In artificial neural networks, a hidden layer is a layer of artificial neurons that is neither an input layer nor an output layer. The simplest examples appear in multilayer perceptrons (MLP), as illustrated in the diagram. An MLP without any hidden layer is essentially just a linear model. With hidden layers and activation functions, however, nonlinearity is introduced into the model. In typical machine learning practice, the weights and biases are initialized, then iteratively updated during training via backpropagation.
Nanosemantics
Nanosemantics Lab is a Russian IT company specializing in natural language processing (NLP), computer vision (CV), speech technologies (ASR/TTS) and creation of interactive dialog interfaces, particularly chatbots and virtual assistants, based on artificial intelligence (AI). The company uses neural network platforms, including its own-made platform PuzzleLib which works on Russian-made microprocessor architecture Elbrus and Russia-based Astra Linux operating system. The company was founded in 2005 by Igor Ashmanov and Natalya Kaspersky. == Profile == The company was one of the first on Russian market to develop dialog interfaces for different branches of businesses, as well as to support community of AI developers. The company's most demanded product, as for beginning of the 2020s, is the automated "online advisers", functioning as chat bots, made for helping customers with usage of commercial products. In 2009 the company released an online service called iii.ru, where visitors were able to create their own AI-based virtual personalities entitles "infs" (for free). A visitor was able to train its own "inf" and let them chat to other "live" visitors as well with other "infs". More than 2.3 million of "infs" were created and trained by visitors over several years. Nanosemantics Lab maintains its own linguistic programming language for AI development called Dialog Language (DL). Popular social networks and instant messaging services may be used as base platforms. Nanosemantics' AI bots support different types of businesses: banks and financial services, telecommunications, retail, travel and automobile industry, home appliances production, etc. Among its solutions, Nanosemantics lists projects for various companies and institutions, among them VTB, Beeline, MTS, Sberbank, Higher School of Economics, Webmoney, Gazpromneft, Rostelecom, Ford Motors, Ministry of Health of the Russian Federation and others. The company uses the term "inf" for naming its numerous types of chat bots. The term was coined by co-founder Igor Ashmanov, head of Ashmanov & Partners. A 2014 scholarly research at Higher School of Economics, called "Basics of Business Informatics", states that such "infs", when used at business, may lower load on employees, collect statistics useful for understanding market demand and also may increase customer loyalty by providing fast and informative answers due to usage of large databases. The same research describes Nanosemantics' project for Russian branch of Ford Motors company, when AI capabilities were used for promoting the car model Ford Kuga. The research pointed out that within 2 months since beginning, the promo-website conducted 47774 talks of visitors with the specialized "inf", which indicated several hundred thousand of questions and the longest chat lasted for 3 hours 10 minutes. One-year promo campaign showed that 28.6% of people who made pre-orders talked to an "inf". In 2016 Nanosemantics launched a SaaS platform aimed at creating customized virtual assistants by users. The company's flagship product is considered to be Dialog Operating System (DialogOS), a professional corporate platform for creating intellectual voice and textual bots. It has its own linguistic programming language for creation of flexible scenarios and ready-studied neural natural language processing modules that are able to understand human interlocutors. In 2021 the company presented technology called NLab Speech ASR which contains a set of neural-networking algorithms for processing audio signals and analysis of texts that were trained and calibrated using speech-based big data marked up manually. The technology allows speed of processing of data up to "6 real-time factor" and precision values in noisy audio data may exceed 82%. In March 2022 the technology was included in Russia's Joint Registry for Russian Programs for Computers and Databases. As well, another technology was included: NLab Speech TTS, which is text-to-speech system that produces synthesized speech from printed text. == Joint projects == Nanosemantics participates in Ashmanov & Partners' projects related to AI. Since 2014, it helps in development of hardware "personal assistant" called Lexy, a solution similar to Amazon Alexa and the analogues. In August 2019 it was announced that Nanosemantics is going to participate in creation of open operating system for creating automated voice assistants. The project was called SOVA (Smart Open Virtual Assistant) and received investment of 300 million roubles (~$4,6 million) from Russian state-maintained National Technological Initiative. The company maintains long-term partnerships with Skolkovo Innovation Center (resident of IT cluster), branch association "Neuronet" and Yandex. Together with USA-based startup Remedy Logic, Nanosemantics has developed a medical diagnostic system for finding, using AI, spinal pathologies in tomography images of human bodies. Among them: central, foraminal and lateral lumbar stenosis, hernias, arthrosis. The system offers options of treatment. Since August 2021 the company