Ai generator based educational presentation

Generater ai wold wide use

1. Generative ai This Photo by Unknown Author is licensed under CC BY-SA-NC

2. What is Generative ai ? Generative AI refers to a class of artificial intelligence systems that have the ability to generate new content, such as images, text, audio, or even videos, that is not explicitly programmed into the system. These systems use machine learning techniques, particularly generative models, to create new data that is similar to, but not an exact replica of, existing data. One popular type of generative model is the Generative Adversarial Network (GAN), introduced by Ian Good fellow and his colleagues in 2014. GANs consist of two neural networks, a generator, and a discriminator, which are trained simultaneously through adversarial training. The generator creates new data, and the discriminator evaluates whether the generated data is real or fake. This iterative process helps the generator improve its ability to create more realistic content over time.

3. Advantages of generative ai Content Generation Data Augmentation Personalization Simulation and Training Drug Discovery Natural Language Processing (NLP) Artificial Creativity, etc. This Photo by Unknown Author is licensed under CC BY-NC-ND

4. Disadvantages of generative ai Ethical Concerns Bias and Fairness Security Risks Lack of Control Resource Intensiveness Data Privacy Overfitting

5. Working of generative ai Generator: The GAN consists of two main components: a generator and a discriminator. The generator's role is to create new data instances. In the context of images, for example, the generator takes random noise as input and transforms it into data that should resemble the training data. Discriminator: The discriminator, on the other hand, is like a detective trying to distinguish between real and fake data. It takes both real data from the training set and generated data from the generator and assigns probabilities of whether the data is real or fake.

6. Adversarial Training : The generator and discriminator are trained simultaneously in a competitive manner. The generator aims to produce data that is indistinguishable from real data, while the discriminator aims to get better at distinguishing between real and fake data. Loss Functions: The generator and discriminator are optimized using loss functions. The generator's loss is related to how well it fools the discriminator, and the discriminator's loss is related to how well it distinguishes between real and generated data. Iterations: The training process involves iteratively improving both the generator and the discriminator. As the generator gets better at creating realistic data, the discriminator adjusts to become more discerning. This adversarial feedback loop continues until a balance is reached. This Photo by Unknown Author is licensed under CC BY-NC-ND

7. Convergence: Ideally, the generator becomes skilled at generating data that is nearly indistinguishable from real data, and the discriminator is no longer able to reliably tell the difference. Generated Output: Once trained, the generator can be used to generate new data that wasn't in the original training set. This generated data is often used for various applications, such as image synthesis, text generation, or other tasks depending on the nature of the input data. This Photo by Unknown Author is licensed under CC BY

8. Examples of generative ai Image Synthesis Text Generation Art and Creativity Music Composition Video Synthesis Drug Discovery Game Design This Photo by Unknown Author is licensed under CC BY-NC This Photo by Unknown Author is licensed under CC BY-NC

9. Thank You Name : Anurag Roll no : 2001511 Vocational it cca