{"id":29190,"date":"2025-09-09T03:00:26","date_gmt":"2025-09-09T00:00:26","guid":{"rendered":"https:\/\/fatihsoysal.com\/blog\/uretim-yapay-zekasinin-ai-gercekten-nasil-yapildigini-ogrenmek\/"},"modified":"2025-09-09T03:00:26","modified_gmt":"2025-09-09T00:00:26","slug":"uretim-yapay-zekasinin-ai-gercekten-nasil-yapildigini-ogrenmek","status":"publish","type":"post","link":"https:\/\/fatihsoysal.com\/blog\/uretim-yapay-zekasinin-ai-gercekten-nasil-yapildigini-ogrenmek\/","title":{"rendered":"\u00dcretim Yapay Zek\u00e2s\u0131n\u0131n (AI) Ger\u00e7ekten Nas\u0131l Yap\u0131ld\u0131\u011f\u0131n\u0131 \u00d6\u011frenmek"},"content":{"rendered":"

\u00dcretim Yapay Zek\u00e2s\u0131n\u0131n (AI) Ger\u00e7ekten Nas\u0131l Yap\u0131ld\u0131\u011f\u0131n\u0131 \u00d6\u011frenmek<\/p>\n

\u00dcretim yapay zek\u00e2s\u0131 (\u00dcYAZ) g\u00fcn\u00fcm\u00fczde bir\u00e7ok sekt\u00f6rde devrim yarat\u0131yor, ancak bu karma\u015f\u0131k sistemlerin nas\u0131l in\u015fa edildi\u011fi \u00e7o\u011fu ki\u015fi i\u00e7in gizemini koruyor. Bu makale, \u00dcYAZ geli\u015ftirmenin ard\u0131ndaki temel ad\u0131mlar\u0131, kar\u015f\u0131la\u015faca\u011f\u0131n\u0131z zorluklar\u0131 ve ba\u015far\u0131l\u0131 bir \u00dcYAZ projesi i\u00e7in gerekli olan teknik becerileri ad\u0131m ad\u0131m a\u00e7\u0131klayarak, bu gizemi \u00e7\u00f6zmenize yard\u0131mc\u0131 olacakt\u0131r. Yeni ba\u015flayanlardan deneyimli geli\u015ftiricilere kadar herkes i\u00e7in faydal\u0131 bilgiler i\u00e7ermektedir. <\/p>\n

\u00dcretim Yapay Zek\u00e2s\u0131: Temel Kavramlar Nelerdir?<\/h2>\n

\u00dcretim yapay zek\u00e2s\u0131, b\u00fcy\u00fck \u00f6l\u00e7ekli veri setlerini i\u015fleyerek ve karma\u015f\u0131k modeller e\u011fiterek, ger\u00e7ek zamanl\u0131 veya yak\u0131n ger\u00e7ek zamanl\u0131 olarak sonu\u00e7 \u00fcreten bir yapay zek\u00e2 t\u00fcr\u00fcd\u00fcr. Bu, basit bir g\u00f6r\u00fcnt\u00fc tan\u0131ma sisteminden, otonom ara\u00e7lar\u0131n navigasyonuna veya ki\u015fiselle\u015ftirilmi\u015f i\u00e7erik \u00f6nerilerine kadar geni\u015f bir yelpazede uygulama bulur. \u00d6nemli kavramlar aras\u0131nda \u015funlar yer al\u0131r:<\/p>\n

