AI Models: Types, Examples, and Everything You Need to Know

In November of 2023, major consulting firm PwC poured a billion dollars into AI training for its employees. Every single employee received AI training, and staffers will get chatbot assistants. In order to be successful, PwC decided that AI was the best way for its employees to source ideas, eliminate rote tasks, and increase productivity. 

AI is changing the landscape for every industry. Over the next decade, it will be the differentiating factor that determines which companies can compete and which ones can’t keep up. Thankfully, you don’t have to be a massive consulting firm to implement AI models into your business. Organizations of all sizes can use AI models to identify trends in data, make predictions and draw conclusions, find and correct inefficiencies, build workflows, and many other tasks that skyrocket the performance of business operations. 

Here’s an overview of what AI models are, how they work, and how they’re already changing the landscape of every industry. 

What Are AI Models?

AI models are programs that are trained on data sets to recognize patterns. Then, developers add in algorithms, which are rules that help the program make decisions. Based on the patterns a program recognizes, it makes decisions or predictions on its own. 

Each AI model is trained to perform a specific task. Examples of common tasks that AI models can do include running and compiling marketing campaign reports, generating computer code, recognizing letters and numbers in texts, and entering data. The more input and training data a model receives, the more accurately it can execute its task. The goal of an AI model is to complete the task effectively without any further human intervention. 

AI models are loosely designed after the way humans think. In some ways, AI models’ thinking and decision-making abilities feel uncannily realistic. For example, chatbots can have compelling, human-like conversations with you. In other ways, AI models are already far exceeding human capacity. Machines can process vast amounts of data much faster than people can, and they can recognize patterns that may escape human eyes. They’re more objective and efficient, too, which removes many of the human limitations that are inherent to our work. 

AI vs. Machine Learning vs Deep Learning 

AI, machine learning, and deep learning are related but distinct concepts. What’s the difference between them? The short answer is that machine learning and deep learning are subsets of artificial intelligence. Many people use the terms interchangeably, which is incorrect. Although there may be some overlap between them and they build off each other, it’s important to know the differences between each one in order to fully understand how AI models work. 

Think of these concepts like a castle. Artificial intelligence is the foundation of the whole building. Based on that foundation, other towers and spires can be built to serve specific purposes, like machine learning and deep learning, but they’re all part of the same structure. Here are brief descriptions of each one. 

Artificial intelligence

Artificial intelligence is a general term that describes the science of making computers think like humans. By mimicking the patterns in human behavior, artificial intelligence can perform many of the repetitive tasks that humans usually do. AI is heavily pattern-based and logical. While it’s not quite as adept as humans in some finer aspects, it’s excellent at handling predictable and repetitive tasks, like organizing data, running chatbots, and identifying trends. 

Machine learning

Machine learning is a branch of AI in which programs—to a limited capacity—think for themselves. Sometimes machine learning uses past trends to make itself more efficient; other times, machine learning looks at data, identifies trends, and makes predictions about what will happen. It can use its existing data to forecast what will happen with other data sets or conclude what will happen in the future. Some examples of this include detecting credit fraud or data breaches based on past attacks, diagnosing problems with computers, and predicting when equipment will fail based on how often its parts need to be replaced. 

Deep learning 

Based on AI principles, deep learning is a process of applying neural networks and algorithms to simulate more complex operations. It’s specifically designed to mimic the way humans think. By compiling layers of neural network nodes, deep learning programs can add hierarchies to data for more nuanced decision-making. The space for ambiguity, rather than binary choices, allows deep learning programs to create art, analyze sentiment in a massive batch of social media posts, recognize voice and speech patterns, and translate text in a natural-sounding way. 

How Do AI Models Work?

Consider this: When you type a question into ChatGPT, you get an answer. However, between that input (your question) and the output (ChatGPT’s answer), there was a complex set of interactions that you didn’t see. Although each AI model is slightly different depending on the task it’s supposed to do, here’s a general idea of how AI models work. 

