AI For Economics Students

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

Escapex

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

In computer science, a Levenshtein automaton for a string w and a number n is a finite-state automaton that can recognize the set of all strings whose Levenshtein distance from w is at most n. That is, a string x is in the formal language recognized by the Levenshtein automaton if and only if x can be transformed into w by at most n single-character insertions, deletions, and substitutions. == Applications == Levenshtein automata may be used for spelling correction, by finding words in a given dictionary that are close to a misspelled word. In this application, once a word is identified as being misspelled, its Levenshtein automaton may be constructed, and then applied to all of the words in the dictionary to determine which ones are close to the misspelled word. If the dictionary is stored in compressed form as a trie, the time for this algorithm (after the automaton has been constructed) is proportional to the number of nodes in the trie, significantly faster than using dynamic programming to compute the Levenshtein distance separately for each dictionary word. It is also possible to find words in a regular language, rather than a finite dictionary, that are close to a given target word, by computing the Levenshtein automaton for the word, and then using a Cartesian product construction to combine it with an automaton for the regular language, giving an automaton for the intersection language. Alternatively, rather than using the product construction, both the Levenshtein automaton and the automaton for the given regular language may be traversed simultaneously using a backtracking algorithm. Levenshtein automata are used in Lucene for full-text searches that can return relevant documents even if the query is misspelled. == Construction == For any fixed constant n, the Levenshtein automaton for w and n may be constructed in time O(|w|). Mitankin studies a variant of this construction called the universal Levenshtein automaton, determined only by a numeric parameter n, that can recognize pairs of words (encoded in a certain way by bitvectors) that are within Levenshtein distance n of each other. Touzet proposed an effective algorithm to build this automaton. Yet a third finite automaton construction of Levenshtein (or Damerau–Levenshtein) distance are the Levenshtein transducers of Hassan et al., who show finite state transducers implementing edit distance one, then compose these to implement edit distances up to some constant.
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Yorick Wilks

