The dependence on external servers for AI applications can pose risks, including data security and reliance on a stable internet connection. While some alternatives exist, most of them still require an internet connection for functioning. This leaves users searching for a solution that combines the power of AI with the comfort of offline usage. Currently,…
The field of research focuses on integrating machine learning (ML) in healthcare for personalized treatment. This innovative approach aims to revolutionize how we understand and apply medical treatments, shifting from one-size-fits-all solutions derived from traditional clinical trials to more nuanced, individualized care. The essence of this research lies in predicting treatment outcomes tailored to individual…
In user-centric applications like personal assistance and customer support, language models are increasingly being deployed as dialogue agents in the rapidly advancing domain of artificial intelligence. These agents are tasked with understanding and responding to various user queries and tasks, a capability that hinges on their ability to adapt to new scenarios quickly. However, customizing…
While generating realistic tabular data, one of the difficulties faced by the researchers is maintaining privacy, especially in sensitive domains like finance and healthcare. As the amount of data and the importance of data analysis is increasing in all fields and privacy concerns are leading to hesitancy in deploying AI models, the importance of maintaining…
Artificial intelligence (AI) systems are expanding and advancing at a significant pace. The two main categories into which AI systems have been divided are Predictive AI and Generative AI. The well-known Large Language Models (LLMs), which have recently gathered massive attention, are the best examples of generative AI. While Generative AI creates original content, Predictive…
With the growth of large language models, natural language processing has been revolutionized. Many LLMs, like GPT-3.5, LLaMA, and Mixtral, came up last year, which helped tackle diverse language tasks. Even though there are many such LLMs now, open-source models have no reliable models for translation tasks. Thorough research has been done to tackle this…
Developing large language models (LLMs) is a significant advancement in artificial intelligence and machine learning. Due to their vast size and complexity, these models have shown remarkable capabilities in understanding and generating human language. However, their extensive parameter count poses challenges regarding computational and memory resources, especially during the training phase. This has led to…
In the realm of conversational AI, the trend toward larger models, exemplified by behemoths like ChatGPT, Bard, and Gemini, has been palpable. The understanding is that increasing model parameters and training data significantly enhances language models’ quality and capabilities. However, the computational demands of these colossal models raise concerns about efficiency. When intelligently combined, can…
The field of natural language processing has been transformed by the advent of Large Language Models (LLMs), which provide a wide range of capabilities, from simple text generation to sophisticated problem-solving and conversational AI. Thanks to their sophisticated architectures and immense computational requirements, these models have become indispensable in cloud-based AI applications. However, deploying these…
In large language models (LLMs), the challenge of keeping information up-to-date is significant. As knowledge evolves, these models must adapt to include the latest information. However, updating LLMs traditionally involves retraining, which is resource-intensive. An alternative approach, model editing, offers a way to update the knowledge within these models more efficiently. This approach has garnered…
