Page 131 - AI Ver 1.0 Class 10
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ways that far exceed its original conception. With incredible advances made in data collection, processing and
computation power, intelligent systems can now be deployed to take over a variety of tasks, enable connectivity
and enhance productivity.
— NITI Aayog: National Strategy for Artificial Intelligence
Artificial Intelligence (AI) is the software engine that drives the Fourth Industrial Revolution. Its impact can already
be seen in homes, businesses and political processes. In its embodied form of robots, it will soon be driving cars,
stocking warehouses and caring for the young and elderly. It holds the promise of solving some of the most pressing
issues facing society, but also presents challenges such as inscrutable “black box” algorithms, unethical use of data
and potential job displacement. As rapid advances in machine learning (ML) increase the scope and scale of AI’s
deployment across all aspects of daily life, and as the technology itself can learn and change on its own, multi-
stakeholder collaboration is required to optimize accountability, transparency, privacy and impartiality to create
trust.
— World Economic Forum
The path for an integrated vision AI is not a well-defined technology and no universally agreed definition
exists. It is rather a cover term for techniques associated with data analysis and pattern recognition. AI is not
a new technology, having existed since the 1950s. While some markets, sectors and individual businesses are
more advanced than others, AI is still at a relatively early stage of development, so that the range of potential
applications, and the quality of most existing applications, have ample margins left for further development
and improvement.
— European Artificial Intelligence (AI) leadership
Humans learn from their past experiences and machines follow instructions given by humans. What if humans
can train the machines to learn from past data and do what you instruct them to do, that too faster and with
accuracy. With AI it is possible for machines to learn from the experience. The machines are just the responses
based on new input thereby performing human-like tasks. Let us understand different terminologies which are
often used with AI.
Machine Learning
Machine Learning is a subset of AI which uses statistical methods to enable machines to improve with experience.
It is one of the most popular techniques to build AI systems across the globe. It is the science of getting
machines to interpret, process and analyse data in order to solve problems. It provides us statistical tools to
explore the data.
Artificial
Intelligence
Machine
Learning
Deep
Learning
Deep Learning
Deep Learning is a subset of machine learning that is inspired by the functionality of our brain cells called
neurons which led to the concept of artificial neural networks. It is a process of implementing neural networks
on high dimensional data to gain insight and form solutions.
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