* Makine \u00d6\u011frenmesi (ML):<\/strong> \u00dcYAZ’\u0131n temel ta\u015f\u0131d\u0131r. Algoritmalar\u0131n verilerden \u00f6\u011frenmesini ve gelecekteki girdiler i\u00e7in tahminler yapmas\u0131n\u0131 sa\u011flar. S\u00fcrekli \u00f6\u011frenme, modelin zamanla iyile\u015fmesini ve yeni bilgilere adapte olmas\u0131n\u0131 sa\u011flar.
\n* Derin \u00d6\u011frenme (DL):<\/strong> ML’nin bir alt k\u00fcmesidir ve yapay sinir a\u011flar\u0131 (YSA) kullanarak karma\u015f\u0131k desenleri ve ili\u015fkileri \u00f6\u011frenir. G\u00f6r\u00fcnt\u00fc i\u015fleme, do\u011fal dil i\u015fleme ve konu\u015fma tan\u0131ma gibi g\u00f6revlerde \u00fcst\u00fcn performans g\u00f6sterir.
\n* B\u00fcy\u00fck Veri:<\/strong> \u00dcYAZ, b\u00fcy\u00fck miktarda veriyle e\u011fitilir. Bu veriler, modelin do\u011fru ve g\u00fcvenilir tahminler yapmas\u0131n\u0131 sa\u011flar. Veri kalitesi ve temizli\u011fi, modelin performans\u0131 i\u00e7in kritik \u00f6neme sahiptir.
\n* Bulut Bili\u015fim:<\/strong> \u00dcYAZ sistemleri genellikle bulut altyap\u0131lar\u0131nda \u00e7al\u0131\u015f\u0131r. Bu, \u00f6l\u00e7eklenebilirlik, esneklik ve maliyet etkinli\u011fi sa\u011flar. AWS, Google Cloud ve Azure gibi b\u00fcy\u00fck bulut sa\u011flay\u0131c\u0131lar\u0131, \u00dcYAZ geli\u015ftirme i\u00e7in gerekli olan ara\u00e7lar\u0131 ve hizmetleri sunar.
\n* Model E\u011fitimi:<\/strong> Modelin performans\u0131n\u0131 optimize etmek i\u00e7in kullan\u0131lan s\u00fcre\u00e7tir. E\u011fitim s\u00fcreci, veri \u00f6n i\u015fleme, model se\u00e7imi, hiperparametre optimizasyonu ve model de\u011ferlendirmesini i\u00e7erir.<\/p>\n

\u00dcretim Yapay Zek\u00e2 Sisteminin Mimarisini Nas\u0131l Tasarlar\u0131m?<\/h2>\n

Bir \u00dcYAZ sisteminin mimarisi, projenin \u00f6zel gereksinimlerine ba\u011fl\u0131 olarak b\u00fcy\u00fck \u00f6l\u00e7\u00fcde de\u011fi\u015febilir. Ancak, genel olarak a\u015fa\u011f\u0131daki bile\u015fenleri i\u00e7erir:<\/p>\n

* Veri Kayna\u011f\u0131:<\/strong> Sistemin girdi verilerini ald\u0131\u011f\u0131 yerdir. Bu, bir veritaban\u0131, bir API veya bir dosya sistemi olabilir.
\n* Veri \u00d6n \u0130\u015fleme:<\/strong> Ham verilerin temizlenmesi, d\u00f6n\u00fc\u015ft\u00fcr\u00fclmesi ve modele uygun hale getirilmesi i\u015flemidir. Bu ad\u0131m, modelin performans\u0131n\u0131 b\u00fcy\u00fck \u00f6l\u00e7\u00fcde etkiler.
\n* Model:<\/strong> Verilerden \u00f6\u011frenen ve tahminler yapan algoritmad\u0131r. Model se\u00e7imi, projenin \u00f6zel gereksinimlerine ba\u011fl\u0131d\u0131r.
\n* \u00c7\u0131kt\u0131:<\/strong> Sistemin \u00fcretti\u011fi sonuca kar\u015f\u0131l\u0131k gelir. Bu, bir tahmin, bir karar veya bir eylem olabilir.
\n* Monit\u00f6ring ve De\u011ferlendirme:<\/strong> Sistemin performans\u0131n\u0131 izlemek ve iyile\u015ftirmeler yapmak i\u00e7in kullan\u0131l\u0131r. Bu, hata oranlar\u0131n\u0131 izlemeyi, model performans\u0131n\u0131 de\u011ferlendirmeyi ve gerekirse modeli yeniden e\u011fitmeyi i\u00e7erir.<\/p>\n

