AI Headshot Examples

AI Headshot Examples — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

  • Nobody (username)

    Nobody (username)

    In many Unix variants, "nobody" is the conventional name of a user identifier which owns no files, is in no privileged groups, and has no abilities except those which every other user has. It is normally not enabled as a user account, i.e. has no home directory or login credentials assigned. Some systems also define an equivalent group "nogroup". == Uses == The pseudo-user "nobody" and group "nogroup" are used, for example, in the NFSv4 implementation of Linux by idmapd, if a user or group name in an incoming packet does not match any known username on the system. It was once common to run daemons as nobody, especially on servers, in order to limit the damage that could be done by a malicious user who gained control of them. However, the usefulness of this technique is reduced if more than one daemon is run like this, because then gaining control of one daemon would provide control of them all. The reason is that processes owned by the same user have the ability to send signals to each other and use debugging facilities to read or even modify each other's memory. Modern practice, as recommended by the Linux Standard Base, is to create a separate user account for each daemon.

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  • Vector quantization

    Vector quantization

    Vector quantization (VQ) is a classical quantization technique from signal processing that allows the modeling of probability density functions by the distribution of prototype vectors. Developed in the early 1980s by Robert M. Gray, it was originally used for data compression. It works by dividing a large set of points (vectors) into groups having approximately the same number of points closest to them. Each group is represented by its centroid point, as in k-means and some other clustering algorithms. In simpler terms, vector quantization chooses a set of points to represent a larger set of points. The density matching property of vector quantization is powerful, especially for identifying the density of large and high-dimensional data. Since data points are represented by the index of their closest centroid, commonly occurring data have low error, and rare data high error. This is why VQ is suitable for lossy data compression. It can also be used for lossy data correction and density estimation. Vector quantization is based on the competitive learning paradigm, so it is closely related to the self-organizing map model and to sparse coding models used in deep learning algorithms such as autoencoder. == Training == One simple training algorithm for vector quantization is: Pick a sample point at random Move the nearest quantization vector centroid towards this sample point, by a small fraction of the distance Repeat A more sophisticated algorithm reduces the bias in the density matching estimation and ensures that all points are used, by including an extra sensitivity parameter: Increase each centroid's sensitivity s i {\displaystyle s_{i}} by a small amount Pick a sample point P {\displaystyle P} at random For each quantization vector centroid c i {\displaystyle c_{i}} , let d ( P , c i ) {\displaystyle d(P,c_{i})} denote the distance of P {\displaystyle P} and c i {\displaystyle c_{i}} Find the centroid c i {\displaystyle c_{i}} for which d ( P , c i ) − s i {\displaystyle d(P,c_{i})-s_{i}} is the smallest Move c i {\displaystyle c_{i}} towards P {\displaystyle P} by a small fraction of the distance Set s i {\displaystyle s_{i}} to zero Repeat It is desirable to use a cooling schedule to produce convergence: see Simulated annealing. Another simple method is LBG, which is based on k-means. The algorithm can be iteratively updated with "live" data, rather than by picking random points from a data set, but this will introduce some bias if the data are temporally correlated over many samples. == Applications == Vector quantization is used for lossy data compression, lossy data correction, pattern recognition, density estimation and clustering. Lossy data correction, or prediction, is used to recover data missing from some dimensions. It is done by finding the nearest group with the data dimensions available, then predicting the result based on the values for the missing dimensions, assuming that they will have the same value as the group's centroid. For density estimation, the area/volume that is closer to a particular centroid than to any other is inversely proportional to the density (due to the density matching property of the algorithm). === Use in data compression === Vector quantization, also called "block