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Self-supervised Learning
Self-supervised learning (SSL) refers to a machine learning paradigm, and corresponding methods, for processing unlabelled data to obtain useful representations that can help with downstream learning tasks. The most salient thing about SSL methods is that they do not need human-annotated labels, which means they are designed to take in datasets consisting entirely of unlabelled data samples. Then the typical SSL pipeline consists of learning supervisory signals (labels generated automatically) in a first stage, which are then used for some supervised learning task in the second and later stages. For this reason, SSL can be described as an intermediate form of unsupervised and supervised learning. The typical SSL method is based on an artificial neural network or other model such as a decision list. The model learns in two steps. First, the task is solved based on an auxiliary or pretext classification task using pseudo-labels which help to initialize the model parameters. Second ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
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Machine Learning
Machine learning (ML) is a field of inquiry devoted to understanding and building methods that 'learn', that is, methods that leverage data to improve performance on some set of tasks. It is seen as a part of artificial intelligence. Machine learning algorithms build a model based on sample data, known as training data, in order to make predictions or decisions without being explicitly programmed to do so. Machine learning algorithms are used in a wide variety of applications, such as in medicine, email filtering, speech recognition, agriculture, and computer vision, where it is difficult or unfeasible to develop conventional algorithms to perform the needed tasks.Hu, J.; Niu, H.; Carrasco, J.; Lennox, B.; Arvin, F.,Voronoi-Based Multi-Robot Autonomous Exploration in Unknown Environments via Deep Reinforcement Learning IEEE Transactions on Vehicular Technology, 2020. A subset of machine learning is closely related to computational statistics, which focuses on making pred ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
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Convolutional Neural Networks
In deep learning, a convolutional neural network (CNN, or ConvNet) is a class of artificial neural network (ANN), most commonly applied to analyze visual imagery. CNNs are also known as Shift Invariant or Space Invariant Artificial Neural Networks (SIANN), based on the shared-weight architecture of the Convolution, convolution kernels or filters that slide along input features and provide translation-equivariant map, equivariant responses known as feature maps. Counter-intuitively, most convolutional neural networks are not translation invariant, invariant to translation, due to the downsampling operation they apply to the input. They have applications in computer vision, image and video recognition, recommender systems, image classification, image segmentation, medical image computing, medical image analysis, natural language processing, brain–computer interfaces, and financial time series. CNNs are regularization (mathematics), regularized versions of multilayer perceptrons. ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
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Polysemy
Polysemy ( or ; ) is the capacity for a sign (e.g. a symbol, a morpheme, a word, or a phrase) to have multiple related meanings. For example, a word can have several word senses. Polysemy is distinct from ''monosemy'', where a word has a single meaning. Polysemy is distinct from homonymy—or homophony—which is an accidental similarity between two or more words (such as ''bear'' the animal, and the verb ''bear''); whereas homonymy is a mere linguistic coincidence, polysemy is not. In discerning whether a given set of meanings represent polysemy or homonymy, it is often necessary to look at the history of the word to see whether the two meanings are historically related. Dictionary writers often list polysemes (words or phrases with different, but related, senses) in the same entry (that is, under the same headword) and enter homonyms as separate headwords (usually with a numbering convention such as ''¹bear'' and ''²bear''). Polysemes A polyseme is a word or phrase ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
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Word Sense Disambiguation
Word-sense disambiguation (WSD) is the process of identifying which sense of a word is meant in a sentence or other segment of context. In human language processing and cognition, it is usually subconscious/automatic but can often come to conscious attention when ambiguity impairs clarity of communication, given the pervasive polysemy in natural language. In computational linguistics, it is an open problem that affects other computer-related writing, such as discourse, improving relevance of search engines, anaphora resolution, coherence, and inference. Given that natural language requires reflection of neurological reality, as shaped by the abilities provided by the brain's neural networks, computer science has had a long-term challenge in developing the ability in computers to do natural language processing and machine learning. Many techniques have been researched, including dictionary-based methods that use the knowledge encoded in lexical resources, supervised machine l ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
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Natural Language Processing
Natural language processing (NLP) is an interdisciplinary subfield of linguistics, computer science, and artificial intelligence concerned with the interactions between computers and human language, in particular how to program computers to process and analyze large amounts of natural language data. The goal is a computer capable of "understanding" the contents of documents, including the contextual nuances of the language within them. The technology can then accurately extract information and insights contained in the documents as well as categorize and organize the documents themselves. Challenges in natural language processing frequently involve speech recognition, natural-language understanding, and natural-language generation. History Natural language processing has its roots in the 1950s. Already in 1950, Alan Turing published an article titled " Computing Machinery and Intelligence" which proposed what is now called the Turing test as a criterion of intelligence, ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
