AI News, metadata artificial intelligence
Don’t Skip a Beat!
Effectively using metadata is crucial for media and entertainment (M&E) companies today and using artificial intelligence (AI) and other tools that enable us to harvest it at scale to gain knowledge about our data fast is becoming just as important, according to David Flynn, CEO and co-founder of Hammerspace, a software-as-a-service company that specializes in delivering unstructured data across the hybrid cloud.
Today’s data workflows involve “finding what you need, getting it into the applications, getting it to the users that need it [and] having them be able to sort through what is it that’s actually relevant to work with,” he said, calling it an “extremely manual process [that’s] time-consuming and very wasteful.” Using the enterprise sector’s typical tools, to try and get out of that mess, an organization would just “stick the data in a big silo,” but that “restricts the access,” or you would “copy the data around to multiple storage systems and then people don’t know where to find it,” he said.
That’s why it’s so important “to keep that metadata very closely attached to the data itself,” he said, adding: “If your metadata is detached, then you can’t put it in a loop to where you can filter what data you want to talk about so that you can feed it AI to have it become more competent at recognizing things and use that ultimately to drive more value and data.” In addition, metadata “comes from being able to embed the rules about how the data should be presented and what data should be presented,” he went on to say.
AIDR 2019 Call for Submissions
Supported by the NSF scientific data reuse initiative, AIDR 2019: Artificial Intelligence for Data Discovery and Reuse is a conference aiming to find innovative solutions to accelerate the dissemination and reuse of scientific data in the data revolution.
At the same time, increasing numbers of new data formats, greater data complexity, a lack of consistent data standards across disciplines, metadata or links between data and publications makes it even more challenging to evaluate data quality, reproduce results, and reuse data for new discoveries.
Quality submissions from academia, industry and other communities are all welcomed. (All deadlines are 11:59pm EST) Submission deadline: February 1, 2019 February 22, 2019 Author notification: February 28, 2019 March 8, 2019 Early bird registration deadline: March 15, 2019 March 22, 2019 Conference: May 13-15, 2019 The conference program committee invites Abstract submissions for presentations and panels that address applications of AI/ML to challenges related to the discovery, reuse and management of data across disciplinary domains.
- On 26. februar 2021
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