quantization" or "pattern matching quantization" is often used in lossy data compression. It works by encoding values from a multidimensional vector space into a finite set of values from a discrete subspace of lower dimension. A lower-space vector requires less storage space, so the data is compressed. Due to the density matching property of vector quantization, the compressed data has errors that are inversely proportional to density. The transformation is usually done by projection or by using a codebook. In some cases, a codebook can be also used to entropy code the discrete value in the same step, by generating a prefix coded variable-length encoded value as its output. The set of discrete amplitude levels is quantized jointly rather than each sample being quantized separately. Consider a k-dimensional vector [ x 1 , x 2 , . . . , x k ] {\displaystyle [x_{1},x_{2},...,x_{k}]} of amplitude levels. It is compressed by choosing the nearest matching vector from a set of n-dimensional vectors [ y 1 , y 2 , . . . , y n ] {\displaystyle [y_{1},y_{2},...,y_{n}]} , with n < k. All possible combinations of the n-dimensional vector [ y 1 , y 2 , . . . , y n ] {\displaystyle [y_{1},y_{2},...,y_{n}]} form the vector space to which all the quantized vectors belong. Only the index of the codeword in the codebook is sent instead of the quantized values. This conserves space and achieves more compression. Twin vector quantization (VQF) is part of the MPEG-4 standard dealing with time domain weighted interleaved vector quantization. === Video codecs based on vector quantization === Bink video Cinepak Daala is transform-based but uses pyramid vector quantization on transformed coefficients Digital Video Interactive: Production-Level Video and Real-Time Video Indeo Microsoft Video 1 QuickTime: Apple Video (RPZA) and Graphics Codec (SMC) Sorenson SVQ1 and SVQ3 Smacker video VQA format, used in many games The usage of video codecs based on vector quantization has declined significantly in favor of those based on motion compensated prediction combined with transform coding, e.g. those defined in MPEG standards, as the low decoding complexity of vector quantization has become less relevant. === Audio codecs based on vector quantization === AMR-WB+ CELP CELT (now part of Opus) is transform-based but uses pyramid vector quantization on transformed coefficients Codec 2 DTS G.729 iLBC Ogg Vorbis TwinVQ === Use in pattern recognition === VQ was also used in the eighties for speech and speaker recognition. Recently it has also been used for efficient nearest neighbor search and on-line signature recognition. In pattern recognition applications, one codebook is constructed for each class (each class being a user in biometric applications) using acoustic vectors of this user. In the testing phase the quantization distortion of a testing signal is worked out with the whole set of codebooks obtained in the training phase. The codebook that provides the smallest vector quantization distortion indicates the identified user. The main advantage of VQ in pattern recognition is its low computational burden when compared with other techniques such as dynamic time warping (DTW) and hidden Markov model (HMM). The main drawback when compared to DTW and HMM is that it does not take into account the temporal evolution of the signals (speech, signature, etc.) because all the vectors are mixed up. In order to overcome this problem a multi-section codebook approach has been proposed. The multi-section approach consists of modelling the signal with several sections (for instance, one codebook for the initial part, another one for the center and a last codebook for the ending part). === Use as clustering algorithm === As VQ is seeking for centroids as density points of nearby lying samples, it can be also directly used as a prototype-based clustering method: each centroid is then associated with one prototype. By aiming to minimize the expected squared quantization error and introducing a decreasing learning gain fulfilling the Robbins-Monro conditions, multiple iterations over the whole data set with a concrete but fixed number of prototypes converges to the solution of k-means clustering algorithm in an incremental manner. === Generative adversarial networks (GAN) === VQ has been used to quantize a feature representation layer in the discriminator of generative adversarial networks. The feature quantization (FQ) technique performs implicit feature matching. It improves the GAN training, and yields an improved performance on a variety of popular GAN models: BigGAN for image generation, StyleGAN for face synthesis, and U-GAT-IT for unsupervised image-to-image translation.

