AI For Students With Adhd

AI For Students With Adhd — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

Visual Expert

Visual Expert is a static code analysis tool, extracting design and technical information from software source code by reverse-engineering, used by programmers for software maintenance, modernization or optimization. It is designed to parse several programming languages at the same time (PL/SQL, Transact-SQL, PowerBuilder...) and analyze cross-language dependencies, in addition to each language's source code. Visual Expert checks source code against hundreds of code inspection rules for vulnerability assessment, bug fix, and maintenance issues. == Features == Cross-references exploration: Impact Analysis, E/R diagrams, call graphs, CRUD matrix, dependency graphs. Software documentation: a documentation generator produces technical documentation and low-level design descriptions. Inspect the code to detect bugs, security vulnerabilities and maintainability issues. Native integration with Jenkins. Reports on duplicate code, unused objects and methods and naming conventions. Calculates software metrics and source lines of code. Code comparison: finds differences between several versions of the same code. Performance analysis: identifies code parts that slow down the application because of their syntax - it extracts statistics about code execution from the database and combines it with the static analysis of the code. == Usage == Visual Expert is used in several contexts: Change impact analysis: evaluating the consequences of a change in the code or in a database. Avoiding negative side effects when evolving a system. Static Application Security Testing (SAST): detecting and removing security issues. Continuous Integration / Continuous Inspection : adding a static code analysis job in a CI/CD workflow to automatically verify the quality and security of a new build when it is released. Program comprehension: helping programmers understand and maintain existing code, or modernize legacy systems. Transferring knowledge of the code, from one programmer to another. Software sizing: calculating the size of an application, or a piece of code, in order to estimate development efforts. Code review: improving the code by finding and removing code smells, dead code, code causing poor performances or violations of coding conventions. == Limitations == As a static code analyzer, Visual Expert is limited to the programming languages supported by its code parsers - Oracle PL/SQL, SQL Server Transact-SQL, PowerBuilder. A preliminary reverse engineering is required. Visual Expert does it automatically, but its duration depends on the size of the code parsed. Users must wait for the parsing completion prior to using the features, or schedule it in advance. They must also allocate sufficient hardware resources to support their volume of code. Visual Expert is based on a client/server architecture: the code analysis is running on a Windows PC - preferably a server. The information extracted from the code is stored in a RDBMS, communicating with a client application installed on the programmer's computer - no web client is available. This requires that the code, the parsers, the RDBMS and the programmers’ computers are connected to the same LAN or VPN. == History == 1995- 1998 - Prog and Doc - Initial version distributed on the French market 2001 - Visual Expert 4.5 2003 - Visual Expert 5 2007 - Visual Expert 5.7 2010 - Visual Expert 6.0 2015 - Visual Expert 2015 - Server component added to schedule code analyses 2016 - Visual Expert 2016 - Oracle PL/SQL code parser, code inventory (lines of code, number of objects…) 2017 - Visual Expert 2017 - SQL Server T-SQL code parser, Code comparison, CRUD matrix 2018 - Visual Expert 2018 - DB Code Performance Analysis, integration with TFS 2019 - Visual Expert 2019 - Generation of E/R diagrams from the code 2020 - Visual Expert 2020 - Object dependency matrix, naming consistency verification, integration with GIT and SVN 2021 - Visual Expert 2021 - Continuous Code Inspection, integration with Jenkins 2022 - Visual Expert 2022 - Support for cloud-based repositories and large volumes of code 2023 - Visual Expert 2023 - Performance tuning for PowerBuilder 2024 - Visual Expert 2024 - New web UI to simplify deployment and use among large teams. 2025 - Visual Expert 2025 - AI-based features to explain code, generate comments, and optimize queries
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Jürgen Schmidhuber

