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.

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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.

Technology companies have produced varied responses to concerns about the effects of the design of their conversational AI systems. Some have claimed that their voice assistants are in fact not gendered or human-like -- despite design features suggesting the contrary. We compare these claims to user perceptions by analysing the pronouns they use when referring to AI assistants. We also examine systems' responses and the extent to which they generate output which is gendered and anthropomorphic. We find that, while some companies appear to be addressing the ethical concerns raised, in some cases, their claims do not seem to hold true. In particular, our results show that system outputs are ambiguous as to the humanness of the systems, and that users tend to personify and gender them as a result.

You may also have heard machine learning and AI used interchangeably. AI includes machine learning, but machine learning doesn't fully define AI. Machine learning and AI both have strong engineering components. You find AI and machine learning used in a great many applications today. Artificial Intelligence (AI) is a huge topic today, and it's getting bigger all the time thanks to the success of technologies such as Siri.