{"id":32884,"date":"2018-11-13T15:48:50","date_gmt":"2018-11-13T20:48:50","guid":{"rendered":"https:\/\/digital.hbs.edu\/platform-rctom\/submission\/ford-using-machine-learning-to-create-humanized-vehicles\/"},"modified":"2018-11-13T18:44:57","modified_gmt":"2018-11-13T23:44:57","slug":"ford-using-machine-learning-to-humanize-vehicles","status":"publish","type":"hck-submission","link":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/ford-using-machine-learning-to-humanize-vehicles\/","title":{"rendered":"Ford: Using Machine Learning To Humanize Vehicles"},"content":{"rendered":"<p>&nbsp;<\/p>\n<p>With the rise of the likes of Alexa, Google home, and Siri, consumer demand for highly personalized and adaptive products is increasing, and the automotive industry is no different. \u201cPeople today want to interact with technology whether that\u2019s a virtual assistant in their homes or their cars\u201d said Dr Rana El Kaliouby, CEO of Affectiva, the global leader in Artificial Emotional Intelligence<sup>1<\/sup>. In an effort to maintain market share, the research team at Ford was able to spot this trend early on. They teamed up with Affectiva to integrate machine learning techniques to produce empathetic vehicles with a personalized experience. According to Dimitar Filev, Executive Technical Leader at Ford, incorporating machine learning is a byproduct of growing consumer demand for new features and the abundance of information fueled by high computational power in current vehicles.<sup>2<\/sup><\/p>\n<p><strong><em>A car that understands how you feel<\/em><\/strong><\/p>\n<figure id=\"attachment_32794\" aria-describedby=\"caption-attachment-32794\" style=\"width: 416px\" class=\"wp-caption alignright\"><a href=\"https:\/\/d3.harvard.edu\/platform-rctom\/wp-content\/uploads\/sites\/4\/2018\/11\/Screen-Shot-2018-11-13-at-3.25.27-PM.png\"><img loading=\"lazy\" decoding=\"async\" class=\"wp-image-32794\" src=\"https:\/\/d3.harvard.edu\/platform-rctom\/wp-content\/uploads\/sites\/4\/2018\/11\/Screen-Shot-2018-11-13-at-3.25.27-PM-1024x684.png\" alt=\"\" width=\"416\" height=\"273\" \/><\/a><figcaption id=\"caption-attachment-32794\" class=\"wp-caption-text\">\u00a0 \u00a0 \u00a0 Affectiva&#8217;s emotion detection software (Source: MIT News)<\/figcaption><\/figure>\n<p>Ford\u2019s product development strategy is based on using sentiment analysis software to detect the emotions of the driver in the car. Through an emotion-detecting algorithm they are able to capture a wide range of facial expressions conveying feelings including anger, joy, drowsiness, surprise, and engagement in real-time<sup>1<\/sup>. The applications of such technology in the automotive industry are limitless and can put Ford on the forefront of creating cars that adapt to drivers and passenger complex cognitive and emotional states. Ford is not alone, other companies in different industries like Facebook, Google, Mercedes are also investing in such algorithms to decode their users\u2019 emotions. It is estimated that market size for such affective machine learning solutions will reach $41 billion by 2022<sup>3<\/sup>.<\/p>\n<p>&nbsp;<\/p>\n<p>In January 2018, Ford revealed a prototype with the first application of cognitive learning, the \u2018<a href=\"https:\/\/www.youtube.com\/watch?v=AFpt6jziFsU\">Buzz Car<\/a>\u2019. They used wearable sensors to measure drivers\u2019 blood pressure and whenever they get excited while speeding up, the car flashes almost 200,000 LED on its exterior to channel this \u2018buzz\u2019 through stunning animations. After series of testing, researches at Ford realized that driving is the runner up for activities that brings excitement to people after riding roller coasters<sup>4<\/sup>. It is followed by a number of seemingly rejuvenating activities like kissing or watching a Game of Thrones episode. This puts more pressure on Ford to deliver a more empathetic driving experience, coupled with the fact that other competitors like Mercedes and Tesla are going in the same direction.