United Kingdom


Webinar: Predicting symptomatic COVID-19

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ZOE and King's College data science and machine learning teams have been working around the clock to create a machine learning model that uses Symptom Tracker data to predict COVID-19 in the UK. Based on data from the COVID Symptom Tracker app and the assumptions that we lay out below, we estimate that there are a total of 1.9m people in the UK with symptomatic COVID (aged 20-69 only) as of 1st April 2020. Jonathan Wolf, CEO of ZOE explains the model in our webinar below. You can find a daily feed of maps here. We used machine learning* on this data to learn which symptoms are most predictive of a positive test.


HRI 2020 Online Day One

Robohub

HRI2020 has already kicked off with workshops and the Industry Talks Session on April 3, however the first release of videos has only just gone online with the welcome from General Chairs Tony Belpaeme, ID Lab, University of Ghent and James Young, University of Manitoba. There is also a welcome from the Program Chairs Hatice Gunes from University of Cambridge and Laurel Riek from University of San Diego, requesting that we all engage with the participants papers and videos. The theme of this year's conference is "Real World Human-Robot Interaction," reflecting on recent trends in our community toward creating and deploying systems that can facilitate real-world, long-term interaction. This theme also reflects a new theme area we have introduced at HRI this year, "Reproducibility for Human Robot Interaction," which is key to realizing this vision and helping further our scientific endeavors. This trend was also reflected across our other four theme areas, including "Human-Robot Interaction User Studies," "Technical Advances in Human-Robot Interaction," "Human-Robot Interaction Design," and "Theory and Methods in Human-Robot Interaction."


Researchers want your voice to train coronavirus-detecting AI

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Researchers behind an AI app that detects coronavirus in your voice have asked for volunteers to help by uploading audio of them coughing, breathing, and talking. Scientists from Cambridge University will use the data to develop machine learning algorithms that analyze a voice for symptoms of COVID-19. The COVID-19 Sounds App joins a growing list of tools using voice analysis to diagnose the coronavirus. The method remains unproven, but the team believes the sounds made by COVD-19 patients are so specific that they can reveal who has the disease. "Having spoken to doctors, one of the most common things they have noticed about patients with the virus is the way they catch their breath when they're speaking, as well as a dry cough, and the intervals of their breathing patterns," said Cambridge University Professor Cecilia Mascolo, who led the development of the app.


Seminars to probe potential for machine learning in weather prediction

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ECMWF is organising a series of seminars given by international experts to explore aspects of the use of machine learning in weather prediction and climate studies. The first will take place on 28 April and will be live-streamed. Sherman Lo and Ritabrata Dutta from the University of Warwick will present a statistical methodology to predict precipitation at 0.1 resolution using lower-resolution model fields of air temperature, geopotential, specific humidity, total column water vapour and wind velocity. On 9 June, Annalisa Bracco from the School of Earth and Atmospheric Sciences at the Georgia Institute of Technology will talk about spatiotemporal complexity and time-dependent networks in mid- to late Holocene simulations. In subsequent seminars, Maxime Taillardat (Météo-France) will present examples of operational ensemble post-processing using machine learning; Alberto Arribas (UK Met Office) will talk about work at the Met Office Informatics Lab; and Nal Kalchbrenner (Google) will talk about now-casting applications at Google.


Artificial Intelligence could help diagnose COVID-19 using X-RAYS

Daily Mail - Science & tech

An artificial intelligence programme could be used to more quickly predict the outcome of coronavirus patients by studying X-rays of their chest. Developers at the Oxford-based data-visualisation company, Zegami, have created a machine learning model that can diagnose the virus from the images. However, the team say that in order to get better and more detailed results their AI needs to be trained on a wider range of X-ray images from infected patients. The team believe it could have an artificial intelligence system in place within a matter of weeks to study the disease if it gets enough X-ray images. Zegami CEO, Roger Noble, has written an open letter to the Oxford Health NHS Foundation Trust asking for more images to train the AI model.


