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Uniform convergence in probability : The Sauer–Shelah lemma relates the shattering number Π h ( m ) (m) to the VC Dimension. Lemma: Π H ( m ) ≤ ( e m d ) d (m)\leq \left(\right)^ , where d is the VC Dimension of the concept class H . Corollary: Π H ( m ) ≤ m d (m)\leq m^ . |
Uniform convergence in probability : and are the sources of the proof below. Before we get into the details of the proof of the Uniform Convergence Theorem we will present a high level overview of the proof. Symmetrization: We transform the problem of analyzing | Q P ( h ) − Q ^ x ( h ) | ≥ ε (h)-_(h)|\geq \varepsilon ... |
AsoSoft text corpus : The AsoSoft text corpus is the first large-scale Kurdish text corpus, collected and processed by the AsoSoft research and development group. It contains 458,000 documents (188 million tokens) that are collected from sources such as websites, news agencies, books, and magazines. The corpus is parti... |
AsoSoft text corpus : Official website AsoSoft-Text-Corpus on GitHub |
Autoencoder : An autoencoder is a type of artificial neural network used to learn efficient codings of unlabeled data (unsupervised learning). An autoencoder learns two functions: an encoding function that transforms the input data, and a decoding function that recreates the input data from the encoded representation. ... |
Autoencoder : Autoencoders are often trained with a single-layer encoder and a single-layer decoder, but using many-layered (deep) encoders and decoders offers many advantages. Depth can exponentially reduce the computational cost of representing some functions. Depth can exponentially decrease the amount of training d... |
Autoencoder : (Oja, 1982) noted that PCA is equivalent to a neural network with one hidden layer with identity activation function. In the language of autoencoding, the input-to-hidden module is the encoder, and the hidden-to-output module is the decoder. Subsequently, in (Baldi and Hornik, 1989) and (Kramer, 1991) gen... |
Autoencoder : The two main applications of autoencoders are dimensionality reduction and information retrieval (or associative memory), but modern variations have been applied to other tasks. |
Autoencoder : Representation learning Sparse dictionary learning Deep learning |
Autoencoder : Bank, Dor; Koenigstein, Noam; Giryes, Raja (2023). "Autoencoders". Machine Learning for Data Science Handbook. Cham: Springer International Publishing. doi:10.1007/978-3-031-24628-9_16. ISBN 978-3-031-24627-2. Goodfellow, Ian; Bengio, Yoshua; Courville, Aaron (2016). "14. Autoencoders". Deep learning. Ada... |
Synaptic transistor : A synaptic transistor is an electrical device that can learn in ways similar to a neural synapse. It optimizes its own properties for the functions it has carried out in the past. The device mimics the behavior of the property of neurons called spike-timing-dependent plasticity, or STDP. |
Synaptic transistor : Its structure is similar to that of a field effect transistor, where an ionic liquid takes the place of the gate insulating layer between the gate electrode and the conducting channel. That channel is composed of samarium nickelate (SmNiO3, or SNO) rather than the field effect transistor's doped s... |
Synaptic transistor : A synaptic transistor has a traditional immediate response whose amount of current that passes between the source and drain contacts varies with voltage applied to the gate electrode. It also produces a much slower learned response such that the conductivity of the SNO layer varies in response to ... |
AI slop : "AI slop", often simply "slop", is a derogatory term for low-quality media, including writing and images, made using generative artificial intelligence technology. Coined in the 2020s, the term has a derogatory connotation akin to "spam". It has been variously defined as "digital clutter", "filler content pro... |
AI slop : As large language models (LLMs) and image diffusion models accelerated the creation of high-volume but low-quality written content and images, discussion commenced for the appropriate term for the volume. Terms proposed included "AI garbage", "AI pollution", and "AI-generated dross". Early uses of the term "s... |
AI slop : AI image and video slop proliferated on social media in part because it was revenue-generating for its creators on Facebook and TikTok. This incentivizes individuals from developing countries to create images that appeal to audiences in the United States which attract higher advertising rates. The journalist ... |
AI slop : In August 2024, The Atlantic noted that AI slop was becoming associated with the political right in the United States, who were using it for shitposting and engagement farming on social media, the technology offering "cheap, fast, on-demand fodder for content". AI slop is frequently used in political campaign... |
AI slop : In November 2024, Coca-Cola used artificial intelligence to create three commercials as part of their annual holiday campaign. These videos were immediately met with negative reception from both casual viewers and artists, with animator Alex Hirsch, creator of Gravity Falls, criticizing the company's decision... |
AI slop : Fantastical promotional graphics for the 2024 Willy's Chocolate Experience event, characterized as "AI-generated slop", misled audiences into attending an event that was held in a cheaply decorated warehouse. Tickets were marketed through Facebook advertisements showing AI-generated imagery, with no genuine p... |
