Types of AI Models: Your Ultimate Guide

From virtual assistants like Alexa and large language models such as Llama or GPT to self-driving cars, artificial intelligence is reshaping your daily lives. As different AI models gain prominence across nearly every industry, their extensive and transformative impacts are increasingly evident.

But what exactly makes AI so powerful? What processes enable machines to think, learn, and even outperform human capabilities in certain tasks?

This article guides you on various types of AI models that can be beneficial to your business. You can also explore how to build an LLM pipeline that uses these models. 

What Is an AI Model?

An AI (Artificial Intelligence) model is a program that helps you perform specific business tasks autonomously, without manual intervention. Similar to a human brain, it can learn, solve problems, and make predictions. Instead of learning through personal experiences like humans, AI models acquire knowledge from large datasets and apply mathematical techniques and algorithms to derive insights.

For example, if you want an AI model to distinguish between pictures of phones and laptops, consider training it using many labeled images of each. To understand the differences, the model then analyzes these images to detect patterns, such as size, keyboard, build materials, and screen design. Once the model is sufficiently trained, it can start making predictions on new, unseen inputs. If you provide a new image, the AI model can guess whether it’s a phone or a laptop based on what it has learned from the previous examples.

You can improve the AI model’s accuracy with more data. Additional information helps the model better understand the difference between phones and laptops. So, the performance of AI models is truly dependent on the quantity and quality of the data they are trained on.

Apart from this image recognition task, you can apply AI models to several workflows. These include natural language processing (NLP), anomaly detection, predictive modeling and forecasting, and robotics.

How Do You Create an AI Model?

The following section highlights the step-by-step approach to creating an AI model:

Identify the Problem and Goals

You must start by defining the business problem your AI model will solve, whether it’s a classification, regression, or recommendation. It is crucial to outline what you aim to achieve and identify any challenges you might encounter. This initial planning will guide your model development and ensure it aligns with your business objectives. Collaboration with an IT professional or consulting firm can provide expert advice and strategic guidance.

Data Preparation and Gathering

As data forms the foundation of your AI model, you must gather datasets that accurately reflect real-world scenarios. As your data can be structured, unstructured, static, or streaming, ensure that you clean and pre-process it thoroughly to remove inconsistencies. Proper data labeling and management are also essential for model training.

Design for AI Model Architecture

Based on your specific problem, choose the appropriate algorithm to design your AI model architecture. Algorithms can include rule-based learning, deep learning, and natural language processing (NLP). The architecture greatly affects performance, so experiment with different configurations to find the most effective one.

Deep learning techniques are highly efficient for image-related tasks, text, and audio. NLP models like transformers would be better for managing complex contextual relationships.

Training, Validation, and Testing Data Splitting

Once you design the architecture, you must divide your collected datasets into three sets as follows:

• Training Set: You can use approximately 70% of the total dataset to train the AI model.‍
• Validation Set: Use 15% of the remaining dataset for validation, in which you can fine-tune your AI model for further enhancement.
• ‍Testing Set: Reserve the final 15% to evaluate how well your model performs on new input data.

Model Training

Model training is the key step in building your AI model. During this stage, you must input the training data into your model and use backpropagation to adjust its internal parameters incrementally. This stage requires substantial computational resources, and using modern frameworks like TensorFlow and PyTorch can help streamline the process.

Hyperparameter Tuning

When a model is very simple, it might fail to capture the hidden data patterns, leading to underfitting. Conversely, if the model is highly complex, it may overfit by learning noisy data. Fine-tuning hyperparameters such as batch size, learning rate, and regularization methods helps you maintain a balance between underfitting and overfitting. You can also experiment with different parameter settings to find the optimal configuration.

Model Assessment

Using the validation dataset, you can evaluate the model’s effectiveness. Metrics like accuracy, precision, recall, and F1-score will provide insights into how your model is performing. To enhance its performance, refine the model based on the assessment results.

Testing and Deployment

Test your model with the testing sets. During this phase, you must ensure the model meets real-world use cases. If it performs satisfactorily, you can proceed with deployment.

