AI Generator Jokes

AI Generator Jokes — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

• 

  C3D Toolkit

  C3D Toolkit is a proprietary cross-platform geometric modeling kit software developed by Russian C3D Labs (previously part of ASCON Group). It's written in C++ . It can be licensed by other companies for use in their 3D computer graphics software products. The most widely known software in which C3D Toolkit is typically used are computer aided design (CAD), computer-aided manufacturing (CAM), and computer-aided engineering (CAE) systems. C3D Toolkit provides routines for 3D modeling, 3D constraint solving, polygonal mesh-to-B-rep conversion, 3D visualization, and 3D file conversions etc. == History == Nikolai Golovanov is a graduate of the Mechanical Engineering department of Bauman Moscow State Technical University as a designer of space launch vehicles. Upon his graduation, he began with the Kolomna Engineering Design bureau, which at the time employed the future founders of ASCON, Alexander Golikov and Tatiana Yankina. While at the bureau, Dr Golovanov developed software for analyzing the strength and stability of shell structures. In 1989, Alexander Golikov and Tatiana Yankina left Kolomna to start up ASCON as a private company. Although they began with just an electronic drawing board, even then they were already conceiving the idea of three-dimensional parametric modeling. This radical concept eventually changed flat drawings into three-dimensional models. The ASCON founders shared their ideas with Nikolai Golovanov, and in 1996 he moved to take up his current position with ASCON. As of 2012 he was involved in developing algorithms for C3D Toolkit. In 2012 the earliest version of the C3D Modeller kernel was extracted from KOMPAS-3D CAD. It was later adopted to a range of different platforms and advertised as a separate product. == Overview == It incorporates five modules: C3D Modeler constructs geometric models, generates flat projections of models, performs triangulations, calculates the inertial characteristics of models, and determines whether collisions occur between the elements of models; C3D Modeler for ODA enables advanced 3D modeling operations through the ODA's standard "OdDb3DSolid" API from the Open Design Alliance; C3D Solver makes connections between the elements of geometric models, and considers the geometric constraints of models being edited; C3D B-Shaper converts polygonal models to boundary representation (B-rep) bodies; C3D Vision controls the quality of rendering for 3D models using mathematical apparatus and software, and the workstation hardware; C3D Converter reads and writes geometric models in a variety of standard exchange formats. == Features == == Development == == Applications == Since 2013 - the date the company started issuing a license for the toolkit -, several companies have adopted C3D software components for their products, users include: Recently, C3D Modeler has been adapted to ODA Platform. In April 2017, C3D Viewer was launched for end users. The application allows to read 3D models in common formats and write it to the C3D file format. Free version is available.