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  • Maghi King

    Maghi King

    Margaret (Maghi) Daniel King is a retired British computational linguist known for her work on evaluating the quality of machine translation. She is an honorary professor in the Department of Translation Technology of the University of Geneva in Switzerland, and the former director of the Dalle Molle Institute for Semantic and Cognitive Studies at the University of Geneva. == Education and career == King read classics, Ancient History and Philosophy (Greats) at the University of Oxford, worked as a computer programmer, and became a lecturer in the Department of Computation at the University of Manchester Institute of Science and Technology. She moved to the Dalle Molle Institute for Semantic and Cognitive Studies (ISSCO) in 1974. In 1976, ISSCO became part of the University of Geneva, and she continued there, becoming ISSCO's director in 1978. She remained director until her retirement in 2006. == Recognition == King is a Fellow of the European Association for Artificial Intelligence (formerly ECCAI), elected in 1999.

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  • Separating words problem

    Separating words problem

    In theoretical computer science, the separating words problem is the problem of finding the smallest deterministic finite automaton that behaves differently on two given strings, meaning that it accepts one of the two strings and rejects the other string. It is an open problem how large such an automaton must be, in the worst case, as a function of the length of the input strings. == Example == The two strings 0010 and 1000 may be distinguished from each other by a three-state automaton in which the transitions from the start state go to two different states, both of which are terminal in the sense that subsequent transitions from these two states always return to the same state. The state of this automaton records the first symbol of the input string. If one of the two terminal states is accepting and the other is rejecting, then the automaton will accept only one of the strings 0010 and 1000. However, these two strings cannot be distinguished by any automaton with fewer than three states. == Simplifying assumptions == For proving bounds on this problem, it may be assumed without loss of generality that the inputs are strings over a two-letter alphabet. For, if two strings over a larger alphabet differ then there exists a string homomorphism that maps them to binary strings of the same length that also differ. Any automaton that distinguishes the binary strings can be translated into an automaton that distinguishes the original strings, without any increase in the number of states. It may also be assumed that the two strings have equal length. For strings of unequal length, there always exists a prime number p whose value is logarithmic in the smaller of the two input lengths, such that the two lengths are different modulo p. An automaton that counts the length of its input modulo p can be used to distinguish the two strings from each other in this case. Therefore, strings of unequal lengths can always be distinguished from each other by automata with few states. == History and bounds == The problem of bounding the size of an automaton that distinguishes two given strings was first formulated by Goralčík & Koubek (1986), who showed that the automaton size is always sublinear. Later, Robson (1989) proved the upper bound O(n2/5(log n)3/5) on the automaton size that may be required. This was improved by Chase (2020) to O(n1/3(log n)7). There exist pairs of inputs that are both binary strings of length n for which any automaton that distinguishes the inputs must have size Ω(log n). Closing the gap between this lower bound and Chase's upper bound remains an open problem. Jeffrey Shallit has offered a prize of 100 British pounds for any improvement to Robson's upper bound. == Special cases == Several special cases of the separating words problem are known to be solvable using few states: If two binary words have differing numbers of zeros or ones, then they can be distinguished from each other by counting their Hamming weights modulo a prime of logarithmic size, using a logarithmic number of states. More generally, if a pattern of length k appears a different number of times in the two words, they can be distinguished from each other using O(k log n) states. If two binary words differ from each other within their first or last k positions, they can be distinguished from each other using k + O(1) states. This implies that almost all pairs of binary words can be distinguished from each other with a logarithmic number of states, because only a polynomially small fraction of pairs have no difference in their initial O(log n) positions. If two binary words have Hamming distance d, then there exists a prime p with p = O(d log n) and a position i at which the two strings differ, such that i is not equal modulo p to the position of any other difference. By computing the parity of the input symbols at positions congruent to i modulo p, it is possible to distinguish the words using an automaton with O(d log n) states.

