Intelligence is getting Artificial as Machines do Learn with Deep learning Algorithms
Shivam Shukla ???
Senior Leader with Xpertise in Global ITSM, Gen AI, LLM, Prompt Engg, Google Colab, BOT, Copilot, IT Ops / ITIL & Data Sc, Transformn x Automn, M&A, Mgmt and Intgrn( Ex. NXP, DXC.Tech, CSC, EXL Service, Wipro)
Artificial Intelligence, Machine Learning and Deep Learning are the terms of computer science but also sort of confuse us all, this content below discusses some points on the basis of which we can differentiate between these three terms and make reference to what is happening around us.
Overview
Artificial Intelligence : The word Artificial Intelligence comprises of two words “Artificial” and “Intelligence”. Artificial refers to something which is made by human or non natural thing and Intelligence means ability to understand or think. There is a misconception that Artificial Intelligence is a system, but it is not a system .AI is implemented in the system. There can be so many definition of AI, one definition can be “It is the study of how to train the computers so that computers can do things which at present human can do better.”Therefore It is a intelligence where we want to add all the capabilities to machine that human contain.
Machine Learning : Machine Learning is the learning in which machine can learn by its own without being explicitly programmed. It is an application of AI that provide system the ability to automatically learn and improve from experience. Here we can generate a program by integrating input and output of that program. One of the simple definition of the Machine Learning is “Machine Learning is said to learn from experience E w.r.t some class of task T and a performance measure P if learners performance at the task in the class as measured by P improves with experiences.”
Deep Learning : Deep learning is a subset of ML. It uses some ML techniques to solve real-world problems by tapping into neural networks that simulate human decision-making. Deep learning can be expensive, and requires massive datasets to train itself on. That's because there are a huge number of parameters that need to be understood by a learning algorithm, which can initially produce a lot of false-positives. For instance, a deep learning algorithm could be instructed to "learn" what a cat looks like. It would take a very massive data set of images for it to understand the very minor details that distinguish a cat from, say, a cheetah or a panther or a fox.
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