Artificial Intelligence (AI) is always seen as this radical technology that will transform our lives and even take our jobs. However, what most people don’t realise is that at the heart of AI are multiple models based on several underlying technologies trained to perform specific tasks.
So, you might wonder what exactly is the AI model and how it changes our understanding of AI. Let’s have a brief dive into it.
What is an AI model?
An AI model can be described as a software program that has been trained on a set of data to perform specific tasks like recognising certain patterns. The AI models also use decision-making algorithms to learn from the training data and often apply this learning to achieve specific pre-defined objectives.
AI models are being used in many different fields with different levels of complexity and purposes, including computer vision, robotics, and natural language processing. The overarching goal of an AI model is mainly to solve business problems and it does this by ensuring the algorithm predefined for this purpose is able to reason over and learn from this data.
What are the types of machine learning models?
Based on the means used to create them, there are various types of AI models. The three most common approaches used in data science right now are supervised learning, unsupervised learning, and semi-supervised learning models. It is important to understand that all machine learning models are AI models.
- Supervised machine learning models: These AI models are built using supervised machine learning and these models are trained by people, often those with specific expertise in a subject matter. While designing these models, the subject matter experts review new data points and label them. They are also found to mark the training data as either “responsive” or “unresponsive.” These kinds of models are often used to perform predictive analyses.
- Unsupervised machine learning models: These AI models are developed with the help of unsupervised machine learning, an approach incorporating more automation. These AI models are trained by software and can even be found to mimic the training methodology provided by people. These models are found effective to categorise input data or identify patterns or trends without the initial help of humans. The unsupervised machine learning models are typically used for descriptive analyses and can also be used to summarise the content.
- Semi-supervised machine learning models: As the name implies, the semi-supervised machine learning models act as a middle ground between supervised and unsupervised machine learning. It combines the best aspects of both models wherein subject matter experts label a small amount of data to start training a model. The partially-trained model is then subjected to a larger dataset to create “pseudo-labelling.” The resulting model can then be used for descriptive or predictive purposes.
How is the AI model generally used?
An AI model’s general use can be seen all around us. The predictive algorithm that accurately recommends the next best song on Spotify or a new TV show on Netflix is possible because of an AI model trained on your usage pattern.
In the healthcare industry, an AI model can be seen helping doctors and other medical staff diagnose paediatric diseases. These AI models are also being used to summarise communications, write articles, and detect credit card fraud. There is a good chance that your bank is using an AI model to rank your creditworthiness before processing your loan.
Other general use cases include detecting opinion spam on e-commerce, social media, and other sites. AI models are also being used to filter hate speech on social media platforms. Google is using AI models to detect and filter out email spam and even to predict your text while writing an email.
Today, user experience on platforms like Netflix, Amazon, and YouTube is incomplete without AI models being used to optimise your experience.
What are the common AI models?
While there are multiple different AI models, some of the most popular ones can be easily found in an AI model library. These include:
- Deep neural networks: One of the most popular AI/ML models is deep neural networks. The design for this deep learning model was inspired by the human brain and its neural network. It uses layers of artificial neurons to combine multiple inputs and provide a single output value. They are widely used in mobile app development to provide image and speech recognition services and natural language processing. Neural networks represent the cutting edge of AI and help power computer vision applications.
- Linear regression: If you are working in statistics then consider linear regression to be your AI model friend. Based on supervised learning models, these models are capable of identifying the relationship between input and output variables. This type of AI model can predict the value of a dependent variable based on the value of an independent variable. You will find them used in industries such as healthcare, insurance, e-commerce, and banking.
- Logistic regression: Closely related to the linear regression model, logistic regression is another popular AI model. It differs from the linear regression model because it is only used to solve classification-based problems. This is the best model to use 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: These AI models are highly considered to be straightforward and efficient and use available data from past decisions to arrive at a conclusion. The easiest way to look at decision trees is to see them as a basic if/then pattern. Decision trees are AI models that can be used to solve both regression and classification problems.
- Random forest: If one decision tree is powerful then imagine a forest full of decision trees. That’s the premise of the random forest AI model where each decision tree returns its own result or decision. The decision from one decision tree is merged with the result from every other tree in the forest to create a combined result to deliver an accurate final prediction or decision. For a large data set, the random forest model is considered to be great and is central to modern predictive analytics.
The end goal for AI is to reach a point where artificial general intelligence is achieved to match human intelligence. However, to get to that stage, we will first need AI models that are stronger, trained with quality data, and able to process information like the human brain. AI is important but AI models are equally important.