AI Chat Video

AI Chat Video — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

GPTs

GPTs are custom versions of ChatGPT with added instructions and extra knowledge. GPTs can be used and created from the GPT Store. Any user can easily create them without any programming knowledge. GPTs can be tailored for specific writing styles, topics, or tasks. The ability to create GPTs was introduced in November 2023, and by January 2024, more than 3 million GPTs had been published. == Features and uses == GPTs can be configured to answer complex questions in specific fields, solve problems, provide image-based information, or create digital content. They can be programmed as educational tools, purchasing guides, or technical advisors, as well as for many others applications. GPTs are accessed from the GPT Store section of the ChatGPT web page. The “Explore GPT” link opens the store where the most popular GPTs in each section are highlighted. The GPTs are organized by categories. The store also uses a rating system based on user experiences similar to that used by other app stores such as Apple's App Store or Google Play. Those with the best ratings appear at the top of each category. According to La Vanguardia, the most popular categories are: Personal assistants Learning to program Image generation Creative writing Gaming Entertainment It is expected that in the future the creators of GPTs will be able to monetize them. Companies like Moderna are using GPTs to assist in various specific business tasks. The company has created 750 GPTs for its own internal use. == Configuration == Creating GPTs does not require prior programming knowledge. Free users can use existing GPTs but cannot create their own. Paying subscribers can use the editor on the ChatGPT site to configure the GPT's name, image and description, instructions and access to APIs, along with visibility options. == Criticism == The implementation and use of GPTs has not been without criticism. The GPT Store has been criticized for the proliferation of low-quality GPTs and spam due to a lack of effective moderation. There are also concerns about data privacy and security, as GPTs may collect and use personal information in ways that are not always transparent to users.
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Tamara Broderick

Tamara Ann Broderick is an American computer scientist at the Massachusetts Institute of Technology. She works on machine learning and Bayesian inference. == Education and early career == Broderick is from Parma Heights, Ohio. She attended Laurel School and graduated in 2003. Whilst at high school she took part in the inaugural Massachusetts Institute of Technology Women's Technology Program. She studied mathematics at Princeton University, earning a bachelor's degree in 2007. She was a Marshall scholar, allowing her to pursue graduate research at the University of Cambridge. She was a runner-up in the Association for Women in Mathematics Alice T. Shafer Prize for Excellence in Mathematics. She was co-president of the Princeton Math Club and organised a competition for high school maths teams. She won the Phi Beta Kappa Prize for the highest academic average at Princeton University. During her undergraduate degree, Broderick worked on dark matter haloes with Rachel Mandelbaum. Broderick moved to the United Kingdom for her graduate studies, earning a Master of Advanced Studies for completing Part III of the Mathematical Tripos at the University of Cambridge in 2009. Her Master's thesis looked at the Nomon selection method, improving the efficiency of communications. She returned to America in 2009, joining University of California, Berkeley for her Master's and PhD. Her graduate research was supported by the Berkeley Fellowship and a National Science Foundation Fellowship. Her PhD thesis Clusters and features from combinatorial stochastic processes looked at clustering and speeding up the analysis of large, streaming data sets. In 2013 she was selected for the Berkeley EECS Rising Stars conference. == Research and career == Broderick joined Massachusetts Institute of Technology as an assistant professor in 2015. She is interested in Bayesian statistics and graphical models. She was the recipient of a Google Faculty Research Grant and International Society for Bayesian Analysis Lifetime Members Junior Researcher Award. She was awarded an Army Research Office young investigator program award to investigate machine-learning to quantify uncertainty in data analysis. Broderick is also Alfred P. Sloan Foundation scholar. === Academic service === In 2018, Broderick spoke at the Harvard University Institute for Applied Computational Science Women in Data Science conference. She spoke about Bayesian inference at the 2018 International Conference on Machine Learning. She led a three-day Masterclass on machine learning at University College London in June 2018. Broderick is a scientific advisor for AI.Reverie and WiML (Women in Machine Learning). She has developed a high-school level introduction to machine learning with the Women's Technology Program (WTP). Software she has developed is available on her website. === Awards and honors === Broderick was awarded the Evelyn Fix Memorial Medal and Citation and the International Society for Bayesian Analysis Savage Award for her doctoral thesis. She was awarded a National Science Foundation CAREER Award to scale her machine learning techniques. She was a 2021 Leadership Academy winner of the Committee of Presidents of Statistical Societies.
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Trie

