Difference Between Machine Learning and Artificial Intelligence

In the Machine Learning vs Artificial intelligence debate, it is important to know that both of them are computer science terms and are often used interchangeably. Though both, Machine Learning (ML) and Artificial intelligence (AI) are related to each other but in different ways. AI is a branch that focuses on creating intelligent machines that do tasks which otherwise require human intelligence, like visual perception, speech recognition, decision-making, and natural language processing. AL consists of developing algorithms and systems that can reason, learn, and make the right decisions based on input data. On the other hand, Machine Learning (ML) is a part of AI involving teaching machines to learn from data without being explicitly programmed. The increasing popularity of AI and ML products clearly indicates that more businesses have started using the same to drive better decisions and get insights in real time.

In this blog we will discuss the key differences between Machine Learning vs Artificial intelligence, throwing light on their definitions, applications, and much more.

 

Artificial Intelligence (AI)

Artificial Intelligence refers to the simulation of human intelligence in machines, enabling them to perform tasks that typically require human intelligence. These tasks consist of learning, reasoning, perception, problem-solving, and understanding of language. Also, artificial intelligence is the study to train the computers so that they can perform the tasks done by humans in a better way.

Today, artificial intelligence backs several technologies we use, including smart devices and voice assistants like Siri on Apple devices. Companies deploy numerous techniques like natural language processing and computer vision to streamline tasks, accelerate decision making, and enable customer conversations with chatbots.

 

Key Highlights of AI

• AI allows machines to simulate human intelligence for solving problems
• The purpose is to deploy an intelligent system that can do complex tasks
• AI has a wide scope of applications
• AI uses technologies that help in decision-making.
• Whether it’s structured, semi-structured, or unstructured data, AI can manage all.

 

Common Fields of AI

A few of the most common fields which are a part of artificial intelligence include:

• Machine learning (ML): Machine learning is an important aspect of AI wherein algorithms are trained on data sets to become machine learning models who can perform specific tasks.
• Deep learning: Deep learning is a subset of ML wherein artificial neural networks are used for doing more challenging reasoning tasks without human intervention. 
• Natural Language Processing (NLP): NLP focuses on creating software that can interpret human communication.
• Robotics: It is a division of AI, which focuses on creating robots capable of learning and performing complex tasks in real world environments. 

 

Machine Learning (ML)

ML lays more emphasis on developing algorithms enabling machines to learn patterns and make data-driven decisions. Machine Learning algorithms use various statistical techniques letting computers enhance their overall performance on a specific task over time. By experimenting with machine learning, programmers can evaluate the effectiveness of the solutions and how they can improve the perception, cognition, and action of a computer system.

 

Key Highlights of Machine Learning

• By using ML, machines can learn autonomously from past data.
• ML helps increase the accuracy of the output.
• Machine learning has limited scope of applications as compared to artificial intelligence.
• In regard to data, ML can just use structured and semi-structured data.
• ML uses statistical models to learn and can self-correct them when provided with new data.

 

Artificial Intelligence vs Machine learning: Key Differences

Beneath are the key differences between Machine Learning vs Artificial intelligence that help know more about them:

Artificial Intelligence	Machine Learning
AI is a technology simulating human behaviour.	ML is a subset of AI, allowing machines to learn from data without explicit programming.
AI aims to create smart computer systems like humans do to solve challenging issues.	ML’s goal is enabling machines to learn from data, providing accurate output.
In AI, intelligent systems perform tasks like humans.	ML teaches machines with data for specific tasks and accurate results.
AI encompasses Machine Learning and Deep Learning.	Deep Learning is a primary subset of Machine Learning.
AI has a broad scope in creating intelligent systems.	ML has a more limited focus.
AI works on creating diverse intelligent system complex tasks.	ML creates machines for specific, trained tasks.
AI maximises the chances of success.	ML emphasises accuracy and pattern recognition.
AI applications include Siri, Expert Systems, intelligent humanoid robots.	ML applications include recommender systems, Google search algorithms, and Facebook tagging suggestions.
AI can be Weak AI, General AI, or Strong AI based on capabilities.	ML types include Supervised, Unsupervised, and Reinforcement learning.
AI involves learning, reasoning, and self-correction.	ML involves learning and self-correction with new data.
AI handles Structured, semi-structured, and unstructured data.	ML deals primarily with Structured and semi-structured data.

 

How Kodehash Uses AI and Machine Learning

To be successful in any sector, businesses must be able to transform their data into actionable insight. Despite a lot of differences in Machine Learning vs Artificial intelligence, both of them help businesses in automating a variety of manual processes involving data, decision making, and much more.

By taking professional help from Kodehash Technologies, businesses can get data-driven insights with enhanced speed and efficiency. Connect with us, your trusted software solutions provider, to elevate your technological journey.