AI vs Machine Learning: What are the Differences?

Within the last decade, the term AI vs machine learning has become buzzwords that are often discussed together, and the terms are sometimes used interchangeably, but they don't mean the same thing. Instead, Machine learning is a significant subset of Artificial Intelligence.

AI and ML technologies are all around us, from the digital voice assistants in our devices to the recommendations you see on Netflix. Despite both technologies entering several human domains today, there's still ambiguity and confusion regarding their differences, similarities, and primary applications.

Here's a more in-depth look at the difference between AI and machine learning, the different types, and how these technologies compare.

What is AI?

AI, which stands for artificial intelligence, consists of two words, "Artificial" and "Intelligence", meaning "a human-made thinking power". It refers to machines or systems that mimic human intelligence to perform tasks and can constantly improve themselves based on the information they collect. AI manifests in many forms.

The AI system does not need to be pre-programmed; instead, it uses such algorithms that can work with its intelligence. It includes machine learning algorithms, such as deep learning neural networks and reinforcement learning methods.

Applications of AI

Many technology companies and their users use modern artificial intelligence. Some typical AI applications include:

• Digital Assistants
• Google Maps and Ride-Hailing Applications
• Face Detection and Recognition
• Text Editors and Autocorrect

Types of Artificial Intelligence (AI):

There are three types of artificial intelligence:

1. Artificial Narrow Intelligence
2. Artificial General Intelligence
3. Artificial Super Intelligence

Features of Artificial Intelligence:

Various features of artificial intelligence make it unique. Some of them include the following:

Eliminate tedious tasks:

Humans often get bored with repetitive tasks, but you will never experience boredom with artificial intelligence machines. The AI machine will continue to perform the job as per your instruction, no matter how often you ask them to do it.

Cloud Computing:

AI needs a lot of data for computers to learn; physical data storage can be a big problem. Artificial intelligence capabilities collaborate with the cloud computing environment so that organisations can work strategically and efficiently.

What is Machine Learning?

Machine learning (ML) is a subset of artificial intelligence that focuses on building systems that learn or improve performance based on the data they consume. Today, ML is at work all around us. When we interact with shops online, banks, or use social media, machine learning algorithms make our experience efficient, secure, and smooth.

There are dedicated development teams specialising in machine learning projects that deal with the complete cycle of Artificial Intelligence implementation.

One benefit of machine learning is that the developers' team working with ML projects always has highly skilled and qualified specialists on board; thus, they can start implementing the project immediately.

Applications of machine learning:

• Speech Recognition
• Predictive Analysis
• Recommendation systems
• Online video streaming applications

Types of Machine Learning:

1. Supervision learning
2. Unsupervised learning
3. Reinforcement learning

Features of Machine Learning:

Some features of machine learning make it unique. These include:

Accuracy in data analysis: Machine learning has made exploring a massive volume of data easier in a few steps. It can generate accurate results with fast and efficient real-time data processing algorithms.

Boost Business Intelligence: ML combined with big data generates an outstanding level of business intelligence that helps companies to take strategic initiatives. 

Difference Between Artificial Intelligence and Machine Learning:

Scope:

AI is an all-encompassing term that describes a machine with some level of human intelligence. It is considered a broad concept, while machine learning is a more specific notion with a limited scope.

AI practitioners develop intelligent systems that can perform complex tasks like a human. ML researchers, on the other hand, spend time teaching machines to accomplish a specific job and accurate private outputs.

The scope of AI is enormous; it contains fields such as robotics, natural language processing, and expert systems, while the area of ML is limited, it consists of ML algorithms such as random forest, k-means, support vector machines, decision trees, etc.

Due to this difference, it's fair to say that professionals using AI or ML utilise different elements of computer science and data for their projects.

Success vs Accuracy:

Another difference between machine learning and artificial intelligence solutions is that AI aims to increase the chances of success, while ML aims to boost accuracy and identify patterns of the predicted output and not the success ratio. Success is not in ML as applicable as it is in AI applications. 

Further, AI aims to find the optimal solution for its users, and ML is used to find a solution, optimal or not.

In machine learning, there's a concept called the 'accuracy paradox' in which ML models achieve high accuracy value but can give practitioners a false assumption as the dataset could be highly imbalanced.

Unique outcomes:

Artificial intelligence is a much broader concept than machine learning and can be applied in ways that help users achieve the desired outcome. AI employs logic methods, mathematics, and reasoning to accomplish tasks, while ML can only learn, self-correct or adapt when it is introduced to new data. Apparently, ML has more constrained capabilities than AI.

ML models can only reach a fixed outcome, but AI focuses more on creating an intelligent system to accomplish more than just one result.

Final Thoughts

In AI vs ML, Both artificial intelligence and machine learning are being broadly used in several ways. There are various real-world examples of both technologies. Thanks to AI and ML, we don't even realise, and our work is done.

So to sum it up, AI solves tasks that require human intelligence and ML solves tasks after learning from data and providing predictions.

Or in other words, the main difference between ML and AI is – AI is the umbrella term for the field of study of developing human intelligence in machines and computers while ML aims to build machines that learn from users' experiences and improve themselves without any human intervention.

All the ML tasks we do are part of AI, but not all AI tasks are ML.

Depending on what your goal is, choosing the right technology by understanding thoroughly the differences between machine learning vs artificial intelligence  would help you accelerate and get better results for your software project.