Jürgen Schmidhuber (born 17 January 1963) is a German computer scientist noted for his work in the field of artificial intelligence, specifically artificial neural networks. He has been described by media outlets as a leading pioneer of modern artificial intelligence. He is a scientific director of the Dalle Molle Institute for Artificial Intelligence Research in Switzerland. He is also director of the Artificial Intelligence Initiative and professor of the Computer Science program in the Computer, Electrical, and Mathematical Sciences and Engineering (CEMSE) division at the King Abdullah University of Science and Technology (KAUST) in Saudi Arabia. He is best known for his work on long short-term memory (LSTM), a type of neural network architecture which was the dominant technique for various natural language processing tasks in research and commercial applications in the 2010s. He also introduced principles of dynamic neural networks, meta-learning, generative adversarial networks and linear transformers, all of which are widespread in modern AI. == Career == Schmidhuber completed his undergraduate (1987) and PhD (1991) studies at the Technical University of Munich in Munich, Germany. His PhD advisors were Wilfried Brauer and Klaus Schulten. He taught there from 2004 until 2009. From 2009 to 2021, he was a professor of artificial intelligence at the Università della Svizzera Italiana in Lugano, Switzerland. He has served as the director of Dalle Molle Institute for Artificial Intelligence Research (IDSIA), a Swiss AI lab, since 1995. Since 2021, he has also been the director of the AI Initiative at the King Abdullah University of Science and Technology (KAUST). In 2014, Schmidhuber formed a company, NNAISENSE, to work on commercial applications of artificial intelligence in fields such as finance, heavy industry and self-driving cars. Sepp Hochreiter, Jaan Tallinn, and Marcus Hutter are advisers to the company. Sales were under US$11 million in 2016; however, Schmidhuber states that the current emphasis is on research and not revenue. NNAISENSE raised its first round of capital funding in January 2017. Schmidhuber's overall goal is to create an all-purpose AI by training a single AI in sequence on a variety of narrow tasks, but as of 2026 he has said that the focus of NNAISENSE has shifted from artificial general intelligence to asset management. == Research == In the 1980s, backpropagation did not work well for deep learning with long credit assignment paths in artificial neural networks. To overcome this problem, Schmidhuber (1991) proposed a hierarchy of recurrent neural networks (RNNs) pre-trained one level at a time by self-supervised learning. It uses predictive coding to learn internal representations at multiple self-organizing time scales, facilitating downstream deep learning. The RNN hierarchy can be collapsed into a single RNN, by distilling a higher level chunker network into a lower level automatizer network. In 1993, a chunker solved a deep learning task whose depth exceeded 1000. In 1991, Schmidhuber published adversarial neural networks that contest with each other in the form of a zero-sum game, where one network's gain is the other network's loss. The first network is a generative model that models a probability distribution over output patterns. The second network learns by gradient descent to predict the reactions of the environment to these patterns. This was called "artificial curiosity". In 2014, this principle was used in the creation of the generative adversarial network, which Schmidhuber describes as a special case of artificial curiosity where the environmental reaction is 1 or 0 depending on whether the first network's output is in a given set. Schmidhuber supervised the 1991 diploma thesis of his student Sepp Hochreiter which he considered "one of the most important documents in the history of machine learning". It studied the neural history compressor and analyzed and overcame the vanishing gradient problem. This led to the creation of long short-term memory (LSTM), a type of recurrent neural network. The name LSTM was introduced in a tech report in 1995, leading to the most cited LSTM publication, published in 1997 and co-authored by Hochreiter and Schmidhuber. The standard LSTM architecture was introduced in 2000 by Felix Gers, Schmidhuber, and Fred Cummins. Today's "vanilla LSTM" using backpropagation through time was published with his student Alex Graves in 2005, and its connectionist temporal classification (CTC) training algorithm in 2006. CTC was applied to end-to-end speech recognition with LSTM. In 2014, the state of the art was training “very deep neural network” with 20 to 30 layers. Stacking too many layers led to a steep reduction in training accuracy, known as the "degradation" problem. In May 2015, Rupesh Kumar Srivastava, Klaus Greff, and Schmidhuber used LSTM principles to create the highway network, a feedforward neural network with hundreds of layers, much deeper than previous networks. In Dec 2015, the residual neural network (ResNet) was published, which is a variant of the highway network. In 1992, Schmidhuber published fast weights programmer, an alternative to recurrent neural networks. It has a slow feedforward neural network that learns by gradient descent to control the fast weights of another neural network through outer products of self-generated activation patterns, and the fast weights network itself operates over inputs. This was later shown to be equivalent to the unnormalized linear transformer. In 2011, Schmidhuber's team at IDSIA with his postdoc Dan Ciresan also achieved dramatic speedups of convolutional neural networks (CNNs) using graphics processing units (GPUs), based on CNN designs introduced much earlier by Kunihiko Fukushima. An earlier CNN on GPU by Chellapilla et al. (2006) was 4 times faster than an equivalent implementation on CPU. The deep CNN of Dan Ciresan et al. (2011) at IDSIA was 60 times faster and achieved the first superhuman performance in a computer vision contest in August 2011. Between 15 May 2011 and 10 September 2012, these CNNs won four more image competitions and improved the state of the art on multiple image benchmarks. The approach has become central to the field of computer vision. == Credit disputes == Schmidhuber has controversially argued that he and other researchers have been denied adequate recognition for their contribution to the field of deep learning, in favour of Geoffrey Hinton, Yoshua Bengio and Yann LeCun, who shared the 2018 Turing Award for their work in deep learning. He wrote a "scathing" 2015 article arguing that Hinton, Bengio and LeCun "heavily cite each other" but "fail to credit the pioneers of the field". In a statement to the New York Times, Yann LeCun wrote that "Jürgen is manically obsessed with recognition and keeps claiming credit he doesn't deserve for many, many things... It causes him to systematically stand up at the end of every talk and claim credit for what was just presented, generally not in a justified manner." Schmidhuber replied that LeCun did this "without any justification, without providing a single example", and published details of numerous priority disputes with Hinton, Bengio and LeCun. The term "schmidhubered" has been jokingly used in the AI community to describe Schmidhuber's habit of publicly challenging the originality of other researchers' work, a practice seen by some in the AI community as a "rite of passage" for young researchers. Some suggest that Schmidhuber's significant accomplishments have been underappreciated due to his confrontational personality. == Recognition == Schmidhuber received the Helmholtz Award of the International Neural Network Society in 2013, and the Neural Networks Pioneer Award of the IEEE Computational Intelligence Society in 2016 for "pioneering contributions to deep learning and neural networks." He is a member of the European Academy of Sciences and Arts. He has been referred to as the "father of modern AI", the "father of generative AI", and the "father of deep learning". Schmidhuber himself, however, has called Alexey Grigorevich Ivakhnenko the "father of deep learning", and gives credit to many even earlier AI pioneers. The New York Times ran a profile under the headline "When A.I. Matures, It May Call Jürgen Schmidhuber 'Dad'", highlighting his early work on deep learning and his long‑term vision for self‑improving AI. == Views == Schmidhuber is a proponent of open source AI, and believes that they will become competitive against commercial closed-source AI. Since the 1970s, Schmidhuber wanted to create "intelligent machines that could learn and improve on their own and become smarter than him within his lifetime." He differentiates between two types of AIs: tool AI, such as those for improving healthcare, and autonomous AIs that set their own goals, perform their own research, and explore the universe. He has worked on both types for de
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The Best Free AI Avatar Generator for Beginners

