Guide to Types of AI Models and How They Work

When you think of AI (Artificial Intelligence) models, you may automatically think of generative AI like OpenAI’s ChatGPT (a Generative Pre-training Transformer) and Dall-E-2 (the tech startup’s Ai image generator). 

However, AI models have been used in technology and other fields since the 1950s. In some capacity, you’ve probably unwittingly used a form of AI throughout most of your life. The incorporation of AI in every facet of life is well underway, and in the future, the technology will only become more advanced.

From neural networks to transformers and more, let’s take a look at different types of AI models and how they work.   

What is an AI model?

An AI model is a software tool trained on an extensive data set to make decisions and recognize patterns without further human intervention and training. Although AI models aren’t sentient, they’ve gone through rapid advancement in the past several years, and are able to achieve (and sometimes even exceed) the output goals behind their training.

However, one size does not fit all — there are different AI model categories, and then types of AI models within each category. Often, various AI models are used within the same system to accurately complete a multitude of tasks. 

For example, Ultralytic’s  YOLO-V8 (You Only Look Once), a complex series that’s trained in many vision AI tasks employs several AI models to ensure speed and accuracy in object and image identification and more.

Types of AI model learning techniques

AI models are trained to learn via two main techniques, and their subsequent capabilities may depend on which method is used. Here’s a look at the main techniques used to train AI and how they work.

What is machine learning?

Machine learning uses AI to train machines to make accurate future predictions and learn behavior based on large data input, identifiable patterns, and repetitive experience. All machine learning models can increase their performance success as they continue to learn.   

Essentially, machine learning is the application of AI to allow machines to evolve and learn overtime, and is considered a type of subset of an AI model.

Machine learning covers three different training methods:  

• Reinforcement learning: Reinforcement learning teaches software on how to interact with a machine’s environment. This allows the model to achieve its goals using a trial and error process and then adapting.
• Supervised learning: In the supervised learning method, machines are given specific database input by human trainers and are guided to learn to recognize the patterns found in the labeled database. Supervised learning allows a machine to make accurate predictions on new data based on these patterns.
• Unsupervised learning: Unsupervised learning works similarly to supervised learning, as machines are fed large amounts of database input. However, this method doesn’t involve any guidance or interaction from human trainers. This allows machines to develop insights and begin to recognize patterns without focused training.

Deep learning for AI models  

Deep learning for AI models is a subset of machine learning, which in turn, is a subset of AI. This sounds more confusing than it is — here’s how deep learning works: The method uses a complex, multi-layered neural network AI model to mimic the decision-making process of the human brain.

Deep learning mimics the way the human brain functions by recognizing patterns in data, text, images, sounds, and more to provide insights, answers, and solutions.This method primarily trains AI via unsupervised learning, and is responsible for most of the AI models we use today. Alexa and Siri, autonomous vehicles like Teslas, and ChatGPT are all trained via deep learning.

How algorithms work as AI models  

Algorithms don’t imitate human intelligence or thought process, but rather, are instructions given to a system that tell it what to do and how to perform. On the other hand, AI models are trained to make decisions and perform independently.

However, algorithms play a vital role in developing AI, and are used to support the performance and learning process of AI models. 

How different types of AI models work 

All AI models process large amounts of data input to make accurate predictions, learn to work more effectively, and interpret the data they receive. However, initial human interaction and training is vital to ensure they will work properly.

There are certainly hiccups along the way — for example, GPT models are prone to “hallucinations” and may share false statements as fact. However, as AI models grow and learn, the chances for major error decrease. 

Different types of AI models work through the following methods:

• Acting on future data based on prior data input
• Creating correlations by processing multiple patterns
• Generating content or images based on data input
• Mining data through algorithms and statistics
• Learning insights by processing data input
• Recognizing patterns to make accurate predictions 

Examples of AI models: From neural networks to transformers

All AI models fall under the broader categories of machine learning and deep learning. However, specific models are created to do different things. In a nuanced, complex program, you may find multiple models at work. 

From neural networks to transformers, here are some examples of the most commonly used AI models.

Neural networks

Neural networks are machine-learning and human brain-inspired AI models that are developed using neuron-like interconnected nodes to process information transmission. These AI models can learn on their own once they’ve been extensively trained by human developers.

A crucial component in ushering in the next stage of AI, neural networks are used as the foundation for innovative, life-changing AI programs. For example, virtual assistants, tailor-made Amazon purchase suggestions, and ride-sharing apps all use neural networks.

Decision trees

Decision tree AI models are algorithms trained to narrow down answers based on the questions they’re asked. These models take a “tree-like” approach to providing results by processing multiple algorithms and breaking down data subsets into branch clusters.

For example, decision trees are extremely useful in website search engines, like IMDb and in online bank loan approval applications. Every answer you provide to the questions you’re asked by a decision tree AI model helps it to ignore unrelated data and give accurate results.

Large language models

Large language models (LLMs), are currently the most public-facing AI models. For example, GPTs like ChatGPT and Anthropic’s Claude are LLMs. LLMs process extensive amounts of language from human trainers and web content to understand text. 

LLMs generate human-like text and can help to answer nuanced questions, hold conversations with users, and generate content. These AI models can provide outlines and topic points of emails, website copy, and articles, and generate headline ideation.

Random forests

Random forests are multiple decision trees (typically 500-1000) that work together to make decisions and provide results. Random forests are used to reduce mistakes and improve accuracy in predictions and answers. These AI algorithm models handle huge data sets and play a pivotal role in AI for industries such as healthcare.

Random forests can also help protect your cybersecurity by detecting spam email and cybercriminal threats.

Transformers

Transformers consist of a deep learning neural network AI model, and are designed to decipher large sequences of data, such as sentences with punctuation, and understand the context and relationship of words.

These AI models are fantastic translation tools, and can accurately convert text from one language to another. For example, Google Translate is a transformer AI model.

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