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The UK government is using a'racially biased' online passport photo checker – despite an updated version being available for more than a year. HM Passport Office has confirmed an improved version of the facial detection system, developed by its software vendor, has still not rolled out. The facial detection system is part of the UK government's online passport application service, which lets Brits apply for, renew, replace or update their passport and pay for it online. The technology informs people when it thinks a photo uploaded for a passport may not meet strict requirements – but this has been shown to lead to'racist' mistakes. Users of colour have received messages saying'it looks like your mouth is open', based on the size of their lips, as well other messages like'it looks like your eyes are closed' and'we can't find the outline of your head' due to their skin colour.

ZEISS Germany, Nikon Instruments, Ariadne.ai, Mindpeak, Aiforia, Celly.AI Corporation, SVision LLC, Scopio Lab, AlexaPath, MicroscopeIT, Nanotronics, AiScope, Thermo Fisher, Ash Vision, Sigtuple, GoMicro, MantiScope, Cognex, Paige.AI, Motic, and Pleora Technologies among others are the players in the artificial intelligence microscopy market.Brooklyn, New York, March 10, 2021 (GLOBE NEWSWIRE) -- According to a new market research report published by Global Market Estimates, the Artificial Intelligence Microscopy Market will grow with a CAGR value of 7.2 percent from 2021 to 2026. The market for AI in microscopy will increase with the rising prevalence of infectious disease, cancer, and other disorders that require routine blood morphology analysis. Moreover, with the rising need for advanced live-cell imaging, cloud sharing, and efficient lab workflow, clubbed with the rising research activities in the field of drug testing and toxicology, the market will grow rapidly from 2020 to 2021. Browse 151 Market Data Tables and 111 Figures spread through 181 Pages and in-depth TOC on “Global Artificial Intelligence Microscopy Market - Forecast to 2026" https://www.globalmarketestimates.com/market-report/global-artificial-intelligence-microscopy-market-2824 Key Market Insights Optical or light microscopy is estimated to be the largest segment as per market share or market revenue generation from 2021 to 2026Cancer disease diagnosis and prevention is the major driving factor for this segment to grow rapidlyThe market for independent & private laboratories will be dominant from 2021 to 2026ZEISS Germany, Nikon Instruments, Ariadne.ai, Mindpeak, Aiforia, Celly.AI Corporation, SVision LLC, Scopio Lab, AlexaPath, MicroscopeIT, Nanotronics, AiScope, Thermo Fisher, Ash Vision, Sigtuple, GoMicro, MantiScope, Cognex, Paige.AI, Motic, and Pleora Technologies among others are the players in the artificial intelligence microscopy market Browse the Report @ https://www.globalmarketestimates.com/market-report/global-artificial-intelligence-microscopy-market-2824 Imaging Modalities Outlook (Revenue, USD Billion, 2019-2026) Optical MicroscopyElectron MicroscopyScanning Probe Microscopy Application Outlook (Revenue, USD Billion, 2019-2026) Clinical PathologyNeuron MorphologyCell BiologyPharmacology & ToxicologyOncologyOthers Product Type Outlook (Revenue, USD Billion, 2019-2026) AI-Enabled Cloud SoftwareAI-Enabled Microscopes End-User Outlook (Revenue, USD Billion, 2019-2026) Hospital LaboratoriesIndependent & Private LaboratoriesAcademic Research LabsPharmaceutical & Biotechnology LaboratoriesContract Research Organizations Regional Outlook (Revenue, USD Billion, 2019-2026) North America The U.S.CanadaMexico Europe GermanyUKFranceSpainItalyRest of Europe Asia Pacific ChinaIndiaJapanSouth KoreaAustraliaRest of APAC Central & South America BrazilArgentinaRest of CSA Middle East & Africa Saudi ArabiaUAERest of MEA Website: Global Market Estimates CONTACT: Contact: Yash Jain Email address: yash.jain@globalmarketestimates.com Phone Number: +16026667238

As transparency becomes key for robotics and AI, it will be necessary to evaluate the methods through which transparency is provided, including automatically generated natural language (NL) explanations. Here, we explore parallels between the generation of such explanations and the much-studied field of evaluation of Natural Language Generation (NLG). Specifically, we investigate which of the NLG evaluation measures map well to explanations. We present the ExBAN corpus: a crowd-sourced corpus of NL explanations for Bayesian Networks. We run correlations comparing human subjective ratings with NLG automatic measures. We find that embedding-based automatic NLG evaluation methods, such as BERTScore and BLEURT, have a higher correlation with human ratings, compared to word-overlap metrics, such as BLEU and ROUGE. This work has implications for Explainable AI and transparent robotic and autonomous systems.

