1. Introduction to AI

What is AI?

Artificial Intelligence (AI) refers to the field of computer science that aims to create machines or systems capable of performing tasks that typically require human intelligence. These tasks include reasoning, problem-solving, understanding language, and recognizing patterns.

  • Artificial Narrow Intelligence (ANI): This is AI designed for a specific task. For example, a machine learning model used to recommend videos on YouTube is an ANI.
  • Artificial General Intelligence (AGI): AGI refers to a theoretical form of AI that can perform any intellectual task a human can. This is still in the research phase and has not yet been achieved.
  • Artificial Superintelligence (ASI): This is the next stage beyond AGI, where AI surpasses human intelligence in all aspects.

Key Terminology

  • Machine Learning (ML): A subset of AI where algorithms learn patterns from data to make predictions or decisions. There are three primary types:

    • Supervised Learning: The model is trained on labeled data.
    • Unsupervised Learning: The model finds patterns in unlabeled data.
    • Reinforcement Learning: The model learns by interacting with an environment and receiving feedback.

  • Deep Learning: A subset of machine learning that uses neural networks with many layers (hence “deep”) to learn from large amounts of data. It is particularly effective for tasks like image recognition, speech recognition, and language processing.

  • Natural Language Processing (NLP): This is the field that focuses on the interaction between computers and human language, enabling computers to process, analyze, and understand text or speech in a way that is meaningful.

Discriminative Models vs. Generative Models

Discriminative models and generative models are two fundamental types of machine learning models that serve different purposes and are used in various applications.

Discriminative Models

Discriminative models are designed to predict the probability of a target variable given a set of input features. They learn to distinguish between different classes or labels and are typically used for classification tasks.

Key Characteristics:

  • Focus on prediction: Discriminative models aim to predict the target variable accurately.
  • Conditional probability: They model the conditional probability of the target variable given the input features, P(Y|X).
  • Classification-oriented: Discriminative models are widely used for classification tasks, such as spam detection, sentiment analysis, and image classification.

Examples of Discriminative Models:

  • Logistic Regression
  • Support Vector Machines (SVMs)
  • Neural Networks (e.g., Multilayer Perceptron)

Generative Models

Generative models, on the other hand, are designed to model the underlying distribution of the data. They learn to generate new data samples that are similar to the existing data.

Key Characteristics:

  • Focus on data generation: Generative models aim to generate new data samples that resemble the existing data.
  • Joint probability: They model the joint probability of the input features and the target variable, P(X, Y).
  • Data generation-oriented: Generative models are used for tasks such as data augmentation, anomaly detection, and image/video generation.

Examples of Generative Models:

  • Generative Adversarial Networks (GANs)
  • Variational Autoencoders (VAEs)
  • Gaussian Mixture Models (GMMs)

Key Differences

  • Purpose: Discriminative models focus on prediction, while generative models focus on data generation.
  • Probability modeling: Discriminative models model conditional probabilities, whereas generative models model joint probabilities.
  • Applications: Discriminative models are widely used for classification tasks, while generative models are used for data generation, anomaly detection, and more.

In summary, discriminative models are designed for prediction tasks, while generative models are designed for data generation and modeling the underlying data distribution. Both types of models have their strengths and are used in various applications in machine learning and artificial intelligence.