In computer science, a trie (, ), also known as a digital tree or prefix tree, is a specialized search tree data structure used to store and retrieve strings from a dictionary or set. Unlike a binary search tree, nodes in a trie do not store their associated key. Instead, each node's position within the trie determines its associated key, with the connections between nodes defined by individual characters rather than the entire key. Tries are particularly effective for tasks such as autocomplete, spell checking, and IP routing, offering advantages over hash tables due to their prefix-based organization and lack of hash collisions. Every child node shares a common prefix with its parent node, and the root node represents the empty string. While basic trie implementations can be memory-intensive, various optimization techniques such as compression and bitwise representations have been developed to improve their efficiency. A notable optimization is the radix tree, which provides more efficient prefix-based storage. While tries store character strings, they can be adapted to work with any ordered sequence of elements, such as permutations of digits or shapes. A notable variant is the bitwise trie, which uses individual bits from fixed-length binary data (such as integers or memory addresses) as keys. == History, etymology, and pronunciation == The idea of a trie for representing a set of strings was first abstractly described by Axel Thue in 1912. Tries were first described in a computer context by René de la Briandais in 1959. The idea was independently described in 1960 by Edward Fredkin, who coined the term trie, pronouncing it (as "tree"), after the middle syllable of retrieval. However, other authors pronounce it (as "try"), in an attempt to distinguish it verbally from "tree". == Overview == Tries are a form of string-indexed look-up data structure, which is used to store a dictionary list of words that can be searched on in a manner that allows for efficient generation of completion lists. A prefix trie is an ordered tree data structure used in the representation of a set of strings over a finite alphabet set, which allows efficient storage of words with common prefixes. Tries can be efficacious on string-searching algorithms such as predictive text, approximate string matching, and spell checking in comparison to binary search trees. A trie can be seen as a tree-shaped deterministic finite automaton. == Operations == Tries support various operations: insertion, deletion, and lookup of a string key. Tries are composed of nodes that contain links, which either point to other suffix child nodes or null. As for every tree, each node except the root is pointed to by only one other node, called its parent. Each node contains as many links as the number of characters in the applicable alphabet (although tries tend to have a substantial number of null links). In some cases, the alphabet used is simply that of the character encoding—resulting in, for example, a size of 128 in the case of ASCII. The null links within the children of a node emphasize the following characteristics: Characters and string keys are implicitly stored in the trie, and include a character sentinel value indicating string termination. Each node contains one possible link to a prefix of strong keys of the set. A basic structure type of nodes in the trie is as follows: Node {\displaystyle {\text{Node}}} may contain an optional Value {\displaystyle {\text{Value}}} , which is associated with the key that corresponds to the node. === Searching === Searching for a value in a trie is guided by the characters in the search string key, as each node in the trie contains a corresponding link to each possible character in the given string. Thus, following the string within the trie yields the associated value for the given string key. A null link during the search indicates the inexistence of the key. The following pseudocode implements the search procedure for a given string key in a rooted trie x. In the above pseudocode, x and key correspond to the pointer of the trie's root node and the string key, respectively. The search operation takes O ( m ) {\displaystyle O(m)} time, where m {\displaystyle m} is the size of the string parameter key. In a balanced binary search tree, on the other hand, it takes O ( m log ⁡ n ) {\displaystyle O(m\log n)} time, in the worst case, since key needs to be compared with O ( log ⁡ n ) {\displaystyle O(\log n)} other keys and each comparison takes O ( m ) {\displaystyle O(m)} time, in the worst case. The trie occupies less space, in comparison with a binary search tree, in the case of a large number of short strings, since nodes share common initial string subsequences and store the keys implicitly. === Insertion === Insertion into a trie is guided by using the character sets as indexes to the children array until the last character of the string key is reached. Each node in the trie corresponds to one call of the radix sorting routine, as the trie structure reflects the execution pattern of the top-down radix sort. If null links are encountered before reaching the last character of the string key, new nodes are created. The input value is assigned to the value of the last node traversed, which is the node that corresponds to the key. === Deletion === Deletion of a key–value pair from a trie involves finding the node corresponding to the key, setting its value to null, and recursively removing nodes that have no children. The procedure begins by examining key; an empty string indicates arrival at the node corresponding to the (original) key, in which case its value is set to null. If the node, then, has null value and no children, it is removed from the trie by returning null; otherwise, the node is kept by returning the node itself. == Replacing other data structures == === Replacement for hash tables === A trie can be used to replace a hash table, over which it has the following advantages: Searching for a node with an associated key of size m {\displaystyle m} has the complexity of O ( m ) {\displaystyle O(m)} , whereas an imperfect hash function may have numerous colliding keys, and the worst-case lookup speed of such a table would be O ( N ) {\displaystyle O(N)} , where N {\displaystyle N} denotes the total number of nodes within the table. Tries do not need a hash function for the operation, unlike a hash table; there are also no collisions of different keys in a trie. Within a trie, keys can be efficiently sorted lexicographically. However, tries are less efficient than a hash table when the data is directly accessed on a secondary storage device such as a hard disk drive that has higher random access time than the main memory. == Implementation strategies == Tries can be represented in several ways, corresponding to different trade-offs between memory use and speed of the operations. Using a vector of pointers for representing a trie consumes enormous space; however, memory space can be reduced at the expense of running time if a singly linked list is used for each node vector, as most entries of the vector contains nil {\displaystyle {\text{nil}}} . Techniques such as alphabet reduction may reduce the large space requirements by reinterpreting the original string as a longer string over a smaller alphabet. For example, a string of n bytes can alternatively be regarded as a string of 2n four-bit units. This can reduce memory usage by a factor of eight; but lookups need to visit twice as many nodes in the worst case. Another technique includes storing a vector of 256 ASCII pointers as a bitmap of 256 bits representing ASCII alphabet, which reduces the size of individual nodes dramatically. === Bitwise tries === Bitwise tries are used to address the enormous space requirement for the trie nodes in a naive simple pointer vector implementations. Each character in the string key set is represented via individual bits, which are used to traverse the trie over a string key. The implementations for these types of trie use vectorized CPU instructions to find the first set bit in a fixed-length key input (e.g. GCC's __builtin_clz() intrinsic function). Accordingly, the set bit is used to index the first item, or child node, in the 32- or 64-entry based bitwise tree. Search then proceeds by testing each subsequent bit in the key. This procedure is also cache-local and highly parallelizable due to register independency, and thus performant on out-of-order execution CPUs. === Compressed tries === Radix tree, also known as a compressed trie, is a space-optimized variant of a trie in which any node with only one child gets merged with its parent; elimination of branches of the nodes with a single child results in better metrics in both space and time. This works best when the trie remains static and set of keys stored are very sparse within their representation space. One more approach for static tries is to "pack" the trie by storing disjoint
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AI Virtual Assistants: Free vs Paid (2026)

