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  2. Artificial intelligence engineering - Wikipedia

    en.wikipedia.org/wiki/Artificial_intelligence...

    Key topics include machine learning, deep learning, natural language processing and computer vision. Many universities now offer specialized programs in AI engineering at both the undergraduate and postgraduate levels, including hands-on labs, project-based learning, and interdisciplinary courses that bridge AI theory with engineering practices.

  3. Machine learning - Wikipedia

    en.wikipedia.org/wiki/Machine_learning

    A machine learning model is a type of mathematical model that, once "trained" on a given dataset, can be used to make predictions or classifications on new data. During training, a learning algorithm iteratively adjusts the model's internal parameters to minimize errors in its predictions. [86]

  4. Adversarial machine learning - Wikipedia

    en.wikipedia.org/wiki/Adversarial_machine_learning

    Adversarial machine learning is the study of the attacks on machine learning algorithms, and of the defenses against such attacks. [1] A survey from May 2020 revealed practitioners' common feeling for better protection of machine learning systems in industrial applications.

  5. 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. [1]

  6. Reinforcement learning - Wikipedia

    en.wikipedia.org/wiki/Reinforcement_learning

    Reinforcement learning (RL) is an interdisciplinary area of machine learning and optimal control concerned with how an intelligent agent should take actions in a dynamic environment in order to maximize a reward signal. Reinforcement learning is one of the three basic machine learning paradigms, alongside supervised learning and unsupervised ...

  7. Applications of artificial intelligence - Wikipedia

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

    Machine learning can be used to combat spam, scams, and phishing. It can scrutinize the contents of spam and phishing attacks to attempt to identify malicious elements. [15] Some models built via machine learning algorithms have over 90% accuracy in distinguishing between spam and legitimate emails. [16]

  8. Incremental learning - Wikipedia

    en.wikipedia.org/wiki/Incremental_learning

    The aim of incremental learning is for the learning model to adapt to new data without forgetting its existing knowledge. Some incremental learners have built-in some parameter or assumption that controls the relevancy of old data, while others, called stable incremental machine learning algorithms, learn representations of the training data ...

  9. Generative adversarial network - Wikipedia

    en.wikipedia.org/wiki/Generative_adversarial_network

    Deep learning – Branch of machine learning; Diffusion model – Deep learning algorithm; Generative artificial intelligence – AI system capable of generating content in response to prompts; Synthetic media – Artificial production, manipulation, and modification of data and media by automated means