Difference Between Artificial Intelligence and Machine Learning

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Artificial Intelligence vs Machine Learning

Ask any layman about Artificial Intelligence (AI) and Machine Learning (ML). Most of them will say that both are the same. To some extent, they are right, considering how close they relate to each other. Also, if they are wrong, then the media coverage is to blame. Media coverage brings overwhelming hypes to these two terms. It almost makes people feel that they are the same. But, there is a difference between Artificial Intelligence and Machine Learning that you need to know.

Not just one, there are various differences – both big and small. They distinguish between the two and draw a fine line in the middle. However, describing how different AI and ML are will first need to elaborate on what they are. Only based on that, the differences will be visible.

In this blog, you will read about the subtle differences between the two. Knowing these will be vital to make a successful entry into the world of Artificial Intelligence and Machine Learning.

Let’s Start With Artificial Intelligence (AI)

What is Artificial Intelligence?

AI or Artificial Intelligence is an old concept and the umbrella term. Under it falls all the terms like Machine Learning, Deep Learning, Neural Network, etc. Still, it’s important to note that it stayed a vast and vague topic for many years.

In a simple way, AI is a technology that solves complex problems. Even computers do that, one may ask. But, AI does them in a human-like manner. AI targets solving problems and tasks in a human-like way. It helps to ensure that the result is more accurate and satisfactory.

Development of AI targets making results that are closer to humans. ‘Reasoning’ and ‘Logical thinking’ are the two things that set it apart from machines.

As a result, AI-powered devices or programs do better than traditional computers. It is because AI algorithms are meant to self-develop. Unlike computer algorithms, AI has the power to adapt and grow. Therefore, AI can do textual analysis, provide better customer service and even drive cars. Before we start talking about the difference between Artificial Intelligence and Machine Learning, let us discuss about –

How is Artificial Intelligence Important?

AI has widespread applications in our everyday lives. Across multiple industries, we see various uses of AI.

AI in Early Days

It was always believed that AI was the science and engineering that made computers. Many years ago, even a pocket calculator was considered to be a wonder of AI. Not exactly how we see a calculator today, isn’t it?

AI in Healthcare

Increased diagnosis power and better treatment are what AI has brought. It is helping doctors enormously in saving patients before health worsens.

AI in Smartphones

Even in our regular lives, we connect with AI algorithms in our smartphones. Google Assistant, Microsoft’s Cortana, Amazon’s Alexa, and Apple’s Siri are some of the most popular ones.

Beyond these, modern-day processors have dedicated NPUs besides having CPU and GPU. An NPU (Neural Processing Unit) is like the brain of AI in smartphones, computers, laptops, etc.

AI in Gaming

The gaming industry will always bow down to AI power. From a basic Chess game to eSports names like CSGO, DOTA, COD, etc., AI has immense contributions.

AI in Media and Entertainment

From Social Media to OTT platforms, AI plays a significant role. Understanding the likes and dislikes of a user is its most effective success.

That is why on YouTube, Netflix, or Facebook, we get various recommendations.

Then there are Hollywood movies, bringing their own versions of AI to the world. Over the years, different sci-fi movies made audiences go awestruck. Movies like the ‘Matrix,’ Marvel’s ‘Iron Man’ series, or even ‘2001: A Space Odyssey’ showed AI like a super technology.

The list continues, and it still has a long way to go. But we are here to discuss the difference between Artificial Intelligence and Machine Learning. So, we will be talking about them in another blog some other day.

Moving Onto Machine Learning (ML)

What is Machine Learning?

The first line about Machine Learning should be – ML is a sub-branch of AI. However, you will keep hearing how the terms are interlinked and treated as the same. It is because most of the AI applications that we see today have ML in its heart.

To explain, while AI is the brain of intelligent machines, ML is the supporting technology. Even the models and the processes run on Machine Learning.

As the name goes, Machine Learning is all about a ‘machine’ who is ‘learning.’ ML relies largely on behavioral rules. It compares and examines large data volumes to find common patterns. The concept and basic idea about ML are less complicated and fuzzy than AI. Of course, it is possible to create artificially intelligent machines or devices. But, Machine Learning is simply more practical. One can make a system, or a gadget carved out of it.

Machine Learning algorithms are also capable of working independently. Just like AI, they can too make progress alone, without human intervention. Asking how is Artificial Intelligence important , will lead you to the applications of ML.

How is Machine Learning Important?

ML is a buzzing word in today’s technological advancements. Everywhere you go, you can see ML applications. They are growing more rapidly than ever before.

ML in Image Recognition

Image recognition is one of the first things that ML brings to the table. It can identify various things like objects, places, persons, etc.

One of the applications of image recognition is in ‘face recognition technologies. Apple uses this technology in their iPhones. You might have also seen popular social media app Facebook automatically recognizes faces in photos.

ML in Speech Recognition

Speech recognition is an important part of Machine Learning. We can find its most famous example is Google’s ‘Search by voice’ option.

Speech recognition is about converting voice instructions into text. Popular speech recognition algorithms are present in Google Assistant, Microsoft’s Cortana, Amazon’s Alexa, and Apple’s Siri.

ML in Traffic Prediction

With the help of Machine Learning, you can get traffic prediction. One such example is the widespread application of Google Maps.

