What’s the difference between AI,Machine Learning & Deep Learning?

From being dismissed as science fiction to becoming an intergral part of multiple,wildly popular movie series especially one starring Arnold Schwarzenegger, AI has been part of our lives longer than we realize.In fact, turing test , which was developed by Alan turing during the period of WW2,has been widely attributed as intelligence test for machines.As a field AI, as seen most Ups and downs in the past 50 years.On one hand it is hailed as a frontier of next technological revolution on other hand it is viewed with fear since it has potential to surpass human intelligence and hence achieve world domination.

Application for AI

Specialized applications of AI, however allow us to use facial recognition and image classification as well as smart personal assistants like Siri and Alexa. This usually leverage multiple algorithms to provide functionality to end-user, but may broadly be classified as AI.

Machine Learning

Machine learning is a subset of properties commonly aggregated under AI techniques.The term was orginally used to describe the process of leveraging algorithms to parse data,build models that could learn from it , and ultimately make predictions using these learnt parameteres.

While it began as a small part of AI ,burgeoning interest has propelled ML to forefront of research and it is now used across domains. Growing hardware support as well as improvements in algorithms ,especially pattern recognition, has led ML being accessible for much larger audience, leading to wider adoption.

Applicaiton of ML

Initially, the primary application of ML were limited to field of computer vision and pattern recognition.This was prior to the stellar success and accuracy it enjoys today.Today we used ML without even being aware of how dependent are we, on day to day life. With Google’s search team trying to replace PageRank Algorithm with an improved ML algorithm named RankBrain, to facebook automatically suggesting friends to tag in a picture,we are surround by ML.

Deep Learning

A key ML approach that remained dormant for a few decades was artificial neural networks. This eventually gained wide acceptance when improved processing capabilities became available. A neural network simulates the activities of brain’s neurons in a layered fashion , and the propagation of data occurs in similar a manner,enabling machines to learn more about a given set of observations and make accurate predictions.The accuracy of these models allows reliable services to be offered to end user, since the fall positives has been eliminated entirely .

Application of DL

DL has a large scale business applications because of its capacity to learn from million of observations at once. Although computationally intensive,it is still the preferred alternative because of its unparalled accuracy. Autonomous vehicles and recommendation systems ( Such as those used by Netflix and Amazon) are among the most popular applications of DL algorithms.

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