<\/p>\n<p><strong><em>From telling a joke to self-driving<\/em><\/strong><\/p>\n<p>Filev believes Ford\u2019s emotional intelligence technology is getting more and more advanced. They are currently developing a feature that adjusts the car\u2019s atmosphere based on the driver\u2019s mood. Tampering with the climate settings, cockpit light, or choice of music to match or change the captured emotion<sup>5<\/sup>. If a driver is happy, the car could play a happy song, and if the driver is sad, it could crack a good joke. In the near future, Ford is planning on rolling out a car that at sometimes could take over the wheel in extreme conditions of anger, drowsiness, stress, or distraction. The aim of this application is to reduce the number of accidents and make driving safer. While using machine and deep learning to produce autonomous vehicles is Ford\u2019s end goal, there are many applications for the empathetic car like cognitive-based suspension adjustment and fuel consumption, and perceptive acceleration and braking<sup>2<\/sup>.<\/p>\n<p><strong><em>A closer look into the future<\/em><\/strong><\/p>\n<p>Studies show that machine learning technologies that detect human emotion through facial expressions can be 93.3% accurate at best<sup>6<\/sup>. This might seem like an acceptable margin of error in other applications like advertisement rating. In Ford\u2019s case, rolling-out any cognitive based self-driving vehicles must have a 100% degree of accuracy. They are creating a very sensitive and risky product that could result in a wide range of unfavorable outcomes, and one bad user experience could be detrimental to the brand. Moreover, for many people driving is about the experience of being in control of your own vehicle. Changing this behavior requires a behavioral shift in their customer base. Consequently, I think Ford should conduct a more vigorous market research to validate the buy in from their customers to avoid designing a great product that people do not really want. This is applicable especially to their short-term goals where all their iterations aim to provide a humanized luxurious driving experience rather than solving a core issue such as decreasing car accidents.<\/p>\n<p>&nbsp;<\/p>\n<p>Emotion based technologies always raise a lot of ethical and existential questions over machines taking power that I believe are very applicable to Ford and the automotive industry; Do we want machines to understand how we feel and control us accordingly? Is this a breach of privacy? What if drivers are going to engage in unlawful acts, will the car lock them in? Does this limit human\u2019s free will? What if cars can communicate with each other, does this raise any security concerns?<\/p>\n<p>(799 Words)<\/p>\n<p>&nbsp;<\/p>\n<p><sup>1<\/sup>\u201cAffectiva and Nuance to Bring Emotional Intelligence to AI-Powered Automotive Assistants,\u201d September 6, 2018, Business Wire, <a href=\"https:\/\/tinyurl.com\/y7ef6dw4\">https:\/\/tinyurl.com\/y7ef6dw4<\/a>, accessed November 2018.<\/p>\n<p><sup>2 <\/sup>Filev, Dimitar. Interview by Medha Agarwal. \u201cAdopting AI in the Enterprise: Ford Motor Company,\u201d O\u2019Reilly, July 20, 2017, <a href=\"https:\/\/tinyurl.com\/ycl9yf2v\">https:\/\/tinyurl.com\/ycl9yf2v<\/a>, accessed November 2018.<\/p>\n<p><sup>3<\/sup>Sophie Kleber, \u201c3 Ways AI Is Getting More Emotional,\u201d 性视界 Business Review, July 31, 2018, <a href=\"https:\/\/tinyurl.com\/y8rjkf8z\">https:\/\/tinyurl.com\/y8rjkf8z<\/a>, accessed November 2018.