Curious about the best AI avatar generator? An AI avatar generator is software that uses machine learning to help you get more done — it combines speed, accuracy, and an interface that just works. Hands-on testing shows real-world results vary, so a short free trial is the smartest way to decide. Whether you are a beginner or a pro, the right AI avatar generator slots into your workflow and pays for itself fast. This guide breaks down the top picks, their pros and cons, and who each one is best for.
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AI Paraphrasing Tools Reviews: What Actually Works in 2026

Comparing the best AI paraphrasing tool? An AI paraphrasing tool is software that uses machine learning to help you get more done — it lowers the barrier so anyone can produce professional output. Privacy matters too: check whether your data trains the model and whether a no-log or enterprise tier is available. Whether you are a beginner or a pro, the right AI paraphrasing tool slots into your workflow and pays for itself fast. We tested the leading options and ranked them by quality, value, and ease of use.
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Visualization (graphics)

Visualization (or visualisation in Commonwealth English; see spelling differences), also known as graphics visualization, is any technique for creating images, diagrams, or animations to communicate a message. Visualization through visual imagery has been an effective way to communicate both abstract and concrete ideas since the dawn of humanity. Examples from history include cave paintings, Egyptian hieroglyphs, Greek geometry, and Leonardo da Vinci's revolutionary methods of technical drawing for engineering purposes that actively involve scientific requirements. Visualization today has ever-expanding applications in science, education, engineering (e.g., product visualization), interactive multimedia, medicine, etc. Typical of a visualization application is the field of computer graphics. The invention of computer graphics (and 3D computer graphics) may be the most important development in visualization since the invention of central perspective in the Renaissance period. The development of animation also helped advance visualization. == Overview == The use of visualization to present information is not a new phenomenon. It has been used in maps, scientific drawings, and data plots for over a thousand years. Examples from cartography include Ptolemy's Geographia (2nd century AD), a map of China (1137 AD), and Minard's map (1861) of Napoleon's invasion of Russia a century and a half ago. Most of the concepts learned in devising these images carry over in a straightforward manner to computer visualization. Edward Tufte has written three critically acclaimed books that explain many of these principles. Computer graphics has from its beginning been used to study scientific problems. However, in its early days the lack of graphics power often limited its usefulness. The recent emphasis on visualization started in 1987 with the publication of Visualization in Scientific Computing, a special issue of Computer Graphics. Since then, there have been several conferences and workshops, co-sponsored by the IEEE Computer Society and ACM SIGGRAPH, devoted to the general topic, and special areas in the field, for example volume visualization. Most people are familiar with the digital animations produced to present meteorological data during weather reports on television, though few can distinguish between those models of reality and the satellite photos that are also shown on such programs. TV also offers scientific visualizations when it shows computer drawn and animated reconstructions of road or airplane accidents. Some of the most popular examples of scientific visualizations are computer-generated images that show real spacecraft in action, out in the void far beyond Earth, or on other planets. Dynamic forms of visualization, such as educational animation or timelines, have the potential to enhance learning about systems that change over time. Apart from the distinction between interactive visualizations and animation, the most useful categorization is probably between abstract and model-based scientific visualizations. The abstract visualizations show completely conceptual constructs in 2D or 3D. These generated shapes are completely arbitrary. The model-based visualizations either place overlays of data on real or digitally constructed images of reality or make a digital construction of a real object directly from the scientific data. Scientific visualization is usually done with specialized software, though there are a few exceptions, noted below. Some of these specialized programs have been released as open source software, having very often its origins in universities, within an academic environment where sharing software tools and giving access to the source code is common. There are also many proprietary software packages of scientific visualization tools. Models and frameworks for building visualizations include the data flow models popularized by systems such as AVS, IRIS Explorer, and VTK toolkit, and data state models in spreadsheet systems such as the Spreadsheet for Visualization and Spreadsheet for Images. == Applications == === Scientific visualization === As a subject in computer science, scientific visualization is the use of interactive, sensory representations, typically visual, of abstract data to reinforce cognition, hypothesis building, and reasoning. Scientific visualization is the transformation, selection, or representation of data from simulations or experiments, with an implicit or explicit geometric structure, to allow the exploration, analysis, and understanding of the data. Scientific visualization