AI models can be used to accomplish many tasks and solve complex problems; even if true, sentient is still more science fiction than scientific fact. Artificial Intelligence models are designed to mimic human behavior, but in many ways, popular AI models outperform humans in data science tasks like analysis.
AI models, including large language models capable of understanding and generating human language, rely on Machine Learning algorithms and artificial neural networks to emulate a logical decision-making process using available information and input data sets. These models are the backbone of modern intelligence tools and are skilled at analyzing information, making decisions and predictions, and providing valuable insights.
Let’s take a closer look at AI models and what they are. We will also see how AI models differ from Machine Learning models and the different types of ML models used to create AI algorithms.
What is an AI Model?
Artificial Intelligence is one of the most fascinating fields of computer science. AI studies aim to create a machine that can replicate human intelligence in real time. True AI tools that can think like humans have not been achieved. However, this doesn’t mean we can’t benefit from using AI algorithms.
Today, we use AI models for several analytical and decision-making tasks. An AI model is a program or algorithm that relies on training data to recognize patterns and make predictions or decisions. The more data points an AI model receives, the more accurate it can be in its data analysis and forecasts.
AI models rely on computer vision, natural language processing, and Machine Learning to recognize different patterns. AI models also use decision-making algorithms to learn from their training, collect and review data points, and ultimately apply their learning to achieve their predefined goals.
Large language models (LLMs) are advanced AI technologies capable of understanding and generating human language.
AI models are very good at solving complex problems with a large amount of data. As a result, they can accurately solve complex problems with a very high degree of accuracy.
What is the Difference Between Machine Learning Models and AI Models?
Many people erroneously conflate Machine Learning and Artificial Intelligence. This is likely because ML is a subset of Artificial Intelligence. However, you should be aware of a critical difference between the two.
As we previously defined, Artificial Intelligence is concerned with creating machines that simulate human thoughts, intelligence, and behaviors.
On the other hand, ML is concerned with providing machines with the ability to learn for themselves from experience and lessons without the need to be explicitly programmed. One such tool that identifies AI content is Attrock AI Content Detector that employs ML as well.
All Machine Learning models are AI models, but not all AI models will necessarily be ML models. This is an important distinction to make and will help you understand this topic in greater detail.
A Machine Learning model is an essential part of this. After all, a big part of human intelligence is learning things and predicting future outcomes based on past experiences and lessons. Similarly, AI models learn in their learning phase based on annotated data.
The Different Types of Machine Learning
There are various types of AI models, and we will go over some of the most prominent ones later in this post, but before we do that, we must go over the different types of ML.
There are three main types of ML:
- Supervised
- Unsupervised
- Semi-supervised
Supervised Machine Learning
In a supervised learning model, a human trains or teaches the algorithm what to look for. Often this person doing the training is a data scientist who is a subject matter expert in whatever task the algorithm is being taught to do.
For example, an algorithm working on image recognition might be taught by a data scientist in which images contain offensive or explicit content. The algorithm then uses this information to find other images with similar content.
AI models built with supervised learning, like support vector machines, are often used to perform predictive analysis. These models use past decisions made by subject matter experts to predict future choices that an expert might make. For instance, an AI trained on a massive dataset of high-quality photos can learn to identify specific objects or scenes within an image, allowing for automatic image tagging or content organization.
Unsupervised ML
AI models don’t have to be developed through human training. Instead, in an unsupervised learning model, software trains the algorithm. In some cases, the training method used by the training software will mimic that of a human, but they don’t necessarily have to teach in the same way.
AI models built with unsupervised learning are often used to perform descriptive analysis. These tasks include content summary, classification, extraction, and video analytics. These types of AI models can identify patterns and categorize data without human training.
Semi-Supervised ML
Semi-supervised learning models combine a little bit of both previous models we have discussed. In this setting, a human does part of the training, and software is left to handle the rest based on the initial training done by the human
Since AI models built with semi-supervised learning get a bit of both learning styles, they can also perform both predictive and descriptive analysis tasks depending on the purpose they are designed for.
The Big Issue in Training ML Models
Bias is the biggest training issue with ML models. The challenge for developers and data scientists is to try and reduce training bias to near-zero. Completely eliminating bias might be impossible, but reducing bias as much as possible is critical.
Training bias can occur in all ML models, even in unsupervised settings. Since an ML model is designed to perform specific tasks, the last thing researchers and data scientists want is bias. A famous example of model bias came from Amazon.
Amazon developed an ML model to vet applicants for development positions and highlight the most impressive resumes for human review. Amazon gave the model the resumes of the best developers currently working in the field for training data.
