Google Ads is updating the'Placements' column in Reports Editor to show the same data that's displayed in the'Placements' section of campaigns and ad groups. This change is rolling out over the coming weeks and will only include manually targeted placements. To be clear, placements refer to the locations on the Google Display Network where an ad appears. Placements can include relevant websites and apps that show Google ads, for example. Previously, the'Placements' column in Reports Editor included data for automatically targeted placements.

Information retrieval (IR) systems need to constantly update their knowledge as target objects and user queries change over time. Due to the power -law nature of linguistic data, learning lexical concepts is a problem resisting standard machine learning approaches: while manual intervention is always possible, a more general and automated solution is desirable. In this work, we propose a novel end -to-end framework that models the interaction between a search engine and users as a virtuous human -in-the-loop inference. The proposed framework is the first to our knowledge combining ideas from psycholinguistics and experiment design to maximize efficiency in IR. We provide a brief overview of the main components and initial simulations in a toy world, showing how inference works end-to-end and discussing preliminary results and next steps.

A member of the search marketing community tweeted his surprise that Google ranked a paywalled web page in the featured snippets. Google's official description of featured snippets precludes content that cannot be clicked through to be read. But other documentation states that paywalled content is acceptable for Google Search as well as Google News. In addition to requirements for structured data that is missing, it's unclear if this featured snippet is a mistake or if Google meant to do that. Scott Hendison (@shendison) tweeted his opinion that paywalled content does not belong in Google search results.

In the last decade, it has been shown that many hard AI tasks, especially in NLP, can be naturally modeled as extreme classification problems leading to improved precision. However, such models are prohibitively expensive to train due to the memory blow-up in the last layer. For example, a reasonable softmax layer for the dataset of interest in this paper can easily reach well beyond 100 billion parameters (>400 GB memory). To alleviate this problem, we present Merged-Average Classifiers via Hashing (MACH), a generic K-classification algorithm where memory provably scales at O(logK) without any strong assumption on the classes. MACH is subtly a count-min sketch structure in disguise, which uses universal hashing to reduce classification with a large number of classes to few embarrassingly parallel and independent classification tasks with a small (constant) number of classes. MACH naturally provides a technique for zero communication model parallelism. We experiment with 6 datasets; some multiclass and some multilabel, and show consistent improvement over respective state-of-the-art baselines. In particular, we train an end-to-end deep classifier on a private product search dataset sampled from Amazon Search Engine with 70 million queries and 49.46 million products. MACH outperforms, by a significant margin,the state-of-the-art extreme classification models deployed on commercial search engines: Parabel and dense embedding models. Our largest model has 6.4 billion parameters and trains in less than 35 hours on a single p3.16x machine. Our training times are 7-10x faster, and our memory footprints are 2-4x smaller than the best baselines. This training time is also significantly lower than the one reported by Google's mixture of experts (MoE) language model on a comparable model size and hardware.

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One of the most difficult problems in Artificial Intelligence is the comprehension of language.

What is Google up to? According to the tech giant, it is in the middle of what will be a revolutionary change to Google's ranking algorithm for at least next five years. The notion of Google is to push Artificial Intelligence (AI) to boost search engine ranking efficiency. Through this new technique, the search engine will understand the questions of an individual in a better manner. This is something which – according to Google – did not comprehend too well in the past.

Alphabet is disrupting healthcare, auto, government contracts, and more with AI. We look at how it got here, where it's headed, and what this means for incumbents. Google was relentless in its pursuit of artificial intelligence even before the current wave of AI commercialization took off. It may be hard to imagine a time when neural networks -- AI algorithms initially inspired by biological neural networks -- were having a dry spell. But researchers were bearish on its commercial scalability as recently as the early 2000s.

Google announced what they called the most important update in five years. What is BERT and how will it impact SEO? According to Google this update will affect complicated search queries that depend on context. "These improvements are oriented around improving language understanding, particularly for more natural language/conversational queries, as BERT is able to help Search better understand the nuance and context of words in Searches and better match those queries with helpful results. Particularly for longer, more conversational queries, or searches where prepositions like "for" and "to" matter a lot to the meaning, Search will be able to understand the context of the words in your query. You can search in a way that feels natural for you."

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