{"id":2198,"date":"2020-03-10T07:36:03","date_gmt":"2020-03-10T14:36:03","guid":{"rendered":"https:\/\/melindaozel.com\/?p=2198"},"modified":"2025-02-21T16:02:34","modified_gmt":"2025-02-21T21:02:34","slug":"big-techs-homogeneous-hiring-habits-are-harming-our-data","status":"publish","type":"post","link":"https:\/\/melindaozel.com\/tr\/big-techs-homogeneous-hiring-habits-are-harming-our-data\/","title":{"rendered":"B\u00fcy\u00fck Teknolojinin Homojen \u0130\u015fe Al\u0131m Al\u0131\u015fkanl\u0131klar\u0131 Verilerimize Zarar Veriyor"},"content":{"rendered":"

T\u00fcketici odakl\u0131 dijital d\u00fcnyam\u0131zda, inovasyon yapmak ve yeni \u00fcr\u00fcnler geli\u015ftirmek i\u00e7in bir aciliyet \u00fcrettik. Makine \u00f6\u011frenimi trendi ve h\u0131zl\u0131 hareket etme ve bir \u015feyleri k\u0131rma - yani \"in\u015fa etme\" - bask\u0131s\u0131 ile Big Tech s\u00fcrekli olarak in\u015faat\u00e7\u0131lara ihtiya\u00e7 duyuyor. Sonu\u00e7 olarak, yaz\u0131l\u0131m m\u00fchendisleri, personel say\u0131s\u0131n\u0131 domine eden ve \u015firketler aras\u0131nda ihale sava\u015flar\u0131na yol a\u00e7an, olduk\u00e7a ra\u011fbet g\u00f6ren bir meta haline geldi. Ancak, makine \u00f6\u011frenimi hedefleri b\u00fcy\u00fcd\u00fck\u00e7e veri ihtiya\u00e7lar\u0131 da b\u00fcy\u00fcyor ve m\u00fchendis merkezli sorunlar\u0131 disiplinler aras\u0131 meselelere d\u00f6n\u00fc\u015ft\u00fcr\u00fcyor. Y\u00fcz takibi i\u00e7in y\u00fcz ifadeleri gibi son derece belirsiz veriler \u00fcreten projeler, verilerin m\u00fchendislik kapsam\u0131n\u0131n \u00f6tesinde anla\u015f\u0131lmas\u0131n\u0131 gerektirir; m\u00fchendislik ve uygulanan tamamlay\u0131c\u0131 alan aras\u0131nda disiplinler aras\u0131 bir evlilik gerektirir. Bu nedenle, teknoloji firmalar\u0131n\u0131n \u00fcr\u00fcn geli\u015ftirme s\u00fcrecine alan uzmanlar\u0131n\u0131 dahil ederek veri b\u00fct\u00fcnl\u00fc\u011f\u00fc konusunda sorumluluk almalar\u0131 kritik \u00f6nem ta\u015f\u0131maktad\u0131r.<\/p>\n

