AI For Business For Dummies

AI For Business For Dummies — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

  • Just This Once

    Just This Once

    Just This Once is a 1993 romance novel written in the style of Jacqueline Susann by a Macintosh IIcx computer named "Hal" in collaboration with its programmer, Scott French. French reportedly spent $40,000 and 8 years developing an artificial intelligence program to analyze Susann's works and attempt to create a novel that Susann might have written. A legal dispute between the estate of Jacqueline Susann and the publisher resulted in a settlement to split the profits, and the book was referenced in several legal journal articles about copyright laws. The book had two small print runs totaling 35,000 copies, receiving mixed reviews. == Creation == The novel's creation spanned the fields of artificial intelligence, expert systems, and natural language processing. Scott French first scanned and analyzed portions of two books by Jacqueline Susann, Valley of the Dolls and Once Is Not Enough, to determine constituents of Susann's writing style, which French stated was the most difficult task. This analysis extracted several hundred components including frequency and type of sexual acts and sentence structure. "Once you're there, the writer's style emerges, part of her actual personality comes out, and the computer can be programmed to make a story." French also created several thousand rules to govern tone, plotting, scenes, and characters. The text generated by Hal, the computer, was intended to mimic what Susann might have written, although the output required significant editing. French credits Hal's work with "almost 100% of the plot, 100% of the theme and style." French estimates that he wrote 10% of the prose, the computer Hal wrote about 25% of the prose, and the remaining two-thirds was more of a collaboration between the two. A typical scenario to write a scene would involve Hal asking questions that French would answer (for example, Hal might ask about the "cattiness factor" involved in a meeting between two key female characters, and French would reply with a range of 1 to 10), and the computer would then generate a few sentences to which French would make minor edits. The process would repeat for the next few sentences until the scene was written. == Legal issues == Jacqueline Susann's publisher was skeptical of the legality of Just This Once, although French doubted that an author's thought processes could be copyrighted. Susann's estate reportedly threatened to sue Scott French but the parties settled out of court; the settlement involved splitting profits between the parties but the terms of the settlement were not disclosed. The publication of Just This Once raised questions in the legal profession concerning how copyright law applies to computer-generated works derived from an analysis of other copyrighted works, and whether the generation of such works infringes on copyright. The publications on this topic suggested that the copyright laws of the time were ill-equipped to deal with computer-generated creative works. == Reception == The book's publisher Steven Shragis of Carol Group said of the novel, "I'm not going to say this is a great literary work, but it's every bit as good as anything out in this field, and better than an awful lot." The novel received some positive early reviews. In USA Today, novelist Thomas Gifford compared Just This Once to another novel in the same genre, American Star by Jackie Collins. Gifford concluded: "If you do like this stuff, you'd be much, much better off with the one written by the computer." The Dead Jackie Susann Quarterly declared that Susann "would be proud. Lots of money, sleaze, disease, death, oral sex, tragedy and the good girl gone bad." Other reviews were mixed. Publishers Weekly wrote, "If the books of Jacqueline Susann and Harold Robbins seem formulaic, this debut novel of sin and success in Las Vegas outdoes them all. And that, in a way, is the point.... All novelty rests in the conceit of computer authorship, not in the story itself." Library Journal stated "French invested eight years and $50,000 in a scheme to use artificial intelligence to fulfill his authentic, if dubious, desire to generate a trashy novel a la Jacqueline Susann. Shallow, beautiful-people characters are flatly conceived and randomly accessed in a formulaic plot ... a sexy, boring morality tale. Of possible interest to computer buffs for its use of Expert Systems and the virtual promise of more worthy possibilities; others should read Susann." Kirkus Reviews wrote: "The deal here is that author French is not the author, he's just the midwife, having allegedly programmed his computer to write about our times just the way Susann would... almost perfectly capturing glamorous Jackie's turgid but E-Z reading prose style and ultrareliable mix of sex, glitz, dope 'n' despair.... One wonders, though, if French's tale spinning PC will do as well on the talkshows as Jackie did. The computer weenies have been trying to tell us for years, garbage in-garbage out."

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  • SNNS

    SNNS

    SNNS (Stuttgart Neural Network Simulator) is a neural network simulator originally developed at the University of Stuttgart. While it was originally built for X11 under Unix, there are Windows ports. Its successor JavaNNS never reached the same popularity. == Features == SNNS is written around a simulation kernel to which user written activation functions, learning procedures and output functions can be added. It has support for arbitrary network topologies and the standard release contains support for a number of standard neural network architectures and training algorithms. == Status == There is currently no ongoing active development of SNNS. In July 2008 the license was changed to the GNU LGPL.