<\/p>\n<p><sup>4 <\/sup>Ford Company, \u201cFord Media Center,\u201d <a href=\"https:\/\/media.ford.com\/content\/fordmedia\/fna\/us\/en\/news\/2018\/01\/23\/want-to-feel-good--forget-kissing--football-and-dancing--get-a-s.html\">https:\/\/media.ford.com\/content\/fordmedia\/fna\/us\/en\/news\/2018\/01\/23\/want-to-feel-good&#8211;forget-kissing&#8211;football-and-dancing&#8211;get-a-s.html<\/a>, accessed November 2018.<\/p>\n<p><sup>5 <\/sup>Ian Thibodeau, \u201cFord wants your new car to pick a song \u2013 or tell a joke,\u201d Detroit News, February 22, 2017, <a href=\"https:\/\/tinyurl.com\/y9vg2zd2\">https:\/\/tinyurl.com\/y9vg2zd2<\/a>, accessed November 2018.<\/p>\n<p><sup>6\u00a0<\/sup>M. S. Bartlett, G. Littlewort, C. Lainscsek, I. Fasel and J. Movellan, &#8220;Machine learning methods for fully automatic recognition of facial expressions and facial actions,&#8221;\u00a0<em>2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No.04CH37583)<\/em>, The Hague, 2004, pp. 592-597 vol.1. <a href=\"http:\/\/ieeexplore.ieee.org\/stamp\/stamp.jsp?tp=&amp;arnumber=1398364&amp;isnumber=30409\">http:\/\/ieeexplore.ieee.org\/stamp\/stamp.jsp?tp=&amp;arnumber=1398364&amp;isnumber=30409<\/a><\/p>\n<p>&nbsp;<\/p>\n<p>&nbsp;<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Imagine a car that understands how you feel in real time and adapts accordingly. It could adjust the cockpit light if it feels that you are sad, or take over the wheel if you are too angry to drive. Is this where the market should go?<\/p>\n","protected":false},"author":11884,"featured_media":32885,"comment_status":"open","ping_status":"closed","template":"","categories":[4596,4285,1658,1571,346],"class_list":["post-32884","hck-submission","type-hck-submission","status-publish","has-post-thumbnail","hentry","category-affectiva","category-facial-recognition","category-ford","category-ford-motor-company","category-machine-learning","hck-taxonomy-organization-ford-motor","hck-taxonomy-industry-auto","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>Ford: Using Machine Learning To Humanize Vehicles - 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\/ford-using-machine-learning-to-humanize-vehicles\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Ford: Using Machine Learning To Humanize Vehicles - Technology and Operations Management\" \/>\n<meta property=\"og:description\" content=\"Imagine a car that understands how you feel in real time and adapts accordingly. It could adjust the cockpit light if it feels that you are sad, or take over the wheel if you are too angry to drive. Is this where the market should go?\" \/>\n<meta property=\"og:url\" content=\"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/ford-using-machine-learning-to-humanize-vehicles\/\" \/>\n<meta property=\"og:site_name\" content=\"Technology and Operations Management\" \/>\n<meta property=\"article:modified_time\" content=\"2018-11-13T23:44:57+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/d3.harvard.edu\/platform-rctom\/wp-content\/uploads\/sites\/4\/2018\/11\/Screen-Shot-2018-11-13-at-3.21.29-PM.png\" \/>\n\t<meta property=\"og:image:width\" content=\"1840\" \/>\n\t<meta property=\"og:image:height\" content=\"956\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/png\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data1\" content=\"5 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/d3.harvard.edu\\\/platform-rctom\\\/submission\\\/ford-using-machine-learning-to-humanize-vehicles\\\/\",\"url\":\"https:\\\/\\\/d3.harvard.edu\\\/platform-rctom\\\/submission\\\/ford-using-machine-learning-to-humanize-vehicles\\\/\",\"name\":\"Ford: Using Machine Learning To Humanize Vehicles - Technology and Operations