Abstract:
Generative AI has demonstrated remarkable capabilities in various domains, generative AI. Language models have gained significant attention for their ability to generate human-like text and exhibit promising capabilities in various applications. By understanding the key steps involved in constructing language models, including data collection, preprocessing, model architecture, and training techniques, we can harness the power of generative AI to create advanced language models but it is not without limitations. This report delves into the potential of generative AI to enhance its capabilities and overcome existing limitations. By investigating techniques such as architecture advancements, fine-tuning strategies, data augmentation, ethical considerations, and multimodal learning, we aim to uncover ways to push the boundaries of generative AI and maximize its potential [1]. Generative AI refers to the field of artificial intelligence that focuses on creating models capable of generating new content, such as images, text, or even music. These models have demonstrated significant potential in various domains, including creative applications, data synthesis, and personalized content generation. Generative AI has the ability to mimic human creativity and produce outputs that can be indistinguishable from those created by humans. The Despite the impressive capabilities of generative AI, there are several limitations that need to be addressed. Some of the key limitations include: Fidelity and Realism: Generative models often struggle to produce outputs that are consistently realistic and faithful to the input data distribution. Generated content may contain artifacts, lack fine details, or fail to capture the complexity of the real world. Mode Collapse: Mode collapse occurs when a generative model fails to capture the entire diversity of the training data distribution and instead generates a limited set of outputs. This can result in repetitive or overly similar generated content. Lack of Control: Controlling the output of generative models remains a challenge. While it is possible to condition models on specific inputs or guide the generation process with additional information, achieving precise control over the generated content is still an active area of research. Interpretability and Explainability: Understanding how generative models arrive at their outputs can be challenging. The inner workings of these models often lack transparency, making it difficult to explain why a particular output was generated.[2] Enhancing the capabilities of generative AI is crucial for unlocking its full potential and expanding its applications. By addressing the limitations, we can:Improve Realism and Fidelity:Advancements in generative AI can lead to models that generate more realistic and high-fidelity content. This opens up opportunities for applications such as computer-generated art, virtual reality, and content creation. Enhance Control and Customization: Empowering users with greater control over generative models allows for tailored content generation, personalization, and interactive experiences. This can be beneficial in fields like fashion, interior design, and personalized marketing. Foster Innovation and Creativity: Enhancing generative AI capabilities encourages innovation by providing tools for creative professionals, artists, and designers to explore new possibilities and push the boundaries of their craft. Practical Applications: Advanced generative models can find applications in areas such as data augmentation, simulation, and synthetic data generation, enabling more efficient training of other machine learning models and addressing data scarcity challenges.[3] To overcome the limitations of generative AI, architectural advancements play a crucial role. Some areas of exploration include Generative Adversarial Networks (GANs): Improvements in GAN architectures, training stability, and loss functions can lead to better generation quality, mode coverage, and faster convergence. Variational Autoencoders (VAEs): Advancements in VAE architectures and latent space modeling can enhance control and disentanglement of generative models, allowing for more precise content manipulation. Flow-based Models: Flow-based models offer flexible and invertible transformations that enable efficient sampling and training, resulting in high-quality generated content. Hybrid Architectures.
Page(s):
420-420
DOI:
DOI not available
Published:
Journal: Abstract Book on International Conference on Food and Applied Sciences (ICFAS-23) 3-5 August 23, Volume: 0, Issue: 0, Year: 2023