The problem? Since the majority of the development industry was male at the time, the model devalued and even discriminated against women applicants.
AI Model Development
Training Data
Training data is the lifeblood of AI model development. It serves as the foundation upon which machine learning models are built, enabling them to make accurate predictions and informed decisions. The quality and quantity of training data are paramount, as they directly influence the effectiveness and precision of an AI model.
There are several types of training data, including:
- Labeled Data: This type of data is annotated with relevant information, such as labels or tags, to guide the AI model in learning specific patterns.
- Unlabeled Data: Unlike labeled data, unlabeled data lacks annotations. It is used in unsupervised learning, where the AI model must independently identify patterns and relationships within the data.
- Raw Data: This unprocessed and unfiltered data is often employed in deep learning models, which excel at handling large volumes of data.
The process of collecting and preparing training data, known as data preprocessing, involves cleaning, transforming, and formatting the data to ensure it is suitable for AI model training.
Common Models
There are several different models, and they all work a little bit differently.
Some of the most popular models you might find in an AI model library include:
- Deep neural networks
- Linear regression
- Logistic regression
- Decision trees
- Random forest
Deep Neural Networks
The deep neural network is one of the most popular AI/ML models. The design for this deep learning model was inspired by the human brain and its neural network. This AI model uses layers of artificial neurons to combine multiple inputs and provide a single output value. Hence the name, deep learning.
Deep learning has been used widely in mobile app development to provide image and speech recognition services and natural language processing. Neural networks also help power computer vision applications.
This AI model represents the cutting edge of Artificial Intelligence (AI). It is very adept at solving complex problems that possess large data sets.
Deep learning will be instrumental in achieving the true computer vision and AI standards that we associate with human intelligence and science fiction stories.
Linear Regression
This AI model is very popular with data scientists working in statistics. Linear regression is based on a supervised learning model. These models are tasked with identifying the relationship between input and output variables.
A linear regression model can predict the value of a dependent variable based on the value of an independent variable. These models are used in linear discriminant analysis for several industries, including healthcare, insurance, eCommerce, and banking.
Logistic Regression
This is another popular AI model, and it is closely related to the linear regression model. However, the logistic regression model is different from the linear regression model because it is only used to solve classification-based problems.
Logistic regression is the best AI model for solving a binary classification problem. This model is adept at predicting the value or class of a dependent data point based on a set of independent variables.
Decision Trees
This AI model is straightforward and also highly efficient. The decision tree uses available data from past decisions to conclude. These trees often follow a basic if/then pattern. For example, if you eat a sandwich at home, then you will not need to buy lunch.
Decision trees can be used to solve both regression and classification problems. In addition, rudimentary decision trees powered the earliest forms of predictive analytics.
Random Forest
If one decision tree is a powerful AI model, how mighty is an entire forest? A random forest is a collection of multiple decision trees.
Each decision tree returns its result or decision, which is then merged with the results from every other tree in the forest. Finally, the combined results make a more accurate final prediction or decision.
The random forest is a great AI model when you have a large data set. This model is used for solving both regression and classification problems.
AI Model Applications
Industries that Have Seen the Largest Growth with AI Modeling
AI modeling has revolutionized numerous industries, driving growth and innovation at an unprecedented pace.
Here are some of the sectors that have experienced the most significant advancements thanks to AI modeling:
- Healthcare
- Finance
- Retail
- Manufacturing
- Transportation
These industries have reaped substantial benefits from AI modeling, including increased efficiency, cost savings, and enhanced decision-making capabilities.
AI Model Deployment
Inference and Prediction
Once an AI model has been meticulously trained, it is ready to be deployed to make predictions or decisions on new, unseen data. This process, known as inference, involves using the trained model to generate output from input data, enabling real-time decision-making and insights.
There are several ways to deploy AI models, each catering to different needs and environments:
- Cloud Deployment
- On-Premises Deployment
- Edge Deployment
The deployment process involves several critical steps:
- Model Serving
- Model Monitoring
- Model Updating
By effective deployment tactics, organizations can unlock the full potential of artificial intelligence and machine learning, achieving significant benefits in terms of efficiency, innovation, and competitiveness.
Final Thoughts
While sentient AI, like the kind made famous in science fiction, is not a reality, there are still many practical, important uses for AI tools and models. For example, if your organization wants to get the most out of its data and make better decisions, using an AI model library is a great way to accomplish this.
However, choosing the right AI tools and implementing the right models can be overwhelming, especially if you don’t have any experience with HiTech development.
So the first step your business can take is to reach out to a development partner to help you find and implement the right AI tools and deep learning models for your organization.
A partner can offer you industry experience and technical expertise when looking for the right AI models to fit your needs.