is the resident of Technology Valley of Moscow State University. Also in 2021, Nanosemantics became a member of Committee on Artificial Intelligence within the Russian Association of Software Developers "Native Soft". The company states as one of its missions support of initiatives aimed at preservation and development of the Russian language. In May 2021, together with Pushkin Institute, the company created a chat bot called Phil, that explains to Russian people meaning of different Russian neologisms, and offers synonyms for them. Bot's vocabulary contains more than 500 neologisms, as well the bot can give advice on jargonisms and other types of specific words. Also in 2021, Nanosemanics Lab has signed the first-ever Russian "Codex of ethics of artificial intelligence". It establishes guidelines for ethical behavior of businesses that implement AI-based solutions. === IT contests === The company regularly organizes All-Russian Turing Test competitions for IT developers. Some of these events are co-organized with Microsoft. During the competitions, judges randomly choose virtual interlocutor and have a short conversation with them. They have to determine if a human or a machine is talking to them. An interlocutor may be either a bot or its human creator or operator. The results are measured in per cent of judges that were successfully convinced by a machine that it was a human. In 2021 Nanosemantics took part in federal project "Artificial Intelligence" by National Technological Initiative. In December 2021 the company together with state enterprise "Resource Center of Universal Design and Rehabilitation Technologies" (RCUD-RT) held an all-Russian hackathon aimed at development of AI solutions for medicine. During 3 days, participants created several training programs for patients with speech disorders. In April 2022, another hackathon by Nanosemantics was held together with MIREA – Russian Technological University. Students were participating and trying to generate algorithms for voice deepfakes. 17 teams contested in creation of software that generated artificial voice of a certain person. == Recognition == Since its foundation, Nanosemantics Lab has received a number of recognitions and awards. Among them are several professional ROTOR awards for the website iii.ru (created in 2009). The website gives the general public the means to create and train virtual assistants, which can then be used on a website or integrated into social networks. In 2013, a virtual assistant called Dana, created for Beeline Kazakhstan, was awarded with professional prize "Crystal Headset" in nomination "the best applying of technology". In 2015, the RBTH international media service included Nanosemantics in its list of "Top 50 Startups" in Russia. In 2016, the company received Russian state-maintained award called Runet Prize in two nominations: "State and Society" and "Technology and Innovation". In 2021, in Velikiy Novgorod, Nanosemantics team has won a hackathon aimed at finding means of discovering corruption schemes in Russian laws. In February 2022 the company won another contest by National Technological Initiative, called "Prochtenie", aimed at creation of AI systems for checking schoolchildren's school essays. The Nanosemantics team was awarded 20 million rubles for "overcoming technological barrier" in contest dedicated to English language, and 12 million for 1st place in special nomination "Structure" in Russian-language essay contest.
Intelligent character recognition
Intelligent character recognition (ICR) is a method of extracting handwritten text from images. It is a more sophisticated type of OCR technology that recognizes different handwriting styles and fonts to intelligently interpret data from physical documents. ICR is used to organize paper-based unstructured data by scanning documents, extracting information, and adapting extracted data for database storage. ICR algorithms collaborate with OCR to automate data entry from forms by removing the need for keystrokes. It has a high degree of accuracy and is a dependable method for processing various handwritten media quickly. == Capabilities == Most ICR software has a self-learning neural network-based algorithms, which automatically update the recognition database for new handwriting patterns. It extends the usefulness of scanning devices for the purpose of document processing, from printed character recognition (a function of OCR) to hand-written matter recognition. Because this process is involved in recognizing hand writing, accuracy levels may, in some circumstances, not be very good but can achieve 97%+ accuracy rates in reading handwriting in structured forms. Often to achieve these high recognition rates several read engines are used within the software and each is given elective voting rights to determine the true reading of characters. In numeric fields, engines which are designed to read numbers take preference, while in alpha fields, engines designed to read hand written letters have higher elective rights. When used in conjunction with a bespoke interface hub, hand-written data can be automatically populated into a back office system avoiding laborious manual keying and can be more accurate than traditional human data entry. === Automated forms processing === An important development of ICR was the invention of automated forms processing in 1993 by Joseph Corcoran who was awarded a patent on the invention. This involved a three-stage process of capturing the image of the form to be processed by ICR and preparing it to enable the ICR engine to give best results, then capturing the