A person can get an idea of the shortest route to a destination. Also, she can understand which route has the most negligible traffic chances. This prediction is significant because of how advanced Machine Learning & Naturalistic Intelligence has become.

ML in eCommerce

Machine Learning reads and understands your choices, likes, and dislikes. That is why you get accurate recommendations while buying something from eCommerce websites.

ML in the Automotive Industry

Self-driving cars are the most common and technologically advanced application of ML. Tesla is one such company that has created several self-driving cars. It is one of the most fascinating ways how Machine Learning is shaping up the modern world.

Just like Artificial Intelligence has a long list of applications, Machine Learning also has the same. Machine Learning is alluring and has different subsets too. However, we should not misinterpret it as some super technology or magic. It is simply the study of data to predict future trends.

Difference Between Artificial Intelligence and Machine Learning: The Classification Battle

Having understood the fundamentals of AI and ML, let us move into the classification battle. The two terms can be classified into various grounds.

In this section, you will see the most common ones of them all.

Types of Artificial Intelligence

Reactive Machines AI

These are the oldest AI machines. They have minimal capabilities with no memory-based functionalities. Contrary to why and how is Artificial Intelligence important today, they didn’t have any such attribute. These machines lacked the power of ‘learning’ from their processes. It just used to run on a limited set of inputs and combinations. Chess bots in earlier days used these AI technologies.

Limited Memory AI

Unlike Reactive Machines, in this generation of AI machines, limited memory capabilities were there. These machines were capable of learning from historical data. They also used to make decisions based on that. A large part of existing AI applications that we are familiar with today falls under this category.

Theory of Mind AI

The last two types of AI are almost present in abundance today. But this one right here is more of a concept or a work under progress. The Theory of Mind AI is the next level of AI that researchers are working on. Once this AI gets to its fullest potential, we can evidence a phenomenal evolution. This AI should be able to understand human emotions and perceive them as humans.

Self-Aware AI

The final stage of AI development is this. However, it exists only in a hypothetical manner. We can conclude this to be the ultimate destination for AI research and activities. Robots with this AI should be able to have self-consciousness. If not decades, we will be seeing this concept materializing with a century for sure.

Artificial Narrow Intelligence (ANI)

This AI covers almost all of the existing AI solutions. It covers most of the easy and complicated processes together. ANI can complete tasks autonomously using human-like capabilities. However, these machines can do no more than what they have programmed for.

Artificial General Intelligence (AGI)

AGI is the ability of an AI device or agent to work as a human. It will learn, perceive, understand and function totally like a human. This type of AI has multiple competencies and can flawlessly form connections across domains. It cuts down training times drastically by having such multi-functional capabilities.

Artificial Super intelligence (ASI)

It is going to be the best form of AI that will bring the pinnacle of AI research. AGI will become the most capable and the most advanced form of Artificial Intelligence on the planet. ASI is expected to have the super-fast processing power, excellent memory retention, and fantastic decision-making capabilities.

Types of Machine Learning

Supervised Machine Learning

Supervised learning is that type of Machine Learning which maps an input to an output. Here, Supervised ML algorithms try to find a connection between the target prediction output and input features. By doing so, they can predict the output values for new data based on those connections.

Unsupervised Machine Learning

It is another type of Machine Learning in the family of different ML algorithms. The main uses of this type of ML are pattern detection and descriptive modeling. Unlike Supervised ML, they do not have any output labels or categories.

Reinforced Machine Learning

Reinforced ML is the third most popular type of Machine Learning. It targets using observations by interacting with the environment. The actions that it takes are meant for either maximizing the rewards or reducing the risks. This Machine Learning method keeps learning continuously from the environment using iteration.

A very popular example can be bots in computer games. Many times we find that they are capable of beating humans. This is possible because they are constantly learning from human interactions.

Difference Between Artificial Intelligence and Machine Learning: In A Nutshell

So, we have crossed every alley that leads to defining the two terms. Here we come to the final comparison to look at some quick differences.

  1. Artificial Intelligence is the power of a machine to simulate human behavior. Whereas, Machine Learning is a process that allows a machine to learn from past behaviors. In Machine Learning, explicit programming is not required.
  2. Primarily, AI aims at making computer systems intelligent and solve problems like humans. Whereas ML aims at bringing accurate outputs by studying input patterns.
  3. In AI, machines are made intelligent to have decision-solving and taking skills like humans. Whereas in ML, the system is given data inputs to perform a particular task and provide accurate results.
  4. AI is an intelligent system that can perform various complex tasks. Whereas ML is not destined to solve new tasks by evolving. Instead, they target solving only what they are trained for in the best way.
  5. AI is all about learning, logical thinking, reasoning, and self-correction. On the other hand, ML is about learning and self-correction when it finds new data.
  6. AI deals with three types of data – structured, semi-structured, and unstructured. ML deals with two types of data – structured and semi-structured.

The Conclusion

So, these were the points that you needed to know about the difference between Artificial Intelligence and Machine Learning. And these are the reasons that students in colleges seek for Artificial Intelligence assignment help for a better understanding of the concepts for better good grades.

Business leaders expect Machine Learning and Artificial Intelligence to make unprecedented changes tomorrow. It is too early to think of revolutionary changes in the short run. However, that isn’t the case in the long run. Technological advancements are pushing to the next level at high speed. We can expect to see smarter, effective, and modern tools to ease processes within the coming years.