 AI to Solve the Data Deluge: AI-Based Data CompressionEric Falk ()
A chapter in Innovative Technologies for Market Leadership, 2020, pp 271-285 from Springer Abstract: Abstract The massive amounts of data, growing as we speak, are one of the, if not the, most accountable reasons of today’s AI systems which on many tasks exhibit human grade performance. Thanks to the enormous amounts of image data that machines can be trained to recognize scenes and steer cars. Quantities of medical imagery lead to machine provided diagnostics, sensor data allows us to detect natural disasters before they occur, and to prepare for them. Times are exciting since researchers find new applications to AI at astonishing pace. However, there is a small concern. How will we handle the ever-growing amounts of data? The consensus is that storage is cheap, yet with load of data it is expensive and unsustainable. The amount of live streamed data is also increasing. In other words, we are well advised to consider data compression again. In this chapter, we will introduce traditional compression terminology and techniques, before surveying novel approaches proposed by industry and academia. It sounds contradictory, but AI may just as well help us to address this problem. Date: 2020 There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it. Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:spr:fuobcp:978-3-030-41309-5_18 Ordering information: This item can be ordered from DOI: 10.1007/978-3-030-41309-5_18 Access Statistics for this chapter More chapters in Future of Business and Finance from Springer |
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