AI slop : Online booksellers and library vendors now have many titles that are written by AI and are not curated into collections by librarians. The digital media provider Hoopla, which supplies libraries with ebooks and downloadable content, has generative AI books with fictional authors and dubious quality, which cos... |
AI slop : The 2024 video game Call of Duty: Black Ops 6 includes assets generated by artificial intelligence. Since the game's initial release, many players had accused Treyarch and Raven Software of using AI to create in-game assets, including loading screens, emblems, and calling cards. A particular example was a loa... |
AI slop : Some films have received backlash for including AI-generated content. The film Late Night with the Devil was notable for its use of AI, which some criticized as being AI slop. Several low-quality AI-generated images were used as interstitial title cards, with one image featuring a skeleton with inaccurate bon... |
AI slop : Dead Internet theory – A conspiracy theory based around bot activity online Enshittification – Pattern of quality decline among online products and services Hallucination (artificial intelligence) – Content generated by AI that contains erroneous information presented as fact Low culture – Media with mass app... |
AI slop : The dictionary definition of slop at Wiktionary |
Neural operators : Neural operators are a class of deep learning architectures designed to learn maps between infinite-dimensional function spaces. Neural operators represent an extension of traditional artificial neural networks, marking a departure from the typical focus on learning mappings between finite-dimensiona... |
Neural operators : Understanding and mapping relationships between function spaces has many applications in engineering and the sciences. In particular, one can cast the problem of solving partial differential equations as identifying a map between function spaces, such as from an initial condition to a time-evolved st... |
Neural operators : Architecturally, neural operators are similar to feed-forward neural networks in the sense that they are composed of alternating linear maps and non-linearities. Since neural operators act on and output functions, neural operators have been instead formulated as a sequence of alternating linear integ... |
Neural operators : Training neural operators is similar to the training process for a traditional neural network. Neural operators are typically trained in some Lp norm or Sobolev norm. In particular, for a dataset i = 1 N ,u_)\_^ of size N , neural operators minimize (a discretization of) L U ( i = 1 N ) := ∑ i = 1... |
Neural operators : neuralop – Python library of various neural operator architectures |
DBRX : DBRX is an open-sourced large language model (LLM) developed by Mosaic ML team at Databricks, released on March 27, 2024. It is a mixture-of-experts transformer model, with 132 billion parameters in total. 36 billion parameters (4 out of 16 experts) are active for each token. The released model comes in either a... |
Novelty detection : Novelty detection is the mechanism by which an intelligent organism is able to identify an incoming sensory pattern as being hitherto unknown. If the pattern is sufficiently salient or associated with a high positive or strong negative utility, it will be given computational resources for effective ... |
Novelty detection : Change detection Outlier Reward system == References == |
Fairness (machine learning) : Fairness in machine learning (ML) refers to the various attempts to correct algorithmic bias in automated decision processes based on ML models. Decisions made by such models after a learning process may be considered unfair if they were based on variables considered sensitive (e.g., gende... |
Fairness (machine learning) : Discussion about fairness in machine learning is a relatively recent topic. Since 2016 there has been a sharp increase in research into the topic. This increase could be partly attributed to an influential report by ProPublica that claimed that the COMPAS software, widely used in US courts... |
Fairness (machine learning) : The use of algorithmic decision making in the legal system has been a notable area of use under scrutiny. In 2014, then U.S. Attorney General Eric Holder raised concerns that "risk assessment" methods may be putting undue focus on factors not under a defendant's control, such as their educ... |
Fairness (machine learning) : Recent works underline the presence of several limitations to the current landscape of fairness in machine learning, particularly when it comes to what is realistically achievable in this respect in the ever increasing real-world applications of AI. For instance, the mathematical and quant... |
Fairness (machine learning) : In classification problems, an algorithm learns a function to predict a discrete characteristic Y , the target variable, from known characteristics X . We model A as a discrete random variable which encodes some characteristics contained or implicitly encoded in X that we consider as s... |
Fairness (machine learning) : An important distinction among fairness definitions is the one between group and individual notions. Roughly speaking, while group fairness criteria compare quantities at a group level, typically identified by sensitive attributes (e.g. gender, ethnicity, age, etc.), individual criteria co... |
