Artificial Intelligence models are becoming increasingly utilized in almost every sector. One main reason companies embrace AI technology is its ability to solve complex problems with minimum human intervention. Adopting AI has helped various companies promote efficiency, automate repetitive tasks, and enhance customer experience.
This article will provide an overview of an AI model and all the key points you need to know about it.
An AI model is a program or an algorithm that utilizes datasets to find patterns within that data. This allows the model to reach certain conclusions and make predictions when provided with the necessary information.
AI models are especially useful for solving complex problems which would otherwise require too much time and resources for humans to uncover. The more information the model receives, the more accurate its predictions will be.
Like any other business process, building and maintaining AI models also needs supervision and control. AI model management is the process of overseeing the development of the model, starting from its initial stages of problem identification and ending with constant monitoring and updating. It aids engineers and developers in identifying issues at their initial stages and provides help in organizational matters and target achievement.
AI and machine learning (ML) models are sometimes used interchangeably. While machine learning is a subset of artificial intelligence, these two terms are essentially different from each other. AI deals with creating machines that simulate human behavior and intelligence. The meaning of ML revolves around training these machines to learn and improve themselves based on previous experience without human intervention.
Stemming from the machine learning theory, an ML model is programmed to find patterns and make predictions by analyzing past data independently, without human interference. For example, product recommendations on Amazon or email spam and malware filtering are all cases of ML models in use. It’s also worth noting that while all ML models are also AI models, not all AI models are necessarily machine learning models.
AI models are revolutionizing business operations. Here are some of the top advantages of embracing AI:
Many processes in multiple industries like healthcare or banking have become less time-consuming. Thanks to AI, medical professionals can now diagnose patients, and lenders can evaluate a borrower’s creditworthiness much faster.
With the help of AI, businesses can cut costs and redirect funding to marketing, advertising, or onboarding experts. For example, The Washington Post already uses AI robots to write the copy for its sports columns.
Rather than spending precious time on mundane and repetitive tasks, humans can outsource these projects to AI. Instead, experts and industry professionals can focus on improving business strategies and networking. AI tools are currently incorporated for tasks such as responding to emails or importing data.
AI has offered unique tools to boost customer experience and satisfaction in numerous companies worldwide. For example, chatbots have made it possible for customers to receive near-immediate responses 24/7.
There are different ways of building AI models. However, the end goal of all methods is the same – recognizing patterns among data and making predictions or decisions. Here are some of the most common types of models used in AI:
This is one of the most basic models, where the algorithm predicts the value of the dependent variable based on the value of the independent variable. It’s widely used in predictive analytics software in healthcare to identify the proper dosage of drugs for patients, as well as in insurance to identify the risks of losses from accidents.
Logistic regression works similarly to linear regression. However, it is only used to solve classification-based problems, meaning that it predicts a class of the dependent variable given the independent variable. For example, it is typically used in credit organizations to categorize loan applicants based on the probability of repayment. It is also arguably the most efficient and best AI model for the supply chain management.
This model was inspired by the functioning of the human brain and its neural networks. It uses several artificial neurons that combine with inputs to provide a single output. Deep neural network models are primarily used in mobile application development to build speech and image recognition, as well as natural language processing.
These models use past decisions to derive outcomes. Decision tree algorithms follow a sequential and hierarchical set of true/false or yes/no questions until they reach a final result. For example, if an animal can fly, then it may have feathers. Decision tree models are used in multiple industries, such as banking, to identify the loan eligibility of borrowers.
A random forest is a collection of randomly selected decision trees. It is a powerful tool that combines the results of multiple decision trees and extracts a more accurate prediction. Random forest models are applied in real life to prevent bank fraud, predict profit or loss while purchasing a particular stock, and detect drug sensitivity in medical patients.
To sum up, an AI model is a powerful tool that helps organizations in multiple industries find common patterns in data and come up with viable solutions to business problems. There are multiple types of models that accelerate the data analyst’s job and provide insights into company data. The world of AI is still in its infancy but if you aim to grow your business, keeping an eye on it is a must nowadays.
