Generative AI sets itself apart from other forms of AI, such as conventional AI, due to its unique capability to generate new data and content instead of merely analyzing existing data and making predictions. Conventional AI is task-specific and rule-based, excelling in efficiency and interpretability. In contrast, generative AI shines in areas of creativity, managing uncertainty, and pioneering applications. It has the capacity to create original music compositions, fabricate realistic images, produce text that resembles human writing, and even design molecules for drug discovery. This technology holds the potential to transform industries by offering unparalleled pathways for creativity and innovation.
The training process for generative AI involves large volumes of data from which it learns underlying patterns and generates new data that reflects the training data. It can create entirely novel content based on the patterns it has learned. On the other hand, conventional AI operates on predefined rules and patterns to carry out specific tasks without generating new content. Generative AI can collaborate with traditional AI to deliver more potent solutions by harnessing the strengths of both methodologies.
Despite the excitement surrounding generative AI’s creative abilities and content generation capabilities, conventional AI continues to be crucial for many applications due to its effectiveness in tasks such as automated insights extraction, predictive modeling, intelligent alerting, and natural language processing. Both forms of AI possess their unique strengths which when used together can extract incremental value from data.
