The International Computer Archive of Modern and Medieval English (ICAME) is an international group of linguists and data scientists working in corpus linguistics to digitise English texts. The organisation was founded in Oslo, Norway in 1977 as the International Computer Archive of Modern English, before being renamed to its current title. Its primary objectives were: collecting and distributing information on English language material available for computer processing; and linguistic research completed or in progress on this material; compiling an archive of corpora to be located at the University of Bergen, from where copies of the material can be obtained at cost. The portal to their materials is hosted at the University of Bergen, where they have set out the aim of the organization to "collect and distribute information on English language material available for computer processing and on linguistic research to compile an archive of English text corpora in machine-readable form, and to make material available to research institutions." Creating computer corpora, i.e. collections of texts in machine-readable form, is the most accessible way to study both transcribed spoken language and various genres of written texts for modern scholars, including both "descriptive and more theoretically-minded linguists". The ICAME group hosts academic conferences that focus on corpus linguistic studies of historical changes and contemporary grammatical descriptions of English, and makes corpora of different varieties of English available to scholars, starting with editions of the 1960s Brown Corpus. Their first academic conference was held in Bergen, Norway in 1979, and scholars who were interested in corpus linguistics continued to meet each spring in different European and English-speaking countries. At these meetings, the compilation and distribution of corpora they enabled played a key role in the creation of the field of corpus linguistics in the 20th century, a precursor to current big data analytics. In summarizing the field, Kennedy's Introduction to Corpus Linguistics notes that "for corpus linguists with an interest in the description of English, the International Computer Archive of Modern and Medieval English has been the major resource". The influence of ICAME on the field has also be laid out in Facchinetti's history, Corpus Linguistics Twenty-five Years On. One influential resource that ICAME made available was a CD of 20 different corpora, including those covering different regional Englishes (such as the Australian Corpus of English, the Wellington Corpus of Spoken New Zealand English, the Kolhapur Corpus of Indian English, the Bergen Corpus of London Teenage Language (COLT), the Helsinki Corpus of Older Scots, and the International Corpus of English—East-African component), as well as versions of the Brown Corpus and the Lancaster-Bergen-Oslo (LOB) corpus tagged for part of speech. ICAME also published an annual journal, the ICAME Journal, formerly ICAME News, that contains articles, conference reports, reviews and notices related to corpus linguistics. The current editors of the ICAME Journal are Merja Kytö and Anna-Brita Stenström.I am wearing a tie clip in the shape of a monkey wrench... The story behind this peculiar piece of jewelry goes back to the early 60s when I was assembling the notorious Brown Corpus and others were using computers to make concordances of William Butler Yeats and other poets. One of my colleagues, a specialist in modem Irish literature, was heard to remark that anyone who would use a computer on good literature was nothing but a plumber. Some of my students responded by forming a linguistic plumber's union, the symbol of which was, of course, a monkey wrench.
VideoThang
VideoThang was free video editing software for Windows 2000, XP, and Vista. The software has three parts to it which are My Stuff, Edit My Stuff, and My Mix. The software accepts MOV, AVI, MPG, MP4, PNG, WMV, FLV, and MP3 standards. Its official website is now no longer available. == Reception == Jan Ozer, of Pcmag, said that the software "suffers from several unfortunate design and implementation flaws that dramatically limit output quality and overall utility." Jon L. Jacobi, of PC World, said that the software "may not be the most flexible multimedia editor in the world, but the trim/zoom basics are there, it's free, and it's so simple to use that just about anyone in the world should be able figure it out." Amit Agarwal, of Digital Inspiration, said that the software "doesn’t offer loads of features like other video editors but is perfect for making quick video slideshows of your pictures that you can upload on the web or share via email."
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Linguistic Data Consortium
The Linguistic Data Consortium is an open consortium of universities, companies and government research laboratories. It creates, collects and distributes speech and text databases, lexicons, and other resources for linguistics research and development purposes. The University of Pennsylvania is the LDC's host institution. The LDC was founded in 1992 with a grant from the US Defense Advanced Research Projects Agency (DARPA), and is partly supported by grant IRI-9528587 from the Information and Intelligent Systems division of the National Science Foundation. The director of LDC is Mark Liberman. It subsumed the previous ACL Data Collection Initiative. Part of the motivation was to support the benchmark-oriented methodology of DARPA's Human Language Technology program. Previously, John R. Pierce directed the committee that produced the ALPAC report (1966), which caused a severe decrease in funding for linguistic AI for about 10 years. Later, Charles Wayne restarted funding in speech and language in the mid-1980s. In order to avoid the criticisms from the ALPAC report, they needed a way to demonstrate objective progress, which led to the benchmark-oriented methodology. DARPA would propose specific quantifiable and testable score targets on benchmarks, and teams being funded would attempt to reach the score targets. It was noted that by 1993, the data needed for training and benchmarking the models was big enough that "Not even the largest companies can easily afford enough of [the needed] data... Researchers at smaller companies and in universities risk being frozen out of the process almost entirely." The LDC provided a central location for creating and dispensing such data. There is a membership fee that has been increased once since its founding.
