From Code to Cognition: The Six Subsets of AI

Published on
February 1, 2024
Tessa McDaniel
Marketing Team Lead

Artificial Intelligence is powered by six subsets that enable the heights that AI has reached today.

Forbes reports that, according to the US International Trade Administration, the UK Artificial Intelligence (AI) market is worth more than £16.9 billion and is expected to grow to £803.7 billion by 2035. However, AI is a rather broad term. We've discussed the difference between AI and Machine Learning (ML) before, but there are actually six subsets of AI that each have a different purpose. 

The Six Subsets of Artificial Intelligence

Neural Networks

It might seem an interesting start choice to start with Neural Networks, but they're the foundation of all AI subsets and the core of Machine Learning algorithms. They're based on the way our brains work - interconnected algorithms, or neurons, firing together to recognize patterns, analyze data, and perform tasks that were previously thought to only have been able to be performed by humans. The development of Neural Networks in AI has led to advancements in image and speech recognition, recommendation engines, and medical diagnosis tools. Their ability to detect subtle patterns in large datasets makes them highly valuable for complex decision-making tasks.

Machine Learning

Machine Learning uses intelligent algorithms to recognize patterns, analyze data, and learn independently without being instructed by humans. ML algorithms learn from model data sets, evolve based on their experience, and make predictions all on their own. Paired with Deep Learning (up next), ML can even mimic human thought processes. As ML algorithms are exposed to more data, they continuously improve, making them an essential part of evolving intelligent systems.

Deep Learning

Deep Learning is a specialized subset of Machine Learning that leverages Neural Networks even more. This complex architecture enables Deep Learning models to handle and interpret vast quantities of unstructured data, such as images, sound, and text, with a high degree of accuracy. Deep Learning has been pivotal in advancing fields like image and speech recognition, language translation, and even self-driving car technology and tumor detection.


Robotics is a physical application of AI that tends to be the first image that comes to people's minds when they picture AI. Robots can interact with the world around them in a multitude of ways in manufacturing operations, drones, and completely self-driving cars like Waymo. Robots are also working alongside humans more and more (affectionately called "cobots" due to their collaboration) and can positively affect productivity, efficiency, and safety. As they continue to take over tasks that are boring, repetitive, and sometimes dangerous for humans, robots allow humans to focus on tasks that require more creativity and complexity.

Natural Language Programming

Natural Language Programming (NLP) is a field at the intersection of computer science, artificial intelligence, and linguistics. It's focused on enabling computers to understand, interpret, and respond to human language in a valuable and meaningful way. NLP involves the application of algorithms to identify and extract the natural language rules, enabling the conversion of unstructured language data into a form that computers can understand. From voice-activated assistants to customer service chatbots and language translation services, NLP is a cornerstone technology in facilitating human-computer interaction.

Genetic Algorithms

Genetic Algorithms are a type of optimization algorithm inspired by the process of natural selection. These algorithms reflect the process of natural evolution, where the fittest individuals are selected for reproduction to produce the offspring of the next generation. In computational terms, genetic algorithms start with a set of potential solutions represented by a population of individuals. These solutions are then evolved iteratively using methods akin to biological mechanisms such as mutation, crossover, and selection. Genetic Algorithms are widely used for solving optimization and search problems, where the solution space is vast and not well understood.

Where Does Generative AI Fit In?

I decided to ask ChatGPT directly to see where it classifies itself - after all, who's more qualified to answer the question? According to it, OpenAI's GPT-3 is an amalgamation of nearly all subsets. Neural Networks power the Machine Learning and Deep Learning algorithms are trained on a variety of data sets up until January 2022, and Natural Language Programming is used for the interface with the human sending prompts. Quite a handful of subsets! Generative AI in Virtuoso uses OpenAI to produce robust data sets, generate JavaScript extensions, and more with some exciting upcoming features. Try out our current GenAI features for yourself, and keep an eye on our LinkedIn for the announcement of when the new AI capabilities drop!


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