Yorick Alexander Wilks FBCS (27 October 1939 – 14 April 2023) was a British computer scientist. He was an emeritus professor of artificial intelligence at the University of Sheffield, visiting professor of artificial intelligence at Gresham College (a post created especially for him), senior research fellow at the Oxford Internet Institute, senior scientist at the Florida Institute for Human and Machine Cognition, and a member of the Epiphany Philosophers. In February 2023, Wilks joined WiredVibe as Director of AI and a Board Member, with the goal of commercializing his previous research and ideas. He remained in this role until his death, which occurred shortly before WiredVibe was acquired by AKY X, a company that continues to build on his legacy and contributions. == Biography == Wilks was born in Gerrards Cross, Buckinghamshire in England. He was educated at Torquay Boys' Grammar School, followed by Pembroke College, Cambridge, where he read Philosophy, joined the Epiphany Philosophers and obtained his Doctor of Philosophy degree (1968) under Professor R. B. Braithwaite for the thesis 'Argument and Proof'; he was an early pioneer in meaning-based approaches to the understanding of natural language content by computers. His main early contribution in the 1970s was called "Preference Semantics" (Wilks, 1973; Wilks and Fass, 1992), an algorithmic method for assigning the "most coherent" interpretation to a sentence in terms of having the maximum number of internal preferences of its parts (normally verbs or adjectives) satisfied. That early work was hand-coded with semantic entries (of the order of some hundreds) as was normal at the time, but since then has led to the empirical determinations of preferences (chiefly of English verbs) in the 1980s and 1990s. A key component of the notion of preference in semantics was that the interpretation of an utterance is not a well- or ill-formed notion, as was argued in Chomskyan approaches, such as those of Jerry Fodor and Jerrold Katz. It was rather that a semantic interpretation was the best available, even though some preferences might not be satisfied. So, in "The machine answered the question with a low whine" the agent of "answer" does not satisfy that verb's preference for a human answerer—which would cause it to be deemed ill-formed by Fodor and Katz—but is accepted as sub-optimal or metaphorical, and, now, conventional. The function of the algorithm is not to determine well-formedness at all but to make the optimal selection of word-senses to participate in the overall interpretation. Thus, in "The Pole answered..." the system will always select the human sense of the agent and not the inanimate one if it gives a more coherent interpretation overall. Preference Semantics is thus some of the earliest computational work—with programs run at Systems Development Corporation in Santa Monica in 1967 in LISP on an IBM360—in the now established field of word sense disambiguation. This approach was used in the first operational machine translation system based principally on meaning structures and built by Wilks at Stanford Artificial Intelligence Laboratory in the early 1970s (Wilks, 1973) at the same time and place as Roger Schank was applying his "Conceptual Dependency" approach to machine translation. The LISP code of Wilks' system was in The Computer Museum, Boston. Wilks was elected a fellow of the American and European Associations for Artificial Intelligence, of the British Computer Society, a member of the UK Computing Research Committee, and a permanent member of ICCL, the International Committee on Computational Linguistics. He was professor of artificial intelligence at the University of Sheffield and a senior research fellow at the Oxford Internet Institute. In 1991 he received a Defense Advanced Projects Agency grant on interlingual pragmatics-based machine translation and in 1994 he received a grant by the Engineering and Physical Sciences Research Council to investigate in the field of large-scale information extraction (LaSIE); in the following years he would obtain more grants to carry on exploring the field of information extraction (AVENTINUS, ECRAN, PASTA...). In the 1990s Wilks also became interested in modelling human-computer dialogue and the team led by David Levy and him as chief researcher won the Loebner Prize in 1997. He was the founding director of the EU funded Companions Project on creating long-term computer companions for people. At his Festschrift in 2007 at the British Computer Society