Artificial Intelligence (AI) and Machine Learning (ML) are some words that incidentally pop out when discussing technology and otherwise. Still, with these words being thrown around, they are not the same thing. If you are confused about AI vs. ML, well, do not worry since you’re not the only one who happens to be so puzzled. This article is expected to clarify the concepts and explain how they are different from each other.
What is AI?
AI is essentially the overall concept of designing machines to perform tasks that require human intelligence. It ranges from simple tasks, such as speech or image recognition, to more complex tasks, including a game of chess or even decision-making, translation, etc. In short, AI mimics human thinking in solving problems across various spheres.
There are classifications of AI into:
Narrow AI:
It refers to a specialized AI which is only limited to doing one thing. Some examples of narrow AI are voice assistants such as Siri and Alexa who can understand spoken language but are not capable of complicated thinking outside that program.
General AI:
It is being thought about and in the making, a machine with cognitive abilities just like the human brain. A general AI would solve problems throughout multiple domains and contexts.
Think of AI as the brain of a smart system that drives innovation in domains like health care, finance, or autonomous vehicles. The “muscle” behind this AI brain usually comes from machine learning.
What is Machine Learning?
ML is part of AI. It is, in fact, the learning technique of automatically training systems without explicit programming. It allows computers to process big data, see its patterns, and then make decisions or predictions.
Here is how it works
1. Input: Huge datasets are used to train ML systems.
2. Training: Algorithms process such data with the view of learning patterns, relationships, and trends.
3. Output: The system makes predictions, classifications, or decisions based on what it has learned.
Popular Types of Machine Learning:
Supervised Learning: The algorithm is trained using labeled data for which the outcome is known, and the system learns to predict it.
Unsupervised Learning: Here, the system receives unlabeled data and tries to figure out the underlying patterns or groups.
Reinforcement Learning: Here, it learns by being in touch with the environment through rewards or penalties.
What are the key differences between AI and Machine Learning?
Scope: AI is the more general concept of machines capable of doing tasks in a smart way, while ML is an application of AI based on the principle that machines can learn from data.
Functionality: AI may be rule-based, traditional AI, or learning-based, ML. However, ML always revolves around learning from and improving with data.
Dependency on Data: Machine Learning is data-dependent. The more you feed it, the better performance it creates. AI, especially narrow AI, can still function inside of its defined ruleset without constantly learning.
Real World Examples
AI Example: The self-driving car utilizes AI to understand its surroundings, identify road signs, pedestrians, and other cars, and deciding its driving decisions.
ML Example: Recommendation system on Netflix. It learns your preferences based on a history of what you watched and will recommend to you shows or movies that you probably like.
