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Get to Know Generative AI, Predictive AI and Machine Learning

June 28, 2024

These days, everyone is talking about artificial intelligence (AI). This exciting technology has swept through every space, from education to cybersecurity, opening up new possibilities. IT professionals are frequently encountering AI features and capabilities in cybersecurity solutions today, and that trend will continue as AI technology continues to take off. However, AI and machine learning (ML) are complex concepts that can be tricky to grasp. By learning more about AI and ML, as well as their applications in technologies like generative AI and predictive AI, we can better understand how this type of technology can drive innovation.

What is AI and ML?

AI and ML are different but complementary. There is no AI without ML. AI aims to create systems capable of performing tasks that typically require human intelligence while ML is a key technique used to achieve this goal by enabling systems to learn from and adapt to data. Understanding AI and ML starts with learning the science behind each technology.

Artificial intelligence (AI)

Artificial Intelligence (AI) refers to the simulation of human intelligence in machines designed to think and learn like humans. This encompasses a range of technologies and methodologies that enable computers to perform tasks typically requiring human intelligence, such as visual perception, speech recognition, decision-making and language translation. AI aims to create systems that can adapt to new inputs, improve performance over time and handle complex scenarios through advanced algorithms and data processing techniques.

Machine learning (ML)

Machine Learning (ML) is a subset of AI focused on the development of algorithms and statistical models that allow computers to learn from and make predictions or decisions based on data. Rather than being explicitly programmed to perform specific tasks, ML systems improve their performance as they are exposed to more data over time. This learning process enables applications such as image recognition, natural language processing and predictive analytics, making ML a powerful tool for uncovering patterns and insights within large datasets.

What are the core components of AI and ML?

Both AI and ML have unique core components with some overlap. Breaking those components down makes it easy to see how these two complementary technologies work together to create new possibilities.

The core components of AI

AI is often used as an umbrella term that encompasses a variety of technologies within the same family. Some core components of AI include:

  • Machine learning: A subset of AI that involves the development of algorithms that allow computers to learn from and make predictions or decisions based on data.
  • Natural language processing (NLP): Enables machines to understand and respond to human language.
  • Computer vision: Allows machines to interpret and make decisions based on visual inputs.
  • Robotics: Involves creating intelligent machines that can perform tasks autonomously.

3 applications of machine learning

Machine learning is a subset of AI that involves the development of algorithms and statistical models that enable computers to learn from and make predictions or decisions based on data. Instead of being explicitly programmed to perform a task, ML systems use patterns and inference to improve their performance over time. Key aspects of ML include:

1. Supervised learning: The algorithm is trained on a labeled dataset, which means that each training example is paired with an output label. The goal is to learn a mapping of that data set and its possibilities from inputs to outputs.

Examples: Regression, classification.

2. Unsupervised learning: The algorithm is used on data that does not have labeled responses. The system tries to learn the underlying structure of the data.

Examples: Clustering, association.

3. Semi-supervised learning: Combines both labeled and unlabeled data during training, often with a small amount of labeled data and a large amount of unlabeled data.

4. Reinforcement learning: The algorithm learns by interacting with an environment, making decisions and receiving feedback through rewards or penalties.

Examples: Game playing, robotics.

What is generative and predictive AI?

The most common types of AI that an IT professional may encounter are generative and predictive AI. These are two distinct approaches within the field of artificial intelligence, each serving different purposes and employing different techniques. This overview can make it easier to understand the basics of these technologies

Generative AI

Purpose: Generative AI focuses on creating new data instances that resemble existing data. Its goal is to generate content that is original yet plausible.


  • Generative adversarial networks (GANs): GANs consist of two neural networks, a generator and a discriminator, that compete with each other. The generator creates new data instances, while the discriminator evaluates them against real data, guiding the generator to produce increasingly realistic data.
  • Variational autoencoders (VAEs): VAEs learn the underlying distribution of the data and can generate new data points by sampling from this distribution.
  • Language models: Models like GPT-3 can generate human-like text based on the patterns and structures learned from vast amounts of text data.


  • Content creation: Generating text, images, music and videos. For example, generating realistic images of people who don’t exist or creating articles, stories and poetry.
  • Data augmentation: Creating synthetic data to augment training datasets, improving the performance of machine learning models.
  • Simulation and modeling: Generating scenarios for simulation in fields like finance, healthcare and gaming.

Predictive AI

Purpose: Predictive AI focuses on making predictions about future events or outcomes based on historical data. Its goal is to forecast or classify based on patterns identified in existing data.


  • Supervised learning: Using labeled datasets to train models that can predict outcomes for new, unseen data. Techniques include regression, classification and time series analysis.
  • Unsupervised learning: Finding patterns and relationships in data without explicit labels. Techniques include clustering and association analysis.
  • Reinforcement learning: Training models to make sequences of decisions by rewarding desired behaviors and penalizing undesired ones.


  • Risk assessment: Predicting the likelihood of credit defaults, fraud detection and insurance risk.
  • Customer insights: Forecasting customer behavior, churn prediction and personalized recommendations.
  • Operational efficiency: Predictive maintenance, supply chain optimization and demand forecasting.
  • Healthcare: Predicting disease outbreaks, patient outcomes and treatment effectiveness.

What are the key differences between generative and predictive AI?

Every branch of AI technology has a unique specialty use. Generative AI is about creating new data that mirrors existing data while predictive AI is focused on using past data to predict future outcomes. Both play crucial roles in the advancement of artificial intelligence, addressing different needs and opening up diverse possibilities across various industries. Here is a look at the difference in potential usage between generative AI and predictive AI.

  1. Objective:
    1. Generative AI: Creates new, synthetic data similar to existing data.
    1. Predictive AI: Analyzes historical data to make forecasts or classifications.
  2. Output:
    1. Generative AI: Produces new content (e.g., images, text, audio).
    1. Predictive AI: Provides predictions or insights (e.g., future trends, classifications).
  3. Methods:
    1. Generative AI: Uses models like GANs, VAEs and language models.
    1. Predictive AI: Employs supervised, unsupervised and reinforcement learning techniques.
  4. Applications:
    1. Generative AI: Creative content generation, data augmentation, simulation.
    1. Predictive AI: Risk management, customer behavior analysis, operational optimization.


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