University of Glasgow - Schools - School of Humanities Sgoil nan Daonnachdan - Latest News - PhD studentship: Automation in the practice of archaeological survey

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Thanks to AHRC Collaborative Doctoral Partnership funding held jointly by Historic Environment Scotland (HES) and the University of Glasgow, we are offering a 45 month (3.75 years) PhD scholarship on developing approaches to integrate automation-led detection routines into workflows used in the professional practice of archaeological prospection and landscape archaeology, notably for large scale heritage management. The supervisors will be Dr Rachel Opitz (Archaeology) and Dr Jan Paul Siebert (Computer Science) at University of Glasgow, and Dr Lukasz Banaszek and Mr David Cowley (HES). Automated detection routines have been viewed as potentially useful or even transformative for several decades, and recent progress in artificial intelligence (AI) based in machine learning and computer vision has moved these approaches from potentially interesting to practically implementable across a variety of applications. Within archaeology, the potential of AI-led approaches and heavily automated image processing for partially automating the identification of archaeological features and landscape changes has been demonstrated in several studies. Their implementation has brought measurable benefits, leading to increased investment in their development.


6 Issues Marketers Need to Consider for Successful AI Implementations

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Despite the many unanswered questions that remain about the use of artificial intelligence (AI) in the workplace and in customer-facing and servicing departments, the growth of AI appears unstoppable. Even as early as two years ago, research from the UK-based digital marketing agency Big Rock found after interviewing 100 senior marketers globally, that AI applications, even at that stage had become one of the marketing departments mainstays. The interviews showed -- again at that stage -- that 55% of companies were either currently implementing or actively investigating some form of AI initiative within their marketing practices. Meaning, AI was already shaking things up in the industry. Unsurprisingly, the research read, this inevitable rise of AI technologies in marketing is causing a major shift in the way companies work.


Codebase Ventures eyeing pharma investment opportunities amid pandemic; World High Life subsidiary reports record monthly sales

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Codebase Ventures Inc (CSE:CODE) (OTCQB:BKLLF) reported that Love Hemp, a CBD supplier in the UK, and a subsidiary of World High Life Plc, in which the company is invested, has seen record monthly sales via its retail presence and e-commerce site. In an update on the early-stage investor's holdings, Codebase also told investors it is "actively pursuing" pharmaceutical opportunities that could have a positive impact on the current global coronavirus pandemic. World High Life is focused on backing or acquiring companies operating in the CBD wellness and medicinal cannabis industry and its wholly-owned subsidiary, Love Hemp is a leading CBD supplier in the United Kingdom. Elsewhere, the firm said its Arcology investment - an AI (artificial intelligence) blockchain ecosystem - is advancing its presence among dAPP developers by launching a project on GitHub, the world's largest source code sharing platform. DApp stands for decentralized application and such an app has its backend code running on a decentralized peer-to-peer network.


Men who are 'couch potatoes' are more likely to want to be muscular and hit the gym, study shows

Daily Mail - Science & tech

Experts found that men from wealthy western countries like the UK are more motivated to workout than their Nicaraguan and Ugandan counterparts. However, in all three countries, men that watch more television -- and are therefore exposed more to images of idealised bodies -- wanted to be muscular more. Men who are'couch potatoes' -- those spending a lot of time watching TV -- are more likely to want to be muscular and hit the gym, a study has found Psychologist Tracey Thornborrow of the University of Lincoln and colleagues examined British men's obsession with getting a muscular physique -- along with related phenomena like relying on protein shakes, unhealthy dieting and steroid use. Comparing British men with those from Nicaragua and Uganda, the team assessed each man's body mass index, along with their feelings about peer pressure and their ideal appearance. Participants also ranked the perceived level of muscularity of their current body and their ideal body on the so-called'Male Adiposity and Muscularity Scale.' Designed by the Person Perception Lab at the University of Lincoln, the new scale makes use of two-dimensional images created from 3D software, providing a more realistic range of body types and sizes based on measurements of real people.


The race problem with AI: 'Machines are learning to be racist'

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Artificial intelligence (AI) is already deeply embedded in so many areas of our lives. Society's reliance on AI is set to increase at a pace that is hard to comprehend. AI isn't the kind of technology that is confined to futuristic science fiction movies – the robots you've seen on the big screen that learn how to think, feel, fall in love, and subsequently take over humanity. No, AI right now is much less dramatic and often much harder to identify. Artificial intelligence is simply machine learning.