'AI Procedures' can refer to the steps or processes involved in developing, implementing, and maintaining an AI system. These procedures typically involve a combination of programming, data collection and analysis, and algorithm design, among other tasks.

Here are some common procedures involved in developing an AI system:

1. Training and Validation: AI models are trained using various algorithms and machine learning techniques, which involve feeding the models large amounts of data to enable them to identify patterns and learn from experience. Validation is then done to assess the accuracy and performance of the trained model.

2. Continuous Learning and Improvement: AI models can continue to learn and improve over time, with new data and feedback from users. This requires ongoing monitoring and updating of the model to ensure it remains accurate and effective.
   
3. Problem Identification: The first step in developing an AI system is to identify the problem or task that the system will be designed to solve or automate.
  
4. Model Design: The AI model must be designed and developed, which involves choosing the appropriate algorithms, architectures, and parameters based on the problem and data set.
  
5. Maintenance & Upgrades: Once the AI model is developed and tested, it can be deployed in the production environment, and regular maintenance and updates are required to ensure that it continues to perform optimally.
  
6. Data Collection and Preparation: Collecting data is a crucial first step in AI development. This can involve identifying relevant data sources, such as public datasets, company data, or data from third-party providers. Once the data is collected, it needs to be prepared for use in AI models. This involves cleaning the data, handling missing values, and ensuring that it is structured in a way that can be effectively used by the model.
  
7. Training and Validation: Once the data is collected and prepared, the next step is to train the AI model. This involves selecting appropriate algorithms and machine learning techniques and feeding the model large amounts of data. The model will learn from the data by identifying patterns and relationships, and these insights will be used to make predictions or decisions. Validation is then done to assess the accuracy and performance of the trained model.
  
8. Algorithm Selection: Choosing the right algorithm or technique for a specific AI task is crucial for the success of an AI project. Different algorithms have different strengths and weaknesses, and the choice of algorithm will depend on the type of data, the complexity of the task, and the performance requirements. For example, decision trees are good for classification tasks, while neural networks are good for image recognition.
  
9. Model Deployment & Training: Once an AI model is trained and validated, it can be deployed into a production environment to perform its intended task. This involves integrating the model into existing systems and workflows. The model may need to be optimized for performance and scalability, and appropriate security measures should be put in place to protect sensitive data.
 
10. Continuous Learning and Improvement: AI models can continue to learn and improve over time, with new data and feedback from users. This requires ongoing monitoring and updating of the model to ensure it remains accurate and effective. This can involve retraining the model with new data, or fine-tuning the model's parameters to improve its performance.

11. User Feedback: It's important to gather feedback from users and stakeholders to ensure the system is meeting their needs and expectations. User feedback can also help identify areas for improvement and guide future development.

12. Software updates: Like any other software system, AI systems require regular updates to fix bugs, improve performance, and add new features. It's important to stay current with software updates to ensure the system is running optimally.

1. Machine Learning Algorithms:

* AlphaGo Zero: A reinforcement learning algorithm that achieved superhuman performance in the game of Go.
* Bayesian Networks: Probabilistic graphical models used for representing relationships between variables.
* Decision Trees: Tree-like structures used for classification and regression tasks.
* Deep Learning: A type of machine learning using artificial neural networks with multiple layers.
* K-Nearest Neighbors: Classifies data points based on the similarity to their closest neighbors.
* Linear Regression: Predicts a continuous output based on a linear relationship with input features.
* Support Vector Machines: Classifies data points by finding a hyperplane that best separates different classes.

2. AI Applications:

* Computer Vision: Analyzing and understanding visual information like images and videos.
* Natural Language Processing: Processing and understanding human language.
* Robotics: Designing and building robots that can interact with the physical world.
* Recommender Systems: Suggesting relevant items or products to users based on their past behavior.
* Speech Recognition: Converting spoken language into text.
* Text Generation: Generating human-like text for various purposes.

3. AI Techniques:

* Evolutionary Algorithms: Finding optimal solutions by mimicking natural selection.
* Heuristics: Problem-solving techniques that use approximate methods for efficiency.
* Search Algorithms: Finding solutions by exploring different possibilities systematically.
* Symbolic AI: Reasoning with symbols and rules to represent knowledge.

AI development is a complex and iterative process that requires a multidisciplinary team with expertise in programming, data science, machine learning, and domain-specific knowledge. Each step in the AI procedure is critical for the success of the project, and careful attention must be paid to ensure that the AI model is accurate, reliable, and ethical.

Effective AI development requires a multidisciplinary team with expertise in programming, data science, machine learning, and domain-specific knowledge.