Despite recent advances in Visual QuestionAnswering (VQA), it remains a challenge todetermine how much success can be attributedto sound reasoning and comprehension ability.We seek to investigate this question by propos-ing a new task ofrationale generation. Es-sentially, we task a VQA model with generat-ing rationales for the answers it predicts. Weuse data from the Visual Commonsense Rea-soning (VCR) task, as it contains ground-truthrationales along with visual questions and an-swers. We first investigate commonsense un-derstanding in one of the leading VCR mod-els, ViLBERT, by generating rationales frompretrained weights using a state-of-the-art lan-guage model, GPT-2. Next, we seek to jointlytrain ViLBERT with GPT-2 in an end-to-endfashion with the dual task of predicting the an-swer in VQA and generating rationales. Weshow that this kind of training injects com-monsense understanding in the VQA modelthrough quantitative and qualitative evaluationmetrics

Current summarization systems only produce plain, factual headlines, but do not meet the practical needs of creating memorable titles to increase exposure. We propose a new task, Stylistic Headline Generation (SHG), to enrich the headlines with three style options (humor, romance and clickbait), in order to attract more readers. With no style-specific article-headline pair (only a standard headline summarization dataset and mono-style corpora), our method TitleStylist generates style-specific headlines by combining the summarization and reconstruction tasks into a multitasking framework. We also introduced a novel parameter sharing scheme to further disentangle the style from the text. Through both automatic and human evaluation, we demonstrate that TitleStylist can generate relevant, fluent headlines with three target styles: humor, romance, and clickbait. The attraction score of our model generated headlines surpasses that of the state-of-the-art summarization model by 9.68%, and even outperforms human-written references.

This paper presents an empirical study of conversational question reformulation (CQR) with sequence-to-sequence architectures and pretrained language models (PLMs). We leverage PLMs to address the strong token-to-token independence assumption made in the common objective, maximum likelihood estimation, for the CQR task. In CQR benchmarks of task-oriented dialogue systems, we evaluate fine-tuned PLMs on the recently-introduced CANARD dataset as an in-domain task and validate the models using data from the TREC 2019 CAsT Track as an out-domain task. Examining a variety of architectures with different numbers of parameters, we demonstrate that the recent text-to-text transfer transformer (T5) achieves the best results both on CANARD and CAsT with fewer parameters, compared to similar transformer architectures.

Gartner has released its 2020 Data Science and Machine Learning Platforms Magic Quadrant, and we are excited to announce that Databricks has been recognized as a Leader. Gartner evaluated 17 vendors for their completeness of vision and ability to execute. We are confident the following attributes contributed to the company's success: The biggest advantage of Databricks' Unified Data Analytics Platform is its ability to run data processing and machine learning workloads at scale and all in one place. Customers praise Databricks for significantly reducing TCO and accelerating time to value, thanks to its seamless end-to-end integration of everything from ETL to exploratory data science to production machine learning. With Databricks, data teams can build reliable data pipelines with Delta Lake, which adds reliability and performance to existing data lakes.

The Big Reboot is a two-part exploration of how we prepare society for the potential impacts of technological disruption, job automation, and the continuing shifts taking place in the global economy. In this first discussion we look at practical strategies for i) raising skills and digital literacy across society, and ii) generating the new ventures and job openings required to fill the employment gap left by those that are displaced by technology. We are reaching peak hysteria in the debate about the potential impact of artificial intelligence (AI) and automation on tasks, roles, jobs, employment, and incomes. On an almost weekly basis, we see projections of wholesale job devastation through automation. These doom-laden forecasts vie with outlandishly optimistic forecasts from AI vendors and consultants suggesting that millions of new roles will be created because of our smart new tech toys.

WASHINGTON: The National Geospatial-Intelligence Agency (NGA) will announce plans in May to contract with commercial companies to for analyze satellite and other imagery data of military targets, says David Gauthier, head of NGA's new(ish) Commercial and Business Operations Group. While the first contracts will be small, the move is a big step toward the spy agency's goal of creating a "hybrid" pool of data that combines commercial imagery with low-resolution but high re-revisit rates with traditional high-resolution that is less timely Intelligence Community imagery provided by the National Reconnaissance Office (NRO) and others. "We do foresee in the future a hybrid architecture, where we definitely require both national systems for their capabilities, and commercial systems for their capabilities," he said. While Gauthier wouldn't provide a budget for the new effort, he told me earlier this week that the plan is to evaluate the capabilities of a number of commercial companies to meet NGA's needs. "I don't want to discuss numbers at this time, but we are still operating at small scale and plan on contracting with multiple vendors to compare and contrast their capabilities," he said.

Apple Inc. acquired Voysis, an artificial intelligence startup that developed a platform for digital voice assistants to better understand people's natural language. Dublin, Ireland-based Voysis focused on improving digital assistants inside online shopping apps, so the software could respond more accurately to voice commands from users. A now-removed company webpage said the technology could narrow product search results by processing shopping phrases such as "I need a new LED TV" and "My budget is $1,000." Voysis provided this AI to other companies to incorporate it into their own apps and voice assistants. An Apple spokesman said the company "buys smaller technology companies from time to time, and we generally do not discuss our purpose or plans."

AI is transforming the practice of medicine. It's helping doctors diagnose patients more accurately, make predictions about patients' future health, and recommend better treatments. To help make this transformation possible worldwide, you need to gain practical experience applying machine learning to concrete problems in medicine. We've gathered experts in the AI and medicine field to share their career advice and what they're working on. We'll also be celebrating the launch of our new AI For Medicine Specialization!

When the 1970s and 1980s were colored by banking crises, regulators from around the world banded together to set international standards on how to manage financial risk. Those standards, now known as the Basel standards, define a common framework and taxonomy on how risk should be measured and managed. This led to the rise of professional financial risk managers, which was my first job. The largest professional risk associations, GARP and PRMIA, now have over 250,000 certified members combined, and there are many more professional risk managers out there who haven't gone through those particular certifications. We are now beset by data breaches and data privacy scandals, and regulators around the world have responded with data regulations.

Experts at the University of Copenhagen, Denmark, have begun using artificial intelligence to create computer models that calculate the risk of a corona patient's needing intensive care or a ventilator. As coronavirus patients are hospitalized, it is difficult for doctors to predict which of them will require intensive care and a respirator. Many different factors come into play, some yet to be fully understood by doctors . As such, computer scientists at the University of Copenhagen are now developing computer models based on artificial intelligence that calculate the risk of an individual patient's need for a ventilator or intensive care. The new initiative is being conducted in a collaboration with Rigshospitalet and Bispebjerg Hospital.