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Acquiring and sharing high-quality data

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Manage episode 248276632 series 61203
内容由O'Reilly Media提供。所有播客内容(包括剧集、图形和播客描述)均由 O'Reilly Media 或其播客平台合作伙伴直接上传和提供。如果您认为有人在未经您许可的情况下使用您的受版权保护的作品,您可以按照此处概述的流程进行操作https://zh.player.fm/legal

In this episode of the Data Show, I spoke with Roger Chen, co-founder and CEO of Computable Labs, a startup focused on building tools for the creation of data networks and data exchanges. Chen has also served as co-chair of O’Reilly’s Artificial Intelligence Conference since its inception in 2016. This conversation took place the day after Chen and his collaborators released an interesting new white paper, Fair value and decentralized governance of data. Current-generation AI and machine learning technologies rely on large amounts of data, and to the extent they can use their large user bases to create “data silos,” large companies in large countries (like the U.S. and China) enjoy a competitive advantage. With that said, we are awash in articles about the dangers posed by these data silos. Privacy and security, disinformation, bias, and a lack of transparency and control are just some of the issues that have plagued the perceived owners of “data monopolies.”

In recent years, researchers and practitioners have begun building tools focused on helping organizations acquire, build, and share high-quality data. Chen and his collaborators are doing some of the most interesting work in this space, and I recommend their new white paper and accompanying open source projects.

Sequence of basic market transactions in the Computable Labs protocol. Source: Roger Chen, used with permission.

We had a great conversation spanning many topics, including:

  • Why he chose to focus on data governance and data markets.
  • The unique and fundamental challenges in accurately pricing data.
  • The importance of data lineage and provenance, and the approach they took in their proposed protocol.
  • What cooperative governance is and why it’s necessary.
  • How their protocol discourages an unscrupulous user from just scraping all data available in a data market.

Related resources:

  continue reading

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Artwork
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Manage episode 248276632 series 61203
内容由O'Reilly Media提供。所有播客内容(包括剧集、图形和播客描述)均由 O'Reilly Media 或其播客平台合作伙伴直接上传和提供。如果您认为有人在未经您许可的情况下使用您的受版权保护的作品,您可以按照此处概述的流程进行操作https://zh.player.fm/legal

In this episode of the Data Show, I spoke with Roger Chen, co-founder and CEO of Computable Labs, a startup focused on building tools for the creation of data networks and data exchanges. Chen has also served as co-chair of O’Reilly’s Artificial Intelligence Conference since its inception in 2016. This conversation took place the day after Chen and his collaborators released an interesting new white paper, Fair value and decentralized governance of data. Current-generation AI and machine learning technologies rely on large amounts of data, and to the extent they can use their large user bases to create “data silos,” large companies in large countries (like the U.S. and China) enjoy a competitive advantage. With that said, we are awash in articles about the dangers posed by these data silos. Privacy and security, disinformation, bias, and a lack of transparency and control are just some of the issues that have plagued the perceived owners of “data monopolies.”

In recent years, researchers and practitioners have begun building tools focused on helping organizations acquire, build, and share high-quality data. Chen and his collaborators are doing some of the most interesting work in this space, and I recommend their new white paper and accompanying open source projects.

Sequence of basic market transactions in the Computable Labs protocol. Source: Roger Chen, used with permission.

We had a great conversation spanning many topics, including:

  • Why he chose to focus on data governance and data markets.
  • The unique and fundamental challenges in accurately pricing data.
  • The importance of data lineage and provenance, and the approach they took in their proposed protocol.
  • What cooperative governance is and why it’s necessary.
  • How their protocol discourages an unscrupulous user from just scraping all data available in a data market.

Related resources:

  continue reading

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