Imagine teaching a child to recognize objects. You show them many pictures of a cat and a dog, and they gradually learn to tell the difference. AI works similarly but with a lot more data and mathematical precision.
Here’s a simplified breakdown:
1. Data Collection
- AI needs data to learn. This data can be anything: text, images, sounds, etc.
- Example: To teach AI about cats and dogs, you’d collect thousands of pictures of cats and dogs.
2. Training
- The AI is fed the data and starts learning patterns.
- It uses complex algorithms (like a set of rules) to understand these patterns.
- Example: The AI looks at many pictures of cats and dogs and learns features that distinguish them, like shape, color, and size.
3. Model Creation
- The learning process creates a model, which is essentially the AI’s brain.
- This model can now make predictions or decisions based on new data.
- Example: After training, the AI can look at a new picture and tell you whether it’s a cat or a dog.
4. Testing and Validation
- The AI’s accuracy is tested using a different set of data that it hasn’t seen before.
- Adjustments are made to improve accuracy.
- Example: Show the AI new pictures of cats and dogs to see if it identifies them correctly.
5. Deployment
- Once accurate, the AI model is deployed to perform tasks in real-world applications.
- Example: The trained AI can now be used in an app to identify cats and dogs in user-uploaded photos.
AI learns from data by identifying patterns. It creates a model that can make predictions or decisions based on what it has learned. The more data it gets, and the better the algorithms, the smarter the AI becomes. For more information about AI and how LLM's work please feel free to reach out to us and ask questions.
