Serge Belamant (born 1953) is a French-born South African entrepreneur best known for designing the Universal Electronic Payment System (UEPS) and the Chip Offline Pre-authorised Card (COPAC). He founded the cash-payments company Net1 UEPS Technologies in 1989, led it through dual listings on the NASDAQ and the Johannesburg Stock Exchange, and oversaw the contentious welfare-payments contract with the South African Social Security Agency (SASSA) until his retirement in 2017. Since 2018 he has been non-executive chair of London-based buy-now-pay-later fintech Zilch. == Early life and education == Belamant moved from France to South Africa with his family in 1967 and matriculated from Highlands North Boys' High School, Johannesburg. In 1972 he entered the University of the Witwatersrand to study civil engineering but switched to computer science and applied mathematics in his second year. He left the university without a degree and later took short courses in information systems at the University of South Africa (UNISA). == Early career and SASWITCH (1981–1989) == Belamant worked for Control Data Corporation as a systems analyst for a decade before joining SASWITCH Ltd in 1985. Economic sanctions had left the consortium's national ATM network dependent on unsupported Christian Rovsing computers. Belamant led a rebuild on fault-tolerant Stratus hardware and wrote protocol-translation software that allowed fourteen banks to connect without altering their host systems. By 1988 SASWITCH was handling about three million ATM transactions a month, according to the Competition Commission. The switch—now run by BankservAfrica—remains the backbone of South Africa's shared ATM network. == Net1 UEPS Technologies (1989–2017) == === Founding and UEPS === In 1989, Serge Belamant developed the Universal Electronic Payment System (UEPS), enabling secure, real-time transactions even in areas with limited connectivity. In the same year, he founded NET1 UEPS Technologies Inc., serving as its CEO and Director. === COPAC for VISA === In 1995, VISA tasked Belamant with designing the Chip Offline Pre-authorized Card (COPAC), a technology still widely used in chip-enabled credit and debit cards. A year later, he listed his company APLITEC (Applied Technology Holdings Limited) on the Johannesburg Stock Exchange. === Listings and acquisitions === In 1999, Belamant acquired Cash Payment Services (CPS) from First National Bank of South Africa, modernizing its welfare payment system to serve millions in rural areas. In 2005, he led NET1 Technologies to an IPO, listing it as NET1 UEPS Technologies Inc. on the Nasdaq. A secondary listing on the Johannesburg Stock Exchange (JSE) followed in 2008. === SASSA contract === Under Belamant's leadership, NET1 managed welfare payments for the South African Social Security Agency (SASSA), handling payments for over 10 million beneficiaries monthly. Despite criticism over handling the SASSA contract, investigations by the U.S. Department of Justice and the South African Constitutional Court found no wrongdoing. == Zilch (2018–present) == Belamant co-founded London-based "buy-now-pay-later" firm Zilch Technology in 2018 and serves as non-executive chair. Zilch reported £145 million in annual-recurring revenue and 4.5 million customers in January 2025. == Patents == Belamant is listed as inventor on more than a dozen payment-security patents, including: "Funds transfer system" (US RE36,788, 2000) – the basis for UEPS. "Financial transactions with a varying PIN" (WO 2014/037869, 2014).
