The healthcare experience may have a chance to get better, thanks to AI. Also in this week’s roundup, the data paradox: why AI needs data and data needs AI. Plus, find out how AI might impact the future cost of car insurance, and why it’s so important that you start thinking of your data as an asset.
How AI is helping improve the healthcare experience — three use cases
By Nick Ismail, contributing writer for InformationAge.com
With increasing amounts of data being generated in healthcare, how can organisations leverage AI and analytics to improve care and help make the healthcare system run more efficiently for an overall better patient and provider experience?
The Data Paradox: Artificial Intelligence Needs Data; Data Needs AI
By Joe McKendrick, contributing writer for Forbes.com
Artificial intelligence is a data hog; effectively building and deploying AI and machine learning systems require large data sets. “The development of a machine learning algorithm depends on large volumes of data, from which the learning process draws many entities, relationships, and clusters,” says Philip Russom of TDWI. “To broaden and enrich the correlations made by the algorithm, machine learning needs data from diverse sources, in diverse formats, about diverse business processes.”
Car insurance and more could get cheaper (and, a lot fairer) thanks to artificial intelligence
By Eric Allen Been, contributing writer for TechRepublic.com
The Internet of Things (IoT) has become a crucial part of manufacturing and business transformation. According to a report from Statista (paywall), the total worldwide volume of IoT endpoints data will reach 79.4 zettabytes by 2025, and their number will approach 75 billion the same year.
Unlocking Value With Privacy-Preserving Machine Learning
By Ellison Anne Williams, contributing writer for CPOMagazine.com
To succeed in the digital economy, organizations need to view their data as an asset and unlock its value. This drive has led to ‘data is the new oil’ headlines that are conceptually true, but vastly oversimplify the resource itself. Unlike oil which can be concretely described and defined, data has depth and layers, and quite often, sensitivities. And, its value, meaning, and protection requirements shift depending on the circumstances in which it is obtained and used. Frequently a snapshot of something much larger, data is only as valuable as the insights that can be extracted from it. To address this unprecedented need, machine learning (ML) is proving to be a powerful tool for uncovering the value locked within data. However, with power comes responsibility, and in order to leverage ML effectively, businesses must also understand and mitigate the risks that these capabilities can introduce.
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