We’ve all had days where we’ve found ourselves sifting through countless old emails, looking for one particular invoice. An artificial intelligence model can do this type of sifting much faster, with little human intervention, freeing you up to do more complex tasks or those that require a personal touch.
AI models can’t yet replicate human intelligence, but they can mimic many cognitive aspects of human behavior. Think of ecommerce chatbots that can read a few lines of text from a customer and provide helpful, detailed customer service in response.
AI systems are proliferating, taking over many tasks that were once the sole domain of humans. You may have already interacted with a trained model if you did some online shopping, or even if you simply logged into your email account.
Here’s a breakdown of different types of AI models, from the relatively simple to highly complex.
What is an AI model?
An AI model is a computer program trained to identify patterns in data. AI stands for “artificial intelligence,” and such models are built to mimic the powers of human intelligence. This is made possible through a mix of machine learning (ML), deep learning, natural language processing (NLP), and statistical modeling.
Through a process called model development, computer scientists train AI models using input and output variables. Input variables are raw data that AI systems analyze in their central processing units (CPUs). Output variables take the form of predictions or classifications based on what the model learned from the data.
One common type of AI model is a classification model. Classification models are trained to categorize data points into predefined classes. For instance, such a model might analyze an email and predict whether it’s spam, based on various features in the text. The spam classification is a dependent variable; the AI model only flags an email as spam if it detects certain language or a suspicious sender address. These would be termed “independent variables.”
AI models can be much more complex. For instance, an autonomous vehicle must use multiple decision trees based on multiple data troves to decide whether to brake or accelerate.
How do AI models work?
AI models work by processing data through mathematical formulas known as algorithms to learn patterns and relationships, enabling them to make predictions or decisions without explicit programming. These models typically function as artificial neural networks. They consist of layers of interconnected nodes (neurons, much like those in the human brain) that process input data, extract features, and generate output predictions.
Training
The process of feeding data into an AI model is called training. During training, the model constantly adjusts itself based on the training data it receives. Imagine a student studying for an exam. Given the neural pathways of the human brain, the more practice problems the student solves, the better they understand the material. Similarly, the more input data an AI model processes, the better it becomes at recognizing patterns and making accurate predictions. In this sense, an AI model seeks to simulate human intelligence.
Applications
Once they’re done training AI models, data scientists deploy them for real-world use. This could involve integrating an AI model into a software application (like a recommendation system on a shopping website) or using it to analyze data streams in real time (such as in fraud detection systems). Even after deploying AI models, computer scientists can continue the model training.
This is particularly true of machine learning models, or ML models. As it encounters new raw data, an AI ML model can refine its existing understanding and potentially adapt to changing circumstances.
Types of AI models
There are many different artificial intelligence (AI) models in the world of computer science, each suited to perform specific tasks or process specific data types. Here’s a breakdown of some types of AI models that fall under the umbrellas of machine learning and deep learning:
Machine learning models
Machine learning (ML) models are trained on labeled data, where each data point has a corresponding answer or classification. This is known as supervised learning. (Unsupervised learning uses unlabeled data.) These models learn to identify patterns in the data and use them to make predictions on new data. Here are some popular types of machine learning models:
- Linear regression model. Used for continuous predictions, companies use linear regression models for forecasting, such as estimating housing prices based on independent variables like square footage.
- Logistic regression model. A logistic regression model is used for binary classification tasks, such as predicting whether an email is spam or not.
- Decision trees. These models make predictions by following a tree-like structure based on a series of Yes/No questions about the data, each a dependent variable based on prior data gathered, which makes them useful for tasks like customer churn prediction.
- Support vector machines (SVM). These powerful models assist classification tasks involving high-dimensional data and work by finding a clear separation line—called a hyperplane—between different categories in the data.
- Linear discriminant analysis (LDA). Similar to SVMs, this type of model finds a separation line between categories in the data; however, linear discriminant analysis is a good option for simpler, well-understood data, while SVMs offer more power and flexibility for complex tasks involving data analysis.
- K-nearest neighbors (KNN). This machine learning model classifies data points based on the similarity to their closest neighbors in the training data, which is why companies often deploy AI models like these for image recognition.
- Random forest. This ensemble model combines multiple decision trees, improving accuracy and reducing the risk of overfitting to the training data.
Deep learning models
Deep learning models are a subcategory of machine learning inspired by the structure and function of the human brain. Deep learning neural networks contain multiple layers, allowing them to learn complex patterns from vast amounts of data. Here are some prominent deep learning models:
- Convolutional neural networks (CNNs). This deep learning model excels at image and video recognition because it’s designed to capture spatial relationships between pixels in data.
- Recurrent neural networks (RNNs). These models are well-suited for sequential data like text or speech, and they can process information by considering its order and context.
- Generative adversarial networks (GANs). These involve two competing neural networks: a generator that creates new data and a discriminator that tries to distinguish real data from generated data. GANs are used for generative AI tasks such as image generation, text generation, style transfer, and data augmentation.
- Large language models (LLMs). These are advanced deep learning models trained on massive amounts of text data, allowing them to generate text, translate languages, write different kinds of creative content, and answer questions in an informative way.
- Foundation models. These AI models receive human training and are designed to perform a wide range of tasks by learning foundational representations from vast amounts of data, making them fine-tuned for natural language processing (NLP) and video processing.
AI models FAQ
How are AI models used in business?
AI models are used in business to automate tasks, improve decision-making, enhance customer experiences, and optimize processes in areas such as marketing, finance, operations, and customer service.
What marketing tools use AI models?
Personalized recommendation engines, predictive analytics platforms, chatbots for customer engagement, and sentiment analysis tools for social media monitoring are all marketing tools that use AI models.
Is ChatGPT an AI model?
Yes, ChatGPT is an AI model. Specifically, it’s a large language model (LLM) developed by OpenAI that is capable of generating text based on the input it receives.
