AI vs. Machine Learning vs. Deep Learning

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AI vs. Machine Learning vs. Deep Learning

With the wave of 4.0 industries and the blow of AI technologies, from college students to global firms, all of us might hear at least once about Artificial Intelligence (AI) and Machine Learning (ML). What about Deep Learning (DL)? What are the differences between AI, ML, and DL? Is what you need to know if you would like to learn more about Industry 4.0 and Artificial Intelligence.

Artificial Intelligence: Machines (or computers) that mimic “cognitive” functions that associate with the human mind, such as “learning” and “problem-solving.”
Machine Learning: Machine learning algorithms build a mathematical model based on sample data, known as “training data,” to make predictions or decisions without being explicitly programmed to perform the task. Also, Machine Learning is a subset of artificial intelligence.
Deep Learning: It is part of a broader family of machine learning methods based on artificial neural networks. Deep learning is a class of machine learning algorithms that use multiple layers to extract higher-level features from raw input progressively.
(Source: Wiki definition)

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What is AI?

AI, which is defined as a part of the computer science industry, relates to the automation of smart behaviors. It creates intelligent machines that are programmed to mimic the human mind, such as learning and problem-solving.
In simple terms, we define AI as the brain-mind machine, invented by a human. It is able to think and learn critically as the human’s mind, which solves information more quickly and scientifically on a larger scale than the human’s brain.

However, frequently, AI technologies have regularly some restrictions. For instance, Alexa – a fantastic housekeeper – one of the most famous symbols in artificial intelligence applications is unable to overcome the Turing test.
In a word, what we implement with Al today merely defined as “Narrow AI.” These technologies have the capabilities of executing the detailed task as well as the humans do. We might see some real-life Narrow AI, such as the face recognition of Facebook or email filter system, which reflect the intelligence of a human. Hence, we can refer to some questions such as: How can it be? Where does the secret come?

What is ML?

Machine Learning is a broad term to describe the motion of training machine learning techniques to accomplish practical problems. In detail, it refers to the efficiency of the engine to implement a job better after several times with the upgraded information. In other words, the basic techniques of machine learning are manipulating algorithms to analyze relevant information, then it makes decisions or determines related objects. Instead of generating more software with detailed guidance or activities to implement specific functions, the machine will be educated by adopting a vast array of data and algorithms to learn how to carry out a task.

Without ML, AI technologies will be frequently further restricted. Because the machines deliver the empowers to search for anything without clarified code. For example, if you want to design a program that is capable of recognizing cats in a photo, you will do the following things:

  • First, you provide a lot of description of cat’s characteristics for AI to identify such as color, size, body shape
  • Then, you continuously provide images with the label name “cat” to identify efficiently
  • After receiving full data about a cat, the machine will know how to find out a cat in a photo with the X, Y or C hints. It will estimate 95 percent if that is not a cat.

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What is DL?

When AI was invented, it helps us to reap many significant steps technologies industry. That is Deep Learning, which is a subset of machine learning, adopts as a brain network – neutral networks. It manages to solve the in-depth information as the human mind does. The differences are that scientists don’t need to set up a program for DL to learn about the cat’s appearance. We solely provide the essential information for the machines, it will learn by itself. These steps will give you a closer view:

  • Proving many images of cats.
  • The algorithms will check images to get information about mutual traits between many photos.

Every image will be a decryption key for many levels, from significant layers to smaller pieces. If specific shapes or lines repeat many times, the algorithms will tag it as an essential characteristic. After analyzing enough, the machine will learn which the most evident proofs about cats are. And what scientists need to do is providing the raw data.

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In summary, DL is a subset of ML, in which the machine educates itself several times to accomplish a task. DL requires more input data and more processing power than ML. It sounds complicated, but it has been launched by many giant technology companies such as Facebook, Amazon. One of the most famous names of ML is AlphaGo – a machine is able to play chess with itself until it is capable of prediction of the next moves precisely to win many champions in the world.

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