If you work for a business or organisation, you’ll be encountering AI on a regular basis. This glossary is aimed at busy professionals who want to understand what the AI terminology means.

Agentic AI: Autonomous, independent AI systems that can make decisions and take actions. They can learn and improve. Unlike task-specific AI, which follows predefined instructions, agentic AI plans, adapts and responds to their environment to achieve their goals.

AGI (Artificial General Intelligence): AI capable of understanding, learning and applying knowledge in a similar way to human intelligence. There are predictions that we are ‘nearly there’, whilst some say that it may not be achieveable.

AI avatar: An AI avatar is as a digital portrayal of a human within a virtual environment. Within a marketing context, they can be used for customer service, product or service demonstrations, personalised shopping and sales presentations.

AI Council: A group of people within an organisation, most likely from different departments / functions, who guide the development of AI. The Council ensures that AI is aligned with the organisation’s business goals and standards.

AI expertise: The development of in-house AI knowledge. This can be achieved through training programmes and hiring AI professionals.

AI Leaders: According to IBM, “Leaders look for the intersection of opportunity, need and internal capabilities to develop an action-oriented roadmap; foster organisation-wide alignment through clear and authentic communication; and understand that a strong data foundation delivers the flexibility to customise AI.”

AI ROI: The value produced from AI investment, in relation to the costs incurred.

AI strategy: An organisation’s AI strategic plan may include objectives, KPIs, use cases, resources, initial AI tools, change management, internal communication, training, compliance, budget, milestones and metrics.

AI strategic plan: Elements can include:
* Setting the vision
* Identify AI use cases
* Evaluating the landscape
* Building a data foundation
* Developing infrastructure
* Fostering talent and skills
* Addressing ethics and compliance
* AI implementation roadmap
* Allocating resources
* AI monitoring / research
* Managing organisational change
* Building partnerships
* Mitigating risks
* Measuring success

Algorithm: A set of rules used by a computer to accomplish a task. An algorithm accepts input, such as a dataset and produces an output, such as identifying patterns within the data. Algorithms are found in chatbots and social media websites.

API (Application Programming Interface): A set of protocols and rules that allows different software applications to communicate.

Artificial Intelligence (AI): The study and advancement of computer systems capable of executing tasks that usually necessitate human intelligence, such as visual comprehension, speech recognition, decision-making and language translation.

ASI (Artificial Super Intelligence): AI that goes beyond human intelligence, with the ability to improve itself rapidly and outperform people in most cognitive tasks. ASI remains theoretical. See IBM’s article: What is artificial superintelligence?

Big Data: Datasets of such substantial size or intricacy that conventional data processing applications are ill-equipped to handle them effectively.

Chatbot: A chatbot is a software application created to engage with people via text or voice instructions, emulating human-to-machine conversation.

ChatGPT: (Chat Generative Pre-trained Transformer): A website browser based, free to use, publicly accessible chatbot. You type in a question, it types out an answer.

Computer vision: Machines that can interpret what they ‘see’ and make decisions, based on this information. Examples include quality control by inspecting products on assembly lines, and image recognition systems in self-driving cars.

Context window: This looks like a search bar within ChatGPT and other AI systems. It is where you write your question or enter your command.

Deep learning: (which is used by ChatGPT) is a subset of machine learning. It uses structures called neural networks to find patterns within data and generate outputs. Deep learning neural networks draw inspiration from the organisation of animal and human brains, featuring multiple layers of elementary computational units referred to as neurons. They are particularly well-suited for intricate learning tasks, such as image feature extraction and speech analysis.

Emergent capabilities: These are unanticipated features or abilities that an AI system develops during its design or use. They may involve creating new strategies, interpreting novel data types, or solving problems in unexpected ways. These capabilities result from the system’s interaction with its data and environment.

Generative AI: See Large Language Model.

GPT: A GPT in ChatGPT is a Generative Pre-trained Transformer, which is a type of artificial intelligence model designed to generate human-like text by predicting the next word in a sequence based on the context of the preceding words.

Large Language Model: An LLM understands and generates natural (human) language. It is an Artificial Intelligence (AI) algorithm. Deep learning techniques and large amounts of data are used to generate new content. Examples include ChatGPT from OpenAI and Claude from Anthropic.

Long-horizon reasoning: The ability of an LLM to comprehend and respond to complex, multi-step tasks or scenarios which require a sequence of steps over a longer time period.

Machine Learning: Helping machines to discover their own algorithms to solve problems, without needing to be told what to do by human-developed algorithms.

Natural Language Processing (NLP): Giving a computer the ability to understand human language, both in text and spoken form.

Neural network: A system that comprises a collection of nodes that (more or less) mimic the processing abilities of the human brain.

OpenAI: OpenAI is the company behind ChatGPT has received significant investment from Microsoft.

Prompts: This refers to the instruction or command that you enter into the context window. A great deal has been written about prompt engineering and how to write effective prompts. In broad terms, if you are specific in your prompt, you will get better results.

Tokens: Large Language Model tokens are the textual units used by models such as ChatGPT to comprehend and analyse language. Tokens can be as short as a single character or extend to the length of an individual English word. The total count of tokens within an input text impacts several aspects, including cost and processing duration. Most LLMs impose a maximum threshold on the quantity of tokens they can accommodate in a single interaction.

Unstructured data: Does not conform to a data model and lacks a formal structure. Unstructured data are often more like ‘normal’ business information, for example CRM notes.

Use case: A usage scenario, or in other words, how AI / ChatGPT can help you. The term is often applied to software (“it can do this and it can also do that”).