Big Tech'in k\u00fclt\u00fcr\u00fcnde m\u00fchendislik d\u0131\u015f\u0131 rollere \u00f6ncelik vermek olmasa da, di\u011fer sekt\u00f6rler makine \u00f6\u011freniminde g\u00fcncel uzmanl\u0131\u011f\u0131n \u00f6nemini kabul etmektedir. \u00d6rne\u011fin biyoteknoloji alan\u0131, hem t\u0131p uzmanlar\u0131 hem de biyoteknoloji uzmanlar\u0131 aras\u0131ndaki i\u015fbirli\u011fine dayan\u0131r. ve<\/em> m\u00fchendisler. Bu durumda i\u015fbirli\u011fine duyulan ihtiya\u00e7 a\u00e7\u0131kt\u0131r, ancak daha belirsiz uzmanl\u0131k alanlar\u0131nda ihtiya\u00e7 her zaman belirgin de\u011fildir. Daha \u00f6nce de de\u011finildi\u011fi gibi, bilinmezlik ve anlay\u0131\u015f eksikli\u011finden muzdarip \u00f6nemli bir alan y\u00fcz izlemedir. Y\u00fcz filtreleri, \u00fcr\u00fcn reklamlar\u0131 i\u00e7in \u00f6zellik alg\u0131lama (\u00f6rne\u011fin ruj \u00fcr\u00fcnlerini test etmek i\u00e7in dudak alg\u0131lama veya g\u00f6zl\u00fckler i\u00e7in g\u00f6z alg\u0131lama) ve avatarlar dahil olmak \u00fczere y\u00fcz izleme i\u00e7in yayg\u0131n kullan\u0131m durumlar\u0131 nispeten zarars\u0131zd\u0131r. Ancak, ceza adaleti sistemi, sigorta sekt\u00f6r\u00fc veya siber g\u00fcvenlik d\u00fcnyas\u0131ndaki uygulamalarla duygu tespiti, davran\u0131\u015f izleme ve aldatma tespiti gibi daha az \"sevimli\" kullan\u0131m durumlar\u0131 d\u00fc\u015f\u00fcn\u00fcld\u00fc\u011f\u00fcnde, bir makine \u00f6\u011frenimi modelinin performans\u0131 tart\u0131\u015fmal\u0131 hale gelir; ve yanl\u0131\u015f \u015fekilde yap\u0131l\u0131rsa tehlikeli hale gelir. Y\u00fcz tan\u0131ma teknolojisi ve bu teknolojinin farkl\u0131 \u00fclke ve sekt\u00f6rlerde d\u00fczensiz kullan\u0131m\u0131yla ilgili halihaz\u0131rda bilinen pek \u00e7ok sorun var. Y\u00fcz ifadesi takibi sadece bir ki\u015fiyi tan\u0131mlamakla kalmay\u0131p, o ki\u015finin davran\u0131\u015flar\u0131n\u0131 g\u00f6zlemleyip sonu\u00e7lar \u00e7\u0131kard\u0131\u011f\u0131ndan, \u00e7ok daha istilac\u0131 olma kapasitesine sahiptir.<\/p>\n

Y\u00fcz takibi i\u00e7in ifade verilerinin temel \u00f6nemine ra\u011fmen, B\u00fcy\u00fck Teknoloji genellikle ifade verisi kalitesine \u00f6ncelik vermemektedir. \u00dcr\u00fcn y\u00f6neticileri, m\u00fchendislik y\u00f6neticileri, kullan\u0131c\u0131 ara\u015ft\u0131rmac\u0131lar\u0131 ve yaz\u0131l\u0131m m\u00fchendisleri genellikle bir uzman\u0131n sa\u011flayabilece\u011fi derinlemesine anlay\u0131\u015ftan faydalanmak yerine kendi y\u00fczeysel anlay\u0131\u015flar\u0131na ve ge\u00e7ici ara\u015ft\u0131rmalar\u0131na g\u00fcvenmektedir. Yaz\u0131l\u0131m m\u00fchendisleri algoritma olu\u015fturmada ustala\u015fm\u0131\u015f olsalar da, genellikle verinin i\u00e7inde ne oldu\u011funa dair \u00fcst\u00fcnk\u00f6r\u00fc bir bilgiye sahiptirler. Y\u00fcksek bask\u0131 alt\u0131ndaki i\u015f y\u00fckleri ve kendi uzmanl\u0131k bilgilerine odaklanmalar\u0131 g\u00f6z \u00f6n\u00fcne al\u0131nd\u0131\u011f\u0131nda, m\u00fchendislerin ince ifade verilerini inceleme veya duygu ara\u015ft\u0131rmalar\u0131ndaki karma\u015f\u0131k kavramlara hakim olma konusunda ek bir uzmanl\u0131k geli\u015ftirmeleri m\u00fcmk\u00fcn de\u011fildir.<\/p>\n