  Read more →
• 

  Sparse dictionary learning

  Sparse dictionary learning (also known as sparse coding or SDL) is a representation learning method which aims to find a sparse representation of the input data in the form of a linear combination of basic elements as well as those basic elements themselves. These elements are called atoms, and they compose a dictionary. Atoms in the dictionary are not required to be orthogonal, and they may be an over-complete spanning set. This problem setup also allows the dimensionality of the signals being represented to be higher than any one of the signals being observed. These two properties lead to having seemingly redundant atoms that allow multiple representations of the same signal, but also provide an improvement in sparsity and flexibility of the representation. One of the most important applications of sparse dictionary learning is in the field of compressed sensing or signal recovery. In compressed sensing, a high-dimensional signal can be recovered with only a few linear measurements, provided that the signal is sparse or near-sparse. Since not all signals satisfy this condition, it is crucial to find a sparse representation of that signal such as the wavelet transform or the directional gradient of a rasterized matrix. Once a matrix or a high-dimensional vector is transferred to a sparse space, different recovery algorithms like basis pursuit, CoSaMP, or fast non-iterative algorithms can be used to recover the signal. One of the key principles of dictionary learning is that the dictionary has to be inferred from the input data. The emergence of sparse dictionary learning methods was stimulated by the fact that in signal processing, one typically wants to represent the input data using a minimal amount of components. Before this approach, the general practice was to use predefined dictionaries such as Fourier or wavelet transforms. However, in certain cases, a dictionary that is trained to fit the input data can significantly improve the sparsity, which has applications in data decomposition, compression, and analysis, and has been used in the fields of image denoising and classification, and video and audio processing. Sparsity and overcomplete dictionaries have immense applications in image compression, image fusion, and inpainting. == Problem statement == Given the input dataset X = [ x 1 , . . . , x K ] , x i ∈ R d {\displaystyle X=[x_{1},...,x_{K}],x_{i}\in \mathbb {R} ^{d}} we wish to find a dictionary D ∈ R d × n : D = [ d 1 , . . . , d n ] {\displaystyle \mathbf {D} \in \mathbb {R} ^{d\times n}:D=[d_{1},...,d_{n}]} and a representation R = [ r 1 , . . . , r K ] , r i ∈ R n {\displaystyle R=[r_{1},...,r_{K}],r_{i}\in \mathbb {R} ^{n}} such that both ‖ X − D R ‖ F 2 {\displaystyle \|X-\mathbf {D} R\|_{F}^{2}} is minimized and the representations r i {\displaystyle r_{i}} are sparse enough. This can be formulated as the following optimization problem: argmin D ∈ C , r i ∈ R n ∑ i = 1 K ‖ x i − D r i ‖ 2 2 + λ ‖ r i ‖ 0 {\displaystyle {\underset {\mathbf {D} \in {\mathcal {C}},r_{i}\in \mathbb {R} ^{n}}{\text{argmin}}}\sum _{i=1}^{K}\|x_{i}-\mathbf {D} r_{i}\|_{2}^{2}+\lambda \|r_{i}\|_{0}} , where C ≡ { D ∈ R d × n : ‖ d i ‖ 2 ≤ 1 ∀ i = 1 , . . . , n } {\displaystyle {\mathcal {C}}\equiv \{\mathbf {D} \in \mathbb {R} ^{d\times n}:\|d_{i}\|_{2}\leq 1\,\,\forall i=1,...,n\}} , λ > 0 {\displaystyle \lambda >0} C {\displaystyle {\mathcal {C}}} is required to constrain D {\displaystyle \mathbf {D} } so that its atoms would not reach arbitrarily high values allowing for arbitrarily low (but non-zero) values of r i {\displaystyle r_{i}} . λ {\displaystyle \lambda } controls the trade off between the sparsity and the minimization error. The minimization problem above is not convex because of the ℓ0-"norm" and solving this problem is NP-hard. In some cases L1-norm is known to ensure sparsity and so the above becomes a convex optimization problem with respect to each of the variables D {\displaystyle \mathbf {D} } and R {\displaystyle \mathbf {R} } when the other one is fixed, but it is not jointly convex in ( D , R ) {\displaystyle (\mathbf {D} ,\mathbf {R} )} . === Properties of the dictionary === The dictionary D {\displaystyle \mathbf {D} } defined above can be "undercomplete" if n < d {\displaystyle n d {\displaystyle n>d} with the latter being a typical assumption for a sparse dictionary learning problem. The case of a complete dictionary does not provide any improvement from a representational point of view and thus isn't considered. Undercomplete dictionaries represent the setup in which the actual input data lies in a lower-dimensional space. This case is strongly related to dimensionality reduction and techniques like principal component analysis which require atoms d 1 , . . . , d n {\displaystyle d_{1},...,d_{n}} to be orthogonal. The choice of these subspaces is crucial for efficient dimensionality reduction, but it is not trivial. And dimensionality reduction based on dictionary representation can be extended to address specific tasks such as data analysis or classification. However, their main downside is limiting the choice of atoms. Overcomplete dictionaries, however, do not require the atoms to be orthogonal (they will never have a basis anyway) thus allowing for more flexible dictionaries and richer data representations. An overcomplete dictionary which allows for sparse representation of signal can be a famous transform matrix (wavelets transform, fourier transform) or it can be formulated so that its elements are changed in such a way that it sparsely represents the given signal in a best way. Learned dictionaries are capable of giving sparser solutions as compared to predefined transform matrices. == Algorithms == As the optimization problem described above can be solved as a convex problem with respect to either dictionary or sparse coding while the other one of the two is fixed, most of the algorithms are based on the idea of iteratively updating one and then the other. The problem of finding an optimal sparse coding R {\displaystyle R} with a given dictionary D {\displaystyle \mathbf {D} } is known as sparse approximation (or sometimes just sparse coding problem). A number of algorithms have been developed to solve it (such as matching pursuit and LASSO) and are incorporated in the algorithms described below. === Method of optimal directions (MOD) === The method of optimal directions (or MOD) was one of the first methods introduced to tackle the sparse dictionary learning problem. The core idea of it is to solve the minimization problem subject to the limited number of non-zero components of the representation vector: min D , R { ‖ X − D R ‖ F 2 } s.t. ∀ i ‖ r i ‖ 0 ≤ T {\displaystyle \min _{\mathbf {D} ,R}\{\|X-\mathbf {D} R\|_{F}^{2}\}\,\,{\text{s.t.}}\,\,\forall i\,\,\|r_{i}\|_{0}\leq T} Here, F {\displaystyle F} denotes the Frobenius norm. MOD alternates between getting the sparse coding using a method such as matching pursuit and updating the dictionary by computing the analytical solution of the problem given by D = X R + {\displaystyle \mathbf {D} =XR^{+}} where R + {\displaystyle R^{+}} is a Moore-Penrose pseudoinverse. After this update D {\displaystyle \mathbf {D} } is renormalized to fit the constraints and the new sparse coding is obtained again. The process is repeated until convergence (or until a sufficiently small residue). MOD has proved to be a very efficient method for low-dimensional input data X {\displaystyle X} requiring just a few iterations to converge. However, due to the high complexity of the matrix-inversion operation, computing the pseudoinverse in high-dimensional cases is in many cases intractable. This shortcoming has inspired the development of other dictionary learning methods. === K-SVD === K-SVD is an algorithm that performs SVD at its core to update the atoms of the dictionary one by one and basically is a generalization of K-means. It enforces that each element of the input data x i {\displaystyle x_{i}} is encoded by a linear combination of not more than T 0 {\displaystyle T_{0}} elements in a way identical to the MOD approach: min D , R { ‖ X − D R ‖ F 2 } s.t. ∀ i ‖ r i ‖ 0 ≤ T 0 {\displaystyle \min _{\mathbf {D} ,R}\{\|X-\mathbf {D} R\|_{F}^{2}\}\,\,{\text{s.t.}}\,\,\forall i\,\,\|r_{i}\|_{0}\leq T_{0}} This algorithm's essence is to first fix the dictionary, find the best possible R {\displaystyle R} under the above constraint (using Orthogonal Matching Pursuit) and then iteratively update the atoms of dictionary D {\displaystyle \mathbf {D} } in the following manner: ‖ X − D R ‖ F 2 = | X − ∑ i = 1 K d i x T i | F 2 = ‖ E k − d k x T k ‖ F 2 {\displaystyle \|X-\mathbf {D} R\|_{F}^{2}=\left|X-\sum _{i=1}^{K}d_{i}x_{T}^{i}\right|_{F}^{2}=\|E_{k}-d_{k}x_{T}^{k}\|_{F}^{2}} The next steps of the algorithm include rank-1 approximation of the residual matrix E k {\displaystyle E_{k}} , updating d k {\displaystyle d_{k}} and enforcing the s

