After a year with no new major blockbusters, Jo Sung-hee's Space Sweepers arrives as a breath of fresh air. It's not a perfect movie, nor a particularly innovative one, but the science-fiction adventure--touted as the first Korean space blockbuster--is certainly fun, with colorful performances and impressive CGI, and a worthy substitute for a new Star Wars or Marvel movie. However, its presence in a year of absences isn't the only thing that makes it noteworthy. Unlike nearly all of the movies from those two dominant franchises, Space Sweepers is led by people of color. The main characters are a crew of Koreans, and the film is one of the rare space operas that doesn't posit that English has somehow become a universal language.

Amazon announced that their voice assistant Alexa can now sign business agreements with health providers under the Health Insurance Portability and Accountability Act, or HIPAA. Third-party health developers can meet the rules that govern how sensitive health information is shared, and major health providers and companies have launched a number of voice programs to help users manage chronic conditions. If you have an Amazon Echo at home, you need to dive into the privacy settings. There are a few important things to lockdown. Don't forget to turn off voice purchasing if you never use that feature, or at least set up a PIN.

After years of being forced to use Apple services on the iPhone by default, those restrictions are finally easing up a bit. As noticed by MacRumors earlier today, the iOS 14.5 beta appears to let you set third-party music services as default with Siri. This means you can ask Siri to play a particular song or album and it'll go straight to Spotify or YouTube Music. Currently, Siri only searches and plays things from whatever music you have in the Apple Music app, be it your own collection of songs or the Apple Music subscription catalog. It sounds like after iOS 14.5 is installed, Siri will ask you what music service you want to use when you ask it to play a song.

Warnings about misinformation are now regularly posted on Twitter, Facebook, and other social media platforms, but not all of these cautions are created equal. New research from Rensselaer Polytechnic Institute shows that artificial intelligence can help form accurate news assessments--but only when a news story is first emerging. These findings were recently published in Computers in Human Behavior Reports by an interdisciplinary team of Rensselaer researchers. They found that AI-driven interventions are generally ineffective when used to flag issues with stories on frequently covered topics about which people have established beliefs, such as climate change and vaccinations. However, when a topic is so new that people have not had time to form an opinion, tailored AI-generated advice can lead readers to make better judgments regarding the legitimacy of news articles.

The capabilities of natural language models trained on large-scale data have increased immensely over the past few years. Downstream applications are at risk of inheriting biases contained in these models, with potential negative consequences especially for marginalized groups. In this paper, we analyze the occupational biases of a popular generative language model, GPT-2, intersecting gender with five protected categories: religion, sexuality, ethnicity, political affiliation, and name origin. Using a novel data collection pipeline we collect 396k sentence completions of GPT-2 and find: (i) The machine-predicted jobs are less diverse and more stereotypical for women than for men, especially for intersections; (ii) Fitting 262 logistic models shows intersectional interactions to be highly relevant for occupational associations; (iii) For a given job, GPT-2 reflects the societal skew of gender and ethnicity in the US, and in some cases, pulls the distribution towards gender parity, raising the normative question of what language models _should_ learn.

This work explores learning agent-agnostic synthetic environments (SEs) for Reinforcement Learning. SEs act as a proxy for target environments and allow agents to be trained more efficiently than when directly trained on the target environment. We formulate this as a bi-level optimization problem and represent an SE as a neural network. By using Natural Evolution Strategies and a population of SE parameter vectors, we train agents in the inner loop on evolving SEs while in the outer loop we use the performance on the target task as a score for meta-updating the SE population. We show empirically that our method is capable of learning SEs for two discrete-action-space tasks (CartPole-v0 and Acrobot-v1) that allow us to train agents more robustly and with up to 60% fewer steps. Not only do we show in experiments with 4000 evaluations that the SEs are robust against hyperparameter changes such as the learning rate, batch sizes and network sizes, we also show that SEs trained with DDQN agents transfer in limited ways to a discrete-action-space version of TD3 and very well to Dueling DDQN.

A blank stare and dark face, just like in the movie Red Heat: Russians are global leaders in hiding their emotions. But a new generation of tech entrepreneurs is trying to get under the tough guys' skin and also inside their brains. Among pioneers of emotional intelligent tech in Russia are Moscow-based startups Neurodata Lab and NTechLab. Rosbank, a subsidiary of Paris-based Société Générale financial group, is now testing an emotion recognition technology developed by Neurodata Lab at its call centers. The solution calculates a "Customer Satisfaction Index" in real time by analyzing both voice and speech.

Anyone who's been to a concert knows that something magical happens between the performers and their instruments. It transforms music from being just "notes on a page" to a satisfying experience. A University of Washington team wondered if artificial intelligence could recreate that delight using only visual cues--a silent, top-down video of someone playing the piano. The researchers used machine learning to create a system, called Audeo, that creates audio from silent piano performances. When the group tested the music Audeo created with music-recognition apps, such as SoundHound, the apps correctly identified the piece Audeo played about 86% of the time.

Want the best Coventry and Warwickshire news delivered straight to your inbox? An adaptable industrial robot that can'think' like a human has been developed at an engineering centre on the edge of Coventry. However anyone concerned about the seemingly unstoppable advance of artificial intelligence and the prospect of robots taking over the world can be assured it is purely something to make manufacturing more efficient. The new invention, with the decision-making capability of a human operator, has been developed by robotics experts at the Manufacturing Technology Centre (MTC) located in Ansty Park. Using a combination of machine learning and visual recognition, the robot can be taught to make assembly decisions based on the components put in front of it.

This article introduces a neural network-based signal processing framework for intelligent reflecting surface (IRS) aided wireless communications systems. By modeling radio-frequency (RF) impairments inside the "meta-atoms" of IRS (including nonlinearity and memory effects), we present an approach that generalizes the entire IRS-aided system as a reservoir computing (RC) system, an efficient recurrent neural network (RNN) operating in a state near the "edge of chaos". This framework enables us to take advantage of the nonlinearity of this "fabricated" wireless environment to overcome link degradation due to model mismatch. Accordingly, the randomness of the wireless channel and RF imperfections are naturally embedded into the RC framework, enabling the internal RC dynamics lying on the edge of chaos. Furthermore, several practical issues, such as channel state information acquisition, passive beamforming design, and physical layer reference signal design, are discussed.