Publishers, booksellers, and anyone interested in making books discoverable in the era of AI search: we’re halfway through a two-part Tech Forum webinar mini-series that will interest you.
On September 3, we had the pleasure of hosting metadata expert Tricia McCraney, Head of Account Management for North America at Virtusales Biblio, for her talk: Get discovered: How to use your metadata to conquer AI search. If you missed her session, don’t worry, you can now watch the recording at your leisure.
Next, joining us on September 17 is Ariel Hudnall, Managing Director of Serif, who will lead Get discovered: Adapt your marketing for the AI search era, a presentation that will go over how AI is changing the way books are discovered and what tactical — and technical — changes you can make to your promotions to ensure your books aren’t left out of AEO, GEO, SEO, and retail search systems.
If you’re planning to attend Ariel’s session, we recommend familiarizing yourself with the terms below.
A mini AI glossary
Artificial Intelligence: A branch of computer science focused on creating intelligent machines that can learn, adapt, and perform tasks typically requiring human intelligence. Applications include machine learning, natural language processing, and robotics.
Source: Glossary of AI-related terms (University of Saskatchewan).
AI model types by technique
Deep Learning is a subset of machine learning that uses large multi-layer neural networks to automatically learn complex patterns from data. Instead of a person manually programming features to look for, these models discover increasingly abstract representations on their own. Deep learning powers many current applications like self-driving cars, speech recognition, and image recognition. Its hierarchy is similar to that of neurons in the brain.
Source: Artificial Intelligence Glossary (Stanford University).
Machine Learning (ML): A subset of artificial intelligence where computer systems can learn from data, recognize patterns, and make predictions or decisions without being explicitly programmed. Machine learning algorithms improve as they process more data, enabling applications like image recognition, speech translation, and recommendation systems.
Source: Glossary of AI-related terms (University of Saskatchewan).
Large Language Model (LLM): A model of artificial intelligence that uses deep learning algorithms to process and generate human-like language. These models are characterized by their size (number of parameters) and their capacity to handle complex language tasks. They are trained on large datasets of text and can perform a wide range of language tasks such as translation, summarization, and text generation. Common examples of Large Language Models include the GPT series (GPT-4, GPT-3, etc.) developed by OpenAI; BERT, developed by Google; Claude, developed by Anthropic; and LLaMA, developed by Stanford University.
Source: Glossary of AI-related terms (University of Saskatchewan).
Related – GPT: GPT stands for Generative Pre-trained Transformer. It refers to a type of large language model that can process and generate text and other content.
Source: AI Glossary (George Brown Polytechnic)
AI model types by purpose
Analytic AI Models: Also known as “descriptive” or “diagnostic” artificial intelligence (AI) models, analytic AI models focus on analyzing data to gain insights into past and current events or situations. Analytical AI models help users understand trends, patterns, and relationships within datasets to make informed decisions and optimize processes. Once these models uncover insights—such as which products are selling the most or which medical treatments are most effective—they can inform users’ decisions.
Source: Glossary of AI-related terms (University of Saskatchewan).
Generative Artificial Intelligence (GenAI): Refers to AI systems that can create new content like text, images, music, code, or video. These systems learn patterns from training data and generate novel outputs that resemble the original data, often powered by architectures like GANs, transformers, diffusion models, and variational autoencoders. These models power applications including chatbots, code generation, and creative tools. They also raise questions about the potential for misuse including creating misinformation and deepfakes.
Source: Artificial Intelligence Glossary (Stanford University).
Predictive AI Models: These artificial intelligence (AI) models are designed to analyze historical and current data to make accurate predictions. By identifying patterns and trends, predictive AI models can forecast events, behaviours, or results in various fields such as finance, marketing, and healthcare. In other words, predictive models forecast what might happen based on past events.
Source: Glossary of AI-related terms (University of Saskatchewan).
Reasoning Model: It’s a type of AI system designed to solve complex problems by generating a logical, step-by-step sequence of thought. Unlike models that provide an immediate answer, these systems explicitly break down a problem into intermediate steps, mimicking a human-like process of deduction and analysis. This capability makes them more transparent and effective at tackling tasks that require multi-step logic, such as mathematical word problems, planning, and diagnosing complex issues.
Source: Artificial Intelligence Glossary (Stanford University).
Related – Agentic AI: This term refers to AI systems designed to act as autonomous or semi-autonomous agents: they can set or interpret goals, plan and sequence actions, use tools (like web browsers, code, or APIs), make decisions based on feedback, and adapt over time to complete tasks. Unlike a purely reactive chatbot that only responds turn-by-turn, agentic AI is oriented around ongoing task execution—breaking down objectives, coordinating steps, and sometimes operating with minimal human oversight within defined constraints.
Source: Artificial Intelligence Glossary (Stanford University).
Types of AI tools
AI tools now support a wide range of tasks, including writing, summarizing, searching, coding, designing, translating, tutoring, note-taking, data analysis, image generation, video creation, and workflow automation.
Some common types of AI tools include:
- Chatbots: ChatGPT, Microsoft Copilot, Gemini, Claude, Perplexity
- Writing and editing tools: Grammarly, Microsoft Editor, Wordtune
- Image generation tools: DALL-E, Adobe Firefly, Midjourney, Canva AI
- Coding tools: GitHub Copilot, Cursor, Replit AI
- Meeting and transcription tools: Microsoft Teams AI features, Otter.ai, Fireflies.ai
- Research and discovery tools: Elicit, Perplexity, Semantic Scholar AI features
- Productivity and workflow tools: Microsoft Copilot, Notion AI, Zapier AI
Source: AI Basics (George Brown Polytechnic)
Ownership and deployment
Open Source: This refers to software where its original design, or “blueprint,” is made freely available for anyone to see and use. This public access allows a community of users and developers to study the software, fix issues, and add new features.
Source: Artificial Intelligence Glossary (Stanford University).
Closed Source, also called proprietary software, refers to software whose underlying code is restricted and not available for the public to view, modify, or use. A company or developers maintain exclusive control over how it works and is used.
Source: Artificial Intelligence Glossary (Stanford University).
Self-Hosted LLM: This refers to a large language model that runs on infrastructure you control, whether that’s a local server, an on-premise data center, or a private cloud environment. Instead of sending requests to OpenAI’s API or Anthropic’s servers, you run inference locally. The model weights live on your hardware, and your prompts never leave your network.
This is different from using a managed API, where the model runs on the provider’s infrastructure and you pay per token. It’s also different from fully managed platforms that handle deployment for you but still process data externally.
Source: Self-Hosted LLM Guide (Prem)
Search and indexing terms
Crawled: One of OpenAI’s bots visited your site and read it, typically to pull relevant info to include in an answer to a user’s query that triggered live web search. This likely contributes to OpenAI’s searchable web index (although the company has not publicly revealed details of the underlying system).
Source: How to get indexed by ChatGPT [2026] (Hubspot)
Indexed: After crawling your site, OpenAI stored what it found there. Getting indexed does not guarantee you will be surfaced, but it does make it a possibility.
Source: How to get indexed by ChatGPT [2026] (Hubspot)
Surfaced: The content that OpenAI crawled and indexed from your site gets included in a ChatGPT-generated answer. Just because content from your site is surfaced in a ChatGPT answer doesn’t mean your brand or website was also mentioned/linked to in that answer.
Source: How to get indexed by ChatGPT [2026] (Hubspot)
Tech Forum will continue to host professional development webinars on a wide range of topics. Subscribe to the Tech Forum newsletter to be notified of upcoming events.
