What is machine learning?

What is the difference between AI and machine learning?

rtificial intelligence and machine learning are part of computer science that are correlated with each other. These two technologies are the most trending technologies which are used for creating intelligent systems.

Although these are two related technologies and sometimes people use them as a synonym for each other, but still both are the two different terms in various cases.

Although these are two related technologies and sometimes people use them as a synonym for each other, but still both are the two different terms in various cases.

On a broad level, we can differentiate both AI and ML as:

AI is a bigger concept to create intelligent machines that can simulate human thinking capability and behavior, whereas, machine learning is an application or subset of AI that allows machines to learn from data without being programmed explicitly.

Artificial Intelligence

Artificial intelligence is a field of computer science that makes a computer system that can mimic human intelligence. It is comprised of two words “Artificial” and “intelligence”, which means “a human-made thinking power.” Hence we can define it as,

Artificial intelligence is a technology using which we can create intelligent systems that can simulate human intelligence.

You can read the “What is Artificial Intelligence” article for more…

The Artificial intelligence system does not require to be pre-programmed, instead of that, they use such algorithms which can work with their own intelligence. It involves machine learning algorithms such as Reinforcement learning algorithms and deep learning neural networks. AI is being used in multiple places such as Siri, Google, AlphaGo, AI in Chess playing, etc.

Photo by sk on Unsplash

Based on capabilities, AI can be classified into three types:

  • Weak AI
  • General AI
  • Strong AI

Currently, we are working with weak AI and general AI. The future of AI is Strong AI for which it is said that it will be intelligent than humans.

Key differences between Artificial Intelligence (AI) and Machine learning (ML):

robot by giphy
  • Artificial intelligence is a technology that enables a machine to simulate human behavior.
  • Machine learning is a subset of AI which allows a machine to automatically learn from past data without programming explicitly.
  • The goal of AI is to make a smart computer system like humans to solve complex problems.
  • The goal of ML is to allow machines to learn from data so that they can give accurate output.
  • In AI, we make intelligent systems to perform any task like a human.
  • In ML, we teach machines with data to perform a particular task and give an accurate result.
  • AI has a very wide range of scope.
  • Machine learning has a limited scope.
  • AI is working to create an intelligent system that can perform various complex tasks.
  • Machine learning is working to create machines that can perform only those specific tasks for which they are trained.
  • AI system is concerned about maximizing the chances of success.
  • Machine learning is mainly concerned with accuracy and patterns.
  • The main applications of AI are Siri, customer support using catboats, Expert System, Online game playing, intelligent humanoid robot, etc.
  • The main applications of machine learning are Online recommender systems, Google search algorithms, Facebook auto friend tagging suggestions, Rampco Machine Learning Software, etc.
  • On the basis of capabilities, AI can be divided into three types, which are, Weak AI, General AI, and Strong AI.
  • Machine learning can also be divided into mainly three types are Supervised Learning, Unsupervised Learning, and Reinforcement Learning.
  • AI completely deals with Structured, semi-structured, and unstructured data.
  • Machine learning deals with Structured and semi-structured data.

Rampco Machine Learning Software develop a data-driven Machine Learning (ML) model that can use past actual production input parameters and their corresponding output parameters to estimate the system output based on its inputs.

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for more information, you can check out our website on 👉 rampco.ca

We develop a data-driven Machine Learning model that can use actual production input parameters and their output parameters to estimate the output.

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