Management\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/d3.harvard.edu\\\/platform-rctom\\\/#website\"},\"primaryImageOfPage\":{\"@id\":\"https:\\\/\\\/d3.harvard.edu\\\/platform-rctom\\\/submission\\\/ford-using-machine-learning-to-humanize-vehicles\\\/#primaryimage\"},\"image\":{\"@id\":\"https:\\\/\\\/d3.harvard.edu\\\/platform-rctom\\\/submission\\\/ford-using-machine-learning-to-humanize-vehicles\\\/#primaryimage\"},\"thumbnailUrl\":\"https:\\\/\\\/d3.harvard.edu\\\/platform-rctom\\\/wp-content\\\/uploads\\\/sites\\\/4\\\/2018\\\/11\\\/Screen-Shot-2018-11-13-at-3.21.29-PM.png\",\"datePublished\":\"2018-11-13T20:48:50+00:00\",\"dateModified\":\"2018-11-13T23:44:57+00:00\",\"breadcrumb\":{\"@id\":\"https:\\\/\\\/d3.harvard.edu\\\/platform-rctom\\\/submission\\\/ford-using-machine-learning-to-humanize-vehicles\\\/#breadcrumb\"},\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\\\/\\\/d3.harvard.edu\\\/platform-rctom\\\/submission\\\/ford-using-machine-learning-to-humanize-vehicles\\\/\"]}]},{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\\\/\\\/d3.harvard.edu\\\/platform-rctom\\\/submission\\\/ford-using-machine-learning-to-humanize-vehicles\\\/#primaryimage\",\"url\":\"https:\\\/\\\/d3.harvard.edu\\\/platform-rctom\\\/wp-content\\\/uploads\\\/sites\\\/4\\\/2018\\\/11\\\/Screen-Shot-2018-11-13-at-3.21.29-PM.png\",\"contentUrl\":\"https:\\\/\\\/d3.harvard.edu\\\/platform-rctom\\\/wp-content\\\/uploads\\\/sites\\\/4\\\/2018\\\/11\\\/Screen-Shot-2018-11-13-at-3.21.29-PM.png\",\"width\":1840,\"height\":956},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\\\/\\\/d3.harvard.edu\\\/platform-rctom\\\/submission\\\/ford-using-machine-learning-to-humanize-vehicles\\\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Home\",\"item\":\"https:\\\/\\\/d3.harvard.edu\\\/platform-rctom\\\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Submissions\",\"item\":\"https:\\\/\\\/d3.harvard.edu\\\/platform-rctom\\\/submission\\\/\"},{\"@type\":\"ListItem\",\"position\":3,\"name\":\"Ford: Using Machine Learning To Humanize Vehicles\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\\\/\\\/d3.harvard.edu\\\/platform-rctom\\\/#website\",\"url\":\"https:\\\/\\\/d3.harvard.edu\\\/platform-rctom\\\/\",\"name\":\"Technology and Operations Management\",\"description\":\"MBA Student Perspectives\",\"potentialAction\":[{\"@type\":\"性视界Action\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\\\/\\\/d3.harvard.edu\\\/platform-rctom\\\/?s={search_term_string}\"},\"query-input\":{\"@type\":\"PropertyValueSpecification\",\"valueRequired\":true,\"valueName\":\"search_term_string\"}}],\"inLanguage\":\"en-US\"}]}<\/script>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"Ford: Using Machine Learning To Humanize Vehicles - Technology and Operations Management","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/ford-using-machine-learning-to-humanize-vehicles\/","og_locale":"en_US","og_type":"article","og_title":"Ford: Using Machine Learning To Humanize Vehicles - Technology and Operations Management","og_description":"Imagine a car that understands how you feel in real time and adapts accordingly. It could adjust the cockpit light if it feels that you are sad, or take over the wheel if you are too angry to drive. Is this where the market should go?","og_url":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/ford-using-machine-learning-to-humanize-vehicles\/","og_site_name":"Technology and Operations Management","article_modified_time":"2018-11-13T23:44:57+00:00","og_image":[{"width":1840,"height":956,"url":"https:\/\/d3.harvard.edu\/platform-rctom\/wp-content\/uploads\/sites\/4\/2018\/11\/Screen-Shot-2018-11-13-at-3.21.29-PM.png","type":"image\/png"}],"twitter_card":"summary_large_image","twitter_misc":{"Est. reading