  Read more →
• 

  AI Bug Finders: Free vs Paid (2026)

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

  Read more →
• 

  Lenhart Schubert

  Lenhart Karl Otto Schubert is a professor of Computer Science at the University of Rochester, as well as a member of the Center for Language Sciences and the Center for Computation and the Brain. Schubert is a prominent researcher in the field of common sense reasoning. == Biography == Schubert received his Ph.D. from the University of Toronto in 1970. He was on the faculty of the University of Alberta between 1973 and 1988 and joined the faculty at the University of Rochester in 1988. He was elected fellow of Association for Advancement of Artificial Intelligence in 1993 for "fundamental contributions in NLP, esp. in the formalization, representation, and practical implementation of non-first order concepts".

  Read more →
• 

  VGACAD

  VGACAD was the parent of a suite of shareware graphic utilities made for the MS-DOS operating system used in the IBM PC and clones. It was popular for editing and capturing images using BSAVE (graphics image format) and provided an early graphic editing suite compatible with multiple graphic cards and resolutions, used on the IBM PC. == Usage == Written by Lawrence Gozum in 1987, it was the genesis of multiple versions and improvements over 10 years. Ran with his brother, Marvin initially helped with design ideas, strategic focus, technical support calls, and managing the early shareware business. The growth of the VGACAD suite grew quickly to preoccupy most of their time. Lawrence then focused more of his efforts on software and formed Applied Insights, to manage VGACAD and its offspring, VidFun, and Ai Picture Explorer. At its peak, its users ranged from individuals, Federal government offices, museums and major newspapers. == Features == VGACAD was a misnomer, and meant VGA-Computer Assisted Drawing, rather than computer-aided design, as CAD is commonly referred to today. Its longevity was due to its color accuracy, speed, small size, and that its suite of small utilities often worked stand-alone. One called VGACAP, for 'capture', dumped video memory into a file that could later be converted to popular graphic image formats, later made commonplace when Microsoft Windows programmed the print screen key to dump graphics into the clipboard. However, VGACAP ran insulated apart from early versions of Windows, and thus could capture screens were applications prohibited such function.

  Read more →
• 

  Best AI Analytics Tools in 2026

  Curious about the best AI analytics tool? An AI analytics tool 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 analytics tool 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.

  Read more →
• 

  AI Essay Writers: Free vs Paid (2026)

  Looking for the best AI essay writer? An AI essay writer is software that uses machine learning to help you get more done — it can save you hours every week by automating repetitive work. Most options offer a generous free tier, with paid plans unlocking higher limits, faster processing, and team features. Whether you are a beginner or a pro, the right AI essay writer slots into your workflow and pays for itself fast. Read on for hands-on impressions, pricing tiers, and the standout features that matter.

