Generative AI is an advanced subset of artificial intelligence that focuses on the creation of new, original content rather than just processing existing data.
What is the difference between Generative AI and AI?
AI systems are powered by algorithms and data to excel at specific tasks. On the other hand, generative AI’s goal is to reach human level intellect. AI is programmed to solve very specific problems but Generative AI is designed to solve any problem a human can.
Generative AI uses the principles of neural networks, particularly generative models like Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs). These models learn from vast amounts of data to develop a deep understanding of the underlying patterns and structures. Once trained, generative AI systems can generate new data that closely resembles the training data, such as images, music, text, or even realistic human faces.
However, the rapid progress of generative AI has also raised ethical concerns. With the capability to create highly realistic fake content, there are risks associated with the potential misuse of such technology, like deepfakes for misinformation or identity fraud.
Despite these concerns, generative AI has incredible potential for positive impact. It can drive innovation by augmenting human creativity and problem-solving capabilities. For instance, it can generate new product designs, assist in drug discovery, or even help in designing sustainable urban planning.