Trying to pick the best AI virtual assistant? An AI virtual assistant 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 virtual assistant 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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Vote Compass

Vote Compass is an interactive, online voting advice application developed by political scientists and run during election campaigns. It surveys users about their political views and, based on their responses, calculates the individual alignment of each user with the parties or candidates running in a given election contest. It is operated by a social enterprise called Vox Pop Labs in partnership with locale-specific news organizations, including the Wall Street Journal, Vox Media, the Canadian and Australian Broadcasting Corporations, Television New Zealand, France24, RTL Group, and Grupo Globo. Vote Compass also operates under the trademarks Boussole électorale and Wahl-Navi for French- and German-language iterations, respectively. == Background == Vote Compass was developed by Clifton van der Linden, a professor in the Department of Political Science at McMaster University. It is run by van der Linden along with a team of social and statistical scientists from Vox Pop Labs. Although inspired by European Voting Advice Applications, van der Linden explicitly rejects this terminology, arguing that Vote Compass was "never intended to account for every variable that influences voter choice and its results should not be interpreted as voting advice." == Methodology == Using a Likert scale, users indicate their responses to a series of policy propositions designed to discriminate between candidates' policies on prominent issues relevant to the election. Propositions are crafted in collaboration with political scientists local to each jurisdiction in which Vote Compass is run. Based on a candidate or political party's public disclosures (i.e. party manifestos, policy proposals, official websites, speeches, media releases, statements made in the legislature, etc.) they are calibrated on the same propositions and scales as are users. A series of aggregation algorithms calculate the overall distance between the user and the candidates or parties. There have been claims that Vote Compass surveys have the potential to become push polling, if the survey questions posed are poorly designed.
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AI Paraphrasing Tools: Free vs Paid (2026)

In search of the best AI paraphrasing tool? An AI paraphrasing tool is software that uses machine learning to help you get more done — it turns a rough idea into a polished result in seconds. When choosing one, weigh output quality, pricing, export formats, and how well it fits the tools you already use. Whether you are a beginner or a pro, the right AI paraphrasing tool slots into your workflow and pays for itself fast. Below we compare features, pricing, and real output so you can choose with confidence.
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Xu Li (computer scientist)

