Difference Of AI, ML And DL

Difference Of AI ML And DL

With the advent of new and groundbreaking technological advancements in computing, particularly storage, computing power and networking There is an ongoing fascination with emerging technologies that are driving these advancements. Of these, artificial intelligence (AI) and machine learning (ML) deep learning (DL) are the ones that are discussed. These acronyms are frequently utilized in the same manner in a variety of discussions. They’re similar but they are not the same. The purpose in this article is to clarify the distinction between the three phrases.


How Is Machine Learning Different From Artificial Intelligence

The first thing to note is that each of these acronyms falls within the Artificial Intelligence (AI) umbrella. AI does not exist as a stand-alone technology but rather an umbrella an amalgamation of technologies that encompasses everything that makes machines more intelligent that learn and improve over years of experience and time. Machine Learning (ML) is not the same as AI but is one of the subsets of AI. ML is the AI system that is able to self-learn using an algorithm which can come in a variety of types. A ML system is able to learn over the passage of time and experience. Thus, it is possible to claim the ML system will become more intelligent and smarter with time without the intervention of humans.


Deep Learning (DL) is an extension that is machine learning (ML) applied to huge datasets.

Let’s look at each one to see what they are like.

What Is Artificial Intelligence

AI allows high-performance computing machines to behave like humans, but without human intervention. It is an expansive and inter-disciplinary field in computer science. The AI system can be classified into three kinds

Different types of Artificial Intelligence System

The classification of AI is determined based on their ability to replicate human behaviour, the technology they use to accomplish this as well as their use to the world, and the concept of mind. By using these characteristics for comparison the systems of artificial intelligence (both hypothetical and actual) are classified into three categories:

  1. Artificial Narrow Intelligence (ANI) or ANI systems. It is a system with a goal which is designed to accomplish one specific task.
  2. Artificial General Intelligence, also known as AGI systems. It can help machines be able to learn, comprehend and behave in a manner that is similar to what humans act in the context of a particular circumstance. This, in essence, lets a machine mimic the human.
  3. Artificial Super Intelligence or ASI. It’s in the development phase and is not yet fully implemented. AI systems enable machines to demonstrate the ability to outperform even the brightest human and can therefore control every human.

In short, Artificial Intelligence deals with Reasoning and Problem Solving knowledge representation, decision making, auto-learning, Natural Language Processing (NLP), Perception, Motion and Manipulation, Social network analysis and issues that involve general intelligence.


To get a better understanding of the various the different types of artificial intelligence, here’s a picture:


What Is Machine Learning?

Machine Learning is one of the subsets of Artificial Intelligence that uses statistical learning algorithms to construct systems that are able to automatically learn and grow through experiences, without having to be explicitly programmed.

The majority of us utilize machines learning within our every daily lives when we utilize applications like recommendation systems like Netflix, Youtube, Spotify and search engines such as Google and Yahoo; voice assistants such as amazon alexa and google home. With Machine Learning we teach the algorithm by giving it lots of data, and then allowing it to understand the information processed.

The ML algorithms can be divided into three categories: Supervised Learning, Unsupervised and Reinforcement learning.

Machine Learning or ML is an aspect of AI that employs statistical learning algorithms to create intelligent and sophisticated systems that automatically improve and learn without human intervention. Review and recommendations system available on Amazon is an instance of the ML. Machine learning algorithms are divided in four categories based on the intended purpose.

  1. Supervised learning
  2. Unsupervised Learning
  3. Semi-supervised Learning
  4. Reinforcement Learning

When we use supervised learning techniques, we supply the computer with labeled learning data which contains the input/predictors, and we display it the right results (output) and from this data, the computer will be able to identify patterns that allow it to predict the new data elements. Supervised learning is the process of mapping connections and dependences between desired predictions output as well as the input elements to help us predict the output value for data that is not seen based on the relationships we have learned. The primary kinds of supervised-learning problems include classification and regression problems. Some of the most well-known algorithms include Nearest Neighbor, Naive Bayes, Decision Trees, Linear Regression, Support Vector Machines (SVM) and Neural Networks.

Unsupervised learning is when the machine is taught using non-labeled data without outcomes categories, or even labels, and consequently, the algorithm is unable to model relationships using labels. Instead these algorithms use the data input to search for patterns, identify rules and then summarize and group the data elements based on certain parameters. This is all performed to gain valuable information from the data and provide it in a more effective way to the user. The most well-known algorithms for unsupervised learning are Clustering algorithms as well as Association rule-based learning algorithms. K-means Clustering and Association Rules are main unsupervised learning algorithms.


Semi-supervised learning is a combination of these two methods. In many situations in the real world the cost of labeling is very expensive because it requires highly expert human specialists to perform labeling. Thus, we usually label only a few data points and apply semi-supervised algorithms for building the model. These techniques capitalize from the reality that, even while the group members of the data that are not labeled are not known but the group parameters of the data contain important information.

Reinforcement Learning is a more advanced type in machine-learning. It makes use of the information gathered through interactions with the surrounding environment to perform actions that maximize the reward or reduce the consequences for the agent. The algorithm that is the basis of this system is called an agent. The agent constantly learns from its interactions in the context so as to be able to comprehend the whole spectrum of possible state. Agents can automatically identify the best behaviour within a certain environment to ensure that they can perform at their best. Reinforcement learning is defined by an exact type of problem. All its solutions are classified as algorithms for reinforcement learning. The most popular reinforcement learning techniques are Q-Learning, Time-Difference (TD) as well as Deep Adversarial Networks. The algorithms for reinforcement learning are widely utilized in computer-based board games like Chess and Go, robotic surgery, and autonomous vehicles.


What Is Deep Learning?

Deep learning, also known as DL is an aspect of AI and an ML subset. DL operates in the exact manner as the human brain functions. Like that of Human brain’s Neuron the Artificial Neuron called perceptron starts functioning when the data comes in. Then, it processes the information through patterns, decodes the data, and tries to reduce the loss, and provides a satisfactory end result.

DL systems concentrate on the learning of data representations, instead of focussing on specific algorithms for a particular task. They make use of Deep Neural Networks that are modelled on the structure and functions that the brain of humans. It is linked to learning from previous experiences. DL systems assist the computer model filter the input data by layer to determine and classify data. Deep Learning process information in the same way that the brain of a human. It is utilized in various technologies like driverless cars. The DL network architectures are divided as Convolution Neural Networks, Recurrent Neural Networks, and Recursive Neural Networks.


Deep Learning is employed for Natural Language Processing (NLP) including drug discovery and toxicology Bioinformatics, genetics, and many more. Deep networks are composed of several layers through which require data to pass through before creating the final product. Deep Learning improves AI by making it possible to use a variety of its applications.


To sum up, AI is a superset of ML, and it is a superset of DL. The first AI was introduced to the world. It was then followed by ML and then DL completes the trifecta, making possible everything that was just a dream today. Keep in mind that data science provides insights, Machine learning makes predictions, and Artificial Intelligence creates actions.

You may also like...