Search


How to provide relevant Search Results - Paperless Lab Academy

#artificialintelligence

The relevance of search results is essential for finding information. Indeed, a user will almost never look further than the first few results of a search engine. It is therefore necessary that the relevant information is ranked as high as possible so that the information sought by the user is found in the first results. The order, or "ranking" of search results is essential for search engines, which will therefore use more or less complex algorithms to display the results that users will find most relevant first. It is usually not possible to find the algorithms used by popular search engines.


ExpertFile COVID-19 Search Engine Connects Journalists, Experts

#artificialintelligence

Curated Online Resource Puts Journalists a Click Away From Hundreds of Healthcare, Economic, Industry and Social Science Experts for Quick and Reliable Sources on the Current Coronavirus Pandemic. In response to unprecedented demand for expert sources and fact-based insights during the COVID-19 pandemic, ExpertFile has launched the COVID-19 Experts Search Engine, a specialized online resource designed to help newsrooms around the world;access reliable experts to speak on a variety of topics related to the coronavirus. With millions affected worldwide by the COVID-19 pandemic, the dangers of misinformation and factual inaccuracy pose a potentially devastating impact on society. As the largest curated, open-access search engine of international expert sources, ExpertFile worked quickly and in close consultation with its members -- including healthcare professionals, university academics, NGO's, corporations, industry associations and journalists -- to build the COVID-19 Experts Search Engine. "Facts matter more than opinions when real lives are at stake. We understand that journalists need evidence-based information, and they need it quickly," said Peter Evans, Co-Founder & CEO of ExpertFile.


Resolving single-cell heterogeneity from hundreds of thousands of cells through sequential hybrid clustering and NMF

#artificialintelligence

We describe a new iteration of ICGS that outperforms state-of-the-art scRNA-Seq detection workflows when applied to well-established benchmarks. This approach combines multiple complementary subtype detection methods (HOPACH, sparse-NMF, cluster "fitness", SVM) to resolve rare and common cell-states, while minimizing differences due to donor or batch effects. Using data from multiple cell atlases, we show that the PageRank algorithm effectively down-samples ultra-large scRNA-Seq datasets, without losing extremely rare or transcriptionally similar yet distinct cell-types and while recovering novel transcriptionally distinct cell populations. We believe this new approach holds tremendous promise in reproducibly resolving hidden cell populations in complex datasets.


COVID-Consumers: Pessimistic, but spending more online - Search Engine Land

#artificialintelligence

Consumer sentiment has turned sharply negative as the virus has disrupted every aspect of daily American life. According to a consumer survey from Engine, 88% of consumers in the U.S. are now concerned about the pandemic. And according to another survey of roughly 2,600 U.S. adults from L.E.K. Consulting and Civis (.pdf), between 80% and 90% of adults expect a recession next year. In addition to measuring consumer sentiment, the survey explored how the coronavirus has shifted buying patterns across industries. Generally, the survey finds "significant increases in at-home activities, particularly cooking at home, watching television, browsing social media and exercising at home."


Council Post: Five Ways AI Is Changing The Game Of Content Marketing

#artificialintelligence

Can an individual keep up with the advancing of technology? Can a team of individuals stay up to date in today's digital-dominant world without the assistance of technology? Think of all the tasks that need to be performed before any piece of content goes live: keyword research, reviewing data, analytics and trends, search engine optimization and content writing -- from personalizing to optimizing. Tasks like these can quickly add up, and marketers are spending valuable writing hours researching, testing and optimizing. However, as the digital world expands and technology grows increasingly advanced, there's no denying that artificial intelligence (AI) is going to -- and has already started to -- change the game of content marketing: You see it all the time -- chatbots on websites providing detailed information to consumers who want answers immediately.


computing movies - Google Search

#artificialintelligence

This is a list of films about computers, featuring fictional films in which activities involving computers play a central role in the development of the plot. Not Rated 95 min Biography, Drama, History. Jun 6, 2015 -- Benjamin, a young German computer whiz, is invited to join a subversive hacker group that wants to be noticed on the world's stage. Many Hollywood films played on the big screen are devoted to computer science, whether fiction or not. Computer-related movies have been produced since the--...


How to Use Artificial Intelligence in SEO [2020 Updates]

#artificialintelligence

Artificial Intelligence (AI) has been the major buzzword in the digital marketing world for the past few years, mainly due to the rapid advancements in machine learning technologies. AI is now a relatively familiar idea among marketers, and it's no longer a sci-fi term associated with the distant future. This is also true in the field of SEO. Machine learning technologies have now become a very important component of search engine algorithms. Meaning, if we can understand AI and how it can help SEO, we can further improve our SEO results.


Optimizing Generalized PageRank Methods for Seed-Expansion Community Detection

Neural Information Processing Systems

Landing probabilities (LP) of random walks (RW) over graphs encode rich information regarding graph topology. Generalized PageRanks (GPR), which represent weighted sums of LPs of RWs, utilize the discriminative power of LP features to enable many graph-based learning studies. Previous work in the area has mostly focused on evaluating suitable weights for GPRs, and only a few studies so far have attempted to derive the optimal weights of GPRs for a given application. We take a fundamental step forward in this direction by using random graph models to better our understanding of the behavior of GPRs. In this context, we provide a rigorous non-asymptotic analysis for the convergence of LPs and GPRs to their mean-field values on edge-independent random graphs.


Staying up to Date with Online Content Changes Using Reinforcement Learning for Scheduling

Neural Information Processing Systems

From traditional Web search engines to virtual assistants and Web accelerators, services that rely on online information need to continually keep track of remote content changes by explicitly requesting content updates from remote sources (e.g., web pages). We propose a novel optimization objective for this setting that has several practically desirable properties, and efficient algorithms for it with optimality guarantees even in the face of mixed content change observability and initially unknown change model parameters. Experiments on 18.5M URLs crawled daily for 14 weeks show significant advantages of this approach over prior art. Papers published at the Neural Information Processing Systems Conference.


How Amazon Is Using AI To Better Understand Customer Search Queries

#artificialintelligence

Being an early adopter of artificial intelligence and automation, Amazon always had an edge in using AI to improve its business efficiencies. Not only has it been using AI to enhance its customer experience but has been heavily focused internally. From using AI to predict the number of customers willing to buy a new product to running a cashier-less grocery store, Amazon's AI capabilities are designed to provide customised recommendations to its customers. According to a report, Amazon's recommendation engine is driving 35% of its total sales. One of the main areas where Amazon is applying continuous AI is to better understand their customer search queries and what is the reason they are looking for a particular product.