AI Art Enhancer

AI Art Enhancer — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

  • Escapex

    Escapex

    Escapex, stylized as escapex, was a mobile app developer specializing in white-label fan engagement apps for celebrities. It was founded by Sephi Shapira in 2014 and has raised $18 million in funding. It allows celebrities to reach fans directly, as well as receiving revenue from fans through its freemium model. == Overview == Shapira is Israeli and previously founded Interchan and MassiveImpact. He graduated from Ben-Gurion University of the Negev. The company has raised $18 million in funding. Its 2018 revenue was $5.5 million. In 2016, the company had 57 employees split between Tel Aviv and New York City. The company's General Manager is Joe Cuello, formerly an executive at MTV, then Chief Creative Officer at TuneCore. Their director of social engagement is Rafe Lopresti-Oakes. A press release from the company described the service as having a "proprietary loyalty program" which allows "monetization of social engagement through e-commerce and in-app advertising". App launches typically offered a contest for one fan to meet the celebrity. The app also allows Escapex to collect and monetize user profiles for advertising. The New York Times described the concept of Escapex, musing, "If people love you, why not make money from them?". == Notable apps == The company has created over 350 applications, including: Enrique Iglesias, June 2016 or earlier Akon, June 2016 or earlier Ricky Martin, June 2016 or earlier Rohan Marley and the Bob Marley estate, February 2017 Marc Anthony, March 2017 Prince Royce, March 2017 Jeremy Renner, March 2017, making over $35,000 per month in April 2019 Galen Gering, June 2017 Yandel, June 2017 Greg Vaughan, June 2017 Jason Thompson, June 2017 Niecy Nash, September 2017 Tyler Posey, September 2017 Osric Chau, January 2018 Chris D'Elia Alessandra Ambrosio, making over $35,000 per month in April 2019 Abigail Ratchford, making over $35,000 per month in April 2019 Amber Rose, making over $35,000 per month in April 2019 Dita Von Teese Tommy Chong === Bollywood stars === Escapex has a large roster of Bollywood celebrities, including: Sunny Leone, December 2016 Remo D'Souza, January 2017 Amy Jackson, March 2017 Kajal Aggarwal, March 2017 Nargis Fakhri, April 2017 Disha Patani Sonam Kapoor Salman Khan == Jeremy Renner app == Renner released a mobile app called "Jeremy Renner" (Android) and "Jeremy Renner Official" (iOS) in March 2017. FastCompany wrote extensively about Renner's app in April 2019, calling it "a surprising new kind of social media". The Ringer's Kate Knibbs, explaining how self-referential the app is, summarized it stating "Jeremy Renner’s Jeremy Renner app is the Jeremy Renner of apps." The community developed to include memes, selfies, and a "Happy Rennsday" event on Wednesdays. As early as October 2017 there were claims of censorship, bullying, and "contest-rigging". In September 2019, comedian Stefan Heck wrote about discovering that any replies through the app would appear as if they were sent by Renner himself in push notifications. Heck wrote about notifications making it appear Renner was a big enthusiast of "porno"; other users made it appear Renner was a big fan of Casey Anthony. Renner had to ask Escapex to shut down the app the following day, stating "The app has jumped the shark. Literally." In September 2020, comedian/writer Caroline Goldfarb and actress Sarah Ramos launched The Renner Files podcast, a six-part series investigating the Jeremy Renner app.

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  • Dr.Fill

    Dr.Fill

    Dr.Fill is a computer program that solves American-style crossword puzzles. It was developed by Matt Ginsberg and described by Ginsberg in an article in the Journal of Artificial Intelligence Research. Ginsberg claims in that article that Dr.Fill is among the top fifty crossword solvers in the world. == History == Dr.Fill participated in the 2012 American Crossword Puzzle Tournament, finishing 141st of approximately 650 entrants with a total score of just over 10,000 points. The appearance led to a variety of descriptions of Dr.Fill in the popular press, including The Economist, the San Francisco Chronicle and Gizmodo. A description of Dr.Fill appeared on the front page of the March 17, 2012 New York Times. Dr.Fill's score in 2013 improved to 10,550, which would have earned it 92nd place. Videos of the program solving the problems from the tournament are available on YouTube. The score in 2014 improved further to 10,790, which would have tied for 67th place. A video of the program solving the first six puzzles from that tournament, together with a talk given by Ginsberg describing its performance, can be found on YouTube. Dr.Fill has largely continued to improve since the 2014 event. In 2015, it scored 10,920 points and finished in 55th place. In 2016, it scored 11,205 points and finished in 41st place. In 2017, it scored 11,795 and finished in 11th place. In 2018, it scored 10,740 points, dropping to 78th place. Dr.Fill returned to "form" in 2019, once again scoring 11,795 and finishing in 14th place. The 2020 ACPT was cancelled due to COVID-19, and Dr.Fill participated as a non-competitor in the Boswords tournament instead. The program outperformed the humans, scoring 11,218 points (fast solves with a total of one mistake) while the best scoring human scored 10,994 points (slower solves but no mistakes). The 2021 ACPT was virtual, again due to COVID-19. The Dr.Fill effort was joined by the Berkeley NLP Group, creating a hybrid system named Berkeley Crossword Solver, and Dr.Fill won the main event, scoring 12,825 points with Erik Agard, the highest scoring human, scoring 12,810 points. The tournament was won by Tyler Hinman (12,760 points), who completed the championship puzzle perfectly in three minutes. Dr.Fill also completed that puzzle perfectly, but in 49 seconds. After winning the tournament, Ginsberg announced on August 8, 2021, that both he and Dr.Fill would be retiring from crosswords. == Algorithm == As described by Ginsberg, Dr.Fill works by converting a crossword to a weighted constraint satisfaction problem and then attempting to maximize the probability that the fill is correct. Probabilities for individual words or phrases in the puzzle are computed using relatively simple statistical techniques based on features such as previous appearances of the clue, number of Google hits for the fill, and so on. In doing this, Dr.Fill is attempting to solve a problem similar to that tackled by the Jeopardy!-playing program Watson; Dr.Fill runs on a laptop instead of a supercomputer and Ginsberg remarks that Watson is far more effective than Dr.Fill at solving this portion of the problem. Instead of computational horsepower, Dr.Fill relies on the constraints provided by crossing words to refine its answers. A variety of techniques from artificial intelligence are applied to attempt to find the most likely fill. These include a small amount of lookahead, limited discrepancy search, and postprocessing. Ginsberg remarks that postprocessing was chosen over branch and bound because the two techniques are mutually incompatible and postprocessing was found to be more effective in this domain.