\u00d6rne\u011fin, bir \u00fcretim hatt\u0131nda kusurlu \u00fcr\u00fcnleri tespit eden bir \u00dcYAZ sistemi i\u00e7in, veri kayna\u011f\u0131 bir kamera sistemi olabilir, veri \u00f6n i\u015fleme g\u00f6r\u00fcnt\u00fc i\u015fleme tekniklerini i\u00e7erebilir, model bir derin \u00f6\u011frenme modeli olabilir ve \u00e7\u0131kt\u0131 kusurlu \u00fcr\u00fcnlerin tespit edilmesi olabilir.<\/p>\n

Bir \u00dcretim Yapay Zek\u00e2 Modeli Nas\u0131l E\u011fitilir?<\/h2>\n

Model e\u011fitimi, \u00dcYAZ geli\u015ftirmenin en zaman al\u0131c\u0131 ve kaynak yo\u011fun a\u015famalar\u0131ndan biridir. Bu s\u00fcre\u00e7, a\u015fa\u011f\u0131daki ad\u0131mlar\u0131 i\u00e7erir:<\/p>\n

1. Veri Toplama ve Haz\u0131rlama:<\/strong> Yeterli miktarda y\u00fcksek kaliteli veri toplamak ve temizlemek, modelin ba\u015far\u0131l\u0131 bir \u015fekilde e\u011fitilmesi i\u00e7in kritik \u00f6neme sahiptir. Verilerin etiketlenmesi, \u00f6zellikle denetimli \u00f6\u011frenme modelleri i\u00e7in gereklidir.
\n2. Model Se\u00e7imi:<\/strong> Projenin \u00f6zel gereksinimlerine ba\u011fl\u0131 olarak uygun bir model se\u00e7mek gerekir. Farkl\u0131 model t\u00fcrleri farkl\u0131 performans \u00f6zellikleri sunar.
\n3. Hiperparametre Optimizasyonu:<\/strong> Modelin performans\u0131n\u0131 optimize etmek i\u00e7in hiperparametreleri ayarlamak gerekir. Bu, deneme yan\u0131lma yoluyla veya otomatik hiperparametre optimizasyon teknikleri kullan\u0131larak yap\u0131labilir.
\n4. E\u011fitim:<\/strong> Se\u00e7ilen model, haz\u0131rlanan veriler \u00fczerinde e\u011fitilmelidir. E\u011fitim s\u00fcresi, veri setinin b\u00fcy\u00fckl\u00fc\u011f\u00fcne ve modelin karma\u015f\u0131kl\u0131\u011fna ba\u011fl\u0131 olarak de\u011fi\u015febilir.
\n5. De\u011ferlendirme:<\/strong> E\u011fitilen modelin performans\u0131, farkl\u0131 metrikler kullan\u0131larak de\u011ferlendirilmelidir. Bu, modelin performans\u0131n\u0131 iyile\u015ftirmek i\u00e7in gerekli ad\u0131mlar\u0131n belirlenmesine yard\u0131mc\u0131 olur.<\/p>\n

