What's the difference between Artificial Intelligence, Machine Learning and Deep Learning?

What's the difference between artificial intelligence, machine learning, and deep learning? - Artificial intelligence, machine learning, and deep learning are currently very popular and attract the attention of many parties. However, many of us still do not fully comprehend the meaning of these terms.

Many also think that the three terms describe the same thing, but they don't.

What are the definitions of these three terms? Check out the following explanation:

Artificial Intelligence

In simple terms, AI (the abbreviation for "artificial intelligence") is a system or machine that imitates human intelligence to perform tasks and can continuously improve its capabilities based on the information that has been collected. AI has manifested itself in several forms. Some examples are:

1. Chatbots use AI to understand customer problems faster and provide more efficient answers.
2. Smart Assistant uses AI to parse important information from large amounts of text data to better manage user schedules.
3. Recommendation Engine, which uses AI to provide automatic recommendations for TV shows based on users' viewing habits or product recommendations based on purchase history on e-commerce platforms

Artificial intelligence, or AI, is more concerned with thinking machine processes and data analysis. Although AI displays images of human-like robots that have intelligent thinking abilities, it is not meant to replace humans.

When talking about AI, we are actually not far from these two terms: machine learning and deep learning. because both are sub-fields of artificial intelligence.

Machine Learning

Machine learning is a term to refer to computers that learn from data. This field is a slice of the computer science and statistics majors where algorithms are used to perform specific tasks without being explicitly programmed. In contrast, machine learning recognizes patterns in data and makes predictions when new data arrives.

In general, the learning process of this algorithm can be supervised or unsupervised, depending on the data used to train it.

Machine learning incorporates "classic" algorithms for various tasks such as clustering, regression, and classification. Machine learning algorithms must be trained on data by labeling it.The more data that is given to the algorithm, the better the results will be.

Machine learning is actually a more narrow part of the AI field. This field uses techniques (including deep learning) that allow machines to use previous experience or data to complete a given task. The learning process is based on the following steps:

1. Enter the data into the algorithm. (At this step, we can provide additional information to the model, for example, by performing feature extraction.)
2. Use that data to train the model.
3. Test and apply the model.
4. Use the deployed model to perform auto-prediction tasks. (In other words, call and use the model used to receive the predictions returned by the model.)

Then what is the difference between AI and machine learning?

Artificial intelligence is a field of computer science that allows computer systems to mimic human intelligence. The term is made up of two words, "artificial intelligence" and "intelligence," which mean "man-made thinking power." Artificial intelligence systems do not need to be preprogrammed, but instead use algorithms that can work on their own intelligence.

On the other hand, machine learning allows computer systems to make predictions or take some decisions using pre-existing data without the need to be explicitly programmed. You do this by studying a lot of structured and semi-structured data so that the model you create can produce accurate output or provide predictions based on that data.

Machine learning only works for certain domains, like when we create a model to detect dog images, then the output will only give results for dog images, but if we provide new data, such as cat images, then it is no longer relevant or fails to predict correctly.

Machine learning is now widely used in various places, such as for online product recommendation systems, Google search algorithms, email spam filters, automatic Facebook friend tagging suggestions, etc.

Briefly, the difference between AI and machine learning can be seen in the following table:

Differentiator	Artificial Intelligence	Machine Learning
Generated output	In the form of knowledge	In the form of data
Goals to be achieved	Develop a system that is able to imitate or approach human intelligence in solving problems.	Develop algorithms that can learn independently from training data.
Task scope	Create intelligent systems to perform any task like a human.	Teach machines with data to perform specific tasks and provide accurate results.
Sub field	Machine learning and deep learning are two derived fields of AI.	Only deep learning is a derivative field of Machine Learning
Point of Focus	AI systems focus on maximizing the chances of success.	Machine learning has a focus on accuracy and patterns.
Supported data types	Unstructured, semi-structured, to structured data	Only structured and semi-structured data
Implementation example	Chatbots, Smart Assistants (Siri, Alexa, Google Assistant), Expert systems etc	Netflix movie recommendations, Google search algorithm, Facebook photo tagging, etc

Deep Learning

Deep learning is defined as an algorithm that analyzes data with a logical structure similar to the way humans draw conclusions. Please note that deep learning can only be achieved by using supervised and semi-supervised learning.

To achieve this, deep learning systems use a layered algorithmic structure called an artificial neural network (ANN). Such ANN designs are inspired by the biological neural networks of the human brain, leading to a much more capable learning process than standard machine learning models.

Today, deep learning is used in many fields. Automated steering systems, for example, use deep learning to detect objects such as STOP signs or pedestrians. The military uses deep learning to identify objects from satellites, for example, to find safe or unsafe zones for its troops. Of course, the electronics industry is not exempt. Smart home assistant devices such as Amazon Alexa rely on deep learning algorithms to respond to voice commands and know the preferences of their users.

Then what is so special about deep learning compared to machine learning?

Please note that deep learning is a subfield (derivative) of machine learning. However, it has better advantages than ordinary machine learning.

What are they?

First and foremost, standard machine learning algorithms have rather simple structures, such as linear regression or decision trees, whereas deep learning algorithms are based on artificial neural networks. These multi-layered ANNs, like the human brain, are complex and interrelated.

Second, deep learning algorithms require less human intervention. The Deep Learning algorithm does not require the help of a software engineer to identify features but is able to perform automatic feature engineering through its neural network. This is somewhat different from ordinary machine learning, which requires the intervention of an engineer to help extract features as additional data for the model.

Third, deep learning requires more data than machine learning algorithms to function properly. If machine learning needs a thousand data points, deep learning can need millions. Due to the complex multi-layer structure, deep learning systems require large data sets to eliminate fluctuations and make high-quality interpretations.

The following is a comparison table for machine learning vs. deep learning:

Differentiator	Machine Learning	Deep Learning
Number of data points	Can make predictions based on limited data.	It requires training data on a large scale to make better predictions.
Hardware dependency	Can work on low-end machines. does not necessitate a large computation	It requires a machine with high specifications because it is used to perform intensive matrix multiplication operations. The GPU can efficiently optimize this operation.
Featureization process	Requires features to be accurately identified and created by users.	Learn features from the data provided and build new features independently, without human intervention.
Learning approaches	Breaking the process down into smaller stages. Then combine the results of each stage into one output.	The way we learn is based on the end-to-end process of solving problems.
Execution time	Requires relatively little time for training (the training stage), ranging from a few seconds to several hours.	It usually takes a long time to train because deep learning algorithms involve many layers.
Output	The output is usually a numeric value, such as a score or classification.	The output can be in different formats, such as text, score or sound.

Conclusion

As a closing conclusion, AI is a field that covers machine learning, deep learning, and any field related to methods of improving intelligent machines to solve problems in human life.