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  • Transaction logic

    Transaction logic

    Transaction Logic is an extension of predicate logic that accounts in a clean and declarative way for the phenomenon of state changes in logic programs and databases. This extension adds connectives specifically designed for combining simple actions into complex transactions and for providing control over their execution. The logic has a natural model theory and a sound and complete proof theory. Transaction Logic has a Horn clause subset, which has a procedural as well as a declarative semantics. The important features of the logic include hypothetical and committed updates, dynamic constraints on transaction execution, non-determinism, and bulk updates. In this way, Transaction Logic is able to declaratively capture a number of non-logical phenomena, including procedural knowledge in artificial intelligence, active databases, and methods with side effects in object databases. Transaction Logic was originally proposed in 1993 by Anthony Bonner and Michael Kifer and later described in more detail in An Overview of Transaction Logic and Logic Programming for Database Transactions. The most comprehensive description appears in Bonner & Kifer's technical report from 1995. In later years, Transaction Logic was extended in various ways, including concurrency, defeasible reasoning, partially defined actions, and other features. In 2013, the original paper on Transaction Logic has won the 20-year Test of Time Award of the Association for Logic Programming as the most influential paper from the proceedings of ICLP 1993 conference in the preceding 20 years. == Examples == === Graph coloring === Here tinsert denotes the elementary update operation of transactional insert. The connective ⊗ is called serial conjunction. === Pyramid stacking === The elementary update tdelete represents the transactional delete operation. === Hypothetical execution === Here <> is the modal operator of possibility: If both action1 and action2 are possible, execute action1. Otherwise, if only action2 is possible, then execute it. === Dining philosophers === Here | is the logical connective of parallel conjunction of Concurrent Transaction Logic. == Implementations == A number of implementations of Transaction Logic exist: The original implementation. An implementation of Concurrent Transaction Logic. Transaction Logic enhanced with tabling. An implementation of Transaction Logic has also been incorporated as part of the Flora-2 knowledge representation and reasoning system. All these implementations are open source.