in London a volume of his own papers was presented along with a volume of essays in his honour. He was awarded the Antonio Zampolli prize in honour of his lifetime work at the LREC 2008 conference on 28 May 2008, and the Lifetime Achievement Award at the ACL 2008 conference on 18 June 2008. In 2009, he was awarded the British Computer Society's Lovelace Medal, its annual award for research achievement, and was awarded the Fellowship of the Association for Computing Machinery. In 1998, Wilks became head of the Department of Computer Science of the University of Sheffield, where he had started working in the year 1993 as professor of artificial intelligence, a post he still held. In 1993 he became the founding director of the Institute of Language, Speech and Hearing (ILASH). Wilks also set up the Natural Language Processing Group of the University of Sheffield. In 1994 he (along with Rob Gaizauskas and Hamish Cunningham) designed GATE, an advanced NLP architecture that has been widely distributed. National Life Stories conducted an oral history interview (C1672/24) with Yorick Wilks in 2016 for its Science and Religion collection held by the British Library. Wilks died on 14 April 2023, at the age of 83. == Awards == Wilks received many awards: (2009) Elected Fellow of the Association for Computing Machinery (2009) Lovelace Medal by the British Computer Society (2008) Zampolli Prize (ELRA, awarded at LREC in Marrakech, Morocco) (2008) Lifetime Achievement Award (Association for Computational Linguistics, in Columbus) (2006) Visiting Professor, University of Oxford (2004) Elected to UK Computing Research Committee (2004) Elected Fellow, British Computer Society (2003) Visiting Fellow, Oxford Internet Institute (1998) Elected Fellow of European Association for Artificial Intelligence (1997) Elected Fellow, EPSRC College of Computing (1991) Visiting Fellow, Trinity Hall, Cambridge (1991) Elected Fellow of the American Association for Artificial Intelligence (1983) Royal Society Travel Fellowship (1983) Commonwealth of Australia Visiting Professor (1981) Visiting Sloan Fellow, University of California, Berkeley (1980) Invited Participant in the Nobel Symposium on Language, Stockholm (1979) NATO Senior Scientist Fellowship (1979) Visiting Sloan Fellow, Yale University (1975) SRC Senior Visiting Fellowship, University of Edinburgh == Membership == Wilks was an active member of the following associations: Association for Computational Linguistics Society for the Study of AI and Simulation of Behaviour Association for Computing Machinery Cognitive Science Society British Society for the Philosophy of Science American Association for Artificial Intelligence Aristotelian Society == Selected works == === Books === Wilks, Y. (2019) Artificial Intelligence: Modern Magic or Dangerous Future?.Icon Books. New illustrated edition, 2023, MIT Press. Wilks, Y. (2015) Machine Translation: its scope and limits. Springer Wilks, Y (ed.) (2010) Close Engagements with Artificial Companions: Key Social, Psychological and Design issues. John Benjamins; Amsterdam Wilks, Y., Brewster, C. (2009) Natural Language Processing as a Foundation of the Semantic Web. Now Press: London. Wilks, Y. (2007) Words and Intelligence I, Selected papers by Yorick Wilks. In K. Ahmad, C. Brewster & M. Stevenson (eds.), Springer: Dordrecht. Wilks, Y. (ed. and with introduction and commentaries). (2006) Language, cohesion and form: selected papers of Margaret Masterman. Cambridge: Cambridge University Press. Wilks, Y., Nirenburg, S., Somers, H. (eds.) (2003) Readings in Machine Translation. Cambridge, MA: MIT Press. Wilks, Y.(ed.). (1999) Machine Conversations. Kluwer: New York. Wilks, Y., Slator, B., Guthrie, L. (1996) Electric Words: dictionaries, computers and meanings. Cambridge, MA: MIT Press. Ballim, A., Wilks, Y. (1991) Artificial Believers. Norwood, NJ: Erlbaum. Wilks, Y.(ed.). (1990) Theoretical Issues in Natural Language Processing. Norwood, NJ: Erlbaum. Wilks, Y., Partridge, D. (eds. plus three YW chapters and an introduction). (1990) The Foundations of Artificial Intelligence: a sourcebook. Cambridge: Cambridge University Press. Wilks, Y., Sparck-Jones, K.(eds.). (1984) Automatic Natural Language Processing, paperback edition. New York: Wiley. Originally published by Ellis Horwood. Wilks, Y., Charniak, E. (eds and principal authors). (1976) Computational Semantics—an Introduction to Artificial Intelligence and
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AI Resume Builders: Free vs Paid (2026)

