An AI model is a computer program that uses algorithms to mimic human intelligence, enabling automated systems to perform tasks that have historically required human cognition and decision-making.

But what exactly is an AI model and how does it work? This guide covers AI model fundamentals, types, and implementation — everything you need to understand these powerful tools driving business transformation.

What is an AI model?

An AI model is a computer program trained on data to recognize patterns, make predictions, and perform specific tasks without explicit programming for each scenario.

Think of AI-powered chatbots — they use AI models to understand your questions and generate responses. The model processes your input, references its training data, and provides relevant answers autonomously.

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, and deep learning are related but distinct concepts:

• AI: The broad field of creating intelligent machines

• Machine Learning: A subset of AI that learns from data

• Deep Learning: A subset of ML that uses neural networks

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.

While not yet at the human brain's level, AI can analyze huge volumes of data faster than a data scientist can, though the brain still performs perceptual recognition tasks much faster than even a powerful computer.

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, which is composed of around 10^11 neurons, sends information through its network. 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.

AI model lifecycle and deployment

An AI model is a business asset requiring structured lifecycle management to ensure accuracy, compliance, and value.

The AI model lifecycle includes four key stages:

• Training: Teaching the model with quality data

• Testing: Validating accuracy and performance

• Deployment: Integrating into production systems

• Monitoring: Ongoing evaluation and improvement

Training AI models

The foundation of any effective AI model is the data it's trained on. This stage involves feeding a chosen algorithm vast amounts of high-quality, relevant data. For enterprises, it's critical to use data that is clean, unbiased, and respects privacy and permissions.

Testing and validation

Before a model is deployed, it must be rigorously tested. This involves evaluating its performance against a separate set of data to check for accuracy, consistency, and potential biases. Validation confirms that the model behaves as expected and can make reliable predictions on new, unseen data.

Model deployment and integration

Once validated, the model is deployed into a production environment. This means integrating it with existing applications, workflows, and systems, like Slack, Microsoft Teams, or a browser extension. Proper identity and permission controls are essential here to ensure users only receive answers they are authorized to see.

Performance evaluation and correction

An AI model is not a 'set it and forget it' tool. Its performance must be continuously monitored to track accuracy and relevance over time. This process of correction ensures the model becomes a continuously improving, trusted layer of truth for the entire organization.

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 multiple tasks without additional training.

Common applications include:

• Question answering and chat

• Text generation and summarization

• Code writing and debugging

• Educational assistance

OpenAI's ChatGPT is a prime example of foundation model capabilities.

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, and the computational power used for training these models is doubling every six months, leading to a rapid increase in their capabilities.

LLMs excel in customer service through sentiment analysis and understanding customer emotions.

Business applications include:

• Social media monitoring

• Review analysis

• Brand perception tracking

• Customer support automation

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.

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; for example, one economist found that using LLMs made him 10 – 20% more productive in his work.

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.

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