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  • PauseAI

    PauseAI

    PauseAI is a global political movement founded in the Netherlands with the stated aim of achieving global coordination to stop the development of more powerful general artificial intelligence systems, at least until it is known how to build them safely, and keep them under democratic control. The movement was established in Utrecht in May 2023 by software entrepreneur Joep Meindertsma. == Proposal == PauseAI's stated goal is to "implement a temporary pause on the training of the most powerful general AI systems". Their website lists some proposed steps to achieve this goal: Set up an international AI safety agency, similar to the IAEA. Only allow training of general AI systems if their safety can be guaranteed. Only allow deployment of models after no dangerous capabilities are present. == Background == During the late 2010s and early 2020s, a rapid improvement in the capabilities of artificial intelligence models known as the AI boom was underway, which included the release of large language model GPT-3, its more powerful successor GPT-4, and image generation models Midjourney and DALL-E. This led to an increased concern about the risks of advanced AI, causing the Future of Life Institute to release an open letter calling for "all AI labs to immediately pause for at least six months the training of AI systems more powerful than GPT-4". The letter was signed by thousands of AI researchers and industry CEOs such as Yoshua Bengio, Stuart Russell, and Elon Musk. == History == Founder Joep Meindertsma first became worried about the existential risk from artificial intelligence after reading philosopher Nick Bostrom's 2014 book Superintelligence: Paths, Dangers, Strategies. He founded PauseAI in May 2023, putting his job as the CEO of a software firm on hold. Meindertsma claimed the rate of progress in AI alignment research is lagging behind the progress in AI capabilities, and said "there is a chance that we are facing extinction in a short frame of time". As such, he felt an urge to organise people to act. PauseAI's first public action was to protest in front of Microsoft's Brussels lobbying office in May 2023 during an event on artificial intelligence. In November of the same year, they protested outside the inaugural AI Safety Summit at Bletchley Park. The Bletchley Declaration that was signed at the summit, which acknowledged the potential for catastrophic risks stemming from AI, was perceived by Meindertsma to be a small first step. But, he argued "binding international treaties" are needed. He mentioned the Montreal Protocol and treaties banning blinding laser weapons as examples of previous successful global agreements. In February 2024, members of PauseAI gathered outside OpenAI's headquarters in San Francisco, in part due to OpenAI changing its usage policy that prohibited the use of its models for military purposes. On 13 May 2024, protests were held across thirteen countries before the AI Seoul Summit, including the United States, the United Kingdom, Brazil, Germany, Australia, and Norway. Meindertserma said that those attending the summit "need to realize that they are the only ones who have the power to stop this race". Protesters in San Francisco held signs reading "When in doubt, pause", and "Quit your job at OpenAI. Trust your conscience". Jan Leike, head of the "superalignment" team at OpenAI, resigned two days later due to his belief that "safety culture and processes [had] taken a backseat to shiny products".

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  • IMazing

    IMazing

    iMazing is mobile device management software that allows users to transfer files and data between iOS devices (iPhone, iPad and iPod Touch) and macOS or Windows computers, in addition to many other features beyond the scope of what Apple's own tools enable. == History == Developed by DigiDNA, iMazing was initially released in 2008 as DiskAid, enabling users to transfer data and files from the iPhone or iPod Touch to Mac or Windows computers. DiskAid was renamed iMazing in 2014. Version 2.0 was released on September 13, 2016. In August 2021, version 2.14 of iMazing added a spyware detection feature. The feature is based on Amnesty International’s Mobile Verification Toolkit to detect Pegasus Spyware following the publication of Pegasus Project. == Description == With iMazing, an iPhone or iPad can be used similarly to an external hard drive. It performs tasks that iTunes doesn’t offer, including incremental backups of iOS devices, browsing and exporting text and voicemail messages, managing apps, encryption, and migrating data from an old phone to a new one. The menu bar app iMazing Mini enables automatic, wireless and encrypted backups of iPhones. The iMazing HEIC Converter is a free desktop app for Mac and PC that lets users convert photos from HEIC format to JPG or PNG.

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  • ProVisual Engine

    ProVisual Engine

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

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  • OpenVX

    OpenVX

    OpenVX is an open, royalty-free standard for cross-platform acceleration of computer vision applications. It is designed by the Khronos Group to facilitate portable, optimized and power-efficient processing of methods for vision algorithms. This is aimed for embedded and real-time programs within computer vision and related scenarios. It uses a connected graph representation of operations. == Overview == OpenVX specifies a higher level of abstraction for programming computer vision use cases than compute frameworks such as OpenCL. The high level makes the programming easy and the underlying execution will be efficient on different computing architectures. This is done while having a consistent and portable vision acceleration API. OpenVX is based on a connected graph of vision nodes that can execute the preferred chain of operations. It uses an opaque memory model, allowing to move image data between the host (CPU) memory and accelerator, such as GPU memory. As a result, the OpenVX implementation can optimize the execution through various techniques, such as acceleration on various processing units or dedicated hardware. This architecture facilitates applications programmed in OpenVX on different systems with different power and performance, including battery-sensitive, vision-enabled, wearable displays. OpenVX is complementary to the open source vision library OpenCV. OpenVX in some applications offers a better optimized graph management than OpenCV. == History == OpenVX 1.0 specification was released in October 2014. OpenVX sample implementation was released in December 2014. OpenVX 1.1 specification was released on May 2, 2016. OpenVX 1.2 was released on May 1, 2017. Updated OpenVX adopters program and OpenVX 1.2 conformance test suite was released on November 21, 2017. OpenVX 1.2.1 was released on November 27, 2018. OpenVX 1.3 was released on October 22, 2019. == Implementations, frameworks and libraries == AMD MIVisionX Archived 2019-08-05 at the Wayback Machine - for AMD's CPUs and GPUs. Cadence - for Cadence Design Systems's Tensilica Vision DSPs. Imagination - for Imagination Technologies's PowerVR GPUs Synopsys - for Synopsys' DesignWare EV Vision Processors Texas Instruments’ OpenVX (TIOVX) - for Texas Instruments’ Jacinto™ ADAS SoCs. NVIDIA VisionWorks - for CUDA-capable Nvidia GPUs and SoCs. OpenVINO - for Intel's CPUs, GPUs, VPUs, and FPGAs.