Ongoing Evaluation and Enhancement

Continuously monitor and update your AI model to adapt to changing data patterns. Gather user feedback to understand the model’s performance and make necessary adjustments to keep it accurate and relevant.

5 Types of AI Models

Let’s take a look at the five different AI model types:

Machine Learning

Machine Learning (ML) is a subset of AI technology that helps you focus on developing a model that can learn from large volumes of data. ML models can recognize and detect patterns in each data type. Eventually, these models can make predictions on the test data.

ML models include different types of algorithms, and here are two of them:

• Support Vector Machines (SVM): SVM is used for classification and regression tasks. The goal of SVM is to find a hyperplane, a line in 2D or 3D space that serves as a decision boundary that separates the data into different classes.
  SVM helps you to maximize the margin, which is the distance between the hyperplane and the nearest data points from each class. These nearest points are called support vectors.

• Decision Trees: A decision tree is an ML model that uses a tree-like graph to help you predict the value of a target class. Each node in the tree denotes a decision based on a particular feature, and the branches indicate the outcomes of those decisions. The process continues until a decision is made at a leaf node, representing the final prediction.

When to Use?

Here are some scenarios on when to use machine learning models:

• Finance: Banks and financial institutions use ML models to predict and detect fraudulent transactions by analyzing suspicious patterns in transaction data. The model can also develop trading algorithms to predict stock prices and execute trades at the right time.
• ‍E-commerce: Online retailers like Amazon and Netflix use ML to recommend products or content to users based on their past behavior. The models can help predict demand and prevent stockouts.

Real-World Application

A practical example of an ML model is a spam email filter. Most email services like Gmail or Outlook use machine learning algorithms to automatically filter suspicious emails from your inbox. You do not have to manually sort through junk emails, saving time and effort. The filter also prevents phishing attacks by identifying and blocking unusual messages before they reach you.

Supervised Learning

Supervised learning is a category of machine learning in which the model can be trained using labeled datasets. This indicates that each training example has an input-output pair, where the output or label is known to the models. It is widely used in tasks like image classification and regression analysis.

A few of the supervised learning models are as follows:

• K-Nearest Neighbors (k-NN): KNN is a simple supervised learning model for classification and regression problems. It makes predictions based on the similarity of data points in the feature space. Distance metrics such as Euclidean, Manhattan, and Minkowski are used to calculate the difference between a particular data point and all points in the training dataset.

• Naive Bayes Algorithm: Naive Bayes is a probabilistic classification algorithm based on Bayes’ theorem with the assumption of feature independence. It is particularly effective for large datasets and is widely used for text classification, such as spam detection and sentiment analysis.

When to Use?

Let’s look at the use cases of supervised learning models:

• Classification Problems: You can use a supervised learning model to identify objects within images, such as recognizing cats, dogs, or specific landmarks in photos. In the same way, a model for sentiment analysis allows you to classify text in categories like positive, negative, and neutral to understand public opinion.
• ‍Regression Tasks: A supervised learning model helps predict house prices based on attributes like location, size, and number of bedrooms. It also enables you to estimate future sales figures based on historical data about market trends and seasonal effects.

Real-World Application

Credit scoring is a practical example of a supervised learning model. Financial institutions use this model to assess the creditworthiness of applicants. By analyzing features like income, credit history, and debt level, the algorithm learns to predict the likelihood of an applicant defaulting on a loan. This helps banks and lenders make smart decisions about credit issuing, reducing risk and improving financial outcomes.

Unsupervised Learning

Unsupervised learning is another machine learning algorithm in which the model is trained on data without labels. Instead of learning from input-output pairs, the algorithm tries to uncover patterns and structures in the data by itself.

Some of the unsupervised learning models are as follows:

• K-Means Clustering: K-Means is an unsupervised learning model used to divide a dataset into K distinct, non-overlapping clusters based on their features. The algorithm iteratively allocates data points to the nearest cluster center and updates the center based on the mean of the assigned points until convergence.

