This article is about Machine Learning
Exploring the Potential of Machine Learning
By NIIT Editorial
Published on 07/03/2021
Artificial Intelligence owes its most recent strides in advancement to steps taken in its sub-segment, machine learning. Through the application of its subject areas such as supervised learning and unsupervised learning, algorithm developers have innovated methods to introduce autonomous learning models for computers. As a result, machines can gather data, perform an action, take into account new learnings and implement them the next time around to near perfection. In the grand scheme of things, machine learning will play a significant role as we shall see in the pointers mentioned below.
Search Engine Results
Google has been factoring in machine learning for many years to guide the rankings on its search engine results page (SERP). The key parameters guiding its ranking algorithms include the total traffic being drawn by each website, total bounce rate, total time spent on the website, so on and so forth. The cumulative of this calculation then determines which results are to be shown on the first page and in what order. The upcoming years spell more of the same old same old as far as machine learning integrations in its ranking algorithms are concerned.
If business analysts can study any number of factors voraciously with machine learning then the concept can be applied to end-to-end value chains to optimize outcomes. Product development could take on new forms working with multi-pronged approaches to untether customer experiences that deliver maximum user satisfaction. Revenues could be streamlined to max out incentives. In other words, organizational resources could be bundled in a manner that ensures high-outcome settings and productive environments for the collective workforce.
Quantum computing is beginning to find its feet with corporations such as IBM, and Google working with pilot quantum computers. Their computational setups can solve high-scale mathematical problems that conventional computers take days/years to decode. With computer chips reaching their tiniest possible aperture, the commercial scope of quantum computers is anybody’s guess with their true potential remaining untapped. But one thing is for sure, the combination of machine learning and quantum computers will allow digital businesses to venture onto previously uncharted waters.
Open-source frameworks such as TensorFlow, Keras, and PyTorch have standardized machine learning into their built-ups. In other words, users are being handed fully functional machine learning setups that they can begin using without in-depth expertise of the same. They signal the onset of an industry that minimizes user-end functions and off-load the greatest extent of computations to the code that learns through self-supervision. Without machine learning, we may never have reached this stage and you should expect more of the same in the future.
Artificial Intelligence has levels to it and machine learning will continue to push its bounds further. Smart cities, the Internet of Everything (IoE), autonomous cars, and self-directed robots in futuristic warehouses will boom thanks to machine learning.
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