Fairness (machine learning) : Causal fairness measures the frequency with which two nearly identical users or applications who differ only in a set of characteristics with respect to which resource allocation must be fair receive identical treatment. An entire branch of the academic research on fairness metrics is devo... |
Fairness (machine learning) : Fairness can be applied to machine learning algorithms in three different ways: data preprocessing, optimization during software training, or post-processing results of the algorithm. |
Fairness (machine learning) : Algorithmic bias Machine learning Representational harm == References == |
Temporal annotation : Temporal annotation is the study of how to automatically add semantic information regarding time to natural language documents. It plays a role in natural language processing and computational linguistics. |
Temporal annotation : Temporal annotation involves the application of a semantic annotation to a document. Significant temporal annotation standards include TimeML, ISO-TimeML and TIDES. These standards typically include annotations for some or all of temporal expressions (or timexes), events, temporal relations, tempo... |
Temporal annotation : Successful temporal annotation enables systems to find out when facts asserted in texts are true, to build timelines, to extract plans, and to discover mentions of change. This has had applications in many domains, such as information extraction, digital history, processing social media, and clini... |
Temporal annotation : The TempEval task series sets a shared temporal annotation task, and has run at SemEval three times, attracting system entries from around the world. The task originally centred on determining the types of temporal relations only. In TempEval-2 and -3, this expanded to include event and timex anno... |
Temporal annotation : Computational semantics Natural language processing SemEval TimeML |
Temporal annotation : Boguraev, B. and Ando, R.K. (2005), TimeML-Compliant Text Analysis for Temporal Reasoning. Proceedings of IJCAI. Derczynski, L. (2013), Determining the Types of Temporal Relations in Discourse, PhD thesis, University of Sheffield. Pustejovsky et al. (2003), The TimeBank Corpus, Proceedings of the ... |
Temporal annotation : TimeML.org THYME project Pheme project |
Sparrow (chatbot) : Sparrow is a chatbot developed by the artificial intelligence research lab DeepMind, a subsidiary of Alphabet Inc. It is designed to answer users' questions correctly, while reducing the risk of unsafe and inappropriate answers. One motivation behind Sparrow is to address the problem of language mod... |
Sparrow (chatbot) : Sparrow is a deep neural network based on the transformer machine learning model architecture. It is fine-tuned from DeepMind's Chinchilla AI pre-trained large language model (LLM), which has 70 Billion parameters. Sparrow is trained using reinforcement learning from human feedback (RLHF), although ... |
Sparrow (chatbot) : Sparrow's training data corpus is mainly in English, meaning it performs worse in other languages. When adversarially probed by study participants it breaks the rules 8% of the time; however, this is still three times lower than the baseline prompted pre-trained model (Chinchilla). |
Sparrow (chatbot) : AI safety Commonsense reasoning Ethics of artificial intelligence Natural language processing Prompt engineering |
Sparrow (chatbot) : White paper Blog post |
Automated essay scoring : Automated essay scoring (AES) is the use of specialized computer programs to assign grades to essays written in an educational setting. It is a form of educational assessment and an application of natural language processing. Its objective is to classify a large set of textual entities into a ... |
Automated essay scoring : Most historical summaries of AES trace the origins of the field to the work of Ellis Batten Page. In 1966, he argued for the possibility of scoring essays by computer, and in 1968 he published his successful work with a program called Project Essay Grade (PEG). Using the technology of that tim... |
Automated essay scoring : According to a recent survey, modern AES systems try to score different dimensions of an essay's quality in order to provide feedback to users. These dimensions include the following items: Grammaticality: following grammar rules Usage: using of prepositions, word usage Mechanics: following ru... |
Automated essay scoring : From the beginning, the basic procedure for AES has been to start with a training set of essays that have been carefully hand-scored. The program evaluates surface features of the text of each essay, such as the total number of words, the number of subordinate clauses, or the ratio of uppercas... |
Automated essay scoring : Any method of assessment must be judged on validity, fairness, and reliability. An instrument is valid if it actually measures the trait that it purports to measure. It is fair if it does not, in effect, penalize or privilege any one class of people. It is reliable if its outcome is repeatable... |
Automated essay scoring : AES has been criticized on various grounds. Yang et al. mention "the over-reliance on surface features of responses, the insensitivity to the content of responses and to creativity, and the vulnerability to new types of cheating and test-taking strategies." Several critics are concerned that s... |
