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InstructGPT
Generative Pre-trained Transformer 3 (GPT-3) is a large language model released by OpenAI in 2020. Like its predecessor, GPT-2, it is a decoder-only transformer model of deep neural network, which supersedes recurrence and convolution-based architectures with a technique known as "attention". This attention mechanism allows the model to focus selectively on segments of input text it predicts to be most relevant. GPT-3 has 175 billion parameters, each with 16-bit precision, requiring 350GB of storage since each parameter occupies 2 bytes. It has a context window size of 2048 tokens, and has demonstrated strong " zero-shot" and " few-shot" learning abilities on many tasks. On September 22, 2020, Microsoft announced that it had licensed GPT-3 exclusively. Others can still receive output from its public API, but only Microsoft has access to the underlying model. Background According to ''The Economist'', improved algorithms, more powerful computers, and a recent increase in t ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
GPT-3
Generative Pre-trained Transformer 3 (GPT-3) is a large language model released by OpenAI in 2020. Like its predecessor, GPT-2, it is a decoder-only transformer model of deep neural network, which supersedes recurrence and convolution-based architectures with a technique known as "attention". This attention mechanism allows the model to focus selectively on segments of input text it predicts to be most relevant. GPT-3 has 175 billion parameters, each with 16-bit precision, requiring 350GB of storage since each parameter occupies 2 bytes. It has a context window size of 2048 tokens, and has demonstrated strong " zero-shot" and " few-shot" learning abilities on many tasks. On September 22, 2020, Microsoft announced that it had licensed GPT-3 exclusively. Others can still receive output from its public API, but only Microsoft has access to the underlying model. Background According to ''The Economist'', improved algorithms, more powerful computers, and a recent increase i ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Generative Pre-trained Transformer
A generative pre-trained transformer (GPT) is a type of large language model (LLM) and a prominent framework for generative artificial intelligence. It is an Neural network (machine learning), artificial neural network that is used in natural language processing by machines. It is based on the Transformer (deep learning architecture), transformer deep learning architecture, pre-trained on large data sets of unlabeled text, and able to generate novel human-like content. As of 2023, most LLMs had these characteristics and are sometimes referred to broadly as GPTs. The first GPT was introduced in 2018 by OpenAI. OpenAI has released significant #Foundation models, GPT foundation models that have been sequentially numbered, to comprise its "GPT-''n''" series. Each of these was significantly more capable than the previous, due to increased size (number of trainable parameters) and training. The most recent of these, GPT-4o, was released in May 2024. Such models have been the basis fo ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
OpenAI
OpenAI, Inc. is an American artificial intelligence (AI) organization founded in December 2015 and headquartered in San Francisco, California. It aims to develop "safe and beneficial" artificial general intelligence (AGI), which it defines as "highly autonomous systems that outperform humans at most economically valuable work". As a leading organization in the ongoing AI boom, OpenAI is known for the GPT family of large language models, the DALL-E series of text-to-image models, and a text-to-video model named Sora (text-to-video model), Sora. Its release of ChatGPT in November 2022 has been credited with catalyzing widespread interest in generative AI. The organization has a complex corporate structure. As of April 2025, it is led by the Nonprofit organization, non-profit OpenAI, Inc., Delaware General Corporation Law, registered in Delaware, and has multiple for-profit subsidiaries including OpenAI Holdings, LLC and OpenAI Global, LLC. Microsoft has invested US$13 billion ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Machine Learning
Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of Computational statistics, statistical algorithms that can learn from data and generalise to unseen data, and thus perform Task (computing), tasks without explicit Machine code, instructions. Within a subdiscipline in machine learning, advances in the field of deep learning have allowed Neural network (machine learning), neural networks, a class of statistical algorithms, to surpass many previous machine learning approaches in performance. ML finds application in many fields, including natural language processing, computer vision, speech recognition, email filtering, agriculture, and medicine. The application of ML to business problems is known as predictive analytics. Statistics and mathematical optimisation (mathematical programming) methods comprise the foundations of machine learning. Data mining is a related field of study, focusing on exploratory data analysi ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Automatic Summarization
Automatic summarization is the process of shortening a set of data computationally, to create a subset (a summary) that represents the most important or relevant information within the original content. Artificial intelligence algorithms are commonly developed and employed to achieve this, specialized for different types of data. Text summarization is usually implemented by natural language processing methods, designed to locate the most informative sentences in a given document. On the other hand, visual content can be summarized using computer vision algorithms. Image summarization is the subject of ongoing research; existing approaches typically attempt to display the most representative images from a given image collection, or generate a video that only includes the most important content from the entire collection. Video summarization algorithms identify and extract from the original video content the most important frames (''key-frames''), and/or the most important video seg ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Supervised Learning
