{"id":30586,"date":"2018-11-13T14:34:05","date_gmt":"2018-11-13T19:34:05","guid":{"rendered":"https:\/\/digital.hbs.edu\/platform-rctom\/submission\/image-recognition-at-facebook-how-machine-learning-is-helping-computers-and-people-who-are-blind-see-digital-photos\/"},"modified":"2018-11-13T14:34:05","modified_gmt":"2018-11-13T19:34:05","slug":"image-recognition-at-facebook-how-machine-learning-is-helping-computers-and-people-who-are-blind-see-digital-photos","status":"publish","type":"hck-submission","link":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/image-recognition-at-facebook-how-machine-learning-is-helping-computers-and-people-who-are-blind-see-digital-photos\/","title":{"rendered":"Image recognition at Facebook: How machine learning is helping computers \u2014 and people who are blind \u2014 \u2019see\u2019 digital photos"},"content":{"rendered":"<p><b>Image recognition enables Facebook to deliver value to users and advertisers <\/b><\/p>\n<p><span style=\"font-weight: 400\">Every day, Facebook users share more than two billion photos across Facebook\u2019s suite of products (Facebook, Messenger, Instagram and WhatsApp)<sup>1<\/sup><\/span><span style=\"font-weight: 400\">\u00a0\u2014 making Facebook one of the largest and fastest growing repositories of images, and the largest photo sharing service in the world.<sup>2<\/sup><br \/>\n<\/span><span style=\"font-weight: 400\"> While photos have been a major part of Facebook\u2019s growth and success, they also present unique opportunities, challenges, and limitations for the company as it expands.<\/span><\/p>\n<p><span style=\"font-weight: 400\">Photos provide Facebook with an uncontaminated glimpse into the behavior and preferences of Facebook\u2019s 2.27 billion monthly active users (MAUs).<sup>3<\/sup><\/span><span style=\"font-weight: 400\"> By categorizing the photos that users upload and engage with (e.g. using Facebook\u2019s reaction tool<sup>4<\/sup><\/span><span style=\"font-weight: 400\">), Facebook can better understand how users spend their time and the types of content they\u2019re most likely to find interesting. <\/span><\/p>\n<p><span style=\"font-weight: 400\">As Facebook grapples with its slowest revenue and user growth rates in history (see chart below)<sup>5<\/sup><\/span><span style=\"font-weight: 400\">, it is crucial that the company identifies new ways to deliver value. Robust image recognition technology will allow Facebook to collect more meaningful data about its users, which Facebook can then monetize by enabling its advertisers to target content more strategically. <\/span><\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-large\" src=\"https:\/\/media-bii.businessinsider.com\/images\/5bda04dcc957013aaa76df9d-1920-1419.jpg\" width=\"1920\" height=\"1419\" \/><\/p>\n<p><span style=\"font-weight: 400\">Image recognition technology will also enable Facebook to deliver on its mission of &#8220;giving people the power to build community and bringing the world closer together.&#8221;<sup>6<\/sup><\/span><span style=\"font-weight: 400\"> Historically, fulfilling this mission has been especially challenging as Facebook attempted to reach users who are blind or have low vision (approximately 285 million people, globally<sup>7<\/sup><\/span><span style=\"font-weight: 400\">). By integrating image recognition technology with Facebook\u2019s existing VoiceOver capabilities, individuals who are blind are able to experience the world of Facebook independently (without the help of friends or volunteers), thereby growing Facebook\u2019s overall user base.<sup>8<\/sup><\/span><\/p>\n<p><iframe loading=\"lazy\" title=\"Introducing Automatic Alt Text\" src=\"https:\/\/player.vimeo.com\/video\/161529744?dnt=1&amp;app_id=122963\" width=\"640\" height=\"360\" frameborder=\"0\" allow=\"autoplay; fullscreen; picture-in-picture; clipboard-write\"><\/iframe><\/p>\n<p><b>Improving the accuracy of image recognition using \u2018wild\u2019 data<\/b><\/p>\n<p><span style=\"font-weight: 400\">Traditionally, image recognition models have been trained on datasets of photos that have been manually annotated by humans<\/span><span style=\"font-weight: 400\">.<sup>9<\/sup> However, there are certain limitations to these models: relatively few of these databases exist because it is both labor- and computationally-intensive to build these datasets and to train machine learning models based on the data<\/span><span style=\"font-weight: 400\">.<sup>10<\/sup> One of the most commonly used image datasets, ImageNet, which has been used to train image recognition models at IBM<sup>11<\/sup><\/span><span style=\"font-weight: 400\">, Microsoft<sup>12<\/sup><\/span><span style=\"font-weight: 400\">, and Google<sup>13<\/sup><\/span><span style=\"font-weight: 400\">, only comprises approximately 14 million images<\/span><span style=\"font-weight: 400\">.<sup>14<\/sup>\u00a0As Facebook endeavored to create image recognition technologies to extract and organize information from billions of user photos, a more robust solution was needed. <\/span><\/p>\n<p><span style=\"font-weight: 400\">To address this challenge, researchers at Facebook experimented with training its image recognition network on public images uploaded through Instagram<sup>15<\/sup><\/span><span style=\"font-weight: 400\">\u00a0\u2014 a service Facebook acquired in 2012.<sup>16<\/sup><\/span><span style=\"font-weight: 400\">\u00a0Rather than manually annotating each picture, Facebook tested whether user-generated hashtags could approximate human-annotations for training purposes.<sup>17<\/sup><\/span><span style=\"font-weight: 400\">\u00a0By using a dataset comprised of 3.5 billion Instagram photos, Facebook was able to achieve an all time record-high score of 85.4 percent on image recognition accuracy \u2014\u00a0a two-percent increase over the previous record.<sup>18<\/sup><\/span><\/p>\n<p><b>The limitations and implications of \u2018wild\u2019 data<\/b><\/p>\n<p><span style=\"font-weight: 400\">While this research revealed that it is possible to use organic or \u201cwild\u201d datasets to train image recognition networks, the accuracy and scalability of Facebook\u2019s image recognition model is limited by the diversity and quality of the user-uploaded photos and the user-generated \u201chashtags.\u201d To address this limitation, Facebook will need to crowdsource a more robust dataset of annotated images for training these models. <\/span><\/p>\n<p><span style=\"font-weight: 400\">In line with Facebook\u2019s broader business objectives, one way to build this dataset would be to attract and retain a more geographically, racially, socioeconomically diverse user base \u2014 but doing so takes time. In the interim, Facebook could partner with other global technology companies (e.g. WeChat) to crowdsource images, thereby improving the overall accuracy of image recognition algorithms. <\/span><\/p>\n<p><span style=\"font-weight: 400\">Furthermore, as Facebook continues to explore leveraging user-generated content to improve its image recognition tools, they must carefully consider the privacy implications of this new model. Given the long-term nature of this initiative, it may be worthwhile to consider the strategic benefits of a formal partnership with academic institutions or policy groups to establish best practices for user-sourced image recognition models. In the absence of formal privacy regulations or frameworks, technology companies will be responsible for determining the appropriate applications of user content \u2014 ultimately posing the question: are users able and capable of identifying if (and when) these applications cross a line when it comes to privacy?<\/span><\/p>\n<p><em>(797 words)<\/em><\/p>\n<p>&nbsp;<\/p>\n<p><strong>Sources<\/strong><\/p>\n<p><sup>1<\/sup><span style=\"font-weight: 400\"> Facebook, \u201cUsing Artificial Intelligence to Help Blind People \u2018See\u2019 Facebook,\u201d <\/span><a href=\"https:\/\/newsroom.fb.com\/news\/2016\/04\/using-artificial-intelligence-to-help-blind-people-see-facebook\/\"><span style=\"font-weight: 400\">https:\/\/newsroom.fb.com\/news\/2016\/04\/using-artificial-intelligence-to-help-blind-people-see-facebook\/<\/span><\/a><span style=\"font-weight: 400\">, accessed November 11, 2018<\/span><\/p>\n<p><sup>2<\/sup><span style=\"font-weight: 400\"> Newton, Casey. \u201cThe big pictures.\u201d The Verge, May 17, 2017. <\/span><a href=\"https:\/\/www.theverge.com\/2017\/5\/17\/15650096\/google-photos-new-features-shared-libraries-printed-books-io-2017\"><span style=\"font-weight: 400\">https:\/\/www.theverge.com\/2017\/5\/17\/15650096\/google-photos-new-features-shared-libraries-printed-books-io-2017<\/span><\/a><span style=\"font-weight: 400\">, accessed November 11, 2019<\/span><\/p>\n<p><sup>3<\/sup><span style=\"font-weight: 400\"> \u201cFacebook Reports Third Quarter 2018 Results,\u201d press release, October 30, 2018, <\/span><a href=\"https:\/\/investor.fb.com\/investor-news\/press-release-details\/2018\/Facebook-Reports-Third-Quarter-2018-Results\/default.aspx\"><span style=\"font-weight: 400\">https:\/\/investor.fb.com\/investor-news\/press-release-details\/2018\/Facebook-Reports-Third-Quarter-2018-Results\/default.aspx<\/span><\/a><span style=\"font-weight: 400\">, accessed November 12, 2018<\/span><\/p>\n<p><sup>4<\/sup> <span style=\"font-weight: 400\">Facebook, \u201cReactions Now Available Globally,\u201d <\/span><a href=\"https:\/\/newsroom.fb.com\/news\/2016\/02\/reactions-now-available-globally\/\"><span style=\"font-weight: 400\">https:\/\/newsroom.fb.com\/news\/2016\/02\/reactions-now-available-globally\/<\/span><\/a><span style=\"font-weight: 400\">, accessed November 12, 2018<\/span><\/p>\n<p><sup>5<\/sup>\u00a0<span style=\"font-weight: 400\">Business Insider Intelligence, \u201cFacebook Q3 revenue and user growth decelerate, but the company will seek growth in Stories and video,\u201d October 31, 2018, via Business Insider Intelligence, <\/span><a href=\"https:\/\/intelligence.businessinsider.com\/post\/facebook-q3-revenue-and-user-growth-decelerate-but-the-company-will-seek-growth-in-stories-and-video-2018-10\"><span style=\"font-weight: 400\">https:\/\/intelligence.businessinsider.com\/post\/facebook-q3-revenue-and-user-growth-decelerate-but-the-company-will-seek-growth-in-stories-and-video-2018-10<\/span><\/a><span style=\"font-weight: 400\">, accessed November 12, 2018<\/span><\/p>\n<p><sup>6<\/sup>\u00a0<span style=\"font-weight: 400\">Facebook, \u201cCompany Info,\u201d <\/span><a href=\"https:\/\/newsroom.fb.com\/company-info\/\"><span style=\"font-weight: 400\">https:\/\/newsroom.fb.com\/company-info\/<\/span><\/a><span style=\"font-weight: 400\">, accessed November 11, 2018<\/span><\/p>\n<p><sup>7<\/sup>\u00a0<span style=\"font-weight: 400\">World Health Organization. Global Data on Visual Impairments 2010. Accessed November\u00a0<\/span><span style=\"font-weight: 400\">11, 2018<\/span><\/p>\n<p><sup>8<\/sup>\u00a0<span style=\"font-weight: 400\">Facebook, \u201c<\/span><span style=\"font-weight: 400\">Under the hood: Building accessibility tools for the visually impaired on Facebook\u201d<\/span> <a href=\"https:\/\/code.fb.com\/ios\/under-the-hood-building-accessibility-tools-for-the-visually-impaired-on-facebook\/\"><span style=\"font-weight: 400\">https:\/\/code.fb.com\/ios\/under-the-hood-building-accessibility-tools-for-the-visually-impaired-on-facebook\/<\/span><\/a><span style=\"font-weight: 400\">, accessed November 11, 2018<\/span><\/p>\n<p><sup>9<\/sup>\u00a0<span style=\"font-weight: 400\">Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A.C., Fei-Fei, L.: ImageNet Large Scale Visual Recognition Challenge. IJCV (2015) <\/span><\/p>\n<p><sup>10<\/sup>\u00a0<span style=\"font-weight: 400\">Mahajan et al., \u201cExploring the Limits of Weakly Supervised Pretraining,\u201d Facebook Working Paper, <\/span><a href=\"https:\/\/research.fb.com\/wp-content\/uploads\/2018\/05\/exploring_the_limits_of_weakly_supervised_pretraining.pdf\"><span style=\"font-weight: 400\">https:\/\/research.fb.com\/wp-content\/uploads\/2018\/05\/exploring_the_limits_of_weakly_supervised_pretraining.pdf<\/span><\/a><span style=\"font-weight: 400\">, accessed November 2018.<\/span><\/p>\n<p><sup>11<\/sup>\u00a0<span style=\"font-weight: 400\">IBM, \u201c<\/span><span style=\"font-weight: 400\">IBM Research achieves record deep learning performance with new software technology,\u201d<\/span> <a href=\"https:\/\/www.ibm.com\/blogs\/research\/2017\/08\/distributed-deep-learning\/\"><span style=\"font-weight: 400\">https:\/\/www.ibm.com\/blogs\/research\/2017\/08\/distributed-deep-learning\/<\/span><\/a><span style=\"font-weight: 400\">, accessed November 11, 2018<\/span><\/p>\n<p><sup>12<\/sup>\u00a0<span style=\"font-weight: 400\">Microsoft, \u201cTraining Deep Neural Networks on ImageNet Using Microsoft R Server and Azure GPU VMs,\u201d <\/span><a href=\"https:\/\/blogs.technet.microsoft.com\/machinelearning\/2016\/11\/15\/imagenet-deep-neural-network-training-using-microsoft-r-server-and-azure-gpu-vms\/\"><span style=\"font-weight: 400\">https:\/\/blogs.technet.microsoft.com\/machinelearning\/2016\/11\/15\/imagenet-deep-neural-network-training-using-microsoft-r-server-and-azure-gpu-vms\/<\/span><\/a><span style=\"font-weight: 400\">, accessed November 12, 2018<\/span><\/p>\n<p><sup>13<\/sup>\u00a0<span style=\"font-weight: 400\">Google, \u201cAutoML for large scale image classification and object detection,\u201d <\/span><a href=\"https:\/\/ai.googleblog.com\/2017\/11\/automl-for-large-scale-image.html\"><span style=\"font-weight: 400\">https:\/\/ai.googleblog.com\/2017\/11\/automl-for-large-scale-image.html<\/span><\/a><span style=\"font-weight: 400\">, accessed November 12, 2018<\/span><\/p>\n<p><sup>14<\/sup>\u00a0<span style=\"font-weight: 400\">Image-Net, <\/span><a href=\"http:\/\/www.image-net.org\"><span style=\"font-weight: 400\">http:\/\/www.image-net.org<\/span><\/a><span style=\"font-weight: 400\">, accessed November 12, 2018<\/span><\/p>\n<p><sup>15<\/sup>\u00a0<span style=\"font-weight: 400\">Facebook Code, \u201cAdvancing state-of-the-art image recognition with deep learning on hashtags\u201d, <\/span><a href=\"https:\/\/code.fb.com\/ml-applications\/advancing-state-of-the-art-image-recognition-with-deep-learning-on-hashtags\/\"><span style=\"font-weight: 400\">https:\/\/code.fb.com\/ml-applications\/advancing-state-of-the-art-image-recognition-with-deep-learning-on-hashtags\/<\/span><\/a><span style=\"font-weight: 400\">, accessed November 11, 2018<\/span><\/p>\n<p><sup>16<\/sup> <span style=\"font-weight: 400\">Facebook, \u201cFacebook to acquire Instagram,\u201d <\/span><a href=\"https:\/\/newsroom.fb.com\/news\/2012\/04\/facebook-to-acquire-instagram\/\"><span style=\"font-weight: 400\">https:\/\/newsroom.fb.com\/news\/2012\/04\/facebook-to-acquire-instagram\/<\/span><\/a><span style=\"font-weight: 400\">, accessed November 12, 2018<\/span><\/p>\n<p><sup>17<\/sup>\u00a0<span style=\"font-weight: 400\">Facebook Code, \u201cAdvancing state-of-the-art image recognition with deep learning on hashtags\u201d, <\/span><a href=\"https:\/\/code.fb.com\/ml-applications\/advancing-state-of-the-art-image-recognition-with-deep-learning-on-hashtags\/\"><span style=\"font-weight: 400\">https:\/\/code.fb.com\/ml-applications\/advancing-state-of-the-art-image-recognition-with-deep-learning-on-hashtags\/<\/span><\/a><span style=\"font-weight: 400\">, accessed November 11, 2018<\/span><\/p>\n<p><sup>18<\/sup>\u00a0<span style=\"font-weight: 400\">Mahajan et al., \u201cExploring the Limits of Weakly Supervised Pretraining,\u201d Facebook Working Paper, <\/span><a href=\"https:\/\/research.fb.com\/wp-content\/uploads\/2018\/05\/exploring_the_limits_of_weakly_supervised_pretraining.pdf\"><span style=\"font-weight: 400\">https:\/\/research.fb.com\/wp-content\/uploads\/2018\/05\/exploring_the_limits_of_weakly_supervised_pretraining.pdf<\/span><\/a><span style=\"font-weight: 400\">, accessed November 2018.<\/span><\/p>\n<p>&nbsp;<\/p>\n","protected":false},"excerpt":{"rendered":"<p>As we become increasingly reliant on machines to perform our daily tasks \u2014 from answering our emails to driving our cars \u2014 we are exposed to the limitations of technology as it attempts to perform complex human operations such as natural language processing and image recognition. To address this, Facebook is leveraging its repository of user-uploaded photos to improve accuracy of image recognition technologies to enable computers \u2014 and people who are blind \u2014 to \u2018see\u2019 in natural contexts.<\/p>\n","protected":false},"author":11656,"featured_media":30618,"comment_status":"open","ping_status":"closed","template":"","categories":[4561,4286,346,344],"class_list":["post-30586","hck-submission","type-hck-submission","status-publish","has-post-thumbnail","hentry","category-accessibility","category-image-recognition","category-machine-learning","category-product-development","hck-taxonomy-organization-facebook","hck-taxonomy-industry-technology","hck-taxonomy-country-united-states"],"connected_submission_link":"https:\/\/d3.harvard.edu\/platform-rctom\/assignment\/rc-tom-challenge-2018\/","yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.3 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Image recognition at Facebook: How machine learning is helping computers \u2014 and people who are blind \u2014 \u2019see\u2019 digital photos - Technology and Operations Management<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/image-recognition-at-facebook-how-machine-learning-is-helping-computers-and-people-who-are-blind-see-digital-photos\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Image recognition at Facebook: How machine learning is helping computers \u2014 and people who are blind \u2014 \u2019see\u2019 digital photos - Technology and Operations Management\" \/>\n<meta property=\"og:description\" content=\"As we become increasingly reliant on machines to perform our daily tasks \u2014 from answering our emails to driving our cars \u2014 we are exposed to the limitations of technology as it attempts to perform complex human operations such as natural language processing and image recognition. 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