What is the Difference Between Artificial Intelligence Machine Learning and Deep Learning

What is the Difference Between Artificial Intelligence | Machine Learning and Deep Learning

Introduction

AI has been adopted in the current society as a key aspect of the technology that is applied in almost all the fields. However, terms such as Machine Learning (ML) and Deep learning (DL) are sometimes used synonymously with AI which causes most people to have little understanding of what each of them is. For anyone that seeks to establish an interaction with the field of technological growth AI, ML, and DL, it is essential to understand the differences between them all. In this article, the reader will learn the definitions of these three concepts, how they are similar, and how they are different and the uses of each.

What does AI stands for?

The term Artificial Intelligence (AI) is a large branch of computer science aimed at the development of the intelligent systems and programs. Some of the activities involve reasoning, learning, solving or perceiving and even creativity. The notion of AI can be considered as rather vast and it belongs to the sphere of scientific discussion for approximately half a century more precisely since the middle of the twentieth century when such scholars as A Turing started to discuss the possibility of the existence of intelligent machines.

AI can be classified into two categories:

Narrow AI: 

Otherwise known as Narrow AI, it is an AI system made to complete one certain function like voice recognition or image categorization. These systems are therefore effective, however, as has been mentioned above, they function in a confined capacity.

General AI:

This is also referred to as Strong AI and comprises of the most advanced form of artificial intelligence which has the capacity to complete any task that would normally require a human intellect. The general AI is still in its conceptual phase currently.

Examples of AI Applications:

• Self driving cars and other vehicles for movement on the roads and the traffic system.
• Smart speech recognition devices such as Apple’s Siri and Amazon’s Alexa which can take commands.
• AI-based recommendation platforms including the Netflix and Amazon recommendation platforms.

What is Machine Learning (ML)?

Specifically, that means defining what Machine Learning is as a subject of focus in this current information age.

 Machine Learning is one of the areas of AI that deals with making algorithms that allows computer to learn from data and improves its performance as time goes on without coding each of the procedures required for the given task. This is different from conventional programming, where one instructs a computer to perform a number of operations, in machine learning he feeds the system information and it makes predictions based on it.

 Most ML are faced with big data and hence learn from such and the workings involve learning patterns and relationships. This means that the more these models are trained, to a particular set of data the more capable they are in predicting new data accurately.

 Key Aspects of Machine Learning:

Supervised Learning:

Here the data fed to the model is labeled that means the input data come with the right answer already. The model in question is trained on what is referred to as training data, and learns to recognize and predict inputs and/or outputs that relate to this data.

Unsupervised Learning:

The model is provided with data that does not have any labels and has to in some way categorize the data such as grouping similar data.

Reinforcement Learning: 

This learning is done through the model’s interaction with its environment and through the award or penalty system.

Examples of ML Applications:

• Smart filters that help the user to deal with spam messages in the most efficient way.
• Examples of the Smart Maintenance applications are: Predictive maintenance systems which help to predict when an equipment is likely to fail.
• On the basis of the user behavior, we will be engaging in the marketing campaign different depending on the wants and desires of the citizens.

What is Deep Learning (DL)?

Deep Learning (DL) can be considered as a subfield of Machine Learning which utilizes artificial Neural Networks with a large number of layers to reveal complex and nonlinear relationships between features in a large dataset. 

These are artificial neural networks whose architecture tries to closely resemble the human brain and consists of layers of heterogeneous nodes, known as neurons that get to learn from data.

 Hence, deep learning is one of the major branches of AI that has had significant impact especially in the area of image and speech recognition. DL models have the coping point of not requiring the user to define features in the raw data as is seen in conventional models of machine learning.

Key Features of Deep Learning:

Neural Networks: 

DL models are made up of several neurons which are capable of processing data. The fact meaning that the more layers the network has, the more complex patterns it can learn.

Backpropagation: This is the procedure of adjusting the weights of the neurons within the network by using the error rate; the difference between the network output and the theoretical output.

High Data Requirements: Despite the recognized performance, DL models are reliant on big data and powerful computationally resources to train.

Examples of DL Applications:

• Automated image recognition systems that enable identification of one or many things, people or even scenes in images including videos.
• AI invention-like models the precisely capture natural language intention and creation abilities such as chatbots.
• It is technology that involves self-driving and which work with the help of visual and sensor information processing in real-time for driving on the roads.

Differences between AI, Machine learning and Deep learning

While Artificial Intelligence, Machine Learning, and Deep Learning are interconnected, they each have distinct characteristics:

Scope of Artificial Intelligence, Machine Learning, and Deep Learning : 

Artificial intelligence is the most general concept that refers to any technology that makes the machine be able to perform tasks that are otherwise performed by human beings. While AI deals with making decisions based on the results of data analysis, ML can be considered as a part of it and provides learning from data features.

Complexity:

While creating an ML algorithm is already complicated when compared to simple linear regression models, DL models are more complex as they use neural networks that have several layers.

Data Requirements: While the traditional ML models are effective with smaller datasets, the DL models can only perform well with large amounts of data.

Performance: 

• Despite the fact that DL models are mostly able to provide more accurate solutions as compared to traditional ML models particularly in remote sensing image recognition and natural language processing, they also require higher resources.
• This relationship between AI, ML, and DL is expressed in the following sections of this work:
• To visualize the relationship between these concepts, imagine three concentric circles:
• The biggest circle is Artificial Intelligence which includes all systems to possess AI.
• AI acronym includes ML as systems that are capable of learning from data.
• Within ML, there is DL as it defines the systems that apply deep neural networks for learning.

As this kind of hierarchical structure puts forward, the compound exact is that Deep Learning is a part of Machine Learning but all of Machine Learning is not Deep Learning.

And all the AI does not need Machine Learning.

 Applications in Real-World Scenarios

To better understand how AI, ML, and DL are used, let’s look at some real-world examples:

• AI Applications: AI is implemented in areas of decision making for instance self driven cars, these depend on sensors, ML models and DL networks to cater for the necessary decisions to be made.
• ML Applications: Subscription-based services like Netflix with their recommendation models utilize Machine Learning algorithms that propose content based on users’ preferences.
• DL Applications: Facial recognition technology is one of the key application of Deep Learning where nested neural networks analyze and identify explicit features of faces in images or video clips.

 Artificial Intelligence, Machine Learning, and Deep Learning : The Future

To some extent, predicting future prospects of advanced technologies such as AI, ML, and DL is not that challenging because they are bound to progressively improve with time. Emerging trends include:

Explainable AI: 

Attempts to increase the interpretability of the AI models in diverse fields, particularly, such significant domains as medicine and finance.

Edge AI:

Localledge: the implementation of AI models directly to smartphones and other IoT devices thereby enabling real-time computations without the need for a cloud server.

Continual Learning: AI systems that can learn and get better through the data that is fed to it with no need for re-training the systems.

Conclusion

Education on the distinctions between Artificial Intelligence, Machine Learning, and Deep Learning is important to recognize the working of today’s technology and technologies’ future transformation. AI is the big picture, machine learning is AI that allows them to learn from data and deep learning is the best way to combat complex problems. And while these technologies advance, so too must citizens’ understanding of their applications and differences in order to come to terms with the effects they will have on industries and the wider society.