Comparing the best AI resume builder? An AI resume builder 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 resume builder 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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Model compression

Model compression is a machine learning technique for reducing the size of trained models. Large models can achieve high accuracy, but often at the cost of significant resource requirements. Compression techniques aim to compress models without significant performance reduction. Smaller models require less storage space, and consume less memory and compute during inference. Compressed models enable deployment on resource-constrained devices such as smartphones, embedded systems, edge computing devices, and consumer electronics computers. Efficient inference is also valuable for large corporations that serve large model inference over an API, allowing them to reduce computational costs and improve response times for users. Model compression is not to be confused with knowledge distillation, in which a smaller "student" model is trained to imitate the input-output behavior of a larger "teacher" model (as opposed to using the "teacher"'s trained parameters or the "teacher"'s training targets). == Techniques == Several techniques are employed for model compression. === Pruning === Pruning sparsifies a large model by setting some parameters to exactly zero. This effectively reduces the number of parameters. This allows the use of sparse matrix operations, which are faster than dense matrix operations. Pruning criteria can be based on magnitudes of parameters, the statistical pattern of neural activations, Hessian values, etc. === Quantization === Quantization reduces the numerical precision of weights and activations. For example, instead of storing weights as 32-bit floating-point numbers, they can be represented using 8-bit integers. Low-precision parameters take up less space, and takes less compute to perform arithmetic with. It is also possible to quantize some parameters more aggressively than others, so for example, a less important parameter can have 8-bit precision while another, more important parameter, can have 16-bit precision. Inference with such models requires mixed-precision arithmetic. Quantized models can also be used during training (rather than after training). PyTorch implements automatic mixed-precision (AMP), which performs autocasting, gradient scaling, and loss scaling. === Low-rank factorization === Weight matrices can be approximated by low-rank matrices. Let W {\displaystyle W} be a weight matrix of shape m × n {\displaystyle m\times n} . A low-rank approximation is W ≈ U V T {\displaystyle W\approx UV^{T}} , where U {\displaystyle U} and V {\displaystyle V} are matrices of shapes m × k , n × k {\displaystyle m\times k,n\times k} . When k {\displaystyle k} is small, this both reduces the number of parameters needed to represent W {\displaystyle W} approximately, and accelerates matrix multiplication by W {\displaystyle W} . Low-rank approximations can be found by singular value decomposition (SVD). The choice of rank for each weight matrix is a hyperparameter, and jointly optimized as a mixed discrete-continuous optimization problem. The rank of weight matrices may also be pruned after training, taking into account the effect of activation functions like ReLU on the implicit rank of the weight matrices. == Training == Model compression may be decoupled from training, that is, a model is first trained without regard for how it might be compressed, then it is compressed. However, it may also be combined with training. The "train big, then compress" method trains a large model for a small number of training steps (less than it would be if it were trained to convergence), then heavily compress the model. It is found that at the same compute budget, this method results in a better model than lightly compressed, small models. In Deep Compression, the compression has three steps. First loop (pruning): prune all weights lower than a threshold, then finetune the network, then prune again, etc. Second loop (quantization): cluster weights, then enforce weight sharing among all weights in each cluster, then finetune the network, then cluster again, etc. Third step: Use Huffman coding to losslessly compress the model. The SqueezeNet paper reported that Deep Compression achieved a compression ratio of 35 on AlexNet, and a ratio of ~10 on SqueezeNets.
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AI Code Generators Reviews: What Actually Works in 2026

Trying to pick the best AI code generator? An AI code generator is software that uses machine learning to help you get more done — it scales effortlessly from a single task to thousands. The best picks balance beginner-friendly simplicity with the depth power users need, and they ship updates often. Whether you are a beginner or a pro, the right AI code 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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Anil K. Jain (computer scientist, born 1948)

