Applications of Generative Machine Learning
- Natural Language Generation (NLG): Instances like GPT-3 can process human-like written text when prompted, thereby leading to possible applications in chatbots, content generation or language translation.
- Image Synthesis: Using Generative Adversarial Networks (GANs) is possible to create the pictures which can be used in not only arts and design, but also computer graphics.
- Data Augmentation: Generative models are capable of generating new data points and adding it to the training datasets to improve their ability to tolerate anomalies and generalize what they have learnt.
- Anomaly Detection: Generative models can be trained to determine that data follows a normal distribution pattern and look for any abnormalities that vary out from this distribution in a significant way.
- Drug Discovery: Generative models can be applied to new molecular structures so that the experts in drug discovery can try unseen chemicals compounds rapidly and widely.
What is Generative Machine Learning?
Generative Machine Learning is an interesting subset of artificial intelligence, where models are trained to generate new data samples similar to the original training data. In this article, we’ll explore the fundamentals of generative machine learning, compare it with discriminative models, delve into its applications, and conclude with insights into its significance in the AI landscape.