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  • MindSpore

    MindSpore

    MindSpore is an open-source software framework for deep learning, machine learning and artificial intelligence developed by Huawei. == Overview == MindSpore provides support for Python by allowing users to define models, control flow, and custom operators using native Python syntax. Unlike graph-based frameworks that require users to learn DSL or complex APIs, MindSpore adopts a source-to-source (S2S) automatic differentiation approach, allowing Python code to be automatically transformed into optimized computational graphs. It has support for custom OpenHarmony-based HarmonyOS NEXT single core framework system built for HarmonyOS, includes an AI system stack that comes with Huawei's built LLM model called PanGu-Σ with full MindSpore framework support. Alongside, OpenHarmony Native device-side AI support for training interface and ArkTS programming interface for its NNRt (Neural Network Runtime) backend configurations via MindSpore Lite AI framework codebase introduced in API 11 Beta 1 of OpenHarmony 4.1. MindSpore platform runs on Ascend AI chips and Kirin alongside other HiSilicon NPU chips. CANN (Compute Architecture of Neural Networks), heterogeneous computing architecture for AI developed by Huawei. With CANN backend in OpenCV DNN, giving developers ability to run created AI models on the Ascend, Kirin and other HiSilicon NPU enabled chips. It supports cross platform development such as Android, iOS, Windows, global OpenHarmony-based distro, Eclipse Oniro, Linux-based EulerOS alongside OpenEuler Huawei's server OS platforms, macOS and Linux. == History == On April 24, 2024, Huawei's MindSpore 2.3.RC1 was released to open source community with Foundation Model Training, Full-Stack Upgrade of Foundation Model Inference, Static Graph Optimization, IT Features and new MindSpore Elec MT (MindSpore-powered magnetotelluric) Intelligent Inversion Model.

