While neural conversation models have shown great potentials towards generating informative and engaging responses via introducing external knowledge, learning such a model often requires knowledge-grounded dialogues that are difficult to obtain. To overcome the data challenge and reduce the cost of building a knowledgegrounded dialogue system, we explore the problem under a zero-resource setting by assuming no context-knowledge-response triples are needed for training. To this end, we propose representing the knowledge that bridges a context and a response and the way that the knowledge is expressed as latent variables, and devise a variational approach that can effectively estimate a generation model from a dialogue corpus and a knowledge corpus that are independent with each other. Evaluation results on three benchmarks of knowledge-grounded dialogue generation indicate that our model can achieve comparable performance with stateof-the-art methods that rely on knowledge-grounded dialogues for training, and exhibits a good generalization ability over different topics and different datasets.

For all Reviewers: Thank you for the valuable comments that help us improve the work. Wizard) to train a knowledge-grounded generation model. GT-knowledge in the input K knowledge sentences on WoW seen and WoW unseen are 37.7% and 37.4% respectively. Finally, to speed up training, we use the number 10. CMU_DoG (pseudo supervision created by selecting GT-knowledge using Sim(.,.) with the response), and the results For REALM, the notification date of ICML 2020 is quite close to the submission date of NeurlPS 2020. For Reviewer #2: We will follow your suggestions on the improvement of clarity in the final version.

A New Jersey Mayor has shared new footage of'glowing orbs transforming into drones' over Long Island, adding more intrigue to this ongoing mystery. Michael Melham, the Mayor of Belleville, has been outspoken about the unexplained phenomena plaguing his state and the greater tri-state area since mid-November when the drones first appeared. He shared the bizarre footage on X, saying the clips'appears to show glowing orbs turning into drones. Verified not to be planes via flight tracker. In a recent interview with NewsNation, Melham said he is still getting reports of drone sightings'all over New Jersey, and even Long Island.' 'Here in New Jersey, we are about 500 mayors strong, we are still waiting for answers because our residents are still gravely concerned over what's flying just over our homes,' he said.

LinkedIn is facing a class-action lawsuit over allegations of using private messages to train its AI model. The lawsuit, filed in the U.S. District Court in the Northern District of California, has accused the Microsoft-owned professional networking site of "unlawfully disclosing its Premium customers' private messages to third parties" and "concealing" its practices by "stealthily altering its privacy policies and statements." A key part of the lawsuit accused LinkedIn of disclosing private InMail messages to third parties to train its model. A spokesperson for LinkedIn said, "we are not using member messages to train models as alleged in the complaint." The issue of attaining training data for AI models is a contentious one, and LinkedIn is not the first company to be accused of misconduct.

This is the part of the article where I usually drop a witty line to spark your curiosity or offer a fresh perspective on something intriguing. There I was, minding my own bidness and getting some work done, when I got a Slack notification from Jason (ZD's big boss). He sent me an article about Meta's plans to integrate AI-generated user profiles and content across its social media platforms. His caption: "What do you think about this?" That's corporate talk for, "I'm subtly telling you what to do, and you should write about this…" Not gonna lie, I had to check the date because it had to be April 1st. Why would Meta want to generate AI users?

This week, OpenAI is introducing a research preview called Operator. I initially wanted to do a hands-on, but once I found out that you need a Pro account (which costs 200 per month), I decided to watch the various OpenAI demos, share them with you, and then share my thoughts. Altman did say that users of the 20-per-month Plus plan would eventually be able to use Operator. Operator is an AI agent. Fundamentally, it simulates keyboard and mouse clicks in a browser, reading the screen, and performing actions. Also: Have a genealogy mystery?

Welcome to a new newsletter, with a bit of a new direction. While our mid-week edition tackles news specifics, this end-of-the-week missive combines the biggest news with more context, more things to read and watch, recommendations, easter eggs, inside baseball and stuff that interests our readers, alongside the breaking news, reviews and features you expect from Engadget. We'd love your feedback on what you'd like to see covered in these meatier editions -- hit me up at tma(at)engadget.com. Luckily for me, we kick things off with Samsung's big Unpacked event, launching three new phones and teasing two -- yes, two! -- more coming soon. Everything Samsung announced, including prices and launch dates (February 8 -- I'll save you a click), we collated here, but it was largely a fallow year for Galaxy S hardware, barring a substantially more powerful chip.

We consider the problem of generating rankings that are fair towards both users and item producers in recommender systems. We address both usual recommendation (e.g., of music or movies) and reciprocal recommendation (e.g., dating). Following concepts of distributive justice in welfare economics, our notion of fairness aims at increasing the utility of the worse-off individuals, which we formalize using the criterion of Lorenz efficiency. It guarantees that rankings are Pareto efficient, and that they maximally redistribute utility from better-off to worse-off, at a given level of overall utility. We propose to generate rankings by maximizing concave welfare functions, and develop an efficient inference procedure based on the Frank-Wolfe algorithm. We prove that unlike existing approaches based on fairness constraints, our approach always produces fair rankings. Our experiments also show that it increases the utility of the worse-off at lower costs in terms of overall utility.

We consider the problem of generating rankings that are fair towards both users and item producers in recommender systems. We address both usual recommendation (e.g., of music or movies) and reciprocal recommendation (e.g., dating). Following concepts of distributive justice in welfare economics, our notion of fairness aims at increasing the utility of the worse-off individuals, which we formalize using the criterion of Lorenz efficiency. It guarantees that rankings are Pareto efficient, and that they maximally redistribute utility from better-off to worse-off, at a given level of overall utility. We propose to generate rankings by maximizing concave welfare functions, and develop an efficient inference procedure based on the Frank-Wolfe algorithm. We prove that unlike existing approaches based on fairness constraints, our approach always produces fair rankings. Our experiments also show that it increases the utility of the worse-off at lower costs in terms of overall utility.

Mere moments after his swearing in Monday, President Donald Trump made a proclamation to attendees of his inauguration: "It shall henceforth be the policy of the United States government that there are only two genders: male and female." Trump then signed an executive order disparaging what the White House called "gender ideology" and claiming that a person's sex is "not changeable and [is] grounded in fundamental and incontrovertible reality." Trump's order, which was widely seen as an unscientific attempt to roll back the rights of transgender and gender-expansive people, also instructs federal agencies "to require that government-issued identification documents, including passports, visas, and Global Entry cards, accurately reflect the holder's sex," rather than their gender identity. It was one of 78 orders signed on Monday, some of which were part of Trump's attempts to end Biden-era policies that "socially engineer race and gender into every aspect of public and private life." While the executive order only affects federal policy, the broader implications are vast.