There is a flood of articles on AI these days, especially with the rise of ChatGPT. To make sense of it, let’s review the key terms associated with AI, particularly as they relate to business development, digital strategy, and digital transformation.
In this article, we’ve compiled the most important AI terms to help you grasp the foundational elements necessary for AI implementation. We’ll continue to expand this list as new AI concepts gain traction in business and technology discussions.
Application Programming Interface (API):
Software programs communicate with each other using a set of rules and definitions. APIs enable developers to build complex systems by integrating functionalities from various sources.
Artificial Intelligence (AI):
A branch of computer science focused on creating systems that perform tasks requiring human intelligence, such as learning, problem-solving, language understanding, perception, and decision-making.
Neural Networks:
AI models inspired by the human brain are designed to process data and identify patterns, making them ideal for analyzing large datasets.
Machine Learning (ML):
A subset of AI where algorithms learn and improve through data exposure rather than explicit programming.
Deep Learning:
A branch of machine learning using multi-layered neural networks to analyze and draw insights from large, unstructured datasets like images or audio.
Foundation Models (FM):
Large-scale deep learning models trained on vast amounts of unstructured data can perform a wide range of tasks, such as GPT-4 or DALL·E 2.
Transformers:
Key components in foundation models are that these neural networks use “attention heads” to understand context in sequential data like text.
Generative AI:
AI is built on foundation models and is capable of creating text, images, or music while also supporting non-generative tasks like classification.
Graphics Processing Unit (GPU):
Initially developed for rendering graphics, GPUs are now essential for deep learning applications due to their processing power.
Large Language Models (LLMs):
A class of foundation models trained on extensive text datasets, enabling tasks like summarization, text generation, and knowledge extraction.
Pre-training:
This is the initial phase of training AI models on a broad dataset to establish a baseline understanding before fine-tuning for specific tasks.
Fine-tuning:
Adapting a pre-trained model to improve performance on a specific task by training it on a smaller, specialized dataset.
Chatbots:
AI systems designed to simulate human conversations. Advanced versions, like ChatGPT, use conversational AI for more intelligent, two-way interactions.
MLOps:
A practice focused on maintaining, integrating, and automating machine learning models throughout their lifecycle—from data management to deployment.
Prompt Engineering:
Designing and refining input prompts for generative AI models to achieve accurate and desired outputs.
Bias:
Systematic errors in AI models caused by training on flawed or incomplete data.
Structured Data:
Organized, easily searchable data (e.g., in databases or spreadsheets) used to train machine learning models effectively.
Unstructured Data:
Unformatted data (e.g., text, images, audio) requiring advanced techniques for processing and analysis.
Data Mining:
Techniques used to identify patterns, correlations, or anomalies within structured or unstructured data.
If you want to stay updated on digital strategy and AI adoption, bookmark our insights page or follow IZARA on LinkedIn for new perspectives.
© Lars Kirstein | lk@izara.com
