AI System User

An AI System User is a system user that is an artificial intelligence practitioner who interacts with AI-powered systems to accomplish intelligent tasks.

• AKA: Artificial Intelligence System User, AI Practitioner, AI Tool User.
• Context:
  • It can typically interact with Machine Learning Models through prediction interfaces.
  • It can typically provide Training Data for model improvement.
  • It can typically interpret AI Outputs within domain contexts.
  • It can typically configure AI Parameters for task optimization.
  • It can typically evaluate AI Performance using quality metrics.
  • ...
  • It can often utilize Pre-Trained Models for transfer learning tasks.
  • It can often apply AI Tools across multiple domains.
  • It can often combine AI Services for complex workflows.
  • It can often implement AI Safety Practices for responsible usage.
  • ...
  • It can range from being a Novice AI System User to being an Expert AI System User, depending on its AI system user proficiency level.
  • It can range from being a Single-Tool AI System User to being a Multi-Tool AI System User, depending on its AI system user tool diversity.
  • It can range from being a Consumer AI System User to being an Enterprise AI System User, depending on its AI system user deployment scale.
  • It can range from being a Supervised AI System User to being an Autonomous AI System User, depending on its AI system user independence level.
  • It can range from being a Traditional AI System User to being a Generative AI System User, depending on its AI system user technology focus.
  • ...
  • It can navigate AI Ethics Considerations in deployment decisions.
  • It can manage AI System Limitations through workaround strategys.
  • It can contribute to AI Communitys via use case sharing.
  • It can influence AI Product Development through user feedback.
  • ...
• Example(s):
  • LLM System Users, such as:
    • ChatGPT Users employing conversational AI interfaces.
    • Claude Users utilizing advanced reasoning capabilitys.
    • LLM Power Users maximizing language model potentials.
  • Computer Vision Users, such as:
    • Image Recognition Users classifying visual content.
    • Object Detection Users identifying spatial elements.
    • Face Recognition Users verifying personal identitys.
  • ML Platform Users, such as:
    • AutoML Users leveraging automated machine learning.
    • Cloud AI Users accessing scalable AI services.
    • Edge AI Users deploying local inference models.
  • Voice AI Users interacting with speech recognition systems.
  • Recommendation System Users receiving personalized suggestions.
  • ...
• Counter-Example(s):
  • Traditional Software Users, who use non-AI systems only.
  • AI Developers, who build AI systems rather than use them.
  • Manual Process Users, who avoid automation technology.
• See: System User, Artificial Intelligence, Machine Learning, LLM System User, LLM Power User, AI Safety, AI Ethics, Pre-Trained Model, AI Tool.

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