https://omeka.lawrencecatania.com/files/original/2b393b90b5ad8854896dfe234ffe6543.jpg a628bc38849962a51217f40e01effe39 https://omeka.lawrencecatania.com/files/original/de07f85b612efc177040244e84128c2b.jpg 865f3f7598bb88e3668096f358699f28 https://omeka.lawrencecatania.com/files/original/08516a6c4de034cd446c8c07f8ea21d2.jpg 480efeccb78e48e9e43dc59732fb3055 https://omeka.lawrencecatania.com/files/original/937cc5a195d88f4cda38c2b636bf4120.jpg ecc47c004bb897a7e817b7f381c8b113 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Rabbit Holes Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource --show me algorithm first known as "BackRub"-- Description An account of the resource The algorithm first known as BackRub is the foundation of what we now know as Google Search. Developed in 1996 by Larry Page and Sergey Brin at Stanford University, it was originally a research project named for its unique ability to analyze "backlinks" to determine a website's importance. The Core Algorithm: PageRank While the search engine was called BackRub, the specific mathematical algorithm it used is called PageRank. How it Works: Unlike early search engines that ranked pages based on how many times a keyword appeared, BackRub treated every link from one website to another as a "vote of confidence". Weighted Authority: The algorithm didn't just count links; it analyzed the quality of those links. A link from a highly-regarded, "important" website carried more weight than a link from an obscure one. The Goal: By analyzing this web of connections (the "back-rubs"), the algorithm could surface the most relevant and trustworthy information for any given search. Evolution to Google The name "BackRub" was used from 1996 until approximately September 1997. The founders eventually chose the name Google—a play on the mathematical term "googol" (the number 1 followed by 100 zeros)—to reflect their mission to organize the nearly infinite amount of information on the web. Would you like to see the original mathematical formula for PageRank or more details on how it has changed today? --original mathematical formula-- The original mathematical formula for the algorithm used by BackRub (later named PageRank) was first published by Larry Page and Sergey Brin in their 1998 research paper, The Anatomy of a Large-Scale Hypertextual Web Search Engine. The PageRank Formula The formula defines the importance of a page ( ) based on the ranks of all pages that link to it: Key Components : The PageRank of page , which is the value the algorithm is trying to calculate. (Damping Factor): A probability constant, typically set to 0.85. It represents the likelihood that a "random surfer" will continue clicking on links rather than getting bored and jumping to a random new page. : The PageRank of page which links to page . : The total number of outbound links on page . : Often referred to as the "teleportation" or "reset" factor, ensuring that even pages with no incoming links have a baseline score. Why This Was Revolutionary Before this, search engines mostly looked for keywords on a page. PageRank's recursive nature meant that a link from an "important" site (one with a high ) was worth more than many links from obscure sites. Essentially, the rank of a page is a "vote" that is divided equally among all the links it sends out. Would you like to see a step-by-step example of how this calculation works for a small network of three or four pages? --show me latest search engine algorithm-- The latest iteration of the search engine algorithm is not a single formula like PageRank, but a complex, multi-layered system that incorporates Generative AI and Machine Learning. As of April 2026, the search engine landscape includes "Core Updates" and the use of large language models like Gemini. The current algorithm uses AI-generated answer synthesis. Google uses a customized version of the Gemini model for search. It has advanced capabilities, such as multi-step reasoning and planning. AI Overviews (formerly SGE) uses generative AI to provide a summarized response at the top of search results. It uses high-quality sources from the search index to support its information. Current ranking systems include: March 2026 Core Update: This update, released March 27, 2026, aimed at surfacing relevant, satisfying content and refining quality signals like originality and credibility. RankBrain (2015): This machine learning component helps the search engine understand the intent behind ambiguous queries. BERT (2019): This system helps understand the context of words in a search query. MUM (2021): The Multitask Unified Model can understand information across different languages and media types like images and video. Key ranking signals prioritize the Helpful Content System, which evaluates websites based on: Experience. Expertise. Authoritativeness. Trustworthiness. Information on optimizing content for these new AI-driven search results, or details about recent updates like the March 2026 Spam Update, are available --show me mathematical formula including block where necessary for AI-- Modern AI-driven search engines use a Transformer-based architecture. This system processes queries through high-dimensional vector spaces where the core mathematical engine is the Scaled Dot-Product Attention. 1. The Core AI Formula: Scaled Dot-Product Attention The AI calculates the relationship between each word (token) in a query and web content using this formula: (Query): A matrix representing the "search intent". (Key): A matrix representing the "content labels" of web pages. (Value): A matrix containing the actual information to be retrieved. : A scaling factor that prevents the numbers from becoming too large. Softmax: A function that converts the raw scores into a probability distribution (0 to 1). 2. The Transformer Block In a search engine, this attention formula is wrapped in a Transformer Block. This allows the system to understand nuances like sarcasm, context, and intent. A simplified mathematical representation of one layer's output ( ) looks like this: : The initial "vector" representation of a search query. LayerNorm: Normalizes the data. FeedForward: A neural network layer that processes the attention-weighted data. 3. Vector Similarity (Ranking) Once the AI has "understood" the query and the page, it uses Cosine Similarity to see how closely they match in a multi-dimensional space. How it works: If the search is for "how to fix a leaking faucet," the algorithm uses the attention formula to find the relationship between "fix," "leaking," and "faucet." It matches this against the "values" ( ) of articles that explain plumbing repairs.