As Artificial Intelligence (AI) models become more important and widespread in almost every sector, it is increasingly important for businesses to understand how these models work and the potential implications of using them.
This guide will provide an overview of AI models and their various applications. We will explore popular use cases of AI models and discuss how they can be used effectively in business and other settings.
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In this article, we will cover the following:
- Concepts of AI vs. ML vs. DL
- What is an AI model
- What is an ML model
- What is a DL model
- Value of AI models for businesses
- The most popular AI models
So let’s get started without further ado!
AI vs. Machine Learning vs. Deep Learning
First, it is important to gain a clear understanding of the basic concepts of artificial intelligence types. We often find the terms Artificial Intelligence and Machine Learning or Deep Learning being used interchangeably. Therefore, there is some confusion about what a machine learning model is and how it is different from an AI model.
Artificial Intelligence (AI)
Artificial Intelligence (AI) is a subfield within computer science associated with constructing machines that can simulate human intelligence. AI research deals with the question of how to create computers that are capable of intelligent behavior.
Machine Learning (ML)
Machine Learning (ML) is a subset within AI associated with providing machines the ability to learn from experience without the need to be programmed explicitly. Or in simple words, ML or machine learning is a part of AI. So while all ML models are, by default, AI models, the opposite may not always be true.
In ML, it’s important to distinguish between supervised vs. unsupervised learning, and a hybrid version named semi-supervised learning. In short, supervised learning is where the algorithm is given a set of training data. Supervised models learn from ground truth data that was labeled manually by data scientists. In computer vision, this process is called image annotation. The model uses this data to learn (AI training) how to make predictions on new data (AI inferencing).
On the other hand, unsupervised learning is where the algorithm is given raw data that is not annotated. Here, the algorithm is not explicitly told what to do with it and must learn how to make predictions by itself. This type of ML model is suitable to perform specific tasks on distinct data types, for example, fraud detection or financial analysis, that require identifying a hidden structure in unlabeled data.
Deep Learning (DL)
Deep learning (DL) is a subset of machine learning, which is a subset of artificial intelligence. Deep learning is concerned with algorithms that can learn to recognize patterns in data, whereas machine learning is more general and deals with algorithms that can learn any kind of task.
What Is An AI Model?
In simple terms, an AI model is a tool or algorithm which is based on a certain data set through which it can arrive at a decision – all without the need for human interference in the decision-making process.
An AI model is a program or algorithm that utilizes a set of data that enables it to recognize certain patterns. This allows it to reach a conclusion or make a prediction when provided with sufficient information, often a huge amount of data. Hence, AI models are particularly suitable for solving complex problems while providing higher efficiency/cost savings and accuracy compared to simple methods.
If you are looking for a more detailed guide about the topic, we recommend our complete guide about the differences between Deep Learning vs. Machine Learning.
What Is An ML Model?
A machine learning model, or ML model, is a kind of AI model that uses a mathematical formula to make predictions about future events. It is trained on a set of data and then used to make predictions about new data. Some common examples of ML models include regression models and classification models.
What Is A DL Model?
A deep learning model, or a DL model, is a neural network that has been trained to learn how to perform a task, such as recognizing objects in digital images and videos, or understanding human speech.
Deep learning models are trained by using large sets of data and algorithms that enable the model to learn how to perform the task. The more data the model is trained on, the better it can learn to perform the task.
The term “deep” of “deep learning” refers to the fact that DL models are composed of multiple layers of neurons, or processing nodes. The deeper the model, the more layers of neurons it has. This allows the model to learn more complex tasks by breaking them down into smaller and smaller pieces.
For example, ResNet is a deep learning model for computer vision tasks such as image recognition. It is one of the deepest models currently available, with a version that contains 152 layers (ResNet-152).
YOLO, or “You Only Look Once,” is a deep learning model for real-time object detection. Surpassing YOLOv4 and YOLOR, the latest version, YOLOv7, is super fast and very accurate, the current state of the art for several AI vision tasks.