Automated essay scoring : Most resources for automated essay scoring are proprietary. eRater – published by Educational Testing Service Intellimetric – by Vantage Learning Project Essay Grade – by Measurement, Inc. == References == |
Document structuring : Document Structuring is a subtask of Natural language generation, which involves deciding the order and grouping (for example into paragraphs) of sentences in a generated text. It is closely related to the Content determination NLG task. |
Document structuring : Assume we have four sentences which we want to include in a generated text It will rain on Saturday It will be sunny on Sunday Max temperature will be 10 °C on Saturday Max temperature will be 15 °C on Sunday There are 24 (4!) orderings of these messages, including (1234) It will rain on Saturday... |
Document structuring : There are three basic approaches to document structuring: schemas, corpus-based, and heuristic. Schemas are templates which explicitly specify sentence ordering and grouping for a document (as well as Content determination information). Typically they are constructed by manually analysing a corpu... |
Document structuring : Perhaps the ultimate document structuring challenge is to generate a good narrative—in other words, a text which starts by setting the scene and giving an introduction/overview; then describes a set of events in a clear fashion, so readers can easily see how the individual events are related and ... |
Wadhwani Institute for Artificial Intelligence : Wadhwani AI, based in Mumbai, Maharashtra, is an independent, non-profit institute. Founded in 2018, it is dedicated to developing Artificial intelligence solutions for social good. Their mission is to build AI-based innovations and solutions for underserved communities ... |
Wadhwani Institute for Artificial Intelligence : The institute was founded with a $30 million philanthropic effort by the Wadhwani brothers, Romesh Wadhwani and Sunil Wadhwani. The institute was inaugurated and dedicated to the nation by Narendra Modi, the 14th Prime Minister of India. In 2019, the institute received a... |
Classic monolingual word-sense disambiguation : Classic monolingual Word Sense Disambiguation evaluation tasks uses WordNet as its sense inventory and is largely based on supervised / semi-supervised classification with the manually sense annotated corpora: Classic English WSD uses the Princeton WordNet as it sense inv... |
Classic monolingual word-sense disambiguation : During the first Senseval workshop the HECTOR sense inventory was adopted. The reason for adopting a previously unknown sense inventory was mainly to avoid the use of popular fine-grained word senses (such as WordNet), which could make the experiments unfair or biased. Ho... |
Classic monolingual word-sense disambiguation : Comparison of methods can be divided in 2 groups by amount of words to test. The difference consists in the amount of analysis and processing: all-words task implies disambiguating all the words of the text lexical sample consists in disambiguating some previously chosen ... |
Classic monolingual word-sense disambiguation : During the evaluation of WSD systems two main performance measures are used: Precision: the fraction of system assignments made that are correct Recall: the fraction of total word instances correctly assigned by a system If a system makes an assignment for every word, the... |
Classic monolingual word-sense disambiguation : Word sense disambiguation Other variants of WSD evaluations Word sense WordNet SemEval == References == |
Logic form : Logic forms are simple, first-order logic knowledge representations of natural language sentences formed by the conjunction of concept predicates related through shared arguments. Each noun, verb, adjective, adverb, pronoun, preposition and conjunction generates a predicate. Logic forms can be decorated wi... |
Logic form : Vasile Rus (2002). Logic Form for WordNet Glosses. Ph.D. thesis, Southern Methodist University. Vasile Rus and Dan Moldovan (September 2002). "High performance logic form transformation". International Journal on Artificial Intelligence Tools. 11 (3): 437–454. doi:10.1142/S0218213002000976. Dan Moldovan an... |
Phrase structure grammar : The term phrase structure grammar was originally introduced by Noam Chomsky as the term for grammar studied previously by Emil Post and Axel Thue (Post canonical systems). Some authors, however, reserve the term for more restricted grammars in the Chomsky hierarchy: context-sensitive grammars... |
Phrase structure grammar : In 1956, Chomsky wrote, "A phrase-structure grammar is defined by a finite vocabulary (alphabet) Vp, and a finite set Σ of initial strings in Vp, and a finite set F of rules of the form: X → Y, where X and Y are strings in Vp." |
Phrase structure grammar : In linguistics, phrase structure grammars are all those grammars that are based on the constituency relation, as opposed to the dependency relation associated with dependency grammars; hence, phrase structure grammars are also known as constituency grammars. Any of several related theories fo... |
METEOR : METEOR (Metric for Evaluation of Translation with Explicit ORdering) is a metric for the evaluation of machine translation output. The metric is based on the harmonic mean of unigram precision and recall, with recall weighted higher than precision. It also has several features that are not found in other metri... |
