Which Is Best Artificial Intelligence Or Machine Learning?

Artificial intelligence and machine learning are two buzzwords that have received a lot of attention. They appear to be a competitor or a term that we use interchangeably at times. So, vague perception of both concepts is creating some confusion. It is important to clarify the difference between these two terms of modern technology.

AI refers to the broader term of machines having the ability to perform tasks efficiently. How machines perform tasks we call “smart.” Other terms you may have come across are machine learning and deep learning. Normally people use these terms interchangeably with artificial intelligence. Therefore, it becomes difficult to identify the difference between all these terms.

AI vs Machine Learning:

To understand what each term stands for, it’s necessary to learn their difference. So, at this point, we’ll go over the fundamental differences between them.

What Is Artificial Intelligence And How Does It Work?

Artificial intelligence (AI) is a branch of computer science. It is the component that is used to make a computer system that mimics human intelligence. So, it is safe to define artificial intelligence as:

It is a technology that is used to develop intelligent systems which are capable of simulating human intelligence.

There is no need to preprogram artificial intelligence. They use algorithms that are capable of working with their intelligence. These algorithms include machine algorithms like “reinforcement learning algorithm” and deep learning neural networks. You can utilize AI at multiple places like Google, AlphaGo, or playing chess with a computer. Considering the capabilities of AI, we can distribute it into three kinds:

Weak Or Narrow AI:

This is a kind of artificial intelligence that implements a limited part of the mind focused on a narrow task. This can be useful for testing hypotheses about minds but can’t mind. Many current systems that claim to be artificial intelligence are working on weak or narrow AI. But we can’t call them weak AI in a traditional sense. A narrow AI system, such as Goggle Assistant, is currently available. But we can’t ultimately call it a weak AI. Because it works on a limited pre-defined range of functions.

AGI (Artificial General Intelligence):

It refers to an intelligent agent’s possible ability to learn cognitive activities that humans can perform. You can also use this term for those computer systems that experience consciousness.

Basic Attributes Of AGI:

This technology can solve puzzles and make decisions under uncertainty.
It represents common sense knowledge like a plan and learning.
Possess the capacity to speak effectively in a natural language.

It has the ability to combine all of the skills listed above to achieve a specific goal. This is not it for AGI, it has other capabilities too:

It can take input as a human as an ability to see or hear.
Gives output in the form of object manipulation or any other movement.

Strong Artificial Intelligence Or Full AI:

It refers to the intelligence that develops a system that can mimic human brain capabilities. This is more a kind of philosophy than an actual approach. This theory states that a machine can typically react as a human brain. On the other hand, weak AI is applicable because it demonstrates what AI is.

What Is Machine Learning And How Does It Work?

The goal of machine learning is to extract meaningful knowledge from data. As a result, we can define machine learning as follows:

Machine learning itself is not artificial intelligence. It is a subfield of it. This allows devices to utilize previous data without having to be specifically coded.

The systems enable to forecast anything or make effective decisions using past data by machine learning. This kind of learning uses massive structured and unstructured data. This helps in analyzing that machine learning can produce exact results or predictions using data. There are various places at which we can take benefit from machine learning. An example of places at which we use it is Email spam filter, Auto friend tagging suggestion of Facebook.

With Python and R scripts, there are three types of machine learning algorithms:

Supervised Learning:

This algorithm contains dependent variables which it has to predict from the given independent variables. We create a function to get the desired output by using the variable. The algorithms which are an example of supervised learning are:

Regression
Decision Tree
Random Forest
KNN
Logistic Regression

Unsupervised Learning:

We don’t need any kind of the desired outcome in this algorithm. It is useful for classifying populations into different categories. Like, we need to classify clients in a specific category for a marketing purpose. The following are some instances of unsupervised learning algorithms:

Apriori algorithm
K-Means

Reinforcement Learning:

This algorithm is used to make our machine trained for making certain decisions. Experts train a machine in the following manner to make it capable of doing so.

They expose the machine to a specific environment where it trains itself by continuously experiencing trial and error. This machine uses past experiences as a source of learning. It tries its best to capture the best possible knowledge from all experiences. That kind of behavior makes it perfect for making effective business decisions.

Bottom Line:

In the recent world, machine learning is more in demand. Because it can manage a pile of tasks and make innovative decisions. This has been specifically adapted by today’s marketers. The AI has been around for so long, even at that time people were not aware of its core potential. We have experienced many false starts on the road of AI. But in the form of machine learning marketers have found something new and shiny. Certainly, today we tend to move towards our goals at the fastest pace.

In the domain of artificial intelligence, we’ve witnessed incredible progress. This advancement has ultimately led us to machine learning. Even though there is a huge difference between machine learning and AI. But both technologies are important and have a great future ahead. Moreover, there is room for improvement too in both technologies.