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  • Manifold regularization

    Manifold regularization

    In machine learning, manifold regularization is a technique for using the shape of a dataset to constrain the functions that should be learned on that dataset. In many machine learning problems, the data to be learned do not cover the entire input space. For example, a facial recognition system may not need to classify any possible image, but only the subset of images that contain faces. The technique of manifold learning assumes that the relevant subset of data comes from a manifold, a mathematical structure with useful properties. The technique also assumes that the function to be learned is smooth: data with different labels are not likely to be close together, and so the labeling function should not change quickly in areas where there are likely to be many data points. Because of this assumption, a manifold regularization algorithm can use unlabeled data to inform where the learned function is allowed to change quickly and where it is not, using an extension of the technique of Tikhonov regularization. Manifold regularization algorithms can extend supervised learning algorithms in semi-supervised learning and transductive learning settings, where unlabeled data are available. The technique has been used for applications including medical imaging, geographical imaging, and object recognition. == Manifold regularizer == === Motivation === Manifold regularization is a type of regularization, a family of techniques that reduces overfitting and ensures that a problem is well-posed by penalizing complex solutions. In particular, manifold regularization extends the technique of Tikhonov regularization as applied to Reproducing kernel Hilbert spaces (RKHSs). Under standard Tikhonov regularization on RKHSs, a learning algorithm attempts to learn a function f {\displaystyle f} from among a hypothesis space of functions H {\displaystyle {\mathcal {H}}} . The hypothesis space is an RKHS, meaning that it is associated with a kernel K {\displaystyle K} , and so every candidate function f {\displaystyle f} has a norm ‖ f ‖ K {\displaystyle \left\|f\right\|_{K}} , which represents the complexity of the candidate function in the hypothesis space. When the algorithm considers a candidate function, it takes its norm into account in order to penalize complex functions. Formally, given a set of labeled training data ( x 1 , y 1 ) , … , ( x ℓ , y ℓ ) {\displaystyle (x_{1},y_{1}),\ldots ,(x_{\ell },y_{\ell })} with x i ∈ X , y i ∈ Y {\displaystyle x_{i}\in X,y_{i}\in Y} and a loss function V {\displaystyle V} , a learning algorithm using Tikhonov regularization will attempt to solve the expression arg min f ∈ H 1 ℓ ∑ i = 1 ℓ V ( f ( x i ) , y i ) + γ ‖ f ‖ K 2 {\displaystyle {\underset {f\in {\mathcal {H}}}{\arg \!\min }}{\frac {1}{\ell }}\sum _{i=1}^{\ell }V(f(x_{i}),y_{i})+\gamma \left\|f\right\|_{K}^{2}} where γ {\displaystyle \gamma } is a hyperparameter that controls how much the algorithm will prefer simpler functions over functions that fit the data better. Manifold regularization adds a second regularization term, the intrinsic regularizer, to the ambient regularizer used in standard Tikhonov regularization. Under the manifold assumption in machine learning, the data in question do not come from the entire input space X {\displaystyle X} , but instead from a nonlinear manifold M ⊂ X {\displaystyle M\subset X} . The geometry of this manifold, the intrinsic space, is used to determine the regularization norm. === Laplacian norm === There are many possible choices for the intrinsic regularizer ‖ f ‖ I {\displaystyle \left\|f\right\|_{I}} . Many natural choices involve the gradient on the manifold ∇ M {\displaystyle \nabla _{M}} , which can provide a measure of how smooth a target function is. A smooth function should change slowly where the input data are dense; that is, the gradient ∇ M f ( x ) {\displaystyle \nabla _{M}f(x)} should be small where the marginal probability density P X ( x ) {\displaystyle {\mathcal {P}}_{X}(x)} , the probability density of a randomly drawn data point appearing at x {\displaystyle x} , is large. This gives one appropriate choice for the intrinsic regularizer: ‖ f ‖ I 2 = ∫ x ∈ M ‖ ∇ M f ( x ) ‖ 2 d P X ( x ) {\displaystyle \left\|f\right\|_{I}^{2}=\int _{x\in M}\left\|\nabla _{M}f(x)\right\|^{2}\,d{\mathcal {P}}_{X}(x)} In practice, this norm cannot be computed directly because the marginal distribution P X {\displaystyle {\mathcal {P}}_{X}} is