Artificial Intelligence (AI) Model
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An Artificial Intelligence (AI) Model is a computational model that can solve AI tasks.
- Context:
- It can (typically) be trained with an AI Model Training System.
- It can (typically) be trained on Large Training Datasets.
- It can (often) face challenges related to bias, fairness, and interpretability.
- It can range from being a Small AI Model to being an Large AI Model.
- It can range from being a Closed-Source AI Model to being an Open-Source AI Model.
- It can range from being a Uni-Model AI Model to being an Multi-Modal AI Model, for natural language processing, computer vision, and robotics.
- It can evolve with continuous learning and updates based on new data.
- It can incorporate various techniques such as neural networks, decision trees, support vector machines, and genetic algorithms.
- It can be deployed in real-world environments to automate tasks, provide insights, and enhance decision-making processes.
- It can be evaluated and benchmarked using metrics like accuracy, precision, recall, F1 score, and mean squared error.
- ...
- Example(s):
- A Convolutional Neural Network (CNN) used for image classification.
- A Recurrent Neural Network (RNN) applied in sequence prediction.
- A Generative Adversarial Network (GAN) used for generating realistic images.
- A Transformer-based Language Model for natural language understanding and NL generation.
- ...
- Counter-Example(s):
- A Traditional Software Program that follows explicitly coded instructions without learning from data.
- A Rule-Based Expert System that does not utilize machine learning techniques.
- See: LLM Model, Multimodal Model, Supervised Learning, Unsupervised Learning, Reinforcement Learning, Deep Learning, Neural Network, Bias in AI, Interpretability in AI.