AI vs ML

- Overview

While artificial intelligence (AI) encompasses the idea that machines can imitate human intelligence, machine learning does not. Machine learning (ML) aims to teach machines how to perform specific tasks and provide accurate results by recognizing patterns.

You may hear people use AI and ML interchangeably, especially when discussing big data, predictive analytics, and other digital transformation topics. This confusion is understandable because AI and ML are closely related. However, these trending technologies differ in several aspects, including scope, applications, etc.

AI and ML products are proliferating as enterprises use them to process and analyze large amounts of data, drive better decisions, generate recommendations and insights instantly, and create accurate forecasts and projections.

So, what exactly is the difference between AI and ML, how are ML and AI connected, and what do these terms mean for the practice of organizations today? We’ll break down AI  vs. ML and explore how these two innovative concepts are related and how they differ.

- Artificial Intelligence (AI)

• AI allows machines to simulate human intelligence to solve problems
• The goal is to develop an intelligent system that can perform complex tasks
• We build systems that can solve complex tasks just like humans
• AI application fields are wide
• AI uses technology in systems to mimic human decision-making
• AI works on all types of data: structured, semi-structured and unstructured
• AI systems use logic and decision trees to learn, reason, and self-correct 

- Machine Learning (ML)

• ML allows machines to learn autonomously from past data
• The goal is to build machines that can learn from data to improve the accuracy of their output
• We use data to train machines to perform specific tasks and provide accurate results
• ML has limited scope
• ML uses self-learning algorithms to produce predictive models
• ML can only work with structured and semi-structured data
• ML systems rely on statistical models to learn and can self-correct when provided with new data

[More to come ...]