unknown, but it can be estimated from the provided data. === Graph-based approach of the Laplacian norm === When the distances between input points are interpreted as a graph, then the Laplacian matrix of the graph can help to estimate the marginal distribution. Suppose that the input data include ℓ {\displaystyle \ell } labeled examples (pairs of an input x {\displaystyle x} and a label y {\displaystyle y} ) and u {\displaystyle u} unlabeled examples (inputs without associated labels). Define W {\displaystyle W} to be a matrix of edge weights for a graph, where W i j {\displaystyle W_{ij}} is a similarity built from distance measure between the data points x i {\displaystyle x_{i}} and x j {\displaystyle x_{j}} (so that more close implies higher W i j {\displaystyle W_{ij}} ). Define D {\displaystyle D} to be a diagonal matrix with D i i = ∑ j = 1 ℓ + u W i j {\displaystyle D_{ii}=\sum _{j=1}^{\ell +u}W_{ij}} and L {\displaystyle L} to be the Laplacian matrix D − W {\displaystyle D-W} . Then, as the number of data points ℓ + u {\displaystyle \ell +u} increases, L {\displaystyle L} converges to the Laplace–Beltrami operator Δ M {\displaystyle \Delta _{M}} , which is the divergence of the gradient ∇ M {\displaystyle \nabla _{M}} . Then, if f {\displaystyle \mathbf {f} } is a vector of the values of f {\displaystyle f} at the data, f = [ f ( x 1 ) , … , f ( x l + u ) ] T {\displaystyle \mathbf {f} =[f(x_{1}),\ldots ,f(x_{l+u})]^{\mathrm {T} }} , the intrinsic norm can be estimated: ‖ f ‖ I 2 = 1 ( ℓ + u ) 2 f T L f {\displaystyle \left\|f\right\|_{I}^{2}={\frac {1}{(\ell +u)^{2}}}\mathbf {f} ^{\mathrm {T} }L\mathbf {f} } As the number of data points ℓ + u {\displaystyle \ell +u} increases, this empirical definition of ‖ f ‖ I 2 {\displaystyle \left\|f\right\|_{I}^{2}} converges to the definition when P X {\displaystyle {\mathcal {P}}_{X}} is known. === Solving the regularization problem with graph-based approach === Using the weights γ A {\displaystyle \gamma _{A}} and γ I {\displaystyle \gamma _{I}} for the ambient and intrinsic regularizers, the final expression to be solved becomes: arg min f ∈ H 1 ℓ ∑ i = 1 ℓ V ( f ( x i ) , y i ) + γ A ‖ f ‖ K 2 + γ I ( ℓ + u ) 2 f T L f {\displaystyle {\underset {f\in {\mathcal {H}}}{\arg \!\min }}{\frac {1}{\ell }}\sum _{i=1}^{\ell }V(f(x_{i}),y_{i})+\gamma _{A}\left\|f\right\|_{K}^{2}+{\frac {\gamma _{I}}{(\ell +u)^{2}}}\mathbf {f} ^{\mathrm {T} }L\mathbf {f} } As with other kernel methods, H {\displaystyle {\mathcal {H}}} may be an infinite-dimensional space, so if the regularization expression cannot be solved explicitly, it is impossible to search the entire space for a solution. Instead, a representer theorem shows that under certain conditions on the choice of the norm ‖ f ‖ I {\displaystyle \left\|f\right\|_{I}} , the optimal solution f ∗ {\displaystyle f^{}} must be a linear combination of the kernel centered at each of the input points: for some weights α i {\displaystyle \alpha _{i}} , f ∗ ( x ) = ∑ i = 1 ℓ + u α i K ( x i , x ) {\displaystyle f^{}(x)=\sum _{i=1}^{\ell +u}\alpha _{i}K(x_{i},x)} Using this result, it is possible to search for the optimal solution f ∗ {\displaystyle f^{}} by searching the finite-dimensional space defined by the possible choices of α i {\displaystyle \alpha _{i}} . === Functional approach of the Laplacian norm === The idea beyond the graph-Laplacian is to use neighbors to estimate the Laplacian. This method is akin to local averaging methods, that are known to scale poorly in high-dimensional problems. Indeed, the graph Laplacian is known to suffer from the curse of dimensionality. Luckily, it is possible to leverage expected smoothness of the function to estimate thanks to more advanced functional analysis. This method consists of estimating the Laplacian operator using derivatives of the kernel reading ∂ 1 , j K ( x i , x ) {\displaystyle \partial _{1,j}K(x_{i},x)} where ∂ 1 , j {\displaystyle \partial _{1,j}} denotes the partial derivatives according to the j-th coordinate of the first variable. This second approach to the Laplacian norm is to put in relation with meshfree methods, that contrast with the finite difference method in PDE. == Applications == Manifold regularization can extend a variety of algorithms that can be expressed using Tikhonov regularization, by choosing an appropriate loss function V {\displaystyle V} and hypothesis space H {\displaystyle {\mathcal {H}}} . Two commonly used examples are the families of support vector machines and regularized least squares algorithm