Single particle analysis
Single particle analysis is a group of related computerized image processing techniques used to analyze images from transmission electron microscopy (TEM). These methods were developed to improve and extend the information obtainable from TEM images of particulate samples, typically proteins or other large biological entities such as viruses. Individual images of stained or unstained particles are very noisy, making interpretation difficult. Combining several digitized images of similar particles together gives an image with stronger and more easily interpretable features. An extension of this technique uses single particle methods to build up a three-dimensional reconstruction of the particle. Using cryo-electron microscopy it has become possible to generate reconstructions with sub-nanometer, near-atomic resolution resolution first in the case of highly symmetric viruses, and now in smaller, asymmetric proteins as well. == Techniques == Single particle analysis can be done on both negatively stained and vitreous ice-embedded transmission electron cryomicroscopy (CryoTEM) samples. Single particle analysis methods are, in general, reliant on the sample being homogeneous, although techniques for dealing with conformational heterogeneity are being developed. Images (micrographs) are taken with an electron microscope using charged-coupled device (CCD) detectors coupled to a phosphorescent layer (in the past, they were instead collected on film and digitized using high-quality scanners). The image processing is carried out using specialized software programs, often run on multi-processor computer clusters. Depending on the sample or the desired results, various steps of two- or three-dimensional processing can be done. === Alignment and classification === Biological samples, and especially samples embedded in thin vitreous ice, are highly radiation sensitive, thus only low electron doses can be used to image the sample. This low dose, as well as variations in the metal stain used (if used) means images have high noise relative to the signal given by the particle being observed. By aligning several similar images to each other so they are in register and then averaging them, an image with higher signal-to-noise ratio can be obtained. As the noise is mostly randomly distributed and the underlying image features constant, by averaging the intensity of each pixel over several images only the constant features are reinforced. Typically, the optimal alignment (a translation and an in-plane rotation) to map one image onto another is calculated by cross-correlation. However, a micrograph often contains particles in multiple different orientations and/or conformations, and so to get more representative image averages, a method is required to group similar particle images together into multiple sets. This is normally carried out using one of several data analysis and image classification algorithms, such as multi-variate statistical analysis and hierarchical ascendant classification, or k-means clustering. Often data sets of tens of thousands of particle images are used, and to reach an optimal solution an iterative procedure of alignment and classification is used, whereby strong image averages produced by classification are used as reference images for a subsequent alignment of the whole data set. === Image filtering === Image filtering (band-pass filtering) is often used to reduce the influence of high and/or low spatial frequency information in the images, which can affect the results of the alignment and classification procedures. This is particularly useful in negative stain images. The algorithms make use of fast Fourier transforms (FFT), often employing Gaussian shaped soft-edged masks in reciprocal space to suppress certain frequency ranges. High-pass filters remove low spatial frequencies (such as ramp or gradient effects), leaving the higher frequencies intact. Low-pass filters remove high spatial frequency features and have a blurring effect on fine details. === Contrast transfer function === Due to the nature of image formation in the electron microscope, bright-field TEM images are obtained using significant underfocus. This, along with features inherent in the microscope's lens system, creates blurring of the collected images visible as a point spread function. The combined effects of the imaging conditions are known as the contrast transfer function (CTF), and can be approximated mathematically as a function in reciprocal space. Specialized image processing techniques such as phase flipping and amplitude correction / Wiener filtering can (at least partially) correct for the CTF, and allow high resolution reconstructions. === Three-dimensional reconstruction === Transmission electron microscopy images are projections of the object showing the distribution of density through the object, similar to medical X-rays. By making use of the projection-slice theorem a three-dimensional reconstruction of the object can be generated by combining many images (2D projections) of the object taken from a range