AI, ML, DL, Gen AI

Artificial intelligence (AI): The overarching field of AI, which creates intelligent systems that perform human-like tasks

• Example: Siri and Alexa are examples of AI systems that can perform human-like tasks such as answering questions, setting reminders, and controlling smart home devices.

• Machine learning (ML): A subset of AI that uses statistical techniques for prediction based on patterns

• Example: Spam filters that learn to identify and block unwanted emails are an example of ML, where the system analyzes patterns in email data to make predictions about future messages.

• Deep learning (DL): A type of ML based on neural networks that are capable of learning complex patterns from large datasets

• Example: Facial recognition systems used in smartphones and social media platforms are powered by deep learning, which can learn complex patterns in large datasets of facial images.

• Generative AI: A subset of DL that creates new data based on learned patterns, often without retraining

• Example: Text-generating models like Amazon Nova Lite and image-generating models like Amazon Nova Canvas are examples of generative AI, which can create new content (such as articles, stories, or images) based on the patterns they've learned from their training data.

In generative AI, a model is the result of applying a machine learning algorithm to training data. Models encapsulate the patterns, relationships, and rules learned from the data, so that the AI system can generate new content or make predictions when given new inputs.

The quality of a generative AI model is critically dependent on both the training data and the ML algorithm that you use. High-quality, diverse training data helps the model learn a wide range of patterns and nuances, while an appropriate algorithm ensures effective learning from this data.

Model development is often iterative. Initial models might have limitations or biases. You can address these issues by refining training data, adjusting algorithms, or fine-tuning model parameters. AWS services such as SageMaker AI help with this iterative process by providing tools for model training, evaluation, and deployment.

Be aware that a model is only as good as the information it was trained on. It is important to carefully curate data and continuously monitor and update models to make sure they remain accurate and relevant over time.