• Apriori Algorithm: The Apriori algorithm helps to mine frequent item sets and generate association rules in large datasets. It starts with single items and then progressively joins them to form larger item sets while pruning those without minimum support threshold. Once frequent sets are determined, the algorithm generates association rules, evaluating them based on metrics like confidence to uncover strong relationships between items.

When to Use?

• Customer Segmentation: Clustering algorithms can help you group customers based on purchasing behavior, demographic information, or preferences. This enables targeted marketing strategies without prior knowledge of customer segments.
• ‍Dimensionality Reduction: When dealing with high-dimensional data, unsupervised learning techniques allow you to reduce the number of features while preserving key information. This simplifies the analysis and visualization of complex datasets, such as genomics or image processing.

Real-World Application

Market basket analysis is an example of an unsupervised learning algorithm. Retailers use association rule learning algorithms, like Apriori, to identify items that frequently co-occur in transactions. Analyzing transaction data, the model detects patterns such as which products are often bought together. This helps retailers optimize product placements and create effective promotional strategies, improving the shopping experience and increasing sales.

Deep Learning

Deep learning is another category of machine learning model that utilizes neural networks with multiple layers to model intricate patterns in data. It is helpful for tasks involving large amounts of data and complex relationships.

Here are a few deep-learning models:

• Convolutional Neural Networks (CNNs): CNNs are a group of deep neural networks primarily used for image processing tasks. They use convolutional layers to automatically and adaptively learn spatial hierarchies of features such as edges, textures, and shapes from images.

• Recurrent Neural Networks (RNNs): RNNs are designed for sequential data and are capable of learning temporal dependencies. They use feedback connections to maintain context across sequences, making them suitable for time series prediction.

When to Use?

• Speech Recognition: Deep learning models employ neural networks to process audio signs and recognize spoken words. It enables applications like voice-to-text conversion to understand and respond to voice commands in text.
• ‍Autonomous Vehicles: Deep learning models process and interpret data from sensors and cameras in autonomous vehicles. These models help detect and classify objects like pedestrians or traffic signs and predict the movement of surrounding entities. By analyzing this data, deep learning algorithms enable vehicles to make driving decisions, navigate safely, and adapt to dynamic road conditions.

Real-World Application

Medical imaging analysis uses deep learning models like CNN to help interpret X-rays, CT scans, MRIs, and ultrasounds to diagnose, monitor, and treat medical conditions. Such models can automatically identify and classify abnormalities like tumors from these images and predict disease progression. This technology supports radiologists and clinicians by providing more accurate diagnoses to improve patient care and treatment planning.

Natural Language Processing (NLP) Models

Natural Language Processing (NLP) is another ML technology that analyzes, understands, and generates human language. As the volume of text data increases, NLP has become vital for extracting valuable insights and automating numerous tasks.

Let’s understand some of the NLP models:

• Transformers: Transformers are a class of models that rely on the self-attention mechanism to process and generate text. They have changed NLP by improving the ability to handle long-range dependencies and content. The examples are BERT (Bidirectional Encoder Representations from Transformer) and GPT (Generative Pre-trained Transformer).
• ‍Token Embeddings: Token embeddings are an NLP technique for representing words in a continuous vector space where semantically similar tokens are closer together. Examples include Word2Vec, GloVe, and FastText.

When to Use?

Here are a few use cases of the NLP model:

• Machine Translation: NLP models like transformers are used to translate text from one language to another. Google uses these models to provide accurate translations and facilitate cross-linguistic communication.
• ‍Named Entity Recognition (NER): NLP models help to identify and classify entities within text, such as names of people, organizations, and locations. It is useful for information extraction tasks and can be applied to automated news, text summarization, and customer support.

Real-World Application

Virtual assistants such as Siri and Google Assistant are some examples of NLP models in action. They use NLP algorithms to interpret and respond to user questions in human language.

How Can Airbyte Help Build an LLM Pipeline that Leverages AI Models?

Large language models (LLMs) are becoming powerful in many applications. However, they require relevant and accurate data from several sources to function properly. To streamline data integration tasks, you can leverage no-code tools like Airbyte.

