6 Common AI Techniques
1. Generative AI
- AI technique that is able to generate something. It could be text, images, videos, or whatever other artifacts
- ChatGPT, Google Gemini, and other Large Language Models (LLMs) are using this technique
2. Non-Generative Machine Learning
- Category shouldn’t have even existed before Generative AI became popular in recent years
- In past years called “Classic Machine Learning” and “Deep Learning”
- Now, because of the Gen AI hype, they all become “Normal Machine Learning” techniques
- This category includes basic linear regression, clustering, classification, decision trees, etc.
3. Simulation
- AI technique allows us to create a model for a process or a system in the real world
- We can input a lot of combinations of parameters
- Purpose is to be able to answer lots of “what-if” questions
4. Optimization
- AI technique finds the best parameters in a formula or equation
- For example, we know that discounts will attract customers to buy more products from a supermarket. However, if the price is too low, the profit may disappear. Therefore, optimisation techniques can help us to find the “balance point” to maximise our profit
- AI technique is not necessarily machine learning
- We may create a set of pre-defined rules based on scientific evidence or even rules of thumb from the domain experts
- Such a “rule-based system” will help us make better decisions
- Graphs do not refer to any particular algorithms of machine learning but a data structure that represents objects and their relationships in a different way
- For example, a node in a graph stands for a data point, and edges represent the connections between this node and other nodes. Such a data structure could help other processes work more accurately than a tabular-based data structure.
Ref: christopher-tao-5717a274