focuses and emphasizes the representation of higher order data using primarily graphics and animation techniques. It is a very important part of visualization and maybe the first one, as the visualization of experiments and phenomena is as old as science itself. Traditional areas of scientific visualization are flow visualization, medical visualization, astrophysical visualization, and chemical visualization. There are several different techniques to visualize scientific data, with isosurface reconstruction and direct volume rendering being the more common. === Data and information visualization === Data visualization is a related subcategory of visualization dealing with statistical graphics and geospatial data (as in thematic cartography) that is abstracted in schematic form. Information visualization concentrates on the use of computer-supported tools to explore large amount of abstract data. The term "information visualization" was originally coined by the User Interface Research Group at Xerox PARC and included Jock Mackinlay. Practical application of information visualization in computer programs involves selecting, transforming, and representing abstract data in a form that facilitates human interaction for exploration and understanding. Important aspects of information visualization are dynamics of visual representation and the interactivity. Strong techniques enable the user to modify the visualization in real-time, thus affording unparalleled perception of patterns and structural relations in the abstract data in question. === Educational visualization === Educational visualization is using a simulation to create an image of something so it can be taught about. This is very useful when teaching about a topic that is difficult to otherwise see, for example, atomic structure, because atoms are far too small to be studied easily without expensive and difficult to use scientific equipment. === Knowledge visualization === The use of visual representations to transfer knowledge between at least two persons aims to improve the transfer of knowledge by using computer and non-computer-based visualization methods complementarily. Thus properly designed visualization is an important part of not only data analysis but knowledge transfer process, too. Knowledge transfer may be significantly improved using hybrid designs as it enhances information density but may decrease clarity as well. For example, visualization of a 3D scalar field may be implemented using iso-surfaces for field distribution and textures for the gradient of the field. Examples of such visual formats are sketches, diagrams, images, objects, interactive visualizations, information visualization applications, and imaginary visualizations as in stories. While information visualization concentrates on the use of computer-supported tools to derive new insights, knowledge visualization focuses on transferring insights and creating new knowledge in groups. Beyond the mere transfer of facts, knowledge visualization aims to further transfer insights, experiences, attitudes, values, expectations, perspectives, opinions, and estimates in different fields by using various complementary visualizations. See also: picture dictionary, visual dictionary === Product visualization === Product visualization involves visualization software technology for the viewing and manipulation of 3D models, technical drawing and other related documentation of manufactured components and large assemblies of products. It is a key part of product lifecycle management. Product visualization software typically provides high levels of photorealism so that a product can be viewed before it is actually manufactured. This supports functions ranging from design and styling to sales and marketing. Technical visualization is an important aspect of product development. Originally technical drawings were made by hand, but with the rise of advanced computer graphics the drawing board has been replaced by computer-aided design (CAD). CAD-drawings and models have several advantages over hand-made drawings such as the possibility of 3-D modeling, rapid prototyping, and simulation. 3D product visualization promises more interactive experiences for online shoppers, but also challenges retailers to overcome hurdles in the production of 3D content, as large-scale 3D content production can be extremel
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Wang-Chiew Tan

Wang-Chiew Tan is a Singaporean computer scientist specializing in data management and natural language processing. Her work in data management includes data provenance (or data lineage) and data integration. She is currently a Research Scientist at Facebook AI, and was previously the Director of Research at Megagon Labs in Mountain View, California. At Megagon Labs, Tan was the lead researcher on a study with the University of Tokyo that concluded that the company of other people is more effective than pets at making people happy. == Education and career == Tan earned her bachelor's degree in computer science (first-class) at the National University of Singapore, and completed her Ph.D. at the University of Pennsylvania. Her 2002 dissertation, Data Annotations, Provenance, and Archiving, was jointly supervised by Peter Buneman and Sanjeev Khanna. Before working at Megagon, she has been a professor of computer science at the University of California, Santa Cruz beginning in 2002, and, from 2010 to 2012, was on leave from Santa Cruz as a researcher at IBM Research - Almaden. == Recognition == Tan was named a Fellow of the Association for Computing Machinery in 2015 "for contributions to data provenance and to the foundations of information integration".
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Klaus-Robert Müller