METEOR : As with BLEU, the basic unit of evaluation is the sentence, the algorithm first creates an alignment (see illustrations) between two sentences, the candidate translation string, and the reference translation string. The alignment is a set of mappings between unigrams. A mapping can be thought of as a line betw... |
METEOR : BLEU F-Measure NIST (metric) ROUGE (metric) Word Error Rate (WER) LEPOR Noun-Phrase Chunking |
METEOR : ^ Banerjee, S. and Lavie, A. (2005) |
METEOR : Banerjee, S. and Lavie, A. (2005) "METEOR: An Automatic Metric for MT Evaluation with Improved Correlation with Human Judgments" in Proceedings of Workshop on Intrinsic and Extrinsic Evaluation Measures for MT and/or Summarization at the 43rd Annual Meeting of the Association of Computational Linguistics (ACL-... |
METEOR : The METEOR Automatic Machine Translation Evaluation System (including link for download) |
LIVAC Synchronous Corpus : LIVAC is an uncommon language corpus dynamically maintained since 1995. Different from other existing corpora, LIVAC has adopted a rigorous and regular "Windows" approach in processing and filtering massive media texts from representative Chinese speech communities such as Beijing, Hong Kong,... |
LIVAC Synchronous Corpus : Accessing media texts, manual input, etc. Text unification including conversion from simplified to traditional Chinese characters, stored as Big5 and Unicode versions Automatic word segmentation Automatic alignment of parallel texts Manual verification, part-of-speech tagging Extraction of wo... |
LIVAC Synchronous Corpus : Categories used include general terms and proper names, such as: general names, surnames, semi titles; geographical, organizations and commercial entities, etc.; time, prepositions, locations, etc.; stack-words; loanwords; case-word; numerals, etc. Construction of databases of proper names, p... |
LIVAC Synchronous Corpus : Compilation of Pan-Chinese dictionaries or local dictionaries Information technology research, such as predictive Chinese text input for mobile phones, automatic speech to text conversion, opinion mining Comparative studies on linguistic and cultural developments in the Pan-Chinese regions, e... |
LIVAC Synchronous Corpus : British National Corpus Oxford English Corpus Corpus of Contemporary American English (COCA) 語料庫 |
LIVAC Synchronous Corpus : Official website Chilin (HK)'s website |
Nouvelle AI : Nouvelle artificial intelligence (AI) is an approach to artificial intelligence pioneered in the 1980s by Rodney Brooks, who was then part of MIT artificial intelligence laboratory. Nouvelle AI differs from classical AI by aiming to produce robots with intelligence levels similar to insects. Researchers b... |
Nouvelle AI : The differences between nouvelle AI and symbolic AI are apparent in early robots Shakey and Freddy. These robots contained an internal model (or "representation") of their micro-worlds consisting of symbolic descriptions. As a result, this structure of symbols had to be renewed as the robot moved or the w... |
Nouvelle AI : BEAM robotics Behavior-based robotics Cognitive science Intelligence Reactive planning Scruffy AI |
Nouvelle AI : What is AI - Nouvelle AI Nouvelle AI Reactive planning and nouvelle AI AI - new foundations at EnCYClopædia Britannica |
Generative adversarial network : A generative adversarial network (GAN) is a class of machine learning frameworks and a prominent framework for approaching generative artificial intelligence. The concept was initially developed by Ian Goodfellow and his colleagues in June 2014. In a GAN, two neural networks compete wit... |
Generative adversarial network : There is a veritable zoo of GAN variants. Some of the most prominent are as follows: |
Generative adversarial network : Other than for generative and discriminative modelling of data, GANs have been used for other things. GANs have been used for transfer learning to enforce the alignment of the latent feature space, such as in deep reinforcement learning. This works by feeding the embeddings of the sourc... |
Generative adversarial network : In 1991, Juergen Schmidhuber published "artificial curiosity", neural networks in a zero-sum game. The first network is a generative model that models a probability distribution over output patterns. The second network learns by gradient descent to predict the reactions of the environme... |
Generative adversarial network : Artificial intelligence art – Visual media created with AI Deepfake – Realistic artificially generated media Deep learning – Branch of machine learning Diffusion model – Deep learning algorithm Generative artificial intelligence – AI system capable of generating content in response to p... |
Generative adversarial network : Knight, Will. "5 Big Predictions for Artificial Intelligence in 2017". MIT Technology Review. Retrieved January 5, 2017. Karras, Tero; Laine, Samuli; Aila, Timo (2018). "A Style-Based Generator Architecture for Generative Adversarial Networks". arXiv:1812.04948 [cs.NE]. This Person Does... |
Digital cloning : Digital cloning is an emerging technology, that involves deep-learning algorithms, which allows one to manipulate currently existing audio, photos, and videos that are hyper-realistic. One of the impacts of such technology is that hyper-realistic videos and photos makes it difficult for the human eye ... |
Digital cloning : Artificial intelligence Deepfake Deep learning Digital media Post-mortem privacy Virtual actor Virtual human – Computer simulation of a person == References == |
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