Who benefits from schema. org?
Schema.org, linked data, and knowledge graphs are powerful tools for organizing and navigating vast amounts of information. Much of the current energy around these tools is related to SEO and search engines, especially Google, who depend on them to provide a better search experience. These same tools help commercial and corporate publishers deliver better, and more unique, web experiences to researchers and other content consumers.
We all have a stake in how well these tools work, so we need to understand the process of creating and managing them, and how stakeholders share the cost, risk, and benefit of the raw material, technical development, and maintenance.
Content strategist Michael Andrews‘ deep dive into the history and process behind schema.org’s management is an enlightening read for stakeholders.
Taming the tail: adventures in improving AI economics
Martin Casado and Matt Bornstein focus on the business models and challenges of machine learning companies and products, which are more unique than you might realize and something we need to learn a lot more about. We recommended an earlier article of theirs on the differences between the business models of AI companies and software companies. This article is a follow-up and provides some guidance on how to deal with some of the challenges previously identified. Especially interesting is their example of long-tailed distributions to illustrate the importance problem understanding.
Headless meets serverless – a tierless architecture for frictionless enterprise
The components of modern enterprise IT architectures have changed considerably in the last few years. The use of APIs, microservices, XaaS (everything as a service), headless, and serverless approaches have, individually and especially in conjunction, become strategically critical. As Phil Wainewright puts it…
As these connected digital technologies mesh together, they begin to reshape the nature of the enterprise, opening up new ways to collaborate, connect and do business. We are still at the very beginning of adjusting to what this means for how we live and work.
Wainewright explains what these technologies are, describes related activity and trends, and makes a case for a tierless model. His article is relevant and will be helpful to both IT and business managers.
The field of natural language processing is chasing the wrong goal
Researchers are too focused on whether AI systems can ace tests of dubious value. They should be testing whether systems grasp how the world works.
The Gilbane Advisor is curated by Frank Gilbane for content technology, computing, and digital experience professionals. The focus is on strategic technologies. We publish more or less twice a month except for August and December. We do not sell or share personal data.
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