What is AI / ML / DL

• Deep Learning (DL) is a part of Machine Learning (ML) and Machine Learning is a part of Artificial Intelligence .so,
• Artificial Intelligence is a broad field that creates intelligent machines. While Machine Learning is mainly focused on algorithms and models that learn using data. On the other hand Deep learning uses a neural network to process and analyze complex data and It is a specialized form of machine learning.
• In summary, These three fields are interconnected. And because of enabling machines to perform tasks that were once thought to be exclusive by humans various industries recolonized.

History :

The history of AI/ML and DL spans several decades. within this time period, these three fields make significant milestones and advancement.

Artificial intelligence come in 1950.

1. Early foundation of Ai (1950s -1960s)

• John McCarthy coined the term “Artificial intelligence in 1950 . And the Ai was officially established.
• Researchers focused on developing rule-based systems and symbolic reasoning, aiming to create machines capable of human-like problem-solving.
• The Dartmouth Conference in 1956 is often considered the birthplace of AI, where researchers gathered to discuss the potential of creating intelligent machines.

2. AI winter ( 1970s — 1980s )

• Progress in AI research faced challenges and disillusionment due to high expectations and limited computational power.
• Funding and interest declined during this period, leading to what is known as the “AI winter.”

Machine Learning Comes in 1980

• The birth of machine learning happened in 1950–1960 creating the first self-learning program called the “ Samuel Checkers-playing Program “.
• But between 1970 -1980 Machine Learning faced some issues and limitations because of lack of computational power and Data availability.
• As a result, researchers created some Algorithms for example decision trees and neural networks, which showed promise.
• So 1980 the Emergence of Machine Learning happened as an alternative approach to AI.
• In 1990–2000 Machine Learning such as vector machines Bayesian networks, and ensemble methods, gained popularity.
• And in that period application AI expands including Computer vision natural language processing and robotics, so we called this time period as Renaissance of AI.

Deep Learning comes in 2010

• Deep Learning origin and development can be traced back to early 2000 but gained significant popularity in 2010.
• Not only Deep Learning the neural network also comes in 2000.
• The term “deep learning” became more widely used to describe the application of neural networks with multiple layers and complex architectures around that time.

Machine Learning

• This is a subset of AI and this focus on algorithms. Machine Learning enables machines to learn from data and make predictions without explicit programming. So Machine Learning helps to revolutionize industries through automation and personalized recommendation.

Machine Learning vs Traditional Programming

• In programming, we used Data to generate an output but in Machine Learning things are different because we are working on data.
• But not all the problems are not suitable for machine Learning. ML is used where traditional programming does not work it means we used Machine Learning where we cannot give a solution based on the algorithm.

Current AI / ML solutions

• Recommendation System.
• Computer vision.
• Natural Language Processing.
• Language Translate.
• Clustering.
• Forecasting.
• Reinforcement Learning.
• General Adversarial Networks.

Deep Learning

• This is a Subset of AI and Deep learning using Artificial Neural Networks inspired by the human brain Deep Learning excels at learning hierarchical representations from raw data and has achieved breakthroughs in image and speech recognition, natural language processing, and more.

• Computer vision, natural language processing, speech recognition, and many others.
• It has been widely applied to tasks such as image and video classification, object detection, language translation, sentiment analysis and even playing complex games.
• Deep learning’s ability to automatically learn intricate patterns and representations from large datasets has contributed to its effectiveness in solving complex problems and driving advancement to artificial intelligence.

Types of Deep learning Models and their Users

• Convolutional Neural Network

Computer vision and Image classification. And also this model is using in self-driving cars and mobile phones.

• Recurrent Neural Network

1. Process Sequences ( Ex: Text, Speech, audio). In the modern world we are using virtual assistance like Siri, Alexa, and google so this model is using in that assistance.
2. And also to translate language also using this model.

• Reinforcement Learning

1. Well-known games, used in Roberts.
2. This is one of the important parts of deep learning because from this model machines are learning by doing some activities so by using this model Deep learning can play complex games and can do some complex activities

• Generative Adversarial Network

1. Generating new artifacts ( Ex: photos, music, videos ).
2. By implementing this model to a machine we can draw and do many more graphic designing works from that Machines.
3. And also we called this model GAN

Important

The Computer can do things it has seen before.
For example :
Sometimes Face recognition systems cannot detect black or white people because of their skin.
So It’s important to train the machine to all the data sets represent.

Conclusion

• Artificial Intelligence, Machine Learning, and deep learning brought the world to a new era. Because these new technologies are doing thing that was thought only can be done by a human.
• And most importantly Ai / ML and DL are still developing. so in the future, these three things will make a new generation of mankind.
• So this article is containing the basic information about artificial intelligence, machine Learning, and deep learning and the history of those three things.