1. Introduction to Artificial Intelligence (AI)

Definition

Artificial Intelligence (AI) refers to the simulation of human intelligence in machines that are designed to think and act like humans.

These systems can perform tasks that typically require human intelligence, such as understanding natural language, recognizing patterns, solving problems, and making decisions.

Examples of AI Applications:

Key Concepts

• Machine Learning (ML): A subset of AI that involves algorithms learning from data to make decisions without being explicitly programmed
• Deep Learning: A further subset of ML that uses neural networks with many layers (deep networks) to analyze complex data patterns
• Generative AI: A branch of AI that focuses on creating new content, such as text, images, or code, by learning from existing data, often using models like neural networks

Source: AWS Skillbuilder

2. Machine Learning (ML)

Definition

Machine Learning (ML) is a method of data analysis that automates analytical model building.

It's based on the idea that systems can learn from data, identify patterns, and make decisions with minimal human intervention.

Types of Data

Training Process

In ML, the training process involves feeding large amounts of data to an algorithm so that it can learn to make predictions or decisions. This involves:

• Algorithms: The mathematical models that process the data
• Features: The measurable properties or characteristics of the data
• Inference: The process of using the trained model to make predictions on new, unseen data
  
  

Machine Learning Styles

2.1. Model Deployment and Inference

Model Artifacts

Once a machine learning model is trained, the output is a set of model artifacts, which include:

• Trained Parameters: The weights and biases learned during training
• Model Definition: A description of the model's architecture and logic
• Inference Code: Software that uses the model artifacts to make predictions

Hosting Options

There are two main ways to deploy and use a machine learning model:

Example Use Cases

• Real-Time Inference: Fraud detection during online transactions
• Batch Inference: Monthly sales forecasts using historical data
  

2.2. Model Evaluation and Challenges

Overfitting vs. Underfitting

• Overfitting
  • Occurs when a model performs exceptionally well on training data but poorly on new data
  • It essentially "memorizes" the training data rather than generalizing from it
• Underfitting
  • Happens when a model is too simple to capture the underlying trend of the data
  • It leads to poor performance on both training and new data

Bias and Fairness

• Bias
  • Refers to the tendency of a model to make systematic errors due to imbalanced or biased training data
  • For example, if a loan approval model is trained predominantly on data from a certain demographic, it might unfairly reject applications from other groups
• Fairness
  • Ensuring that models make equitable decisions across different groups
  • This can be achieved by carefully curating training data and continuously monitoring model performance for any signs of bias

Example

— Loan Approval Model: If the training data lacks diversity, the model might incorrectly learn that certain demographic groups are less likely to be approved, leading to biased outcomes.

3. Deep Learning

Definition

Deep Learning is a subset of machine learning that utilizes neural networks with multiple layers (hence "deep").

These networks are inspired by the human brain and are particularly effective at recognizing complex patterns in large datasets.

Source: AWS Skillbuilder

Neural Network Structure

• Input Layer: Receives the initial data
• Hidden Layers: Multiple layers where the data is processed. Each layer extracts features and passes them to the next layer
• Output Layer: Produces the final prediction or classification

Applications

• Image Classification: Identifying objects within images (e.g., identifying fish in photos)
• Natural Language Processing (NLP): Understanding and generating human language (e.g., translating languages or summarizing text)

Deep Learning vs. Traditional ML

4. Generative AI

Definition

Generative AI refers to models that generate new content based on training data. These models are particularly powerful in tasks like creating text, images, music, and even software code.

Techniques

• Transformers: A type of model architecture that processes sequences of data (e.g., sentences) in parallel, making them efficient for training on large datasets

Applications

• Content Creation: Writing articles, generating images, composing music, etc.
• Language Models: Understanding and generating human language (e.g., chatbots, translation services)

Example

— Generative AI can analyze vast amounts of text data to create a coherent, contextually relevant response to a user's query, making it useful in applications like chatbots and virtual assistants.