Klaus-Robert Müller (born 1964 in Karlsruhe, West Germany) is a German computer scientist and physicist, most noted for his work in machine learning and brain–computer interfaces. == Career == Klaus-Robert Müller received his Diplom in mathematical physics and PhD in theoretical computer science from the University of Karlsruhe. Following his Ph.D. he went to Berlin as a postdoctoral fellow at GMD (German National Research Center for Computer Science) Berlin (now part of Fraunhofer Institute for Open Communication Systems), where he started building up the Intelligent Data Analysis (IDA) group. From 1994 to 1995 he was a research fellow at Shun'ichi Amari's lab at the University of Tokyo. 1999 Müller became an associate professor for neuroinformatics at the University of Potsdam, transitioning to the full professorship for Neural Networks and Time Series Analysis in 2003. Since 2006 he holds the chair for Machine Learning at Technische Universität Berlin. Since 2012 he holds a distinguished professorship at Korea University in Seoul. He co-founded and is co-director of the Berlin Big Data Center (BBDC) of TU Berlin. As of 2017, 29 former doctoral or postdoctoral researchers of Klaus-Robert Müller have become full professors themselves. Bernhard Schölkopf and Alexander J. Smola were supervised by him as members of his research group. Since 2020 he is director of the Berlin Institute for the Foundations of Learning and Data (BIFOLD), a German National AI Competence Center, and director of the European Laboratory for Learning and Intelligent Systems (ELLIS) unit Berlin. In 2020/2021 he spent his sabbatical at Google Brain as a principal scientist. == Research == Müller has contributed extensively to several major interests of machine learning, including support vector machines (SVMs) and kernel methods, and artificial neural networks. He pioneered applying new methods of pattern recognition in domains like brain–computer interfaces, using them for patients with Locked-in syndrome. He is one of the leading computer scientists affiliated with Germany. His current research interests include: Statistical learning theory (Support Vector Machines, Deep Neural Networks, Boosting) Learning of non-stationarity data Fusion of structured heterogeneous multi-modal data, co-adaptation Applications: MEG, EEG, NIRS, ECoG, EMG, Brain Computer Interfaces, computational neuroscience, computer vision, genomic data analysis, computational chemistry and atomistic simulations, digital pathology == Honours and awards == Klaus-Robert Müller was elected a fellow of the German National Academy of Sciences Leopoldina in 2012. In 2017 he was elected member of the Berlin-Brandenburg Academy of Sciences and Humanities and also external scientific member of the Max Planck Society. In 2021 he was elected member of the German Academy of Science and Engineering. His work was honoured with several awards, including: 2026 Gottfried Wilhelm Leibniz Prize 2025 IEEE Neural Network Pioneer Award 2024 Feynman Prize in Nanotechnology 2023 Hector Fellow 2025, 2024, 2023, 2022, 2021, 2020, and 2019 Clarivate Highly Cited Researcher 2017 Vodafone Innovations Award 2017 2014 Science Prize of Berlin 2014 by the Governing Mayor of Berlin 2014 European Research Council Panel Consolidator Grants 2009 Best Paper award by IEEE Engineering in Medicine and Biology Society EMBS 2006 SEL-ALCATEL Research Prize for Technical Communication 1999 Olympus Award for Pattern Recognition == Books == with Holzinger, Andreas; et al., eds. (2022). xxAI – Beyond Explainable Artificial Intelligence. Lecture Notes in Computer Science. Vol. 13200. Springer Cham. doi:10.1007/978-3-031-04083-2. ISBN 978-3-031-04082-5. with Schütt, Kristof T.; et al., eds. (2020). Machine Learning Meets Quantum Physics. Lecture Notes in Physics. Vol. 968. Springer Cham. doi:10.1007/978-3-030-40245-7. ISBN 978-3-030-40244-0. S2CID 242406994. with Samek, Wojciech; et al., eds. (2019). Explainable AI: Interpreting, Explaining and Visualizing Deep Learning. Lecture Notes in Computer Science. Vol. 11700. Springer Cham. doi:10.1007/978-3-030-28954-6. ISBN 978-3-030-28953-9. with Montavon, Grégoire; et al., eds. (2012). Neural Networks: Tricks of the Trade. Lecture Notes in Computer Science. Vol. 7700 (2nd ed.). Springer Berlin, Heidelberg. doi:10.1007/978-3-642-35289-8. ISBN 978-3-642-35288-1. S2CID 39578794.
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Bernhard Schölkopf