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  • Structured sparsity regularization

    Structured sparsity regularization

    Structured sparsity regularization is a class of methods, and an area of research in statistical learning theory, that extend and generalize sparsity regularization learning methods. Both sparsity and structured sparsity regularization methods seek to exploit the assumption that the output variable Y {\displaystyle Y} (i.e., response, or dependent variable) to be learned can be described by a reduced number of variables in the input space X {\displaystyle X} (i.e., the domain, space of features or explanatory variables). Sparsity regularization methods focus on selecting the input variables that best describe the output. Structured sparsity regularization methods generalize and extend sparsity regularization methods, by allowing for optimal selection over structures like groups or networks of input variables in X {\displaystyle X} . Common motivation for the use of structured sparsity methods are model interpretability, high-dimensional learning (where dimensionality of X {\displaystyle X} may be higher than the number of observations n {\displaystyle n} ), and reduction of computational complexity. Moreover, structured sparsity methods allow to incorporate prior assumptions on the structure of the input variables, such as overlapping groups, non-overlapping groups, and acyclic graphs. Examples of uses of structured sparsity methods include face recognition, magnetic resonance image (MRI) processing, socio-linguistic analysis in natural language processing, and analysis of genetic expression in breast cancer. == Definition and related concepts == === Sparsity regularization === Consider the linear kernel regularized empirical risk minimization problem with a loss function V ( y i , f ( x ) ) {\displaystyle V(y_{i},f(x))} and the ℓ 0 {\displaystyle \ell _{0}} "norm" as the regularization penalty: min w ∈ R d 1 n ∑ i = 1 n V ( y i , ⟨ w , x i ⟩ ) + λ ‖ w ‖ 0 , {\displaystyle \min _{w\in \mathbb {R} ^{d}}{\frac {1}{n}}\sum _{i=1}^{n}V(y_{i},\langle w,x_{i}\rangle )+\lambda \|w\|_{0},} where x , w ∈ R d {\displaystyle x,w\in \mathbb {R^{d}} } , and ‖ w ‖ 0 {\displaystyle \|w\|_{0}} denotes the ℓ 0 {\displaystyle \ell _{0}} "norm", defined as the number of nonzero entries of the vector w {\displaystyle w} . f ( x ) = ⟨ w , x i ⟩ {\displaystyle f(x)=\langle w,x_{i}\rangle } is said to be sparse if ‖ w ‖ 0 = s < d {\displaystyle \|w\|_{0}=s 0 {\displaystyle w_{j}>0} . However, as in this case groups may overlap, we take the intersection of the complements of those groups that are not set to zero. This intersection of complements selection criteria implies the modeling choice that we allow some coefficients within a particular group g {\displaystyle g} to be set to zero, while others within the same group g {\displaystyle g} may remain positive. In other words, coefficients within a group may differ depending on the several group memberships that each variable within the group may have. ==== Union of groups: latent group Lasso ==== A different approach is to consider union of groups for variable selection. This approach captures the modeling situation where variables can be selected as long as they belong at least to one group with positive coefficients. This modeling perspective implies that we want to preserve group structure. The formulation of the union of groups approach is also referred to as latent group Lasso, and requires to modify the group ℓ 2 {\displaystyle \ell _{2}} norm considered above and introduce the following regularizer R ( w ) = i n f { ∑ g ‖ w g ‖ g : w = ∑ g = 1 G w ¯ g } {\displaystyle R(w)=inf\left\{\sum _{g}\|w_{g}\|_{g}:w=\sum _{g=1}^{G}{\bar {w}}_{g}\right\}} where w ∈ R d {\displaystyle w\in {\mathbb {R^{d}} }} , w g ∈ G g {\displaystyle w_{g}\in G_{g}} is the vector of coefficients of group g, and w ¯ g ∈ R d {\displaystyle {\bar {w}}_{g}\in {\mathbb {R^{d}} }} is a vector with coefficients w g j {\displaystyle w_{g}^{j}} for all variables j {

