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AI Models: A Complete Guide for Beginners and Experts

November 28, 2023

LaMDA, GPT, and others… These days, it seems like everyone’s curious about AI models and their abilities. Why all the excitement, though? Because various types of AI models are popping up in all kinds of industries really fast. Lots of companies are actively turning to AI development services to make their processes smoother and provide better customer service. But do you really know what AI models are all about?

What is an AI Model?

Now, it’s hard to think of any industry that doesn’t lean on AI in some way. In this era of “big data,” consider the vast volumes of information we amass. Processing this data would be a Herculean task without AI’s helping hand. Looking ahead, the integration of intelligent machines into various sectors is set to deepen, making it crucial for professionals in different fields to pinpoint the AI model that best suits their industry’s demands. But before we dive into the diverse array of AI models available today, let’s first unpack what is an AI model.

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An AI model is a program trained on a set of data to recognize patterns or make decisions without further human intervention. It applies different algorithms to relevant data inputs to achieve the tasks or output it’s been programmed for.

The Evolution of AI Models

Back in the early days of AI, things were pretty hands-made. Researchers and engineers had to manually create algorithms, designing them to pull out the bits of data they needed. These algorithms were based on what the experts knew and felt was right, and each one was made for specific tasks. But this approach had its problems. It struggled with too much data – a challenge known as the “curse of dimensionality.” Also, scaling up these algorithms for bigger, tougher tasks was really hard. 

Then came deep learning models. These models changed the game because they learned directly from the data without needing specific instructions. They used layers of artificial neurons that process data and trained through a method called backpropagation. Deep learning has been a big hit, useful in loads of areas like image recognition, speech and language recognition, and even gaming. But despite these models being great, they needed a lot of labeled data, which took time and money to get. To fix this, experts developed tricks like pre-training AI models and fine-tuning.

Pre-training means teaching a neural network with a big dataset, usually without much supervision, to learn general stuff. This way, you won’t need as much labeled data, and it can be used as is or further customized to suit the specific needs of the application.

Recently, generative models such as the GPT-3 language model and its newer versions, as well as image generation models such as DALL-E and StableDiffusion, have gained great importance in the field of AI. They’re good at creating content that seems human-made, like texts and images.

AI Models

AI research is heading towards building AI that can learn and adapt independently without much help from us. These autonomous AI systems will keep learning and improving, making decisions in complicated situations. Some key parts of autonomous AI are reinforcement learning, learning from human feedback, self-supervised learning, generative AI, and active learning. 

Reinforcement learning lets AI know from trial and error, and self-supervised learning finds patterns in data without clear instructions. Active learning helps AI pick the best data to learn from, so it doesn’t need as much to train.

Most Widespread Types of AI Models

In the next step, we will examine those AI models examples and highlight their most important characteristics.  

Supervised Learning Models

Supervised learning is when you teach an AI with data that’s already labeled. You give it a bunch of examples, and it figures out a rule to guess the labels for new stuff it sees. Basically, you’re giving the AI historical data and asking it to see the patterns to predict new things. 

There are two main types of supervised learning: regression and classification. Regression tries to guess an outcome based on what it knows, even when there are lots of possible answers. Classification sorts data into different categories based on which one it thinks is most likely.

Linear Regression

Linear Regression is a common type of supervised learning used a lot in statistics. It looks at the link between what you put in and what you get out. In simple terms, it predicts one thing based on another.

Decision Trees

The Decision Tree model is like a map that helps you decide based on past choices. It’s straightforward, really popular, and gets its name because it splits data up in a way that looks like a tree, with branches leading to different conclusions.

Random Forest

Random Forest is a type of model that’s good for sorting data (classification) and predicting things (regression). It works by using a bunch of decision trees. Think of it like creating a forest from these trees. Each tree is trained on different data subsets. The model merges the results and all their answers using a bagging method. This way, it makes better predictions.

Random Forest

Source: Wikipedia 

Neural Networks

One of the most popular AI/ML models, Neural Networks, is set up a bit like the human brain, with lots of layers and connections called artificial neurons. These networks are great for recognizing speech, figuring out what’s in pictures, and understanding language.

Support Vector Machines (SVM)

SVM is a fast and effective model, especially when you don’t have a lot of data. It’s really good at sorting data into two groups (binary classification). Even though there are newer methods like neural networks, SVM is quicker and works better with small datasets, like when you’re classifying text. It’s a type of supervised learning, so it’s used for sorting things into groups, spotting outliers, and guessing numbers.

Supervised Learning Models
Popular Use-CasesPredicting sales trends
Image recognition
Customer segmentation
Popular AlgorithmsLinear Regression
Decision Trees
Random Forests
GoalTo predict or classify data based on past examples
ExamplesEmail spam filtering
Credit scoring systems
Medical diagnosis

Unsupervised Learning Models

AI models can also be made using unsupervised machine learning. This method involves more automatic steps. The models are trained by programs, often in ways that kind of copy how humans would train them. AI models created this way are usually used for figuring out and explaining data. They help summarize and sort information as well as find patterns or rules in it.

K-Means Clustering

K-Means Clustering is a basic kind of unsupervised learning algorithm. It’s used to split a bunch of data into different groups or clusters. These groups are made based on how similar the data points are to each other. You’ll see it used in cases like customer segmentation and image compression.

Hierarchical Clustering

Hierarchical Clustering is another way to group data. It’s different from K-Means because it sorts data into a sort of family tree of clusters. You can actually see how the data groups up, step by step. This sort of AI transparency is really handy when you have data that’s kind of layered or has levels to it.

Principal Component Analysis (PCA)

Then there’s Principal Component Analysis, or PCA. It’s a way to simplify complicated data while keeping the important bits. PCA finds the most important parts of a dataset and squishes them down, making it easier to work with. This is helpful for data visualization and noise reduction.