Bernhard Schölkopf (born 20 February 1968) is a German computer scientist known for his work in machine learning, especially on kernel methods and causality. He is a director at the Max Planck Institute for Intelligent Systems in Tübingen, Germany, where he heads the Department of Empirical Inference. He is also an affiliated professor at ETH Zürich, honorary professor at the University of Tübingen and Technische Universität Berlin, and chairman of the European Laboratory for Learning and Intelligent Systems (ELLIS). == Research == === Kernel methods === Schölkopf developed SVM methods achieving world record performance on the MNIST pattern recognition benchmark at the time. With the introduction of kernel PCA, Schölkopf and coauthors argued that SVMs are a special case of a much larger class of methods, and all algorithms that can be expressed in terms of dot products can be generalized to a nonlinear setting by means of what is known as reproducing kernels. Another significant observation was that the data on which the kernel is defined need not be vectorial, as long as the kernel Gram matrix is positive definite. Both insights together led to the foundation of the field of kernel methods, encompassing SVMs and many other algorithms. Kernel methods are now textbook knowledge and one of the major machine learning paradigms in research and applications. Developing kernel PCA, Schölkopf extended it to extract invariant features and to design invariant kernels and showed how to view other major dimensionality reduction methods such as LLE and Isomap as special cases. In further work with Alex Smola and others, he extended the SVM method to regression and classification with pre-specified sparsity and quantile/support estimation. He proved a representer theorem implying that SVMs, kernel PCA, and most other kernel algorithms, regularized by a norm in a reproducing kernel Hilbert space, have solutions taking the form of kernel expansions on the training data, thus reducing an infinite dimensional optimization problem to a finite dimensional one. He co-developed kernel embeddings of distributions methods to represent probability distributions in Hilbert Spaces, with links to Fraunhofer diffraction as well as applications to independence testing. === Causality === Starting in 2005, Schölkopf turned his attention to causal inference. Causal mechanisms in the world give rise to statistical dependencies as epiphenomena, but only the latter are exploited by popular machine learning algorithms. Knowledge about causal structures and mechanisms is useful by letting us predict not only future data coming from the same source, but also the effect of interventions in a system, and by facilitating transfer of detected regularities to new situations. Schölkopf and co-workers addressed (and in certain settings solved) the problem of causal discovery for the two-variable setting and connected causality to Kolmogorov complexity. Around 2010, Schölkopf began to explore how to use causality for machine learning, exploiting assumptions of independence of mechanisms and invariance. His early work on causal learning was exposed to a wider machine learning audience during his Posner lecture at NeurIPS 2011, as well as in a keynote talk at ICML 2017. He assayed how to exploit underlying causal structures in order to make machine learning methods more robust with respect to distribution shifts and systematic errors, the latter leading to the discovery of a number of new exoplanets including K2-18b, which was subsequently found to contain water vapour in its atmosphere, a first for an exoplanet in the habitable zone. == Education and employment == Schölkopf studied mathematics, physics, and philosophy in Tübingen and London. He was supported by the Studienstiftung and won the Lionel Cooper Memorial Prize for the best M.Sc. in Mathematics at the University of London. He completed a Diplom in Physics, and then moved to Bell Labs in New Jersey, where he worked with Vladimir Vapnik, who became co-adviser of his PhD thesis at TU Berlin (with Stefan Jähnichen). His thesis, defended in 1997, won the annual award of the German Informatics Association. In 2001, following positions in Berlin, Cambridge and New York, he founded the Department for Empirical Inference at the Max Planck Institute for Biological Cybernetics, which grew into a leading center for research in machine learning. In 2011, he became founding director at the Max Planck Institute for Intelligent Systems. With Alex Smola, Schölkopf co-founded the series of Machine Learning Summer Schools. He also co-founded a Cambridge-Tübingen PhD Programme and the Max Planck-ETH Center for Learning Systems. In 2016, he co-founded the Cyber Valley research consortium. He participated in the IEEE Global Initiative on "Ethically Aligned Design". Schölkopf is co-editor-in-Chief of the Journal of Machine Learning Research, a journal he helped found, being part of a mass resignation of the editorial board of Machine Learning (journal). He is among the world’s most cited computer scientists. Alumni of his lab include Ulrike von Luxburg, Carl Rasmussen, Matthias Hein, Arthur Gretton, Gunnar Rätsch, Matthias Bethge, Stefanie Jegelka, Jason Weston, Olivier Bousquet, Olivier Chapelle, Joaquin Quinonero-Candela, and Sebastian Nowozin. As of late 2023, Schölkopf is also a scientific advisor to French research group Kyutai which is being funded by Xavier Niel, Rodolphe Saadé, Eric Schmidt, and others. == Awards and recognition == Schölkopf’s awards include the Royal Society Milner Award and, shared with Isabelle Guyon and Vladimir Vapnik, the BBVA Foundation Frontiers of Knowledge Award in the Information and Communication Technologies category. He was the first scientist working in Europe to receive this award. He was elected a Fellow of the Royal Society in 2026.
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Dispo

