AI, Bots, Chatbots, Large Language Model

The Spaghetti Soup of Artificial Intelligence (AI) Definitions for Business

By 6 Minute Read

AI Definitions

Everyone knows what the AI acronym stands for, but what happens when we dig a bit deeper? Making sense of artificial intelligence definitions is a challenge that data scientists face each day but what about the rest of us?

With a spaghetti soup of terms and acronyms, the terminology landscape of Ai definitions can leave a person utterly confused. So we thought we’d attempt a mini-glossary to try and help some of the people that are looking to AI solutions for their business but don’t need to get into the weeds of the technology and just want to get the simplest artificial intelligence definition. And, by the way, this is not an extensive list but it may help you decipher some of the more commonly used acronyms and add some new vocabulary to a growing list of AI terms.

Let’s start with some of the acronyms, a list that seems to expand and confuse. There’s AI, Conversational AI, Generative AI, Large language model, NLP, NLU, DL, ML, VA, IVA, chatbots, Ai bots….and on goes the endless list.

‘A’ can stand for artificial, as in the Artificial Intelligence abbreviation, but it can also mean automation, as in Robotic Process Automation. ‘L’ represents Learning…but it can also mean Language. ‘N’ is pretty common for Natural, as in Natural Language Processing, but if you dig deeper into AI technology it can mean Neural. I won’t even get into that here. ‘I’ is easy – it represents Intelligence – right? Well, then you see mention of conversational UI where ‘I’ is for Interface. And you wonder why things get confusing? A true alphabet soup! So here’s a quick list of some of the common terms and how we define them.

Artificial Intelligence (AI)

Considered a branch of computer science, artificial intelligence (AI) is the theory and practice of computers simulating and imitating human behavior, such as speech and visual recognition, decision-making, language understanding, language translation, etc. The history of AI dates well back in time so, as a concept, it is not new and certain aspects of AI such as robotics have already become mainstream.

But what has really caused the recent surge in the adoption of AI lies in the fact that the technology has become more accessible and affordable. Open-sourcing by cloud tech giants like Amazon and Google has made essential AI knowledge and skills more widely available enabling developers to get up to speed very quickly and apply AI, without requiring in-depth knowledge of machine learning. Combine this with the advances in the processing power of computers to deal with massive volumes of data that can fuel AI projects, and the market for AI blossomed. More recently, the emergence of Generative AI and Large Language Models has added an extra exciting layer to all this AI talk.  So let’s explore some definitions.

Chatbot or AI Chatbot

A chatbot is a computer program or AI-powered system designed to simulate conversation with human users through text or speech interactions. Chatbots use natural language processing (NLP) and machine learning algorithms to understand user input, interpret intent, and generate appropriate responses. They can be deployed across various platforms, including websites, messaging apps, and voice assistants, to provide assistance, answer questions, perform tasks, or facilitate transactions. Chatbots can range from simple rule-based systems that follow predefined scripts to more advanced AI-driven models (i.e. AI Chatbots) capable of learning and adapting to user behavior over time. The terms “Chatbot” and “AI Chatbot” are often used interchangeably, but there can be a subtle distinction in how the terms are used.

Chatbots are widely used in customer service, virtual assistance, sales, marketing, and other domains to enhance user experiences, automate routine tasks, and improve operational efficiency.

Conversational AI

Conversational AI is a form of artificial intelligence that understands and simulates human conversation through the use of AI bots powered by natural language processing (NLP). It allows customers to express intent, via voice or messaging, and for AI-powered bots to execute on that intent, automate the required tasks, and fulfill the customer need.

Conversational AI represents a significant shifts towards using natural language to do things like transact, book things, search items, interact, and access services, with limited or no need for a human agent. It goes beyond just the conversation and can automate underlying processes and tasks needed to execute on the customer’s need. Natural language is a very fluid and non-procedural area but the business has defined processes. So they need to blend together and offer a  high degree of flexibility and versatility in orchestrating the right tasks at the right time.

Conversational AI applications include chatbots, virtual assistants, and customer support systems, all of which aim to provide efficient, personalized, and responsive interactions with users.

