Twitter announced a new feature that encourages Android Twitter users to read an article before reading it. This raised suspicions that Twitter was tracking user clicks. The move is part of Twitter's stated goal to encourage "informed discussion." Often people share a link without reading the article. That results in click bait titles getting widely promoted regardless of the content.

A critical component of data management and enrichment pipelines is connecting large datasets from various sources to form a holistic view; to make connections between entities across data sources. Oftentimes, these entities -- such as individuals, organizations, or addresses -- may not have a unique identifier that can be used as a key to detect duplicates or to merge datasets on. ThinkData has developed a scalable entity resolution engine to solve these problems. After experimenting with both deep learning and traditional NLP techniques, the team has found the best balance of accuracy and performance. Specifically, we have achieved near-parity in accuracy compared to Magellan (the leading entity resolution project in research), albeit with much better performance metrics and greater scalability.

We introduce MARGE, a pre-trained sequence-to-sequence model learned with an unsupervised multi-lingual multi-document paraphrasing objective. MARGE provides an alternative to the dominant masked language modeling paradigm, where we self-supervise the reconstruction of target text by retrieving a set of related texts (in many languages) and conditioning on them to maximize the likelihood of generating the original. We show it is possible to jointly learn to do retrieval and reconstruction, given only a random initialization. The objective noisily captures aspects of paraphrase, translation, multi-document summarization, and information retrieval, allowing for strong zero-shot performance on several tasks. For example, with no additional task-specific training we achieve BLEU scores of up to 35.8 for document translation. We further show that fine-tuning gives strong performance on a range of discriminative and generative tasks in many languages, making MARGE the most generally applicable pre-training method to date.

Productive, Self-Service Data Science - June 30 Data science is a core part of an organization's digital transformation strategy. In this latest DSC webinar discover how American Family Insurance's use of the Alation Data Catalog is enabling more productive data science outcomes with trusted, curated data. Productive, Self-Service Data Science - June 30 Data science is a core part of an organization's digital transformation strategy. In this latest DSC webinar discover how American Family Insurance's use of the Alation Data Catalog is enabling more productive data science outcomes with trusted, curated data.

Learning to rank (LTR from now on) is the application of machine learning techniques, typically supervised, in the formulation of ranking models for information retrieval systems. With LTR becoming more and more popular (Apache Solr supports it from Jan 2017 and Elasticsearch has an Open Source plugin released in 2018), organizations struggle with the problem of how to evaluate the quality of the models they train. This talk explores all the major points in both Offline and Online evaluation. Setting up correct infrastructures and processes for a fair and effective evaluation of the trained models is vital for measuring the improvements/regressions of a LTR system. The talk is intended for: – Product Owners, Search Managers, Business Owners – Software Engineers, Data Scientists, and Machine Learning Enthusiast Expect to learn: the importance of Offline testing from a business perspective how Offline testing can be done with Open Source libraries how to build a realistic test set from the original data set in input avoiding common mistakes in the process the importance of Online testing from a business perspective A/B testing and Interleaving approaches: details and Pros/ Cons common mistakes and how they can false the obtained results Join us as we explore real-world scenarios and dos and don'ts from the e-commerce industry!

We aim for mobile robots to function in a variety of common human environments. Such robots need to be able to reason about the locations of previously unseen target objects. Landmark objects can help this reasoning by narrowing down the search space significantly. More specifically, we can exploit background knowledge about common spatial relations between landmark and target objects. For example, seeing a table and knowing that cups can often be found on tables aids the discovery of a cup. Such correlations can be expressed as distributions over possible pairing relationships of objects. In this paper, we propose an active visual object search strategy method through our introduction of the Semantic Linking Maps (SLiM) model. SLiM simultaneously maintains the belief over a target object's location as well as landmark objects' locations, while accounting for probabilistic inter-object spatial relations. Based on SLiM, we describe a hybrid search strategy that selects the next best view pose for searching for the target object based on the maintained belief. We demonstrate the efficiency of our SLiM-based search strategy through comparative experiments in simulated environments. We further demonstrate the real-world applicability of SLiM-based search in scenarios with a Fetch mobile manipulation robot.