In machine learning, supervised learning (SL) is a paradigm where a Statistical model, model is trained using input objects (e.g. a vector of predictor variables) and desired output values (also known as a ''supervisory signal''), which are often human-made labels. The training process builds a function that maps new data to expected output values. An optimal scenario will allow for the algorithm to accurately determine output values for unseen instances. This requires the learning algorithm to Generalization (learning), generalize from the training data to unseen situations in a reasonable way (see inductive bias). This statistical quality of an algorithm is measured via a ''generalization error''. Steps to follow To solve a given problem of supervised learning, the following steps must be performed: # Determine the type of training samples. Before doing anything else, the user should decide what kind of data is to be used as a Training, validation, and test data sets, trainin ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Fine-tuning (machine Learning)
In deep learning, fine-tuning is an approach to transfer learning in which the parameters of a pre-trained neural network model are trained on new data. Fine-tuning can be done on the entire neural network, or on only a subset of its layers, in which case the layers that are not being fine-tuned are "frozen" (i.e., not changed during backpropagation). A model may also be augmented with "adapters" that consist of far fewer parameters than the original model, and fine-tuned in a parameter-efficient way by tuning the weights of the adapters and leaving the rest of the model's weights frozen. For some architectures, such as convolutional neural networks, it is common to keep the earlier layers (those closest to the input layer) frozen, as they capture lower-level features, while later layers often discern high-level features that can be more related to the task that the model is trained on. Models that are pre-trained on large, general corpora are usually fine-tuned by reusing their p ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Dataset (machine Learning)
In machine learning, a common task is the study and construction of algorithms that can learn from and make predictions on data. Such algorithms function by making data-driven predictions or decisions, through building a mathematical model from input data. These input data used to build the model are usually divided into multiple data sets. In particular, three data sets are commonly used in different stages of the creation of the model: training, validation, and test sets. The model is initially fit on a training data set, which is a set of examples used to fit the parameters (e.g. weights of connections between neurons in artificial neural networks) of the model. The model (e.g. a naive Bayes classifier) is trained on the training data set using a supervised learning method, for example using optimization methods such as gradient descent or stochastic gradient descent. In practice, the training data set often consists of pairs of an input vector (or scalar) and the correspondin ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Text Corpus
In linguistics and natural language processing, a corpus (: corpora) or text corpus is a dataset, consisting of natively digital and older, digitalized, language resources, either annotated or unannotated. Annotated, they have been used in corpus linguistics for statistical statistical hypothesis testing, hypothesis testing, checking occurrences or validating linguistic rules within a specific language territory. Overview A corpus may contain texts in a single language (''monolingual corpus'') or text data in multiple languages (''multilingual corpus''). In order to make the corpora more useful for doing linguistic research, they are often subjected to a process known as annotation. An example of annotating a corpus is part-of-speech tagging, or ''POS-tagging'', in which information about each word's part of speech (verb, noun, adjective, etc.) is added to the corpus in the form of ''tags''. Another example is indicating the Lemma (morphology), lemma (base) form of each word ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Generative Artificial Intelligence
Generative artificial intelligence (Generative AI, GenAI, or GAI) is a subfield of artificial intelligence that uses generative models to produce text, images, videos, or other forms of data. These models Machine learning, learn the underlying patterns and structures of their training data and use them to produce new data based on the input, which often comes in the form of natural language Prompt (natural language), prompts. Generative AI tools have become more common since an "AI boom" in the 2020s. This boom was made possible by improvements in transformer (machine learning model), transformer-based deep learning, deep neural networks, particularly large language models (LLMs). Major tools include chatbots such as ChatGPT, Microsoft Copilot, Copilot, Gemini (chatbot), Gemini, Grok (chatbot), Grok, and DeepSeek (chatbot), DeepSeek; text-to-image models such as Stable Diffusion, Midjourney, and DALL-E; and text-to-video models such as Sora (text-to-video model), Sora and Veo ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Attention Is All You Need
"Attention Is All You Need" is a 2017 landmark research paper in machine learning authored by eight scientists working at Google. The paper introduced a new deep learning architecture known as the transformer, based on the attention mechanism proposed in 2014 by Bahdanau et al. It is considered a foundational paper in modern artificial intelligence, and a main contributor to the AI boom, as the transformer approach has become the main architecture of a wide variety of AI, such as large language models. At the time, the focus of the research was on improving Seq2seq techniques for machine translation, but the authors go further in the paper, foreseeing the technique's potential for other tasks like question answering and what is now known as multimodal Generative AI. The paper's title is a reference to the song " All You Need Is Love" by the Beatles. The name "Transformer" was picked because Jakob Uszkoreit, one of the paper's authors, liked the sound of that word. An early de ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
