A core interest in building Artificial Intelligence (AI) agents is to let them interact with and assist humans. One example is Dynamic Search (DS), which models the process that a human works with a search engine agent to accomplish a complex and goal-oriented task. Early DS agents using Reinforcement Learning (RL) have only achieved limited success for (1) their lack of direct control over which documents to return and (2) the difficulty to recover from wrong search trajectories. In this paper, we present a novel corpus-level end-to-end exploration (CE3) method to address these issues. In our method, an entire text corpus is compressed into a global low-dimensional representation, which enables the agent to gain access to the full state and action spaces, including the under-explored areas. We also propose a new form of retrieval function, whose linear approximation allows end-to-end manipulation of documents. Experiments on the Text REtrieval Conference (TREC) Dynamic Domain (DD) Track show that CE3 outperforms the state-of-the-art DS systems.

Google has updated its video structured data help document with new information on some of the latest enhancements to video search results. The updated document has details on how to mark timestamps on YouTube videos, how to monitor performance in Search Console, and more screenshots to show the new structured data types in action. The Google video structured data doc got a big refresh – More screenshots! Here's a rundown of the new information that was added. Google may display timestamps alongside YouTube videos in search results which help searchers jump directly to a specific part of the video.

Google is adding new data to Search Console giving retailers insight into the performance of product rich results in Google Search. The data can be found in a new Search Appearance in the Search Console performance report, which captures stats such as total clicks, impressions, average click-through rate, and average position. Websites that are eligible to appear in Google's product search results will find a new Search Appearance type called "Product results." "Website owners need to understand the impact of these rich results. The Google Search Console Performance report provides key metrics like clicks and impressions to help webmasters understand and optimize the performance of their website results on Google Search. These metrics can further be segmented by device, geography and queries."

Everything you need to know about Duchess of Sussex Meghan Markle and her new life as part of the British royal family. There's something about that "Markle sparkle" that has the world transfixed, seeing as Meghan Markle has now been named the world's "most powerful dresser" in a 2019 report from Lyst, a fashion search engine. It was a big year for the Duchess of Sussex, who stylishly seized the spotlight at dozens of public appearances and royal tours, and even when introducing the world to baby Archie -- and according to Lyst, shoppers took notice. There's something about that "Markle sparkle" that has the world transfixed, as Meghan Markle has been named the world's most powerful dresser of 2019. According to Lyst's annual Year in Fashion roundup, each of the Duchess' numerous fashion statements sparked a 216-percent average increase in search for similar items.

GoogleBot continually gets even smarter when resolving both paid PPC advertising and when displaying earned search results. This article endeavors to demystify the concept of how Google predictive search works around user intent and positive search experiences. Digital marketers, SEO's, SEM, and AdWords professionals who have a working knowledge of these processes find the keys to offering meaningful user voice activated activity on the Internet. Search engine's core task is to point people to the best information. Since Internet users express one idea in many different ways, by using Google search predictions, you can reach web surfers of any age, anywhere, and any time of day. Wikipedia says, "Predictive analytics encompasses a variety of statistical techniques from predictive modeling, machine learning, and data mining that analyze current and historical facts to make predictions about future or otherwise unknown events. Every business wants to leverage their data for optimal ...

Facebook is rolling out new advertising features that use machine learning to dynamically customize ads for individual users. This gives advertisers the ability to serve personalized ads when they may otherwise lack the time and resources required to deliver personally relevant ad experiences. "Facebook machine learning combines data and signals from our platform, with insights you share, in order to make predictions for who the right people are for a given message. As people take different actions on and off Facebook, it creates intent signals that help us deliver a more tailored ad experience. We do this for both Organic and Paid content."

We consider the problem of adaptively PAC-learning a probability distribution $\mathcal{P}$'s mode by querying an oracle for information about a sequence of i.i.d. samples $X_1, X_2, \ldots$ generated from $\mathcal{P}$. We consider two different query models: (a) each query is an index $i$ for which the oracle reveals the value of the sample $X_i$, (b) each query is comprised of two indices $i$ and $j$ for which the oracle reveals if the samples $X_i$ and $X_j$ are the same or not. For these query models, we give sequential mode-estimation algorithms which, at each time $t$, either make a query to the corresponding oracle based on past observations, or decide to stop and output an estimate for the distribution's mode, required to be correct with a specified confidence. We analyze the query complexity of these algorithms for any underlying distribution $\mathcal{P}$, and derive corresponding lower bounds on the optimal query complexity under the two querying models.

Imagine having a data collection of hundreds of thousands to millions of images without any metadata describing the content of each image. How can we build a system that is able to find a sub-set of those images that best answer a user's search query? What we will basically need is a search engine that is able to rank image results given how well they correspond to the search query, which can be either expressed in a natural language or by another query image. The way we will solve the problem in this post is by training a deep neural model that learns a fixed length representation (or embedding) of any input image and text and makes it so those representations are close in the euclidean space if the pairs text-image or image-image are "similar". I could not find a data-set of search result ranking that is big enough but I was able to get this data-set: http://jmcauley.ucsd.edu/data/amazon/

Question Answering (QA) systems are used to provide proper responses to users' questions automatically. Sentence matching is an essential task in the QA systems and is usually reformulated as a Paraphrase Identification (PI) problem. Given a question, the aim of the task is to find the most similar question from a QA knowledge base. In this paper, we propose a Multi-task Sentence Encoding Model (MSEM) for the PI problem, wherein a connected graph is employed to depict the relation between sentences, and a multi-task learning model is applied to address both the sentence matching and sentence intent classification problem. In addition, we implement a general semantic retrieval framework that combines our proposed model and the Approximate Nearest Neighbor (ANN) technology, which enables us to find the most similar question from all available candidates very quickly during online serving. The experiments show the superiority of our proposed method as compared with the existing sentence matching models.

Inhibition can take place at the level of neurotransmitters in the synaptic cleft, neurons can inhibit each other's fire rate, it can be s h own at a physiological level - for instance by measuring the EEG, and finally it can be investigated on a purely behavioral level. Behavioral inhibition typically means something like'making a content/action less accessible or suppressing it altogether' in order to enhance processing of relevant information . In cognition, thus, the concept of inhibition implies cognitive mechanisms that actively lower currently irrelevant or inter fering information. Psychological theories that posit the existence of inhibitory mechanisms in our mind have elicited much research across diverse fields of C ognitive P sychology like perception, attention, action control, and memory but have also been tra nsferred to other research fields like D evelopmental P sychology as, fo r instance, understanding the aging brain or the developing brain is closely linked to understanding how the brain handles irrelevant or interfering information - that is how or whether the brain can inhibit such information. The two areas in Cognitive Psychology in which inhibition is traditionally investigated to the largest extent are the research fields of attention and memory. In attention research, typically the interference due to distracting stimuli or actions is analyzed in experimental paradigms that try to tap a specific form of cognitive inhibition. For example, in the Negative Priming task (for a review, Frings, Schneider, & Fox, 2015) it is typically analyzed how an irrelevant distractor stimulus is inhibited. In the cuing task that elicits the inhibition of return effect (Posner, Choate, Rafal, & Vaughn, 1985) it is typically analyzed how an irrelevant location is inhibited. In task switchin g (Kiesel et al., 2010) lowering competition by a just previously performed task while currently executing a novel task is achieved by inhibiting that previous task.