Collaborating Authors

What Role Does Artificial Intelligence Play in Content Recommendations?


Marketers see great potential value in using artificial intelligence (AI) to support the use case of recommending highly targeted content to users in real time. That use case scored the highest among 49 use cases presented to marketers in the 2021 State of Marketing AI report by Drift and the Marketing Artificial Intelligence Institute. That use case scored a 3.96, putting it on the cusp of "high value" (4.0), with 5.0 being "transformative." The AI marketing use cases that trailed in the top five include: "Most websites you go to today for businesses, a human is writing the rules to say which content to recommend," Paul Roetzer, CEO and founder of the Marketing Artificial Intelligence Institute, told CMSWire in a CX Decoded Podcast. "What are the related articles? There is some basic tagging system for if they read this, then read that. Most of them are human-powered. They don't have a Netflix or a Spotify type algorithm that's actually learning preferences, knows the last 15 articles someone read, and how far along he got into them. Therein lies potential, however it's something marketers and customer experience professionals remain hopeful about: 54% of them told CMSWire researchers in the State of Digital Customer Experience 2021 report they see AI having significant impacts on digital customer experience over the next two to five years. And most of them see "gaining actionable customer insights" (27%) as the area where they see the most potential. Roetzer said it is hard to find really good solutions to do this out-of-the-box. Noz Urbina of Urbina Consulting agreed, calling the technology nascent. The bigger question for marketers beyond what kind of tools are out there is do we have the data to support the use case, according to Roetzer. And do we have a strong foundation of metadata, content tagging and content taxonomies, according to Urbina. "You need enough data, for one," Roetzer said. "Sometimes the problem is smaller data, not necessarily the cost.

Buyers Meeting Point - How does an AI think about taxonomy?


Emerging technologies are'all the rage' in procurement today – especially for rules-based tasks that have to be quickly and reliably executed at scale. Transaction categorization is no exception from the trend. There are a number of ways to leverage technology for categorizing procurement activity, but they are not all created equal. Some employ top-down business rules'under the hood' while others leverage AI, and while the end result might appear the same at first glance, this could not be further from the truth. When taxonomy information is assigned to a transaction, the goal is to represent an accurate and actionable truth about the business activity.

9 WordPress Plugins That Use Artificial Intelligence


In the last year the biggest minds of the globe like Elon Musk, Stephen Hawking and Bill Gates began to publicly worry about artificial intelligence (AI) that is slowly entering our everyday lives. Other tech leaders like Google's Eric Schmidt claim that AI is not something we need to necessarily be afraid of. Artificial intelligence makes it possible for a software to learn from experience, and make decisions based on its acquired knowledge. This feature results in an enhanced usability, as the AI algorithm observes the behaviour of individual users and with time adapts to their unique needs. There are many of these smart WordPress plugins that utilize AI as their core function.

A Taxonomy of Automated Assistants

Communications of the ACM

Automated cars are in our future--and starting to be in our present. In 2014, the Society of Automotive Engineers (SAE) published the first version of a taxonomy for degree of automation in vehicles from Level 0 (not automated) to Level 5 (fully automated, no human intervention necessary).8 Since then, this taxonomy has gained wide acceptance--to the point where everyone from the U.S. government (used by the NHTSA5) to auto manufacturers to the popular press are talking in terms of "skipping level 3" or "everyone wants a level 5 car."1 As technology gets developed and improved, having an accepted taxonomy helps ensure people can talk to each other and know they are talking about the same thing. It is time for one of our computing organizations (perhaps ACM?) to develop an analogous taxonomy for automated assistants.

Semantic Kernel Forests from Multiple Taxonomies

Neural Information Processing Systems

When learning features for complex visual recognition problems, labeled image exemplars alone can be insufficient. While an \emph{object taxonomy} specifying the categories' semantic relationships could bolster the learning process, not all relationships are relevant to a given visual classification task, nor does a single taxonomy capture all ties that \emph{are} relevant. In light of these issues, we propose a discriminative feature learning approach that leverages \emph{multiple} hierarchical taxonomies representing different semantic views of the object categories (e.g., for animal classes, one taxonomy could reflect their phylogenic ties, while another could reflect their habitats). For each taxonomy, we first learn a tree of semantic kernels, where each node has a Mahalanobis kernel optimized to distinguish between the classes in its children nodes. Then, using the resulting \emph{semantic kernel forest}, we learn class-specific kernel combinations to select only those relationships relevant to recognize each object class.