Our accustomed systems of retrieving particular bits of information no longer fill the needs of many people. Searching traditional indexes of print publications has been aided by computerized databases, but still usually requires time-consuming serial searching of one database after the other, and then moving on to other methods of searching for internet sources. And what if the information being sought is a sound byte? A video clip? Yesterday's e-mail exchange between respected scientists? Artificial intelligence may hold the key to information retrieval in an age where widely different formats contain the information being sought, and the universe of knowledge is simply too big and growing too rapidly for successful searching to proceed at a human's slow speed.
Search Engines is a software system that helps to carry out web searches. They search the World Wide Web in a systematic way for particular information specified by users, such as a list of web sites, news stories, a map, a directory listing or a biography of a celebrity. They are web search engines that search using a spider to systematically index the content of web sites. The term "search engine" can be used for the software system, the service that delivers web content, or both. In recent years, search engine optimization (SEO) has become a very popular way for web site owners to attract more traffic to their web sites.
Google is developing a new feature called Big Moments, which will compete with rivals Facebook and Twitter in delivering the latest breaking news updates during major events. The COVID-19 pandemic forced the search engine to react quickly and constantly to its users' needs for the latest and most authoritative information, according to Google. A team at Google has been working on the project for over a year, after the company struggled to provide the latest updates on the U.S. Capitol attack in January and Black Lives Matter protests last summer, says The Information, a Silicon Valley-basedtechnology news site. Big Moments hopes to build upon Google's Full Coverage feature, which it launched in Google News in 2018 and later integrated with its search engine in March of 2021. Full Coverage allows users to tap into a news headline and see how that story is reported from a variety of sources.
Whether you are a customer searching for your favorite products online, a writer looking for the latest statistics, or a business owner learning SEO skills, you are using a search engine to get answers. And search engines are pretty interesting! You open up your favorite one, add some related keywords and click to search. Within a fraction of a second, you get thousands of results for your entered keyword. Search engines can perform the way they do because of the algorithms they have and a lot of brilliant people powering them.
Are you fascinated by the possibilities of machine learning systems and is it important to you that these technologies are used fairly? As a PhD Candidate, your research aims to answer the question how information retrieval systems based on machine learning can be used in a non-discriminatory and fair way. Information retrieval and recommender systems based on machine learning can be used to make decisions about people. Government agencies can use such systems to detect welfare fraud, insurers can use them to predict risks and to set insurance premiums, and companies can use them to select the best people from a list job applicants. Such systems can lead to more efficiency, and could improve our society in many ways.
Google has announced a new redesign of its search tools, making it more visual and adding in extra contextual information about its results. At its Search On event, the web giant also announced new features for Google Chrome and its Google Lens artificially-intelligent photo software. The main aesthetic change are visually browsable results, "for searches where you need inspiration" such as "pour painting ideas", Google says, which will surface a series of pictures at the top of search results without having to navigate to the Images tab. It will also bring in more contextual information, rolled out over the coming months, with a new'Things to know" section that includes "different dimensions people typically search for". For those searching how to paint with acrylics, for example, underneath the top result will be a series of drop-down results that include a step-by-step guide, tips, or style options.
Search engine optimization is the process of driving traffic to a website through organic search results. This means that people are finding your content organically in search engines like Google, Yahoo, and Bing. Given that Google owns both YouTube and Gmail, it's no surprise that videos and emails are two big ways to rank for SEO. This comprehensive SEO guide will walk you through all the best tips to optimize your content for SEO. You'll learn how to build links, use keywords effectively, write engaging copy, create video content that attracts viewers, and more!
A new research collaboration between France and the UK casts doubt on growing industry confidence that synthetic data can resolve the privacy, quality and availability issues (among other issues) that threaten progress in the machine learning sector. Among several key points addressed, the authors assert that synthetic data modeled from real data retains enough of the genuine information as to provide no reliable protection from inference and membership attacks, which seek to deanonymize data and re-associate it with actual people. Furthermore, the individuals most at risk from such attacks, including those with critical medical conditions or high hospital bills (in the case of medical record anonymization) are, through the'outlier' nature of their condition, most likely to be re-identified by these techniques. 'Given access to a synthetic dataset, a strategic adversary can infer, with high confidence, the presence of a target record in the original data.' The paper also notes that differentially private synthetic data, which obscures the signature of individual records, does indeed protect individuals' privacy, but only by significantly crippling the usefulness of the information retrieval systems that use it.
In this blog post we'll take a look at how information is delivered to human beings by machines. There are in fact different strategies that identify not only the context of information retrieval, but also user intent and means of delivery. We'll look into what information retrieval is, how user intent defines the objective and how this objective is achieved by specific information delivery systems. Information Retrieval (IR) is the process of gaining knowledge from a source of data from the environment. This environment can be explored in several ways to obtain such information, depending on the its state and the state of the user.
With the rapid development of artificial intelligence, conversational bots have became prevalent in mainstream E-commerce platforms, which can provide convenient customer service timely. To satisfy the user, the conversational bots need to understand the user's intention, detect the user's emotion, and extract the key entities from the conversational utterances. However, understanding dialogues is regarded as a very challenging task. Different from common language understanding, utterances in dialogues appear alternately from different roles and are usually organized as hierarchical structures. To facilitate the understanding of dialogues, in this paper, we propose a novel contextual dialogue encoder (i.e. DialogueBERT) based on the popular pre-trained language model BERT. Five self-supervised learning pre-training tasks are devised for learning the particularity of dialouge utterances. Four different input embeddings are integrated to catch the relationship between utterances, including turn embedding, role embedding, token embedding and position embedding. DialogueBERT was pre-trained with 70 million dialogues in real scenario, and then fine-tuned in three different downstream dialogue understanding tasks. Experimental results show that DialogueBERT achieves exciting results with 88.63% accuracy for intent recognition, 94.25% accuracy for emotion recognition and 97.04% F1 score for named entity recognition, which outperforms several strong baselines by a large margin.
This paper tackles a recently proposed Video Corpus Moment Retrieval task. This task is essential because advanced video retrieval applications should enable users to retrieve a precise moment from a large video corpus. We propose a novel CONtextual QUery-awarE Ranking~(CONQUER) model for effective moment localization and ranking. CONQUER explores query context for multi-modal fusion and representation learning in two different steps. The first step derives fusion weights for the adaptive combination of multi-modal video content. The second step performs bi-directional attention to tightly couple video and query as a single joint representation for moment localization. As query context is fully engaged in video representation learning, from feature fusion to transformation, the resulting feature is user-centered and has a larger capacity in capturing multi-modal signals specific to query. We conduct studies on two datasets, TVR for closed-world TV episodes and DiDeMo for open-world user-generated videos, to investigate the potential advantages of fusing video and query online as a joint representation for moment retrieval.