Deploy an AI model
To deploy and run an AI model, a computing device or server is needed that provides a lot of processing power and storage. ML frameworks like TensorFlow, PyTorch, or Caffe2 let you run an AI model with a few lines of code. While prototyping is simple, the management of AI pipelines and computing resources at scale is very complex and requires sophisticated infrastructures. This is why most AI projects fail to move beyond the PoC phase.
There is a range of AI hardware suitable for different tasks. Graphics Processing Units (GPU) are widely used for training and inference workloads (NVIDIA Jetson). Central Processing Units (CPU) are used primarily for inference, but also training workloads (e.g., Intel Xeon). Coprocessors and AI accelerators include Intel VPU, Google Coral TPU, and Qualcomm NPU.
In the early days, the Cloud was the only way to provide sufficient computing resources for AI workloads. Hosted platforms to deploy AI models include Viso Suite, Hugging Face, Google Colab, or Amazon SageMaker.
In recent years, a new paradigm called Edge Computing made it possible to deploy models to the network edge (Edge AI). Running AI models at the Edge made it possible to build real-world applications that are more efficient, private, and robust.
This trend is accelerated by advances in deep learning that led to model frameworks becoming much lighter, smaller, and faster (Edge-optimized models include TensorFlow Lite or YOLOv7 Lite). Such AI models require several times cheaper hardware to run, leading to immense cost advantages.
At viso.ai, we provide automated infrastructure to deploy DL models faster and more efficiently. Our enterprise platform Viso Suite provides a visual no-code interface to automate the deployment of AI models to the Edge and the Cloud.
The value of AI models in business
Data and artificial intelligence are becoming increasingly important in business. Data is being generated at an unprecedented rate, and companies rely on AI models to make use of it. When applied to real-world problems, AI models can solve tasks that would otherwise be too difficult or time-consuming for humans to do.
We see a few key strategies for how applying AI models will impact businesses:
- Strategy #1: Collect data to build AI models
- Strategy #2: Use AI models to generate new data
- Strategy #3: Use AI models to understand data
- Strategy #4: Use AI models to automate tasks
Let’s have a closer look at those strategies.
1. Collect data to train AI models
The ability to collect data for training is of utmost value when competitors have no or limited access to data, or when it is difficult to obtain. Data enables businesses to train AI models and continuously re-train (improve) existing models.
There are many ways to collect data, including scraping it from the web, or through the use of sensors or cameras. In general, access to large amounts of data enables the training of better-performing AI models and thus the development of competitive advantages.
2. AI models can be used to generate new data
A model can generate new data similar to the training data, for example, by using a Generative Adversarial Network (GAN). New, generative AI models provide image generation capabilities to create art and photorealistic images (such as DALL-E 2).
AI models can also be used to generate completely new data sets (synthetic data) or artificially bloat existing data (data augmentation) to train more robust algorithms.
3. AI models can be used to analyze existing data
Model inference is the process of using a model to predict the output for a given input. This is done by applying the model algorithm to new input data (existing data or real-time sensory data) that the model has never “seen” before and then interpreting the results.
Practical AI applications usually use model inference to “apply” a trained model in business tasks, for example, to perform person recognition or object detection and tracking in a video stream.
4. AI models can be used to automate tasks
To apply AI models in business, they are integrated into pipelines. A pipeline consists of several steps, including data acquisition, transformation, data analysis, and data output.
In computer vision applications, a vision pipeline acquires the video stream and applies image processing before feeding individual images into the DL model. When applied in manufacturing, for example, this can be used to automate visual inspection or perform automated object counting of bottles on conveyor belts.
Overall, AI models can help businesses to become more efficient, competitive, and profitable, by allowing them to make better decisions based on data analysis. In the future, AI models will likely become even more important in business, as more and more companies adopt them to gain a competitive advantage.
Next, we will list the top, most used AI models which you need to know. Then, we will go through the entire list and describe them one by one.
