Exploring the Foundations and Applications of Generative Artificial Intelligence.pptx

Exploring the Foundations and Applications of Generative Artificial Intelligence.pptx

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  Introduction to GenerativeArtificial Intelligence
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  Basic definition Generative AI,short for Generative Artificial Intelligence, refers to a class of artificial intelligence systems that have the capability to generate new, original content or data that is similar to, but not an exact copy of, existing information. These systems use various techniques, such as machine learning algorithms and neural networks, to learn patterns and relationships from a given dataset and then generate new content based on that learning.
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  Generative AI Landscape Thegraph given below compares ChatGPT with other influential applications in terms of the time taken to reach the milestone of 1 million users.
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  Evolution The evolution ofgenerative AI has been marked by advancements in machine learning techniques, model architectures, and applications. Here's a brief overview of the key milestones and developments in the field: Early Generative Models: Early generative models, such as Restricted Boltzmann Machines (RBMs), provided a foundation for understanding probabilistic generative processes. Markov Chain Monte Carlo methods were used for sampling from complex probability distributions. Variational Autoencoders (VAEs): VAEs introduced a probabilistic approach to generative modeling, combining ideas from autoencoders and variational inference. VAEs are effective in learning latent representations and generating new data samples.
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  Generative Adversarial Networks(GANs): GANs use a game-theoretic approach involving a generator and a discriminator, leading to impressive results in image generation and other domains. Conditional GANs and Image-to-Image Translation: Conditional GANs extended the GAN framework to generate samples conditioned on specific inputs, enabling tasks like image-to-image translation. Pix2Pix, CycleGAN, and similar models demonstrated the ability to transform images across domains (e.g., turning satellite images into maps).
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  Natural Language Processing(NLP): Transformer-based models, such as OpenAI's GPT (Generative Pre-trained Transformer) series, marked a significant breakthrough in NLP. GPT-3, for example, achieved state-of-the-art performance in a wide range of language tasks. Transfer Learning and Pre-training: Transfer learning became a prevalent paradigm in AI, with models pre-trained on large datasets and fine-tuned for specific tasks. This approach proved successful in various domains, including computer vision and natural language processing. Advanced Architectures and Techniques: Architectures like BERT (Bidirectional Encoder Representations from Transformers) improved language understanding by capturing bidirectional context. Progressive GANs and StyleGAN introduced techniques for high-quality image synthesis, including the generation of realistic faces.
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  Technology The technology ofgenerative AI involves a variety of techniques and models designed to enable machines to generate new data that is similar to existing examples. ● Generative Models: Generative Adversarial Networks (GANs): GANs consist of a generator and a discriminator. The generator creates data, and the discriminator evaluates the authenticity of the generated data. Variational Autoencoders (VAEs): VAEs use an encoder and a decoder to map input data to a latent space and vice versa. ● Neural Networks Deep neural networks serve as the foundation for many generative models. These networks can include convolutional neural networks (CNNs) for image data, recurrent neural networks (RNNs) for sequential data, and transformer architectures for various applications. ● Transformer Architecture The transformer architecture, introduced for natural language processing tasks, has been widely adopted in generative AI. Its attention mechanism allows models to capture complex dependencies in data, making it effective for tasks beyond language processing.
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  ● Reinforcement Learning Somegenerative models incorporate reinforcement learning to improve the quality of generated content. Reinforcement learning is particularly useful when the model needs to interact with an environment and receive feedback to refine its generative capabilities. ● Pre-training and Transfer Learning Transfer learning involves pre-training models on large datasets before fine- tuning them for specific tasks. Pre-trained models, such as OpenAI's GPT series, have demonstrated the effectiveness of leveraging vast amounts of data to achieve strong generalization.
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  Types of GenerativeAImodels ● Generative Adversarial Models ● Transformer based Models ● Diffusion Models ● Variational Autoencoders ● Unimodal Models ● Multimodal Models ● Large Language Models
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  ● learn the(hard or soft) boundary between classes ● providing classification splits (probabilistic or non-probabilistic manner) ● allow you to classify points, without providing a model of how the points are actually generated ● don't have generative properties ● make few assumptions of the model structure ● less tied to a particular structure ● better performance with lots of example data; higher accuracy, which mostly leads to better learning result ● discriminative models can yield superior performance (in part because they have fewer variables to compute) Discriminative ● saves calculation resource ● can outperform generative if assumptions are not satisfied (real world is messy and assumptions are rarely perfectly satisfied) ● not designed to use unlabeled data; are inherently supervised and cannot easily support unsupervised learning ● do not generally function for outlier detection ● do not offer such clear representations of relations between features and classes in the dataset ● yields representations of boundaries (more than generative) ● do not allow one to generate samples from the joint distribution of observed and target variables ● generates lower asymptotic errors
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  Generative AI ● requiresless training samples ● model the distribution of individual classes ● provides a model of how the data is actually generated ● learn the underlying structure of the data ● have discriminative properties ● make some kind of structure assumptions on your model ● decision boundary: where one model becomes more likely ● often outperform discriminative models on smaller datasets because their generative assumptions place some structure on your model that prevent overfitting ● natural use of unlabeled data ● takes all data into consideration, which could result in slower processing as a disadvantage ● generally function for outlier detection ● typically specified as probabilistic graphical models, which offer rich representations of the independence relations in the dataset ● more straightforward to detect distribution changes and update a generative model ● takes the joint probability and predicts the most possible known label ● typically more flexible in expressing dependencies in complex learning tasks ● a flexible framework that could easily cooperate with other needs of the application ● results in higher asymptotic errors faster ● training method usually requires multiple numerical optimization techniques ● will need the combination of multiple subtasks for a solving complex real-world problem