Ne t\u00fcr verilere ihtiya\u00e7 duyuldu\u011funun, bunlar\u0131n nas\u0131l toplanaca\u011f\u0131n\u0131n ve nas\u0131l etiketlenece\u011finin belirlenmesi hassas s\u00fcre\u00e7<\/a>. Yanl\u0131\u015f verileri hedeflemeyi se\u00e7erseniz, bu verileri ne kadar iyi toplad\u0131\u011f\u0131n\u0131z veya etiketledi\u011finiz \u00f6nemli olmayacakt\u0131r. Yararl\u0131 verileri hedefler ancak yanl\u0131\u015f toplarsan\u0131z, bu da ba\u015far\u0131s\u0131z olacakt\u0131r. Tam ve\/veya do\u011fru bir \u015fekilde etiketlemezseniz yine ba\u015far\u0131s\u0131z olacakt\u0131r. Y\u00fcz \u00f6zelliklerindeki morfolojik farkl\u0131l\u0131klar, ifade yorumlamas\u0131ndaki do\u011fal \u00f6nyarg\u0131lar ve hem duygu ara\u015ft\u0131rmac\u0131lar\u0131 hem de y\u00fcz anatomistleri aras\u0131ndaki tart\u0131\u015fmalar nedeniyle, ruj almadan \u00f6nce denemenin \u00f6tesinde niyetlerle y\u00fcz izleme algoritmalar\u0131 geli\u015ftiren herhangi bir grup, veri b\u00fct\u00fcnl\u00fc\u011f\u00fc i\u00e7in hesap verebilirli\u011fi ve etik sorumlulu\u011fu kabul etmelidir.<\/p>\n

Algoritmalar\u0131n\u0131n yap\u0131 ta\u015flar\u0131n\u0131n onlar\u0131 kullananlar taraf\u0131ndan iyi anla\u015f\u0131lmas\u0131n\u0131 sa\u011flamak yerine, B\u00fcy\u00fck Teknoloji'nin talihsiz durumu, b\u00fcy\u00fck miktarda veri toplamak veya elde etmek ve bunlar\u0131 \u00fc\u00e7\u00fcnc\u00fc taraf etiketleyicilere aktarmakt\u0131r. Etiketleyiciler genellikle d\u0131\u015f kaynakl\u0131d\u0131r, s\u00f6zle\u015fmelidir ve neredeyse her zaman de\u011ferlerinin alt\u0131ndad\u0131r. Etiketlerin kalitesini izlemek i\u00e7in standart, \u00e7e\u015fitli Temel Performans G\u00f6stergeleri veya KPI'lar olu\u015fturmak ve uygulamakt\u0131r, ancak Big Tech, geli\u015fmi\u015f verilerin kalitesini me\u015fru bir \u015fekilde denetleyebilecek ki\u015filere kaynak yat\u0131r\u0131m\u0131 yapmamay\u0131 tercih etti\u011finden, KPI'lar genellikle keyfidir ve \u00e7ok az de\u011fere sahiptir. Sorunu daha da derinle\u015ftirecek olursak, m\u00fchendisler temel ger\u00e7ekleri belirleyecek anlay\u0131\u015f derinli\u011fine sahip de\u011fillerse, ger\u00e7ekte neyi \u00f6l\u00e7\u00fcyorlar? Denetimsiz \u00f6\u011frenme var. Ve bir de denetimsiz m\u00fchendislik vard\u0131r.<\/p>\n

