(Mar 24): DevOps Large Language Models - What are Large Language Models and Why the New Buzz?

Large language models (LLMs) are advanced AI systems designed to understand the human language intricacies which allows it; when queried, to generate creative, intelligent responses.

What Makes an LLM

Enormous data sets in petabytes train a successful LLM. Sourced from public domain; books, articles, websites, and other text-based sources. Deep learning techniques are used to help these models understand and generate output similar to human content. LLMs are used in a variety of applications such as Internet search, content creation, language translation, customer service chatbots, financial analysis sites, scientific research repositories, etc.

What is Transforming the AI landscape

Google introduced 'transformer architecture' in 2017, a deep learning model that replaced traditional recurrent and convolutional structures with a new type of architecture that's particularly effective at understanding and contextualizing language, as well as generating text, images, audio, and computer code. A great example is OpenAI's ChatGPT (chatbot generative pretrained transformer).

Accelerating AI functions

Accelerated hardware systems called graphics processing units (GPUs) is a key factor in AI evolution. GPUs, initially developed for graphics, are specialized processors adept at ML tasks due to their unique architecture.

LLM Usage

LLMs for content generation

Gen AI can streamline content creation by generating various types of media, including text, sound, and images.

One popular use of this technology in the enterprise is to develop chatbots that engage in conversational interactions with business users, helping them obtain accurate answers to their questions. LLMs could analyze documents, summarizing unstructured text, and converting unstructured text into structured table formats.

LLMs as logical reasoning engines

LLM are valuable for sentiment analysis and other complex reasoning tasks that involve extracting meaningful insights from text and providing a deeper understanding of human language due to natural language understanding (NLU). NLU focuses on understanding meaning in human communication and extends to textual data; social media posts, reviews, surveys, etc.

LLMs as translation engines

LLMs have transformed text translation between languages, making it easier for people to communicate across linguistic barriers.

LLMs for text retrieval, summarization, and search

LLMs are pretrained on vast amounts of text data, allowing them to grasp the nuances of language and comprehend the meaning of text. They can search through large databases or the Internet in general to locate relevant information based on user-defined queries.