Anil Kumar Jain (born 1948) is an Indian-American computer scientist and University Distinguished Professor in the Department of Computer Science and Engineering at Michigan State University. He is one of the most highly cited researchers in computer science, and is internationally recognized for his foundational contributions to pattern recognition, computer vision, and biometric recognition, particularly in fingerprint recognition and face recognition. Jain is a member of the United States National Academy of Engineering, a Foreign Member of the Chinese Academy of Sciences, and a Foreign Fellow of the Indian National Academy of Engineering. He is a Fellow of the ACM, IEEE, AAAS, IAPR, and SPIE. His research has shaped the field of biometrics and has been applied in systems used worldwide for identity verification, law enforcement, and border security. In 2024, he was awarded the BBVA Foundation Frontiers of Knowledge Award in the category of Information and Communication Technologies. == Early life and education == Born in Basti, India, Jain received his Bachelor of Technology in electrical engineering from the Indian Institute of Technology, Kanpur in 1969. He then moved to the United States, where he earned his M.S. in 1970 and Ph.D. in 1973 from Ohio State University. His doctoral dissertation, titled Some Aspects of Dimensionality and Sample Size Problems in Statistical Pattern Recognition, was supervised by Robert B. McGhee and laid the groundwork for his subsequent research in pattern recognition. == Career == Jain began his academic career at Wayne State University, where he taught from 1972 to 1974. In 1974, he joined the faculty of Michigan State University, where he has remained for over five decades and currently holds the position of University Distinguished Professor. Throughout his career, Jain has conducted pioneering research in data clustering, fingerprint recognition, and face recognition. His work has been published in leading scientific journals including Scientific American, Nature, IEEE Spectrum, and MIT Technology Review. He served as Editor-in-Chief of the IEEE Transactions on Pattern Analysis and Machine Intelligence from 1991 to 1994. Jain has also contributed to national security and policy through his service on several advisory bodies. He served as a member of the U.S. National Academies panels on Information Technology, Whither Biometrics, and Improvised Explosive Devices (IED). He has also served on the Defense Science Board, the Forensic Science Standards Board, and the AAAS Latent Fingerprint Working Group. In 2014, Jain was named Innovator of the Year at Michigan State University for transferring several technologies on face and fingerprint recognition to major players in the biometrics industry. He holds eight U.S. and Korean patents related to biometric technologies. == Research contributions == Jain's research spans pattern recognition, computer vision, machine learning, and biometric recognition. His contributions have been particularly influential in several areas: === Biometric recognition === Jain is considered one of the foremost authorities on biometric recognition systems. His research group at Michigan State University has developed algorithms and systems for fingerprint, face, and iris recognition that have been widely adopted in both academic research and commercial applications. His work on fingerprint matching algorithms has been instrumental in establishing standards for automated fingerprint identification systems (AFIS) used by law enforcement agencies worldwide. In recent years, Jain and his research team have made significant advances in child fingerprint recognition, demonstrating that digital scans of a young child's fingerprint can be correctly recognized one year later with over 99 percent accuracy for children as young as six months old. This research has important implications for child identification in developing countries, where it can be used to track immunization records and provide access to medical care. === Data clustering === Jain's survey article "Data clustering: a review" (1999), co-authored with M. N. Murty and P. J. Flynn, is one of the most highly cited papers in computer science. His 2010 paper "Data Clustering: 50 Years Beyond K-Means" provided a comprehensive overview of the evolution of clustering methods and remains an essential reference in the field. === Statistical pattern recognition === Jain's work on statistical pattern recognition, including his influential survey "Statistical pattern recognition: A review" (2000) with R. P. W. Duin and Jianchang Mao, has shaped the theoretical foundations of the field. == Citation metrics and academic impact == Jain is among the most highly cited researchers in computer science. Based on his Google Scholar profile, he had an h-index of 200 in 2020, which was the highest among computer scientists identified in a survey published by UCLA at the time. As of August 2023, his h-index on Google Scholar is 211. He has since been surpassed by Yoshua Bengio, a researcher of similar subjects (neural networks and deep learning for artificial intelligence), who had an h-index of 224 as of August 2023. Another source reported that as of December 2022, he had the highest discipline h-index (D-index) in computer science. == Honors and awards == Jain has received numerous awards and honors recognizing his contributions to computer science and engineering: === Academy memberships === Member, United States National Academy of Engineering (2016) — elected "for contributions to the engineering and practice of biometrics" Foreign Fellow, Indian National Academy of Engineering (2016) Foreign Member, Chinese Academy of Sciences (2019) Member, The World Academy of Sciences (2019) Fellow, National Academy of Inventors === Professional society fellowships === Fellow, ACM Fellow, IEEE (1988) — for contributions to image processing Fellow, AAAS Fellow, International Association for Pattern Recognition Fellow, SPIE === Major awards === BBVA Foundation Frontiers of Knowledge Award in Information and Communication Technologies (2024) IAPR King-Sun Fu Prize (2008) IEEE W. Wallace McDowell Award (2007) — the highest technical honor awarded by the IEEE Computer Society, for pioneering contributions to theory, technique, and practice of pattern recognition, computer vision, and biometric recognition systems IEEE Computer Society Technical Achievement Award (2003) IAPR Pierre Devijver Award (2002) Humboldt Research Award (2002) Guggenheim Fellowship (2001) Fulbright Fellowship (1998) IEEE ICDM Research Contribution Award (2008) === Best paper awards === IEEE Transactions on Neural Networks (1996) Pattern Recognition journal (1987, 1991, 2005) === Honorary doctorates === Universidad Autónoma de Madrid (2018) Hong Kong University of Science and Technology (2021) == Legacy and endowments == Two endowed funds have been established in Jain's honor at Michigan State University, recognizing his lasting impact on the field and the university. In 2015, a former visiting scholar from Jain's laboratory made an anonymous $400,000 gift to create the Anil K. Jain Endowed Graduate Fellowship, which supports doctoral-level research in pattern recognition, computer vision, and biometric recognition. In 2022, the Anil K. and Nandita K. Jain Endowed Professorship was established through $1 million in contributions from multiple donors, including a substantial gift from the Jain family, to support faculty recruitment and retention in the Department of Computer Science and Engineering. == Selected publications == === Books === 1988. Algorithms For Clustering Data. With Richard C. Dubes. Prentice Hall. 1993. Markov Random Fields: Theory and Applications. With Rama Chellappa eds. Academic Press. 1999. Biometrics: Personal Identification in Networked Society. With Ruud M. Bolle and Sharath Pankanti eds. Springer. 2003. Handbook of Fingerprint Recognition. (2nd edition 2009). With D. Maio, D. Maltoni, S. Prabhakar. Springer. 2005. Handbook of Face Recognition. (2nd edition 2011). With S. Z. Li ed. Springer. 2006. Handbook of Multibiometrics. With A. Ross and K. Nandakumar. Springer. 2007. Handbook of Biometrics. With P. Flynn and A. Ross eds. Springer. 2011. Introduction to Biometrics. With A. Ross and K. Nandakumar. Springer. 2015. Encyclopedia of Biometrics (Second Edition). With Stan Li. Springer. === Research articles === Cross, George R. and Anil K. Jain. "Markov random field texture models". IEEE Transactions on Pattern Analysis and Machine Intelligence (1983): 25–39. Jain, Anil K., and Farshid Farrokhnia. "Unsupervised texture segmentation using Gabor filters". Pattern Recognition 24.12 (1991): 1167–1186. Jain, Anil K., and Douglas Zongker. "Feature selection: Evaluation, application, and small sample performance". IEEE Transactions on Pattern Analysis and Machine Intelligence, 19.2 (1997): 153–158. Jain, Anil K., L. Hong, S. Pankanti, R. Bolle. "An Identity-A
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How to Choose an AI Video Editor

