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

By 5 Minute Read

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 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, NLP, NLU, DL, ML, RPA, VA and then there’s even IA (Intelligent Automation).

‘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.

AI Definition

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. To learn a bit about ServisBOT’s AI approach watch the short video clip.

Conversational AI

Conversational AI is a form of artificial intelligence that understands and simulates human conversation through the use of 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 one of the most significant shifts towards using natural language to do things like transact, book things, search items, interact, and access services, without the need for a human agent. It goes beyond just the conversation to orchestrate and 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.

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.

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.

Robotic Process Automation (RPA)

Robotic Process Automation applies software robots (or bots) to automate processes, eliminating inefficiencies, cutting costs, and improving speed and performance. It is generally applicable to routine, repeatable, rule-based, or predictable business processes and is governed by structured data inputs, rather than conversation. The technology can vary from basic rules-based automation to very complex solutions based on machine learning. At the more basic end of the spectrum, solutions are often an extension of traditional business processes and rules management while at the more advanced end, the complexity means that data scientists are required to build highly custom AI solutions. This means there is no real RPA Bot.

Virtual Assistant (VA)

A Virtual Assistant, also known as a digital virtual assistant, or an AI assistant understands natural language, allowing them to receive spoken commands and carry out specific tasks. A VA is a “conversational, computer-generated character that replicates a conversation to transmit voice- or text-based data to a user via web, kiosk, or mobile interface,” as per Gartner’s definition. A VA 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.

There is much discussion about whether a VA is a chatbot and what the difference is between the two and it depends on how different companies define these terms. At ServisBOT we consider the Virtual Assistant to be similar to a simple Welcome Chatbot or Concierge i.e. the VA plays the role of handling the initial customer interaction and request, interpreting their intent at a high level and then passing this on to the appropriate task-oriented bot to execute on the intent. So the VA is a thin-sliced bot whose role is to manage the conversational interface but hand off to other bots or humans for deeper AI-powered tasks.

Close this Window