In this article we are going to see difference between Artificial Intelligence, Machine Learning, Deep Learning.
|ARTIFICIAL INTELLIGENCE||MACHINE LEARNING||DEEP LEARNING|
|AI stands for Artificial Intelligence, and is basically the study/process which enables machines to mimic human behaviour through particular algorithm.||ML stands for Machine Learning, and is the study that uses statistical methods enabling machines to improve with experience.||DL stands for Deep Learning, and is the study that makes use of Neural Networks(similar to neurons present in human brain) to imitate functionality just like a human brain.|
|AI is the broader family consisting of ML and DL as it’s components.||ML is the subset of AI.||DL is the subset of ML.|
|AI is a computer algorithm which exhibits intelligence through decision making.||ML is an AI algorithm which allows system to learn from data.||DL is a ML algorithm that uses deep(more than one layer) neural networks to analyze data and provide output accordingly.|
|Search Trees and much complex math is involved in AI.||If you have a clear idea about the logic(math) involved in behind and you can visualize the complex functionalities like K-Mean, Support Vector Machines, etc., then it defines the ML aspect.||If you are clear about the math involved in it but don’t have idea about the features, so you break the complex functionalities into linear/lower dimension features by adding more layers, then it defines the DL aspect.|
|The aim is to basically increase chances of success and not accuracy.||The aim is to increase accuracy not caring much about the success ratio.||It attains the highest rank in terms of accuracy when it is trained with large amount of data.|
|Three broad categories/types Of AI are: Artificial Narrow Intelligence (ANI), Artificial General Intelligence (AGI) and Artificial Super Intelligence (ASI)|
Three broad categories/types Of ML are: Supervised Learning, Unsupervised Learning and Reinforcement Learning.
DL can be considered as neural networks with a large number of parameters layers lying in one of the four fundamental network architectures: Unsupervised Pre-trained Networks, Convolutional Neural Networks, Recurrent Neural Networks and Recursive Neural Networks.
|The efficiency Of AI is basically the efficiency provided by ML and DL respectively.||Less efficient than DL as it can’t work for longer dimensions or higher amount of data.||More powerful than ML as it can easily work for larger sets of data.|
|Examples of AI applications include: Google’s AI-Powered Predictions, Ridesharing Apps Like Uber and Lyft, Commercial Flights Use an AI Autopilot, etc.||Examples of ML applications include: Virtual Personal Assistants: Siri, Alexa, Google, etc., Email Spam and Malware Filtering.|
Examples of DL applications include: Sentiment based news aggregation, Image analysis and caption generation, etc.