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  • Katia Sycara

    Katia Sycara

    Ekaterini Panagiotou Sycara (Greek: Κάτια Συκαρά) is a Greek computer scientist. She is an Edward Fredkin Research Professor of Robotics in the Robotics Institute, School of Computer Science at Carnegie Mellon University internationally known for her research in artificial intelligence, particularly in the fields of negotiation, autonomous agents and multi-agent systems. She directs the Advanced Agent-Robotics Technology Lab at Robotics Institute, Carnegie Mellon University. She also serves as academic advisor for PhD students at both Robotics Institute and Tepper School of Business. == Education and early life == Born in Greece, she went to the United States to pursue advanced education through various scholarships, including a Fulbright (1965-1969). She received a B.S. in applied mathematics from Brown University, M.S. in electrical engineering from the University of Wisconsin–Milwaukee, and PhD in computer science from Georgia Institute of Technology. == Research and career == Sycara is a pioneer in the field of semantic web, case-based reasoning, autonomous agents and multi-agent systems. She has authored or co-authored more than 700 technical papers dealing with multi-agent systems, software agents, web services, semantic web, human–computer interaction, human-robot interaction, negotiation, case-based reasoning and the application of these techniques to crisis action planning, scheduling, manufacturing, healthcare management, financial planning and e-commerce.[1] She has led multimillion-dollar research effort funded by DARPA, NASA, AFOSR, ONR, AFRL, NSF and industry. Through an ONR MURI program and though the COABS DARPA program, Prof. Sycara's group has developed the RETSINA multiagent infrastructure, a toolkit that enables the development of heterogeneous software agents that can dynamically coordinate in open information environments (e.g. the Internet). RETSINA has been used in multiple applications including supporting human joint mission teams for crisis response; creating autonomous agents for situation awareness and information fusion; financial portfolio management, negotiations and coalition formation for e-commerce, and coordinating robots for Urban Search and Rescue. Sycara is one of the contributors to the development of OWL-S, the Darpa-sponsored language for Semantic Web services, as well as matchmaking and brokering software for agent discovery, service integration and semantic interoperation. === Academic service === Sycara is the founding Editor-in-Chief of the journal Autonomous Agents and Multi-Agent Systems; Editor-in-Chief, of the Springer Series on Agents; and Area Editor of AI and Management Science, the journal "Group Decision and Negotiation." She is a member of the Editorial Board, the Kluwer book series on "Multiagent Systems, Artificial Societies and Simulated Organizations"; member of the editorial board, the journals "Agent Oriented Software Engineering", "Web Intelligence and Agent Technologies", "Journal of Infonomics", "Fundamenda Informaticae", and "Concurrent Engineering: Research and Applications"; and member of the editorial board of the "ETAI journal on the Semantic Web" (1998–2001). She was on the Editorial Board of "IEEE Intelligent Systems and their Applications" (1992–1996), and "AI in Engineering" (1990–1996). She is a member of the Scientific Advisory Board of France Telecom, 2003-2009; member of the Scientific Advisory Board of the Institute of Informatics and Telecommunications of the Greek National Research Center Demokritos, 2004-2012; member of the AAAI Executive Council (1996–99); member of the OASIS Technical committee on the development of UDDI (Universal Description and Discovery for Interoperability) software which is an industry standard; and an invited expert for W3C (the World Wide Web Consortium) Working Group on Web Services Architecture. She was a founding member of the Board of Directors of the International Foundation of Multiagent Systems (IFMAS), and founding member of the Semantic Web Science Association. Sycara served as the program chair of the Second International Semantic Web Conference (ISWC 2003); general chair, of the Second International Conference on Autonomous Agents (Agents 98); chair of the Steering Committee of the Agents Conference (1999–2001); scholarship chair of AAAI (1993–1999); and the US co-chair for the US-Europe Semantic Web Services Initiative. === Awards and honors === Sycara is a Fellow of Institute of Electrical and Electronics Engineers (IEEE), and a Fellow of American Association for Artificial Intelligence (AAAI). Sycara is the recipient of the 2002 ACM/SIGART Agents Research Award. She is also the recipient of the 2015 Group Decision and Negotiation (GDN) Award of the Institute for Operations Research and the Management Sciences (INFORMS) GDN Section for her outstanding contributions to the field of group decision and negotiation. According to the citation of the award: Katia Sycara is widely acknowledged as one of the leading researchers in the field of autonomous software agents and in particular on problems related to joint decision making and negotiations of such agents. Her work is characterized by a unique combination of methods from Artificial Intelligence and research on human negotiations, and thus has contributed to significant advances in both fields. Sycara's robot teams have won multiple international awards. In the 2005 Robocup Urban Search and Rescue (US Open) held in Atlanta, her team won the First-in-Class Award for Autonomy, and the First-in-Class Award for Mobility. Two years later, again in Atlanta, she led another team that became a world champions in the 2007 International Robocup Search and Rescue Simulation League Competition. In 2008, her robotic team placed third in the Worldwide Robocup Championship Competition in the Urban Search and Rescue Virtual robots League held in Beijing, China. In 2005, she received the Outstanding Alumnus Award from the University of Wisconsin–Milwaukee. She was awarded an Honorary Doctorate from the University of the Aegean in 2004.

