The first time I came across the term, ‘AI models’, it was when I was undertaking a couple of assignments concerning the term. I am writing about it on my personal blog this time and hope you will enjoy reading it.
I believe that artificial intelligence has sunk deep into almost every aspect of people’s lives. For example, we have AI in our music system that plays our favorite songs on command.
We have AI-controlled surveillance systems, and AI-led automation that streamlines the business operations of many companies. And we’ve AI-powered chatbots answering customer queries. We also have AI for writing and translating text and performing facial recognition, data analysis, cybersecurity, and so on.
No need to say that AI has transformed human lives, businesses, and industries by helping them be creative and valuable in their forte.
Talking about businesses, AI-led business transformation has been unmistakable based on the following upsides –
AI Benefits For Businesses
- Accurate & realistic decisions – AI helps organizations gain data-based insights, enabling them to make informed decisions. These decisions are more accurate and realistic, not based on pure instincts influenced by personal biases or preferences.
- Better productivity – Organizations favoring artificial intelligence machines can effectively handle and simplify tasks with considerable volume and velocity. The productivity gains through AI-assisted machines usually stand at a scale beyond human capacity. It’s more noticeable in terms of how AI saves time for workers by removing time-consuming mundane tasks.
- Speedy business – AI helps businesses with productivity gains by enabling shorter cycles and reduced time in handling tasks. This results in better ROI through a reduced timeline enabled by AI.
- Personalized customer experience – AI-led efficiency helps organizations build highly personalized and customized experiences and services.
Conclusively, AI models deliver a lot more benefits to organizations if they understand how to leverage these technologies to their business purposes.
In this blog, I am breaking down AI models, their types, benefits, use cases, and more details to make the blog a comprehensive read for people seeking a profound understanding of AI models.
What Is an AI Model?
First of all, understand that AI or Artificial Intelligence is an intelligent machine that depends on large datasets to get trained and become skilled in making predictions.
The AI models, therefore, are programmatically designed to study the datasets so that they can simulate how humans think and make predictions accordingly.
The models analyze and process multiple data points to identify patterns to make predictions in somewhat exact replications of human intelligence.
A somewhat relatable example of an AI model is the analogy of teaching a student how to identify a dog by showing him a lot of pictures of the animal. It continues until the student learns to identify the dogs in different situations.
You teach an AI model similar way but that involves much more data to be able to learn patterns within the data and make predictions on its own.
How Is an AI Model Different from A Machine Learning Model?
AI models use algorithms to simulate or replicate human intelligence. On the other hand, ML or Machine Learning models self-evolve and learn by teaching machines to operate and optimize themselves.
ML’s efficiency gets better and better over time based on how it makes decisions and learns from them.
Not all AI models are ML models but all ML models are AI models. An AI model is every system that can perform tasks that would require human intelligence (e.g. making predictions, recognizing images, etc.)
AI models represent different aspects within the field of AI. You can say they are the specific representation of AI algorithms trained on large datasets to perform specific tasks.
An Insight into The Value Of Dataset In AI Models
Since we talk about datasets in AI models, know that the quality of data is extremely important for these models to get trained efficiently. Otherwise, the chance of them making wrong predictions is inevitable due to wrong/corrupt training data.
Just as a student’s performance is based on the quality of his learning, similarly, an AI model’s efficiency is evaluated based on different metrics, such as accuracy, precision, and recall.
Are AI and AI Models Two Different Terms?
Though they are related but represent different aspects within an AI landscape.
You can say artificial intelligence is quite a broad field of creating an intelligent system that can perform mundane tasks, such as learning perception, reasoning, problem-solving, and so on.
Since these tasks typically involve human intelligence to get done, the use of AI means saving time and efforts that could otherwise be invested in more heavy-handed, creative projects by humans.
What Does The Term, “AI” Mean?
When we use the term “AI”, we generally relate it to a powerful system encompassing different techniques, methodologies, algorithms, etc. It also combines traditional rule-based systems and machine learning approaches which are modern.
Some Notable Examples of AI Systems
- Recommendation Algorithms – These AI algos (or algorithms) offer tailored recommendations based on a user’s preferences or viewing history of content. Netflix, Amazon, and YouTube fall under this example of AI.
- Search Algorithms – These AI algos are used by search engines, like Google, to help users find relevant results in response to their queries. The reason you find (nearly) accurate answers for your search queries in Google or other search engines is actually because of these algorithms. They analyze and process a lot of technicalities in the background before ranking and displaying relevant results for your search terms.
- Face ID – It is a type of AI application that maps the geometry of a user’s facial features for a secured authentication to unlock the user’s device safely. Apple Inc. has designed and developed Face ID for its iPhone and iPad Pro.