Generative Adversarial Networks (GANs)

GAN is one of the AI-generated models that generates its output by drawing parallels with the input patterns it observes. By doing so, the artificial intelligence model produces the output that could plausibly be produced based on the same input.

Unsupervised Learning Models
Popular Use-CasesAnomaly detection 
Data clustering 
Association mining
Dimensionality reduction
Popular AlgorithmsK-Means Clustering 
Hierarchical Clustering 
Apriori Algorithm
Principal Component Analysis (PCA)
GoalTo discover underlying patterns, groupings, or associations in data without using labeled outcomes or guidance
ExamplesSegmenting customers based on purchasing behavior
Detecting fraudulent activities in banking transactions
Market basket analysis in retail
Reducing the number of features in a dataset for easier analysis

Reinforcement Learning Models

Here, things get a bit more interesting. If you ask how to train an AI model of such type, this runs through trial and error, rewarding them when they make the right decisions.

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Q-learning is a key method in Reinforcement Learning. It’s all about learning from the actions it takes. The model tries different things, sees what works and what doesn’t, and learns from it.

Deep Q Networks (DQNs)

Then, there are Deep Q Networks or DQNs. Think of them as a smarter version of Q-learning. They use deep learning, which means they can handle more complex problems and make better decisions.

Policy Gradient Methods

Last, we have Policy Gradient Methods. Here, the method of AI modeling is a bit different. Instead of just learning from actions, they focus on learning the best ‘policy’ or set of rules to follow. This way, the model learns how to make good decisions in any situation.

Reinforcement Learning Models
Popular Use-CasesRobotic motion
Autonomous transport
Text mining
Optimization for treatment policies in healthcare
Trade execution in finance
Popular AlgorithmsQ-learning 
Deep Q Networks (DQNs)
Policy Gradient Methods
Monte Carlo Methods
GoalTo train models to make a sequence of decisions by rewarding desirable actions and/or punishing undesirable ones
ExamplesAlphaGo playing
Self-driving cars navigating traffic 
Robotic arms in manufacturing 
Content curation in streaming services 

Source: IBM

Pre-trained and Transfer Learning Models

Moving on, we get into the realm of Pre-trained and Transfer Learning Models. These are a big deal because they save a lot of time and effort.

BERT and Natural Language Processing

Take BERT, for instance, which stands for Bidirectional Encoder Representations from Transformers. It’s a mouthful, but it’s revolutionizing how machines understand human language. BERT helps computers get the gist of what we’re saying, making things like search engines way smarter.

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Computer Vision with ResNet and VGG

Then, there’s the fascinating world of computer vision. Models like ResNet and VGG are at the forefront here. They help computers ‘see’ and ‘understand’ images and videos. From recognizing faces to interpreting scenes, these models are making huge strides in how machines interpret visual data.

Pre-trained and Transfer Learning Models
Popular Use-CasesImage recognition
Natural language processing
Speech recognition
Popular AlgorithmsConvolutional Neural Networks (CNNs)
Recurrent Neural Networks (RNNs)
GoalTo leverage existing models to save time and resources in training new models
ExamplesGoogle’s BERT for language understanding, ResNet for image processing

Applications of AI Models

Some studies show that AI-generated models might greatly boost the world’s economy  – by 14% to 16% by 2030. But like other tech breakthroughs, this big change won’t happen simultaneously. To get into this AI-powered future, we’ll need to put money into tech – think computers and servers and hire AI engineers who can meet new market demands and job shifts.

Healthcare and AI

AI models are seriously shaking things up in healthcare. It’s not just about helping doctors with their diagnoses anymore. Now, AI is smart enough to spot things in medical images that even the sharpest docs might miss, like early signs of cancer. And in medicine-making, it’s speeding up the process of finding new drugs.

Finance and AI

AI’s proving to be super handy in finance, especially with trading stocks and sniffing out fraud. It’s way faster and more precise than humans at making those split-second market moves. ​Banks are also getting a hand from AI to determine who’s good for a loan and who’s not.

Entertainment and AI

Entertainment’s got a new player: AI-generated models. For gamers, it means tougher, smarter challenges. If you’re streaming movies or shows, AI’s the one digging through your watch history to recommend what to binge on next. It’s even getting artsy, helping to create new tunes and artworks.

Autonomous Systems and AI

AI is kind of like the brains of self-driving cars, making them drive smarter. Thanks to AI, drones are flying better than ever. In the robotics industry, technology is enhancing the utility and safety of robots, whether they operate in factories or assist in homes.

Final words

What’s next, you ask? Well, even though we’re not in a sci-fi movie with super-smart AI, these days, AI is really handy for lots of things. Say your company wants to look closely at its data to make better decisions. Using AI could help a lot with that. But choosing the best AI/ML models or programming languages for your needs can be tough, especially if you’re not a tech whiz.

The first step? Consider getting help from a company that knows all about AI, like Relevant. We have experts who can help you navigate through all the AI options and find something that works just right for your business.

Written by
Product Manager at Relevant Software
Vadim Struk is a seasoned Product Manager at Relevant Software with nearly a decade of experience in the technology industry. During his time at Relevant, Vadim honed his skills in business analysis and product management, making him a key figure in the company's development and strategic planning. His expertise in requirements engineering and management is particularly noteworthy as it involves the painstaking task of gathering, analyzing, and defining what a product must achieve to satisfy the needs of stakeholders and end users. Vadim's role extends beyond the initial stages of product development. He is also actively involved in overseeing the implementation of solutions, ensuring that each stage of the product life cycle aligns with the company's vision and goals.

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