of viewing angles. Proteins in vitreous ice ideally adopt a random distribution of orientations (or viewing angles), allowing a fairly isotropic reconstruction if a large number of particle images are used. This contrasts with electron tomography, where the viewing angles are limited due to the geometry of the sample/imaging set up, giving an anisotropic reconstruction. Filtered back projection is a commonly used method of generating 3D reconstructions in single particle analysis, although many alternative algorithms exist. Before a reconstruction can be made, the orientation of the object in each image needs to be estimated. Several methods have been developed to work out the relative Euler angles of each image. Some are based on common lines (common 1D projections and sinograms), others use iterative projection matching algorithms. The latter works by beginning with a simple, low resolution 3D starting model and compares the experimental images to projections of the model and creates a new 3D to bootstrap towards a solution. Methods are also available for making 3D reconstructions of helical samples (such as tobacco mosaic virus), taking advantage of the inherent helical symmetry. Both real space methods (treating sections of the helix as single particles) and reciprocal space methods (using diffraction patterns) can be used for these samples. === Tilt methods === The specimen stage of the microscope can be tilted (typically along a single axis), allowing the single particle technique known as random conical tilt. An area of the specimen is imaged at both zero and at high angle (~60-70 degrees) tilts, or in the case of the related method of orthogonal tilt reconstruction, +45 and −45 degrees. Pairs of particles corresponding to the same object at two different tilts (tilt pairs) are selected, and by following the parameters used in subsequent alignment and classification steps a three-dimensional reconstruction can be generated relatively easily. This is because the viewing angle (defined as three Euler angles) of each particle is known from the tilt geometry. 3D reconstructions from random conical tilt suffer from missing information resulting from a restricted range of orientations. Known as the missing cone (due to the shape in reciprocal space), this causes distortions in the 3D maps. However, the missing cone problem can often be overcome by combining several tilt reconstructions. Tilt methods are best suited to negatively stained samples, and can be used for particles that adsorb to the carbon support film in preferred orientations. The phenomenon known as charging or beam-induced movement makes collecting high-tilt images of samples in vitreous ice challenging. === Map visualization and fitting === Various software programs are available that allow viewing the 3D maps. These often enable the user to manually dock in protein coordinates (structures from X-ray crystallography, NMR, or a computational model such as one found in the AlphaFold Protein Structure Database) of subunits into the electron density. Several programs can also fit subunits computationally; as of the 2020s using these programs tend to produce better accuracy than manual docking because they can perform labor-intensive tasks such as: The scale of SPA-derived maps depends on knowing the pixel size (angstorms per pixel), which is not always accurate. Programs can automatically correct for this difference by using coordinate data or by using knowledge of chemical bonds. Many proteins are made up of several roughly rigid protein domains linked by flexible parts. Pre-existing coordinate data, whether experimental or computational, may not exactly match the inter-domain positioning of the cyro-EM map. Modern programs can automatically "chop" pre-existing coordinate data into individual domains and fit them in individually. For higher-resolution structures, it is pos
ReRites
ReRites (also known as RERITES, ReadingRites, Big Data Poetry) is a literary work of "Human + A.I. poetry" by David Jhave Johnston that used neural network models trained to generate poetry which the author then edited. ReRites won the Robert Coover Award for a Work of Electronic Literature in 2022. == About the project == The ReRites project began as a daily rite of writing with a neural network, expanded into a series of performances from which video documentation has been published online, and concluded with a set of 12 books and an accompanying book of essays published by Anteism Books in 2019. In Electronic Literature, Scott Rettberg describes the early phases of the project in 2016, when it bore the preliminary name Big Data Poetry. Jhave (the artist name that David Jhave Johnston goes by) describes the process of writing ReRites as a rite: "Every morning for 2 hours (normally 6:30–8:30am) I get up and edit the poetic output of a neural net. Deleting, weaving, conjugating, lineating, cohering. Re-writing. Re-wiring authorship: hybrid augmented enhanced evolutionary". There is video documentation of the writing process. The human editing of the neural network's output is fundamental