Machine Learning (ML) and Generative AI (GenAI) are both powerful subsets of Artificial Intelligence, yet they serve distinct purposes and are built on different conceptual foundations.
While ML focuses on pattern recognition, prediction, and decision-making based on data, Generative AI is designed to create new content, simulate environments, and even generate synthetic training data.
This raises a critical research question:
In what ways can Generative AI be applied to enhance, support, or evolve core Machine Learning models and workflows?
🔍 Points for Discussion:
- Can GenAI generate high-quality synthetic datasets to improve ML performance in data-scarce scenarios?
- How does GenAI contribute to semi-supervised and self-supervised learning?
- Are there examples where generative models have boosted accuracy, generalization, or robustness in traditional ML pipelines?
- What are the computational and ethical trade-offs of merging GenAI with classic ML tasks?
- How can diffusion models, GANs, or LLMs assist in feature engineering, model training, or real-time adaptation?
One of the most powerful ways Generative AI can support traditional Machine Learning is by helping solve the “not enough data” problem, something that comes up a lot in real-world applications.
Take medical imaging as an example. Training a model to detect rare diseases from MRI or CT scans sounds amazing, but the challenge is, those rare cases are exactly that: rare. There's often not enough data to build a reliable model.
That’s where Generative AI, especially models like GANs (Generative Adversarial Networks) and Diffusion Models, comes in as they can actually create high-quality synthetic images that closely resemble real medical scans. These aren’t just random pictures; they follow the same patterns, structures, and styles as real ones.
This is where my thinking stands so far, but I’m sure there’s room for improvement, and I’d love to hear how others are approaching this.
- Badges
- Science topic