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  2. ChatGPT-4 vs. 3.5: Which Version Offers More Money-Making ...

    www.aol.com/chatgpt-4-vs-3-5-170413903.html

    For starters, ChatGPT-4 is multimodal (working with both imagers and text) and more “steerable” than ChatGPT-3.5 in that it can take multiple prompts in greater numbers, which allows it to ...

  3. ChatGPT - Wikipedia

    en.wikipedia.org/wiki/ChatGPT

    ChatGPT is a chatbot and virtual assistant developed by OpenAI and launched on November 30, 2022. Based on large language models (LLMs), it enables users to refine and steer a conversation towards a desired length, format, style, level of detail, and language. Successive user prompts and replies are considered at each conversation stage as context.

  4. GPT-4 - Wikipedia

    en.wikipedia.org/wiki/GPT-4

    Generative Pre-trained Transformer 4 ( GPT-4) is a multimodal large language model created by OpenAI, and the fourth in its series of GPT foundation models. [ 1] It was launched on March 14, 2023, [ 1] and made publicly available via the paid chatbot product ChatGPT Plus, via OpenAI's API, and via the free chatbot Microsoft Copilot. [ 2]

  5. GPT-3 - Wikipedia

    en.wikipedia.org/wiki/GPT-3

    v. t. e. 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 [ 2] transformer model of deep neural network, which supersedes recurrence and convolution-based architectures with a technique known as "attention". [ 3]

  6. AI boom - Wikipedia

    en.wikipedia.org/wiki/AI_boom

    The AI boom, [ 1][ 2] or AI spring, [ 3][ 4] is an ongoing period of rapid progress in the field of artificial intelligence (AI) that started in the late 2010s before gaining international prominence in the early 2020s. Examples include protein folding prediction led by Google DeepMind and generative AI applications developed by OpenAI .

  7. 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] [18] 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.

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