Dispo (formerly David's Disposable) is an American photo sharing and social networking app owned by Dispo, Inc. and co-founded by CEO Daniel Liss, YouTuber David Dobrik, and Natalie Mariduena. When the app initially launched on iOS in December 2019, it briefly charted as the most downloaded free app on the App Store, ahead of both Disney+ and Instagram. The app was rebranded and relaunched as Dispo, expanding from a simple camera app to a full social network in March 2021. It is based on the disposable camera. == History == On December 21, 2019, the app was first launched on the App Store under the name "David's Disposable." In its first week of release, it was downloaded more than a million times, reaching number one among free apps in the App Store. In June 2020, the team decided to rename the app to Dispo, purchasing the Dispo.fun domain on June 21, 2020. The company announced the change in September 2020. The early Dispo team consisted of Dobrik's longtime friend and business associate Natalie Mariduena as its treasurer, entrepreneur and venture capitalist Daniel Liss as chief executive officer, Regynald Augustin as first engineer, and Briana Hokanson as lead designer. In October 2020, the company raised a $4M seed round with backing from Alexis Ohanian's venture fund Seven Seven Six alongside other investors including Unshackled Ventures, Shrug Capital, and Weekend Fund. In February 2021, Axios reported that the app had generated US$20 million in its series A round, led by Spark Capital. At this time, the app was valued at US$200 million. A New York Times profile asked, "Are Disposables the Future of Photosharing?" In March 2021, the app was officially relaunched with new social network features and its invite-only feature was dropped. On March 21, 2021, it was announced that Spark Capital would sever all ties with Dispo in light of several disparaging allegations against David Dobrik and The Vlog Squad. The same day, it was announced that Dobrik would leave the company and step down from the company's board of directors. On March 22, 2021, Seven Seven Six and Unshackled Ventures announced they would be standing by the company and its remaining employees but donating profits to charity. In June, 2021, CEO Daniel Liss announced Dispo's official Series A. Investors and advisors in the new Dispo include Ohanian's Seven Seven Six, Unshackled, Endeavor, photographers Annie Leibovitz and Raven B. Varona, NBA stars Kevin Durant and Andre Iguodala (through their 35 Ventures and F9 Strategies venture firms, respectively). Other participants include Cara Delevingne, Sofia Vergara, Shade Room CEO Angelica Nwandu, Latin World Entertainment CEO Luis Balaguer, and Amplify Africa co-founders Damilare Kujembola and Timi Adeyeba. == Overview == Dispo has been compared to other image sharing and social networking services, most notably Instagram and VSCO, although users cannot immediately see the photos they have taken using the app. When a user attempts to take a photo, the interface mimics the developing process of a disposable camera. Users can take as many photos on the app as they want; they do not appear on the app however, until 9 am the next day. Once the set of photos appear on the app, users can choose to save them or share them with other users in a "roll". == Reception == Screen Rant has called the app "like Clubhouse [referring to the app] but for photos," comparing the early invite-only features of the apps. As it greatly restricts the user's editing options and sets out to offer a more authentic social networking experience, the app has been widely dubbed the "anti-Instagram". Between March 2021 and June 2021, the app reached the top ten in the App Store's photo/video rankings on 5 continents including in the US, Japan, Spain, Germany, Brazil, and Australia. It has been a notable success in Japan, where it opened its first international office in July 2021. In July 2021, NBA number one draft pick Cade Cunningham announced he had selected Dispo as his exclusive social media partner for the NBA draft.
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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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Best AI Blog Writers in 2026

Trying to pick the best AI blog writer? An AI blog writer 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 blog writer 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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Kunihiko Fukushima

Kunihiko Fukushima (Japanese: 福島邦彦, born 16 March 1936) is a Japanese computer scientist, most noted for his work on artificial neural networks and deep learning. He is currently working part-time as a senior research scientist at the Fuzzy Logic Systems Institute in Fukuoka, Japan. == Notable scientific achievements == In 1980, Fukushima published the neocognitron, the original deep convolutional neural network (CNN) architecture. Fukushima proposed several supervised and unsupervised learning algorithms to train the parameters of a deep neocognitron such that it could learn internal representations of incoming data. Today, however, the CNN architecture is usually trained through backpropagation. This approach is now heavily used in computer vision. In 1969 Fukushima introduced the ReLU (Rectifier Linear Unit) activation function in the context of visual feature extraction in hierarchical neural networks, which he called "analog threshold element". (Though the ReLU was first used by Alston Householder in 1941 as a mathematical abstraction of biological neural networks.) As of 2017 it is the most popular activation function for deep neural networks. == Education and career == In 1958, Fukushima received his Bachelor of Engineering in electronics from Kyoto University. He became a senior research scientist at the NHK Science & Technology Research Laboratories. In 1989, he joined the faculty of Osaka University. In 1999, he joined the faculty of the University of Electro-Communications. In 2001, he joined the faculty of Tokyo University of Technology. From 2006 to 2010, he was a visiting professor at Kansai University. Fukushima acted as founding president of the Japanese Neural Network Society (JNNS). He also was a founding member on the board of governors of the International Neural Network Society (INNS), and president of the Asia-Pacific Neural Network Assembly (APNNA). He was one of the board of governors of the International Neural Network Society (INNS) in 1989-1990 and 1993-2005. == Awards == In 2020, Fukushima received the Bower Award and Prize for Achievement in Science. In 2022, Fukushima became a laureate of the Asian Scientist 100 by the Asian Scientist. He also received the IEICE Achievement Award and Excellent Paper Awards, the IEEE Neural Networks Pioneer Award, the APNNA Outstanding Achievement Award, the JNNS Excellent Paper Award and the INNS Helmholtz Award.
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Symbol level