  Read more →
• 

  Tf–idf

  In information retrieval, tf–idf (term frequency–inverse document frequency, TFIDF, TFIDF, TF–IDF, or Tf–idf) is a measure of importance of a word to a document in a collection or corpus, adjusted for the fact that some words appear more frequently in general. Like the bag-of-words model, it models a document as a multiset of words, without word order. It is a refinement over the simple bag-of-words model, by allowing the weight of words to depend on the rest of the corpus. It was often used as a weighting factor in searches of information retrieval, text mining, and user modeling. A survey conducted in 2015 showed that 83% of text-based recommender systems in digital libraries used tf–idf. Variations of the tf–idf weighting scheme were often used by search engines as a central tool in scoring and ranking a document's relevance given a user query. One of the simplest ranking functions is computed by summing the tf–idf for each query term; many more sophisticated ranking functions are variants of this simple model. == Motivations == Karen Spärck Jones (1972) conceived a statistical interpretation of term-specificity called Inverse Document Frequency (idf), which became a cornerstone of term weighting: The specificity of a term can be quantified as an inverse function of the number of documents in which it occurs.For example, the df (document frequency) and idf for some words in Shakespeare's 37 plays might be represented as follows: We see that "Romeo", "Falstaff", and "salad" appears in very few plays, so seeing these words, one could get a good idea as to which play it might be. In contrast, "good" and "sweet" appears in every play and are completely uninformative as to which play it is. == Definition == The tf–idf is the product of two statistics, term frequency and inverse document frequency. There are various ways for determining the exact values of both statistics. A formula that aims to define the importance of a keyword or phrase within a document or a web page. === Term frequency === Term frequency, tf(t,d), is the relative frequency of term t within document d, t f ( t , d ) = f t , d ∑ t ′ ∈ d f t ′ , d {\displaystyle \mathrm {tf} (t,d)={\frac {f_{t,d}}{\sum _{t'\in d}{f_{t',d}}}}} , where ft,d is the raw count of a term in a document, i.e., the number of times that term t occurs in document d. Note the denominator is simply the total number of terms in document d (counting each occurrence of the same term separately). There are various other ways to define term frequency: the raw count itself: tf(t,d) = ft,d Boolean "frequencies": tf(t,d) = 1 if t occurs in d and 0 otherwise; logarithmically scaled frequency: tf(t,d) = log (1 + ft,d); augmented frequency, to prevent a bias towards longer documents, e.g. raw frequency divided by the raw frequency of the most frequently occurring term in the document: t f ( t , d ) = 0.5 + 0.5 ⋅ f t , d max { f t ′ , d : t ′ ∈ d } {\displaystyle \mathrm {tf} (t,d)=0.5+0.5\cdot {\frac {f_{t,d}}{\max\{f_{t',d}:t'\in d\}}}} === Inverse document frequency === The inverse document frequency is a measure of how much information the word provides, i.e., how common or rare it is across all documents. It is the logarithmically scaled inverse fraction of the documents that contain the word (obtained by dividing the total number of documents by the number of documents containing the term, and then taking the logarithm of that quotient): i d f ( t , D ) = log ⁡ N n t {\displaystyle \mathrm {idf} (t,D)=\log {\frac {N}{n_{t}}}} with D {\displaystyle D} : is the set of all documents in the corpus N = | D | {\displaystyle N={|D|}} : total number of documents in the corpus n t = | { d ∈ D : t ∈ d } | {\displaystyle n_{t}=|\{d\in D:t\in d\}|} : number of documents where the term t {\displaystyle t} appears (i.e., t f ( t , d ) ≠ 0 {\displaystyle \mathrm {tf} (t,d)\neq 0} ). If the term is not in the corpus, this will lead to a division-by-zero. It is therefore common to adjust the numerator to 1 + N {\displaystyle 1+N} and the denominator to 1 + | { d ∈ D : t ∈ d } | {\displaystyle 1+|\{d\in D:t\in d\}|} . === Term frequency–inverse document frequency === Then tf–idf is calculated as t f i d f ( t , d , D ) = t f ( t , d ) ⋅ i d f ( t , D ) {\displaystyle \mathrm {tfidf} (t,d,D)=\mathrm {tf} (t,d)\cdot \mathrm {idf} (t,D)} A high weight in tf–idf is reached by a high term frequency (in the given document) and a low document frequency of the term in the whole collection of documents; the weights hence tend to filter out common terms. Since the ratio inside the idf's log function is always greater than or equal to 1, the value of idf (and tf–idf) is greater than or equal to 0. As a term appears in more documents, the ratio inside the logarithm approaches 1, bringing the idf and tf–idf closer to 0. == Justification of idf == Idf was introduced as "term specificity" by Karen Spärck Jones in a 1972 paper. Although it has worked well as a heuristic, its theoretical foundations have been troublesome for at least three decades afterward, with many researchers trying to find information theoretic justifications for it. Spärck Jones's own explanation did not propose much theory, aside from a connection to Zipf's law. Attempts have been made to put idf on a probabilistic footing, by estimating the probability that a given document d contains a term t as the relative document frequency, P ( t | D ) = | { d ∈ D : t ∈ d } | N , {\displaystyle P(t|D)={\frac {|\{d\in D:t\in d\}|}{N}},} so that we can define idf as i d f = − log ⁡ P ( t | D ) = log ⁡ 1 P ( t | D ) = log ⁡ N | { d ∈ D : t ∈ d } | {\displaystyle {\begin{aligned}\mathrm {idf} &=-\log P(t|D)\\&=\log {\frac {1}{P(t|D)}}\\&=\log {\frac {N}{|\{d\in D:t\in d\}|}}\end{aligned}}} Namely, the inverse document frequency is the logarithm of "inverse" relative document frequency. This probabilistic interpretation in turn takes the same form as that of self-information. However, applying such information-theoretic notions to problems in information retrieval leads to problems when trying to define the appropriate event spaces for the required probability distributions: not only documents need to be taken into account, but also queries and terms. == Link with information theory == Both term frequency and inverse document frequency can be formulated in terms of information theory; it helps to understand why their product has a meaning in terms of joint informational content of a document. A characteristic assumption about the distribution p ( d , t ) {\displaystyle p(d,t)} is that: p ( d | t ) = 1 | { d ∈ D : t ∈ d } | {\displaystyle p(d|t)={\frac {1}{|\{d\in D:t\in d\}|}}} This assumption and its implications, according to Aizawa: "represent the heuristic that tf–idf employs." The conditional entropy of a "randomly chosen" document in the corpus D {\displaystyle D} , conditional to the fact it contains a specific term t {\displaystyle t} (and assuming that all documents have equal probability to be chosen) is: H ( D | T = t ) = − ∑ d p d | t log ⁡ p d | t = − log ⁡ 1 | { d ∈ D : t ∈ d } | = log ⁡ | { d ∈ D : t ∈ d } | | D | + log ⁡ | D | = − i d f ( t ) + log ⁡ | D | {\displaystyle H({\cal {D}}|{\cal {T}}=t)=-\sum _{d}p_{d|t}\log p_{d|t}=-\log {\frac {1}{|\{d\in D:t\in d\}|}}=\log {\frac {|\{d\in D:t\in d\}|}{|D|}}+\log |D|=-\mathrm {idf} (t)+\log |D|} In terms of notation, D {\displaystyle {\cal {D}}} and T {\displaystyle {\cal {T}}} are "random variables" corresponding to respectively draw a document or a term. The mutual information can be expressed as M ( T ; D ) = H ( D ) − H ( D | T ) = ∑ t p t ⋅ ( H ( D ) − H ( D | W = t ) ) = ∑ t p t ⋅ i d f ( t ) {\displaystyle M({\cal {T}};{\cal {D}})=H({\cal {D}})-H({\cal {D}}|{\cal {T}})=\sum _{t}p_{t}\cdot (H({\cal {D}})-H({\cal {D}}|W=t))=\sum _{t}p_{t}\cdot \mathrm {idf} (t)} The last step is to expand p t {\displaystyle p_{t}} , the unconditional probability to draw a term, with respect to the (random) choice of a document, to obtain: M ( T ; D ) = ∑ t , d p t | d ⋅ p d ⋅ i d f ( t ) = ∑ t , d t f ( t , d ) ⋅ 1 | D | ⋅ i d f ( t ) = 1 | D | ∑ t , d t f ( t , d ) ⋅ i d f ( t ) . {\displaystyle M({\cal {T}};{\cal {D}})=\sum _{t,d}p_{t|d}\cdot p_{d}\cdot \mathrm {idf} (t)=\sum _{t,d}\mathrm {tf} (t,d)\cdot {\frac {1}{|D|}}\cdot \mathrm {idf} (t)={\frac {1}{|D|}}\sum _{t,d}\mathrm {tf} (t,d)\cdot \mathrm {idf} (t).} This expression shows that summing the Tf–idf of all possible terms and documents recovers the mutual information between documents and term taking into account all the specificities of their joint distribution. Each Tf–idf hence carries the "bit of information" attached to a term x document pair. == Link with statistical theory == Tf–idf is closely related to the negative logarithmically transformed p-value from a one-tailed formulation of Fisher's exact test when the underlying corpus documents satisfy certain idealized assumptions. More recently, tf–idf variants were shown to arise as components in the test st