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  • Top 10 AI Art Generators Compared (2026)

    Top 10 AI Art Generators Compared (2026)

    Shopping for the best AI art generator? An AI art 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 art 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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  • Google Cloud Dataflow

    Google Cloud Dataflow

    Google Cloud Dataflow is a fully managed service for executing Apache Beam pipelines within the Google Cloud Platform ecosystem. Dataflow provides a fully managed service for executing Apache Beam pipelines, offering features like autoscaling, dynamic work rebalancing, and a managed execution environment. Dataflow is suitable for large-scale, continuous data processing jobs, and is one of the major components of Google's big data architecture on the Google Cloud Platform. At its core, Dataflow's architecture is designed to abstract away infrastructure management, allowing developers to focus purely on the logic of their data processing tasks. When a pipeline written using the Apache Beam SDK is submitted, Dataflow translates this high-level definition into an optimized job graph. The service then provisions and manages a fleet of Google Compute Engine workers to execute this graph in a highly parallelized and fault-tolerant manner. This serverless approach, combined with intelligent autoscaling of both the number of workers (horizontal) and the resources per worker (vertical), ensures that jobs have the precise amount of computational power needed at any given time, optimizing both performance and cost. The service's deep integration with the Google Cloud ecosystem makes it a powerful tool for a variety of use cases beyond simple data movement. For real-time analytics, Dataflow can ingest unbounded streams of data from Cloud Pub/Sub, perform complex transformations, and load results into BigQuery for immediate querying. In machine learning workflows, it is commonly used to preprocess and transform massive datasets stored in Cloud Storage, preparing them for training models in Vertex AI. This versatility makes it the central processing engine for modern ETL (Extract, Transform, Load) operations, streaming analytics, and large-scale data preparation within the cloud. == History == Google Cloud Dataflow was announced in June, 2014 and released to the general public as an open beta in April, 2015. In January, 2016 Google donated the underlying SDK, the implementation of a local runner, and a set of IOs (data connectors) to access Google Cloud Platform data services to the Apache Software Foundation. The donated code formed the original basis for Apache Beam. In August 2022, there was an incident where user timers were broken for certain Dataflow streaming pipelines in multiple regions, which was later resolved. Throughout 2023 and 2024, there have been various other updates and incidents affecting Google Cloud Dataflow, as documented in the release notes and service health history. The donation of the Dataflow SDK to the Apache Software Foundation was a pivotal moment, establishing Apache Beam as a unified, open-source programming model for defining both batch and streaming data pipelines. This strategic move decoupled the pipeline definition from the execution engine. As a result, developers could write portable data processing logic that was not locked into Google's ecosystem. A Beam pipeline can be executed on various runners, including Apache Flink, Apache Spark, and, of course, the highly optimized Google Cloud Dataflow service, providing flexibility and future-proofing data processing investments. == Features == Google Cloud Dataflow supports both batch and streaming data processing pipelines. It automatically handles resource provisioning, data sharding, and scaling according to workload, reducing manual configuration needed for large-scale data operations. == Use cases == Dataflow is used for ETL (Extract, Transform, Load) data pipelines, real-time analytics, and event stream processing for companies in industries such as finance, advertising, and IoT.