to this project, and Jhave gives examples of both unedited text extracts and his edited versions in publications about the project. Kyle Booten describes ReRites as "simultaneously dusty and outrageously verdant, monotonously sublime and speckled with beautiful and rare specimens". === Performances === ReRites was first shared with an audience through a series of performances where audience members and poets would participate in reading the automatically generated texts, which appeared on screen so fast that human readers could barely keep up. This has been described as allowing participants to "re-discover[..] the peculiar pleasures of being embodied", or, in Jhave's own words, as a space where human participants were "playing their wits and voices against an evocative infinite deep-learning muse". The first performance was at Brown University's Interrupt Festival in 2019. It has been performed many times since, including at the Barbican Centre in London and Anteism Books. === Print publications === For a single year Jhave published one book of poetry from the ReRites project each month. These twelve volumes are accompanied by a book of essays, all published by Anteism Books. The accompanying essays provide critical responses to the project from poets and scholars including Allison Parrish, Johanna Drucker, Kyle Booten, Stephanie Strickland, John Cayley, Lai-Tze Fan, Nick Montfort, Mairéad Byrne, and Chris Funkhouser. Allison Parrish notes elsewhere that these paratexts to ReRites serve a legitimising function for a genre of poetry that is not yet institutionally acknowledged. === Technical details === Starting in 2016 under the name Big Data Poetry, Jhave generated poems using, in his own words, "neural network code (..) adapted from three corporate github-hosted machine-learning libraries: TensorFlow (Google), PyTorch (Facebook), and AWD-LSTM (SalesForce)". He explains that the "models were trained on a customised corpus of 600,000 lines of poetry ranging from the romantic epoch to the 20th century avant garde". Jhave maintains a GitHub repository with some of the code supporting ReRites. == Reception == ReRites is described by John Cayley as "one of the most thorough and beautiful" poetic responses to machine learning. The work's influence on the field of electronic literature was acknowledged in 2022, when the work won the Electronic Literature Organization's Robert Coover Award for a Work of Electronic Literature. The jury described ReRites as particularly poignant in the time of the pandemic, as it was "a documentation of the performance of the private ritual of writing and the obsessive-compulsive need for writers to communicate — even when no one else is reading". The question of authorship and voice in ReRites has been raised by several critics. Although generated poetry is an established genre in electronic literature, Cayley notes that unlike the combinatory poems created by authors like Nick Montfort, where the author explicitly defines which words and phrases will be recombined, ReRites has "not been directed by literary preconceptions inscribed in the program itself, but only by patterns and rhythms pre-existing in the corpora". In an essay for the Australian journal TEXT, David Thomas Henry Wright asks how to understand authorship and authority in ReRites: "Who or what is the authority of the work? The original data fed into the machine, that is not currently retrievable or discernible from the final works? The code that was taken and adapted for his purposes? Or Jhave, the human editor?" Wright concludes that Jhave is the only actor with any intentionality and therefore the authority of the work. The centrality of the human editor is also emphasised by other scholars. In a chapter analysing ReRites Malthe Stavning Erslev argues that the machine learning misrepresents the dataset it is trained on. While ReRites uses 21st century neural networks, it has been compared to earlier literary traditions. Poet Victoria Stanton, who read at one of the ReRites performances, has compared ReRites to found poetry, while David Thomas Henry Wright compares it to the Oulipo movement and Mark Amerika to the cut-up technique. Scholars also position ReRites firmly within the long tradition of generative poetry both in electronic literature and print, stretching from the I Ching, Queneau's Cent Mille Milliards de Poemes and Nabokov's Pale Fire to computer-generated poems like Christopher Strachey's Love Letter Generator (1952) and more contemporary examples. Jhave describes the process of working with the output from the neural network as "carving". In his book My Life as an Artificial Creative Intelligence, Mark Amerika writes that the "method of carving the digital outputs provided by the language model as part of a collaborative remix jam session with GPT-2, where the language artist and the language model play off each other’s unexpected outputs as if caught in a live postproduction set, is one I share with electronic literature composer David Jhave Johnston, whose AI poetry experiments precede my own investigations."