Silikon Vadisi'ndeki Be\u015f B\u00fcy\u00fck \u015firketten birinde \u00e7al\u0131\u015f\u0131rken, karma\u015f\u0131k y\u00fcz izleme verilerine ili\u015fkin so\u011fukkanl\u0131 zihniyet beni s\u00fcrekli \u015foke ediyordu. Meslekta\u015flar\u0131m algoritma geli\u015ftirmede \u00f6nc\u00fc ki\u015filer olmalar\u0131na ra\u011fmen y\u00fcz anatomisi, temel duygu kavramlar\u0131 ve ifade davran\u0131\u015flar\u0131 hakk\u0131nda y\u00fczeysel bir anlay\u0131\u015fa sahiptiler. Di\u011fer t\u00fcm son derece uzmanl\u0131k gerektiren konular gibi, insan ifadesinin n\u00fcanslar\u0131n\u0131 anlamak da y\u0131llar s\u00fcren yo\u011fun bir \u00e7al\u0131\u015fma ve deneyim gerektirir. Hayat\u0131m\u0131 ve kariyerimi insan y\u00fcz\u00fcn\u00fcn n\u00fcanslar\u0131n\u0131 anlamaya adam\u0131\u015f bir y\u00fcz ifadesi uzman\u0131 olmama ra\u011fmen, \u00f6nemli toplant\u0131lardan ve planlama oturumlar\u0131ndan d\u00fczenli olarak d\u0131\u015flan\u0131yordum. S\u0131k s\u0131k i\u015f arkada\u015flar\u0131m\u0131 modas\u0131 ge\u00e7mi\u015f ve yanl\u0131\u015f ifade referans sayfalar\u0131n\u0131 (ki \u015fu anda bu sayfalar\u0131 kullan\u0131yorum) geli\u015fig\u00fczel kar\u0131\u015ft\u0131r\u0131rken yakal\u0131yordum. buras\u0131 i\u00e7in yeni \u00e7\u00f6z\u00fcmler sunmak<\/a>) bir veri hatt\u0131 plan\u0131n\u0131 bir araya getirme giri\u015fimlerinde bulundum. Uzmanl\u0131\u011f\u0131m s\u0131k s\u0131k denetleyici bir role indirgendi ve becerilerim sakal tiplerini ve sa\u00e7 rengini s\u0131n\u0131fland\u0131rmak gibi alakas\u0131z g\u00f6revlerde k\u00f6t\u00fcye kullan\u0131ld\u0131. Veri tutars\u0131zl\u0131klar\u0131ndaki e\u011filimleri i\u015faretledi\u011fimde veya donan\u0131m sorunlar\u0131n\u0131 \u00f6ng\u00f6rd\u00fc\u011f\u00fcmde, oturtuldum ve \"makine \u00f6\u011frenimi nas\u0131l \u00e7al\u0131\u015f\u0131r\" konusunda bir ders ald\u0131m.<\/p>\n

Y\u00fcz izleme konusunda X y\u0131ll\u0131k deneyim talep eden bir\u00e7ok i\u015f ilan\u0131 g\u00f6r\u00fcyorum, ancak ilgili deneyimi talep etmek yeterli de\u011fil. Nas\u0131l ki bir m\u00fchendisin ge\u00e7mi\u015fte t\u0131p teknolojisinde \u00e7al\u0131\u015fm\u0131\u015f olmas\u0131 onu t\u0131p doktoru yapmaya yetmiyorsa, sadece izleme teknolojisi \u00fczerinde \u00e7al\u0131\u015fm\u0131\u015f olmak da bir m\u00fchendisi y\u00fcz ifadeleri ya da duygular konusunda otorite yapmaya yetmiyor. Uygun uzmanlar\u0131n i\u015fe al\u0131nmas\u0131na daha fazla odaklan\u0131lsa, belki de nadir ve \u00f6zel deneyime sahip m\u00fchendisler bulma m\u00fccadelesi hafifletilebilir; b\u00f6yle bir hafifletme, m\u00fchendislik ile e\u015fle\u015ftirilebilece\u011fi tamamlay\u0131c\u0131 disiplinler aras\u0131nda daha yenilik\u00e7i i\u015fbirli\u011fine yer a\u00e7abilir.<\/p>\n