In knowledge-based systems, agents choose actions based on the principle of rationality to move closer to a desired goal. The agent is able to make decisions based on knowledge it has about the world (see knowledge level). But for the agent to actually change its state, it must use whatever means it has available. This level of description for the agent's behavior is the symbol level. The term was coined by Allen Newell in 1982. For example, in a computer program, the knowledge level consists of the information contained in its data structures that it uses to perform certain actions. The symbol level consists of the program's algorithms, the data structures themselves, and so on.
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Android Auto

Android Auto is a mobile app developed by Google to mirror features of a smartphone (or other Android device) on a car's dashboard information and entertainment head unit. Once an Android device is paired with the car's head unit, the system can mirror some apps on the vehicle's display. Supported apps include GPS mapping and navigation, music playback, SMS, telephone, and Web search. The system supports both touchscreen and button-controlled head units. Hands-free operation through voice commands is available and recommended to reduce driver distraction. Android Auto is part of the Open Automotive Alliance, a joint effort of 28 automobile manufacturers, with Nvidia as tech supplier, available in 36 countries. == History == Android Auto was revealed at Google I/O 2014. The app was released to the public on March 19, 2015. In November 2016, Google implemented an app that would run the Android Auto UI on the mobile device. In July 2019, Android Auto received its first major UI rework, which among other changes, brought an app drawer to Android Auto for the first time. Google also announced that the app's ability to be used on a phone would be discontinued in favor of Google Assistant's drive mode. In December 2020, Google announced the expansion of Android Auto to 36 additional countries in Europe, Indonesia, and more. In April 2021, Android Auto launched in Belgium, Denmark, Netherlands, Norway, Portugal, and Sweden. Google announced in May 2022 a user interface redesign for Android Auto, codenamed CoolWalk, which aims to simplify the app's usage, and make it more adaptable to screens of different orientations and aspect ratios. The redesign incorporates a new split-screen layout, where Google Maps can be displayed alongside a music player. CoolWalk was originally slated to launch in Q3 2022. In June 2022, Android Auto no longer ran directly on a mobile device; the app permitting this was decommissioned, in favor of a Driving Mode built into the Google Assistant app for a similar purpose. In November 2022, the CoolWalk user interface was released in Android Auto's beta program. == Functionality == Android Auto is software that can be utilized from an Android mobile device, acting as a vehicle's dashboard head unit. Once the user's Android device is connected to the vehicle, the head unit will serve as an external display for the Android device, presenting supported software in a car-specific user interface provided by the Android Auto app. In Android Auto's first iterations, the device was required to be connected via USB to the car. For some time, starting in November 2016, Google added the option to run Android Auto as a regular app on an Android device, allowing users to choose whether to use Android Auto on a personal phone or tablet, rather than on a compatible automotive head unit. This app was decommissioned in June 2022 in favor of a Driving Mode built into the Google Assistant app. At CES 2018, Google confirmed that the Google Assistant would be coming to Android Auto later in the year. An Android Auto SDK has been released, allowing third parties to modify their apps to work with Android Auto; initially, only APIs for music and messaging apps were available. == Head unit support == In May 2015, Hyundai became the first manufacturer to offer Android Auto support, making it first available in the 2015 Hyundai Sonata. Automobile manufacturers that will offer Android Auto support in their cars include Abarth, Acura, Alfa Romeo, Aston Martin, Audi, Bentley, Buick, BMW, BYD, Cadillac, Chevrolet, Chrysler, Citroën, Dodge, Ferrari, Fiat, Ford, GMC, Genesis, Holden, Honda, Hyundai, Infiniti, Jaguar Land Rover, Jeep, Kia, Lamborghini, Lexus, Lincoln, Mahindra and Mahindra, Maserati, Maybach, Mazda, Mercedes-Benz, Mitsubishi, Nissan, Opel, Peugeot, Porsche, RAM, Renault, SEAT, Škoda, SsangYong, Subaru, Suzuki, Tata Motors Cars, Toyota, Volkswagen and Volvo. Additionally, aftermarket car-audio systems supporting Android Auto add the technology into host vehicles, including Pioneer, Kenwood, Panasonic, and Sony. == Criticism == In May 2019, Italy filed an antitrust complaint targeting Android Auto, citing a Google policy of allowing third-parties to only offer media and messaging apps on the platform, preventing Enel from offering an app for locating vehicle charging stations. Google announced a new SDK, to be released to select partners in August 2020 and made generally available by the end of the year. == Availability == As of December 2025, Android Auto is available in 46 countries:
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Top 10 AI Background Removers Compared (2026)

Curious about the best AI background remover? An AI background remover is software that uses machine learning to help you get more done — it combines speed, accuracy, and an interface that just works. Hands-on testing shows real-world results vary, so a short free trial is the smartest way to decide. Whether you are a beginner or a pro, the right AI background remover slots into your workflow and pays for itself fast. This guide breaks down the top picks, their pros and cons, and who each one is best for.
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