In the previous article of this series we started our look at Generative AI, and looking deeper into the key concept of Artificial Intelligence itself. in this article we’ll consider another 2 key concepts, Generative Models and Training Data.
Generative Models
Definition: Generative models are a type of artificial intelligence that learn from data to create new, similar examples. They can produce various types of content, like text, images, or music.
Explanation
Think of generative models as creative tools that learn from existing material and then generate new content based on that learning. Here’s how it works:
- Learning from Data:
- Generative models are trained on a dataset that contains many examples of the type of content they will create. For instance, if they are trained on a collection of paintings, they learn the styles, colours, and techniques used in those paintings.
- Creating New Content:
- After learning, the model can generate original content that resembles what it was trained on. It doesn’t copy; instead, it combines and transforms the patterns it has learned.
Key Concepts Related to Generative Models:
- Training Data:
- This is the set of examples (like images, text, or music) used to teach the model. The more diverse and high-quality the training data, the better the generative model can create.
- Output:
- The new content produced by the generative model. For example, it could create a new painting or write a unique story based on what it learned.
Example in Simple Terms:
Imagine you have a friend who loves to draw. You show them lots of pictures of different animals. After looking at these pictures, your friend starts to draw their own animals, inspired by the ones they’ve seen but not copying any of them exactly. They might create a new animal that combines features from various ones they learned about.
In summary, generative models are a part of AI that focus on learning from existing content to create new, original work that resembles that content.
Training Data
Definition: Training data is a collection of information (examples) that is used to teach an artificial intelligence (AI) system how to perform a specific task.
Explanation
Imagine you’re trying to teach a child how to recognise different animals. To do this, you show them many pictures of animals and tell them the name of each one. Over time, the child learns to identify animals by remembering the patterns they’ve seen before. In the same way, training data is used to teach AI systems by giving them lots of examples to learn from.
How It Works:
- Collection:
- The training data can be anything related to the task you want the AI to learn. If you’re teaching AI to recognise images, the data might be pictures of different objects. If you want it to write sentences, the data might be a large set of texts.
- Learning:
- The AI system looks at the training data repeatedly, learning patterns, features, and relationships within the data. Over time, it becomes better at understanding and using that information.
- Application:
- Once the AI has learned from the training data, it can use what it learned to make decisions or create new content.
Key Concepts:
- Examples:
- Each item in the training data is called an “example.” For instance, if you’re teaching an AI to recognise cats, each picture of a cat in the training data is an example.
- Features:
- Features are specific things the AI looks for in the data. In the case of recognising animals, the features might include the shape of the ears, the colour of the fur, or the size of the body.
- Labels:
- If the task requires identifying or classifying something (like recognising cats vs. dogs), each example in the training data might be labelled. For instance, a photo would have the label “cat” or “dog,” so the AI knows what it’s learning to recognise.
Example in Simple Terms:
Imagine you’re teaching a friend to bake. You give them many recipes (the training data) to show them how different cakes are made. Over time, they start to understand common ingredients and methods (the patterns) used in baking. After practicing with those recipes, your friend can bake their own cake, even without following an exact recipe.
In summary, training data is the set of examples AI uses to learn how to do a specific task. The more examples it has, and the more accurate they are, the better the AI can perform its task.
In the next article we’ll be considering the final 2 key concepts, that of Neural Networks and GPTs.
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