# \u00d6rnek: basit bir lineer regresyon modeli e\u011fitimi (scikit-learn k\u00fct\u00fcphanesi kullan\u0131larak)\nfrom sklearn.linear_model import LinearRegression\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import mean_squared_error\n\n# Veri y\u00fckleme ve b\u00f6lme\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\n\n# Model e\u011fitimi\nmodel = LinearRegression()\nmodel.fit(X_train, y_train)\n\n# Tahmin ve de\u011ferlendirme\ny_pred = model.predict(X_test)\nmse = mean_squared_error(y_test, y_pred)\nprint(f\"Ortalama Kare Hata: {mse}\")<\/pre>\n
<\/code><\/p>\n
Ger\u00e7ek D\u00fcnya Senaryolar\u0131: Vaka Analizleri<\/h2>\n
Bir e-ticaret \u015firketinin \u00fcr\u00fcn \u00f6nerisi sistemini ele alal\u0131m. Bu sistem, m\u00fc\u015fterilerin ge\u00e7mi\u015f al\u0131\u015fveri\u015f ge\u00e7mi\u015flerine ve tercihlerine dayal\u0131 olarak \u00fcr\u00fcn \u00f6nerileri sunar.  Bu sistem, b\u00fcy\u00fck miktarda veriyi i\u015fleyen ve ki\u015fiselle\u015ftirilmi\u015f \u00f6neriler \u00fcreten bir \u00dcYAZ sistemi kullan\u0131r.  Sistemin performans\u0131, \u00f6nerilerin alaka d\u00fczeyi ve t\u0131klama oranlar\u0131 gibi metrikler kullan\u0131larak de\u011ferlendirilir.  Ba\u015fka bir \u00f6rnek olarak, bir otonom ara\u00e7 geli\u015ftirmek istedi\u011fimizi d\u00fc\u015f\u00fcnelim.  Burada, kamera g\u00f6r\u00fcnt\u00fclerini, sens\u00f6r verilerini ve GPS verilerini i\u015fleyen ve arac\u0131n g\u00fcvenli bir \u015fekilde s\u00fcr\u00fclmesini sa\u011flayan karma\u015f\u0131k bir \u00dcYAZ sistemi gereklidir. Bu sistemin performans\u0131, g\u00fcvenlik ve verimlilik gibi metriklerle de\u011ferlendirilir.<\/p>\n
\u00dcretim Yapay Zek\u00e2 Sistemlerinde Performans Optimizasyonu Nas\u0131l Yap\u0131l\u0131r?<\/h2>\n
\u00dcretim yapay zek\u00e2 sistemlerinin performans\u0131, bir\u00e7ok fakt\u00f6re ba\u011fl\u0131d\u0131r.  Performans optimizasyonu i\u00e7in a\u015fa\u011f\u0131daki stratejiler kullan\u0131labilir:<\/p>\n
* Model se\u00e7imi:<\/strong>  G\u00f6reve uygun en iyi model t\u00fcr\u00fcn\u00fc se\u00e7mek.
\n* Veri \u00f6n i\u015fleme:<\/strong>  Verilerin temizlenmesi ve d\u00f6n\u00fc\u015ft\u00fcr\u00fclmesi, modelin performans\u0131n\u0131 art\u0131r\u0131r.
\n* Hiperparametre ayar\u0131:<\/strong>  Modelin hiperparametrelerini optimize etmek, performans\u0131n\u0131 b\u00fcy\u00fck \u00f6l\u00e7\u00fcde etkiler.
\n* Donan\u0131m optimizasyonu:<\/strong>  Daha g\u00fc\u00e7l\u00fc donan\u0131m kullanmak, e\u011fitim s\u00fcresini k\u0131salt\u0131r ve performans\u0131 art\u0131r\u0131r.
\n* Paralelle\u015ftirme:<\/strong>  E\u011fitim ve tahmin i\u015flemlerini paralel olarak \u00e7al\u0131\u015ft\u0131rmak, performans\u0131 art\u0131r\u0131r.
