Jun 22, 2022
In Design Forum
Google said its latest major search update, the inclusion Banner Design of the BERT algorithm, will help it better understand the intent behind users' search queries, which should mean more relevant results. BERT will impact 10% of searches, the company said, which means it's likely to impact your brand's organic visibility and traffic — you just might not notice it. Here's our high-level Banner Design review of what we know so far about what Google claims is "one of the greatest advancements in search history." When you're ready to dive deeper, take a look at our companion article: A Deep Dive into BERT: How BERT Launched a Rocket in Natural Language Understanding, by Dawn Anderson. When was BERT rolled out in Google Search? BERT began rolling out to Google's Banner Design search system the week of October 21, 2019 for English queries, including snippets. The algorithm will expand to all languages in which Google offers search, but there's no set timeline yet, Google's Danny Sullivan said. A BERT model is also used to improve snippets in two dozen countries. What is BERT? BERT, which stands for Bidirectional Encoder Banner Design Representations from Transformers, is a neural network-based technique for pre-training in natural language processing. In plain English, it can be used to help Google better discern the context of words in search queries. For example, in the phrases "nine to five" and "a quarter to five", the word "to" has two different meanings, which may be obvious to humans, but less so to search engines. BERT is designed Banner Design to distinguish between these nuances to facilitate more relevant results. Google open-sourced BERT in November 2018. This means anyone can use BERT to train their own language Banner Design processing system for answering questions or for other tasks. What is a Neural Network? Neural networks of algorithms are designed for pattern recognition, to put it very simply. Categorizing image content, recognizing handwriting, and even predicting trends in financial markets are common real-world Banner Design applications for neural networks — not to mention research applications such as click. They train on data sets to recognize patterns. BERT pre-trained using Wikipedia's plain text corpus, Google explained when it open-sourced it.