time":"5 minutes"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"WebPage","@id":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/ford-using-machine-learning-to-humanize-vehicles\/","url":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/ford-using-machine-learning-to-humanize-vehicles\/","name":"Ford: Using Machine Learning To Humanize Vehicles - Technology and Operations Management","isPartOf":{"@id":"https:\/\/d3.harvard.edu\/platform-rctom\/#website"},"primaryImageOfPage":{"@id":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/ford-using-machine-learning-to-humanize-vehicles\/#primaryimage"},"image":{"@id":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/ford-using-machine-learning-to-humanize-vehicles\/#primaryimage"},"thumbnailUrl":"https:\/\/d3.harvard.edu\/platform-rctom\/wp-content\/uploads\/sites\/4\/2018\/11\/Screen-Shot-2018-11-13-at-3.21.29-PM.png","datePublished":"2018-11-13T20:48:50+00:00","dateModified":"2018-11-13T23:44:57+00:00","breadcrumb":{"@id":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/ford-using-machine-learning-to-humanize-vehicles\/#breadcrumb"},"inLanguage":"en-US","potentialAction":[{"@type":"ReadAction","target":["https:\/\/d3.harvard.edu\/platform-rctom\/submission\/ford-using-machine-learning-to-humanize-vehicles\/"]}]},{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/ford-using-machine-learning-to-humanize-vehicles\/#primaryimage","url":"https:\/\/d3.harvard.edu\/platform-rctom\/wp-content\/uploads\/sites\/4\/2018\/11\/Screen-Shot-2018-11-13-at-3.21.29-PM.png","contentUrl":"https:\/\/d3.harvard.edu\/platform-rctom\/wp-content\/uploads\/sites\/4\/2018\/11\/Screen-Shot-2018-11-13-at-3.21.29-PM.png","width":1840,"height":956},{"@type":"BreadcrumbList","@id":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/ford-using-machine-learning-to-humanize-vehicles\/#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https:\/\/d3.harvard.edu\/platform-rctom\/"},{"@type":"ListItem","position":2,"name":"Submissions","item":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/"},{"@type":"ListItem","position":3,"name":"Ford: Using Machine Learning To Humanize Vehicles"}]},{"@type":"WebSite","@id":"https:\/\/d3.harvard.edu\/platform-rctom\/#website","url":"https:\/\/d3.harvard.edu\/platform-rctom\/","name":"Technology and Operations Management","description":"MBA Student Perspectives","potentialAction":[{"@type":"性视界Action","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/d3.harvard.edu\/platform-rctom\/?s={search_term_string}"},"query-input":{"@type":"PropertyValueSpecification","valueRequired":true,"valueName":"search_term_string"}}],"inLanguage":"en-US"}]}},"_links":{"self":[{"href":"https:\/\/d3.harvard.edu\/platform-rctom\/wp-json\/wp\/v2\/hck-submission\/32884","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/d3.harvard.edu\/platform-rctom\/wp-json\/wp\/v2\/hck-submission"}],"about":[{"href":"https:\/\/d3.harvard.edu\/platform-rctom\/wp-json\/wp\/v2\/types\/hck-submission"}],"author":[{"embeddable":true,"href":"https:\/\/d3.harvard.edu\/platform-rctom\/wp-json\/wp\/v2\/users\/11884"}],"replies":[{"embeddable":true,"href":"https:\/\/d3.harvard.edu\/platform-rctom\/wp-json\/wp\/v2\/comments?post=32884"}],"version-history":[{"count":0,"href":"https:\/\/d3.harvard.edu\/platform-rctom\/wp-json\/wp\/v2\/hck-submission\/32884\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/d3.harvard.edu\/platform-rctom\/wp-json\/wp\/v2\/media\/32885"}],"wp:attachment":[{"href":"https:\/\/d3.harvard.edu\/platform-rctom\/wp-json\/wp\/v2\/media?parent=32884"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/d3.harvard.edu\/platform-rctom\/wp-json\/wp\/v2\/categories?post=32884"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}