Predictive vs. generative AI

Generative AI is a type of artificial intelligence that creates new content, whether it's text, images, music or even videos. It does this by learning from existing data and using that knowledge to generate something entirely new. Generative AI, as a concept and a research subject, has been around for some time, but it became widely recognized with the rise of deep learning models like GPT (Generative Pre-trained Transformer).

An example of a generative AI tool is ChatGPT, which can answer questions or write articles based on patterns learned from vast amounts of text. Another example is DALL·E, which can create images based on text prompts.

The focus of predictive AI is to forecast future outcomes based on historical data. It uses patterns and trends from past information to make predictions for things like stock prices, weather forecasts or customer behavior. Predictive AI has been used for decades in different fields to improve decision making.

For example, recommendation systems on Netflix and Amazon use predictive AI to suggest movies or products by predicting what you may like based on your viewing or purchase history.

Predictive AI and generative AI are both powerful technologies, but they serve different purposes via different approaches. Below, let’s compare the two across some key areas.

Purpose

The main purpose of generative AI is to produce entirely new content – something that didn’t exist before.

Predictive AI, on the other hand, analyzes historical data to make informed guesses about future events or behaviors.

Input data

Generative AI learns from vast amounts of existing data, such as text, images or music, to understand patterns and use them to create new content. It doesn't just analyze the data; it uses it as a foundation when creating new examples.

Predictive AI works with structured and targeted datasets, often much smaller – such as historical trends, numbers or patterns – and uses that to predict future outcomes. It needs specific input data related to the task at hand, like past sales data to predict future sales.

Training methodologies

Generative AI models are trained on large datasets via unsupervised or semi-supervised learning techniques. For example, models like GPT use reinforcement learning or transformer architectures to improve their outputs.

Predictive AI, however, is usually trained via supervised learning, where it learns from labeled datasets. The model is shown examples along with the correct outcome, and it learns to predict similar outcomes on its own. Linear regression, decision trees and random forests are commonly used algorithms.

Output

The output of generative AI is new content such as an article, an image, a song or a video. The results are creative and vary significantly depending on the given instructions.

Predictive AI outputs are forecasts or predictions based on data trends such as future sales figures, customer behavior predictions or risk assessments. It’s designed to give insights rather than create new content.

Next, we will discuss the practical use cases of predictive AI vs. generative AI.

Predictive AI

• Recommendation systems: Online platforms use predictive AI to suggest movies, songs or products based on user behavior and preferences.
• Financial forecasting: Banks and financial institutions use predictive AI to forecast market trends, stock prices and customer creditworthiness.
• Healthcare diagnosis: Predictive AI is used in healthcare to predict patient outcomes, diagnose diseases based on symptoms and historical data, and identify potential health risks before they become critical.
• Supply chain management: Predictive AI helps companies forecast demand, optimize inventory and plan more efficient logistics.

Generative AI

• Content creation: Generative AI is used to produce new articles, blog posts and books.
• Art and design: Artists and designers use generative AI to create unique digital art, graphics and music.
• Game development: Generative AI can design new levels, characters and environments for the gaming industry.
• Drug discovery: In healthcare, generative AI helps researchers design new molecules or compounds, which speeds up drug discovery processes.
• Cybersecurity: Many generative-AI-powered cybersecurity tools help in detecting and mitigating cyber threats.
• Software development: Generative AI is used to write code, design algorithms and create unit tests.

Both generative AI and predictive AI have their challenges. Below, we will examine some common ones and discuss strategies to overcome them.

Predictive AI challenges

• Challenge: Predictive AI has a strong reliance on accurate and relevant data. Poor-quality data or missing information can lead to incorrect predictions and unreliable results.
  Solution: Properly clean and preprocess data before feeding it into the model.
  
• Challenge: Predictive models can sometimes become too focused on the training data, which leads to overfitting. This makes the model perform well on historical data but poorly on new data.
  Solution: Use techniques like cross-validation and regularization to prevent overfitting.
  
• Challenge: Predictive AI can struggle with uncertainty when making forecasts in volatile environments like stock markets or customer behavior.
  Solution: Implement probabilistic models that account for uncertainty and provide confidence intervals for predictions.

Generative AI challenges

• Challenge: Generative AI models can reflect biases present in their training data.
  Solution: Audit and filter training data for biases and improve algorithms to reduce the likelihood of biased content.
  
• Challenge: The content generated by AI can sometimes lack coherence, accuracy or creativity, especially when compared to human-created content.
  Solution: Implement feedback loops where human reviewers refine and correct the AI’s output.
  
• Challenge: Training generative models, especially large-scale ones, requires significant computational resources.
  Solution: Explore avenues to optimize models for better efficiency, which will, in turn, reduce computational demands.

To get the most out of their unique strengths, it’s important to understand the differences between predictive and generative AI. By knowing when and how to use each, you can tap into their full potential. We hope this piece has provided useful insights to help you on that journey.