First, programmers must start with a set of data—the larger, the better. They must also have a goal of what they want the AI model to do and what kind of outcomes they want. 

Programmers then start feeding data into the AI model. AI models, inspired by the human brain, have data points that are sometimes referred to as nodes. The AI model identifies patterns in the data set using algorithms, or sets of rules, and forms relationships between the nodes. This creates what’s called a neural network. 

More algorithms work together, forming a complex set of equations and an intricate neural network. When multiple algorithms are applied to a data set, they form a model. The model uses the algorithms to understand the data, identify trends, and make decisions. This neural net is a complex set of calculations that takes input, applies rules, evaluates hierarchies, and makes some degree of assessment. 

An AI model’s end goal is to achieve a task. Eventually, after taking and processing input, the AI model will create an output as it executes its task. The more data it has, the more accurate the output will be. If the output isn’t correct or precise enough, programmers can add more data to their data set or fine-tune some of the algorithms so that the model’s predictions are more accurate.

Types of AI Models

There are many different types of AI models. The three main types are supervised learning, unsupervised learning, and reinforcement learning. Each is useful for a different purpose. 

Supervised learning

Supervised machine learning is when a neural network makes decisions or predictions based on labeled data sets. To help guide the model, the programmers have labeled the data sets, input key features, and target variables to teach the model to mimic human behavior. In a way, humans are giving “training wheels” to the model to help it make decisions. This gives the model a structure to start with, making its predictions more accurate and reducing the chance for errors. Companies can benefit from supervised learning AI models by implementing them for tasks like speech and text recognition, regression analysis, statistical tests, spam filters, fraud detection, KNN algorithms, K-means, and random forest algorithms.

Unsupervised learning

In unsupervised learning, the AI model has its “training wheels” taken off. There’s no human guidance, labeled data sets, or pre-existing relations between data sets. The model has to make decisions, either with the help of another software program or completely on its own. As a result, the program doesn’t have to spit out black-and-white, right-or-wrong answers—it can do more complex tasks like categorizing data. Many use cases for unsupervised learning models involve trend analysis, grouping sentiments of social media posts, identifying traffic patterns, and discovering inefficiencies in manufacturing processes. 

Reinforcement learning

Reinforcement learning is a technique where an AI model is given a goal and then is put in a situation where it must try to reach the goal. For example, an AI model can be programmed to pick stocks with the goal of maximizing returns on investment. The model learns through trial and error; when it succeeds in its task, there’s a reward (such as earning money on stocks), and when it fails, there’s a punishment (like losing money on stocks), which reinforces the algorithms that led it to its winning behavior. The AI model learns from its experiences and becomes more efficient at reaching its goal. Eventually, as the model creates its own data of experiences, it can increasingly accurately predict how to pick lucrative stocks in the future. 

How to Train an AI Model

Whatever kind of task you want your AI model to do, there’s a general workflow to follow. Here are the main steps that programmers take to train and deploy AI models. 

1. Gather data. The more data you have, the more accurate your model will be and the more complex algorithms and decisions it can handle. 
2. Clean the data. This involves getting rid of inaccurate data, as well as annotating and labeling the data sets. For supervised learning, cleaning is how you teach your model to create the outputs you want; for unsupervised learning, it’s how your AI model will identify trends and make predictive patterns. Cleaning the data also includes getting rid of “noise,” which is data that may not necessarily be wrong but will still lead your model to the wrong conclusion. In addition to cleaning your data, you’ll also need to keep it updated, or your model will make decisions based on outdated information. 
3. Choose a model. Know what kind of output you’re looking for and what kinds of algorithms and parameters will help the model reach the right decisions. When you choose a model, you’ll have to take into account the type of model (unsupervised, supervised, or reinforced, as mentioned above), as well as what kinds of resources you have. What is your processing power? How much time do you have? How many tasks will you need your AI model to complete? 
4. Train your model. Give your AI model a test run. You’ll need a training set of data, which is what your model learns from, as well as a validation set, which is the set of correct answers to see how well your model is working. 
5. Test the model. When you test the model, there are a few different ways you can measure success. Remember, there’s a difference between precision (consistency every time the model completes the task) and accuracy (how correct the output actually is compared to real-world results). 
6. Deploy the model. Here’s where you see how well your AI model works in real life. Deploy the model and integrate it into your other systems and data pools. Make sure you have sufficient processing power and frameworks for the model to do all its work. 
7. Fine-tune the algorithms. If your AI model’s predictions are imprecise or inaccurate, you’ll need to make some changes. Many models have bias in their outputs, usually due to incomplete or inaccurate data sets. Continuous learning and optimization are part of the machine learning process, so you’ll keep refining your AI model over time. 

