Artificial Intelligence can be defined as the ability of computers to think, respond and simulate like humans. Artificial Intelligence is a broad term that refers to various fields of study including Machine learning, Fuzzy logic, Robotics, Data Science, Expert Systems, Neural Networks, etc.

Artificial Intelligence a swirling 360°

Artificial Intelligence is one of the most fascinating fields of computer science. Artificial Intelligence studies aim to create a machine that can replicate human intelligence in real time. True AI that can think like a human has 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.

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.

Common AI Models

There are several different AI 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 neural networks have 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. It is very adept at solving complex problems that possess large data sets.

Deep learning neural networks 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 AI 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 arrive at a conclusion. 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. Modern predictive analytics are powered in large part by random forest models.

Jobs in AI

There are two sides to this coin: Robots and AI will take some jobs away from humans — but they will also create new ones. Since 2000, robots and automation systems have slowly phased out many manufacturing jobs — 1.7 million of them. On the flip side, it’s predicted that AI will create 97 million new jobs by 2025.

Artificial intelligence is poised to eliminate millions of current jobs — and create millions of new ones.

WILL ARTIFICIAL INTELLIGENCE (AI) REPLACE JOBS?

AI is and will continue to replace some jobs. Workers in industries ranging from healthcare to agriculture and industrial sectors can all expect to see disruptions in hiring due to AI. But demand for workers, especially in robotics and software engineering, is expected to rise thanks to AI.

HOW MANY JOBS WILL AI REPLACE?

According to the World Economic Forum's "The Future of Jobs Report 2020," AI is expected to replace 85 million jobs worldwide by 2025. Though that sounds scary, the report goes on to say that it will also create 97 million new jobs in that same timeframe.

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