  Read more →
• 

  Tresorit

  Tresorit is a Swiss company providing end-to-end encrypted cloud storage and secure content collaboration services. Founded in 2011, the company primarily serves businesses and organizations with elevated data protection and compliance requirements. Since 2021, Tresorit has been part of Swiss Post's digital business services, which, under the name 'Swiss Post Digital' offer secure communication platforms and connectable software solutions for SMEs, public authorities, and the healthcare sector, among others. == History == Tresorit was founded in 2011 by Hungarian software developers Istvan Lam, Szilveszter Szebeni and Gyorgy Szilagyi with the aim of providing a secure alternative to traditional cloud storage solutions. The company developed a cloud collaboration platform based on client-side end-to-end encryption and a zero-knowledge architecture. In its early years, Tresorit gained attention through a public security challenge inviting researchers to attempt to compromise its encryption system. The initiative received coverage in technology and cybersecurity media. The company initially positioned itself as a secure alternative to conventional cloud storage services and gradually expanded its offering toward enterprise-focused collaboration tools. In 2021, Swiss Post Communications Services acquired a majority stake in Tresorit. The company is now part of Swiss Post, and continues to operate independently within Swiss Post’s digital division, while benefiting from the broader infrastructure and institutional framework of its parent organization. Tresorit has offices in Zurich, Munich, and Budapest. == Products and Services == Tresorit provides a cloud-based platform for secure file storage and collaboration. Its services include encrypted file sharing, email encryption, electronic signatures, and encrypted data rooms for managing sensitive documents and workflows. The platform is available on Windows, macOS, Linux, Android, and iOS. == Technology == Tresorit uses client-side end-to-end encryption based on a zero-knowledge model. Files are encrypted on the user’s device before being uploaded to company servers. According to the company, encryption keys remain under user control, meaning that Tresorit and third parties cannot access the content of stored files. == Security challenge == Between 2013 and 2014, Tresorit organized a public challenge inviting security researchers to attempt to compromise the service's encryption implementation. The challenge received coverage in technology and cybersecurity media. == Acquisition by Swiss Post == In 2021, Swiss Post Communications Services acquired a majority stake in Tresorit as part of Swiss Post’s broader digital services strategy. The company is now part of Swiss Post. == Reception == Tresorit has been covered by international technology and business publications in the context of secure cloud storage and encrypted collaboration services. TechCrunch described the company as an early European provider of end-to-end encrypted cloud services, while The New York Times included it in discussions of secure file-sharing tools. Other publications such as TechRadar and ITPro have reviewed Tresorit in the context of enterprise security and confidential data handling.