List of the Most Popular AI Models
- AI Model #1: Linear Regression
- AI Model #2: Deep Neural Networks
- AI Model #3: Logistic Regression
- AI Model #4: Decision Trees
- AI Model #5: Linear Discriminant Analysis
- AI Model #6: Naive Bayes
- AI Model #7: Support Vector Machines
- AI Model #8: Learning Vector Quantization
- AI Model #9: K-nearest Neighbors
- AI Model #10: Random Forest
Overview of the Most Important AI Models
In the next step, we will look into those AI techniques and highlight their most important characteristics.
1. Linear Regression
Used extensively in statistics, Linear Regression is a model that is based on supervised learning. The main task of this model is to find the relationships between the input and output variables. In simpler words, it predicts the value of a dependent variable based on a given independent variable.
Linear regression models are widely used in various industries, including banking, retail, construction, healthcare, insurance, and many more.
2. Deep Neural Networks
One of the most popular AI/ML models, Deep Neural Networks or DNN, is an Artificial Neural Network (ANN) with multiple (hidden) layers between the input and output layers. Inspired by the neural network of the human brain, these are similarly based on interconnected units known as artificial neurons. To learn more about this AI model, read our guide about how Deep Neural Network models work.
DNN models find application in several areas, including speech recognition, image recognition, and natural language processing (NLP).
3. Logistic Regression
A very popular ML model, Logistic regression is the preferred method for solving binary classification problems. It is a statistical model that can predict the class of the dependent variable from the set of given independent variables.
This is similar to the Linear regression model, except that it is only used in solving classification-based problems.
4. Decision Trees
In the field of Artificial Intelligence, the Decision Tree (DT) model is used to arrive at a conclusion based on the data from past decisions. A simple, efficient, and extremely popular model, Decision Tree is named so because the way the data is divided into smaller portions resembles the structure of a tree. This model can be applied for both regression and classification problems.
5. Linear Discriminant Analysis
Linear Discriminant Analysis, or LDA, is a branch of the Logistic Regression model. This is usually used when two or more classes are to be separated in the output. This model is useful for various tasks in the field of computer vision, medicine, etc.
6. Naive Bayes
Naive Bayes is a simple yet effective AI model useful for solving a range of complicated problems. It is based on the Bayes Theorem and is especially applied for test classification.
The model works on the assumption that the occurrence of any particular feature does not depend on the occurrence of any other feature. Since this assumption is almost never true, the model is called ‘naive’.
It can be used for both binary and multiple-class classifications. Some of its applications include medical data classification and spam filtering.
7. Support Vector Machines
SVM, or Support Vector Machine, is a quick and efficient model that excels in analyzing limited amounts of data. It is applicable to binary classification problems.
Compared to newer technologies such as artificial neural networks, SVM is faster and performs better with a dataset of limited samples – such as in text classification problems.
This is a supervised ML algorithm that can be used for classification, outlier detection, and regression problems.
8. Learning Vector Quantization
Learning Vector Quantization (LVQ) is a type of Artificial Neural Network that works on the winner-takes-all principle. It processes information by preparing a set of codebook vectors that are then used to classify other unseen vectors. It is used for solving multi-class classification problems.
9. K-nearest Neighbors
The K-nearest Neighbors (kNN) model is a simple supervised ML model used for solving both regression and classification problems. This algorithm works on the assumption that similar things (data) exist near each other.
While it is a powerful model, one of its major disadvantages is that the speed slows down with an increase in the data volume.
10. Random Forest
Random Forest is an ensemble learning model useful for solving both regression and classification problems. It operates using multiple decision trees and makes the final prediction using the bagging method.
To simplify, it builds a ‘forest’ with multiple decision trees, each trained on different data subsets, and merges the results together to come up with more accurate predictions.
What’s Next?
To sum up, different AI models are used for solving different problems, from self-driving cars to object detection, face recognition and pose estimation. Therefore, being aware of the models is essential for identifying the one best suited for a particular task. With the rapid improvement in artificial intelligence adoption, these models are certain to be applied across all industries in the near future.
If you are looking for a way to build, deploy and scale AI models with a powerful end-to-end platform, check out Viso Suite. You find the Whitepaper here.
To read more about related topics, we recommend you the following articles about AI and ML for beginners and experts:
