The Cyberspace Administration of China (CAC) said that the 105 apps violated laws by excessively collecting and illegally accessing users’ personal information, according to a statement posted on its site Friday.\nThe CAC has stepped up scrutiny of Chinese apps as authorities beef up protection of personal information protection and seek to prevent breaches of online privacy.\nOne concern in China and elsewhere is that many internet companies have collected excessive user information to target consumers with advertisements.\nProvisions outlining how apps should collect personal information came into effect May 1.\nDozens of other internet companies including Baidu, Tencent and ride-hailing firm Didi Chuxing have also been fined for not properly disclosing investments or acquisitions.
"}}}},{"node":{"postTitle":"ACRCloud, Muffin, Viscovery, Civolution – The Shotcaller","slug":"acrcloud-muffin-viscovery-civolution-the-shotcaller","publishDate":"None","createdLocal":"2021-05-21 14:30:44.891091","feedName":"Image Recognition","postSummary":{"childMarkdownRemark":{"html":"Some of the Major Companies covered in this Research are ACRCloud, Muffin, Viscovery, Civolution, ArcSoft, Gracenote, Beatgrid Media, Enswers, Microsoft, DataScouting, Digimarc, Clarifai, Shazam Entertainment, Google, Nuance Communications & Audible Magic etc.\nfor more information or any query mail at sales@htfmarketreport.comAt last, all parts of the Global Content Recognition Market are quantitatively also subjectively valued to think about the Global just as regional market equally.\nWhat are probably the most encouraging, high-development scenarios for Content Recognition movement showcase by applications, types and regions?\nQ 4.What segments grab most noteworthy attention in Content Recognition Market in 2020 and beyond?\nQ 5. Who are the significant players confronting and developing in Content Recognition Market?
"}}}},{"node":{"postTitle":"AppTek's Workbench Delivers 85% More Efficiency in Computer Vision and Speech Recognition Data Labeling Tasks","slug":"apptek-s-workbench-delivers-85-more-efficiency-in-computer-vision-and-speech-recognition-data-labeling-tasks","publishDate":"None","createdLocal":"2021-05-21 14:30:44.169698","feedName":"Image Recognition","postSummary":{"childMarkdownRemark":{"html":"AppTek Workbench - Automatic Speech Recognition Annotation AppTek Workbench - Computer Vision AnnotationAppTek's Workbench, used by its distributed workforce spanning across 70+ countries, serves as a secure, cloud-based data annotation and labeling platform designed to radically streamline the production of high-quality data sets.\nWorkbench combines audio, text, image and video labeling processes, and features semi-automated speech and CV labeling to more efficiently produce a wide array of outputs.\nIn head-to-head evaluations for video annotation tasks that used AppTek's new semi-automated labeling capabilities, evaluators reported an 85% faster time-to-completion versus manual annotation tasks.\nAudio evaluators also noted an impressive 75% productivity gain to fully segment, transcribe, annotate and label data for the training of speech recognition models.\n\"We are thrilled with the new efficiencies and productivity gains offered by the semi-automation of audio and video labeling tasks inside the Workbench, \" said Katie Nguyen, SVP Data Operations.
"}}}},{"node":{"postTitle":"AppTek Workbench - Automatic Speech Recognition Annotation","slug":"apptek-workbench-automatic-speech-recognition-annotation","publishDate":"None","createdLocal":"2021-05-21 14:30:43.411746","feedName":"Image Recognition","postSummary":{"childMarkdownRemark":{"html":"MCLEAN, Va., May 21, 2021 /PRNewswire/ -- AppTek, a leader in Artificial Intelligence (AI), Machine Learning (ML), Automatic Speech Recognition (ASR), Neural Machine Translation (NMT), Text-to-Speech (TTS) and Natural Language Processing / Understanding (NLP/U) technologies, announced significant productivity gains through improvements to its Workbench Data Annotation and Labeling Platform.\nThis is the result of ML-enabled automation for the production of multi-format, multi-domain, speech, audio, text, image, natural language and computer vision data, which are used to fuel mission critical AI models for federal and enterprise customers.
"}}}},{"node":{"postTitle":"Twitter finds its AI tends to crop out Black people, men from photos","slug":"twitter-finds-its-ai-tends-to-crop-out-black-people-men-from-photos","publishDate":"2021-05-19 00:00:00","createdLocal":"2021-05-20 14:31:04.340196","feedName":"Image Recognition","postSummary":{"childMarkdownRemark":{"html":"It found an 8% difference from demographic parity in favor of women, and a 4% favor toward white individuals.\nThe paper cited several possible reasons, including issues with image backgrounds and eye color, but said none were an excuse.\nTo counter the problem, Twitter recently started showing standard aspect ratio photos in full – without any crop – on its mobile apps and is trying to expand that effort.\nThe findings are another example of the disparate impact from artificial intelligence systems including demographic biases identified in facial recognition and text analysis, the paper said.\nAmazon Inc in 2018 scrapped an AI recruiting tool that showed bias against women.