Visual Turing Test
The Visual Turing Test is “an operator-assisted device that produces a stochastic sequence of binary questions from a given test image”. The query engine produces a sequence of questions that have unpredictable answers given the history of questions. The test is only about vision and does not require any natural language processing. The job of the human operator is to provide the correct answer to the question or reject it as ambiguous. The query generator produces questions such that they follow a “natural story line”, similar to what humans do when they look at a picture. == History == Research in computer vision dates back to the 1960s when Seymour Papert first attempted to solve the problem. This unsuccessful attempt was referred to as the Summer Vision Project. The reason why it was not successful was because computer vision is more complicated than what people think. The complexity is in alignment with the human visual system. Roughly 50% of the human brain is devoted in processing vision, which indicates that it is a difficult problem. Later there were attempts to solve the problems with models inspired by the human brain. Perceptrons by Frank Rosenblatt, which is a form of the neural networks, was one of the first such approaches. These simple neural networks could not live up to their expectations and had certain limitations due to which they were not considered in future research. Later with the availability of the hardware and some processing power the research shifted to image processing which involves pixel-level operations, like finding edges, de-noising images or applying filters to name a few. There was some great progress in this field but the problem of vision which was to make the machines understand the images was still not being addressed. During this time the neural networks also resurfaced as it was shown that the limitations of the perceptrons can be overcome by Multi-layer perceptrons. Also in the early 1990s convolutional neural networks were born which showed great results on digit recognition but did not scale up well on harder problems. The late 1990s and early 2000s saw the birth of modern computer vision. One of the reasons this happened was due to the availability of key, feature extraction and representation algorithms. Features along with the already present machine learning algorithms were used to detect, localise and segment objects in Images. While all these advancements were being made, the community felt the need to have standardised datasets and evaluation metrics so the performances can be compared. This led to the emergence of challenges like the Pascal VOC challenge and the ImageNet challenge. The availability of standard evaluation metrics and the open challenges gave directions to the research. Better algorithms were introduced for specific tasks like object detection and classification. Visual Turing Test aims to give a new direction to the computer vision research which would lead to the introduction of systems that will be one step closer to understanding images the way humans do. == Current evaluation practices == A large number of datasets have been annotated and generalised to benchmark performances of difference classes of algorithms to assess different vision tasks (e.g., object detection/recognition) on some image domain (e.g., scene images). One of the most famous datasets in computer vision is ImageNet which is used to assess the problem of object level Image classification. ImageNet is one of the largest annotated datasets available and has over one million images. The other important vision task is object detection and localisation which refers to detecting the object instance in the image and providing the bounding box coordinates around the object instance or segmenting the object. The most popular dataset for this task is the Pascal dataset. Similarly there are other datasets for specific tasks like the H3D dataset for human pose detection, Core dataset to evaluate the quality of detected object attributes such as colour, orientation, and activity. Having these standard datasets has helped the vision community to come up with well performing algorithms for all these tasks. The next logical step is to create a larger task encompassing of these smaller subtasks. Having such a task would lead to building systems that would understand images, as understanding images would inherently involve detecting objects, localising them and segmenting them. == Details == The Visual Turing Test (VTT) unlike the Turing test has a query engine system which interrogates a computer vision system in the presence of a human co-ordinator. It is a system that generates a random sequence of binary questions specific to the test image, such that the answer to any question k is unpredictable given the true answers to the previous k − 1 questions (also known as history of questions). The test happens in the presence of a human operator who serves two main purposes: removing the ambiguous questions and providing the correct answers to the unambiguous questions. Given an Image infinite possible binary questions can be asked and a lot of them are bound to be ambiguous. These questions if generated by the query engine are removed by the human moderator and instead the query engine generates another question such that the answer to it is unpredictable given the history of the questions. The aim of the Visual Turing Test is to evaluate the Image understanding of a computer system, and an important part of image understanding is the story line of the image. When humans look at an image, they do not think that there is a car at ‘x’ pixels from the left and ‘y’ pixels from the top, but instead they look at it as a story, for e.g. they might think that there is a car parked on the road, a person is exiting the car and heading towards a building. The most important elements of the story line are the objects and so to extract any story line from an image the first and the most important task is to instantiate the objects in it, and that is what the query engine does. === Query engine === The query engine is the core of the Visual Turing Test and it comprises two main parts : Vocabulary and Questions ==== Vocabulary ==== Vocabulary is a set of