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  • Postediting

    Postediting

    Post-editing (or postediting) is the process whereby humans amend machine-generated translation to achieve an acceptable final product. A person who post-edits is called a post-editor. The concept of post-editing is linked to that of pre-editing. In the process of translating a text via machine translation, best results may be gained by pre-editing the source text – for example by applying the principles of controlled language – and then post-editing the machine output. It is distinct from editing, which refers to the process of improving human generated text (a process which is often known as revision in the field of translation). Post-edited text may afterwards be revised to ensure the quality of the language choices are proofread to correct simple mistakes. Post-editing involves the correction of machine translation output to ensure that it meets a level of quality negotiated in advance between the client and the post-editor. Light post-editing aims at making the output simply understandable; full post-editing at making it also stylistically appropriate. With advances in machine translation full post-editing is becoming an alternative to manual translation. Practically all computer-assisted translation (CAT) tools now support post-editing of machine translated output. == Post-editing and machine translation == Machine translation left the labs to start being used for its actual purpose in the late seventies at some big institutions such as the European Commission and the Pan-American Health Organization, and then, later, at some corporations such as Caterpillar and General Motors. First studies on post-editing appeared in the eighties, linked to those implementations. To develop appropriate guidelines and training, members of the Association for Machine Translation in the Americas (AMTA) and the European Association for Machine Translation (EAMT) set a Post-editing Special Interest Group in 1999. After the nineties, advances in computer power and connectivity sped machine translation development and allowed for its deployment through the web browser, including as a free, useful adjunct to the main search engines (Google Translate, Bing Translator, Yahoo! Babel Fish). A wider acceptance of less than perfect machine translation was accompanied also by a wider acceptance of post-editing. With the demand for localisation of goods and services growing at a pace that could not be met by human translation, not even assisted by translation memory and other translation management technologies, industry bodies such as the Translation Automation Users Society (TAUS) expect machine translation and post-editing to play a much bigger role within the next few years. The use of Machine Translation suggests sometimes pre-editing. Human translators possess significantly more sophisticated cognitive abilities than machine translation (MT) systems. They leverage a wealth of life experience, common sense, and multi-sensory input to understand context, identify semantic intent, and add cultural nuances to translations. This remains true even as MT capabilities continue to improve. Unlike MT systems, which primarily focus on literal word-for-word conversion, human translators grasp the underlying meaning and intent, even when information is implicit. They "read between the lines," guided by their understanding of the world. Essentially, MT models excel at text string prediction, not true comprehension. Their success often stems from framing problems as prediction tasks, such as in self-driving cars or fraud detection. Studies have demonstrated that integrating adaptive MT with post-editing interfaces can lead to reductions in technical effort and time, improving overall translation efficiency. These systems are also supported by research that highlights the benefits of adaptive MT in real-world translation scenarios. For example, incremental adaptation in Neural Machine Translation (NMT) for professional post-editors has been shown to improve translation quality and reduce time spent on edits, showcasing how human expertise and machine assistance can complement each other effectively. == Light and full post-editing == For many years, no widely accepted, standardized post-editing guidelines existed; however, in 2017, ISO standard 18587:2017: Translation services — Post-editing of machine translation output — Requirements was published. Studies in the eighties distinguished between degrees of post-editing which, in the context of the European Commission Translation Service, were first defined as conventional and rapid or full and rapid. Light and full post-editing seems the wording most used today. Light post-editing implies minimal intervention by the post-editor, with the aim of ensuring quality is "good enough" or "understandable"; the expectation is that the client will use it for inbound purposes only, often when the text is needed urgently, or has a short time span. Full post-editing involves a greater level of intervention to achieve a degree of quality to be negotiated between client and post-editor; the expectation is that the outcome will be a text that is not only understandable but presented in some stylistically appropriate way, so it can be used for assimilation and even for dissemination, for inbound and for outbound purposes. The quality is expected to be publishable and equivalent to that of a human translation. The assumption, however, has been that it takes less effort for translators to work directly from the source text than to post-edit the machine generated version. With advances in machine translation, this may be changing. For some language pairs and for some tasks, and with engines that have been customised with domain specific good quality data, some clients are already requesting translators to post-edit instead of translating from scratch, in the belief that they will attain similar quality at a lower cost. The light/full classification, developed in the nineties when machine translation still came on a CD-ROM, may not suit advances in machine translation at the light post-editing end either. For some language pairs and some tasks, particularly if the source has been pre-edited, raw machine output may be good enough for gisting purposes without requiring subsequent human intervention. == Post-editing efficiency == Post-editing is used when raw machine translation is not good enough and human translation not required. Industry advises post-editing to be used when it can at least double the productivity of manual translation, even fourfold it in the case of light post-editing (1000 words per hour vs. 250 wph). However, post-editing efficiency is difficult to predict. Various studies from both academia and industry have claimed that post-editing is generally faster than translating from scratch, regardless of language pairs or translators' experience. There is, however, no agreement about how much time can be saved through post-editing in practice (if any at all): While the industry reports on time savings around 40%, some academic studies suggest that time savings under actual working conditions are more likely to be between 0–20%, or that it may depend on the terminological proximity between the source and target languages. Professionals have also reported negative productivity gains where corrections require more time than to translate from scratch. == Post-editing and the language industry == After some thirty years, post-editing is still "a nascent profession". What the right profile of the post-editor is, has not yet been fully studied. Post-editing overlaps with translating and editing, but only partially. Most think the ideal post-editor will be a translator keen to be trained on the specific skills required, but there are some who think a bilingual without a background in translation may be easier to train. Not much is known either on who the actual post-editors are, whether they tend to be professional translators, whether they work mostly as in-house employees or self-employed, and on which conditions. Many professional translators dislike post-editing, among other reasons because it tends to be paid at lower rates than conventional translations, with the International Association of Professional Translators and Interpreters (IAPTI) having been particularly vocal about it.

