We have Google Maps to foresee the real-time traffic, smartphones, applications, autonomous cars, etc. that make our lives easier and more convenient. These are all because of artificial intelligence (AI) and machine learning (ML).

Have you ever considered if they are different?

Certainly, they differ. This post will help you find that difference, which has a key role in AI & machine learning solutions.

Let’s get started with artificial intelligence.

What is artificial intelligence (AI)?

It is actually a computer technology that can think a bit like us. Using this ability resolves any problem by relating things and understanding their connectivity, and intuitions. The stored insights support it to come up with a possible solution.

History and Evolution

Alan Turing is the father of AI, who created the concept of the Turing Test in the 1950s. His findings created a code-breaking computer that was extremely famous during World War II. This innovation helped allied countries understand the secret messages of the German military.

This invention gradually rose to prominence. It empowered machines to think, as we do. It could only be possible if they passed the Turing Test.  Simply put, if the machine is able to respond to questions as we do, it will pass the test.

To introduce this capacity, computer systems are enabled to understand intentions, think logically, and also adapt to the situation. These key capabilities use data-driven logic and mathematics to come out with a practical answer. Else, it might be a dream only.

AI Applications That Are Commonly Used

We have AI-driven technologies that are creating a whole different world of automation. Because of its advent, Robotic Process Automation (RPA) technology is flourishing. It lets insurance or any user-industry or company analyse risks in a better way. The most common of these AI apps are the following:

• Google Search Engine
• Self-driving or auto-run cars by Tesla
• Personalized recommendations, as you see on YouTube or eCommerce sites
• Personal voice assistants like Google Voice, Amazon Alexa, & Apple’s Siri

Two Types of Artificial Intelligence (AI)

Here are the types of AI technology:

• Narrow AI

The application or system whose end-to-end or specific tasks are completely predefined represents Narrow AI.  A chatbot, for instance, can automatically respond to the query of the online customer.

• General AI

General AI is an advanced version, which incorporates machine learning systems for drawing insights, intuitive learning, and understanding them. Certainly, this technology is quicker to think, assess, and perform any alignments in a better way.

Despite the fact that AI is evolved, it cannot completely replicate a human being. The natural feelings or instincts (as found in a person) are still not there.

What is machine learning (ML)?

Machine learning is a part of AI, which helps in modeling or building algorithms. Data collection fuels it with the supply of sample data, which are known as training data.

ML models enrich machines with data-driven insights that help in foreseeing ups, downs, trends, and even the intent of a person. This forecasting is automatically done by using collected databases, which have a range of semi-structured and structured datasets. They produce insights to analyse and discover accurate decisions, strategies, or feasible solutions.

History and Evolution

This concept took rise in 1959 by Arthur Samuel. He launched AI and computer gaming together with ML. His inventions enabled computers to keep on learning without any programming and re-programming.

His idea has exposed machines to a new ML model, which allows models/algorithms to continuously learn, adapt, and develop on their own. In short, this modeling process supports machines with a frequent learning process that produces intelligence.

More and more businesses are investing in these solutions because they want to maximize efficiency, productivity, and revenues with the least investment. These ML models can assist in quick decision-making, predicting, analyzing, and learning new trends or relationships through insights.

Different Types of ML

It has an extra type if you compare it with the AI. The data scientists, who are the right professionals to work with, use its specific type in accordance with the goal or objective.

Let’s get through its all types.

• Supervised ML

This type of machine learning model uses labeled datasets to train algorithms. It can classify data, assess to correlate, and predict the outcome accurately. This type of learning uses very specific inputs and outputs.

• Unsupervised ML

The unsupervised ML prepares and trains unlabeled datasets. The machine learning models themselves scan through them to identify & meaningfully correlate models. In this case, the unlabeled datasets and outputs are decided prior.

• Reinforcement Learning

Reinforcement learning requires a human touch. Data scientists are required to train ML so that it can completely run a multistep process. Certainly, a predefined set of rules or protocols are defined to follow. The scientist’s program ML models to run through the whole preset cycle and provide positive or negative feedback on their performance.

Commonly Used ML Applications

There are multiple well-known companies and brands like Amazon, Google, and Facebook that use these models to achieve business results. Navigation, for example, that is automatically tapped and defined by Google Maps app on a smartphone or desktop is commendable. It helps people to foresee the traffic and suggest optimal routes.

Here are a few applications that are supported by ML:

• Email filtering Feature
• Speech recognition in any device or app
• Computer vision (CV)
• Spam/fraud detection in banking or insurance apps
• Predictive analysis
• Malware threat detection
• Business process automation (BPA)

Artificial Intelligence and Machine Learning Differ

There are certain differences. The following points will show you how they differ and why you cannot use them interchangeably.

1. All types of ML can be AI, but not all types of AI are ML

AI is supported by machine learning that incorporates human intelligence to a certain level to discover limitless scope. On the other hand, ML is more specific in terms of a process. It has a limited scope.

The researchers and data scientists can refine AI to such an extent that it can act or work like a man. On the contrary, ML trains machines to achieve specific tasks or goals and win an output as per desire.

2. AI achieves success, ML is all about accuracy

If you talk about AI, it is more relevant to achieving success. On the flip side, ML requires a high data accuracy rate in terms of discovering patterns. Success has no role when you talk about ML.

As per the aim, AI is dedicated to finding optimal solutions for users whereas, ML just ensures finding solutions. It won’t matter if they are optimal or not.

3. Unique outcomes

When it comes to achieving desirable results, AI comes first. Various methods of logic, mathematics, and reasoning are their lifeline, which helps in achieving success. Opposite it, ML can only learn, adapt, or self-correct because of its limited capabilities.

You may expect to achieve a preset outcome through ML models. But when it comes to AI, it can do anything. You draw intelligence via it that can let you achieve more than a result.

Simply put, ML can forecast through analysis (as in sales). But, AI can bring a number of results and advantages that might not have been predicted before.

Summary

Artificial Intelligence and Machine Learning Differ from each other. AI is more about achieving success plus limitless results that might not be predicted. On the other hand, ML is limited in terms of results and practices. It can only learn, adapt, and autocorrect itself. You can only achieve preset and predicted results with machine learning.