Artificial intelligence (AI) is rapidly becoming a staple in today’s society, with every industry using it to interpret datasets more quickly. But what exactly is an AI model and how does it help you with decision-making? 

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AI models are everywhere — in fact, 86% of IT leaders expect generative AI, for example, to be vital to their company in the near future. It’s a very useful tool, one that mimics human intelligence to make predictions and find patterns in input data. 

But it makes you wonder: what is an AI model? 

This is a question we’ll try to answer in this guide. Dive in to discover what an AI model is, how it works, and some of the most popular types of models. 

What is an AI Model?

An AI model is a computer program trained on specific algorithms that help it replicate human intelligence to make predictions, find patterns, and make decisions. 

Think of all the AI-powered chatbots that have recently appeared. They use various AI models to have conversations with humans and answer questions the user types into a text box. 

In a nutshell, while you don’t interact with the AI model directly, it’s actually powering the chatbot and helping it make decisions autonomously using the training data the developers feed into it. 

The purpose of artificial intelligence models is to do specific tasks and automate decision-making workflows. 

Now that you know what an AI model is, let’s discuss how it differs from machine learning and deep learning. 

What’s the Difference Between AI, Machine Learning, and Deep Learning?

Artificial intelligence, machine learning, deep learning — they all sound similar, right? Wrong! ❌ 

It’s a common misconception that these tools are interchangeable but there’s a slight difference between AI and a machine learning model. 

Artificial Intelligence (AI)

Artificial intelligence is a computer science field that focuses on developing software or machines that simulate human intelligence. AI-powered apps can usually do all kinds of tasks, such as translating content into other languages or generating art and images. 

Don’t worry — it’s not yet at the human brain’s level but it can analyze huge volumes of data faster than a data scientist can. That’s why it often outclasses humans in the data science field. 

Machine Learning (ML)

Machine learning is a branch of AI, possibly one of the biggest. It focuses on helping AI software imitate the way humans learn, through algorithms and datasets. 

Generally, ML models can learn from data on their own which helps them make accurate predictions (called unsupervised learning). But you can also train the algorithm with specific data in a process named supervised learning. 

A good example is any streaming service’s recommendations. They use ML to analyze what a user often watches and offer similar suggestions. 

Deep Learning (DL)

Deep learning is a subset of machine learning that teaches computers to process data by mimicking human neural networks. Basically, DL simulates the brain’s decision-making power to make predictions and recognize data patterns.

This is commonly seen in healthcare, especially in image recognition, as it helps detect diseases in MRIs more easily. Besides, it works to improve its accuracy over time.

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Okay, we’ve established what artificial intelligence, machine learning, and deep learning are. 

Let’s return to AI models and see how they work. 

How Do AI Models Work?

As we’ve already discussed, AI models use multiple algorithms to make predictions and understand patterns in data. It cannot work without these algorithms. 

Basically, developers train the AI model to mimic how a human brain sends information through neurons. But they’re not called neurons, just layers. And we can distinguish between different types of layers: 

• Input layer — Here’s where data enters. 
• Hidden layer — This hidden layer processes data and moves it to other layers. 
• Output layer — The output layer spits out the final result. 

In general, AI models learn from thousands of open-source data items to generate an answer. Unless you teach them, they won’t know the answer to your question. That’s why you can also categorize AI models by intelligence. Which means that the more data they learn from, the more complex they’ll be. 

With this information in mind, let’s talk about discriminative and generative models. 

Discriminative vs. generative models

You can classify machine learning models into two categories: discriminative and generative. 

A generative model is a computer vision model that learns data patterns in an attempt to generate similar output. It forecasts the probability of what the next word will be based on what it has seen before. 

By making correlations, the generative model can generate highly probable outputs. It can either offer autocomplete suggestions or generate entirely new text. You might think that using generative AI is wrong, but 78% of executive leaders believe that the benefits of generative AI outweigh the risks — you can do more in less time, with less effort. 

Examples include transformers, which you can use to identify how different elements in a dataset influence one another. Or diffusion models that apply Gaussian noise to destroy training data and recover it. 

Discriminative models, on the other hand, are algorithms that focus on distinguishing between different categories or classes of data. They don’t model each class individually; instead, they learn the boundaries that separate those classes. 

What’s the purpose? Well, to predict the probability of data belonging to a certain class. 

Think of apps like spam detection. The discriminative model classifies emails as spam based on their content. 

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After making the distinction between these models, let’s talk about the different types of AI models. 

What are the Different Types of AI Models?

Everyone uses AI models nowadays, no matter the industry. 

However, there are various types of AI models with different use cases. In the next paragraphs, let’s explore what each type does and how they optimize your flows. 

Foundation models

Foundation models are pre-trained ML models that perform a wide range of tasks, including answering questions, text generation, writing code, and summarization. 

People mostly use these trained models for self-driving learning, meaning that anyone can use such tools to learn something new or do homework, for example.

Think of platforms such as OpenAI’s ChatGPT, which uses foundation models for different use cases. 