In search of the best AI video editor? An AI video editor 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 video editor 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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Path tracing

Path tracing is a rendering algorithm in computer graphics that simulates how light interacts with objects and participating media to generate realistic (physically plausible) images. It is based on earlier, more limited, ray tracing algorithms. Path tracing is used to create photorealistic images for artistic purposes, and for applications such as architectural rendering and product design. It is also used to render frames for animated films, and visual effects for film and television. Because it can be very accurate and unbiased, it is commonly used to generate reference images when testing the quality of other rendering algorithms. The technique uses the Monte Carlo method to compute estimates of global illumination and simulate the ways different materials reflect (or scatter), transmit, absorb, and emit light. It can incorporate simple modeling of the effects of aperture and lens (depth of field, and bokeh) and shutter speed (motion blur), or more realistic simulation of the optical components in a camera. The algorithm works by describing illumination in a scene using the rendering equation, or light transport equation, and finding an approximate solution using Monte Carlo integration. An inefficient (but accurate) version of the algorithm can be very simple, and involves tracing a ray from the camera, allowing this ray to bounce in random directions as it hits different objects in the scene, and computing the amount of light transmitted along the path to the camera whenever the path encounters a light source. This process is repeated many times for each pixel (each repetition, with generated path and transmitted light, is called a sample), and the results are averaged. One main difference between this algorithm and standard ray tracing is that a single unbranching path is traced each time, while "Whitted-style" or "Cook-style" ray tracing recursively samples branching paths (e.g. when light is both reflected and refracted by a glass object). More practical versions incorporate improvements such as quasi-Monte Carlo methods (techniques that distribute samples more evenly), importance sampling (take more samples of paths that are likely to transport more light), and next event estimation (allow a very limited form of branching, and sample additional paths that connect to the lights more directly). Because path tracing uses random samples there is noise in the final image, which decreases as more samples are taken. Images commonly require many thousands of samples per pixel (spp) to reduce noise to an acceptable level, and denoising techniques (e.g. based on neural networks) are often used. Denoising is usually necessary when path tracing is used for real-time rendering in video games, because relatively few samples can be taken. Many alternative algorithms for path tracing have been developed, although they do not always outperform more straightforward implementations. These include bidirectional path tracing (which traces paths forwards from the light source as well as backwards from the camera), Metropolis light transport, and ways of combining path tracing with photon mapping. Video games often use biased versions of path tracing to improve performance (e.g. limiting the number of bounces in each path). A family of techniques called ReSTIR has been developed that can help real-time path tracing by sharing data between nearby pixels and consecutive frames. == History == Like all ray tracing methods, path tracing is based on ray casting, which Arthur Appel used for computer graphics rendering in the late 1960s. In 1980, John Turner Whitted published a recursive ray tracing algorithm that allows rendering images of scenes containing mirrored surfaces and refractive transparent objects. In 1984, Cook et al. described a form of ray tracing called distributed ray tracing, which uses Monte Carlo integration to render effects such as depth of field, motion blur, reflection from rough surfaces, and area lights. The same year, the radiosity method (not a ray tracing method) was published, which was the first physically based method for rendering diffuse global illumination. In 1986, Jim Kajiya published a paper exploring how to use distributed ray tracing to render physically-based global illumination, and this paper also introduced and named the method called "path tracing". Path tracing and other distributed ray tracing techniques were further refined in the late 1980s and early 1990s by researchers such as James Arvo and Peter Shirley, and by Greg Ward in the open source Radiance software. Despite being theoretically able to render any lighting, the original form of path tracing can sometimes be very inefficient (or noisy) for rendering light that is reflected or refracted before illuminating a visible surface, including diffuse global illumination where light enters an area through narrow gaps, because it traces paths only from the camera. To address this, variations of path tracing that trace paths from both the camera and from light sources, called bidirectional path tracing, were published in 1993 by Eric Lafortune and Yves Willems, and in 1997 by Eric Veach and Leonidas Guibas. In 1997 Veach and Guibas also published an alternative method called Metropolis light transport, which combines bidirectional path tracing with the Metropolis method. Veach's lengthy Ph.D. dissertation described both techniques, along with the theoretical background of path tracing; later, the book Physically Based Rendering (which won an Academy Award for Technical Achievement in 2014) helped to make information about path tracing more widely available. Path tracing requires tracing a large number of paths of light in order to produce an image with a visually acceptable amount of noise. This made path tracing very slow on computers available in the 1980s and 1990s, and noise remained a problem when trying to reproduce the style of earlier computer graphics animated films. Most animated films produced until around 2010, by studios such as Pixar, used rasterization-based rendering, with ray tracing used selectively for reflections (and later for precomputed or cached global illumination). However the speed of computers rapidly increased during the 1990s. Blue Sky Studios pioneered using Monte Carlo ray tracing for global illumination in animation, including in the 1998 short film "Bunny", but they did not disclose the precise techniques used. Path tracing gradually become more practical for film production in the early 2000s. The Arnold renderer, developed by Marcos Fajardo, was used by Sony Pictures Imageworks to produce the feature-length film Monster House, released in 2006. Pixar rewrote their RenderMan software to use path tracing, and released their first feature-length path-traced film Finding Dory in 2016. Although path tracing still had a large computational cost, animation studios discovered that less human labor was required when using it, for example because global illumination no longer needed to be faked by manually placing lights. The amount of noise present in path traced images still caused difficulties, particularly when rendering motion blur (which was used extensively by earlier animated films) but denoising techniques were developed to address this. New techniques were also needed for rendering hair and fur, and to handle the extremely large scenes sometimes required by films. Renderers such as Arnold, and Disney's Hyperion, originally only used CPUs for rendering, but as GPUs became more capable (and APIs such as CUDA, OpenCL, and OptiX were released) researchers and developers began adapting algorithms and implementations to use GPUs. GPUs can dramatically reduce rendering time: for example using a high-end GPU to accelerate portions of the rendering code can make it over 30 times faster than using only a high-end CPU. == Description == Kajiya's 1986 paper defined a recursive integral equation called the rendering equation, which describes a simplified form of light transport. Using Monte Carlo integration for the integral on the right side of the equation leads fairly directly to the path tracing algorithm: I ( x , x ′ ) = g ( x , x ′ ) [ ϵ ( x , x ′ ) + ∫ S ρ ( x , x ′ , x ″ ) I ( x ′ , x ″ ) d x ″ ] {\displaystyle I(x,x')=g(x,x')\left[\epsilon (x,x')+\int _{S}\rho (x,x',x'')I(x',x'')dx''\right]} This expresses I(x,x'), the light arriving at point x from point x', as the product of a geometry term, g(x,x'), which is 0 if there is something blocking the light between the two points and 1 otherwise, and the amount of light leaving point x' and traveling towards x. The light leaving point x' is the sum of the light emitted by the surface at x', and the integral of the light arriving at x' from all other points in the scene (the integration domain S) and being reflected towards x. The factor ρ(x,x',x''), which calculates how much light is reflected, must take into account the angles at which the light is arriving and leaving, and
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AI Code-review Tools Reviews: What Actually Works in 2026