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  • Moore machine

    Moore machine

    In the theory of computation, a Moore machine is a finite-state machine whose current output values are determined only by its current state. This is in contrast to a Mealy machine, whose output values are determined both by its current state and by the values of its inputs. Like other finite state machines, in Moore machines, the input typically influences the next state. Thus the input may indirectly influence subsequent outputs, but not the current or immediate output. The Moore machine is named after Edward F. Moore, who presented the concept in a 1956 paper, “Gedanken-experiments on Sequential Machines.” == Formal definition == A Moore machine can be defined as a 6-tuple ( S , s 0 , Σ , Λ , δ , G ) {\displaystyle (S,s_{0},\Sigma ,\Lambda ,\delta ,G)} consisting of the following: A finite set of states S {\displaystyle S} A start state (also called initial state) s 0 {\displaystyle s_{0}} which is an element of S {\displaystyle S} A finite set called the input alphabet Σ {\displaystyle \Sigma } A finite set called the output alphabet Λ {\displaystyle \Lambda } A transition function δ : S × Σ → S {\displaystyle \delta :S\times \Sigma \rightarrow S} mapping a state and the input alphabet to the next state An output function G : S → Λ {\displaystyle G:S\rightarrow \Lambda } mapping each state to the output alphabet "Evolution across time" is realized in this abstraction by having the state machine consult the time-changing input symbol at discrete "timer ticks" t 0 , t 1 , t 2 , . . . {\displaystyle t_{0},t_{1},t_{2},...} and react according to its internal configuration at those idealized instants, or else having the state machine wait for a next input symbol (as on a FIFO) and react whenever it arrives. A Moore machine can be regarded as a restricted type of finite-state transducer. == Visual representation == === Table === A state transition table is a table listing all the triples in the transition relation δ : S × Σ → S {\displaystyle \delta :S\times \Sigma \rightarrow S} . === Diagram === The state diagram for a Moore machine, or Moore diagram, is a state diagram that associates an output value with each state. == Relationship with Mealy machines == As Moore and Mealy machines are both types of finite-state machines, they are equally expressive: either type can be used to parse a regular language. The difference between Moore machines and Mealy machines is that in the latter, the output of a transition is determined by the combination of current state and current input ( S × Σ {\displaystyle S\times \Sigma } as the domain of G {\displaystyle G} ), as opposed to just the current state ( S {\displaystyle S} as the domain of G {\displaystyle G} ). When represented as a state diagram, for a Moore machine, each node (state) is labeled with an output value; for a Mealy machine, each arc (transition) is labeled with an output value. Every Moore machine M {\displaystyle M} is equivalent to the Mealy machine with the same states and transitions and the output function G ( s , σ ) = G M ( δ M ( s , σ ) ) {\displaystyle G(s,\sigma )=G_{M}(\delta _{M}(s,\sigma ))} , which takes each state-input pair ( s , σ ) {\displaystyle (s,\sigma )} and yields G M ( δ M ( s , σ ) ) {\displaystyle G_{M}(\delta _{M}(s,\sigma ))} , where G M {\displaystyle G_{M}} is M {\displaystyle M} 's output function and δ M {\displaystyle \delta _{M}} is M {\displaystyle M} 's transition function. However, not every Mealy machine can be converted to an equivalent Moore machine. Some can be converted only to an almost equivalent Moore machine, with outputs shifted in time. This is due to the way that state labels are paired with transition labels to form the input/output pairs. Consider a transition s i → s j {\displaystyle s_{i}\rightarrow s_{j}} from state s i {\displaystyle s_{i}} to state s j {\displaystyle s_{j}} . The input causing the transition s i → s j {\displaystyle s_{i}\rightarrow s_{j}} labels the edge ( s i , s j ) {\displaystyle (s_{i},s_{j})} . The output corresponding to that input, is the label of state s i {\displaystyle s_{i}} . Notice that this is the source state of the transition. So for each input, the output is already fixed before the input is received, and depends solely on the present state. This is the original definition by E. Moore. It is a common mistake to use the label of state s j {\displaystyle s_{j}} as output for the transition s i → s j {\displaystyle s_{i}\rightarrow s_{j}} . == Examples == Types according to number of inputs/outputs. === Simple === Simple Moore machines have one input and one output: edge detector using XOR binary adding machine clocked sequential systems (a restricted form of Moore machine where the state changes only when the global clock signal changes) Most digital electronic systems are designed as clocked sequential systems. Clocked sequential systems are a restricted form of Moore machine where the state changes only when the global clock signal changes. Typically the current state is stored in flip-flops, and a global clock signal is connected to the "clock" input of the flip-flops. Clocked sequential systems are one way to solve metastability problems. A typical electronic Moore machine includes a combinational logic chain to decode the current state into the outputs (lambda). The instant the current state changes, those changes ripple through that chain, and almost instantaneously the output gets updated. There are design techniques to ensure that no glitches occur on the outputs during that brief period while those changes are rippling through the chain, but most systems are designed so that glitches during that brief transition time are ignored or are irrelevant. The outputs then stay the same indefinitely (LEDs stay bright, power stays connected to the motors, solenoids stay energized, etc.), until the Moore machine changes state again. ==== Worked example ==== A sequential network has one input and one output. The output becomes 1 and remains 1 thereafter when at least two 0's and two 1's have occurred as inputs. A Moore machine with nine states for the above description is shown on the right. The initial state is state A, and the final state is state I. The state table for this example is as follows: === Complex === More complex Moore machines can have multiple inputs as well as multiple outputs. == Gedanken-experiments == In Moore's 1956 paper "Gedanken-experiments on Sequential Machines", the ( n ; m ; p ) {\displaystyle (n;m;p)} automata (or machines) S {\displaystyle S} are defined as having n {\displaystyle n} states, m {\displaystyle m} input symbols and p {\displaystyle p} output symbols. Nine theorems are proved about the structure of S {\displaystyle S} , and experiments with S {\displaystyle S} . Later, " S {\displaystyle S} machines" became known as "Moore machines". At the end of the paper, in Section "Further problems", the following task is stated: Another directly following problem is the improvement of the bounds given at the theorems 8 and 9. Moore's Theorem 8 is formulated as: Given an arbitrary ( n ; m ; p ) {\displaystyle (n;m;p)} machine S {\displaystyle S} , such that every two of its states are distinguishable from one another, then there exists an experiment of length n ( n − 1 ) 2 {\displaystyle {\tfrac {n(n-1)}{2}}} which determines the state of S {\displaystyle S} at the end of the experiment. In 1957, A. A. Karatsuba proved the following two theorems, which completely solved Moore's problem on the improvement of the bounds of the experiment length of his "Theorem 8". Theorem A. If S {\displaystyle S} is an ( n ; m ; p ) {\displaystyle (n;m;p)} machine, such that every two of its states are distinguishable from one another, then there exists a branched experiment of length at most ( n − 1 ) ( n − 2 ) 2 + 1 {\displaystyle {\tfrac {(n-1)(n-2)}{2}}+1} through which one may determine the state of S {\displaystyle S} at the end of the experiment. Theorem B. There exists an ( n ; m ; p ) {\displaystyle (n;m;p)} machine, every two states of which are distinguishable from one another, such that the length of the shortest experiments establishing the state of the machine at the end of the experiment is equal to ( n − 1 ) ( n − 2 ) 2 + 1 {\displaystyle {\tfrac {(n-1)(n-2)}{2}}+1} . Theorems A and B were used for the basis of the course work of a student of the fourth year, A. A. Karatsuba, "On a problem from the automata theory", which was distinguished by testimonial reference at the competition of student works of the faculty of mechanics and mathematics of Moscow State University in 1958. The paper by Karatsuba was given to the journal Uspekhi Mat. Nauk on 17 December 1958 and was published there in June 1960. Until the present day (2011), Karatsuba's result on the length of experiments is the only exact nonlinear result, both in automata theory, and in similar problems of computational complexity theory.