- Smart Assistants – Amazon Alexa, Google Assistant, and Apple Siri are some of the AI-powered virtual assistants that use Natural Language Processing (NLP) to understand and respond to user queries.
- Self-Driving Cars – These types of cars use machine learning algorithms, cameras, and sensors to help drivers navigate roads, find obstacles, and make real-time decisions.
- Healthcare Management – AI-assisted technologies contribute to healthcare sectors, in terms of medical diagnosis, drug discovery, and personalized treatment plans.
AI models are of different types, such as supervised learning models, unsupervised learning models, reinforcement learning models, deep learning models, etc.
List Of Different Types Of AI Models
In this section of my blog, I am going to write down specific categories of artificial intelligence. I will focus more on the application of specific models and techniques within the AI field.
That’s because categorizing AI into specific categories is nearly impossible based on the fact that it is a constantly evolving field coupled with other factors, such as –
- It is continuously expanding into new areas like healthcare
- There have been new models and paradigms coming up in the AI field
- Because AI sometimes intersects with other fields like neuroscience and robotics, it becomes more suitable for diverse applications, and complex.
- Characteristics of some AI models are interchangeable; meaning you can use them in different application fields. For example, deep learning, which is a subdomain of machine learning, can also be used in generative AI. An example is – Generative Adversarial Networks (GANs) used in Gen AI to create realistic data.
Types Of AI Models
There are basically three types of AI models:
- Generative AI
- Machine Learning
- Deep Learning
Furthermore, machine learning is categorized into three distinct subfields;
- Supervised learning
- Unsupervised learning
- Reinforcement learning
It should be borne in mind that categorizing AI into specific models or subfields is nearly impossible as the AI field continues getting better, with new versions, technologies, models, and applications appearing off and on.
Also, most of the characteristics of AI models are interchangeable, meaning some techniques or principles of a specific AI model can be used in another AI model.
For example, neural networks like CNNs (Convolutional Neural Networks) are used in deep learning as well as in generative AI. The use of CNNs in deep learning involves image recognition and image classification, whereas, in generative AI, it is used for creating realistic images or modifying existing images.
Machine Learning Models
Before I define machine learning models, let me tell you what machine learning stands for. It is very necessary at this point of my narration, given MI (machine learning) is also a vast field of technology.
What is Machine Learning?
Machine Learning is a subdomain of artificial intelligence. It creates techniques for systems and algorithms. These algorithms and systems enable computers to learn from data and make decisions accordingly.
Technically speaking, ML is any system or algorithm that learns from data and makes decisions or predictions on its own based on what it has learned from that data.
So, it learns from data, makes decisions autonomously, and becomes mature over time.
What types of algorithms or systems does machine learning create?
It creates supervised learning algorithms, unsupervised learning algorithms, and reinforcement learning algorithms. Each algorithm has its respective purpose.
For example, supervised learning algorithms learn from labeled data to make predictions and decisions. These algorithms include neural networks, SVM (Support Vector Machine), and Linear regression.
ML systems are different with respective programmed goals.
For example, classification systems assign labels to input data. It doesn’t directly involve creating algorithms. Rather, it creates techniques and frameworks for algorithms to learn from data. It is ML engineers (human programmers) who choose algorithms, apply ML techniques, and train models.
What are Machine Learning Models?
The simple definition of a machine learning model is that it is a computer program that has been trained on a specific dataset so that it can perform specific tasks such as predictions, classifications, or decision-making based on a new set of data.
A trained ML model can identify patterns and make predictions from unseen or new data. The model is the result of machine learning techniques. It learns patterns from new data and makes predictions accordingly.
Types Of Machine Learning Models
Supervised Learning Models
A supervised learning model is one of the main types of machine learning models which are trained using labeled data where input data has a corresponding label. You can also say that the input data is paired with the correct outcome.
The model learns to map inputs to outputs, meaning it identifies the rule that takes the input and turns it into a desired outcome (i.e. prediction or classification).
Note that the most defining characteristic of a supervised learning model is that it is trained on labeled data. There might be some unavoidable cases involving the use of unlabeled data alongside labeled data while training the model. However, the use of labeled data is unquestionably the standard practice here.
Examples of supervised learning models include spam filters, image recognition, weather prediction, etc.
Why Is It Called the Supervised Learning Model?
The main reason why it is called supervised learning is based on the fact that it uses labeled data. In fact, labeled data is a supervisor here, not a human supervisor. This labeled data provides a correct baseline for the ML model, enabling it to learn from the data and make predictions accordingly.
Labeled data is a teacher here consisting of input-output pairs in which case the output or label shows the result for each input. The role of humans, in this context, involves preparing and labeling the data. Once the data is prepared, it works as a supervisor in the learning process.