Large language models (LLMs)

LLMs are deep learning models that understand and interpret language to generate text and converse like a human using natural language processing (NLP). 

Being trained on huge datasets (hence the ‘large’) LLMs can predict the next word in a sentence or phrase. Which gives them the flexibility and scalability necessary to perform various tasks, such as language translation, generating human-like responses, etc. 

You can mostly find LLMs in customer service, as they’re able to detect client emotions through sentiment analysis. By analyzing social media activity or online reviews, you can better understand how people perceive your brand, so you can improve your products and services. 

Neural networks

Think of neural networks as the neurons in the human brain; it’s what these ML models are based on. In a nutshell, they’re a bunch of interconnected nodes that process input data and make predictions based on that data. 

There are multiple types of neural networks, including: 

• Feedforward neural networks (FNNs) — the simplest form of neural connection. 
• Convolutional neural networks (CNNs) — suitable for grid data. 
• Generative adversarial networks (GANs) — consist of general and discriminator neural networks. 
• Long short-term memory networks (LSTMs) — address the vanishing gradient problem. 
• Recurrent neural networks (RNNs) — great for sequential data. 

These models are good for image, video, and speech recognition, machine translation, video games, etc. 

Multimodal models

Multimodal models extract information from different types of data, such as images, audio, video, and even speech. They “see” the visual input through computer vision and get information from it. 

Nowadays, most foundation models have become multimodal. For instance, ChatGPT doesn’t only respond to text prompts, but can also recognize information from images. 

You can also consider some text-to-image generation tools as multimodal AI models.

Why is this model helpful? Because it can generate even better results and help you get the best possible answer. 

Decision trees

Decision trees are flow charts that split the data into subsets based on the answer to a previous question. Think of them as a tree. Each node represents a decision based on a feature, while a branch represents the outcome of that decision. Then, at the end of the branch, you have a leaf with the final result. 

For instance, most spam detectors use decision trees to figure out whether an email is spam or not. They peruse the email and, if they identify multiple ‘no-no’ keywords, they’ll classify it as spam. 

Plus, you can use decision trees to classify customers based on their preferences, behavior, purchase history, etc. This helps marketers offer more personalized content, which increases engagement and reduces churn. 

Random forests

When you put together multiple decision trees, it creates a random forest. It’s basically a learning model that brings individual results and decisions from decision trees into a single, more precise prediction. 

The greatest advantage is that it increases the accuracy of your predictions. You can use it to predict customer behavior and use the insights to create better experiences and interactions. 

Diffusion models

We’ve mentioned diffusion models before, but we didn’t explain them in depth. Let’s do so now. 

Diffusion models work by adding “noise” to images, breaking them into tiny pieces which the model carefully analyzes to discover new patterns. Then, by “de-noising” the image (working in reverse) the model generates new pattern combinations. 

For instance, you want to generate a picture of a cat. The diffusion model knows that cats have small bodies, whiskers, and paws. With this info, the model can recreate these characteristics into an entirely new high-quality image. 

Linear regression models

Linear regression is a type of ML model often used for figuring out the relationship between input and output variables. In a nutshell, it identifies and predicts the linear relationship between two variables. 

For instance, it’s a great model for risk analysts who want to identify where they might be vulnerable. 

Logistic regression models

Logistic regression is a widely used statistical model that focuses on solving binary classification problems based on one or more predictors. This translates into using independent variables to measure and estimate the chances of a specific event occurring. 

You can often find logistic regression models in the medical field, where researchers use them to understand which factors influence a disease. This leads to the development of more accurate testing. 

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Last on our list is offering tips on how to develop a custom AI model. Let’s go through the steps in the following section. 

How to Develop a Custom AI Model

With recent advances in technology, there are plenty of good tools you can use to build a cutting-edge AI model yourself, such as TensorFlow, Vertex AI, or PyTorch. With an AI model, you can drive innovation across the board and make more data-driven decisions.

To get started, here are some of the steps you should follow: 

1. Identify your goals — What are you trying to achieve with the custom AI model? Do you want to improve your customer service or generate text faster? Make sure to set clear objectives that meet your business needs. 
2. Gather data — An AI model is only as good as the data you give it. The more you feed it, the better it’ll be at answering questions. Select the appropriate algorithms and choose datasets that reflect your use cases. 
3. Build the structure — Most tools have a user-friendly interface that you can use to create the AI system. They might even have tutorials and guides to help you set out the right configurations. 
4. Train the model — This step requires you to train your model and ensure what it learns is correct. Keep a close eye on the progress and set it on the right path if it strays. 
5. Validate and deploy — When all’s ready and you’ve tested the model, you can integrate it into your business framework. Make sure to always monitor its performance and update it regularly, as it’s vital for keeping the model accurate and relevant. And fine-tune it to perfection. 

Congrats! You’ve reached the end of the article. Let’s say our parting words. 

Over to You

With the rise of artificial intelligence comes a great challenge: deciding which AI tool to use to streamline your operations and automate plenty of boring, manual tasks.

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