"}}}},{"node":{"postTitle":"Facial Recognition to disrupt event photography?","slug":"facial-recognition-to-disrupt-event-photography","publishDate":"2021-05-19 22:57:27+00:00","createdLocal":"2021-05-20 14:31:02.500463","feedName":"Image Recognition","postSummary":{"childMarkdownRemark":{"html":"Facial Recognition to disrupt event photography?\nSince 2015 Lightroom Classic has had a facial recognition feature to detect faces in a set of images.\nIt’s on the verge of a release that includes facial recognition among other tools to automate and streamline various tasks.\nWhen it comes to schools photography, facial recognition is more reliable due to the photos being straight-to-camera portraits in a controlled studio environment.\n‘Facial recognition is good if a parent is willing to upload their child’s face, or if the school will allow it.
"}}}},{"node":{"postTitle":"Image Recognition Market Global Analysis 2021-2028: Hitachi, Honeywell, Qualcomm, Google, Catchoom Technologies, and Many More. – Brockville Observer","slug":"image-recognition-market-global-analysis-2021-2028-hitachi-honeywell-qualcomm-google-catchoom-technologies-and-many-more-brockville-observer","publishDate":"None","createdLocal":"2021-05-20 14:31:00.158265","feedName":"Image Recognition","postSummary":{"childMarkdownRemark":{"html":"The global demand in Image Recognition market was fully anticipated over the forecast timeframe.\nThe study identifies the untapped avenues, and factors shaping the revenue potential of the Image Recognition market.\nGlobal Image Recognition Market Key Highlights• Compound Annual Growth Rate (CAGR) of the Image Recognition market during the forecast period 2022-2027 estimating the return on investments.\n• Detailed analysis of the influencing factors that will assist the global Image Recognition participants to grow in the next five years with its full potential• Estimation of the global Image Recognition market size, market share by value and by volume, and contribution of the parent market in the global Image Recognition market.\nTable of ContentsChapter One: Report Overview1.1 Study Scope1.2 Key Market Segments1.3 Players Covered: Ranking by Image Recognition Revenue1.4 Market Analysis by Type1.4.1 Global Image Recognition Market Size Growth Rate by Type: 2020 VS 20281.5 Market by Application1.5.1 Global Image Recognition Market Share by Application: 2020 VS 20281.6 Study Objectives1.7 Years ConsideredChapter Two: Global Growth Trends by Regions2.1 Image Recognition Market Perspective (2018-2028)2.2 Image Recognition Growth Trends by Regions2.2.1 Image Recognition Market Size by Regions: 2018 VS 2020 VS 20282.2.2 Image Recognition Historic Market Share by Regions (2018-2020)2.2.3 Image Recognition Forecasted Market Size by Regions (2021-2028)2.3 Industry Trends and Growth Strategy2.3.1 Market Top Trends2.3.2 Market Drivers2.3.3 Market Challenges2.3.4 Porter’s Five Forces Analysis2.3.5 Image Recognition Market Growth Strategy2.3.6 Primary Interviews with Key Image Recognition Players (Opinion Leaders)Chapter Three: Competition Landscape by Key Players3.1 Global Top Image Recognition Players by Market Size3.1.1 Global Top Image Recognition Players by Revenue (2018-2020)3.1.2 Global Image Recognition Revenue Market Share by Players (2018-2020)3.1.3 Global Image Recognition Market Share by Company Type3.2 Global Image Recognition Market Concentration Ratio3.2.1 Global Image Recognition Market Concentration Ratio (CRChapter Five: and HHI)3.2.2 Global Top Chapter Ten: and Top 5 Companies by Image Recognition Revenue in 20203.3 Image Recognition Key Players Head office and Area Served3.4 Key Players Image Recognition Product Solution and Service3.5 Date of Enter into Image Recognition Market3.6 Mergers & Acquisitions, Expansion PlansThe global Image Recognition market research study curated in the report provides information about the current trends and future market dynamics to the market participants.
"}}}},{"node":{"postTitle":"Time to Clear Out the Sexism in Artificial Intelligence","slug":"time-to-clear-out-the-sexism-in-artificial-intelligence","publishDate":"2021-05-20 00:29:14+00:00","createdLocal":"2021-05-20 14:30:58.259133","feedName":"Image Recognition","postSummary":{"childMarkdownRemark":{"html":"The bias seen in images generated by artificial intelligence is pervasive and difficult to address.\nIt’s now well documented that artificial intelligence systems are subject to inherent biases in the results they deliver.\nAI-driven healthcare recommendation engines have been shown to discriminate against members of minority groups, and HR recruiting systems have been shown to be biased against female candidates.\nNow, a recent study shows that sexism against women in artificial intelligence-based image processing runs deeper than thought.\n“This behavior might result from the sexualized portrayal of people, especially women, in internet images, and serves as a reminder of computer vision’s controversial history with Playboy centerfolds and objectifying images,” Steed and Caliskan say.
"}}}}]}},"pageContext":{"limit":8,"skip":2248,"irNumPages":301,"currentPage":282}}}