In a recent LinkedIn post, Andrew Bolis offers a clear and concise guide to understanding the often-complex world of Artificial Intelligence (AI) terminology. Aimed at demystifying the subject for a broad audience, Bolis breaks down essential buzzwords into simple, digestible definitions.
Bolis highlights the potential confusion surrounding AI jargon, stating:
“All the AI buzzwords can be confusing.”
He then proceeds to provide straightforward explanations for terms ranging from the fundamental concept of Artificial Intelligence itself to more nuanced aspects like Deep Learning and Natural Language Processing. Bolis’s approach prioritizes clarity, aiming to make AI accessible to those who may not have a technical background.
Understanding the Core Concepts of AI
Andrew Bolis begins by defining Artificial Intelligence as a foundational concept. According to Bolis:
“Artificial Intelligence – A computer brain that helps and learns.”
This simple definition sets the stage for more specialized terms. Machine Learning, a subset of AI, is described by Bolis as the process where AI learns through examples. He further elaborates on Deep Learning, explaining it as a layered, step-by-step learning process.
Exploring Advanced AI Mechanisms
Bolis delves into the structural and functional components of AI systems. He defines Neural Networks as interconnected AI parts working collaboratively to achieve tasks. The setup guiding AI’s learning process is termed ‘Model Architecture’ by Bolis, while the information AI utilizes to improve is identified as ‘Training Data’.
“Training Data – Info AI uses to get smarter.”
The post also touches upon the stages of AI development. Bolis explains ‘Pretraining’ as an initial reading phase to prepare the AI, followed by ‘Fine-Tuning’ for additional, specialized training. Reinforcement Learning is characterized as a method where AI learns from feedback, specifically rewards or mistakes.
Addressing Common AI Challenges and Applications
Andrew Bolis also addresses potential pitfalls in AI development and application. He defines ‘Overfitting’ as a scenario where an AI memorizes too much data, leading to inaccuracies. This is a critical concept for understanding AI performance limitations.
Furthermore, Bolis explains ‘Natural Language Processing’ (NLP), a key area enabling AI to interact with humans. He defines it as the capability for AI to:
“AI reads and talks like us.”
He also clarifies the concept of a ‘Context Window,’ defining it as the AI’s short-term memory for recent conversational elements. Bolis concludes his post by emphasizing that AI doesn’t need to be overly complicated and encourages readers to share the information.
📝 About This Content
This article is based on insights shared by Andrew Bolis on LinkedIn.
📅 Originally posted on October 1, 2026 | View original post on LinkedIn →