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  • Mealy machine

    Mealy machine

    In the theory of computation, a Mealy machine is a finite-state machine whose output values are determined both by its current state and the current inputs. This is in contrast to a Moore machine, whose output values are determined solely by its current state. A Mealy machine is a deterministic finite-state transducer: for each state and input, at most one transition is possible. == History == The Mealy machine is named after George H. Mealy, who presented the concept in a 1955 paper, "A Method for Synthesizing Sequential Circuits". == Formal definition == A Mealy machine is a 6-tuple ( S , S 0 , Σ , Λ , T , G ) {\displaystyle (S,S_{0},\Sigma ,\Lambda ,T,G)} consisting of the following: a finite set of states S {\displaystyle S} a start state (also called initial state) S 0 {\displaystyle S_{0}} which is an element of S {\displaystyle S} a finite set called the input alphabet Σ {\displaystyle \Sigma } a finite set called the output alphabet Λ {\displaystyle \Lambda } a transition function T : S × Σ → S {\displaystyle T:S\times \Sigma \rightarrow S} mapping pairs of a state and an input symbol to the corresponding next state. an output function G : S × Σ → Λ {\displaystyle G:S\times \Sigma \rightarrow \Lambda } mapping pairs of a state and an input symbol to the corresponding output symbol. In some formulations, the transition and output functions are coalesced into a single function T : S × Σ → S × Λ {\displaystyle T:S\times \Sigma \rightarrow S\times \Lambda } . "Evolution across time" is realized in this abstraction by having the state machine consult the time-changing input symbol at discrete "timer ticks" t 0 , t 1 , t 2 , . . . {\displaystyle t_{0},t_{1},t_{2},...} and react according to its internal configuration at those idealized instants, or else having the state machine wait for a next input symbol (as on a FIFO) and react whenever it arrives. == Comparison of Mealy machines and Moore machines == Mealy machines tend to have fewer states: Different outputs on arcs (n2) rather than states (n). When implemented as electronic circuits (rather than as mathematical abstractions or code): Moore machines are safer to use than Mealy machines: Outputs change at the clock edge (always one cycle later). In Mealy machines, input change can cause output change as soon as logic is done — a big problem when two machines are interconnected – asynchronous feedback may occur if one isn't careful. Mealy machines react faster to inputs: React in the same cycle—they don't need to wait for the clock. In Moore machines, more logic may be necessary to decode state into outputs—more gate delays after clock edge. == Diagram == The state diagram for a Mealy machine associates an output value with each transition edge, in contrast to the state diagram for a Moore machine, which associates an output value with each state. When the input and output alphabet are both Σ, one can also associate to a Mealy automata a Helix directed graph (S × Σ, (x, i) → (T(x, i), G(x, i))). This graph has as vertices the couples of state and letters, each node is of out-degree one, and the successor of (x, i) is the next state of the automata and the letter that the automata output when it is instate x and it reads letter i. This graph is a union of disjoint cycles if the automaton is bireversible. == Examples == === Simple === A simple Mealy machine has one input and one output. Each transition edge is labeled with the value of the input (shown in red) and the value of the output (shown in blue). The machine starts in state Si. (In this example, the output is the exclusive-or of the two most-recent input values; thus, the machine implements an edge detector, outputting a 1 every time the input flips and a 0 otherwise.) === Complex === More complex Mealy machines can have multiple inputs as well as multiple outputs. == Applications == Mealy machines provide a rudimentary mathematical model for cipher machines. Considering the input and output alphabet the Latin alphabet, for example, then a Mealy machine can be designed that given a string of letters (a sequence of inputs) can process it into a ciphered string (a sequence of outputs). However, although a Mealy model could be used to describe the Enigma, the state diagram would be too complex to provide feasible means of designing complex ciphering machines. Moore/Mealy machines are DFAs that have also output at any tick of the clock. Modern CPUs, computers, cell phones, digital clocks and basic electronic devices/machines have some kind of finite state machine to control it. Simple software systems, particularly ones that can be represented using regular expressions, can be modeled as finite state machines. There are many such simple systems, such as vending machines or basic electronics. By finding the intersection of two finite state machines, one can design in a very simple manner concurrent systems that exchange messages for instance. For example, a traffic light is a system that consists of multiple subsystems, such as the different traffic lights, that work concurrently.