words that represent the elements of the images. This vocabulary when used with appropriate grammar leads to a set of questions. The grammar is defined in the next section in a way that it leads to a space of binary questions. The vocabulary V {\displaystyle {\mathcal {V}}} consist of three components: Types of Objects T {\displaystyle {\mathcal {T}}} Type-dependent attributes of objects A ( t ) {\displaystyle {\mathcal {A}}(t)} Type-dependent relationships between two objects R ( t , t ′ ) {\displaystyle {\mathcal {R}}(t,t')} For Images of urban street scenes the types of objects include people, vehicle and buildings. Attributes refer to the properties of these objects, for e.g. female, child, wearing a hat or carrying something, for people and moving, parked, stopped, one tire visible or two tires visible for vehicles. Relationships between each pair of object classes can be either “ordered” or “unordered”. The unordered relationships may include talking, walking together and the ordered relationships include taller, closer to the camera, occluding, being occluded etc. Additionally all of this vocabulary is used in context of rectangular image regions w \in W which allow for the localisation of objects in the image. An extremely large number of such regions are possible and this complicates the problem, so for this test, regions at specific scales are only used which include 1/16 the size of image, 1/4 the size of image, 1/2 the size of image or larger. ==== Questions ==== The question space is composed of four types of questions: Existence questions: The aim of the existence questions is to find new objects in the image that have not been uniquely identified previously. They are of the form : Qexist = 'Is there an instance of an object of type t with attributes A partially visible in region w that was not previously instantiated?' Uniqueness questions: A uniqueness question tries to uniquely identify an object to instantiate it. Quniq = 'Is there a unique instance of an object of type t with attributes A partially visible in region w that was not previously instantiated?' The uniqueness questions along with the existence questions form the instantiation questions. As mentioned earlier instantiating objects leads to other interesting questions and eventually a story line. Uniqueness questions follow the existence questions and a positive answer to it leads to instantiation of an object. Attribute questions: An attribute question tries to find more about the object once it has been instantiated. Such questions can query about a single attribute, conjunction of two attributes or disjunction of two attributes. Qatt(ot) = {'Does object ot have attribute a?' , 'Does object
Sentence embedding
In natural language processing, a sentence embedding is a representation of a sentence as a vector of numbers which encodes meaningful semantic information. State of the art embeddings are based on the learned hidden layer representation of dedicated sentence transformer models. BERT pioneered an approach involving the use of a dedicated [CLS] token prepended to the beginning of each sentence inputted into the model; the final hidden state vector of this token encodes information about the sentence and can be fine-tuned for use in sentence classification tasks. In practice however, BERT's sentence embedding with the [CLS] token achieves poor performance, often worse than simply averaging non-contextual word embeddings. SBERT later achieved superior sentence embedding performance by fine tuning BERT's [CLS] token embeddings through the usage of a siamese neural network architecture on the SNLI dataset. Other approaches are loosely based on the idea of distributional semantics applied to sentences. Skip-Thought trains an encoder-decoder structure for the task of neighboring sentences predictions; this has been shown to achieve worse performance than approaches such as InferSent or SBERT. An alternative direction is to aggregate word embeddings, such as those returned by Word2vec, into sentence embeddings. The most straightforward approach is to simply compute the average of word vectors, known as continuous bag-of-words (CBOW). However, more elaborate solutions based on word vector quantization have also been proposed. One such approach is the vector of locally aggregated word embeddings (VLAWE), which demonstrated performance improvements in downstream text classification tasks. == Applications == In recent years, sentence embedding has seen a growing level of interest due to its applications in natural language queryable knowledge bases through the usage of vector indexing for semantic search. LangChain for instance utilizes sentence transformers for purposes of indexing documents. In particular, an indexing is generated by generating embeddings for chunks of documents and storing (document chunk, embedding) tuples. Then given a query in natural language, the embedding for the query can be generated. A top k similarity search algorithm is then used between the query embedding and the document chunk embeddings to retrieve the most relevant document chunks as context information for question answering tasks. This approach is also known formally as retrieval-augmented generation. Though not as predominant as BERTScore, sentence embeddings are commonly used for sentence similarity evaluation which sees common use for the task of optimizing a Large language model's generation parameters is often performed via comparing candidate sentences against reference sentences. By using the cosine-similarity of the sentence embeddings of candidate and reference sentences as the evaluation function, a grid-search algorithm can be utilized to automate hyperparameter optimization. == Evaluation == A way of testing sentence encodings is to apply them on Sentences Involving Compositional Knowledge (SICK) corpus for both entailment (SICK-E) and relatedness (SICK-R). In the best results are obtained using a BiLSTM network trained on the Stanford Natural Language Inference (SNLI) Corpus. The Pearson correlation coefficient for SICK-R is 0.885 and the result for SICK-E is 86.3. A slight improvement over previous scores is presented in: SICK-R: 0.888 and SICK-E: 87.8 using a concatenation of bidirectional Gated recurrent unit.