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  • Jensen Huang

    Jensen Huang

    Jen-Hsun "Jensen" Huang (Chinese: 黃仁勳; Wade–Giles: Huáng Jén-hsūn; Tâi-lô: N̂g Jîn-hun; born February 17, 1963) is a Taiwanese and American business executive and electrical engineer who is the founder, president, and CEO of Nvidia, the world's most valuable company. As of 2026, Forbes estimates his net worth at over US$200 billion, making him the seventh-wealthiest individual in the world. The son of Taiwanese immigrants, Huang spent his childhood in Taiwan and Thailand before moving to the United States, where he was a student in Kentucky and Oregon. After earning a master's degree from Stanford University, Huang launched Nvidia in 1993 from a Denny's restaurant in San Jose, California, at age 30 and has remained its president and CEO ever since. He led the company out of near-bankruptcy during the 1990s and oversaw its expansion into GPU production, high-performance computing, and artificial intelligence (AI). Under Huang, Nvidia experienced rapid growth during the AI boom, becoming the first company to reach a market capitalization of over $5 trillion in October 2025. In 2021 and 2024, Time magazine included Huang in their list of the most influential people. In 2025, he was named as one of the "Architects of AI" for Time's Person of the Year. == Early life and education == Huang was born in Taipei, Taiwan, on February 17, 1963, and moved to the southern city of Tainan as a child. He is the younger of two sons of Huang Hsing-tai, a chemical engineer at an oil refinery, and Lo Tsai-hsiu, a schoolteacher. They were a middle-class Taiwanese family that relocated often, and were native speakers of Taiwanese Hokkien. Each day, Jensen's mother randomly selected 10 words from the dictionary to teach her sons English. When he was five years old, Huang's family moved to Thailand to support his father's refinery career and remained there for approximately four years. He attended Ruamrudee International School while in Bangkok. In the late 1960s, Hsing-tai traveled from Taiwan to New York City to train under an air conditioning company and, after returning home, resolved to send his sons to the United States. At age nine, Jensen, despite not yet being able to speak English fluently, was sent by his parents to live in the United States. He and his older brother moved in 1973 to live with an uncle in Tacoma, Washington, escaping widespread social unrest in Thailand. Both Huang's aunt and uncle were recent immigrants to Washington state; they accidentally enrolled him and his brother in the Oneida Baptist Institute, a religious reform academy in Kentucky for troubled youth, mistakenly believing it to be a prestigious boarding school. In order to afford the academy's tuition, Jensen's parents sold nearly all their possessions. When he was 10 years old, Huang lived with his older brother in the Oneida boys' dormitory. Each student was expected to work every day, and his brother was assigned to perform manual labor on a nearby tobacco farm. Because he was too young to attend classes at the reform academy, Huang was educated at a separate public school—the Oneida Elementary school in Oneida, Kentucky—arriving as "an undersized Asian immigrant with long hair and heavily accented English" and was frequently bullied and beaten. In Oneida, Huang cleaned toilets every day, learned to play table-tennis, joined the swimming team, and appeared in Sports Illustrated at age 14. He taught his illiterate roommate, a "17-year-old covered in tattoos and knife scars," how to read in exchange for being taught how to bench press. In 2002, Huang said he remembered his life in Kentucky "more vividly than just about any other". Two years after Huang arrived in Oneida, his parents moved to the United States and settled in Beaverton, Oregon, after which the brothers withdrew from school in Kentucky to live back with them. As a teenager, Huang attended Aloha High School in Aloha, Oregon, where he excelled academically. He skipped two grades, graduated at age 16, and became a nationally ranked table-tennis player in addition to being a member of its mathematics, computer, and science clubs. In 1977, the school purchased an Apple II computer. Huang used the machine to play Super Star Trek, a text-based game, and to program in BASIC, creating his own version of Snake. Beginning at age 15, Huang got his first job working the graveyard shift at a local Denny's restaurant as a dishwasher, busboy, and waiter from 1978 to 1983. After high school, he chose to enroll at Oregon State University due to its low in-state tuition. He studied electrical engineering and graduated in 1984 with a bachelor's degree with highest honors. Huang later recalled, "I was the youngest kid in school, in class" and the only student who "looked like a child". Years later, while working as a microchip designer in Silicon Valley, he concurrently pursued graduate night classes at Stanford University, where he earned a master's degree in electrical engineering in 1992. == AMD and LSI Logic == After graduating from college, Huang was a microchip designer in Silicon Valley. He was recruited for positions at Texas Instruments, Advanced Micro Devices (AMD), and LSI Logic, ultimately choosing the California-based AMD due to already being familiar with the company. Huang designed AMD microprocessors while simultaneously attending Stanford and raising his two children. However, when he heard of new chip design processes at LSI Logic, Huang left AMD to assume a role as a technical officer at the LSI Corporation, working under a startup company, Sun Microsystems, where he met engineers Chris Malachowsky and Curtis Priem. LSI was in contract with Sun Microsystems and had introduced Huang to Malachowsky and Priem, who were working on a new graphics accelerator card. While the three produced the card's manufacturing process, the relationship between Malachowsky and Priem became strained as the two disputed the chip's design, leading to infighting; according to Malachowsky, they "broke every tool that LSI Logic had in their standard portfolio". In 1989, Huang, Malachowsky, and Priem finalized the accelerator, which they called the "GX graphics engine". GX was a widespread financial success; the sales of the graphics engine contributed to Sun Microsystem's revenue increasing from $262 million in 1987 to $656 million in 1990, and Huang was promoted to be the director of LSI's CoreWare, a division that manufactured chips for hardware vendors. == Nvidia == === Founding (1993) === When business began to slow for Sun Microsystems after 1990, Huang, along with Priem and Malachowsky, each resigned their jobs to pursue a venture together in making graphics chips for PC games. They initially named their new company "NVision" until Huang suggested that the company be named "Nvidia" based on the Latin word invidia, as Priem wanted competitors to turn "green with envy". They eventually dropped the "i" to honor the NV1 chip that they were then developing. The three met frequently in 1992 at a Denny's roadside diner in East San Jose to formulate a business plan. Huang chose for them to meet at Denny's due to his prior work experience at the restaurant chain and because it was "quieter than home and had cheap coffee". The three founded the company during one meeting at a breakfast booth at the diner. To formally incorporate the company, Huang found a lawyer, James Gaither of Cooley Godward, who demanded the $200 in cash in Huang's pockets to capitalize the company. After that meeting, Huang went back to Priem and Malachowsky to ask each of them for $200 for their respective shares of the company, which meant that Nvidia's initial capital was $600. On April 5, 1993, Huang personally signed Nvidia's original articles of incorporation into effect. Although he left LSI, Huang remained in good standing with the company and was able to secure funding for Nvidia from LSI's CEO, Wilfred Corrigan, who introduced Huang to venture capitalist Don Valentine. An account cited how Huang's presentation pitch went badly. Valentine, the leader of Sequoia Capital, chose to invest in Nvidia through Corrigan's support, as did Sutter Hill Ventures. The funding enabled Nvidia to begin development efforts toward its first chip and to begin paying wages for its employees. By the first day of operation, Huang was made Nvidia's president and CEO. Even though Huang, at age 30, was younger than Priem and Malachowsky, both Priem and Malachowsky believed that he was prepared to be CEO. According to Priem, "we basically deferred to Jensen on day one" and told Huang, "you're in charge of running the company—all the stuff Chris and I don't know how to do". === President and CEO (1993–present) === As of 2024, Huang has been Nvidia's chief executive for over three decades, a tenure described by The Wall Street Journal as "almost unheard of in fast-moving Silicon Valley". He owns 3.6% of Nvidia's stock, which went public in 1999. He earned US$24.6 million as CEO i