Big Tech'in m\u00fchendisli\u011fe odaklanan t\u00fcnel vizyonu, de\u011fi\u015fmesi gereken ihmalk\u00e2r bir al\u0131\u015fkanl\u0131kt\u0131r. Yaz\u0131l\u0131m m\u00fchendisleri makine \u00f6\u011frenimi s\u00fcreci i\u00e7in ger\u00e7ekten gerekli olsa da, makine \u00f6\u011frenimi tutkusu bizi disiplinler aras\u0131 eylem ihtiyac\u0131n\u0131 kabul etmemiz gereken bir noktaya getirdi. N\u00fcanslara ba\u011fl\u0131 sistemler i\u00e7in veriler hafife al\u0131nmamal\u0131d\u0131r - \u00f6zellikle de istilac\u0131 kullan\u0131m durumlar\u0131 i\u00e7in potansiyel ta\u015f\u0131d\u0131klar\u0131 zaman. \u015eirketler veri konusundaki uzmanl\u0131\u011fa m\u00fchendislik uzmanl\u0131\u011f\u0131na g\u00f6sterdikleri sayg\u0131 ve deste\u011fin ayn\u0131s\u0131n\u0131 g\u00f6sterirlerse, algoritmalar\u00a0ve\u00a0<\/em>olu\u015fturulduklar\u0131 veriler daha kapsaml\u0131 ve daha az hatal\u0131 olacakt\u0131r. B\u00fct\u00fcnsel veri sistemlerinin eksikli\u011fi bizi \u00f6nyarg\u0131ya a\u00e7\u0131k, d\u00fczenlenmemi\u015f \u00fcr\u00fcnlerle ba\u015f ba\u015fa b\u0131rakacakt\u0131r. Algoritmalara y\u00f6nelen ve veri kalitesinden uzakla\u015fan bir yat\u0131r\u0131m dengesizli\u011fi, m\u00fchendislik \u00e7abalar\u0131n\u0131n bo\u015fa gitmesine, eksik \u00fcr\u00fcnlere ve etik olmayan teknolojinin yay\u0131lmas\u0131na yol a\u00e7acakt\u0131r.<\/p>\n

T\u00fcm personel say\u0131n\u0131z\u0131 tek bir sepete koymay\u0131n. Sorumlu bir \u015fekilde i\u015fe al\u0131n.<\/p>","protected":false},"excerpt":{"rendered":"

Makine \u00f6\u011frenimi hedefleri b\u00fcy\u00fcd\u00fck\u00e7e, veri ihtiya\u00e7lar\u0131 da b\u00fcy\u00fcyor ve m\u00fchendis merkezli sorunlar\u0131 disiplinler aras\u0131 konulara d\u00f6n\u00fc\u015ft\u00fcr\u00fcyor.<\/p>","protected":false},"author":60486633,"featured_media":2199,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_jetpack_feature_clip_id":0,"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[676756913,676756994,676756915,676756914,676757008,676756993],"tags":[],"class_list":["post-2198","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-action-units","category-ar-vr","category-facial-expressions","category-muscles","category-free","category-tech","generate-columns","tablet-grid-50","mobile-grid-100","grid-parent","grid-50"],"yoast_head":"\nBig Tech's Homogeneous Hiring Habits Are Harming Our Data - Face the FACS | Facial Expressions in Animation, AI, & Behavior<\/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:\/\/melindaozel.com\/tr\/big-techs-homogeneous-hiring-habits-are-harming-our-data\/\" \/>\n<meta property=\"og:locale\" content=\"tr_TR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Big Tech's Homogeneous Hiring Habits Are Harming Our Data\" \/>\n<meta property=\"og:description\" content=\"As machine learning ambitions grow, data needs grow as well, transforming engineer-centric problems into cross-disciplinary matters.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/melindaozel.com\/tr\/big-techs-homogeneous-hiring-habits-are-harming-our-data\/\" \/>\n<meta property=\"og:site_name\" content=\"Face the FACS | Facial Expressions in Animation, AI, & Behavior\" \/>\n<meta property=\"article:publisher\" 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