How Long Does It Take For A Supervised Learning Model To Become Expert In Making Predictions?
The exact timeline for a Supervised learning model to be able to make accurate predictions is not certain, considering there are various factors involved in this context.
Key factors, such as size and diversity of the dataset, model complexity, and architecture, training time, iterations and epochs, computational resources, as well as evaluation and fine-tuning are what specifically determine the timeline for supervised learning to become a skilled predictor.
The more complex models and larger datasets would take hours to days to be expert in making predictions. Simple models and small datasets don’t require much time, in this context.
In addition, it is also a never-ending improvement process coupled with experimentation and patience that factors in to capacitate a supervised learning model proficient in making predictions.
Unsupervised Learning Models
An unsupervised learning model is another type of machine learning model trained on unlabeled data. It doesn’t require human intervention/supervision to execute the task of data labeling. It works on its own to identify patterns, relationships, or structures within the unlabeled data.
Key Use Cases Of Unsupervised Model
- Segmenting customers – Identifying different customer groups based on their purchasing behavior or demographics. This enables to tailor marketing campaigns for specific customer groups.
- Data anomaly detection – Detecting unusual data patterns in transactions that may signal unauthorized or fraudulent activity. Similarly, it identifies unusual network activity that may indicate a cybersecurity breach.
- Recommendation and personalization – The models can recommend products, movies or music according to the behavioral patterns and preferences of users. Similarly, they can create personalized recommendations for users/customers.
- Pattern identification – Unsupervised models can identify patterns in images without labeled data. They are also used in searching images, facial recognition, and medical image analysis.
Does Unsupervised Learning AI Model Completely Rule Out Human Intervention In Its Training Processes?
While it is safe to say that the unsupervised learning AI model doesn’t require human intervention as much as its counterpart supervised model, that’s not the entire truth. It does need human involvement in its training process to some extent.
Here’s why;
- A human expert knows better about what sort of unlabeled data an AI model would require for its training process. It is not an easy task to facilitate a complex data selection process for AI training, as the wrong data feed would negatively impact the response mechanism of an AI model.
- A human expert is needed to ensure that data for a mode’s training has gone through proper cleaning and formatting processes.
- A human data scientist or engineer is required to choose an appropriate algorithm for an unsupervised learning model and set up the mode’s parameters. It enables the model to properly study the data and recognize patterns.
- After the necessary configuration, the model is provided unlabeled data to learn. Here, a human is needed to ensure that the training process is smooth, the machine learns patterns within the data, and timely adjusts parameters when needed.
Semi-Supervised Learning AI Models
These types of AI models are slightly different from unsupervised models in terms of the data they use to get trained. These models rely on both labeled and unlabeled data during training.
Semi-supervised learning AI models come between supervised models that require labeled data, and unsupervised models that require unlabeled data.
Both data types (i.e. labeled and unlabeled data) perform complementary roles respectively to help the semi-supervised models get trained better than either could have done alone.
Use Case Of Semi-Supervised Learning
- In Natural Language Processing (NLP), the use of a semi-supervised learning model involves leveraging huge amounts of unlabeled text data to improve the model’s performance in tasks like sentiment analysis or topic modeling. Also, the model enhances machine translation systems or can be used in improving Named Entity Recognition to recognize entries like names, dates, or locations.
- In speech recognition, semi-supervised learning assists in training speech recognition models on huge datasets containing unlabeled speech data to improve their accuracy and power.
- Semi-supervised learning can be used to train models, like Graph Neural Networks (GNNs), and Entropy Regularization Models, improving their performance with a smaller or limited amount of labeled data. As a result, it will reduce the cost of data labeling and annotation, better decision-making, and greater value to businesses.
What Is The Role Of Labeled Data In Semi-Supervised Learning Models?
Labeled data act as a key foundation for the learning process in semi-supervised learning models, allowing the model to learn the correct outputs for specific inputs as an essential guide to the learning process.
Labeled data helps the model understand the underlying patterns and relationships within the data. It also regularizes the model from overfitting to the unlabeled data.
To say otherwise, it guides the model not to stray far from known data patterns during the learning process from the ocean of unlabeled data.
What is The Role Of Unlabeled Data In Semi-Supervised Learning Models?
Semi-supervised learning AI models are fed unlabeled data to identify hidden patterns and structures within the data. Since such data type contains a vast amount of information, it helps the models advance its learning process more, and achieve greater efficiency.
To say in simple words, unlabeled data help semi-supervised learning models with an infinite amount of information for exploration and refinement.
Semi-supervised learning AI models have some advantages, too.
For example, it doesn’t cost much money and time as it needs less labeled data. By leveraging unlabeled data, the models understand better the less represented or imbalanced classes (i.e. they are unequally distributed classes in data).
