Let’s understand the processes behind Generative Artificial Intelligence and see how a Generative AI model is built and what are its business applications.
Generative AI is a category of Artificial Intelligence that processes the user’s prompt and generates new data based on data it has stored in the database. As far as Artificial Intelligence is concerned, it makes predictions based on the certain patterns it is trained on. A few days back, we published an introductory blog post on Artificial Intelligence and its major application ChatGPT. The fact of the matter is the widespread of AI is not stopping and is consistently making headlines in the world of technology. There have been continuous new developments in natural language processing(NLP) and Machine Learning that are making Artificial Intelligence a must-have component of any software organization. In this blog post, we will go through what is Generative AI and how Generative Artificial Intelligence can add value to your existing AI systems.
We will cover the following points in this article:
- What is Generative AI & how it works? Generative AI explained
- Business benefits of Generative Artificial Intelligence
What is Generative AI & how it works? Generative AI explained
This branch of Artificial Intelligence generates content such as Images, Text, Audio, or Video based on existing data. The term “generative” is used correctly here because it generates something new based on the provided databases.
Generative Artificial Intelligence is mainly based on the following two models:
Transformer-based models: This model takes the user’s input, and generates textual data collected from internet sources. ChatGPT is a major example because GPT-3(Generative Pre-trained Transformer) generates textual responses collected and compiled from multiple sources over the internet.
Generative Adversarial Networks(GANs): GANs are used to generate visual data such as images based on the user’s input. It is based on deep learning which comprises neural networks. In addition, Generative Adversarial Networks(GANs) have two sub-models and both of them are neural networks.
First, Generative Model generates random outputs no matter fake or real. Since its job is to generate data samples so it is a heavyweight process and requires more processing. Secondly, Discriminative Model receives the output generated by the Generative Model as input and learns to differentiate between real and fake data. It is to basically train the Discriminative modeling to recognize the real data prompts. However, the generator starts differentiating correctly after consistent training sessions.
This is a brief overview of the working mechanism of generative artificial intelligence. Due to deep learning and multiple neural network involvement, generative AI not only makes predictions but also generates new content based on the provided database. So, all these components and algorithms empower the systems to generate AI-based solutions.
Business benefits of Generative Artificial Intelligence
This section of the blog post will demonstrate the business applications of generative Artificial Intelligence. Almost every sector of life is leveraging generative AI. Let’s explore it further by going through the following points:
Textual Content Generation: In fact, Generative AI is used to generate textual data such as writing articles, and translating one language into another. The best example of this application is the GPT-3 model that generates text-based responses collected from various resources. Further, Generative AI is heavily being used in building AI-powered chatbots such as ChatGPT. It not only responds like humans but also helps programmers in their coding files. Moreover, Generative Artificial Intelligence can create algorithms, and create plugins.
Image Generation: Systems are powerful enough to create images of various entities even with the input given in the textual form. In addition, it can give a real look to a human face based on the sketch provided. Moreover, a generative AI model can transform one type of image into another. Above all, it can be used in the real-time generation of MRI and other imaging.
Audio/Video Generation: This is another application of Generative AI in which you can generate audio videos. Generative Adversarial Networks(GANs) can generate videos and can predict the next frame if a single frame is provided. Further, it can also improve the resolution of the existing videos. So, the entertainment industry is the main beneficiary of this application as they can use it in filmmaking, songwriting, music recording, and forming various types of an art galleries.
Likewise, there is much more to explore about this branch of Artificial Intelligence. It has brought automation to a wide range of sectors of businesses.
So, here we are going to end this blog post. Hope, you have a better understanding of Generative Artificial Intelligence after going through this article. In addition, we also have gone through generative AI models along with their working patterns. So, we have tried well to answer the question what is Generative AI? In fact, this branch of Artificial Intelligence is giving birth to new ideas of creativity. Moreover, we have published some articles on AI, ChatGPT, and ChatGPT integrations so do not forget to give them a read.
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What is the purpose of generative AI?
The main purpose of generative Artificial Intelligence is to make AI-powered systems that receive user inputs, process them, and generate new content based on the databases it maintains. For more details, please visit this link.
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