Generative Artificial Intelligence: Fundamentals

Abstract

Generative language models have witnessed substantial traction, notably with the introduction of refined models aimed at more coherent user-AI interactions—principally conversational models. The epitome of this public attention has arguably been the refinement of the GPT-3 model into ChatGPT and its subsequent integration with auxiliary capabilities such as search features in Microsoft Bing. Despite voluminous prior research devoted to its developmental trajectory, the model’s performance, and applicability to a myriad of quotidian tasks remained nebulous and task specific. In terms of technological implementation, the advent of models such as LLMv2 and ChatGPT-4 has elevated the discourse beyond mere textual coherence to nuanced contextual understanding and real-world task completion. Concurrently, emerging architectures that focus on interpreting latent spaces have offered more granular control over text generation, thereby amplifying the model’s applicability across various verticals. Within the purview of cyber defense, especially in the Swiss operational ecosystem, these models pose both unprecedented opportunities and challenges. Their capabilities in data analytics, intrusion detection, and even misinformation combatting is laudable; yet the ethical and security implications concerning data privacy, surveillance, and potential misuse warrant judicious scrutiny.

The Influence of Large Language Models on Conversational Marketing and Communication Strategies

“…Delving into the area of computational linguistics and artificial intelligence uncovers the captivating power of Large Language Models (LLMs). These behemoths of linguistic prowess, represented by OpenAI's GPT-3 possessing an astonishing 175 billion parameters, and Google's LaMDA, exemplify the quantum leap in technical advancement (Corchado et al, 2023). They stand as towering monuments, changing our understanding of language generation and comprehension.…”