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1. Algorithms

• Machine Learning Algorithms: These enable computers to learn from data and enhance their capabilities without needing explicit programming.

• Optimization Algorithms: These are utiliz to fine-tune models and identify the best settings for optimal performance.

2. Data

• Training Data: Large datasets are used to train artificial intelligence models. The effectiveness of AI systems relies on both the volume and quality of the input data.

• Testing and Validation Data: Separate datasets are employ to evaluate the performance of the train models.

• Labeled Data: In supervise machine learning, this refers to data that has been tag or categoriz to guide the learning process.

3. Training

• Supervised Learning: The algorithm is train using a label dataset that includes input data and corresponding outputs.

• Unsupervised Learning: The algorithm identifies patterns and relationships in the available data without specific labels.

• Reinforcement Learning: The learner acquires knowledge through interaction with the environment, receiving feedback in the form of rewards or penalties.

4. New version

• The development and use of AI systems must prioritize ethical principles, transparency, accountability, and fairness to ensure that AI technology is used responsibly.

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