Xu Li is a Chinese computer scientist and co-founder and current CEO of SenseTime, an artificial intelligence (AI) company. Xu has led SenseTime since the company's incorporation and helped it independently develop its proprietary deep learning platform. == Education and research == Xu obtained both his bachelor's and master's degrees in computer science from Shanghai Jiao Tong University. He received his doctorate in computer science from the Chinese University of Hong Kong. Xu has published more than 50 papers at international conferences and in journals in the field of computer vision and won the Best Paper Award at the international conference on Non-Photorealistic Rendering and Animation (NPAR) 2012 and the Best Reviewer Award at the international conferences Asian Conference on Computer Vision ACCV 2012 and International Conference on Computer Vision (ICCV) 2015. He has three algorithms that have been included into the visual open-source platform OpenCV, and his "L0 Smoothing" algorithm garnered the most citations in research papers over a span of five years (2011–2015) within the ACM Transactions on Graphics (TOG), a scientific journal that Thomson Reuters InCites has placed first among software engineering journals. == Career == Previously, Xu worked at Lenovo Corporate Research & Development. He was also a visiting researcher at Motorola China R&D Institute, Omron Research Institute, and Microsoft Research. == Selected publications == Jimmy Ren, Xiaohao Chen, Jianbo Liu, Wenxiu Sun, Li Xu, Jiahao Pang, Qiong Yan, Yu-wing Tai, "Accurate Single Stage Detector Using Recurrent Rolling Convolution", (CVPR), 2017. Jimmy SJ. Ren, Yongtao Hu, Yu-Wing Tai, Chuan Wang, Li Xu, Wenxiu Sun, Qiong Yan, "Look, Listen and Learn – A Multimodal LSTM for Speaker Identification", The 30th AAAI Conference on Artificial Intelligence (AAAI), 2016 Jimmy SJ. Ren, Li Xu, Qiong Yan, Wenxiu Sun, "Shepard Convolutional Neural Networks" Advances in Neural Information Processing Systems (NIPS), 2015. Xiaoyong Shen, Chao Zhou, Li Xu, Jiaya Jia, "Mutual-Structure for Joint Filtering" International Conference on Computer Vision (ICCV), (oral presentation), 2015. Jianping Shi, Qiong Yan, Li Xu, Jiaya Jia, "Hierarchical Image Saliency Detection on Extended CSSD" IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2015. Jianping Shi, Xin Tao, Li Xu, Jiaya Jia, "Break Ames Room Illusion: Depth from General Single Images" ACM Transactions on Graphics (TOG), (Proc. ACM SIGGRAPH ASIA2015). Yongtao Hu, Jimmy SJ. Ren, Jingwen Dai, Chang Yuan, Li Xu, Wenping Wang, "Deep Multimodal Speaker Naming" ACM International Conference on Multimedia (MM), 2015. Li Xu, Jimmy SJ. Ren, Qiong Yan, Renjie Liao, Jiaya Jia "Deep Edge-Aware Filters" International Conference on Machine Learning (ICML), 2015. Jianping Shi, Li Xu, Jiaya Jia "Just Noticeable Defocus Blur Detection and Estimation" IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015. Ziyang Ma, Renjie Liao, Xin Tao, Li Xu, Jiaya Jia, Enhua Wu "Handling Motion Blur in Multi-Frame Super-Resolution" IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015. Xiaoyong Shen, Qiong Yan, Li Xu, Lizhuang Ma, Jiaya Jia"Multispectral Joint Image Restoration via Optimizing a Scale Map" IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2015. Jimmy SJ. Ren, Li Xu, "On Vectorization of Deep Convolutional Neural Networks for Vision Tasks" AAAI Conference on Artificial Intelligence (AAAI), 2015. == Awards and honors == Xu was ranked 7th in Fortune magazine's 2018 edition of its 40 Under 40. He was also named "China's Outstanding AI Industry Leader" by The Economic Observer, received the "Innovative Business Leader" Award under NetEase's "Future Technology Talent Awards", and was honored as Sina's "2017 Top Ten Economic Figures". In 2018, Xu was named EY's "Entrepreneur of the Year China" in the Technology category.
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The Best Free AI Logo Maker for Beginners

Curious about the best AI logo maker? An AI logo maker 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 logo maker 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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Human image synthesis

