AI is disrupting multiple industries and is becoming part of everyday life. So, it makes sense to try and find out what society thinks about it. With some many different sources of information (and misinformation) people have different opinions varying from optimism to predictions about the impeding doom of humanity. The Mozilla Foundation published a very interesting report, based on a survey where they asked people what they think of AI. The results are surprising and interesting.
Kyocera Corp. has started developing a device to check human health and immunity from the odor of one's stool, aiming to put it into practical use in three years. In collaboration with AuB Inc., a Tokyo-based startup, Kyocera will analyze data from the device, which will be installed in toilet seats. The Kyoto-based electronics giant will create a system that infers the intestinal environment of the user with the aid of artificial intelligence technology and data collected by AuB, according to Kyocera officials. Kyocera will deliver the results to clients through a smartphone application and propose measures to improve diet and other elements of their lives to improve health, the officials said. As part of the development process, AuB will gather stool samples from 29 players of a youth team belonging to Kyoto Sanga F.C., a professional soccer team.
The NLP community has been focusing a lot on chasing the SOTA on standard and recent leaderboards (GLUE, SentEval...) over the recent years. While this aspiration has led to improvements in model performances, it has also resulted in a worrisome increase in model complexity and computational resources required to train and use the current state-of-the-art models. There is currently a lack of incentive to keep models small and efficient and research the optimal trade-offs between performances and efficiency. SustaiNLP 2020 (co-located with EMNLP 2020) has officially launched a shared-task/competition to promote the development of effective, energy-efficient models for difficult NLU tasks. The competition will end on 08/28.
Given the outsized hold Artificial Intelligence (AI) technology has acquired on public imagination of late, it comes as no surprise that many are wondering what AI can do for the public health crisis wrought by the COVID-19 coronavirus. A casual search of AI and COVID-19 already returns a plethora of news stories, many of them speculative. While AI technology is not ready to help with the magical discovery of a new vaccine, there are important ways it can assist in this fight. Controlling epidemics is, in large part, based on laborious contact tracing and using that information to predict the spread. We live in a time in which we constantly leave digital footprints through our daily life and interactions.
Fear is spreading on social media as people share their thoughts on the deadly coronavirus and the impact of the efforts to combat it. Italian-based artificial intelligence company Expert System has been searching through tens of thousands of social media posts to track feelings towards COVID-19. They used a range of natural language systems to capture the emotional view of different English language social media posts related to the pandemic. The team plan to publish a daily update showing the changing attitudes and emotions surrounding the spread of the virus and efforts to slow it down. For the fourth day in a row fear has been the most dominant emotion expressed in posts, with all negative views increasing across the English-language world.
They also talk about the launch of a global trial of promising treatments. See all of our News coverage of the pandemic here. See all of our Research and Editorials here. Also this week, Nadine Gogolla, research group leader at the Max Planck Institute of Neurobiology, talks with Sarah about linking the facial expressions of mice to their emotional states using machine learning. This week's episode was produced with help from Podigy.
Google is hoping to end low quality video calls by deploying artificial intelligence to "fill in" audio gaps caused by bad connections. WaveNetEQ works by using a library of speech data to realistically continue short segments of conversations. The AI is trained to produce mostly syllable sounds, and can fill gaps of up to 120 milliseconds. It comes as the use of video calls has become increasingly important during the corornavirus crisis. When making a call over the internet, data is split into small chunks called packets.
Sennheiser's second-generation high-end true wireless earbuds gain noise cancelling and longer battery life to do battle with Sony and Apple. The German firm's first earbuds were some of the best-sounding available. Now Sennheiser hopes its £280 Momentum True Wireless 2 can steal the show once again. The first thing you notice is just how big the earbuds are. Despite being slightly smaller than the previous versions they are still large, shaped like a fez with the eartip projecting out of one corner.
Analysis The Electronic Frontier Foundation on Thursday warned that the consequences of the novel coronavirus pandemic – staff cuts, budget cuts, and lack of access to on-site content review systems, among others – have led tech companies to focus even more resources on barely functional moderation systems. Technology platforms have tended to favor automated content moderation over human editorial oversight. The results of such algorithmic policing have been imperfect but, more importantly to those implementing these systems, inexpensive compared to salaried employees or underpaid contractors. Though most of the major tech companies involved in overseeing user-generated posts have been celebrating machine learning for years now, the EFF frets that AI-driven moderation has been talked up a bit too much lately. The advocacy group points to recent public statements by Facebook, Twitter, and YouTube that cite increased reliance on automated tools for content moderation.