information using the ICR engine and finally processing the results to automatically validate the output from the ICR engine. This application of ICR increased the usefulness of the technology and made it applicable for use with real world forms in normal business applications. Modern software applications use ICR as a technology of recognizing text in forms filled in by hand (hand-printed). == Differences between ICR and OCR == === OCR === Optical character recognition (OCR) is commonly considered to apply to any recognition technique that reads machine printed text. An example of a traditional OCR use case would be to translate the characters from an image of a printed document, such as a book page, newspaper clipping, or legal contract, into a separate file that could be searched and updated with a word processor or document viewer. It's also quite helpful for automating the processing of forms. Information can be swiftly extracted from form fields and entered into another application, like a spreadsheet or database, by zonally applying the OCR engine to those fields. Yet, data is typically manually input rather than typed into form fields. Character identification becomes even more challenging while reading handwritten material. The diversity of more than 700,000 printed font variants is tiny compared to the near unlimited variations in hand-printed characters. The recognition program must take into account not just stylistic differences but also the kind of writing implement used, the standard of the paper, errors, hand stability, and smudges or running ink. === ICR === Intelligent character recognition (ICR) makes use of continuously improving algorithms to collect more information about the variances in hand-printed characters and more precisely identify them. ICR, which was created in the early 1990s to aid in the automation of forms processing, enables the conversion of manually entered data into text that is simple to read, search for, and change. When used to read characters that are obviously divided into distinct areas or zones, such as fixed fields seen on many structured forms, it works best. Both OCR and ICR can be configured to read a variety of languages; however, limiting the expected character set to a smaller number of languages will produce better recognition outcomes. ICR cannot read cursive handwriting since it must still be able to assess each character individually. While writing in cursive, it might be difficult to tell where one character ends and another one begins, and there are more differences across samples than when hand-printing text. A more recent method called intelligent word recognition (IWR) focuses on reading a word in context rather than recognizing individual characters. == Intelligent word recognition == Intelligent word recognition (IWR) can recognize and extract not only printed-handwritten information, cursive handwriting as well. ICR recognizes on the character-level, whereas IWR works with full words or phrases. Capable of capturing unstructured information from every day pages, IWR is said to be more evolved than hand print ICR. Not meant to replace conventional ICR and OCR systems, IWR is optimized for processing real-world documents that contain mostly free-form, hard-to-recognize data fields that are inherently unsuitable for ICR. This means that the highest and best use of IWR is to eliminate a high percentage of the manual entry of handwritten data and run-on hand print fields on documents that otherwise could be keyed only by humans.
Eduard Hovy
Eduard Hovy is a Research Professor in the Language Technologies Institute at Carnegie Mellon University. He is one of the original 17 Fellows of the Association for Computational Linguistics. == Biography == Eduard Hovy received M.S. (December 1982) and Ph.D. (May 1987) degrees in Computer Science from Yale University. He was awarded honorary doctorates from the National University of Distance Education (UNED) in Madrid in 2013 and the University of Antwerp in 2015.
Model inversion attack
Model inversion attack is a type of adversarial machine learning attack where an attacker tries to reconstruct or infer sensitive information about a model's training data by analyzing the outputs of a trained machine learning model. Instead of directly querying the underlying dataset, attackers query the model (usually via APIs or prediction interfaces), and leverage patterns in the model responses to infer properties of the original inputs. These attacks leverage the fact that machine learning models encode statistical information about their training data in their parameters and outputs, which can unintentionally leak private or proprietary information. Depending on the access level to the target model, model inversion attacks can be performed in both black-box and white-box settings. In a generic attack, an adversary makes several queries to a model and leverages the responses (e.g. confidence scores, predictions) to train a surrogate or inversion model that learns to approximate the inverse mapping from outputs to inputs. This process may enable the reconstruction of sensitive attributes, e.g., facial features, medical data, or user behavior patterns, from models trained on such data. The technique has been demonstrated against various models like deep neural networks, classification systems etc. The technique has significant privacy risks in areas like healthcare, finance, biometric identification etc. Mitigation strategies include restricting model access, reducing output granularity, using differential privacy and monitoring anomalous query patterns.
