Conversational AI is a type of artificial intelligence (AI) that allows back and forth human-like conversations between a user and a computer.  It is a combination of a number of technologies including natural language ping (NLP) machine learning, deep learning and contextual awareness.

Conversational AI uses natural language understanding (NLU), machine learning (ML), and natural language processing (NLP) to that enables a user to engage in natural conversations with a ‘machine’.

NLU helps interpret meaning from users’ words and remembers what has been said to maintain context and continuity. Once a customer’s intention is understood, ML determines the appropriate response. Using natural language generation (NLG) which is a subset of NLP, the response is shared in understandable human language.

Advanced NLU understands grammatical mistakes, slang, misspellings, short-forms and industry-specific terms – just the same as a human would.

In simple terms, a chatbot is a computer program that can stimulate and process human conversation (written or spoken), enabling humans to have a conversation with a computer.

The most sophisticated chatbots use AI and natural language processing to simulate human-like interactions.

Not all chatbots are based in AI.  There are rules-based chatbots that use a designated script and flow that doesn’t change, regardless of the input from the customer.

LLMs are a type of artificial intelligence model. They are very large (billions to hundreds of billions of parameters) deep-neural-networks that is trained to understand and generate human language. LLMs are trained by going through billions of pages of material in a particular language, learning the patterns and connections between words and phrases. This type of training means that these networks are sensitive to contextual relationships between the elements of that language (words, phrases, etc).

Generative AI is a type of artificial intelligence technology that is capable of generating text, images, code or other types of content, often in response to a prompt entered by a user. For example when using a chatbot, users type questions or instructions into an input field, and the  AI model can generate a human-like response.

Efficiency is the most compelling benefit of generative AI. A business can automate specific tasks, enabling them to focus their time, energy and resources on more important strategic objectives. The benefits of this can range from lower labour costs to greater operational efficiency and new insights into the performance of certain business processes.

Generative AI tools are also being used for idea creation, content planning and scheduling, search engine optimisation, marketing, audience engagement, research and editing and potentially more.

A major concern is the potential for spreading misinformation and harmful content and the social impact of this as well as reputation and potential financial damage to the enterprise. Concerns have also been raised about the automation of tasks by generative AI and issues around workforce and job displacement.

Generative AI and general AI both relate to the field of artificial intelligence, with generative AI a subtype of general AI.
Generative AI uses various machine learning techniques, such as LLMs, to generate new content from patterns learned from training data. These outputs can be text, images, music or anything else that can be represented digitally.

Simply, general AI is the concept of computer systems and robotics that possess human-like intelligence and autonomy. It should be noted that most AI systems are examples of “narrow AI,” in that they’re designed for very specific tasks.

Generative AI is a subset of artificial intelligence, and generative AI models use machine learning techniques to process and generate data. Machine learning is the foundation of AI and is the application of computer algorithms to data to teach a computer to perform a specific task. Machine learning is the process that enables AI systems to make informed decisions or predictions based on the patterns they have learned.

Deep learning models are a class of ML models that imitate the way humans process information. The model consists of several layers of processing (hence the term ‘deep’) to extract high-level features from the data provided. The final layer provides the more human-like insight. Whilst traditional ML models require data to be labelled, this is not the case with deep learning models that can ingest large amounts of unstructured data. They are used to perform more human-like functions such as facial recognition and natural language processing.