Human image synthesis is technology that can be applied to make believable and even photorealistic renditions of human-likenesses, moving or still. It has effectively existed since the early 2000s. Many films using computer generated imagery have featured synthetic images of human-like characters digitally composited onto the real or other simulated film material. Towards the end of the 2010s deep learning artificial intelligence has been applied to synthesize images and video that look like humans, without need for human assistance, once the training phase has been completed, whereas the old school 7D-route required massive amounts of human work. == Timeline of human image synthesis == In 1971 Henri Gouraud made the first CG geometry capture and representation of a human face. Modeling was his wife Sylvie Gouraud. The 3D model was a simple wire-frame model and he applied the Gouraud shader he is most known for to produce the first known representation of human-likeness on computer. The 1972 short film A Computer Animated Hand by Edwin Catmull and Fred Parke was the first time that computer-generated imagery was used in film to simulate moving human appearance. The film featured a computer simulated hand and face (watch film here). The 1976 film Futureworld reused parts of A Computer Animated Hand on the big screen. The 1983 music video for song Musique Non-Stop by German band Kraftwerk aired in 1986. Created by the artist Rebecca Allen, it features non-realistic looking, but clearly recognizable computer simulations of the band members. The 1994 film The Crow was the first film production to make use of digital compositing of a computer simulated representation of a face onto scenes filmed using a body double. Necessity was the muse as the actor Brandon Lee portraying the protagonist was tragically killed accidentally on-stage. In 1999 Paul Debevec et al. of USC captured the reflectance field of a human face with their first version of a light stage. They presented their method at the SIGGRAPH 2000 In 2003 audience debut of photo realistic human-likenesses in the 2003 films The Matrix Reloaded in the burly brawl sequence where up-to-100 Agent Smiths fight Neo and in The Matrix Revolutions where at the start of the end showdown Agent Smith's cheekbone gets punched in by Neo leaving the digital look-alike unnaturally unhurt. The Matrix Revolutions bonus DVD documents and depicts the process in some detail and the techniques used, including facial motion capture and limbal motion capture, and projection onto models. In 2003 The Animatrix: Final Flight of the Osiris a state-of-the-art want-to-be human likenesses not quite fooling the watcher made by Square Pictures. In 2003 digital likeness of Tobey Maguire was made for movies Spider-man 2 and Spider-man 3 by Sony Pictures Imageworks. In 2005 the Face of the Future project was an established. by the University of St Andrews and Perception Lab, funded by the EPSRC. The website contains a "Face Transformer", which enables users to transform their face into any ethnicity and age as well as the ability to transform their face into a painting (in the style of either Sandro Botticelli or Amedeo Modigliani). This process is achieved by combining the user's photograph with an average face. In 2009 Debevec et al. presented new digital likenesses, made by Image Metrics, this time of actress Emily O'Brien whose reflectance was captured with the USC light stage 5 Motion looks fairly convincing contrasted to the clunky run in the Animatrix: Final Flight of the Osiris which was state-of-the-art in 2003 if photorealism was the intention of the animators. In 2009 a digital look-alike of a younger Arnold Schwarzenegger was made for the movie Terminator Salvation though the end result was critiqued as unconvincing. Facial geometry was acquired from a 1984 mold of Schwarzenegger. In 2010 Walt Disney Pictures released a sci-fi sequel entitled Tron: Legacy with a digitally rejuvenated digital look-alike of actor Jeff Bridges playing the antagonist CLU. In SIGGGRAPH 2013 Activision and USC presented a real-time "Digital Ira" a digital face look-alike of Ari Shapiro, an ICT USC research scientist, utilizing the USC light stage X by Ghosh et al. for both reflectance field and motion capture. The end result both precomputed and real-time rendering with the modernest game GPU shown here and looks fairly realistic. In 2014 The Presidential Portrait by USC Institute for Creative Technologies in conjunction with the Smithsonian Institution was made using the latest USC mobile light stage wherein President Barack Obama had his geometry, textures and reflectance captured. In 2014 Ian Goodfellow et al. presented the principles of a generative adversarial network. GANs made the headlines in early 2018 with the deepfakes controversies. For the 2015 film Furious 7 a digital look-alike of actor Paul Walker who died in an accident during the filming was done by Weta Digital to enable the completion of the film. In 2016 techniques which allow near real-time counterfeiting of facial expressions in existing 2D video have been believably demonstrated. In 2016 a digital look-alike of Peter Cushing was made for the Rogue One film where its appearance would appear to be of same age as the actor was during the filming of the original 1977 Star Wars film. In SIGGRAPH 2017 an audio driven digital look-alike of upper torso of Barack Obama was presented by researchers from University of Washington. It was driven only by a voice track as source data for the animation after the training phase to acquire lip sync and wider facial information from training material consisting 2D videos with audio had been completed. Late 2017 and early 2018 saw the surfacing of the deepfakes controversy where porn videos were doctored using deep machine learning so that the face of the actress was replaced by the software's opinion of what another persons face would look like in the same pose and lighting. In 2018 Game Developers Conference Epic Games and Tencent Games demonstrated "Siren", a digital look-alike of the actress Bingjie Jiang. It was made possible with the following technologies: CubicMotion's computer vision system, 3Lateral's facial rigging system and Vicon's motion capture system. The demonstration ran in near real time at 60 frames per second in the Unreal Engine 4. In 2018 at the World Internet Conference in Wuzhen the Xinhua News Agency presented two digital look-alikes made to the resemblance of its real news anchors Qiu Hao (Chinese language) and Zhang Zhao (English language). The digital look-alikes were made in conjunction with Sogou. Neither the speech synthesis used nor the gesturing of the digital look-alike anchors were good enough to deceive the watcher to mistake them for real humans imaged with a TV camera. In September 2018 Google added "involuntary synthetic pornographic imagery" to its ban list, allowing anyone to request the search engine block results that falsely depict them as "nude or in a sexually explicit situation." In February 2019 Nvidia open sources StyleGAN, a novel generative adversarial network. Right after this Phillip Wang made the website ThisPersonDoesNotExist.com with StyleGAN to demonstrate that unlimited amounts of often photo-realistic looking facial portraits of no-one can be made automatically using a GAN. Nvidia's StyleGAN was presented in a not yet peer reviewed paper in late 2018. At the June 2019 CVPR the MIT CSAIL presented a system titled "Speech2Face: Learning the Face Behind a Voice" that synthesizes likely faces based on just a recording of a voice. It was trained with massive amounts of video of people speaking. Since 1 July 2019 Virginia has criminalized the sale and dissemination of unauthorized synthetic pornography, but not the manufacture., as § 18.2–386.2 titled 'Unlawful dissemination or sale of images of another; penalty.' became part of the Code of Virginia. The law text states: "Any person who, with the intent to coerce, harass, or intimidate, maliciously disseminates or sells any videographic or still image created by any means whatsoever that depicts another person who is totally nude, or in a state of undress so as to expose the genitals, pubic area, buttocks, or female breast, where such person knows or has reason to know that he is not licensed or authorized to disseminate or sell such videographic or still image is guilty of a Class 1 misdemeanor.". The identical bills were House Bill 2678 presented by Delegate Marcus Simon to the Virginia House of Delegates on 14 January 2019 and three-day later an identical Senate bill 1736 was introduced to the Senate of Virginia by Senator Adam Ebbin. Since 1 September 2019 Texas senate bill SB 751 amendments to the election code came into effect, giving candidates in elections a 30-day protection period to the elections during which making and distributing digital look-alikes or synthetic fakes of the candidates is an offense. Th
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Is an AI Art Generator Worth It in 2026?