Examples of AI Models 

AI models are quickly changing the way every industry functions. Some of the most powerful and popular AI models that currently exist are:

• ChatGPT. When chatbot ChatGPT was released in November 2022, the entire world gasped. For most people, this was the first accessible and interactive exposure they’d had to AI. ChatGPT has quickly become integrated into many businesses, where employees use it to generate computer code, come up with ideas for marketing campaigns, summarize large amounts of text, and onboard new employees. 
• Midjourney. As an image generator, Midjourney creates graphics based on natural language descriptions. It can create images of just about anything you can think of. For businesses, that means that Midjourney can create social media images, email graphics, brochures, and ads almost instantly. 
• Quillbot. Although Quillbot has quite a few features and tools, it’s mostly a text generator. It can write content for you based on prompts. However, its more refined uses include checking for plagiarism, editing grammar, summarizing texts, creating citations, modifying tone of voice, and changing sentence lengths. It’s great for businesses looking to repurpose a product’s set of messaging points for different audience segments. 
• CapCut. In an era where video content is popular and fast-paced, more people are using video AI models like CapCut. CapCut lets users edit videos using background remover tools, filters, sound effects, text overlays, and templates. Companies no longer need an experienced video team with expensive cameras to create videos with high production value. Companies can adapt to social media trends quickly with user-friendly video AI tools like CapCut. 
• Hugging Face. Hugging Face is a data science tool that focuses on natural language processing and allows users to test and deploy AI models. With in-browser tools, users can train, test,, and deploy the machine learning models they’re building. The company’s goal is to make AI more accessible for everyone. It’s become especially popular after partnering with AWS. Now, companies and individuals can use Hugging Face to demo models, fine-tune algorithms, research best practices, share data sets, and build prototypes. Even the smallest companies can build AI models to save money and automate their repetitive tasks, sort data, make custom tools that gauge social media sentiments, and create chatbots. 
• MuseNet. Created by OpenAI, the same company that made ChatGPT, MuseNet is an AI model for music. Using deep neural networks, MuseNet is a browser-based AI music generator that can compose music using ten different instruments. Will it be the next John Williams or Hans Zimmer? Maybe not, but it’s changing the game for some companies. Businesses can quickly create background music for videos, craft tunes to accompany slogans and ads, and mimic the style of popular composers (which is excellent for making parodies)

All the AI Tools You Need

To stay competitive, you’ll need to embrace the efficiency, accuracy, and speed that AI offers. Domo’s suite of AI tools and services empower your business with secure and flexible AI, along with instant support with AI-powered chat and guidance—right within your flow of work. Domo.AI offers AI-guided experiences, chat-style data exploration, flexible model creation and management, seamless deployment, and superior governance and security.

Some advantages of using Domo’s AI tools include:

• Seamless management, deployment, and optimization of Domo-hosted models for efficient utilization.
• Effortless integration of external models with existing data environments for enhanced performance.
• The ability to create and fine-tune models for improved results.
• A secure and transparent environment that promotes responsible AI practices through built-in usage analytics and governance.
• Pre-built Universal Models for forecasting, anomaly detection, sentiment analysis, and PII detection, eliminating the need for development or training.

Trusted conversational AI that enables everyone to ask questions, uncover insights, and take action.

To learn more about how your organization can speed up productivity and increase product efficiency with Domo’s AI tools, schedule a demo with us today.