Shopping for the best AI code-review tool? An AI code-review tool is software that uses machine learning to help you get more done — it keeps getting smarter as the underlying models improve. Pricing, accuracy, and the size of the model behind the tool are the three factors that most affect daily usefulness. Whether you are a beginner or a pro, the right AI code-review 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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Yejin Choi

Yejin Choi (Korean: 최예진; born 1977) is the Dieter Schwarz Foundation Professor and Senior Fellow at the Department of Computer Science at Stanford University and the Stanford Institute for Human-Centered Artificial Intelligence (HAI) respectively. Her research considers natural language processing and computer vision. == Early life and education == Choi is from South Korea. She attended Seoul National University. After earning a bachelor's degree in Computer Science, Choi moved to the United States, where she joined Cornell University as a graduate student. There she worked with Claire Cardie on natural language processing. After earning her doctorate, Choi joined Stony Brook University as an Assistant Professor of Computer Science. At Stony Brook University Choi developed a statistical technique to identify fake hotel reviews. == Research and career == In 2018 Choi joined the Allen Institute for AI. Her research looks to endow computers with a statistical understanding of written language. She became interested in neural networks and their application in artificial intelligence. She started to assemble a knowledge base that became known as the atlas of machine commonsense (ATOMIC). By the time she had finished the creation of ATOMIC, the language model generative Pre-trained Transformer 2 (GPT-2) had been released. ATOMIC does not make use of linguistic rules, but combines the representations of different languages within a neural network. In 2020, Choi was endowed with the Brett Helsel Professorship, which she held until she became Chair of Computer Science in 2023. She has since made use of Commonsense Transformers (COMET) with Good old fashioned artificial intelligence (GOFAI). The approach combines symbolic reasoning and neural networks. She has developed computational models that can detect biases in language that work against people from underrepresented groups. For example, one study demonstrated that female film characters are portrayed as less powerful than their male counterparts. In 2023, Choi became The Wissner-Slivka Chair of Computer Science. Choi is also a scientific advisor to French research group Kyutai which is being funded by Xavier Niel, Rodolphe Saadé, Eric Schmidt, and others. In 2025, Stanford HAI announced the appointment of Choi as senior fellow and the Dieter Schwarz Foundation HAI Professor and Professor of Computer Science at Stanford University. == Awards and honours == 2013 International Conference on Computer Vision Marr Prize 2016 Institute of Electrical and Electronics Engineers AI One to Watch 2017 Facebook ParlAI Research Award 2018 Anita Borg Early Career Award 2020 Association for the Advancement of Artificial Intelligence Outstanding Paper Award 2021 Conference on Neural Information Processing Systems Outstanding Paper Award 2021 Association for Computational Linguistics Test-of-time Paper Award 2021 Conference on Computer Vision and Pattern Recognition Longuet-Higgins Prize 2022 North American Chapter of the Association for Computational Linguistics Best Paper Award 2022 International Conference on Machine Learning Outstanding Paper Award 2022 MacArthur Fellowship 2023 Association for Computational Linguistics Best Paper Award 2023 TIME100 Archived 2024-12-27 at the Wayback Machine AI 2023 2023 Empirical Methods in Natural Language Processing Outstanding Paper Award 2025 Association for Computational Linguistics Outstanding Paper Award 2025 Association for Computational Linguistics Best Demo Paper Award 2025 TIME100 AI 2025 == Select publications == Ott, Myle; Choi, Yejin; Cardie, Claire; Hancock, Jeffrey T. (2011). "Finding Deceptive Opinion Spam by Any Stretch of the Imagination". Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies. Portland, Oregon, USA: Association for Computational Linguistics: 309–319. arXiv:1107.4557. Bibcode:2011arXiv1107.4557O. ISBN 9781932432879. S2CID 2510724. Kulkarni, Girish; Premraj, Visruth; Ordonez, Vicente; Dhar, Sagnik; Li, Siming; Choi, Yejin; Berg, Alexander C.; Berg, Tamara L. (2013). "BabyTalk: Understanding and Generating Simple Image Descriptions". IEEE Transactions on Pattern Analysis and Machine Intelligence. 35 (12): 2891–2903. Bibcode:2013ITPAM..35.2891K. CiteSeerX 10.1.1.225.5228. doi:10.1109/TPAMI.2012.162. ISSN 1939-3539. PMID 22848128. Choi, Yejin; Cardie, Claire; Riloff, Ellen; Patwardhan, Siddharth (2005). "Identifying sources of opinions with conditional random fields and extraction patterns". Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing - HLT '05. Morristown, NJ, USA: Association for Computational Linguistics. pp. 355–362. doi:10.3115/1220575.1220620.
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Self-verifying finite automaton