Outline of deep learning
The following outline is provided as an overview of, and topical guide to, deep learning: Deep learning is a subfield of machine learning and artificial intelligence based on artificial neural networks with multiple processing layers. It emphasizes representation learning and is widely used in areas such as computer vision, natural language processing, speech recognition, recommender systems, robotics, and generative artificial intelligence. == Ways to categorize deep learning == A field of study A branch of artificial intelligence A subfield of machine learning A subfield of computer science A form of representation learning A class of methods based on artificial neural networks An approach used in computational statistics == History == === Precursors === Cybernetics Perceptron Connectionism Neocognitron Backpropagation === Milestones === LeNet Long short-term memory Deep belief network AlexNet Sequence to sequence learning Generative adversarial network Residual neural network Transformer BERT Generative pre-trained transformer Diffusion model === Related histories === History of artificial intelligence History of machine learning Timeline of machine learning == Core concepts == == Learning settings == Supervised learning Unsupervised learning Self-supervised learning Semi-supervised learning Reinforcement learning Transfer learning Multitask learning Multimodal learning Online machine learning Continual learning == Common tasks == Image classification Object detection Image segmentation Automatic speech recognition Neural machine translation Question answering Automatic summarization Text-to-image model Protein structure prediction == Architectures == === Feedforward and convolutional architectures === Feedforward neural network Multilayer perceptron Convolutional neural network Radial basis function network Residual neural network U-Net === Recurrent and sequence architectures === Recurrent neural network Long short-term memory Gated recurrent unit Sequence to sequence learning Recursive neural network === Representation-learning architectures === Autoencoder Denoising autoencoder Sparse autoencoder Variational autoencoder Restricted Boltzmann machine Deep belief network === Attention and transformer architectures === Attention (machine learning) Transformer BERT Generative pre-trained transformer Vision transformer === Generative and probabilistic architectures === Autoregressive model Diffusion model Energy-based model Generative adversarial network Mixture of experts === Graph and memory architectures === Graph neural network Graph convolutional network Siamese network Neural Turing machine Memory network Echo state network Capsule neural network == Neural network components and techniques == Artificial neuron Activation function Rectified linear unit Sigmoid function Softmax function Embedding Convolution Pooling layer Attention Batch normalization Layer normalization Residual connections == Training and optimization == Backpropagation Gradient descent Stochastic gradient descent Adam optimization Learning rate Loss function Cross-entropy Mean squared error Regularization Dropout Early stopping Batch normalization Data augmentation Transfer learning Knowledge distillation Ensemble learning Curriculum learning == Datasets and benchmarks == CIFAR-10 ImageNet MNIST database Common Objects in Context (COCO) General Language Understanding Evaluation (GLUE) benchmark LibriSpeech SQuAD == Applications == === Computer vision === Computer vision Facial recognition system Image classification Image segmentation Medical imaging Object detection Optical character recognition === Natural language processing === Automatic summarization Chatbot Information retrieval Large language model Natural language processing Neural machine translation Question answering Sentiment analysis === Speech and audio === Automatic speech recognition Music information retrieval Speaker recognition Speech synthesis === Science and medicine === Bioinformatics Computational biology Drug discovery Medical diagnosis Protein structure prediction === Robotics and control === Autonomous car Computer game bot Control theory Robotics === Recommendation, search, and forecasting === Anomaly detection Forecasting Fraud detection Recommender system Search engine === Generative artificial intelligence === Deepfake Generative artificial intelligence Large language model Speech synthesis Text-to-image model === Computer graphics and video games === Deep Learning Anti-Aliasing (DLAA) Deep Learning Super Sampling (DLSS) == Hardware == AMD Instinct AMD XDNA Application-specific integrated circuit Deep learning processor, Neural processing unit (NPU), or Neural Engine Field-programmable gate array General-purpose computing on graphics processing units (GPGPU) Graphics processing unit NVIDIA Deep Learning Accelerator (NVDLA) Tensor processing unit Vision processing unit Wafer-scale integration === Supporting software platforms === CUDA Metal ROCm == Software == === Open-source frameworks and libraries === === Neural network software === EDLUT Emergent Encog JOONE Neuroph NeuroSolutions OpenNN