  Read more →
• 

  Arthur Zimek

  Arthur Zimek is a professor in data mining, data science and machine learning at the University of Southern Denmark in Odense, Denmark. He graduated from LMU Munich in Germany, where he worked with Prof. Hans-Peter Kriegel. His dissertation on "Correlation Clustering" was awarded the "SIGKDD Doctoral Dissertation Award 2009 Runner-up" by the Association for Computing Machinery. He is well known for his work on outlier detection, density-based clustering, correlation clustering, and the curse of dimensionality. He is one of the founders and core developers of the open-source ELKI data mining framework.

  Read more →
• 

  How to Choose an AI Code-review Tool

  Trying to pick the best AI code-review tool? An AI code-review tool 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-review tool slots into your workflow and pays for itself fast. Read on for hands-on impressions, pricing tiers, and the standout features that matter.

  Read more →
• 

  Sinkov statistic

  Sinkov statistics, also known as log-weight statistics, is a specialized field of statistics that was developed by Abraham Sinkov, while working for the small Signal Intelligence Service organization, the primary mission of which was to compile codes and ciphers for use by the U.S. Army. The mathematics involved include modular arithmetic, a bit of number theory, some linear algebra of two dimensions with matrices, some combinatorics, and a little statistics. Sinkov did not explain the theoretical underpinnings of his statistics, or characterized its distribution, nor did he give a decision procedure for accepting or rejecting candidate plaintexts on the basis of their S1 scores. The situation becomes more difficult when comparing strings of different lengths because Sinkov does not explain how the distribution of his statistics changes with length, especially when applied to higher-order grams. As for how to accept or reject a candidate plaintext, Sinkov simply said to try all possibilities and to pick the one with the highest S1 value. Although the procedure works for some applications, it is inadequate for applications that require on-line decisions. Furthermore, it is desirable to have a meaningful interpretation of the S1 values.

  Read more →
• 

  Uniphore

  Uniphore is an American software company that develops artificial intelligence platforms for business use. The company is headquartered in Palo Alto, California, with offices in the United States, United Kingdom, Spain, Israel, United Arab Emirates, and India. Uniphore is known for its "Business AI Cloud," an enterprise AI platform that combines data, knowledge, models, and software agents for use in sales, marketing, and service. The company has also acquired firms in video emotion AI, AI agents, low-code automation, knowledge automation, voice and screen capture, customer data platforms, and data engineering. == History == Uniphore Software Systems was founded by Umesh Sachdev and Ravi Saraogi in 2008 and was incubated at IIT Madras. The company received an initial grant of $100,000 from the National Research Development Corporation. Early work focused on speech technologies for emerging markets. Uniphore partnered with companies that specialized in English and European languages, and adapting the technology for Indian languages and dialects. In 2014, Uniphore released its first flagship products, auMina, along with two other products, Akeira and amVoice. Uniphore raised series A funding, led by Kris Gopalakrishnan (cofounder of Infosys), in April 2015. The next month, Uniphore received additional investment from IDG Ventures. With input from its investors, Uniphore changed its business model from license fee-based income to a software as a service-based subscription fee model in 2015. By June 2016, it had added more than 70 global languages and expanded its services to Southeast Asia, the Middle East, and the United States. The company opened operations in Singapore in October 2016. The company raised Series B funding in October 2017, led by John Chambers and existing investors. Series C funding of $51 million was announced in August 2019 and led by March Capital. Uniphore acquired an exclusive third-party license for robotic process automation technology from NTT DATA in October 2020. In January 2021, Uniphore acquired Emotion Research Lab, a startup based in Spain that uses artificial intelligence and machine learning to analyze video and interpret emotions. The company received $140 million in Series D funding, led by Sorenson Capital Partners, in March 2021, bringing total funding to $210 million. In January 2021, Uniphore acquired Emotion Research Lab. In July 2021, it agreed to acquire Jacada, a provider of low-code/no-code automation; the transaction closed in October 2021. On February 16, 2022, Uniphore announced a $400 million Series E financing led by NEA, which valued the company at $2.5 billion. Hilarie Koplow-McAdams, an NEA venture partner and former Salesforce/New Relic executive, joined Uniphore's board in 2022. Uniphore's board has also included former Cisco CEO John Chambers, former Convergys CEO Andrea J. Ayers, and CrowdStrike CFO Burt Podbere (appointed January 2021). In February 2023, Uniphore acquired UK-based Red Box, a platform for capturing voice and screen recordings used in regulated and large-scale environments. It also acquired France-based Hexagone, a behavioral analytics firm combining computer vision and natural-language techniques. On December 5, 2024, Uniphore announced agreements to acquire ActionIQ, a customer data platform (CDP) vendor, and Infoworks, an enterprise data engineering platform. Uniphore launched the Business AI Cloud on June 9, 2025. The Business AI Cloud consists of a single, unified platform that includes data, knowledge, AI models, and AI agents. Uniphore announced in August 2025 that it had acquired Orby AI and intended to acquire Autonom8 to extend multi-agent and workflow automation capabilities. As of September 2025, Uniphore's customers included the United States Coast Guard, Singapore Police Force, London Underground, DirecTV, JPMorgan Chase, LG, DHL, UPS, Vodafone, Verizon, NTT Data, and as of May 2021, Firstsource. In October 2025, Uniphore raised $260 million in a Series F round at a reported valuation of $2.5 billion. Investors included March Capital, NEA, Nvidia, AMD, Snowflake, and Databricks. In January 2026, KPMG and Uniphore announced a collaboration focused on deploying AI agents powered by specialized small language models. The announcement was made at the World Economic Forum held in Davos. Cognizant and Uniphore announced a partnership in February 2026 to develop industry-specific AI tools for regulated sectors, which would initially focus on life sciences and finance. Uniphore and Rackspace also announced a partnership in March 2026. This partnership was announced in order to create an "Infrastructure-to-Agents" architecture, focusing on Business AI as a private cloud service. == Products == As of 2025, Uniphore's core offering is the Business AI Cloud and Business AI Suite of agentic AI applications. === Business AI Cloud === Uniphore’s Business AI Cloud is a full-stack platform that organizes enterprise data and knowledge for agentic AI applications. The platform enables deployment across clouds and existing data sources. Key layers and capabilities include the following. Agentic layer: Includes prebuilt agents, a natural-language agent builder, and orchestration based on Business Process Model and Notation (BPMN) to run AI workflows across business units. Model layer: Supports an open, interoperable mix of closed and open-source large language models (LLMs). Models can be orchestrated, governed, and replaced as needed. Knowledge layer: Organizes raw data into structured knowledge used for retrieval, explainability, and fine-tuning of small language models (SLMs). Data layer: Connects to data across multiple platforms and clouds through a zero-copy, composable fabric, enabling in-place preparation and supporting data residency and sovereignty requirements. === Business AI Suite === The Uniphore Business AI Suite has various prebuilt AI agents that can be used in customer service, sales, marketing, and human resources. The Uniphore Business AI Suite includes several LOBs (Lines of Business) for business functions with intelligent agents that are prebuilt, but composable. Built on the Uniphore Business AI Cloud, each application combines agentic automation and fine-tuned models. Marketing AI, Customer Service AI, Sales AI, and People AI (for human resources) are included. Competitors include Palantir, Microsoft Azure, Amazon Bedrock, Google's Vertex AI, Databricks, and Snowflake. == Recognition == Deloitte Technology Fast 50 India identified Uniphore as the 17th fastest-growing technology company in India in 2012 and one of the top 500 fastest growing companies in the Asia-Pacific region in 2014. In 2016, Time included Sachdev on its list of "10 millennials who are changing the world" for “building a phone that can understand almost any language”. NASSCOM named Uniphore to its "League of 10" emerging Indian technology companies in 2017. In 2020, the San Francisco Business Times ranked Uniphore as No. 7 among small companies in its list of the best places to work in the San Francisco Bay Area. In 2022, the company was featured on the Forbes AI 50 list. Uniphore was mentioned in the Deloitte Technology Fast 500 list in 2023, 2024, and 2025. In 2025, Inc. included Uniphore in its Best in Business program.

  Read more →
• 

  Richard Zemel

  Richard Stanley Zemel (born 1963) is a Canadian-American computer scientist and professor at Columbia University, Department of Computer Science, and a leading figure in the field of machine learning and computer vision. Zemel studied the history of science at Harvard University and obtained his B.A. in 1984. He continued his study at the Department of Computer Science of the University of Toronto under the supervision of Geoffrey Hinton. He obtained his M.Sc. and Ph.D. both in computer science in 1989 and 1994, respectively.

