What Is The Difference Between Artificial Intelligence and Machine Learning
What is AI, what is ML, how they are connected, and the difference between them
When talking about artificial intelligence (AI) and machine learning (ML), people often use them interchangeably. This is understandable as the two technologies are closely related. But in fact, they differ in several aspects, especially scope and application.
1. What AI and ML are used for?
AI and ML are used to process and analyze large amounts of data, give better decisions, generate recommendations and insights in real time, and make forecasts and predictions.
2. What is AI?
AI is a broad filed. It refers to the use of technologies to build machines and computers that have the ability to sense, reason, act or adapt like a human.
AI is a set of technologies implemented in a system to enable it to reason, learn, and act to solve complex problems.
3. What is ML?
ML is a subset of AI. It uses algorithms to automatically enable a machine or system to learn and improve from experience. ML does not use explicit programming. It relies on self-learning algorithms to extract knowledge from data and learn from data and experience automatically.
ML algorithms improve its performance over time as they are trained by more data.
4. Difference between AI and ML
The goal of AI and Ml is different. AI involves broader goals while ML focuses on more specific goals. The main goal of AI is to develop machines that can perform complex tasks intelligently like a human. ML’s aim to teach a machine how to perform a specific task and provide more accurate results by identifying patterns.
The scope of AI and Ml is different. ML is one of the pathways to AI. AI systems are developed by applying tools such as ML, deep learning, neural networks, computer vision and natural language processing.
As for the applications, AI has a wider application range such as problem-solving, decision-making, and autonomous systems. ML has a narrower applications as it focuses on tasks like pattern recognition and predictive modelling.