When training neural networks as classifiers, it is common to observe an increase in average test loss while still maintaining or improving the overall classification accuracy on the same dataset. In spite of the ubiquity of this phenomenon, it has not been well studied and is often dismissively attributed to an increase in borderline correct classifications. We present an empirical investigation that shows how this phenomenon is actually a result of the differential manner by which test samples are processed. In essence: test loss does not increase overall, but only for a small minority of samples. Large representational capacities allow losses to decrease for the vast majority of test samples at the cost of extreme increases for others. This effect seems to be mainly caused by increased parameter values relating to the correctly processed sample features. Our findings contribute to the practical understanding of a common behaviour of deep neural networks. We also discuss the implications of this work for network optimisation and generalisation.

Omdena collaborative AI projects run for two months and are a unique opportunity to work with AI practitioners from around the world whilst solving grand challenges. You augment both your soft and hard skills and get access to mentors, world-class tools, and courses. Add your name, email & interests here and we will get back to you when we find a project that aligns with your interests. The World Resources Institute (WRI) has been seeking to understand how regional and... In this Challenge 61 technology changemakers are building a machine learning solution for...

Qatar plans to set up an Artificial Intelligence (AI) committee under the country's Transport & Communications Ministry (MoTC). The country's cabinet approved the draft decision establishing the committee on 3 March. It will consist of three representatives from the MoTC, one of whom will head the committee. The committee will help establish follow-up mechanisms and implement the Qatar National AI Strategy, which the country launched in 2019. It will supervise the programmes and initiatives related to AI launched by the state and coordinate with the ministries and relevant authorities in developing plans and programmes "for preparing human cadres in the field of artificial intelligence applications". Qatar's National AI Strategy focuses on education, data access, employment, business, research and ethics.

The Cambridge Analytica scandal would kick off Facebook's largest publicity crisis ever. It compounded fears that the algorithms that determine what people see on the platform were amplifying fake news and hate speech, and that Russian hackers had weaponized them to try to sway the election in Trump's favor. Millions began deleting the app; employees left in protest; the company's market capitalization plunged by more than $100 billion after its July earnings call. In the ensuing months, Mark Zuckerberg began his own apologizing. He apologized for not taking "a broad enough view" of Facebook's responsibilities, and for his mistakes as a CEO. Internally, Sheryl Sandberg, the chief operating officer, kicked off a two-year civil rights audit to recommend ways the company could prevent the use of its platform to undermine democracy. Finally, Mike Schroepfer, Facebook's chief technology officer, asked Quiñonero to start a team with a directive that was a little vague: to examine the societal impact of the company's algorithms. The group named itself the Society and AI Lab (SAIL); last year it combined with another team working on issues of data privacy to form Responsible AI. Quiñonero was a natural pick for the job. He, as much as anybody, was the one responsible for Facebook's position as an AI powerhouse. In his six years at Facebook, he'd created some of the first algorithms for targeting users with content precisely tailored to their interests, and then he'd diffused those algorithms across the company. Now his mandate would be to make them less harmful. Facebook has consistently pointed to the efforts by Quiñonero and others as it seeks to repair its reputation. It regularly trots out various leaders to speak to the media about the ongoing reforms.

The reporting and analysis of current events around the globe has expanded from professional, editor-lead journalism all the way to citizen journalism. Politicians and other key players enjoy direct access to their audiences through social media, bypassing the filters of official cables or traditional media. However, the multiple advantages of free speech and direct communication are dimmed by the misuse of the media to spread inaccurate or misleading claims. These phenomena have led to the modern incarnation of the fact-checker -- a professional whose main aim is to examine claims using available evidence to assess their veracity. As in other text forensics tasks, the amount of information available makes the work of the fact-checker more difficult. With this in mind, starting from the perspective of the professional fact-checker, we survey the available intelligent technologies that can support the human expert in the different steps of her fact-checking endeavor. These include identifying claims worth fact-checking; detecting relevant previously fact-checked claims; retrieving relevant evidence to fact-check a claim; and actually verifying a claim. In each case, we pay attention to the challenges in future work and the potential impact on real-world fact-checking.

In this paper, we present new image segmentation methods based on hidden Markov random fields (HMRFs) and cuckoo search (CS) variants. HMRFs model the segmentation problem as a minimization of an energy function. CS algorithm is one of the recent powerful optimization techniques. Therefore, five variants of the CS algorithm are used to compute a solution. Through tests, we conduct a study to choose the CS variant with parameters that give good results (execution time and quality of segmentation). CS variants are evaluated and compared with non-destructive testing (NDT) images using a misclassification error (ME) criterion.