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  2. Explainable artificial intelligence - Wikipedia

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

    Marvin Minsky et al. raised the issue that AI can function as a form of surveillance, with the biases inherent in surveillance, suggesting HI (Humanistic Intelligence) as a way to create a more fair and balanced "human-in-the-loop" AI. [61] Explainable AI has been recently a new topic researched amongst the context of modern deep learning.

  3. Commonsense knowledge (artificial intelligence) - Wikipedia

    en.wikipedia.org/wiki/Commonsense_knowledge...

    Commonsense knowledge can underpin a commonsense reasoning process, to attempt inferences such as "You might bake a cake because you want people to eat the cake." A natural language processing process can be attached to the commonsense knowledge base to allow the knowledge base to attempt to answer questions about the world. [2]

  4. Right to explanation - Wikipedia

    en.wikipedia.org/wiki/Right_to_explanation

    The field of Explainable AI seeks to provide better explanations from existing algorithms, and algorithms that are more easily explainable, but it is a young and active field. [ 18 ] [ 19 ] Others argue that the difficulties with explainability are due to its overly narrow focus on technical solutions rather than connecting the issue to the ...

  5. Exclusive: Reasoner, a startup from Crashlytics’ cofounder ...

    www.aol.com/finance/exclusive-reasoner-startup-c...

    The AI industry is pushing hard to build reasoning capabilities into the technology, partly to draw closer to the holy grail of human-level or superhuman artificial intelligence, and partly just ...

  6. Ethics of artificial intelligence - Wikipedia

    en.wikipedia.org/wiki/Ethics_of_artificial...

    This has led to advocacy and in some jurisdictions legal requirements for explainable artificial intelligence. [68] Explainable artificial intelligence encompasses both explainability and interpretability, with explainability relating to summarizing neural network behavior and building user confidence, while interpretability is defined as the ...

  7. Explanation-based learning - Wikipedia

    en.wikipedia.org/wiki/Explanation-Based_Learning

    Explanation-based learning (EBL) is a form of machine learning that exploits a very strong, or even perfect, domain theory (i.e. a formal theory of an application domain akin to a domain model in ontology engineering, not to be confused with Scott's domain theory) in order to make generalizations or form concepts from training examples. [1]

  8. 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]

  9. Elon Musk says AI has already gobbled up all human ... - AOL

    www.aol.com/finance/elon-musk-says-ai-already...

    AI takes an immense amount of resources—from endless water to an estimated $1 trillion worth of investor dollars—but Elon Musk warned the technology has already run out of its primary training ...

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