Curious about the best AI art generator? An AI art 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 art generator slots into your workflow and pays for itself fast. Read on for hands-on impressions, pricing tiers, and the standout features that matter.
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Oren Etzioni

Oren Etzioni (born 1964) is Professor Emeritus of Computer Science at the University of Washington, and founding CEO of the Allen Institute for Artificial Intelligence (AI2). Etzioni is a co-founder of Vercept, an AI startup, and founder and CEO of TrueMedia.org, a non-profit dedicated to fighting political deepfakes, which launched in April 2024. He is also the Founder and Technical Director of the AI2 Incubator and a venture partner at the Madrona Venture Group. == Early life and education == Etzioni is the son of Israeli-American intellectual Amitai Etzioni. He was the first student to major in computer science at Harvard University, where he earned a bachelor's degree in 1986. He earned a PhD from Carnegie Mellon University in January, 1991, supervised by Tom M. Mitchell. == University of Washington career == Etzioni joined the University of Washington faculty in 1991, immediately after receiving his PhD. He rose through the ranks to become the Washington Research Foundation Entrepreneurship Professor in Computer Science & Engineering. Etzioni's research has been focused on basic problems in the study of intelligence, machine reading, machine learning and web search. Past projects include Internet Softbots—the study of intelligent agents in the context of real-world software testbeds. In 2003, he started the KnowItAll project for acquiring massive amounts of information from the web. In 2005, he founded and became the director of the university's Turing Center. The center investigated problems in data mining, natural language processing, the Semantic Web and other web search topics. Etzioni coined the term machine reading and helped to create the first commercial comparison shopping agent. He has published over 200 technical papers, and his H-index exceeds 100. == Entrepreneurship == As a faculty member Etzioni was also an active entrepreneur, founding multiple companies and pioneering multiple technologies including MetaCrawler (bought by Infospace), Netbot (bought by Excite in 1997 for $35 million), and ClearForest (bought by Reuters). He founded Farecast, a travel metasearch and price prediction site, which was acquired by Microsoft in 2008 for $115 million. Before founding Farecast, he developed a program originally called Hamlet, that used algorithms to identify patterns in airfare data using data-mining techniques. He also co-founded Decide.com, a website to help consumers make buying decisions using previous price history and recommendations from other users. Decide.com was bought by eBay in September, 2013. Etzioni is also a venture partner at the Madrona Venture Group. He is founder and CEO of TrueMedia.org, a non-profit dedicated to fighting political deepfakes, which launched in April 2024. Etzioni is a co-founder of Vercept, an AI startup formed in 2025. == Founding CEO of AI2 == In September 2013 Etzioni was selected as the Founding CEO of the Allen Institute for Artificial Intelligence by philanthropist Paul G. Allen, and in January 2014 he took a leave of absence from the University of Washington to serve in that role. Etzioni's technical contributions continued at AI2; for example, in 2015, he helped to create the Semantic Scholar search engine. Under Etzioni’s leadership, AI2 grew from zero to over two hundred team members including notable researchers and engineers across several domains of AI. By 2021, its AI2 researchers had published near 700 papers in publications such as AAAI, ACL, CVPR, NeurIPS, and ICLR. Twenty-four of these papers had garnered special-recognition awards. AI2 also offered several key resources and tools to the AI community including the AllenNLP library, Semantic Scholar, and the conservation platforms EarthRanger and Skylight. Ed Lazowska, AI2 Board Member, has stated about Etzioni that he "took the collegial, collaborative culture that he absorbed in his 20+ years as a professor in UW's Allen School and mixed it with the singular focus that drives startups to create an elixir that AI2 folks have been drinking over the last eight years. The result is an exceptional organization of scientists, engineers, and entrepreneurs that's pursuing Paul Allen’s vision of ‘AI for the Common Good’ with extraordinary success.” == Popular press == In addition to his scientific publications, Etzioni has