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Yarowsky Algorithm
In computational linguistics the Yarowsky algorithm is an unsupervised learning algorithm for word sense disambiguation that uses the "one sense per collocation" and the "one sense per discourse" properties of human languages for word sense disambiguation. From observation, words tend to exhibit only one sense in most given discourse and in a given collocation. Application The algorithm starts with a large, untagged corpus, in which it identifies examples of the given polysemous word, and stores all the relevant sentences as lines. For instance, Yarowsky uses the word "plant" in his 1995 paper to demonstrate the algorithm. If it is assumed that there are two possible senses of the word, the next step is to identify a small number of seed collocations representative of each sense, give each sense a label (i.e. sense A and B), then assign the appropriate label to all training examples containing the seed collocations. In this case, the words "life" and "manufacturing" are chosen ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
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Language Model
A language model is a probability distribution over sequences of words. Given any sequence of words of length , a language model assigns a probability P(w_1,\ldots,w_m) to the whole sequence. Language models generate probabilities by training on text corpora in one or many languages. Given that languages can be used to express an infinite variety of valid sentences (the property of digital infinity), language modeling faces the problem of assigning non-zero probabilities to linguistically valid sequences that may never be encountered in the training data. Several modelling approaches have been designed to surmount this problem, such as applying the Markov assumption or using neural architectures such as recurrent neural networks or transformers. Language models are useful for a variety of problems in computational linguistics; from initial applications in speech recognition to ensure nonsensical (i.e. low-probability) word sequences are not predicted, to wider use in machine tran ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
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GPT-3
Generative Pre-trained Transformer 3 (GPT-3) is an autoregressive language model that uses deep learning to produce human-like text. Given an initial text as prompt, it will produce text that continues the prompt. The architecture is a standard transformer network (with a few engineering tweaks) with the unprecedented size of 2048-token-long context and 175 billion parameters (requiring 800 GB of storage). The training method is "generative pretraining", meaning that it is trained to predict what the next token is. The model demonstrated strong few-shot learning on many text-based tasks. It is the third-generation language prediction model in the GPT-n series (and the successor to GPT-2) created by OpenAI, a San Francisco-based artificial intelligence research laboratory. GPT-3, which was introduced in May 2020, and was in beta testing as of July 2020, is part of a trend in natural language processing (NLP) systems of pre-trained language representations. The quality of t ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
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OpenAI
OpenAI is an artificial intelligence (AI) research laboratory consisting of the for-profit corporation OpenAI LP and its parent company, the non-profit OpenAI Inc. The company conducts research in the field of AI with the stated goal of promoting and developing friendly AI in a way that benefits humanity as a whole. The organization was founded in San Francisco in late 2015 by Sam Altman, Elon Musk, and others, who collectively pledged US$1 billion. Musk resigned from the board in February 2018 but remained a donor. In 2019, OpenAI LP received a 1 billion investment from Microsoft. OpenAI is headquartered at the Pioneer Building in Mission District, San Francisco. History In December 2015, Sam Altman, Elon Musk, Greg Brockman, Reid Hoffman, Jessica Livingston, Peter Thiel, Amazon Web Services (AWS), Infosys, and YC Research announced the formation of OpenAI and pledged over 1 billion to the venture. The organization stated it would "freely collabora ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
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BERT (language Model)
Bidirectional Encoder Representations from Transformers (BERT) is a transformer-based machine learning technique for natural language processing (NLP) pre-training developed by Google. BERT was created and published in 2018 by Jacob Devlin and his colleagues from Google. In 2019, Google announced that it had begun leveraging BERT in its search engine, and by late 2020 it was using BERT in almost every English-language query. A 2020 literature survey concluded that "in a little over a year, BERT has become a ubiquitous baseline in NLP experiments", counting over 150 research publications analyzing and improving the model. The original English-language BERT has two models: (1) the BERTBASE: 12 encoders with 12 bidirectional self-attention heads, and (2) the BERTLARGE: 24 encoders with 16 bidirectional self-attention heads. Both models are pre-trained from unlabeled data extracted from the BooksCorpus with 800M words and English Wikipedia with 2,500M words. Architecture BERT is ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
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Google
Google LLC () is an American Multinational corporation, multinational technology company focusing on Search Engine, search engine technology, online advertising, cloud computing, software, computer software, quantum computing, e-commerce, artificial intelligence, and Computer hardware, consumer electronics. It has been referred to as "the most powerful company in the world" and one of the world's List of most valuable brands, most valuable brands due to its market dominance, data collection, and technological advantages in the area of artificial intelligence. Its parent company Alphabet Inc., Alphabet is considered one of the Big Tech, Big Five American information technology companies, alongside Amazon (company), Amazon, Apple Inc., Apple, Meta Platforms, Meta, and Microsoft. Google was founded on September 4, 1998, by Larry Page and Sergey Brin while they were Doctor of Philosophy, PhD students at Stanford University in California. Together they own about 14% of its publicl ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |