Machine learning modeling Definitions

  • A

    AI governance

    AI governance is the idea that there should be a legal framework for ensuring that machine learning (ML) technologies are well researched and developed with the goal of helping humanity navigate the adoption of AI systems fairly.

  • artificial ignorance

    Artificial ignorance is the artificial intelligence (AI) development practice of ignoring insignificant data in order to focus more attention to important information that might be valuable.

  • C

    cognitive modeling

    Cognitive modeling is an area of computer science that deals with simulating human problem-solving and mental processing in a computerized model.

  • concept mining

    Concept mining is the process of searching documents or unstructured text for ideas and topics.

  • G

    generative adversarial network (GAN)

    A generative adversarial network (GAN) is a type of AI machine learning (ML) technique made up of two nets that are in competition with one another in a zero-sum game framework.

  • Google AutoML Vision

    Google AutoML Vision is a machine learning model builder for image recognition, offered as a service from Google Cloud.

  • M

    machine learning bias (algorithm bias or AI bias)

    Machine learning bias, also known as algorithm bias or AI bias, is a phenomenon that occurs when an algorithm produces results that are systematically prejudiced due to erroneous assumptions in the machine learning process.

  • N

    artificial neural network (ANN)

    In information technology (IT), a neural network is a system of hardware and/or software patterned after the operation of neurons in the human brain.

  • P

    predictive modeling

    Predictive modeling is a process that uses data mining and probability to forecast outcomes.

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