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  2. Transformer (deep learning architecture) - Wikipedia

    en.wikipedia.org/wiki/Transformer_(deep_learning...

    A transformer is a deep learning architecture developed by Google and based on the multi-head attention mechanism, proposed in a 2017 paper "Attention Is All You Need". [1] Text is converted to numerical representations called tokens, and each token is converted into a vector via looking up from a word embedding table. [1]

  3. Explainable artificial intelligence - Wikipedia

    en.wikipedia.org/wiki/Explainable_artificial...

    Explainable AI ( XAI ), often overlapping with interpretable AI, or explainable machine learning ( XML ), either refers to an artificial intelligence (AI) system over which it is possible for humans to retain intellectual oversight, or refers to the methods to achieve this. [1] [2] The main focus is usually on the reasoning behind the decisions ...

  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.

  5. Rule-based machine learning - Wikipedia

    en.wikipedia.org/wiki/Rule-based_machine_learning

    v. t. e. Rule-based machine learning (RBML) is a term in computer science intended to encompass any machine learning method that identifies, learns, or evolves 'rules' to store, manipulate or apply. [1] [2] [3] The defining characteristic of a rule-based machine learner is the identification and utilization of a set of relational rules that ...

  6. Machine learning - Wikipedia

    en.wikipedia.org/wiki/Machine_learning

    e. Machine learning ( ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn from data and generalize to unseen data and thus perform tasks without explicit instructions. [1] Recently, artificial neural networks have been able to surpass many previous approaches in ...

  7. Prompt engineering - Wikipedia

    en.wikipedia.org/wiki/Prompt_engineering

    Prompt engineering is enabled by in-context learning, defined as a model's ability to temporarily learn from prompts. The ability for in-context learning is an emergent ability [14] of large language models. In-context learning itself is an emergent property of model scale, meaning breaks [15] in downstream scaling laws occur such that its ...

  8. Claude (language model) - Wikipedia

    en.wikipedia.org/wiki/Claude_(language_model)

    Claude models have then been fine-tuned with Constitutional AI with the aim of making them helpful, honest, and harmless. Constitutional AI. Constitutional AI is an approach developed by Anthropic for training AI systems, particularly language models like Claude, to be harmless and helpful without relying on extensive human feedback.

  9. Generative artificial intelligence - Wikipedia

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

    The capabilities of a generative AI system depend on the modality or type of the data set used. Generative AI can be either unimodal or multimodal; unimodal systems take only one type of input, whereas multimodal systems can take more than one type of input. For example, one version of OpenAI's GPT-4 accepts both text and image inputs.

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