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  • The Best Free AI Text-to-video Tool for Beginners

    The Best Free AI Text-to-video Tool for Beginners

    Shopping for 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 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 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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  • Writer invariant

    Writer invariant

    Writer invariant, also called authorial invariant or author's invariant, is a property of a text which is invariant of its author, that is, it will be similar in all texts of a given author and different in texts of different authors. It can be used to find plagiarism or discover who is real author of anonymously published text. Writer invariant is also an author's pattern of writing a letter in handwritten text recognition. While it is generally recognised that writer invariants exist, it is not agreed what properties of a text should be used. Among the first ones used was distribution of word lengths; other proposed invariants include average sentence length, average word length, noun, verb or adjective usage frequency, vocabulary richness, and frequency of function words, or specific function words. Of these, average sentence lengths can be very similar in works of different authors or vary significantly even within a single work; average word lengths likewise turn out to be very similar in works of different authors. Analysis of function words shows promise because they are used by authors unconsciously.

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  • Michael I. Jordan

    Michael I. Jordan

    Michael Irwin Jordan (born February 25, 1956) is an American scientist, professor at the University of California, Berkeley, research scientist at the Inria Paris, and researcher in machine learning, statistics, and artificial intelligence. Jordan was elected a member of the National Academy of Engineering in 2010 for contributions to the foundations and applications of machine learning. He is one of the leading figures in machine learning, and in 2016 Science reported him as the world's most influential computer scientist. In 2022, Jordan won the inaugural World Laureates Association Prize in Computer Science or Mathematics, "for fundamental contributions to the foundations of machine learning and its application." == Education == Jordan received a Bachelor of Science magna cum laude in psychology from the Louisiana State University in 1978, a Master of Science in mathematics from Arizona State University in 1980, and a Doctor of Philosophy in cognitive science from the University of California, San Diego in 1985. At UC San Diego, Jordan was a student of David Rumelhart and a member of the Parallel Distributed Processing (PDP) Group in the 1980s. == Career and research == Jordan is the Pehong Chen Distinguished Professor at the University of California, Berkeley, where his appointment is split across EECS and Statistics. He was a professor at the Department of Brain and Cognitive Sciences at MIT from 1988 to 1998. In the 1980s Jordan started developing recurrent neural networks as a cognitive model. In recent years, his work is less driven from a cognitive perspective and more from the background of traditional statistics. Jordan popularised Bayesian networks in the machine learning community and is known for pointing out links between machine learning and statistics. He was also prominent in the formalisation of variational methods for approximate inference and the popularisation of the expectation–maximization algorithm in machine learning. === Resignation from Machine Learning === In 2001, Jordan and others resigned from the editorial board of the journal Machine Learning. In a public letter, they argued for less restrictive access and pledged support for a new open access journal, the Journal of Machine Learning Research, which was created by Leslie Kaelbling to support the evolution of the field of machine learning. === Honors and awards === Jordan has received numerous awards, including a best student paper award (with X. Nguyen and M. Wainwright) at the International Conference on Machine Learning (ICML 2004), a best paper award (with R. Jacobs) at the American Control Conference (ACC 1991), the ACM-AAAI Allen Newell Award, the IEEE Neural Networks Pioneer Award, and an NSF Presidential Young Investigator Award. In 2002 he was named an AAAI Fellow "for significant contributions to reasoning under uncertainty, machine learning, and human motor control." In 2004 he was named an IMS Fellow "for contributions to graphical models and machine learning." In 2005 he was named an IEEE Fellow "for contributions to probabilistic graphical models and neural information processing systems." In 2007 he was named an ASA Fellow. In 2010 he was named a Cognitive Science Society Fellow and named an ACM Fellow "for contributions to the theory and application of machine learning." In 2012 he was named a SIAM Fellow "for contributions to machine learning, in particular variational approaches to statistical inference." In 2014 he was named an International Society for Bayesian Analysis Fellow "for his outstanding research contributions at the interface of statistics, computer sciences and probability, for his leading role in promoting Bayesian methods in machine learning, engineering and other fields, and for his extensive service to ISBA in many roles." Jordan is a member of the National Academy of Sciences, a member of the National Academy of Engineering and a member of the American Academy of Arts and Sciences. He has been named a Neyman Lecturer and a Medallion Lecturer by the Institute of Mathematical Statistics. He received the David E. Rumelhart Prize in 2015 and the ACM/AAAI Allen Newell Award in 2009. He also won the 2020 IEEE John von Neumann Medal. In 2016, Jordan was identified as the "most influential computer scientist", based on an analysis of the published literature by the Semantic Scholar project. In 2019, Jordan argued that the artificial intelligence revolution hasn't happened yet and that the AI revolution required a blending of computer science with statistics. In 2022, Jordan was awarded the inaugural World Laureates Association Prize by non-governmental and non-profit international organization World Laureates Association, for fundamental contributions to the foundations of machine learning and its application. For 2024 he received the BBVA Foundation Frontiers of Knowledge Award in the category of "Information and Communication Technologies".

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  • Splitwise

    Splitwise

    Splitwise is an online expense-splitting application software accessible via web browser and mobile app. The app facilitates repayments of shared bills by calculating what each person in a group owes. The primary competitor to the app is Venmo, which only operates in the U.S. Splitwise allows users to create groups with friends to determine what each person owes. All expenses and allocations are added to the app, and Splitwise simplifies the transaction history to determine exactly what payments need to be made to whom to settle outstanding balances. Splitwise stores user information via cloud storage. It was developed and is owned by Splitwise Inc., based in Providence, Rhode Island, United States. == History == The app was launched in February 2011 as SplitTheRent, intended to be used for rent splitting, by Ryan Laughlin, Jon Bittner and Marshall Weir. In September 2013, Splitwise was integrated with Venmo to allow users to settle payments via Venmo. In April 2024, Splitwise partnered with Tink, a Visa payment services company, to incorporate a bank transfer feature directly in the Splitwise app. === Financing === In December 2014, the company raised $1.4 million. In October 2016, the company raised $5 million. In April 2021, Splitwise raised $20 million in funding from series A round run by Insight Partners. == Reception == A 2022 opinion piece in The Guardian by London journalist Imogen West-Knights shared the negative effects of exactly splitting bills among friends and family members. West-Knights argued that Splitwise and similar apps can "turn people into those true enemies of all that is fun and joyful in the world: accountants." However, she said the app does work better when used by couples rather than friend groups. Other reviews noted that the app makes people petty. In contrast, an article published by Condé Nast Traveler describes how Splitwise eliminated stress caused by complicated offline bill splitting, saying it "fixed such a pervasive obstacle in group travel." Coverage by The Wall Street Journal lands somewhere in between the two contrasting views, saying Splitwise and similar apps are helpful, but users need to be prepared for difficult money-related conversations that may arise. An etiquette advisor at Debrett's, said, "The less talk you can have about money on any of these occasions, the better." An editor suggested conversations as simple as asking, "We’re splitting this evenly, right?" before a meal.