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  • Pyramid (image processing)

    Pyramid (image processing)

    Pyramid, or pyramid representation, is a type of multi-scale signal representation developed by the computer vision, image processing and signal processing communities, in which a signal or an image is subject to repeated smoothing and subsampling. Pyramid representation is a predecessor to scale-space representation and multiresolution analysis. == Pyramid generation == There are two main types of pyramids: lowpass and bandpass. A lowpass pyramid is made by smoothing the image with an appropriate smoothing filter and then subsampling the smoothed image, usually by a factor of 2 along each coordinate direction. The resulting image is then subjected to the same procedure, and the cycle is repeated multiple times. Each cycle of this process results in a smaller image with increased smoothing, but with decreased spatial sampling density (that is, decreased image resolution). If illustrated graphically, the entire multi-scale representation will look like a pyramid, with the original image on the bottom and each cycle's resulting smaller image stacked one atop the other. A bandpass pyramid is made by forming the difference between images at adjacent levels in the pyramid and performing image interpolation between adjacent levels of resolution, to enable computation of pixelwise differences. == Pyramid generation kernels == A variety of different smoothing kernels have been proposed for generating pyramids. Among the suggestions that have been given, the binomial kernels arising from the binomial coefficients stand out as a particularly useful and theoretically well-founded class. Thus, given a two-dimensional image, we may apply the (normalized) binomial filter (1/4, 1/2, 1/4) typically twice or more along each spatial dimension and then subsample the image by a factor of two. This operation may then proceed as many times as desired, leading to a compact and efficient multi-scale representation. If motivated by specific requirements, intermediate scale levels may also be generated where the subsampling stage is sometimes left out, leading to an oversampled or hybrid pyramid. With the increasing computational efficiency of CPUs available today, it is in some situations also feasible to use wider supported Gaussian filters as smoothing kernels in the pyramid generation steps. === Gaussian pyramid === In a Gaussian pyramid, subsequent images are weighted down using a Gaussian average (Gaussian blur) and scaled down. Each pixel containing a local average corresponds to a neighborhood pixel on a lower level of the pyramid. This technique is used especially in texture synthesis. === Laplacian pyramid === A Laplacian pyramid is very similar to a Gaussian pyramid but saves the difference image of the blurred versions between each levels. Only the smallest level is not a difference image to enable reconstruction of the high resolution image using the difference images on higher levels. This technique can be used in image compression. === Steerable pyramid === A steerable pyramid, developed by Simoncelli and others, is an implementation of a multi-scale, multi-orientation band-pass filter bank used for applications including image compression, texture synthesis, and object recognition. It can be thought of as an orientation selective version of a Laplacian pyramid, in which a bank of steerable filters are used at each level of the pyramid instead of a single Laplacian or Gaussian filter. == Applications of pyramids == === Alternative representation === In the early days of computer vision, pyramids were used as the main type of multi-scale representation for computing multi-scale image features from real-world image data. More recent techniques include scale-space representation, which has been popular among some researchers due to its theoretical foundation, the ability to decouple the subsampling stage from the multi-scale representation, the more powerful tools for theoretical analysis as well as the ability to compute a representation at any desired scale, thus avoiding the algorithmic problems of relating image representations at different resolution. Nevertheless, pyramids are still frequently used for expressing computationally efficient approximations to scale-space representation. === Detail manipulation === Levels of a Laplacian pyramid can be added to or removed from the original image to amplify or reduce detail at different scales. However, detail manipulation of this form is known to produce halo artifacts in many cases, leading to the development of alternatives such as the bilateral filter. Some image compression file formats use the Adam7 algorithm or some other interlacing technique. These can be seen as a kind of image pyramid. Because those file format store the "large-scale" features first, and fine-grain details later in the file, a particular viewer displaying a small "thumbnail" or on a small screen can quickly download just enough of the image to display it in the available pixels—so one file can support many viewer resolutions, rather than having to store or generate a different file for each resolution.