Deep Learning (DL)

Deep Learning is a subset of AI that imitates how a human brain processes information and data in order to make decisions. Deep Learning relies on vast amounts of unstructured historical data with known outcomes that it can learn from and it applies mathematical techniques to look for patterns in data. But the limitation of DL is on the fact that very often in a customer engagement situation you come across new problems or issues that there is no previous history of or data relating to – so you don’t have enough data to train the system. This is why in order to enable exceptional customer experiences it is often necessary to blend the best of AI with human-assisted interactions.

Generative AI

Generative AI refers to artificial intelligence systems that have the ability to create new content (such as text, images, music, or other forms of data) that is original and not explicitly derived from existing examples or templates. These systems are designed to understand patterns and structures within a given dataset and generate novel outputs based on this learned knowledge. Generative AI algorithms often utilize techniques such as deep learning and probabilistic modeling to generate content that is coherent, realistic, and exhibits characteristics similar to those found in the training data.

Generative AI has applications across various domains, including creative content generation, data synthesis, and simulation, and it continues to advance the frontier of artificial intelligence by enabling machines to exhibit creativity and produce new, valuable outputs autonomously.

Large Language Model (LLM)

A Large Language Model (LLM) is a type of artificial intelligence system that is specifically trained to understand and generate human language at a high level of complexity and sophistication. These models are built using deep learning techniques and are trained on vast amounts of text data sourced from the internet or other textual sources.

LLMs have the ability to process and comprehend natural language input, generate coherent and contextually relevant responses, and perform various language-related tasks such as translation, summarization, and question-answering. They typically consist of millions or even billions of parameters, allowing them to capture intricate linguistic patterns and nuances, resulting in outputs that closely resemble human-written text. Examples of popular LLMs include GPT (Generative Pre-trained Transformer) models developed by OpenAI, such as GPT-3 and GPT-4, which have garnered attention for their remarkable language generation capabilities.

LLM applications can cover things like summarization, translation, search, content creation, document identification, compliance, sentiment analysis and many more interesting and emerging applications for today’s businesses.

Natural Language Processing (NLP)

Natural Language Processing is the area of AI that relates to computers processing natural language and text data, which is unstructured and turning it into structured data. The concept of NLP allows decisions to be made based on this data so that, for example, a chatbot can respond to a request. But the interaction between a computer and natural language is complex.

If we think of voice-activated devices, like Alexa or Google Home,  or even text messages on our messaging apps, understanding language nuances and the context of what a user means is a lot trickier than guiding a person through a set of pre-programmed menus. However, the beauty of NLP is in the fact that it allows the likes of a customer service operation to know exactly what the customer wants rather than try to guess this through guided menus.  The accuracy of NLP technology has greatly improved in recent years, making it attractive for a broad range of business use cases, beyond simple chatbots.

Natural Language Understanding (NLU)

Natural Language Understanding (NLU) refers to the ability of a machine to understand what we say. NLU is considered a subset of NLP and it is about understanding what the data really means so that it can process it accordingly. NLU sounds like it’s the same or similar to NLP but NLU just relates to the understanding of natural language.

Machine Learning (ML)

There are varying definitions of Machine Learning but essentially ML is an area of AI that is based on the idea that computers can learn from data that is presented to them so that they can become progressively smarter at identifying patterns, making decisions and executing appropriate actions with minimal human intervention. There are many different types of machine learning algorithms that help computers successfully interpret data, especially data that the machine hasn’t been exposed to before.

Virtual Assistant or AI Assistant 

An AI Assistant, often referred to simply as a virtual assistant, or digital AI assistant is an artificial intelligence-powered software program designed to assist users with various tasks and activities using natural language interactions. These assistants can understand spoken or typed commands and queries, process them using natural language processing (NLP) algorithms, and provide relevant information, perform tasks, or execute commands accordingly. AI assistants can perform a wide range of functions, including answering questions, setting reminders, scheduling appointments, sending messages, making recommendations, providing navigation assistance, and controlling smart home devices.

 An Ai assistant combines natural language processing, conversation control, domain knowledge, and a visual element (such as images or animation) that adapts to the discussion’s context and content.

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