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  2. Generative pre-trained transformer - Wikipedia

    en.wikipedia.org/wiki/Generative_pre-trained...

    Generative pretraining (GP) was a long-established concept in machine learning applications. [16] [17] It was originally used as a form of semi-supervised learning, as the model is trained first on an unlabelled dataset (pretraining step) by learning to generate datapoints in the dataset, and then it is trained to classify a labelled dataset.

  3. GPT-1 - Wikipedia

    en.wikipedia.org/wiki/GPT-1

    The GPT-1 architecture was a twelve-layer decoder-only transformer, using twelve masked self-attention heads, with 64-dimensional states each (for a total of 768). Rather than simple stochastic gradient descent , the Adam optimization algorithm was used; the learning rate was increased linearly from zero over the first 2,000 updates to a ...

  4. Artificial intelligence - Wikipedia

    en.wikipedia.org/wiki/Artificial_intelligence

    Artificial intelligence (AI), in its broadest sense, is intelligence exhibited by machines, particularly computer systems.It is a field of research in computer science that develops and studies methods and software that enable machines to perceive their environment and use learning and intelligence to take actions that maximize their chances of achieving defined goals. [1]

  5. AutoGPT - Wikipedia

    en.wikipedia.org/wiki/AutoGPT

    AutoGPT is an open-source "AI agent" that, given a goal in natural language, will attempt to achieve it by breaking it into sub-tasks and using the Internet and other tools in an automatic loop. [1] It uses OpenAI's GPT-4 or GPT-3.5 APIs, [2] and is among the first examples of an application using GPT-4 to perform autonomous tasks. [3]

  6. Prompt engineering - Wikipedia

    en.wikipedia.org/wiki/Prompt_engineering

    For example, a prompt may include a few examples for a model to learn from, such as asking the model to complete "maison → house, chat → cat, chien →" (the expected response being dog), [23] an approach called few-shot learning. [24] In-context learning is an emergent ability [25] of large language models.

  7. Neuro-symbolic AI - Wikipedia

    en.wikipedia.org/wiki/Neuro-symbolic_AI

    Henry Kautz's taxonomy of neuro-symbolic architectures [11] follows, along with some examples: Symbolic Neural symbolic is the current approach of many neural models in natural language processing, where words or subword tokens are the ultimate input and output of large language models. Examples include BERT, RoBERTa, and GPT-3.

  8. Grok (chatbot) - Wikipedia

    en.wikipedia.org/wiki/Grok_(chatbot)

    Grok-1.5 was released to all X Premium users on May 15, 2024. [1] On April 4, 2024, an update to X's "Explore" page included summaries of breaking news stories written by Grok, a task previously assigned to a human curation team. [19] On April 12, 2024, Grok-1.5 Vision (Grok-1.5V) was announced.

  9. OpenAI Codex - Wikipedia

    en.wikipedia.org/wiki/OpenAI_Codex

    Codex is a descendant of OpenAI's GPT-3 model, fine-tuned for use in programming applications. OpenAI released an API for Codex in closed beta. [1] In March 2023, OpenAI shut down access to Codex. [2] Due to public appeals from researchers, OpenAI reversed course. [3] The Codex model can still be used by researchers of the OpenAI Research ...