Hospitals and universities across the country can now access thousands of Covid-19 images and scans in a bid to develop artificial intelligence solutions to tackle the virus. NHSX has collected more than 40,000 CT scans, MRIs and X-rays from more than 10,000 patients across 18 NHS trusts over the course of the pandemic. Together they form the National Covid-19 Chest Imaging Database (NCCID) and have been extended to hospitals and universities who are using the images to track patterns of illness. It is hoped the database will speed up diagnosis of coronavirus, ultimately leading to quicker treatment and less pressure on the NHS by predicting things like the need for additional ICU capacity. The British Society of Thoratic Imaging, Royal Surrey NHS Foundation Trust and AI company Faculty are working with NHSX on the database as part of the NHS AI Lab.
This ebook, based on the latest ZDNet / TechRepublic special feature, examines how 5G connectivity will underpin the next generation of IoT devices. Autonomous cars (and other vehicles, such as trucks) may still be years away from widespread deployment, but connected cars are very much with us. The modern automobile is fast becoming a sensor-laden mobile Internet of Things device, with considerable on-board computing power and communication systems devoted to three broad areas: vehicle location, driver behaviour, engine diagnostics and vehicle activity (telematics); the surrounding environment (vehicle-to-everything or V2X communication); and the vehicle's occupants (infotainment). All of these systems use cellular -- and increasingly 5G -- technology, among others. Although 5G networks are still a work in progress for mobile operators, the pace of deployment and launches is picking up.
Most of us now encounter AI on a daily basis without noticing it. Social media feeds which show you the posts you are likely to engage with, music streaming platforms which suggest new music you may enjoy listening to, or chatbots which help renew insurance policies are all using a form of AI. We are now seeing what is commonly defined as "weak AI"; systems programmed with algorithms to reach conclusions and predict future behaviour by learning from data patterns. The more data fed to the system, the more accurate the system becomes in predicting future behaviours. The Scottish Government has flagged the food and drink industry, worth around £14 billion each year, as a key growth sector in its economic strategy.
In this technical talk, Amanda Prorok, Assistant Professor in the Department of Computer Science and Technology at Cambridge University, and a Fellow of Pembroke College, discusses her team's latest research on what, how and when information needs to be shared among agents that aim to solve cooperative tasks. Effective communication is key to successful multi-agent coordination. Yet it is far from obvious what, how and when information needs to be shared among agents that aim to solve cooperative tasks. In this talk, I discuss our recent work on using Graph Neural Networks (GNNs) to solve multi-agent coordination problems. In my first case-study, I show how we use GNNs to find a decentralized solution to the multi-agent path finding problem, which is known to be NP-hard.
Bésame Cosmetics founder and makeup historian Gabriela Hernandez delivers insights into the billion-dollar cosmetic industry. Learn how makeup was deeply impacted by society's perception of women. A make-up artist has become an internet sensation after transforming herself into popular celebrities -- even fooling her friends and phone. Liss Lacao, 29, has recreated the recognizable features of celebrities such as Gordon Ramsay, Dolly Parton, the Queen and British Prime Minister Boris Johnson. She's so good, she's even fooled her iPhone -- which has facial recognition -- and her friends into thinking she was one of the A-listers.
Poppy Gustafsson runs a cutting-edge and gender-diverse cybersecurity firm on the brink of a £3bn stock market debut, but she is happy to reference pop culture classic the Terminator to help describe what Darktrace actually does. Launched in Cambridge eight years ago by an unlikely alliance of mathematicians, former spies from GCHQ and the US and artificial intelligence (AI) experts, Darktrace provides protection, enabling businesses to stay one step ahead of increasingly smarter and dangerous hackers and viruses. Marketing its products as the digital equivalent of the human body's ability to fight illness, Darktrace's AI-security works as an "enterprise immune system", can "self-learn and self-heal" and has an "autonomous response capability" to tackle threats without instruction as they are detected. "It really does feel like we're in this new era of cybersecurity," says Gustafsson, the chief executive of Darktrace. "The arms race will absolutely continue, I really don't think it's very long until this [AI] innovation gets into the hands of attackers, and we will see these very highly targeted and specific attacks that humans won't necessarily be able to spot and defend themselves from. "It's not going to be these futuristic Terminator-style robots out shooting each other, it's going to be all these little pieces of code fighting in the background of our businesses.
