We’ve covered a lot of ground in understanding how both Traditional Machine Learning and Generative AI work.
Now, let’s compare them to highlight how ML and GenAI differ in purpose, structure, and applications. While both use Machine Learning techniques, their goals and methodologies are distinct. Understanding these differences can help you decide which technology to use depending on the problem you want to solve.
3.1: Predicting vs. Creating
The most fundamental difference between Traditional Machine Learning (ML) and Generative AI (GenAI) lies in their core objective:
- Traditional ML is primarily predictive. Its goal is to learn patterns from historical data and apply them to new, unseen data. It excels at tasks like classification, regression, and decision making where the output is based on existing patterns.
- Example: If you have data on house prices over time, traditional ML can predict the price of a new house based on its features like square footage, location, and number of bedrooms. It’s all about mapping inputs to outputs based on learned relationships.
- Traditional ML is Deterministic and in most use cases, repeatable. This makes it usable in scenarios that require the ability to document the algorithm and ensure it has consistent behavior.
- GenAI, on the other hand, is creative. It doesn’t just learn from data to make predictions—it generates new data. This could be a sentence that has never been written before or an image that’s completely original, but still resembles what it has learned from existing data.
- Example: In real estate, instead of just predicting prices, GenAI could create virtual images of homes that don’t yet exist based on architectural styles it has been trained on.
Key Takeaway: Traditional ML answers questions like, “What will happen next?” whereas GenAI answers, “What can we create?” (but it can only create from what it has seen before; it cannot create something that has never been seen).
3.2. Labeled Data vs. Unlabeled or No Data
The kind of data each technology uses is also very different.
- Traditional ML is largely data hungry and often needs Labeled Data to function. In Supervised Learning, for example, you need input-output pairs, where the data is labeled with the correct answers (think of email datasets labeled as spam or not spam). Without this labeled data, it’s difficult for the model to learn effectively.
- Example: In fraud detection, you need a dataset where each transaction is labeled as fraudulent or non-fraudulent. The model learns from these labeled cases and applies that knowledge to new transactions.
- GenAI, particularly models like GANs and Transformers, can work with unlabeled data or even use self-supervised learning. The model learns the distribution of the data itself and creates new examples that match that distribution.
- Example: A model like GPT-4 doesn’t require labeled data. It’s trained on massive amounts of text from books, websites, and articles without labels, learning the relationships between words and sentences. Then, when you ask it to generate a paragraph, it does so based on the patterns it’s learned.
Key Takeaway: Traditional ML often requires labeled data to make predictions, while GenAI can work with large-scale, unlabeled data and create entirely new content.
3.3: Structure of Models – Learning from Data vs. Mimicking Data
- Traditional ML models like decision trees, Support Vector Machines (SVM), and Linear Regression are designed to learn from data to make decisions or predictions. These models generally have a well-defined structure and purpose: they are optimized to find relationships between variables and produce accurate results based on those relationships.
- Example: A decision tree might split a dataset based on the most informative features (like income or credit score) to predict whether someone will repay a loan or not.
- GenAI models, such as GANs and Transformer-based models, are structured to mimic the underlying distribution of the data and generate similar outputs. GANs, for instance, have a unique architecture where two networks (Generator and Discriminator) compete to improve each other, leading to highly realistic outputs.
- Example: In image generation, the Generator network tries to create an image that looks real, while the Discriminator tries to tell if it’s fake. Over time, the Generator gets better at creating convincing images so that the Discriminator can no longer distinguish from real images.
Key Takeaway: Traditional ML is designed to optimize for accurate predictions and decision making, while GenAI focuses on creating realistic data that mimics the training data.
3.4: Applications – Where Each Technology Shines
Traditional ML and GenAI have different strengths and are used in different types of applications:
- Traditional ML is used in areas where prediction, classification, or decision making are the end goals. These models thrive in fields like finance, healthcare, marketing, and more, where the goal is to use past data to inform future actions.
- Examples:
- Credit Scoring: Predicting whether a customer will default on a loan
- Recommendation Systems: Suggesting products to customers based on past purchases
- Supply Chain Forecasting: Predicting demand to optimize inventory
- Examples:
- GenAI excels in creative tasks, like generating new content, art, music, or even new molecular compounds in drug discovery. These models are also being used to simulate environments, create virtual worlds, and enhance human creativity.
- Examples:
- Art and Design: Tools like DALL-E or MidJourney generating artwork from simple text prompts
- Text and Content Creation: GPT-4 generating blog posts, product descriptions, or even entire books
- Healthcare: AI models creating new drug molecules that can potentially treat diseases more effectively (note that these have to go a through testing process, like all drugs, before they can be used in the real world)
- Examples:
Key Takeaway: Traditional ML shines in prediction and decision-making tasks, while GenAI dominates in creative and generative tasks that require producing new, unique content or ideas.
3.5: Explainability vs. Black Box Models
Another critical difference between the two is explainability—how easy it is to understand how the model is making decisions.
- Traditional ML models, like decision trees and linear regression, are often more interpretable. This means you can easily explain why a particular prediction or decision was made by the model. For example, a decision tree allows you to follow a series of decisions or splits that lead to a particular outcome.
- Example: In credit scoring, you can show that a higher credit score and stable income lead to a higher likelihood of loan approval. The decision-making process is transparent.
- GenAI models, especially those like deep Neural Networks or GANs, are often considered Black Boxes. While they are incredibly powerful, it can be difficult to explain why a particular output was generated. For example, a deep learning model that generates a new painting cannot easily explain why it chose certain colors or shapes—it just does so based on what it learned during training.
- Example: When GPT-4 generates a piece of writing, it’s not easy to trace exactly why the model generated a specific sentence. The underlying mechanism is based on complex patterns it learned from millions of texts, making it less interpretable.
Key Takeaway: Traditional ML models tend to be more interpretable, making them easier to explain in industries where transparency is important, such as finance or healthcare. GenAI, while powerful, often functions as a Black Box, which can make it harder to explain its decisions.
3.6: The Future – How These Technologies Complement Each Other
While Traditional ML and GenAI have distinct roles, the future lies in combining the strengths of both. Many industries are already starting to use both technologies together to solve complex problems.
- Example 1: Self-Driving Cars
In autonomous driving, Traditional ML is used to predict road conditions, identify obstacles, and make driving decisions in real time. At the same time, GenAI is used to create simulated driving environments for training purposes. These AI-generated environments help test the car’s driving algorithms in a wide range of conditions—night driving, rain, snow—without the need for real-world testing. - Example 2: Personalized Healthcare
In healthcare, Traditional ML models predict patient outcomes, like the likelihood of developing a certain disease. GenAI can take it further by generating personalized treatment plans or simulating the effects of different drugs, helping doctors make more informed decisions. - Example 3: Financial Risk Modeling
Traditional ML is already widely used in risk modeling to predict market behavior. GenAI can be used to simulate new market scenarios—like extreme economic conditions or rare market events—that traditional data doesn’t capture, providing a more robust risk assessment framework.
Key Takeaway: The combination of Traditional ML’s predictive power and GenAI’s creative capabilities offers limitless potential for industries ranging from healthcare and finance to entertainment and manufacturing. Together, they can solve more complex, multifaceted problems than either could alone.
Conclusion: Applying AI in your business
- Define your use case: what is the business goal you hope to achieve with AI?
- Saying you need it for marketing purposes or FOMO can be a valid business case, just as needing to create a predictive maintenance algorithm to minimize downtime.
- Review and analyze your data
- Review the combination of data and use case to select the best AI technique to apply
- Pilot project