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  • Top 10 AI Pair Programmers Compared (2026)

    Top 10 AI Pair Programmers Compared (2026)

    Looking for the best AI pair programmer? An AI pair programmer is software that uses machine learning to help you get more done — it can save you hours every week by automating repetitive work. Most options offer a generous free tier, with paid plans unlocking higher limits, faster processing, and team features. Whether you are a beginner or a pro, the right AI pair programmer 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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  • Is an AI Content Generator Worth It in 2026?

    Is an AI Content Generator Worth It in 2026?

    Trying to pick the best AI content generator? An AI content generator 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 content generator 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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  • Ginger Software

    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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  • Clarizen

    Clarizen

    Clarizen, Inc. is a project management software and collaborative work management company. Clarizen uses a software as a service business model. Clarizen's features include attaching CAD drawings to a project, moving between the project view and design view and an E-mail reporting feature. In May 2014 Clarizen raised $35 million in venture capital investment led by Goldman Sachs. The round brought investment to $90 million. Previous investors, including Benchmark Capital, Carmel Ventures, DAG Ventures, Opus Capital and Vintage Investment Partners participated. In April 2020, Clarizen appointed Matt Zilli as its new CEO, replacing Boaz Chalamish who is appointed as Executive Chairman. In January 2021 Clarizen was acquired by Planview.

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  • Dan Klein

    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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  • Eurotra

    Eurotra

    Eurotra was a machine translation project established and funded by the European Commission from 1978 until 1992. == History == In 1976, the European Commission started using the commercially developed machine translation system SYSTRAN with a plan to make it work for further languages than originally developed for (Russian-English and English-French), which however turned out to be difficult. This and the potential in existing systems within European research center, led to the decision in 1978 to start the project Eurotra, first through a preparatory Eurotra Coordination Group. Four years later, the European Commission and coordination group gained the approval of the European Parliament. The goal of the project as to create machine translation system for the official languages of the European Community, which at the time were Danish, Dutch, German, English, French, Italian, later including Greek, Spanish and Portuguese. However, as time passed, expectations became tempered; "Fully Automatic High Quality Translation" was not a reasonably attainable goal. The true character of Eurotra was eventually acknowledged to be in fact pre-competitive research rather than prototype development. The project was motivated by one of the founding principles of the EU: that all citizens had the right to read any and all proceedings of the Commission in their own language. As more countries joined, this produced a combinatorial explosion in the number of language pairs involved, and the need to translate every paper, speech and even set of meeting minutes produced by the EU into the other eight languages meant that translation rapidly became the overwhelming component in the administrative budget. To solve this problem Eurotra was devised. The project was unusual in that rather than consisting of a single research team, it had member groups distributed around the member countries, organised along language rather than national lines (for example, groups in Leuven and Utrecht worked closely together), and the secretariat was based at the European Commission in Luxembourg. The actual design of the project was unusual as MT projects go. Older systems, such as SYSTRAN, were heavily dictionary-based, with minor support for rearranging word order. More recent systems have often worked on a probabilistic approach, based on parallel corpora. Eurotra addressed the constituent structure of the text to be translated, going through first a syntactic parse followed by a second parse to produce a dependency structure followed by a final parse with a third grammar to produce what was referred to internally as Intermediate Representation (IR). Since all three modules were implemented as Prolog programs, it would then in principle be possible to put this structure backwards through the corresponding modules for another language to produce a translated text in any of the other languages. However, in practice this was not in fact how language pairs were implemented. The first "live" translation occupied a 4Mb Microvax running Ultrix and C-Prolog for a complete weekend some time in early 1987. The sentence, translated from English into Danish, was "Japan makes computers". The main problem faced by the system was the generation of so-called "Parse Forests" - often a large number of different grammar rules could be applied to any particular phrase, producing hundreds, even thousands of (often identical) parse trees. This used up huge quantities of computer store, slowing the whole process down unnecessarily. While Eurotra never delivered a "working" MT system, the project made a far-reaching long-term impact on the nascent language industries in European member states, in particular among the southern countries of Greece, Italy, Spain, and Portugal. There is at least one commercial MT system (developed by an academic/commercial consortium in Denmark) derived from Eurotra technology.

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