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  • Danqi Chen

    Danqi Chen

    Danqi Chen (Chinese: 陈丹琦; pinyin: Chén Dānqí, IPA: [ʈ͡ʂʰə̌n tan t͡ɕʰǐ]; born in Changsha, China) is a Chinese computer scientist and assistant professor at Princeton University specializing in the AI field of natural language processing (NLP). In 2019, she joined the Princeton NLP group, alongside Sanjeev Arora, Christiane Fellbaum, and Karthik Narasimhan. She was previously a visiting scientist at Facebook AI Research (FAIR). She earned her Ph.D. at Stanford University and her BS from Tsinghua University. Chen is the author of Neural Reading Comprehension and Beyond, a dissertation on using artificial intelligence to access knowledge in ordinary and structured documents. She is the author or co-author of a number of journal articles, including Reading Wikipedia to Answer Open-Domain Questions. Google's SyntaxNet is based on algorithms developed by Danqi Chen and Christopher Manning at Stanford. Her primary research interests are in text understanding and knowledge representation and reasoning. She won a gold medal at the 2008 International Informatics Olympiad. She is known among friends as CDQ. A well known algorithm in competitive programming, CDQ Divide and Conquer, is named after this acronym. She is married to Huacheng Yu, an assistant professor in theoretical computer science at Princeton University.

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  • AI Headshot Generators: Free vs Paid (2026)

    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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  • Jared Kaplan

    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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  • Texture filtering