written commentary on AI for The New York Times, Wired, Nature, and other publications. After reading the idea in a book about AI by Brad Smith and Harry Shum, Etzioni has attempted to create an oath for AI practitioners. In 2018, he published what he called a "Hippocratic Oath for artificial intelligence practitioners" in TechCrunch. == Awards and recognition == In 1993, Etzioni received a National Young Investigator Award. In 2003, Etzioni was elected as AAAI Fellow. In 2005, Etzioni received an IJCAI Distinguished Paper Award for "A Probabilistic Model of Redundancy in Information Extraction". In 2007, he received the Robert S. Engelmore Memorial Award. In 2012 Etzioni was featured as GeekWire's "Geek of the Week". In 2013 Etzioni was voted "Geek of the Year" through GeekWire. In 2022, Etzioni received the 2012 ACL Test-of-Time Paper Award. In 2022, Etzioni, along with Ana-Maria Popescu and Henry Kautz, received the ACM Intelligent User Interfaces Most Impact Award for their 2003 paper, "Towards a Theory of Natural Language Interfaces to Databases". == Personal life == Etzioni has three children, and has said in interviews that family is his number one priority. He is married to Ivone Etzioni, and was previously married to Dr. Ruth Etzioni, a biostatistician at the Fred Hutchinson Cancer Center. Outside of his professional career, Etzioni has a wide range of personal interests. He has attended the Burning Man festival, which he described as a valuable way to step outside his comfort zone. His first computer was a TRS-80, and he has described his car’s GPS as his favorite gadget, joking that he has “no sense of direction.” == Selected publications == === Scholarly publications === Etzioni, Oren (July 1994). "A Softbot-based Interface to the Internet" (PDF). Communications of the ACM. Retrieved March 29, 2018. Etzioni, Oren (December 2008). "Open Information Extraction from the Web" (PDF). Communications of the ACM. Retrieved March 29, 2018. Zamir, Oren; Etzioni, Oren (1998). "Web document clustering". Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval. ACM. pp. 46–54. doi:10.1145/290941.290956. ISBN 978-1-58113-015-7. S2CID 244069. Zamir, Oren; Etzioni, Oren (May 1999). "Grouper: a dynamic clustering interface to Web search results". Computer Networks. 31 (11–16): 1361–1374. CiteSeerX 10.1.1.31.8216. doi:10.1016/S1389-1286(99)00054-7. S2CID 206134308. Popescu, Ana-Maria; Etzioni, Oren (2005). "Extracting product features and opinions from reviews". Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing - HLT '05. pp. 339–346. doi:10.3115/1220575.1220618. Etzioni, Oren; Cafarella, Michael; Downey, Doug; Popescu, Ana-Maria; Shaked, Tal; Sonderland, Stephen; Weld, Daniel; Yates, Alexander (June 2005). "Unsupervised named-entity extraction from the Web: An experimental study". Artificial Intelligence. 165 (1): 91–134. doi:10.1016/j.artint.2005.03.001. Downey, Doug; Etzioni, Oren; Sonderland, Stephen (July 2010). "Grouper: Analysis of a probabilistic model of redundancy in unsupervised information extraction". Artificial Intelligence. 174 (11): 726–748. CiteSeerX 10.1.1.174.2441. doi:10.1016/j.artint.2010.04.024. === Popular articles === Etzioni, Oren (August 4, 2011). "Web Search Needs a Shakeup" (PDF). Nature. Retrieved November 21, 2019. Etzioni, Oren (December 9, 2014). "AI Won't Exterminate Us – It Will Empower Us". Backchannel. Retrieved March 29, 2018. Etzioni, Oren (February 4, 2016). "To Keep AI Safe -- Use AI". Vox. Retrieved November 21, 2019. Etzioni, Oren (April 8, 2016). "Quora Session with Oren Etzioni". Quora. Retrieved March 29, 2018. Etzioni, Oren (June 15, 2016). "Deep Learning Isn't a Dangerous Magic Genie. It's Just Math". Wired. Retrieved March 29, 2018. Etzioni, Oren (September 20, 2016). "No, the Experts Don't Think Superintelligent AI is a Threat to Humanity". MIT Technology Review. Retrieved November 21, 2019. Etzioni, Oren (July 6, 2017). "Artificial intelligence: AI Zooms in on highly influential citations". Nature. Retrieved March 29, 2018. Etzioni, Oren (September 1, 2017). "How to Regulate Artificial Intelligence". The New York Times. Retrieved March 29, 2018. Etzioni, Oren (November 2, 2017). "Workers Displaced by Automation Should Try A New Job: Caregiver". Wired. Retrieved March 29, 2018. Etzioni, Oren (March 14, 2018). "A Hippocratic Oath for artificial intelligence practitioners". Tech Crunch. Retrieved March 29, 2018. Etzioni, Oren (March 7, 2018). "A 'Manhattan Project' for science research". The Hill. Retrieved November 21, 2019. Etzioni, Ore
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Jared Kaplan

