Democratizing Machine Learning Algorithms for Integrated Data-Sharing
The Tango of AI and Big Data
Transforming the Art Museum in the 21st Century.
Explainable AI And The Future Of Machine Learning
Optimal Healthcare Strategy Design In The Digital Era
Ingrid Vasiliu-Feltes, Chief Quality And Innovation Officer, Mednax
Machine Learning And Its Potential Disruptions And Transformations
Sangeeta Edwin, Vice President, Data, Analytics & Insights, Rockwell Automation
AI Summers And Winters And What They Teach Us About The Future
Andreas Merentitis, Director Of Data Science (Global), Olx Group
Artificial Inteligence And The Lost Art Of Auscultation
Edward Kersh, Medical Director, Sutter Care
Thank you for Subscribing to CIO Applications Weekly Brief

Reasons behind the Current Hype Around Machine Learning

With 90 percent of businesses trying to use machine learning, it's time to reconsider the technology's true benefits and capabilities.
Fremont, CA: The complexity of infrastructure or workload requirements is the greatest difficulty organizations confront when using machine learning. A whopping 90 percent of CXOs share this sentiment. To get into the specifics, 88 percent of respondents say they have trouble integrating AI/ML technology, and 86 percent say they have trouble keeping up with the regular changes necessary for data science tools.
Every year, certain technologies gain a greater level of popularity than others. Cloud computing, big data, and cybersecurity are examples of this. Machine learning is now the talk of the town that inspires people to fantasize about the future and the possibilities that it may bring. Even more terrifying are the nightmares, which depict self-learning robots capable of taking over the globe. However, the reality is a long cry from this. It is challenging to understand how statistical and mathematical supervised learning models are used nowadays in machine learning.
Such future visions undoubtedly push us to invest in technology, but they also fuel the so-called hype. According to experts, such scenarios happen when ML gets asked without first addressing the internal data ready or the tool's needs.
It is critical to establish a robust foundation of data for successful project execution when using machine learning, and it necessitates a complete shift in organizational culture and processes.
Before any machine learning development can begin, companies must first focus on 'data readiness.' It entails obtaining clean and consistent data and developing data governance processes and scalable data architectures. Firms must execute long-term data-based plans and policies to build a unified data architecture.
Employees need time to adjust to new technology, and machine learning is no exception.
When computers first became prominent in 1950, many people believed that the future of these robots would be humanoids, particularly in the military. Nobody anticipated, however, that the Internet would genuinely transform the world. Today's scenario is similar, with the latest AI and machine learning algorithms always being overhyped.
I agree We use cookies on this website to enhance your user experience. By clicking any link on this page you are giving your consent for us to set cookies. More info
Featured Vendors
-
Jason Vogel, Senior Director of Product Strategy & Development, Silver Wealth Technologies
James Brown, CEO, Smart Communications
Deepak Dube, Founder and CEO, Datanomers
Tory Hazard, CEO, Institutional Cash Distributors
Jean Jacques Borno, CFP®, Founder & CEO, 1787fp
-
Andrew Rudd, CEO, Advisor Software
Douglas Jones, Vice President Operations, NETSOL Technologies
Matt McCormick, CEO, AddOn Networks
Jeff Peters, President, and Co-Founder, Focalized Networks
Tom Jordan, VP, Financial Software Solutions, Digital Check Corp
Tracey Dunlap, Chief Experience Officer, Zenmonics