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  • OntoUML

    OntoUML

    OntoUML is a language for ontology-driven conceptual modeling. OntoUML is built as a UML extension based on the Unified Foundational Ontology. The foundations of UFO and OntoUML can be traced back to Giancarlo Guizzardi's Ph.D. thesis "Ontological foundations for structural conceptual models". In his work, he proposed a novel foundational ontology for conceptual modeling (UFO) and employed it to evaluate and re-design a fragment of the UML 2.0 metamodel for the purposes of conceptual modeling and domain ontology engineering. == Supporting tools == In 2006, Guizzardi co-founded the Ontology & Conceptual Modeling Research Group (NEMO) located at the Federal University of Espírito Santo (UFES) in Vitória city, state of Espírito Santo, Brazil. Since then, NEMO has been responsible for most of the developments in OntoUML. Several papers about ontologies and OntoUML have been authored by members of the NEMO group.

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  • Tim Houlne

    Tim Houlne

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

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  • Structural synthesis of programs

    Structural synthesis of programs

    Structural synthesis of programs (SSP) is a special form of (automatic) program synthesis that is based on propositional calculus. More precisely, it uses intuitionistic logic for describing the structure of a program in such a detail that the program can be automatically composed from pieces like subroutines or even computer commands. It is assumed that these pieces have been implemented correctly, hence no correctness verification of these pieces is needed. SSP is well suited for automatic composition of services for service-oriented architectures and for synthesis of large simulation programs. == History == Automatic program synthesis began in the artificial intelligence field, with software intended for automatic problem solving. The first program synthesizer was developed by Cordell Green in 1969. At about the same time, mathematicians including R. Constable, Z. Manna, and R. Waldinger explained the possible use of formal logic for automatic program synthesis. Practically applicable program synthesizers appeared considerably later. The idea of structural synthesis of programs was introduced at a conference on algorithms in modern mathematics and computer science organized by Andrey Ershov and Donald Knuth in 1979. The idea originated from G. Pólya’s well-known book on problem solving. The method for devising a plan for solving a problem in SSP was presented as a formal system. The inference rules of the system were restructured and justified in logic by G. Mints and E. Tyugu in 1982. A programming tool PRIZ that uses SSP was developed in the 1980s. A recent Integrated development environment that supports SSP is CoCoViLa — a model-based software development platform for implementing domain specific languages and developing large Java programs. == The logic of SSP == Structural synthesis of programs is a method for composing programs from already implemented components (e.g. from computer commands or software object methods) that can be considered as functions. A specification for synthesis is given in intuitionistic propositional logic by writing axioms about the applicability of functions. An axiom about the applicability of a function f is a logical implication X1 ∧ X2 ∧ ... ∧ Xm → Y1 ∧ Y2 ... Yn, where X1, X2, ... Xm are preconditions and Y1, Y2, ... Yn are postconditions of the application of the function f. In intuitionistic logic, the function f is called a realization of this formula. A precondition can be a proposition stating that input data exists, e.g. Xi may have the meaning “variable xi has received a value”, but it may denote also some other condition, e.g. that resources needed for using the function f are available, etc. A precondition may also be an implication of the same form as the axiom given above; then it is called a subtask. A subtask denotes a function that must be available as an input when the function f is applied. This function itself must be synthesized in the process of SSP. In this case, realization of the axiom is a higher order function, i.e., a function that uses another function as an input. For instance, the formula (state → nextState) ∧ initialState → result can specify a higher order function with two inputs and an output result. The first input is a function that has to be synthesized for computing nextState from state, and the second input is initialState. Higher order functions give generality to the SSP – any control structure needed in a synthesized program can be preprogrammed and used then automatically with a respective specification. In particular, the last axiom presented here is a specification of a complex program – a simulation engine for simulating dynamic systems on models where nextState can be computed from state of the system.

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  • Ramification problem

    Ramification problem

    In philosophy and artificial intelligence (especially, knowledge based systems), the ramification problem is concerned with the indirect consequences of an action. It might also be posed as how to represent what happens implicitly due to an action or how to control the secondary and tertiary effects of an action. It is strongly connected to, and is opposite the qualification side of, the frame problem. Limit theory helps in operational usage. For instance, in KBE derivation of a populated design (geometrical objects, etc., similar concerns apply in shape theory), equivalence assumptions allow convergence where potentially large, and perhaps even computationally indeterminate, solution sets are handled deftly. Yet, in a chain of computation, downstream events may very well find some types of results from earlier resolutions of ramification as problematic for their own algorithms.

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  • Integrated Operations in the High North

    Integrated Operations in the High North

    Integrated Operations in the High North (IOHN, IO High North or IO in the High North) is a unique collaboration project that during a four-year period starting May 2008 is working on designing, implementing and testing a Digital Platform for what in the upstream oil and gas industry is called the next or second generation of Integrated Operations. The work on the Digital platform is focussed on capture, transfer and integration of real-time data from the remote production installations to the decision makers. A risk evaluation across the whole chain is also included. The platform is based on open standards and enables a higher degree of interoperability. Requirements for the digital platform come from use cases defined within the Drilling and Completion, Reservoir and Production and Operations and Maintenance domains. The platform will subsequently be demonstrated through pilots within these three domains. The project was a sidecar initiative for Statoil’s Global Operations Data Integration Project. This was part of a very ambitious Master Plan IT (MapIT), which also included the Real Time Visualization (RTV) tender. The RTV tender aimed to be an ontology-aware information workspace for a wide range of disciplines, as per the IO Capability Stack. Additionally, the sidecar project aimed to increase the semantic web knowledge among suppliers in the industry. This new platform is considered an important enabler for safe and sustainable operations in remote, vulnerable and hazardous areas such as the High North, but the technology is clearly also applicable in more general applications. The IOHN project consortium consists of 23 participants, including operators, service providers, software vendors, technology providers, research institutions and universities. In addition, the Norwegian Defence Force is working with the project to resolve common infrastructural and interoperability challenges. The project is managed by Det Norske Veritas (DNV). Nils Sandsmark was the project manager during the initiation and start-up phase. Frédéric Verhelst took over as project manager from the beginning of 2009. Financing comes from the participants and the Research Council of Norway (RCN) for parts of the project (GOICT and AutoConRig). == Participants == The consortium consists of the following 22 participants (in alphabetical order):

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