Jared Daniel Kaplan is a theoretical physicist and artificial intelligence researcher. He is an associate professor in the Johns Hopkins University Department of Physics & Astronomy, and a co-founder and chief science officer of Anthropic. == Education == Kaplan attended the Illinois Mathematics and Science Academy during high school. He received a bachelor's degree in physics and mathematics from Stanford University and a PhD in physics from Harvard University. His doctoral thesis is titled Aspects of holography, advised by Nima Arkani-Hamed. == Academic career and physics research == Kaplan’s research interests include quantum gravity, holography (AdS/CFT), conformal field theory, and related topics in particle physics and cosmology. He worked as a postdoctoral fellow at SLAC and Stanford University and has been a professor at Johns Hopkins University since 2012. == Machine learning research == Kaplan joined OpenAI in 2019 as a researcher, where he co-authored Scaling Laws for Neural Language Models (2020), which reported that empirically, the performance of language models steadily improves with their size and the amount of data and compute used for training. He is also a co-author of Language Models are Few-Shot Learners (2020), which introduced GPT-3. At the company, he was also involved in the development of Codex. == Anthropic == Kaplan co-founded Anthropic and serves as its chief science officer. In October 2024, Anthropic announced that Kaplan would serve as the company's "Responsible Scaling Officer", overseeing its responsible scaling policy (RSP). In this role, Kaplan determines the safety assessments and precautions to adopt before model release. In December 2025, The Guardian published an interview with Kaplan about AI autonomy and recursive self-improvement timelines. == Honors and recognition == Kaplan was a Hertz Fellow (2005). He has also received a Sloan Research Fellowship and an NSF CAREER award (PHY-1454083). == Selected works == Scaling Laws for Neural Language Models (2020). Language Models are Few-Shot Learners (2020). A Natural Language for AdS/CFT Correlators (2011). == Personal life == As of 2026, Forbes estimated Kaplan's net worth at $3.7 billion. He lives in Pacifica, California, and has a son.
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This is a list of operating systems specifically focused on security. Similar concepts include security-evaluated operating systems that have achieved certification from an auditing organization, and trusted operating systems that provide sufficient support for multilevel security and evidence of correctness to meet a particular set of requirements. == Linux == === Android-based === GrapheneOS is a security-focused, Android-based mobile OS that uses a hardened kernel, C library, custom memory allocator (hardened_malloc), and a hardened Chromium-based browser named Vanadium. It also offers privacy/security features, such as Duress PIN/Password or disabling the USB-C port at a driver/hardware level to avoid exploitation. It deploys exploit mitigations such as hardware-based memory tagging, secure app spawning, restricted dynamic code loading, and more. === Debian-based === Linux Kodachi is a security-focused operating system. Tails is aimed at preserving privacy and anonymity. KickSecure is a security-focused Linux distribution that aims to be "hardened by default". It uses network hardening, kernel hardening, Strong Linux User Account Isolation, better randomness, root access restrictions, and app-specific hardening. Whonix is an anonymity focused operating system based on KickSecure. It consists of two virtual machines, And all communications are routed through Tor. === Other Linux distributions === Alpine Linux is designed to be small, simple, and secure. It uses musl, BusyBox, and OpenRC instead of the more commonly used glibc, GNU Core Utilities, and systemd. Owl - Openwall GNU/Linux, a security-enhanced Linux distribution for servers. Secureblue, a Fedora Silverblue based distro that uses a hardened kernel, custom memory allocator (hardened_malloc), Trivalent, a security-focused, Chromium-based browser inspired by Vanadium, and many other exploit mitigations. == BSD == OpenBSD is a Unix-like operating system that emphasizes portability, standardization, correctness, proactive security, and integrated cryptography. == Xen == Qubes OS aims to provide security through isolation. Isolation is provided through the use of virtualization technology. This allows the segmentation of applications into secure virtual machines.
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In search of the best AI text-to-video tool? An AI text-to-video tool is software that uses machine learning to help you get more done — it turns a rough idea into a polished result in seconds. When choosing one, weigh output quality, pricing, export formats, and how well it fits the tools you already use. Whether you are a beginner or a pro, the right AI text-to-video 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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AI Avatar Generators: Free vs Paid (2026)

Comparing the best AI avatar generator? An AI avatar generator 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 avatar generator 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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