TigerGraph
TigerGraph is a private company headquartered in Redwood City, California. It provides graph database and graph analytics software. == History == TigerGraph was founded in 2012 by programmer Yu, Ruoming, Li, Like and Mingxi, under the name GraphSQL. In September 2017, the company came out of stealth mode under the name TigerGraph with $33 million in funding. It raised an additional $32 million in funding in September 2019 and another $105 million in a series C round in February 2021. Cumulative funding as of March 2021 is $170 million. == Products == TigerGraph's hybrid transactional/analytical processing database and analytics software can scale to hundreds of terabytes of data with trillions of edges, and is used for data intensive applications such as fraud detection, customer data analysis (customer 360), IoT, artificial intelligence and machine learning. It is available using the cloud computing delivery model. The analytics uses C++ based software and a parallel processing engine to process algorithms and queries. It has its own graph query language that is similar to SQL. TigerGraph also provides a software development kit for creating graphs and visual representations. As of Mar 2024, TigerGraph version is up to version 4.2.0 TigerGraph offers free Community Edition for developers, researchers, and educators. It can be obtained from https://dl.tigergraph.com/ == Query Language == GSQL , designed by Mingxi Wu and Alin Deutsch in 2015, is a SQL-like Turing complete query language. GSQL includes additions to make it compliant with the Graph Query Language standard.
Boyfriend Maker
Boyfriend Maker was a dating sim, romance chatbot smartphone app for iOS (iPhone) and Android devices, developed by Japanese studio 36 You Games (styled as 36You) and distributed under the freemium business model. Boyfriend Maker incorporated advanced artificial intelligence chat technology a decade before products such as ChatGPT. According to the developer's website, Boyfriend Maker is an "app that lets you interact and chat with quirky virtual boyfriends". While each virtual boyfriend has certain unique characteristics, the various instances of the boyfriend are powered by a chat engine, that (at least within a language and market) can utilise vocabulary and knowledge acquired in a chat with one user in subsequent chats with other users. == Gameplay == Users gain experience points and in-game coins. Users can customize their virtual boyfriend's appearance by selecting items such as hair, clothing, face, and a necklace. == Apple delisting and reintroduction == In late November 2012, the original iOS Boyfriend Maker app was delisted from the Apple App Store due to "ribald" chat, according to the New York Times. Boyfriend Maker was removed by Apple due to "reports of references to violent sexual acts and pedophilia". Boyfriend Maker had an age rating of 4+, even though the chat bot "responds with often strange and explicit text unsuitable for young children". User-posted chat excerpts indicate that the virtual boyfriend would sometimes transition abruptly to sexual chat in response to a seemingly innocent question. In one user-posted example, in response to the question, "what kind of wedding cake will we have" the boyfriend responds, "a good sex ima be on top of u u gonna ride oon me bitin the pillow gurrl ima fuck da shit out of u". The developer's use of the SimSimi-created third-party chat engine may be responsible for the sexual text. As the virtual boyfriend converses with human users, the SimSimi chat engine acquires vocabulary from users of the game and applies this "learned" vocabulary in chats with other users. The chat engine might also employ lines harvested from human-human chat logs, song lyrics, movies or TV shows. In April 2013, a detuned and presumably tamer version of the app, titled Boyfriend Plus, was permitted on Apple's App Store.