As Artificial Intelligence (AI) models become more significant and prevalent in practically every industry, it is becoming increasingly necessary for organizations to understand how these models work and the potential consequences of deploying them.
This article will provide an overview of artificial intelligence models and their numerous uses. We will look at common applications of AI models and how they may be used effectively in business and other domains.
In this article, we will cover the following topics:
- Concepts of AI vs. ML vs. DL
- What is an AI 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
To begin, it is necessary to understand the fundamental concepts of artificial intelligence types. The phrases Artificial Intelligence, Machine Learning, and Deep Learning are frequently used together. As a result, there is some misunderstanding regarding what a machine learning model is and how it differs from an AI model.
What is Artificial Intelligence (AI)?
Artificial intelligence (AI) is an area of computer science concerned with the development of machines that can simulate human intelligence. AI research is concerned with the subject of how to construct machines capable of intelligent behavior.
What is Machine Learning?
Machine Learning (ML) is an AI subset linked with giving machines the ability to learn from experience without having to be explicitly programmed. In other words, ML, or machine learning, is a subset of AI. While all ML models are, by definition, AI models, this may not necessarily be the case.
What is Deep Learning?
Deep learning (DL) is a subset of machine learning, which is itself a subset of AI. Deep learning algorithms learn to discover patterns in data, whereas machine learning algorithms learn to perform any task.
What Is An AI Model?
In simple terms, an AI model is a tool or algorithm that is based on a certain data set and can make a decision without the need for human intervention in the decision-making process. An AI model is a program or algorithm that uses a set of data to recognize specific patterns. When given enough knowledge, often a massive quantity of data, it can draw a conclusion or make a forecast. As a result, AI models are particularly well-suited to solve complex problems while giving greater efficiency/cost savings and accuracy than basic solutions.
Deploy an AI model
A computing device or server with a lot of processing power and storage is required to deploy and run an AI model. TensorFlow, PyTorch, and Caffe2 are ML frameworks that allow you to run an AI model with just a few lines of code. While prototyping is straightforward, managing AI pipelines and computing resources at scale is quite complicated and necessitates sophisticated infrastructure. This is why most AI initiatives fail to progress past the proof-of-concept stage.
There is a variety of AI hardware available for various tasks. GPUs (Graphics Processing Units) are commonly utilized for training and inference tasks (NVIDIA Jetson). Central Processing Units (CPUs) are generally utilized for inference, although they are also employed for training workloads (e.g., Intel Xeon). Intel VPU and Google AI accelerators are examples of coprocessors and AI accelerators.
The Cloud was the only way to deliver sufficient computer power for AI tasks in the early days. Viso Suite, Hugging Face, Google Colab, and Amazon SageMaker are examples of hosted platforms for AI model deployment.
In recent years, a new paradigm known as Edge Computing has enabled models to be deployed at the network’s edge (Edge AI). Running AI models at the edge enabled the development of more efficient, private, and robust real-world applications.
Deep learning breakthroughs have expedited this trend, resulting in model frameworks that are lighter, smaller, and faster (Edge-optimized models include TensorFlow Lite or YOLOv7 Lite). Such AI models require significantly less expensive technology to run, resulting in significant cost savings.
The value of AI models in business
In business, data and artificial intelligence are becoming increasingly crucial. Companies rely on AI models to make sense of the data that is being generated at an unprecedented rate. When applied to real-world situations, AI models can handle tasks that people might otherwise find too difficult or time-consuming.
We can see many key strategies for how AI models will affect businesses:
1. Collect data to train AI models
When competitors have no or limited access to data, or when it is difficult to obtain, the capacity to collect data for training is extremely valuable. Businesses can use data to train AI models and continuously re-train (upgrade) current models. Data can be collected in a variety of methods, including online scraping and the use of sensors or cameras. Access to vast amounts of data, in general, promotes the training of better-performing AI models and, as a result, the establishment of competitive advantages.
2. AI models can be used to generate new data
A model, for example, can generate new data that is similar to the training data by employing a Generative Adversarial Network (GAN). New generative AI models enable picture production for art and photorealistic photographs (such as DALL-E 2). To train more robust algorithms, AI models can also be used to generate wholly new data sets (synthetic data) or artificially bloat existing data (data augmentation).
3. AI models can be used to analyze existing data
The practice of utilizing a model to predict the output for a given input is known as model inference. This is done by applying the model algorithm to fresh input data (existing data or real-time sensory data) that the model has never “seen” before and then interpreting the results. The model inference is typically used in practical AI applications to “apply” a trained model in business tasks, such as person recognition or item detection and tracking in a video stream.
4. AI models can be used to automate tasks
AI models are integrated into workflows in order to be used in business. Data acquisition, transformation, data analysis, and data output are all steps in a pipeline. A vision pipeline in computer vision applications acquires the video stream and performs image processing before feeding individual images into the DL model. When employed in industry, for example, this can be used to automate visual inspection or to count bottles on conveyor belts.
Overall, AI models can assist businesses in being more efficient, competitive, and profitable by allowing them to make better data-driven decisions. In the future, AI models will likely become even more significant in business, as more and more organizations embrace them to gain a competitive advantage.
Following that, we will outline the top, most commonly utilized AI models that you should be aware of. Then we’ll go over the entire list and describe each one individually.
List of the Most Popular AI Models
In the following step, we will explore various AI algorithms and identify their most important aspects.
1. Linear Regression
Linear Regression is a supervised learning model that is widely used in statistics. This model’s major purpose is to discover the connections between the input and output variables. In other words, it forecasts the value of a dependent variable depending on the value of a particular independent variable. Linear regression models are widely used in a wide range of industries, including banking, retail, construction, healthcare, and insurance.
2. Random Forest
Random Forest is an ensemble learning model that may be used to solve regression and classification issues. It employs many decision trees and uses3. the bagging approach to generate the final forecast. To put it simply, it creates a ‘forest’ of numerous decision trees, each trained on a distinct data subset, and then integrates the results to make more accurate predictions.
3. Deep Neural Networks
Deep Neural Networks (DNN), one of the most common AI/ML models, is an Artificial Neural Network (ANN) with several (hidden) layers between the input and output layers. These are built on linked components known as artificial neurons and are inspired by the neural network of the human brain. To understand more about this AI model, see our explanation of how Deep Neural Network models function. DNN models are used in a variety of fields, including speech recognition, picture recognition, and natural language processing (NLP).
4. Logistic Regression
Logistic regression is a prominent ML model that is used to solve binary classification issues. It is a statistical model that predicts the class of the dependent variable given a collection of independent factors. This model is comparable to the Linear regression model, however, it is solely used to solve classification-based issues.
5. Decision Trees
In the field of Artificial Intelligence, the Decision Tree (DT) model is used to arrive at a conclusion based on the evidence from prior choices. Decision Tree is a simple, efficient, and widely popular model called after the way data is split into smaller sections mimics the structure of a tree. This model may be used to solve regression as well as classification issues.
6. Support Vector Machines
The Support Vector Machine, or SVM, is a fast and efficient model that excels at processing small amounts of data. It is useful for binary classification problems. SVM is faster and performs better with a dataset of restricted samples, such as in text classification issues, than newer technologies like as artificial neural networks. This is a supervised machine learning approach for classification, outlier detection, and regression problems.
Conclusion: What’s Next?
To summarise, many AI models are utilized to solve various challenges ranging from self-driving cars to object identification, face recognition, and pose estimation. As a result, understanding the models is critical for determining which one is best suited for a specific task. With the rapid advancement of artificial intelligence usage, these models are almost expected to be used across all industries in the near future. Let me know in the comments which Ai model you like the most.
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