and Development Foundation for a funding of $8.1 million. The company also raised $3 million from private Israeli and US investors in 2009. In May, 2014 Intel acquired one of Ginger's business units and the rights to use the company's patented technology. == Founders == Before founding Ginger Software, Yael Karov had worked with Rosetta Genomics as its Chief Technology Officer and Vice President of Research and Development from 2003 to 2006, and with ClickSoftware Technologies as a Director of Research and Development from 1990 to 1994. Karov also founded Agentics, a company specializing in free-text classification of e-commerce product information based on natural language processing, in 1996. Avner Zangvil is the co-founder of Ginger Software. Zangvil co-founded Menta Software in 1996 with his brother Arnon Zangvil to develop a product that transforms any Windows-based application into a Web-enabled application usable from any remote computer running a Web browser. Menta was acquired by GraphOn Corporation in 2001. == Technology == Ginger Software uses patented software algorithms in the field of natural language processing. The company claims that the algorithm allows it to correct the written sentences with relatively high accuracy (eliminating up to 95 percent of writing errors), compared to standard spell checkers. Its unique algorithm allows the software to understand the context of the sentence rather than correcting based solely on a word. According to its founder, Karov, the software operates on the logic of sentence context in addition to the memory of a database of words. The company is at the heart of a growing revolution in the world of assistive technology. Ginger claims that the benefits of the software have been leveraged by native English and non-native speakers alike, and have also found value in niche markets like dyslexia management. They further claim that ESL users derive great benefit from the use of the software, as it lets them write error-free English text. Its use also extends to native English speaking business professionals and students who use it as a 'safety net' for their email edits, as well as international students writing in English. More recently, the company has focused on implementing its technology in mobile devices as an integral component of its mobile keyboard products. == Products == Ginger Software products include Ginger Page, a cross-platform writing enhancement app, and Ginger Keyboard which is available for Android devices. Ginger Writer can be used as an online service or installed on your PC or Mac. It supports MS-Word, MS-Outlook, MS-PowerPoint, Microsoft Edge, Chrome, and functions as a writing enhancement app for Android and iOS mobile devices. Its main feature is English grammar and spelling checker that runs seamlessly with the different user interfaces. It also has an advanced paraphrasing tool, contextual synonyms and definitions, translation and a text-to-speech function that enables users to hear sentences before and after correction. Ginger Keyboard for Android replaces the stock keyboard and functions as a productivity boosting keyboard app. Featuring a full set of advanced keyboard features like Stream (swipe-like) typing, adaptive word prediction, a wide variety of customizable themes and emoji, Ginger Keyboard is the only 3rd party keyboard to offer proofreading and other writing tools via one tap access to Ginger Page. == Target segment == Ginger Software started off targeting people with dyslexia. The algorithm underlying the software studies a vast pool of proper sentences in English and builds a model of proper language. The software does not analyze the text at the level of the word, but of the whole sentence. Dyslexics can have trouble choosing the right word – hence the attention to the sentence as a whole. From 2010, Ginger Software included a new target segment in its marketing outreach – users of English as a second language (ESL). Its contextual-based writing correction tool could benefit those who are not proficient in the English language. == Business model == The main business model for consumers is freemium. The free version offers contextual-based grammar and spelling checker with some limitations. Its premium features include unlimited access to Grammar Checker, the grammar and spelling checker, and Sentence Rephraser the rephrasing tool. Ginger Keyboard is free to download and use, although it does offer in-app purchases like themes and theme packs. It also disables your original spell checker. Ginger also provides a powerful Rest API which can correct full documents in one call.

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