SLM vs. LLM: Striking the Balance Between Efficiency and Performance with RAG (10/29)
SLM with RAG
SLM with RAG
Data Market 0. Introduction In todayโs world, weโve become accustomed to the ease of buying and selling products online through platforms like Amazon, eBay, and others. These platforms have created a seamless experience where consumers can purchase goods with just a few clicks. But hereโs a critical question: if data is as valuable as we […]
Introduction Medical data is often locked behind strict privacy regulations, preventing it from being used to its full potential in research and healthcare innovation. However, thereโs a way to generate data that is remarkably similar to your original dataset while keeping sensitive information private. This is made possible through advanced techniques that do not compromise […]
As generative AI breaks new ground in various fields, its potential to revolutionize biology through the creation of synthetic DNA datasets is particularly exciting. What once seemed like science fictionโusing AI to generate DNA sequencesโis now a powerful reality, opening new avenues in research, medicine, and biotechnology. This innovation in DNA data synthesis brings significant […]
Introduction: The Need for Visualizing Relationships Between Data In modern industries, vast amounts of data are generated and utilized every day. This data is collected from various sources, and to use it effectively, it is crucial to understand the relationships between the data points. Graph structures have become an essential tool in clearly representing and […]
synthetic data
AI Data Introduction For AI models to learn effectively, a large amount of data is required. Without proper access to data, the speed and quality of AI development can be significantly hindered. Acquiring the right data for AI has become an essential task. However, industries like finance, healthcare, and the military face challenges when it […]
Data diversity isn’t just a checkbox in modern machine learning. It’s the foundation for building models that generalize well, remain unbiased, and perform reliably in real-world scenarios. Without diverse and representative datasets, even the most advanced algorithms will struggle with bias, underrepresentation, and generalization failures. But how do we measure “diversity” in a way that […]
The quality and quantity of AI datasets are critical to training accurate and effective models. However, gathering real-world data can be expensive, time-consuming, or even impossible in some cases. This is where the phrase โfake it until you make itโ can be applied to AI. By leveraging synthetic data, AI researchers can “fake” their way […]
Recently, the financial industry has been actively adopting AI. There is a strong movement towards building innovative services and AI-based decision-making systems by utilizing customer data. However, privacy regulations and security concerns limit the use of actual data. As a solution to this issue, the demand for synthetic data has been rapidly growing. Synthetic data […]