Some of the apps are including additional incentives for those who say they are vaccinated - such as free credits or access to premium features that usually cost extra, like profile boosts, virtual rose giving and "super likes".

First came the idea of making Covid vaccinations mandatory to go to the pub, while Israel offered free pizza and beer with a shot. Now UK officials have hit on what they hope is an even more persuasive reason for young people to get their jab: more chance of getting a date. In an eye-catching policy coinciding with the rollout of vaccinations for the under-30s beginning this week, the Department of Health and Social Care (DHSC) has teamed up with popular dating apps to encourage take-up of the programme. Users of Tinder, Match, Hinge, Bumble, Badoo, Plenty of Fish, OurTime and Muzmatch will enjoy a series of benefits if they add their vaccination status to their profile, including virtual badges and stickers. Most of the apps are also giving people who say they have been vaccinated free bonuses such as a certain number of "boosts", which promote their profile to potential dates, offering the tantalising prospect of a greater stake in what has already been billed a post-pandemic "summer of love".

Artificial intelligence has been making waves in recent years, enabling us to solve problems faster than traditional computing could ever allow. Recently, for example, Google's artificial intelligence subsidiary DeepMind developed AlphaFold2, a program that solved the protein-folding problem. This is a problem that has had baffled scientists for 50 years. Advances in AI have allowed us to make progress in all kinds of disciplines – and these are not limited to applications on this planet. From designing missions to clearing Earth's orbit of junk, here are a few ways artificial intelligence can help us venture further into space. Do you remember Tars and Case, the assistant robots from the film Interstellar?

Tinder is launching an upgrade to limit awkward run-ins with familiar faces on the dating app. The online dating platform's new "Block Contacts" feature lets users avoid personal contacts, be it exes, family members or colleagues. "We're rolling out Block Contacts as an additional resource empowering members with peace of mind by helping create a worry-free space for them to spark new connections," Bernadette Morgan, group product manager for trust and safety at Tinder, said in a Friday news release. A recent survey commissioned by Tinder found that more than 40% of respondents have come across an ex partner on a dating app. The survey also found 24% of respondents have seen a family member or colleague on the app, and another 10% have seen a professor's profile.

The last couple of weeks brought new gadgets from Apple, Google and others. We got to work testing the new Apple TV 4K, which Devindra Hardawar says is much better than the previous model based on the remote alone. Billy Steele listened to the new Pixel Buds A-Series, which are just as comfortable as last year's version and cost $80 less. Devindra also tested the NVIDIA RTX 3080 Ti, which provided him with some excellent ray tracing performance but will probably be difficult to get your hands on. And Mat Smith cleaned his apartment with the high-end Dyson V15, which uses lasers to help get your floors spotless.

If you're on the market for a new streaming device, chances are you want to make your watching experience better than it already is. Streaming dongles and set-top boxes are ubiquitous these days, but deciphering the differences between them can be challenging. Plus, they're not the only gadgets that can deliver your latest Netflix obsession to your TV screen. Let's break down all of the streaming device options you have today and give you our picks for the best you can buy. It's worth pointing out that if you only use a couple of streaming services (say, Netflix and Hulu), you might not need a standalone streaming device.

Slate has relationships with various online retailers. If you buy something through our links, Slate may earn an affiliate commission. We update links when possible, but note that deals can expire and all prices are subject to change. All prices were up to date at the time of publication. Nancy Jo Sales has been reporting on women's experience of the internet since well before people were aware of the unique dangers it posed.

Crowdsourcing-based content moderation is a platform that hosts content moderation tasks for crowd workers to review user submissions (e.g. text, images and videos) and make decisions regarding the admissibility of the posted content, along with a gamut of other tasks such as image labeling and speech-to-text conversion. In an attempt to reduce cognitive overload at the workers and improve system efficiency, these platforms offer personalized task recommendations according to the worker's preferences. However, the current state-of-the-art recommendation systems disregard the effects on worker's mental health, especially when they are repeatedly exposed to content moderation tasks with extreme content (e.g. violent images, hate-speech). In this paper, we propose a novel, strategic recommendation system for the crowdsourcing platform that recommends jobs based on worker's mental status. Specifically, this paper models interaction between the crowdsourcing platform's recommendation system (leader) and the worker (follower) as a Bayesian Stackelberg game where the type of the follower corresponds to the worker's cognitive atrophy rate and task preferences. We discuss how rewards and costs should be designed to steer the game towards desired outcomes in terms of maximizing the platform's productivity, while simultaneously improving the working conditions of crowd workers.

In recommender systems, a cold-start problem occurs when there is no past interaction record associated with the user or item. Typical solutions to the cold-start problem make use of contextual information, such as user demographic attributes or product descriptions. A group of works have shown that social media background can help predicting temporal phenomenons such as product sales and stock price movements. In this work, our goal is to investigate whether social media background can be used as extra contextual information to improve recommendation models. Based on an existing deep neural network model, we proposed a method to represent temporal social media background as embeddings and fuse them as an extra component in the model. We conduct experimental evaluations on a real-world e-commerce dataset and a Twitter dataset. The results show that our method of fusing social media background with the existing model does generally improve recommendation performance. In some cases the recommendation accuracy measured by hit-rate@K doubles after fusing with social media background. Our findings can be beneficial for future recommender system designs that consider complex temporal information representing social interests.

Personalization of natural language generation plays a vital role in a large spectrum of tasks, such as explainable recommendation, review summarization and dialog systems. In these tasks, user and item IDs are important identifiers for personalization. Transformer, which is demonstrated with strong language modeling capability, however, is not personalized and fails to make use of the user and item IDs since the ID tokens are not even in the same semantic space as the words. To address this problem, we present a PErsonalized Transformer for Explainable Recommendation (PETER), on which we design a simple and effective learning objective that utilizes the IDs to predict the words in the target explanation, so as to endow the IDs with linguistic meanings and to achieve personalized Transformer. Besides generating explanations, PETER can also make recommendations, which makes it a unified model for the whole recommendation-explanation pipeline. Extensive experiments show that our small unpretrained model outperforms fine-tuned BERT on the generation task, in terms of both effectiveness and efficiency, which highlights the importance and the nice utility of our design.