\n* Model s\u0131k\u0131\u015ft\u0131rmas\u0131:<\/strong>  E\u011fitilmi\u015f modeli daha k\u00fc\u00e7\u00fck bir boyuta s\u0131k\u0131\u015ft\u0131rmak, bellek t\u00fcketimini azalt\u0131r ve performans\u0131 art\u0131r\u0131r.<\/p>\n
\u00dcretim Yapay Zek\u00e2 G\u00fcvenli\u011fi: Dikkat Edilmesi Gereken Hususlar<\/h2>\n
\u00dcretim yapay zek\u00e2 sistemlerinin g\u00fcvenli\u011fi, \u00e7ok \u00f6nemlidir.  Yanl\u0131\u015f veya k\u00f6t\u00fc niyetli verilerin sisteme girmesi, yanl\u0131\u015f sonu\u00e7lara ve hatta tehlikeli durumlara neden olabilir.  G\u00fcvenlik \u00f6nlemleri \u015funlard\u0131r:<\/p>\n
\n
Dikkat!<\/h3>\n
  Veri g\u00fcvenli\u011fi, model g\u00fcvenli\u011fi ve sistem g\u00fcvenli\u011fi gibi hususlar \u00fczerinde titizlikle \u00e7al\u0131\u015f\u0131lmal\u0131d\u0131r.  G\u00fcvenlik a\u00e7\u0131klar\u0131n\u0131 azaltmak i\u00e7in d\u00fczenli g\u00fcvenlik denetimleri yap\u0131lmal\u0131 ve g\u00fcncellemeler uygulanmal\u0131d\u0131r.\n<\/p><\/div>\n
* Veri G\u00fcvenli\u011fi:<\/strong> Verilerin g\u00fcvenli bir \u015fekilde depolanmas\u0131 ve korunmas\u0131 gerekir.
\n* Model G\u00fcvenli\u011fi:<\/strong>  Modelin k\u00f6t\u00fc niyetli sald\u0131r\u0131lara kar\u015f\u0131 korunmas\u0131 gerekir.
\n* Sistem G\u00fcvenli\u011fi:<\/strong>  Sistemin genel g\u00fcvenli\u011fi sa\u011flanmal\u0131d\u0131r.  Bu, a\u011f g\u00fcvenli\u011fi, eri\u015fim kontrol\u00fc ve hata y\u00f6netimini i\u00e7erir.<\/p>\n
\u00d6\u011frenme Yol Haritas\u0131<\/h2>\n
[Yeni Ba\u015flayan]:<\/strong>  Temel makine \u00f6\u011frenmesi kavramlar\u0131n\u0131 \u00f6\u011frenin.  Python programlama dilini ve temel veri bilimi k\u00fct\u00fcphanelerini (\u00f6rne\u011fin, NumPy, Pandas, scikit-learn) \u00f6\u011frenin.  Basit makine \u00f6\u011frenmesi modellerini e\u011fitin ve de\u011ferlendirin.  Daha fazla bilgi i\u00e7in (https:\/\/fatihsoysal.com) inceleyebilirsiniz.<\/p>\n
[Orta Seviye]:<\/strong>  Derin \u00f6\u011frenme kavramlar\u0131n\u0131 ve pop\u00fcler derin \u00f6\u011frenme k\u00fct\u00fcphanelerini (\u00f6rne\u011fin, TensorFlow, PyTorch) \u00f6\u011frenin.  Daha karma\u015f\u0131k modeller e\u011fitin ve ger\u00e7ek d\u00fcnya verileriyle \u00e7al\u0131\u015f\u0131n.  Model optimizasyonu ve hiperparametre ayar\u0131 tekniklerini \u00f6\u011frenin.<\/p>\n
[\u0130leri Seviye]:<\/strong>  \u00dcretim yapay zek\u00e2 sistemlerinin mimarisini tasarlay\u0131n ve uygulay\u0131n.  B\u00fcy\u00fck veri setleriyle \u00e7al\u0131\u015f\u0131n ve bulut bili\u015fim platformlar\u0131n\u0131 kullan\u0131n.  Model g\u00fcvenli\u011fi ve performans optimizasyonu tekniklerini uygulay\u0131n.  Ara\u015ft\u0131rma makaleleri okuyarak ve a\u00e7\u0131k kaynak kodlu projeler \u00fczerinde \u00e7al\u0131\u015farak uzmanl\u0131\u011f\u0131n\u0131z\u0131 geli\u015ftirin.<\/p>\n
Sonu\u00e7<\/h2>\n
