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  2. Artificial intelligence engineering - Wikipedia

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

    An AI engineer's workload revolves around the AI system's life cycle, which is a complex, multi-stage process. [24] This process may involve building models from scratch or using pre-existing models through transfer learning, depending on the project's requirements. [ 25 ]

  3. Requirements engineering - Wikipedia

    en.wikipedia.org/wiki/Requirements_engineering

    Requirements engineering (RE) [1] is the process of defining, documenting, and maintaining requirements [2] in the engineering design process. It is a common role in systems engineering and software engineering .

  4. Category:Artificial intelligence engineering - Wikipedia

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

    Artificial Intelligence (AI) engineering is the interdisciplinary field focused on designing, developing, and deploying AI systems. It combines principles from data and software engineering to create robust, scalable, and efficient solutions for complex tasks.

  5. The rise of the "AI engineer" and what it means for the ... - AOL

    www.aol.com/working-ai-changing-software...

    AI is opening new career tracks for software developers who want to shift to different roles. Developers at an AI roundtable said that the tech job market is fluctuating rapidly with gen AI. This ...

  6. Requirements analysis - Wikipedia

    en.wikipedia.org/wiki/Requirements_analysis

    In systems engineering and software engineering, requirements analysis focuses on the tasks that determine the needs or conditions to meet the new or altered product or project, taking account of the possibly conflicting requirements of the various stakeholders, analyzing, documenting, validating, and managing software or system requirements.

  7. Hardware for artificial intelligence - Wikipedia

    en.wikipedia.org/wiki/Hardware_for_artificial...

    Specialized computer hardware is often used to execute artificial intelligence (AI) programs faster, and with less energy, such as Lisp machines, neuromorphic engineering, event cameras, and physical neural networks. Since 2017, several consumer grade CPUs and SoCs have on-die NPUs. As of 2023, the market for AI hardware is dominated by GPUs. [1]