Artificial intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think and act like humans. These intelligent machines can be trained to perform various tasks by processing large amounts of data and recognizing patterns in the data. AI can be classified into two main categories: narrow or general. Narrow AI is designed to perform a specific task, while general AI is designed to perform any intellectual task that a human can. Some examples of AI include voice assistants such as Siri and Alexa, self-driving cars, and facial recognition software.
Clients frequently approach us with AI projects. This typically leads to discussions about the idiosyncrasies of Large Language Models (LLM), prompt engineering, parameter-efficient fine-tuning, and reinforcement learning from human feedback. Usually, the conversation leads to Retrieval-Augmented Generation.
In recent years, large language models (LLMs) have seen significant innovations and improvements in how they function and produce content. However, with this technology, there still could be some tweaks in how models can produce more authoritative responses with cited sources to boot. Retrieval-Augmented Generation was introduced to help the accuracy and access models have to external sources.
This blog post delves into the advanced capabilities of generative models, showcasing how they optimize maintenance strategies in the dynamic landscape of Internet of Things (IoT) solutions. From enhancing anomaly detection to reducing unplanned downtime, discover how generative models revolutionize predictive maintenance, offering a glimpse into the future of asset management. Join us on this journey of innovation and efficiency as we unravel the potential of generative models in shaping the future of IoT-driven maintenance strategies.
Chatbots are an important staple of maintaining a company website. It helps direct any customer questions more directly to the employees at both parties’ convenience. However, their design could be more flexible. By including generative AI in a chatbot’s design, we could see the rise of a more dynamic relationship between customers and the automated front of a company.
As the Internet of Things (IoT) continues to evolve, the integration of Generative Artificial Intelligence (AI) with edge computing emerges as a powerful synergy. This combination holds immense potential for enabling intelligent decision-making at the edge, transforming how IoT devices operate and interact. Because of the distances imposed by the allocation of IoT devices, this does introduce new challenges as to how devices are run, maintained, and protected.
The landscape of online advertising has undergone a profound transformation in recent years, thanks to the integration of Artificial Intelligence. One of the key factors driving this change is the emphasis on personalization. This technology will only continue to serve a crucial role in personalization strategies for elevating digital marketing efforts.
DreamBooth is a deep learning generation model used to fine-tune existing text-to-image models, developed by researchers from Google Research and Boston University in 2022. Originally developed using Google's own Imagen text-to-image model, DreamBooth implementations can be applied to other text-to-image models, where it can allow the model to generate more fine-tuned and personalised outputs after training on three to five images of a subject.
Using Stable Diffusion to create personalized AI art is possible; However, it takes a little bit of work. Stable Diffusion is an open-source text-to-image AI model that can generate amazing images from given texts in seconds. This first article will help you get the default Stable Diffusion installed on your local machine and check that it can run.