Artificial Intelligence, Machine Learning, and Deep Learning Explanation?

Today, we often hear the term AI (Artificial Intelligence) in everyday life.

Take, for example, new technology products that have emerged, such as smartphones or laptops. The manufacturers of these technology products embed the AI ​​label as one of the advantages of the new technology product features they launch.

In smartphone products, technology product manufacturers usually use AI for features in the camera section.

As another example, if you often use virtual assistants (such as Siri or Google Assistant), behind the virtual assistant’s ability to answer questions or carry out the commands you mention, there is actually a role for AI.

So, what is AI actually? AI, which is an acronym for Artificial Intelligence, consists of two syllables. Artificial can be interpreted as “artificial”, while Intelligence is defined as “intelligence”. So, AI means artificial intelligence.

AI is a computer system (machine) that has human-like intelligence. In this case, AI is capable of learning (acquiring information and rules for using information). Reasoning (using rules to reach conclusions), and self-correcting independently.

In simple terms, AI is a computer system that can imitate human thinking in completing a job.

Currently, the most common examples of AI applications are in the latest technology products or services, research, consumer behavior analysis for companies/organizations, detecting fraud, market projections or sales forecasts, monitoring security on the internet and IT, and automating work.

Classification on AI

A. Main Classification of AI:

Strong AI:

AI system with cognitive abilities like humans in general. When given a task or command that has not been recognized, this AI system has enough intelligence to find a solution for every task or command it performs.

Weak AI (Weak AI):

An AI system designed and trained for a specific task. Example: Apple Siri and Google Assistant

B. Other AI Classifications:

Reactive Machines

An example of this AI is Deep Blue, which has the ability to identify pieces on the chessboard and predict moves to win the game. Unfortunately, this AI system does not have the ability to be applied in various situations.

• Limited Memory:

AI capable of making future decisions. For example, a self-driving vehicle that can use its past travel experience to make decisions on future (next) trips.

• Theory of Mind (Theory of Mind):

An AI system that has its own beliefs, desires, and intentions that influence the decisions it makes. This kind of AI doesn’t exist yet

• Self-Awareness (Self-Awareness):

Machines that have the self-awareness to understand the situation and can process information to identify what other people are feeling. This kind of AI doesn’t exist yet.

Machine Learning

Machine learning (ML) aka machine learning is a sub-field of AI. ML uses statistical learning algorithms to build and develop systems that have the ability to learn from data, identify patterns, and make decisions with very minimal human intervention.

ML can also be defined as a technique that enables increased performance on multiple tasks through experience. ML’s focus is on gaining insight so that it can make data-driven decisions (ML uses data to answer questions).

Unconsciously, most of us use ML in our daily lives when using services such as the recommendation systems on the entertainment platforms Netflix, Youtube, and Spotify, or the search engines Google Chrome and Mozilla Firefox.

For companies in today’s digital era, ML adoption is very important because it can help companies in things such as providing insight into customer behavior trends and business operational patterns, as well as supporting the development of new products.

Many of today’s leading companies, such as Facebook, Google, and Uber, make ML a major part of their operations. This is because ML has now become a significant competitive differentiator for many companies.

Deep Learning

In simple terms, Deep Learning (DL) or deep learning is part of ML, which has the ability to imitate the workings of the human brain through a neural network whose architecture is very diverse.

This neural network can imitate the way the human brain works in processing data and creating patterns for use in decision making.

Neural networks are able to learn unsupervised from unstructured or unlabeled data.

For some cases ML can be very complex, so it takes additional methods so that a machine can imitate the workings of the human brain. In this case, DL can be relied on.

DL can solve more complex problems such as computer vision (the ability of machines to recognize objects in image and video data), speech recognition (recognizing data in the form of sound), and NLP/Natural Language Processing (recognizing data in text form) using Artificial Neural Networks. (ANN).

For information, DL is capable of driving many artificial intelligence (AI), applications and services that enhance automation, perform analytical and physical tasks without human intervention.

Examples of current products or services that use DL technologies such as virtual assistants, voice-activated TVs, and credit card fraud detection, as well as new technologies such as self-driving or self-driving cars.

• Weak AI (Weak AI):

An AI system designed and trained for a specific task. Example: Apple Siri and Google Assistant

B. Other AI Classifications:

• Reactive Machines

An example of this AI is Deep Blue, which has the ability to identify pieces on the chessboard and predict moves to win the game. Unfortunately, this AI system does not have the ability to be applied in various situations.

• Limited Memory:

AI capable of making future decisions. For example, a self-driving vehicle that can use its past travel experience to make decisions on future (next) trips.

• Theory of Mind (Theory of Mind):

An AI system that has its own beliefs, desires, and intentions that influence the decisions it makes. This type of AI does not yet exist.

• Self-Awareness (Self-Awareness):

Machines that have the self-awareness to understand the situation and can process information to identify what other people are feeling. This kind of AI doesn’t exist yet.

Machine Learning

Machine learning (ML) aka machine learning is a sub-field of AI. ML uses statistical learning algorithms to build and develop systems that have the ability to learn from data, identify patterns, and make decisions. With very minimal human intervention.

ML can also be defined as a technique that enables increased performance on multiple tasks through experience. ML’s focus is on gaining insight so that it can make data-driven decisions (ML uses data to answer questions).

Unconsciously, most of us use ML in our daily lives when using services such as the recommendation systems on the entertainment. Platforms Netflix, Youtube, and Spotify, or the search engines Google Chrome and Mozilla Firefox.

For companies in today’s digital era, ML adoption is very important. Because it can help companies in things such as providing insight into customer behavior trends and business operational patterns, as well as supporting the development of new products.

Many of today’s leading companies, such as Facebook, Google, and Uber, make ML a major part of their operations. This is because ML has now become a significant competitive differentiator for many companies.

In simple terms, Deep Learning (DL) or deep learning is part of ML. Which has the ability to imitate the workings of the human brain through a neural network whose architecture is very diverse.

This neural network can imitate the way the human brain works in processing data and creating patterns for use in decision making. Neural networks are able to learn unsupervised from unstructured or unlabeled data.

For some cases ML can be very complex, so it takes additional methods so that a machine can imitate the workings of the human brain. In this case, DL can relied on.

DL can solve more complex problems such as computer vision (the ability of machines to recognize objects in image and video data), speech recognition (recognizing data in the form of sound), and NLP/Natural Language Processing (recognizing data in text form) using Artificial Neural Networks. ANN).

For information, DL is capable of driving many artificial intelligence (AI), applications and services that enhance automation, perform analytical and physical tasks without human intervention.

Examples of current products or services that use DL technologies such as virtual assistants, voice-activated TVs, and credit card fraud detection, as well as new technologies such as self-driving or self-driving cars.