  Read more →
• 

  Constrained conditional model

  A constrained conditional model (CCM) is a machine learning and inference framework that augments the learning of conditional (probabilistic or discriminative) models with declarative constraints. The constraint can be used as a way to incorporate expressive prior knowledge into the model and bias the assignments made by the learned model to satisfy these constraints. The framework can be used to support decisions in an expressive output space while maintaining modularity and tractability of training and inference. Models of this kind have recently attracted much attention within the natural language processing (NLP) community. Formulating problems as constrained optimization problems over the output of learned models has several advantages. It allows one to focus on the modeling of problems by providing the opportunity to incorporate domain-specific knowledge as global constraints using a first order language. Using this declarative framework frees the developer from low level feature engineering while capturing the problem's domain-specific properties and guarantying exact inference. From a machine learning perspective it allows decoupling the stage of model generation (learning) from that of the constrained inference stage, thus helping to simplify the learning stage while improving the quality of the solutions. For example, in the case of generating compressed sentences, rather than simply relying on a language model to retain the most commonly used n-grams in the sentence, constraints can be used to ensure that if a modifier is kept in the compressed sentence, its subject will also be kept. == Motivation == Making decisions in many domains (such as natural language processing and computer vision problems) often involves assigning values to sets of interdependent variables where the expressive dependency structure can influence, or even dictate, what assignments are possible. These settings are applicable not only to Structured Learning problems such as semantic role labeling, but also for cases that require making use of multiple pre-learned components, such as summarization, textual entailment and question answering. In all these cases, it is natural to formulate the decision problem as a constrained optimization problem, with an objective function that is composed of learned models, subject to domain- or problem-specific constraints. Constrained conditional models form a learning and inference framework that augments the learning of conditional (probabilistic or discriminative) models with declarative constraints (written, for example, using a first-order representation) as a way to support decisions in an expressive output space while maintaining modularity and tractability of training and inference. These constraints can express either hard restrictions, completely prohibiting some assignments, or soft restrictions, penalizing unlikely assignments. In most applications of this framework in NLP, following, Integer Linear Programming (ILP) was used as the inference framework, although other algorithms can be used for that purpose. == Formal Definition == Given a set of feature functions { ϕ i ( x , y ) } {\displaystyle \{\phi _{i}(x,y)\}} and a set of constraints { C i ( x , y ) } {\displaystyle \{C_{i}(x,y)\}} , defined over an input structure x ∈ X {\displaystyle x\in X} and an output structure y ∈ Y {\displaystyle y\in Y} , a constraint conditional model is characterized by two weight vectors, w and ρ {\displaystyle \rho } , and is defined as the solution to the following optimization problem: a r g m a x y ∑ i w i ϕ i ( x , y ) − ∑ ρ i C i ( x , y ) {\displaystyle argmax_{y}\sum _{i}w_{i}\phi _{i}(x,y)-\sum \rho _{i}C_{i}(x,y)} . Each constraint C i ∈ C {\displaystyle C_{i}\in C} is a boolean mapping indicating if the joint assignment ( x , y ) {\displaystyle (x,y)} violates a constraint, and ρ {\displaystyle \rho } is the penalty incurred for violating the constraints. Constraints assigned an infinite penalty are known as hard constraints, and represent unfeasible assignments to the optimization problem. == Training paradigms == === Learning local vs. global models === The objective function used by CCMs can be decomposed and learned in several ways, ranging from a complete joint training of the model along with the constraints to completely decoupling the learning and the inference stage. In the latter case, several local models are learned independently and the dependency between these models is considered only at decision time via a global decision process. The advantages of each approach are discussed in which studies the two training paradigms: (1) local models: L+I (learning + inference) and (2) global model: IBT (Inference based training), and shows both theoretically and experimentally that while IBT (joint training) is best in the limit, under some conditions (basically, ”good” components) L+I can generalize better. The ability of CCM to combine local models is especially beneficial in cases where joint learning is computationally intractable or when training data are not available for joint learning. This flexibility distinguishes CCM from the other learning frameworks that also combine statistical information with declarative constraints, such as Markov logic network, that emphasize joint training. === Minimally supervised CCM === CCM can help reduce supervision by using domain knowledge (expressed as constraints) to drive learning. These settings were studied in and. These works introduce semi-supervised Constraints Driven Learning (CODL) and show that by incorporating domain knowledge the performance of the learned model improves significantly. === Learning over latent representations === CCMs have also been applied to latent learning frameworks, where the learning problem is defined over a latent representation layer. Since the notion of a correct representation is inherently ill-defined, no gold-standard labeled data regarding the representation decision is available to the learner. Identifying the correct (or optimal) learning representation is viewed as a structured prediction process and therefore modeled as a CCM. This problem was covered in several papers, in both supervised and unsupervised settings. In all cases research showed that explicitly modeling the interdependencies between representation decisions via constraints results in an improved performance. == Integer linear programming for natural language processing applications == The advantages of the CCM declarative formulation and the availability of off-the-shelf solvers have led to a large variety of natural language processing tasks being formulated within the framework, including semantic role labeling, syntactic parsing, coreference resolution, summarization, transliteration, natural language generation and joint information extraction. Most of these works use an integer linear programming (ILP) solver to solve the decision problem. Although theoretically solving an Integer Linear Program is exponential in the size of the decision problem, in practice using state-of-the-art solvers and approximate inference techniques large scale problems can be solved efficiently. The key advantage of using an ILP solver for solving the optimization problem defined by a constrained conditional model is the declarative formulation used as input for the ILP solver, consisting of a linear objective function and a set of linear constraints. == Resources == CCM Tutorial Predicting Structures in NLP: Constrained Conditional Models and Integer Linear Programming in NLP

  Read more →