    Texture filtering

    In computer graphics, texture filtering or texture smoothing is the method used to determine the texture color for a texture mapped pixel, using the colors of nearby texels (ie. pixels of the texture). Filtering describes how a texture is applied at many different shapes, size, angles and scales. Depending on the chosen filter algorithm, the result will show varying degrees of blurriness, detail, spatial aliasing, temporal aliasing and blocking. Depending on the circumstances, filtering can be performed in software (such as a software rendering package) or in hardware, eg. with either real time or GPU accelerated rendering circuits, or in a mixture of both. For most common interactive graphical applications, modern texture filtering is performed by dedicated hardware which optimizes memory access through memory cacheing and pre-fetch, and implements a selection of algorithms available to the user and developer. There are two main categories of texture filtering: magnification filtering and minification filtering. Depending on the situation, texture filtering is either a type of reconstruction filter where sparse data is interpolated to fill gaps (magnification), or a type of anti-aliasing (AA) where texture samples exist at a higher frequency than required for the sample frequency needed for texture fill (minification). There are many methods of texture filtering, which make different trade-offs between computational complexity, memory bandwidth and image quality. == The need for filtering == During the texture mapping process for any arbitrary 3D surface, a texture lookup takes place to find out where on the texture each pixel center falls. For texture-mapped polygonal surfaces composed of triangles typical of most surfaces in 3D games and movies, every pixel (or subordinate pixel sample) of that surface will be associated with some triangle(s) and a set of barycentric coordinates, which are used to provide a position within a texture. Such a position may not lie perfectly on the "pixel grid," necessitating some function to account for these cases. In other words, since the textured surface may be at an arbitrary distance and orientation relative to the viewer, one pixel does not usually correspond directly to one texel. Some form of filtering has to be applied to determine the best color for the pixel. Insufficient or incorrect filtering will show up in the image as artifacts (errors in the image), such as 'blockiness', jaggies, or shimmering. There can be different types of correspondence between a pixel and the texel/texels it represents on the screen. These depend on the position of the textured surface relative to the viewer, and different forms of filtering are needed in each case. Given a square texture mapped on to a square surface in the world, at some viewing distance the size of one screen pixel is exactly the same as one texel. Closer than that, the texels are larger than screen pixels, and need to be scaled up appropriately — a process known as texture magnification. Farther away, each texel is smaller than a pixel, and so one pixel covers multiple texels. In this case an appropriate color has to be picked based on the covered texels, via texture minification. Graphics APIs such as OpenGL allow the programmer to set different choices for minification and magnification filters. Note that even in the case where the pixels and texels are exactly the same size, one pixel will not necessarily match up exactly to one texel. It may be misaligned or rotated, and cover parts of up to four neighboring texels. Hence some form of filtering is still required. == Mipmapping == Mipmapping is a standard technique used to save some of the filtering work needed during texture minification. It is also highly beneficial for cache coherency - without it the memory access pattern during sampling from distant textures will exhibit extremely poor locality, adversely affecting performance even if no filtering is performed. During texture magnification, the number of texels that need to be looked up for any pixel is always four or fewer; during minification, however, as the textured polygon moves farther away potentially the entire texture might fall into a single pixel. This would necessitate reading all of its texels and combining their values to correctly determine the pixel color, a prohibitively expensive operation. Mipmapping avoids this by prefiltering the texture and storing it in smaller sizes down to a single pixel. As the textured surface moves farther away, the texture being applied switches to the prefiltered smaller size. Different sizes of the mipmap are referred to as 'levels', with Level 0 being the largest size (used closest to the viewer), and increasing levels used at increasing distances. == Filtering methods == This section lists the most common texture filtering methods, in increasing order of computational cost and image quality. === Nearest-neighbor interpolation === Nearest-neighbor interpolation is the simplest and crudest filtering method — it simply uses the color of the texel closest to the pixel center for the pixel color. While simple, this results in a large number of artifacts - texture 'blockiness' during magnification, and aliasing and shimmering during minification. This method is fast during magnification but during minification the stride through memory becomes arbitrarily large and it can often be less efficient than MIP-mapping due to the lack of spatially coherent texture access and cache-line reuse. === Nearest-neighbor with mipmapping === This method still uses nearest neighbor interpolation, but adds mipmapping — first the nearest mipmap level is chosen according to distance, then the nearest texel center is sampled to get the pixel color. This reduces the aliasing and shimmering significantly during minification but does not eliminate it entirely. In doing so it improves texture memory access and cache-line reuse through avoiding arbitrarily large access strides through texture memory during rasterization. This does not help with blockiness during magnification as each magnified texel will still appear as a large rectangle. === Linear mipmap filtering === Less commonly used, OpenGL and other APIs support nearest-neighbor sampling from individual mipmaps whilst linearly interpolating the two nearest mipmaps relevant to the sample. === Bilinear filtering === In Bilinear filtering, the four nearest texels to the pixel center are sampled (at the closest mipmap level), and their colors are combined by weighted average according to distance. This removes the 'blockiness' seen during magnification, as there is now a smooth gradient of color change from one texel to the next, instead of an abrupt jump as the pixel center crosses the texel boundary. Bilinear filtering for magnification filtering is common. When used for minification it is often used with mipmapping; though it can be used without, it would suffer the same aliasing and shimmering problems as nearest-neighbor filtering when minified too much. For modest minification ratios, however, it can be used as an inexpensive hardware accelerated weighted texture supersample. The Nintendo 64 used an unusual version of bilinear filtering where only three pixels are used known as 3-point texture filtering, instead of four due to hardware optimization concerns. This introduces a noticeable "triangulation bias" in some textures. === Trilinear filtering === Trilinear filtering is a remedy to a common artifact seen in mipmapped bilinearly filtered images: an abrupt and very noticeable change in quality at boundaries where the renderer switches from one mipmap level to the next. Trilinear filtering solves this by doing a texture lookup and bilinear filtering on the two closest mipmap levels (one higher and one lower quality), and then linearly interpolating the results. This results in a smooth degradation of texture quality as distance from the viewer increases, rather than a series of sudden drops. Of course, closer than Level 0 there is only one mipmap level available, and the algorithm reverts to bilinear filtering. === Anisotropic filtering === Anisotropic filtering is the highest quality filtering available in current consumer 3D graphics cards. Simpler, "isotropic" techniques use only square mipmaps which are then interpolated using bi– or trilinear filtering. (Isotropic means same in all directions, and hence is used to describe a system in which all the maps are squares rather than rectangles or other quadrilaterals.) When a surface is at a high angle relative to the camera, the fill area for a texture will not be approximately square. Consider the common case of a floor in a game: the fill area is far wider than it is tall. In this case, none of the square maps are a good fit. The result is blurriness and/or shimmering, depending on how the fit is chosen. Anisotropic filtering corrects this by sampling the texture as a non-square shape. The goal is