Airbyte is a data integration and replication platform that allows you to transfer data from multiple sources and load it into your preferred destination. It offers 350+ pre-built data connectors to load structured and semi-structured data into your desired destination. You can even load unstructured data into prominent vector databases, such as Pinecone, Weaviate, and Milvus. These data stores make it easier for you to share your data with LLM Frameworks like LangChain to develop AI-enabled applications.

Let’s explore some of the features that Airbyte offers:

• GenAI Workflow Support: Airbyte supports RAG-specific transformations, such as chunking and embedding, which enable you to transform and store data in a single operation.‍
• Custom Connectors: If the source you seek is unavailable, you can build custom connectors using the Connector Development Kit (CDK).‍
• Change Data Capture: The CDC feature allows you to capture changes in the source data to reflect at the destination. This feature enables you to train your LLM models with the updated data.‍
• PyAirbyte: Airbyte offers PyAirbyte, which enables you to extract data from different sources using Airbyte connectors within your Python workflows.

You can utilize these features to train your LLM model on data coming from dispersed sources. For example, consider a situation where you want to create your own LLM chatbot that answers questions related to any cryptocurrency. To achieve reliable responses, you must train your chatbot with data extracted from a crypto exchange.

The code below assists you in extracting data from CoinAPI, a prominent API for crypto markets, using PyAirbyte:

The first step in this process will be to install the PyAirbyte library in your local system. For this, you can run the code below in your preferred code editor:

%pip install --quiet airbyte

After installing the PyAirbyte, you can follow the code below to extract data from CoinAPI:

import airbyte as ab

To create and configure CoinAPI as the source connector, you can follow the code below:

source = ab.get_source(
    “source-coin-api”,
    config={
        “api_key”: ab.get_secret(“COIN_API_KEY”),
        “environment”: “production”,
        “symbol_id”: “COINBASE_SPOT_INDEX_USD”,
        “period”: “1DAY”,
        “start_date”: “2024-01-01T00:00:00”,
    },
    streams="*",
)

In the above code, mention your CoinAPI API key. You can also modify the period, symbol_id, and start_date parameters according to your requirements. Now, you can verify the config and creds by running ‘check’:

source.check()

After getting a success report from the above code, you can list the available streams for the CoinAPI source:

source.get_available_streams()

Read data from the source into the default cache:

cache = ab.get_default_cache()
result = source.read(cache=cache)

You can now extract Open, High, Low, Close, and Volume data for the chosen crypto symbol. To read the data from the cache into a Pandas Dataframe:

ohlcv_df = cache["ohlcv_historical_data"].to_pandas()

You can follow this in-depth tutorial to transform and visualize the data. After completing these steps, you can also use ML and AI libraries like TensorFlow or PyTorch to develop AI applications for your data.

Conclusion

As AI technology develops rapidly, using these models will be crucial for staying competitive and optimizing business workflows. By understanding different types of AI models, your business can leverage its power to improve operational efficiency, enhance decision-making, and encourage innovation.

FAQs

What are the four types of AI systems?

Let’s take a look at the four types of AI systems:

• Reactive Machines: These AI systems react to specific inputs with predefined responses and do not retain past experiences for future use.
• ‍Limited Memory: These machines can use past experience to inform future decisions but have limited memory scope.
• ‍Theory of mind: This type of AI is still theoretical and aims to understand how human emotions, beliefs, and intentions interact more naturally.‍
• Self-Aware: An advanced AI type involving systems with self-awareness and consciousness. These systems would have their beliefs, desires, and self-concepts.

What are the three core beliefs of AI?

Here are the three core beliefs of AI:

• Data-driven Decision Making: AI systems depend on large volumes of data to understand patterns and make decisions.‍
• Automation: AI is designed to automate repetitive, complex tasks, reducing human error. With automation, AI can optimize workflows, increasing productivity and cost savings.‍
• Adaptability: AI systems can adapt to new information and scale their operations, making them suitable for more applications.