Go back far enough in time, and you will eventually reach a stage where all the words for concepts that…
Category: Machine Learning
Machine Learning, often shortened to ML, is a general term for algorithms that use kernels, or propagation graphs, to analyze patterns and act upon them through differential geometry. Machine learning is essentially trained by analyzing large amounts of specific kinds of data, then using the kernel to classify new content, and is used heavily in speech and visual recognition, threat and risk analysis, and gaming.
Articles in this section will typically be more technically focused, while more general content will usually be found under the related rubric of Artificial Intelligence. For more information, see here.
Knowledge Graph Prompts, Exemplified
In my last article (Why Prompts Are The Future of Knowledge Graphs), I explored why a prompt-based approach to knowledge…
Why Prompts Are the Future of Knowledge Graphs
I thought, briefly, of doing another big animated piece for this issue of The Cagle Report. Still, I suspect I’m…
An Interview with ChatGPT About Knowledge Graphs
A great deal has been written on ChatGPT and its implications for semantics and knowledge graphs. However, it occurred to…
ChatGPT (LLMs) vs. Knowledge Graphs
This is the second of three articles about ChatGPT and Knowledge Graphs. In the first article, I looked at how…
Making Sense of Data Features
Spend any time at all in the machine learning space, and pretty soon you will encounter the term “feature”. It’s…
Seeking Out the Future of Search
Way back in 1991, Tim Berners-Lee, then a young English software developer working at CERN in Geneva, Switzerland, came up with an intriguing way of combining a communication protocol for retrieving content (HTTP) with a descriptive language for embedding such links into documents (HTML). Shortly thereafter, as more and more people began to create content on these new HTTP servers, it became necessary to be able to provide some kind of mechanism to find this content.
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