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  2. Applications of artificial intelligence - Wikipedia

    en.wikipedia.org/wiki/Applications_of_artificial...

    The use of AI in banking began in 1987 when Security Pacific National Bank launched a fraud prevention task-force to counter the unauthorized use of debit cards. [60] Kasisto and Moneystream use AI. Banks use AI to organize operations for bookkeeping, investing in stocks, and managing properties. AI can adapt to changes during non-business ...

  3. Category:Applications of artificial intelligence - Wikipedia

    en.wikipedia.org/wiki/Category:Applications_of...

    AI Factory; AI in education; AI-assisted reverse engineering; AI-assisted targeting in the Gaza Strip; AI: When a Robot Writes a Play; Aidoc; Aisera; AIVA; Alice (Microsoft) Anyword; Apple Intelligence; Artificial imagination; Outline of artificial intelligence; Artificial Intelligence Applications Institute; Artificial intelligence in architecture

  4. Foundation model - Wikipedia

    en.wikipedia.org/wiki/Foundation_model

    A foundation model, also known as large X model (LxM), is a machine learning or deep learning model that is trained on vast datasets so it can be applied across a wide range of use cases. [1] Generative AI applications like Large Language Models are often examples of foundation models.

  5. Agentic AI is a ‘new labor model, new productivity model, and a new economic model,’ says Salesforce’s Marc Benioff Fortune Editors Updated December 18, 2024 at 4:48 PM

  6. Meta releases AI model that can check other AI models' work - AOL

    www.aol.com/news/meta-releases-ai-model-check...

    The ability to use AI to evaluate AI reliably offers a glimpse at a possible pathway toward building autonomous AI agents that can learn from their own mistakes, two of the Meta researchers behind ...

  7. Open-source artificial intelligence - Wikipedia

    en.wikipedia.org/wiki/Open-source_artificial...

    Open-source artificial intelligence is an AI system that is freely available to use, study, modify, and share. [1] These attributes extend to each of the system's components, including datasets, code, and model parameters, promoting a collaborative and transparent approach to AI development. [1]

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