Version space learning
Version space learning is a logical approach to machine learning, specifically binary classification. Version space learning algorithms search a predefined space of hypotheses, viewed as a set of logical sentences. Formally, the hypothesis space is a disjunction H 1 ∨ H 2 ∨ . . . ∨ H n {\displaystyle H_{1}\lor H_{2}\lor ...\lor H_{n}} (i.e., one or more of hypotheses 1 through n are true). A version space learning algorithm is presented with examples, which it will use to restrict its hypothesis space; for each example x, the hypotheses that are inconsistent with x are removed from the space. This iterative refining of the hypothesis space is called the candidate elimination algorithm, the hypothesis space maintained inside the algorithm, its version space. == The version space algorithm == In settings where there is a generality-ordering on hypotheses, it is possible to represent the version space by two sets of hypotheses: (1) the most specific consistent hypotheses, and (2) the most general consistent hypotheses, where "consistent" indicates agreement with observed data. The most specific hypotheses (i.e., the specific boundary SB) cover the observed positive training examples, and as little of the remaining feature space as possible. These hypotheses, if reduced any further, exclude a positive training example, and hence become inconsistent. These minimal hypotheses essentially constitute a (pessimistic) claim that the true concept is defined just by the positive data already observed: Thus, if a novel (never-before-seen) data point is observed, it should be assumed to be negative. (I.e., if data has not previously been ruled in, then it's ruled out.) The most general hypotheses (i.e., the general boundary GB) cover the observed positive training examples, but also cover as much of the remaining feature space without including any negative training examples. These, if enlarged any further, include a negative training example, and hence become inconsistent. These maximal hypotheses essentially constitute a (optimistic) claim that the true concept is defined just by the negative data already observed: Thus, if a novel (never-before-seen) data point is observed, it should be assumed to be positive. (I.e., if data has not previously been ruled out, then it's ruled in.) Thus, during learning, the version space (which itself is a set – possibly infinite – containing all consistent hypotheses) can be represented by just its lower and upper bounds (maximally general and maximally specific hypothesis sets), and learning operations can be performed just on these representative sets. After learning, classification can be performed on unseen examples by testing the hypothesis learned by the algorithm. If the example is consistent with multiple hypotheses, a majority vote rule can be applied. == Historical background == The notion of version spaces was introduced by Mitchell in the early 1980s as a framework for understanding the basic problem of supervised learning within the context of solution search. Although the basic "candidate elimination" search method that accompanies the version space framework is not a popular learning algorithm, there are some practical implementations that have been developed (e.g., Sverdlik & Reynolds 1992, Hong & Tsang 1997, Dubois & Quafafou 2002). A major drawback of version space learning is its inability to deal with noise: any pair of inconsistent examples can cause the version space to collapse, i.e., become empty, so that classification becomes impossible. One solution of this problem is proposed by Dubois and Quafafou that proposed the Rough Version Space, where rough sets based approximations are used to learn certain and possible hypothesis in the presence of inconsistent data.
Zoubin Ghahramani
Zoubin Ghahramani FRS (Persian: زوبین قهرمانی; born 8 February 1970) is a British-Iranian machine learning and AI researcher, Vice President of Research at Google DeepMind and Professor of Information Engineering at the University of Cambridge. He has been a Fellow of St John's College, Cambridge since 2009. He held appointments at University College London from 1998 to 2005 and was Associate Research Professor in the Machine Learning Department at Carnegie Mellon University from 2003 to 2012. He was the Chief Scientist of Uber from 2016 until 2020. He joined Google Brain in 2020 as Senior Research Director, becoming a VP of Research in 2021, and heading Google Brain until its merger with DeepMind to form Google DeepMind. He was a founding Cambridge Liaison Director of the Alan Turing Institute and also founding Deputy Director of the Leverhulme Centre for the Future of Intelligence. Ghahramani contributed to the Royal Society's Machine Learning Report in 2017 and led the UK's Future of Compute Review, in 2023. == Education == Ghahramani was educated at the American School of Madrid in Spain and the University of Pennsylvania where he was awarded a dual degree in Cognitive Science and Computer Science in 1990. He obtained his Ph.D. in Cognitive Neuroscience from the Department of Brain and Cognitive Sciences at the Massachusetts Institute of Technology, supervised by Michael I. Jordan and Tomaso Poggio. == Research and career == Following his Ph.D., Ghahramani moved to the University of Toronto in 1995 as a Postdoctoral Fellow in the Artificial Intelligence Lab, working with Geoffrey Hinton. From 1998 to 2005, he was a member of the faculty at the Gatsby Computational Neuroscience Unit, University College London. Ghahramani has made significant contributions in the areas of Bayesian machine learning (particularly variational methods for approximate Bayesian inference), as well as graphical models and computational neuroscience. His current research focuses on nonparametric Bayesian modelling and statistical machine learning. He has also worked on artificial intelligence, information retrieval, bioinformatics and statistics which provide the mathematical foundations for handling uncertainty, making decisions, and designing learning systems. He has published over 300 papers, receiving over 100,000 citations (an h-index of 132). He co-founded Geometric Intelligence in 2014, with Gary Marcus, Doug Bemis, and Ken Stanley, which was acquired by Uber in 2016. Afterwards, he transferred to Uber's AI Labs in 2016, and later became VP of AI and Chief Scientist at Uber. In 2020 he joined Google and became VP of Research and head of Google Brain in 2021 until its merger with DeepMind in April 2023. == Awards and honors == Ghahramani was elected Fellow of the Royal Society (FRS) in 2015. His certificate of election reads: Zoubin Ghahramani is a world leader in the field of machine learning, significantly advancing the state-of-the-art in algorithms that can learn from data. He is known in particular for fundamental contributions to probabilistic modeling and Bayesian nonparametric approaches to machine learning systems, and to the development of approximate variational inference algorithms for scalable learning. He is one of the pioneers of semi-supervised learning methods, active learning algorithms, and sparse Gaussian processes. His development of novel infinite dimensional nonparametric models, such as the infinite latent feature model, has been highly influential.He was awarded the Royal Society Milner Award in 2021 in recognition of 'his fundamental contributions to probabilistic machine learning'.