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.

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.

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."

We are at a very exciting juncture in the development of artificial intelligence (AI). We are starting to see implementations of the third wave of the technology, which involves machines far surpassing human capabilities in various application domains, creating all kinds of opportunities for businesses. To leverage this to its full potential, companies need to rethink how they operate and put AI at the heart of everything they do. The first AI wave started with statistics-based systems. The best-known use is likely the information retrieval algorithms used by big internet companies like Google in the early years of AI, such as PageRank search engine.

It took me a long time to realise that search is the biggest problem in NLP. Just look at Google, Amazon and Bing. These are multi-billion dollar businesses possible only due to their powerful search engines. My initial thoughts on search were centered around unsupervised ML, but I participated in Microsoft Hackathon 2018 for Bing and came to know the various ways a search engine can be made with deep learning. Do you find this in-depth technical education about NLP applications to be useful?

Current state-of-the-art approximate nearest neighbor search (ANNS) algorithms generate indices that must be stored in main memory for fast high-recall search. This makes them expensive and limits the size of the dataset. We present a new graph-based indexing and search system called DiskANN that can index, store, and search a billion point database on a single workstation with just 64GB RAM and an inexpensive solid-state drive (SSD). Contrary to current wisdom, we demonstrate that the SSD-based indices built by DiskANN can meet all three desiderata for large-scale ANNS: high-recall, low query latency and high density (points indexed per node). On the billion point SIFT1B bigann dataset, DiskANN serves 5000 queries a second with 3ms mean latency and 95% 1-recall@1 on a 16 core machine, where state-of-the-art billion-point ANNS algorithms with similar memory footprint like FAISS and IVFOADC G P plateau at around 50% 1-recall@1.

Experimental data has revealed that in addition to feedforward connections, there exist abundant feedback connections in a neural pathway. Although the importance of feedback in neural information processing has been widely recognized in the field, the detailed mechanism of how it works remains largely unknown. Here, we investigate the role of feedback in hierarchical information retrieval. Specifically, we consider a hierarchical network storing the hierarchical categorical information of objects, and information retrieval goes from rough to fine, aided by dynamical push-pull feedback from higher to lower layers. We elucidate that the push (positive) and pull (negative) feedbacks suppress the interferences due to neural correlations between different and the same categories, respectively, and their joint effect improves retrieval performance significantly.

This paper explores the task of interactive image retrieval using natural language queries, where a user progressively provides input queries to refine a set of retrieval results. Moreover, our work explores this problem in the context of complex image scenes containing multiple objects. We propose Drill-down, an effective framework for encoding multiple queries with an efficient compact state representation that significantly extends current methods for single-round image retrieval. We show that using multiple rounds of natural language queries as input can be surprisingly effective to find arbitrarily specific images of complex scenes. Furthermore, we find that existing image datasets with textual captions can provide a surprisingly effective form of weak supervision for this task.

The ability to measure similarity between documents enables intelligent summarization and analysis of large corpora. Past distances between documents suffer from either an inability to incorporate semantic similarities between words or from scalability issues. As an alternative, we introduce hierarchical optimal transport as a meta-distance between documents, where documents are modeled as distributions over topics, which themselves are modeled as distributions over words. We then solve an optimal transport problem on the smaller topic space to compute a similarity score. We give conditions on the topics under which this construction defines a distance, and we relate it to the word mover's distance.

The implementation of AI in ecommerce should come as no surprise. Online businesses have always been quick to adopt new technologies, and this is how the industry thrives; enhancing the customer experience, discovering new markets, and driving further sales. And with the continued development of AI technology like chatbots, visual search, and personalized recommendations, the world of ecommerce is transforming again. But just how effective and useful is AI-powered tech? Where is it being used? In this article, we'll look at the most popular implementations of AI in ecommerce to determine what they do, how they are being used, and first steps for practical application.