We estimate that there are as many as 500,000 papers relevant to COVID-19 that were published before the outbreak, including papers related to the outbreaks of SARS in 2002 and MERS in 2012. Any one of these might contain the key information that leads to effective treatment or a vaccine for COVID-19. This is why we and our colleagues at Lawrence Berkeley National Lab are using the latest artificial intelligence techniques to build COVIDScholar, a search engine dedicated to COVID-19. COVIDScholar includes tools that pick up subtle clues like similar drugs or research methodologies to recommend relevant research to scientists. AI can't replace scientists, but it can help them gain new insights from more papers than they could read in a lifetime.

Giphy is an app in its own right, but most people appear to use it in the context of other services -- as an add-on to Apple's iMessage tool for texting, for example, or as a way to send viral images to coworkers using Slack. In acquiring the company, Facebook signaled Friday it did not plan to change GIHPY's core functionality, pledging it would invest further in content while assuring developers "will continue to have the same access" to the underlying code.

This story was originally published by Next Avenue. Read all of Next Avenue's COVID-19 coverage geared toward keeping older generations informed, safe and prepared. Job hunting is never easy. But the coronavirus pandemic is creating challenges unlike any we've ever seen, with unemployment expected to hit 16% or higher and employers laying off or furloughing millions. The job search engine site Indeed says job postings in late April were more than a third lower than a year ago.

Artificial Intelligence is the buzzing word in digital marketing headlines forever. The impact of Artificial Intelligence is the only source for marketers that affects digital marketing activities. The transformation of digital marketing is due to advanced technologies bringing the business resolution. Search engine optimization is the only task for businesses to find their potential audience traffic to their company websites. Now the Artificial Intelligence SEO is the targeted marketing strategy for the companies.

Vertex nomination is a lightly-supervised network information retrieval (IR) task in which vertices of interest in one graph are used to query a second graph to discover vertices of interest in the second graph. Similar to other IR tasks, the output of a vertex nomination scheme is a ranked list of the vertices in the second graph, with the heretofore unknown vertices of interest ideally concentrating at the top of the list. Vertex nomination schemes provide a useful suite of tools for efficiently mining complex networks for pertinent information. In this paper, we explore, both theoretically and practically, the dual roles of content (i.e., edge and vertex attributes) and context (i.e., network topology) in vertex nomination. We provide necessary and sufficient conditions under which vertex nomination schemes that leverage both content and context outperform schemes that leverage only content or context separately. While the joint utility of both content and context has been demonstrated empirically in the literature, the framework presented in this paper provides a novel theoretical basis for understanding the potential complementary roles of network features and topology.

To build Sounding Board, we develop a system architecture that is capable of accommodating dialog strategies that we designed for socialbot conversations. The architecture consists of a multi-dimensional language understanding module for analyzing user utterances, a hierarchical dialog management framework for dialog context tracking and complex dialog control, and a language generation process that realizes the response plan and makes adjustments for speech synthesis. Additionally, we construct a new knowledge base to power the socialbot by collecting social chat content from a variety of sources. An important contribution of the system is the synergy between the knowledge base and the dialog management, i.e., the use of a graph structure to organize the knowledge base that makes dialog control very efficient in bringing related content to the discussion. Using the data collected from Sounding Board during the competition, we carry out in-depth analyses of socialbot conversations and user ratings which provide valuable insights in evaluation methods for socialbots. We additionally investigate a new approach for system evaluation and diagnosis that allows scoring individual dialog segments in the conversation. Finally, observing that socialbots suffer from the issue of shallow conversations about topics associated with unstructured data, we study the problem of enabling extended socialbot conversations grounded on a document. To bring together machine reading and dialog control techniques, a graph-based document representation is proposed, together with methods for automatically constructing the graph. Using the graph-based representation, dialog control can be carried out by retrieving nodes or moving along edges in the graph. To illustrate the usage, a mixed-initiative dialog strategy is designed for socialbot conversations on news articles.

Google's AI team has released a new tool to help researchers traverse through a trove of coronavirus papers, journals, and articles. The COVID-19 research explorer tool is a semantic search interface that sits on top of the COVID-19 Open Research Dataset (CORD-19). The team says that traditional search engines are sufficient at answering queries such as "What are the symptoms of coronavirus?" or "Where can I get tested in my country?". However, when it comes to more pointed questions from researchers, these search engines and their keyword-based approach fail to deliver accurate results. Google's new tool helps researchers solve that problem.

Passage retrieval in a conversational context is essential for many downstream applications; it is however extremely challenging due to limited data resources. To address this problem, we present an effective multi-stage pipeline for passage ranking in conversational search that integrates a widely-used IR system with a conversational query reformulation module. Along these lines, we propose two simple yet effective query reformulation approaches: historical query expansion (HQE) and neural transfer reformulation (NTR). Whereas HQE applies query expansion, a traditional IR query reformulation technique, NTR transfers human knowledge of conversational query understanding to a neural query reformulation model. The proposed HQE method was the top-performing submission of automatic systems in CAsT Track at TREC 2019. Building on this, our NTR approach improves an additional 18% over that best entry in terms of NDCG@3. We further analyze the distinct behaviors of the two approaches, and show that fusing their output reduces the performance gap (measured in NDCG@3) between the manually-rewritten and automatically-generated queries to 4 from 22 points when compared with the best CAsT submission.

In a paper published this week on the preprint server Arxiv.org, They say that CovidQA, which is a work in progress, could help gauge the accuracy of chatbots and search engines that answer topics about the novel coronavirus. Countries, health systems, and nonprofits around the world are employing AI natural language tools to triage potential COVID-19 patients. But as our investigation in early April revealed, chatbots, in particular, rely on inconsistent medical data sources and privacy practices. Data sets like CovidQA could be used to empirically compare the accuracy of the answers supplied by COVID-19 chatbots, exposing gaps in their knowledge and giving users greater peace of mind.

In this paper we propose a new evaluation challenge and direction in the area of High-level Video Understanding. The challenge we are proposing is designed to test automatic video analysis and understanding, and how accurately systems can comprehend a movie in terms of actors, entities, events and their relationship to each other. A pilot High-Level Video Understanding (HLVU) dataset of open source movies were collected for human assessors to build a knowledge graph representing each of them. A set of queries will be derived from the knowledge graph to test systems on retrieving relationships among actors, as well as reasoning and retrieving non-visual concepts. The objective is to benchmark if a computer system can "understand" non-explicit but obvious relationships the same way humans do when they watch the same movies. This is long-standing problem that is being addressed in the text domain and this project moves similar research to the video domain. Work of this nature is foundational to future video analytics and video understanding technologies. This work can be of interest to streaming services and broadcasters hoping to provide more intuitive ways for their customers to interact with and consume video content.