A year into the severe acute respiratory syndrome coronavirus 2 pandemic, we are experiencing waves of new variants emerging. Some of these variants have worrying functional implications, such as increased transmissibility or antibody treatment escape. Lythgoe et al. have undertaken in-depth sequencing of more than 1000 hospital patients' isolates to find out how the virus is mutating within individuals. Overall, there seem to be consistent and reproducible patterns of within-host virus diversity. The authors observed only one or two variants in most samples, but a few carried many variants. Although the evidence indicates strong purifying selection, including in the spike protein responsible for viral entry, the authors also saw evidence for transmission clusters associated with households and other possible superspreader events. After transmission, most variants fizzled out, but occasionally some initiated ongoing transmission and wider dissemination. Science , this issue p. [eabg0821] ### INTRODUCTION Genome sequencing at an unprecedented scale during the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic is helping to track spread of the virus and to identify new variants. Most of this work considers a single consensus sequence for each infected person. Here, we looked beneath the consensus to analyze genetic variation within viral populations making up an infection and studied the fate of within-host mutations when an infection is transmitted to a new individual. Within - host diversity offers the means to help confirm direct transmission and identify new variants of concern. ### RATIONALE We sequenced 1313 SARS-CoV-2 samples from the first wave of infection in the United Kingdom. We characterized within-host diversity and dynamics in the context of transmission and ongoing viral evolution. ### RESULTS Within-host diversity can be described by the number of intrahost single nucleotide variants (iSNVs) occurring above a given minor allele frequency (MAF) threshold. We found that in lower-viral-load samples, stochastic sampling effects resulted in a higher variance in MAFs, leading to more iSNVs being detected at any threshold. Based on a subset of 27 pairs of high-viral-load replicate RNA samples (>50,000 uniquely mapped veSEQ reads, corresponding to a cycle threshold of ~22), iSNVs with a minimum 3% MAF were highly reproducible. Comparing samples from two time points from 41 individuals, taken on average 6 days apart (interquartile ratio 2 to 10), we observed a dynamic process of iSNV generation and loss. Comparing iSNVs among 14 household contact pairs, we estimated transmission bottleneck sizes of one to eight viruses. Consensus differences between individuals in the same household, where sample depth allowed iSNV detection, were explained by the presence of an iSNV at the same site in the paired individual, consistent with direct transmission leading to fixation. We next focused on a set of 563 high-confidence iSNV sites that were variant in at least one high-viral-load sample (>50,000 uniquely mapped); low-confidence iSNVs unlikely to represent genomic diversity were excluded. Within-host diversity was limited in high-viral-load samples (mean 1.4 iSNVs per sample). Two exceptions, each with >14 iSNVs, showed variant frequencies consistent with coinfection or contamination. Overall, we estimated that 1 to 2% of samples in our dataset were coinfected and/or contaminated. Additionally, one sample was coinfected with another coronavirus (OC43), with no detectable impact on diversity. The ratio of nonsynonymous to synonymous ( dN/dS ) iSNVs was consistent with within-host purifying selection when estimated across the whole genome [ dN/dS = 0.55, 95% confidence interval (95% CI) = 0.49 to 0.61] and for the Spike gene ( dN/dS = 0.60, 95% CI = 0.45 to 0.82). Nevertheless, we observed Spike variants in multiple samples that have been shown to increase viral infectivity (L5F) or resistance to antibodies (G446V and A879V). We observed a strong association between high-confidence iSNVs and a consensus change on the phylogeny (153 cases), consistent with fixation after transmission or de novo mutations reaching consensus. Shared variants that never reached consensus (261 cases) were not phylogenetically associated. ### CONCLUSION Using robust methods to call within-host variants, we uncovered a consistent pattern of low within-host diversity, purifying selection, and narrow transmission bottlenecks. Within-host emergence of vaccine and therapeutic escape mutations is likely to be relatively rare, at least during early infection, when viral loads are high, but the observation of immune-escape variants in high-viral-load samples underlines the need for continued vigilance. ![Figure] Diagram showing low SARS-CoV-2 within-host genetic diversity and narrow transmission bottleneck. Individuals with high viral load typically have few, if any, within-host variants. Narrow transmission bottlenecks mean that the major variant in the source individual was typically transmitted and the minor variants lost. Occasionally, the minor variant was transmitted, leading to a consensus change, or multiple variants were transmitted, resulting in a mixed infection. Credit: FontAwesome, licensed under CC BY 4.0. Extensive global sampling and sequencing of the pandemic virus severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have enabled researchers to monitor its spread and to identify concerning new variants. Two important determinants of variant spread are how frequently they arise within individuals and how likely they are to be transmitted. To characterize within-host diversity and transmission, we deep-sequenced 1313 clinical samples from the United Kingdom. SARS-CoV-2 infections are characterized by low levels of within-host diversity when viral loads are high and by a narrow bottleneck at transmission. Most variants are either lost or occasionally fixed at the point of transmission, with minimal persistence of shared diversity, patterns that are readily observable on the phylogenetic tree. Our results suggest that transmission-enhancing and/or immune-escape SARS-CoV-2 variants are likely to arise infrequently but could spread rapidly if successfully transmitted. : /lookup/doi/10.1126/science.abg0821 : pending:yes
Over the last decade, we have heard a lot of doom-saying about how artificial intelligence (AI) would result in the loss of huge numbers of jobs However, the picture (across both public and private sectors) is now starting to look not only more nuanced but also more positive. A 2017 report from consultancy PWC suggested that embedding AI across all sectors is likely to create thousands of jobs. In the UK, one estimate suggests that it could contribute as much as 5% of GDP within 10 years. That’s not to say that we won’t lose jobs, because we undoubtedly will. However, they will be
The World Economic Forum's Centre for the Fourth Industrial Revolution, in partnership with the UK government, has developed guidelines for more ethical and efficient government procurement of artificial intelligence (AI) technology. Governments across Europe, Latin America and the Middle East are piloting these guidelines to improve their AI procurement processes.