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  2. Prompt engineering - Wikipedia

    en.wikipedia.org/wiki/Prompt_engineering

    Prompt engineering is the process of structuring an instruction that can be interpreted and understood by a generative artificial intelligence (AI) model. [ 1 ] [ 2 ] A prompt is natural language text describing the task that an AI should perform. [ 3 ]

  3. Generative artificial intelligence - Wikipedia

    en.wikipedia.org/wiki/Generative_artificial...

    Generative AI systems trained on words or word tokens include GPT-3, GPT-4, GPT-4o, LaMDA, LLaMA, BLOOM, Gemini and others (see List of large language models). They are capable of natural language processing , machine translation , and natural language generation and can be used as foundation models for other tasks. [ 51 ]

  4. Midjourney - Wikipedia

    en.wikipedia.org/wiki/Midjourney

    Midjourney is a generative artificial intelligence program and service created and hosted by the San Francisco-based independent research lab Midjourney, Inc. Midjourney generates images from natural language descriptions, called prompts, similar to OpenAI's DALL-E and Stability AI's Stable Diffusion. [1] [2] It is one of the technologies of ...

  5. GPT-J - Wikipedia

    en.wikipedia.org/wiki/GPT-J

    GPT-J was designed to generate English text from a prompt. It was not designed for translating or generating text in other languages or for performance without first fine-tuning the model for a specific task. [2] Nonetheless, GPT-J performs reasonably well even without fine-tuning, even in translation (at least from English to French). [8]

  6. Artificial intelligence art - Wikipedia

    en.wikipedia.org/wiki/Artificial_intelligence_art

    [96] Generative AI models typically work based on user-entered word-based prompts, especially in the case of diffusion models, and this word-related bias may lead to biased results. Along with this, generative AI can perpetuate harmful stereotypes regarding women.

  7. Text-to-image personalization - Wikipedia

    en.wikipedia.org/wiki/Text-to-image_personalization

    Typical text-to-image models represent words (and sometimes parts-of-words) as tokens, or indices in a predefined dictionary. During generation, an input prompt is converted into such tokens, each of which is converted into a ‘word-embedding’: a continuous vector representation which is learned for each token as part of the model's training.

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