Artificial Intelligence (AI) Technology
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An Artificial Intelligence (AI) Technology is a computational technology that enables the creation of intelligent systems (that can perform intelligence-requiring tasks).
- Context:
- It can (typically) include AI Technology-Supporting Know-How, such as machine learning techniques (for training models), AI data engineering methodologies (for data preparation and data processing), and algorithm design know-how (for creating complex decision-making frameworks).
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- It can (often) be developed by AI Engineers.
- It can (often) be associated with AI Research.
- It can (often) be associated with AI Applications.
- It can (often) require Computational Resources and Data.
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- It can range from being a Simple AI Technology to being Complex AI Technology.
- It can range from being a Narrow AI Technology to being General AI Technology.
- It can range from being a Standalone AI Technology to being a Networked AI Technology.
- It can range from being a Rule-based AI to being Machine Learning-based AI Technology (such as deep learning technology).
- It can range from being a Deterministic AI Technology to being a Probabilistic AI Technology.
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- It can be associated with an AI Talent Measure.
- It can raises various AI Ethics considerations.
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- Example(s):
- Fundamental AI Components, such as:
- AI Algorithms, which define the step-by-step procedures used in data processing and automated reasoning to perform specific tasks,
- AI Tasks, which represent the various activities and operations that AI systems are designed to perform, such as classification, prediction, and decision-making,
- AI Systems, which are integrated setups of AI technologies that work together to perform complex functions and solve real-world problems,
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- Application-Specific AI Technologies, such as:
- Autonomous Vehicle Technology that uses AI for navigation and decision making,
- Virtual Assistant Technology that employs AI for understanding and responding to user queries,
- Healthcare Diagnostic AI that analyzes medical images and patient data to assist in disease detection and diagnosis,
- Financial Trading AI that utilizes market data and economic indicators for automated trading and investment decisions,
- Recommender System Technology that personalizes content and product suggestions based on user preferences and behavior,
- Cybersecurity AI that detects and responds to network threats and anomalies in real-time,
- Smart Home Technology that optimizes energy use, security, and comfort through AI-driven home automation.
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- AI Methodologies and Approaches, such as:
- ML Technology that leverages machine learning algorithms to predict outcomes based on data patterns,
- NLP Technology that processes and understands human language for applications like chatbots and translation services,
- Vision Technology that enables computers to interpret and process visual information from the world,
- RL Technology that uses reinforcement learning for developing systems that learn optimal behaviors through trial and error.
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- Domain-Specific AI Technology, such as:
- Legal-Domain AI Technology, such as Legal Document Analysis AI and Case Prediction AI.
- Healthcare AI Technology, such as Medical Imaging AI and Drug Discovery AI.
- Financial AI Technology, such as Algorithmic Trading AI and Fraud Detection AI.
- Educational AI Technology, such as Adaptive Learning AI and Automated Grading AI.
- Environmental AI Technology, such as Climate Modeling AI and Wildlife Tracking AI.
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- Fundamental AI Components, such as:
- Counter-Example(s):
- Conventional Software Technology, which operate based solely on predefined algorithms and lack the ability to learn from data,
- See: Cognitive Computing, Machine Learning, Robotics, Data Mining, AI Tech Prediction.