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  • NovelAI

    NovelAI

    NovelAI is an online cloud-based, SaaS model, and a paid subscription service for AI-assisted storywriting and text-to-image synthesis, originally launched in beta on June 15, 2021, with the image generation feature being implemented later on October 3, 2022. NovelAI is owned and operated by Anlatan, which is headquartered in Wilmington, Delaware. == Features == NovelAI uses GPT-based large language models (LLMs) to generate storywriting and prose. It has several models, such as Calliope, Sigurd, Euterpe, Krake, and Genji, with Genji being a Japanese-language model. The service also offers encrypted servers and customizable editors. For AI art generation, which generates images from text prompts, NovelAI uses a custom version of the source-available Stable Diffusion text-to-image diffusion model called NovelAI Diffusion, which is trained on a Danbooru-based dataset. NovelAI is also capable of generating a new image based on an existing image. The NovelAI terms of service states that all generated content belongs to the user, regardless if the user is an individual or a corporation. Anlatan states that generated images are not stored locally on their servers. == History == On April 28, 2021, Anlatan officially launched NovelAI. On June 15, 2021, Anlatan released their finetuned GPT-Neo-2.7B model from EleutherAI named Calliope, after the Greek Muses. A day later, they released their Opus-exclusive GPT-J-6B finetuned model named Sigurd, after the Norse/Germanic hero. On March 21, 2023, Nvidia and CoreWeave announced Anlatan being one of the first CoreWeave customers to deploy NVIDIA's H100 Tensor Core GPUs for new LLM model inferencing and training. On April 1, 2023, Anlatan added ControlNet features to their text-to-image NovelAI Diffusion model. On May 16, 2023, Anlatan announced that they named their H100 cluster Shoggy, a reference to H.P. Lovecraft's Shoggoths, which was used to pre-train an undisclosed 8192 token context LLM in-house model. == Reception and controversy == Following the implementation of image generation, NovelAI became a widely-discussed topic in Japan, with some online commentators noting that its image synthesis features are very adept at producing close impressions of anime characters, including lolicon and shotacon imagery, while others have expressed concern that it is a paid service reliant on a diffusion model, while the original machine learning training data consists of images used without the consent of the original artists. Attorney Kosuke Terauchi notes that, since a revision of the law in 2018, it is no longer illegal in Japan for machine learning models to scrape copyrighted content from the internet to use as training data; meanwhile, in the United States where NovelAI is based, there is no specific legal framework which regulates machine learning, and thus the fair use doctrine of US copyright law applies instead. Danbooru has posted an official statement in regards to NovelAI's use of the site's content for AI training, expressing that Danbooru is not affiliated with NovelAI, and does not endorse nor condone NovelAI's use of artists' artworks for machine learning. FayerWayer described NovelAI as a service capable of generating hentai. Manga artist Izumi Ū commented that while the manga style art generated by NovelAI is highly accurate, there are still imperfections in the output, although he views these as human-like in a favourable light nonetheless. In response to the topic of NovelAI, Narugami, founder of the Japanese freelance artist commissioning website Skeb, stated on October 5, 2022 that the use of AI image generation is prohibited on the platform since 2018. Illustrations using NovelAI have been posted on social media and illustration posting sites, and by October 13, 2,111 works tagged with #NovelAI were posted on Pixiv. Pixiv has stated that it is not considering a complete elimination of creations that use AI, though it requires AI-generated posts to be marked as such and allows users to filter them out. == Incidents == On October 6, 2022, NovelAI experienced a data breach where its software's source code was leaked.

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  • Apache cTAKES

    Apache cTAKES

    Apache cTAKES: clinical Text Analysis and Knowledge Extraction System is an open-source Natural Language Processing (NLP) system that extracts clinical information from electronic health record unstructured text. It processes clinical notes, identifying types of clinical named entities — drugs, diseases/disorders, signs/symptoms, anatomical sites and procedures. Each named entity has attributes for the text span, the ontology mapping code, context (family history of, current, unrelated to patient), and negated/not negated. cTAKES was built using the UIMA Unstructured Information Management Architecture framework and OpenNLP natural language processing toolkit. == Components == Components of cTAKES are specifically trained for the clinical domain, and create rich linguistic and semantic annotations that can be utilized by clinical decision support systems and clinical research. These components include: Named Section identifier Sentence boundary detector Rule-based tokenizer Formatted list identifier Normalizer Context dependent tokenizer Part-of-speech tagger Phrasal chunker Dictionary lookup annotator Context annotator Negation detector Uncertainty detector Subject detector Dependency parser patient smoking status identifier Drug mention annotator == History == Development of cTAKES began at the Mayo Clinic in 2006. The development team, led by Dr. Guergana Savova and Dr. Christopher Chute, included physicians, computer scientists and software engineers. After its deployment, cTAKES became an integral part of Mayo's clinical data management infrastructure, processing more than 80 million clinical notes. When Dr. Savova's moved to Boston Children's Hospital in early 2010, the core development team grew to include members there. Further external collaborations include: University of Colorado Brandeis University University of Pittsburgh University of California at San Diego Such collaborations have extended cTAKES' capabilities into other areas such as Temporal Reasoning, Clinical Question Answering, and coreference resolution for the clinical domain. In 2010, cTAKES was adopted by the i2b2 program and is a central component of the SHARP Area 4. In 2013, cTAKES released their first release as an Apache Software Foundation incubator project: cTAKES 3.0. In March 2013, cTAKES became an Apache Software Foundation Top Level Project (TLP).

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