Reinforcement Learning AI Models
These types of AI models learn through trial and error by interacting with their environment. Here, the RL (Reinforcement Learning) agent (i.e. the AI system learning) self-learn in its interacted environment.
The environment means a physical, or simulated, or abstract world/playground the agent interacts with practices its skills, and receives feedback.
The interesting thing is, RL agent receives rewards and penalties that correspondingly influence their decision-making abilities.
Positive rewards encourage the agent to repeat the action causative of the positive rewards. Negative rewards (also called penalties) refer to the situation when the agent deviates from its intended goal or path.
Penalties are there to ensure that the agent doesn’t behave against its planned goal.
Key Reinforcement Learning Use Cases
- In robotics, RL trains robots to execute tasks like navigation, manipulation, and assembly. Developing autonomous vehicles, drones, and robots are also use cases of RL agents.
- The use of RL in gaming involves learning to play complex games like Go, Chess, etc.
- The RL also comes in handy for algorithmic trading in which it provides a framework for agents to learn trading strategies through trial and error. Eventually, the RL agents learn the complex stock market environment navigate it based on its learning experience, and make informed trading decisions.
Examples of reinforcement learning models are in games (StarCraft), Robotics, and recommendation systems (Netflix or Amazon).
As for the applications, the RL models help robots learn tasks in an environment lacking predefined rules, like warehouses or disaster zones. It can be used in finance in which it learns market data to optimize investment strategies for investors or banks.
RL algorithms come in handy to manage traffic by optimizing traffic light patterns and minimizing congestion.
Deep Learning AI Models
Deep Learning is a subset of machine learning. It performs well in learning vast amounts of data to identify complex patterns that humans may not discern easily.
Deep learning models excel at extracting complex patterns from text, images, and sound data, thanks to their multi-layered neural networks.
Once trained on large datasets, the models can make accurate predictions on new, unseen data. Deep learning specifically uses artificial neural networks, with the models’ architecture inspired by the structure and function of the human brain.
The models contain multiple layers of neurons, enabling them to learn and decipher complex data patterns.
Use Cases Of Deep Learning Models
- In image and video processing, the use of deep learning involves identifying people, scenes, and objects within the images. It locates/classifies objects within images or videos, and creates new images or videos based on available data.
- In NLP, the use of deep learning involves translating text from one to another language. It determines the sentiments/tone of the text, summarizing text, and in chatbots and virtual assistance.
- The use of deep learning in healthcare involves analyzing medical images like MRIs, and X-rays to diagnose diseases, and drug discovery,. It ensures tailoring treatments to individual patients.
- In finance, deep learning involves identifying fraudulent activities, and creditworthiness assessment of loan applicants.
- Deep learning can also be used in recommending products/content based on user’s preferences, or their watching/reading history.
Generative AI Models
Generative AI models are a subset of machine learning. It can generate new content, such as text, images, video, music, or code, according to the prompts given to a specific model. The quality of content generated by these models largely depends on the quality of training they have gone through on large datasets.
Therefore, these models depend on high-quality training data to be able to generate answers for users’ prompts accurately and efficiently.
Types of gen AI models include Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Transformer models.
- GANs – These models contain two neural networks called a Generator and a Discriminator. These networks work against each other, in which the first one creates new data. The second one evaluates its authenticity. The purpose here is to create authentic and realistic content.
- VAEs – This generative model generates new data. For this, it uses a probabilistic approach that allows the model to address uncertainty and variability in the data effectively. As a result, it facilitates the creation of new content in a more flexible and powerful way. Understand that VAEs generate content resembling the same content or data the model is trained on, like images or text.
- Transformer – Transformer Gen AI models define a type of neural network architecture. It is popular for revolutionizing NLP (natural language processing) tasks, like text summarization, text generation, question answering, sentiment analysis, and conversational agents (e.g. chatbots).
Conclusion
The above-detailed rundown on AI, its types, and key values in various application areas leaves us in no doubt as to acknowledging the sheer power of the technology in influencing human lives and the business world on a large scale.
From simple content generation to analyzing and predicting complex data patterns, the footprints of AI on our human lives and businesses are unmistakably game-changing.
At the same time, I tend to believe that these powerful technologies need to be in check through appropriate laws. It will ensure to prevent misuse of these technologies from running rampant worldwide. That’s because the world today is already drowned to the neck with the deluge of misinformation, fake news, and toxic content.
Under such circumstances, while cutting-edge AI models are beneficial for businesses and humans, their suitable application must be in alignment with laws and regulations to prevent any danger of misinformation or identity theft due to the misuse of the technologies.
What’s your take on the post?
I’d highly appreciate your feedback as it always motivates me to write something valuable for my readers.