aimojo free WordPress plugin

Aimojo™ transforms WordPress into a hyper-relevant, context aware and intelligent powerhouse in minutes. Now supports updated versions of WooCommerce! Using patent-pending feature/tag dimensionalization methods within the Affinitomics Cloud, the plugin creates AI constructs from pages, posts, and custom post types. These constructs are then used to allow information to self-organize based on contextual value. This makes link lists and menus contextual and dynamic – making sites sticky and visitors more likely to convert. Applied to searches (Google CSE), Affinitomics improves results by as much as 9x, imparting context and massively reducing noise. Categories and traditional tags create flat index structures with little actual relational value. Some plugins try to impart contextual value by either requiring hard-coded relationships or forcing WordPress to calculate tag counts and concordances in an effort to find contextually valuable matches. Plugins that do the latter cause WordPress to perform tens of thousands more calculations than normal, bogging servers and slowing performance. Some hosts have banned the use of these plugins. Aimojo™ for WordPress uses a RESTful API to communicate with the Affinitomics™ Cloud, storing AI constructs, and calculating contextual relationships and values. Free of the the computational load, WordPress benefits, becoming a hyper-contextual information system that dynamically molds itself to the users needs. Additional Information Affinitomics vs Tags also here “Affinitomics™ sounds intimidating – It must take lots of training or a PhD to comprehend.” This couldn’t be further from the truth. If you know how tags work, you can use Affinitomics. Skim this article, and you’ll understand Affinitomics and know how to use them. And we promise, no classes, no visits to MIT, and no scientists are required. When people tag documents or web pages, they generally put everything deemed pertinent into the tags. And since search engines rely on these tags, people involved in search engine optimization often attach quite a number of tags and keywords to the document. These tags – all stored in the same place and separated by commas – are what scientists call a “bag-of-words” or “bag-of-features.” They call them that because there is no structure to the meta-data that the tags provide. Scientists also call this “flat” because all the tags have the same value, and are all used the same way. When a page is searched, the search algorithm usually awards the tags and keywords a higher value if they are also found within the structure of the document. This is called concordance. In the world of intelligent systems, concordance is barely a passing grade. It’s ok for sorting a response from a search engine, but not much else. Affinitomics™ makes a simple change to this paradigm – the same tags are simply sorted based on their relationship to the subject matter of the document (or picture, or video, or song, etc.). This simple change makes a world of difference. It changes tags from a “bag-of-words” to a “dimensional feature space” – making them much more valuable and useful to any number of machine learning and artificial intelligence algorithms. How are the tags sorted? That’s a good question with a deceptively simple answer. If you look at any set of tags you’ll discover that there are usually two, and sometimes three types. 1) Some tags describe the subject’s particular features; 2) some describe what the subject goes with or occurs with; and, 3) sometimes, there are tags that describe what the subject doesn’t go with, conflicts with, or dislikes. By dividing these tags into Descriptors (what it is), Draws (as in drawing closer), and Distances (as in keeping a distance from), the feature space becomes multi-dimensional, thus imparting more information for sorting and classification algorithms. Essentially this makes information self-aware – understanding what it is, what it matches, and what it doesn’t match or is antithetical to. As an example, the following are tags for a St. Bernard Dog: dog, big, k9, furry, eats a lot, good with kids, likes snow, chases cars, chases cats. It’s easy to derive Affinitomics from these tags. “dog, big, k9, furry” are all easily recognizable as Descriptors. The Draws are easy to recognize as well, and we can take a shortcut in writing them that will differentiate them from Descriptors. They become: +eating, +kids, +snow. We also take a shortcut on what are easy to spot as Distances, and they become: -cars, -cats. By separating the tags into three types of Affinitomics, not only have they become more useful for the computer system, they are actually easier to write and take up less space. Traditional Tags look like this: dog, big, k9, furry, eats a lot, good with kids, likes snow, chases cars, chases cats Whereas the features in an Affinitomic Archetype look like this: dog, big, k9, furry, +eating, +kids, +snow, -cars, -cats So now you know how to write Affinitomics, you can see that it takes much less time than writing tags, and by categorizing tags into Descriptors, Draws and Distances, you’ve made the computer much happier. It’s like sorting laundry – it takes the same amount of time and results come out in the wash. With these Affinitomics instead of tags, algorithms can much more quickly determine matches, affinities, and sort values. Extra Credit Affinitomics are even more valuable with attenuation – telling the system how much to value Draws and Distances. For example: How much does the dog like to eat? Or which does it hate more; cars or cats? The attenuated Affinitomics for the St. Bernard answer those questions like this: dog, big, k9, furry, +eating2, +kids, +snow4, -cars2, -cats5 You’ll notice that it’s still less data than the tags, even though the Affinitomics now represent a three dimensional feature space which is far more valuable for knowledge retrieval, discovery, and machine learning. Because of this, Affinitomics can be evaluated, sorted, and grouped much faster and more accurately than tags. In addition, since the Affinitomics essentially make the information self-ranking and self-sorting, systems that use Affinitomics don’t require categories. There you have it. You now know how to create Affinitomic Archetypes – a fancy way of saying that you understand how and why you should sort your laundry, errr, tags.