In automata theory, a self-verifying finite automaton (SVFA) is a special kind of a nondeterministic finite automaton (NFA) with a symmetric kind of nondeterminism introduced by Hromkovič and Schnitger. Generally, in self-verifying nondeterminism, each computation path is concluded with any of the three possible answers: yes, no, and I do not know. For each input string, no two paths may give contradictory answers, namely both answers yes and no on the same input are not possible. At least one path must give answer yes or no, and if it is yes then the string is considered accepted. SVFA accept the same class of languages as deterministic finite automata (DFA) and NFA but have different state complexity. == Formal definition == An SVFA is represented formally by a 6-tuple, A=(Q, Σ, Δ, q0, Fa, Fr) such that (Q, Σ, Δ, q0, Fa) is an NFA, and Fa, Fr are disjoint subsets of Q. For each word w = a1a2 … an, a computation is a sequence of states r0,r1, …, rn, in Q with the following conditions: r0 = q0 ri+1 ∈ Δ(ri, ai+1), for i = 0, …, n−1. If rn ∈ Fa then the computation is accepting, and if rn ∈ Fr then the computation is rejecting. There is a requirement that for each w there is at least one accepting computation or at least one rejecting computation but not both. == Results == Each DFA is a SVFA, but not vice versa. Jirásková and Pighizzini proved that for every SVFA of n states, there exists an equivalent DFA of g ( n ) = Θ ( 3 n / 3 ) {\displaystyle g(n)=\Theta (3^{n/3})} states. Furthermore, for each positive integer n, there exists an n-state SVFA such that the minimal equivalent DFA has exactly g ( n ) {\displaystyle g(n)} states. Other results on the state complexity of SVFA were obtained by Jirásková and her colleagues.
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Qstack

Qstack is a cloud management platform developed by GreenQloud, a cloud computing software company founded in Reykjavik, Iceland in February 2010. Qstack enables its users to manage multiple clouds and hybrid deployments through a single self-service portal. Qstack is in continuous development, incorporating developments within infrastructure, cloud, and application management solutions. The next release of Qstack is slated for June 2017. == History == In 2014 when Jonsi Stefansson joined as CEO, Greenqloud pivoted its operational focus to development of Qstack with beta launch in the fall of 2015, and began offering support, technical services and certifications for the software. == Features == Qstack is hypervisor agnostic (KVM, VMware, Hyper-V) and can manage private clouds in multiple locations as well as AWS, Azure, and EC2-compatible public clouds from its user interface. Qstack combines proprietary software with open-source components, and the company claims to harden them to meet the strict security standards often required by enterprise deployments. Qstack features VM templates for Windows, Linux, and other operating systems. It also features full SSH/RDP access to instances, virtual routers, firewalls, and load balancers built into the interface. == Reception == In a 2015 review, IDG columnist J. Peter Bruzzese praised Qstack’s user interface for its ease-of-use and clean look.
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AI Presentation Makers: Free vs Paid (2026)

Curious about the best AI presentation maker? An AI presentation 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 presentation maker slots into your workflow and pays for itself fast. This guide breaks down the top picks, their pros and cons, and who each one is best for.
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AI Headshot Generators: Free vs Paid (2026)

Curious about the best AI headshot generator? An AI headshot 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 headshot 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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