How does Generative AI work?
The Generative AI works on complex algorithms and neural network architectures, like Generative Adversarial Networks (GANs) and Transformers. These models are trained on large datasets, from which they learn patterns, styles, and structures. The AI then uses this training to generate new content that mimics the learned material. For example, a Generative AI trained on cat images to generate new image of cat in a similar style. Let’s understand working of Generative AI in detail.
- Learning of Data: In Generative AI the first step is to learn from large amount of datasets for which AI is designed to generate such as code, text, images, code or all of these. For Example, ChatGPT 3.5 that is trained to generate any type of text content, code, and many more but it cannot generate images whereas ChatGPT 4 is trained to generate images also according to the instruction given by user.
- Understanding Patterns: After the training of AI with the large sets of data. It became capable to understand the pattern and rules inherent in that data. The AI identifies these patterns using algorithms. For example, if we trained AI with the images of cat it will learn the pattern how their eyes, hairs, ears, nose, etc. look like or it can be anything we can train AI to recognize the text in the images, speech etc.
- Creating New Content: After understanding the patterns, Generative AI can able to start creating new content. The AI can generate new pieces that is similar to original data but unique using the patterns it got learned. For example, an AI trained on pop music can compose a new piece that sounds like it was written by a pop music composer, even though it is entirely original.
- Refinement and Variation: Refinement is also a part of Generative AI. It generate multiple variations, evaluate them, and then refine the generated data based on the feedback. For example, AI generated a music there is a need of pitch variation then AI refine it based on the goals and feedback.
- Generative Models: Generative Models are crucial part of Generative AI and It used specific types of machine learning models. One common type is the Generative Adversarial Network (GAN). In a GAN, two neural networks – a generator and a discriminator – work against each other. The generator creates new content, and the discriminator evaluates it. Over time, this adversarial process leads to increasingly sophisticated and convincing creations.
Differences between Conversational AI and Generative AI
Artificial intelligence has evolved significantly in the past few years, making day-to-day tasks easy and efficient. Conversational AI and Generative AI are the two subsets of artificial intelligence that rapidly advancing the field of AI and have become prominent and transformative. Both technologies make use of machine learning and natural language processing to serve distinct purposes and work on different principles. These technologies, though distinct in their applications and principles, both leverage the power of machine learning(ML) and natural language processing(NLP) to transform various industries.
In this article, let us explore what is Generative and conversational AI and how they work, and also let us compare generative AI and conversational AI by focusing on their respective abilities and features.
Conversational AI and Generative AI
Table of Content
- What is Conversational AI?
- What is Generative AI?
- How does Generative AI work?
- How does Conversational AI work?
- Differences between Conversational AI vs.Generative AI
- Conclusion
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