By Tony Fyler, contributing writer for Techhq.com
Machine learning (ML) and data science are used almost interchangeably by the general public for the kind of mystic data sorcery on which much of the future is understood to depend. But they’re two different, intertwined things. Let’s break down the data on data science and machine learning to get a clearer idea of what’s what.
By Contributing writers for McKinsey.com
Generative artificial intelligence (AI) describes algorithms (such as ChatGPT) that can be used to create new content, including audio, code, images, text, simulations, and videos. Recent new breakthroughs in the field have the potential to drastically change the way we approach content creation.
By Srikanth, contributing writer for Techiexpert.com
The idea of “learning” is the direct contrast between AI and machine learning. For a computer to learn how to evaluate information as a person does, we may give it a lot of data using machine learning.
By Scott Clark, contributing writer for Cmswire.com
Data silos are a problem for many businesses, and often create barriers to information sharing and collaboration across departments within an organization. Although AI and associated technologies are not a panacea for siloed data, they can provide brands with ways to minimize the otherwise tedious efforts to manually eliminate data silos.
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