Fabrity blog

By integrating large language models with proprietary data sources and systems, AI virtual assistants can unlock powerful new capabilities across industries. These advances range from streamlining customer service and employee support to personalizing product recommendations and facilitating seamless e-commerce experiences. In this article, we will explore ten potential use cases for AI-powered virtual assistants, demonstrating their transformative impact on business operations and customer interactions.

10 business use cases for Generative AI

As developing large language models (LLMs) incurs significant costs, small language models (SLMs) present a cost-effective alternative. SLMs, with fewer parameters and simpler architectures, offer efficient performance for specific tasks, making them suitable for businesses prioritizing cost, speed, and data privacy. In this article we introduce SLMs, explore their advantages over LLMs, and discuss their practical applications in various business scenarios.

Read more on Fabrity: https://fabrity.com/blog/small-language-models-slms-a-gentle-introduction/

Small Language Models - a gentle introduction

Much has been written about applying AI to manufacturing processes, yet practical examples often remain vague or elusive. To address this, we have delved deeper to gather specific implementations of AI in the manufacturing industry. In this article, you will discover how AI can transform even the most complex industrial processes, enabling companies to embrace smart manufacturing.

In particular, we want to share four industry-specific use cases where artificial intelligence has proved its transformative power. They include:

- AI-enhanced oil analysis in the aviation industry,
- self-programming robots for tedious, repetitive tasks,
- optimization of a copper smelting process,
- reduction of costly downtime in a power plant.

AI in manufacturing - 4 nonobvious implementations

Explore the power of Large Language Models like GPT-4, which transform text generation and comprehension through advanced transformer architecture. Learn how pretraining and fine-tuning processes make these models essential for tasks in customer service, content creation, and more. Discover the importance of ethical considerations and the exciting future of multimodal capabilities in AI. Dive into the full article on Fabrity’s blog for a comprehensive look.

LLMs - a simple introduction

In this article, we explore how Azure IoT Hub drives innovation across various sectors by enabling real-time connectivity and management of IoT devices. In manufacturing, it helps with real-time monitoring and predictive maintenance, while in healthcare, it supports remote patient monitoring and smart medical devices. The energy sector benefits from optimized energy consumption and smart grid management, and retail uses IoT for enhanced customer experiences. Azure IoT Hub also enables connected vehicles in the automotive industry, precision farming in agriculture, and smart city infrastructure management for sustainability, showing its broad impact on operational efficiency across industries.

7 practical implementations of Azure IoT Hub

In this article, we provide insights into choosing the most suitable large language model (LLM) for your needs in the evolving AI landscape. It emphasizes evaluating key factors such as model size, performance, and cost efficiency. Understanding the balance between general-purpose models and specialized fine-tuned ones is crucial, as well as considering open-source versus proprietary options. The article highlights the importance of aligning LLM selection with specific business goals, use cases, and technical requirements, helping organizations navigate the complexity of LLM choices to drive better AI outcomes in 2024.

Finding the best LLM - a guide for 2024

Explore responsible and ethical AI implementation in business with practical strategies for integrating generative AI effectively.

Fabrity blog

Fabrity Talks