The COVID-19 global pandemic has resulted in international efforts to understand, track, and mitigate the disease, yielding a significant corpus of COVID-19 and SARS-CoV-2-related publications across scientific disciplines. As of May 2020, 128,000 coronavirus-related publications have been collected through the COVID-19 Open Research Dataset Challenge [23]. Here we present CO-Search, a retriever-ranker semantic search engine designed to handle complex queries over the COVID-19 literature, potentially aiding overburdened health workers in finding scientific answers during a time of crisis. The retriever is built from a Siamese-BERT[18] encoder that is linearly composed with a TF-IDF vectorizer [19], and reciprocal-rank fused [5] with a BM25 vectorizer. The ranker is composed of a multi-hop question-answering module[1], that together with a multi-paragraph abstractive summarizer adjust retriever scores. To account for the domain-specific and relatively limited dataset, we generate a bipartite graph of document paragraphs and citations, creating 1.3 million (citation title, paragraph) tuples for training the encoder. We evaluate our system on the data of the TREC-COVID[22] information retrieval challenge. CO-Search obtains top performance on the datasets of the first and second rounds, across several key metrics: normalized discounted cumulative gain, precision, mean average precision, and binary preference.

AI BI: Fueling Deeper Business Insights and Edge - June 30 Today, businesses are being asked to do more with less, while finding new ways to not only stay competitive, but also outpace the competition. In this latest DSC webinar, hear from two organizations that are coupling their data with artificial intelligence (AI) to power their business intelligence (BI) forecasts and dashboards. AI BI: Fueling Deeper Business Insights and Edge - June 30 Today, businesses are being asked to do more with less, while finding new ways to not only stay competitive, but also outpace the competition. In this latest DSC webinar, hear from two organizations that are coupling their data with artificial intelligence (AI) to power their business intelligence (BI) forecasts and dashboards.

LONDON – Beijing dismissed as "ridiculous" a Harvard Medical School study of hospital traffic and search engine data that suggested the novel coronavirus may already have been spreading in China last August, and scientists said it offered no convincing evidence of when the outbreak began. The research, which has not been peer-reviewed by other scientists, used satellite imagery of hospital parking lots in Wuhan -- where the disease was first identified in late 2019 -- and data for symptom-related queries on search engines for terms such as "cough" and "diarrhea." The study's authors said increased hospital traffic and symptom search data in Wuhan preceded the documented start of the coronavirus pandemic, in December 2019. "While we cannot confirm if the increased volume was directly related to the new virus, our evidence supports other recent work showing that emergence happened before identification at the Huanan Seafood market (in Wuhan)," they said. Paul Digard, an expert in virology at the University of Edinburgh, said that using search engine data and satellite imagery of hospital traffic to detect disease outbreaks "is an interesting idea with some validity."

Beijing has dismissed as "ridiculous" a Harvard Medical School study of hospital traffic and search engine data that suggested the new coronavirus may already have been spreading in China last August, and scientists said it offered no convincing evidence of when the outbreak began. Chinese Foreign Ministry spokeswoman Hua Chunying, asked about the research at a news briefing on Tuesday, said: "I think it is ridiculous, incredibly ridiculous, to come up with this conclusion based on superficial observations such as traffic volume." The research, which has not been peer-reviewed by other scientists, used satellite imagery of hospital parking lots in Wuhan - where the disease was first identified in late 2019 - and data for symptom-related queries on search engines for things such as "cough" and "diarrhoea". The study's authors said increased hospital traffic and symptom search data in Wuhan preceded the documented start of the coronavirus pandemic in December 2019. "While we cannot confirm if the increased volume was directly related to the new virus, our evidence supports other recent work showing that emergence happened before identification at the Huanan Seafood market (in Wuhan)," they said.