Peltarion Synapse SNNS === Platforms, tools, and deployment === Amazon SageMaker Google Colab Hugging Face Kaggle Kubeflow MLflow ONNX OpenVINO TensorFlow Hub == Algorithms for deep learning and neural networks == Backpropagation Conjugate gradient method Generalized Hebbian algorithm Gradient descent Levenberg–Marquardt algorithm Perceptron Quasi-Newton method Wake-sleep algorithm == Methods and related topics == === Representation and metric learning === Contrastive learning Embedding Feature learning Manifold learning Metric learning === Generative modeling === Autoregressive model Diffusion model Generative adversarial network Generative model Variational inference === Efficient and scalable deep learning === Knowledge distillation Low-rank approximation Mixture of experts Quantization Sparsity === Reliability, safety, and interpretability === Adversarial machine learning AI alignment Algorithmic bias Catastrophic forgetting Differential privacy Explainable artificial intelligence Federated learning Hallucination (artificial intelligence) == Conferences and workshops == Annual Meeting of the Association for Computational Linguistics Conference on Computer Vision and Pattern Recognition Conference on Neural Information Processing Systems International Conference on Computer Vision International Conference on Learning Representations International Conference on Machine Learning == Organizations == === Research laboratories and institutions === Allen Institute for AI Alberta Machine Intelligence Institute European Laboratory for Learning and Intelligent Systems Google DeepMind Meta AI Mila Microsoft Research Vector Institute === Companies === Anthropic Cerebras Cohere DeepSeek Mistral AI OpenAI Stability AI xAI == Publications == === Books === Deep Learning – Ian Goodfellow and Yoshua Bengio Neural Networks and Deep Learning – Michael Nielsen Perceptrons – Marvin Minsky and Seymour Papert === Journals === IEEE Transactions on Neural Networks and Learning Systems Neural Networks Neural Computation == Influential persons ==
SYSTRAN
SYSTRAN, founded by Dr. Peter Toma in 1968, is one of the oldest machine translation companies. SYSTRAN has done extensive work for the United States Department of Defense and the European Commission. SYSTRAN provided the technology for Yahoo! Babel Fish until May 30, 2012, among others. It was used by Google's language tools until 2007. SYSTRAN is used by the Dashboard Translation widget in macOS. Commercial versions of SYSTRAN can run on Microsoft Windows (including Windows Mobile), Linux, and Solaris. Historically, SYSTRAN systems used rule-based machine translation (RbMT) technology. With the release of SYSTRAN Server 7 in 2010, SYSTRAN implemented a hybrid rule-based/statistical machine translation (SMT) technology which was the first of its kind in the marketplace. As of 2008, the company had 59 employees of whom 26 are computational experts and 15 computational linguists. The number of employees decreased from 70 in 2006 to 59 in 2008. In January 2024, ChapsVision acquired Systran. == History == With its origin in the Georgetown machine translation effort, SYSTRAN was one of the few machine translation systems to survive the major decrease of funding after the ALPAC Report of the mid-1960s. The company was established in La Jolla in California to work on translation of Russian to English text for the United States Air Force during the Cold War. Large numbers of Russian scientific and technical documents were translated using SYSTRAN under the auspices of the USAF Foreign Technology Division (later the National Air and Space Intelligence Center) at Wright-Patterson Air Force Base, Ohio. The quality of the translations, although only approximate, was usually adequate for understanding content. The company headquarters is in Paris, while its U.S. headquarters is in San Diego, CA. During the dot-com boom, the international language industry started a new era, and SYSTRAN entered into agreements with a number of translation integrators, the most successful of these being WorldLingo. In 2016, the Harvard NLP group and SYSTRAN founded OpenNMT, an open source ecosystem for neural machine translation and neural sequence learning. This has enabled machine translation software with learning capabilities, dramatically increasing MT translation quality. The project has since been used in several research and industry applications, and its open source ecosystem is currently maintained by SYSTRAN and Ubiqus. == Business situation == Most of SYSTRAN's revenue comes from a few customers. 57.1% comes from the 10 main customers and the three largest customers account for 10.9%, 8.9%, and 8.9% of its revenues, respectively. Revenues had been declining in the early 2000s: 10.2 million euros in 2004, 10.1 million euros in 2005, 9.3 million euros in 2006, 8.8 million euros in 2007, and 7.6 million euros in 2008, before seeing a rebound in 2009 with 8.6 million euros. == Languages == The following is a list of the languages in which SYSTRAN translate from and to English: Russian into English in 1968 and English into Russian in 1973 for the Apollo–Soyuz project.