Market Insight: Ten Applications Of Large Language Models For Industry
Executive Summary
This report highlights the potential of large language models (LLMs), first brought to the mainstream by OpenAI’s ChatGPT. It details their inner workings and outlines how they can be integrated as agents within current technologies, data structures and industrial procedures, to create substantial market impact. We explore the Transformer architecture, its significance, and its limitations. Drawing upon meticulous analysis of recent academic research, commercial product launches, and contributions from the open-source AI community, this report guides readers through comprehensive explanations and intuitive graphical depictions of LLMs functioning as chatbots, reasoning companions, information retrieval systems and agent-style task completion aides. Our findings illuminate a future in which data management, machine learning and LLMs intersect to fuel unparalleled efficiency and innovation in the heavy industries sector.
Table of contents
Advances in data management and machine learning techniques have become rocket fuel for AI developmentTransformer-based LLMs dominate today’s generative AI landscape
Ten applications of LLMs for industrial operations
Table of figures
Figure 1. The transformer architecture enables deep language understandingFigure 2. Encoders distil the meaning of sentences
Figure 3. Transformer decoder LLMs generate text
Figure 4. LLMs can model and predict real-world cause and effect
Figure 5. Retrieval-augmented generation (RAG) can eliminate LLM hallucination
Figure 6. LLM agents can use tools to complete tasks
Organisations mentioned
ABB, Anthropic, Apple, AspenTech, AVEVA, C3 AI, Cognite, DataStax, GitHub, Google, Hexagon, HighByte, Hitachi Vantara, International Electrotechnical Commission (IEC), Meta, Microsoft, NVIDIA, OpenAI, Rockwell Automation, Salesforce Research, Seoul National University, Siemens, SparkCognition, Stability AI, Technology Innovation Institute, ThirdAI, Timeseer.aiAbout the authors
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