\u00dcretim yapay zek\u00e2s\u0131, bir\u00e7ok sekt\u00f6rde devrim yaratma potansiyeline sahip g\u00fc\u00e7l\u00fc bir teknolojidir.  Bu makale, \u00dcYAZ sistemlerinin nas\u0131l in\u015fa edildi\u011fini ve bu sistemlerin performans\u0131n\u0131 nas\u0131l optimize edebilece\u011finizi a\u00e7\u0131klam\u0131\u015ft\u0131r.  Ancak, bu karma\u015f\u0131k bir alan olup, s\u00fcrekli \u00f6\u011frenme ve geli\u015ftirme gerektirir.  Daha fazla bilgi i\u00e7in (https:\/\/fatihsoysal.com) ziyaret edebilirsiniz.<\/p>\n
S\u0131k\u00e7a Sorulan Sorular<\/h3>\n
* \u00dcretim yapay zek\u00e2s\u0131 i\u00e7in hangi programlama dili en iyisidir?<\/strong> Python, yayg\u0131n olarak kullan\u0131lan ve g\u00fc\u00e7l\u00fc k\u00fct\u00fcphanelere sahip bir dildir.
\n* \u00dcretim yapay zek\u00e2s\u0131n\u0131n maliyeti nedir?<\/strong> Maliyet, projenin karma\u015f\u0131kl\u0131\u011f\u0131n\u0131, veri setinin b\u00fcy\u00fckl\u00fc\u011f\u00fcn\u00fc ve kullan\u0131lan altyap\u0131y\u0131 i\u00e7eren bir\u00e7ok fakt\u00f6re ba\u011fl\u0131d\u0131r.
\n* \u00dcretim yapay zek\u00e2s\u0131 hangi sekt\u00f6rlerde kullan\u0131l\u0131r?<\/strong>  E-ticaret, sa\u011fl\u0131k, finans, otomotiv ve \u00fcretim gibi bir\u00e7ok sekt\u00f6rde kullan\u0131lmaktad\u0131r.
\n* \u00dcretim yapay zek\u00e2s\u0131n\u0131n etik sorunlar\u0131 nelerdir?<\/strong>  \u00d6nyarg\u0131, gizlilik ve g\u00fcvenlik gibi etik sorunlar dikkate al\u0131nmal\u0131d\u0131r.
\n* \u00dcretim yapay zek\u00e2s\u0131n\u0131n gelece\u011fi nedir?<\/strong>  Yapay zek\u00e2 teknolojilerindeki geli\u015fmeler, \u00dcYAZ'\u0131n daha da geli\u015fmi\u015f ve yayg\u0131n bir \u015fekilde kullan\u0131lmas\u0131n\u0131 sa\u011flayacakt\u0131r.<\/p>\n
Yazar: Fatih Soysal<\/p>\n","protected":false},"excerpt":{"rendered":"\u00dcretim Yapay Zek\u00e2s\u0131n\u0131n (AI) Ger\u00e7ekten Nas\u0131l Yap\u0131ld\u0131\u011f\u0131n\u0131 \u00d6\u011frenmek \u00dcretim yapay zek\u00e2s\u0131 (\u00dcYAZ) g\u00fcn\u00fcm\u00fczde bir\u00e7ok sekt\u00f6rde devrim yarat\u0131yor, ancak…","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"csco_page_header_type":"","csco_page_load_nextpost":"","csco_page_subscribe_form":"","csco_page_contact_form":"","footnotes":""},"categories":[1],"tags":[],"class_list":{"0":"post-29190","1":"post","2":"type-post","3":"status-publish","4":"format-standard","6":"category-genel","7":"cs-entry","8":"cs-video-wrap"},"yoast_head":"\n\u00dcretim Yapay Zek\u00e2s\u0131n\u0131n (AI) Ger\u00e7ekten Nas\u0131l Yap\u0131ld\u0131\u011f\u0131n\u0131 \u00d6\u011frenmek<\/title>\n<meta name=\"description\" content=\"\u00dcretim yapay zek\u00e2s\u0131 (\u00dcYAZ) g\u00fcn\u00fcm\u00fczde bir\u00e7ok sekt\u00f6rde devrim yarat\u0131yor, ancak bu karma\u015f\u0131k sistemlerin nas\u0131l in\u015fa edildi\u011fi \u00e7o\u011fu ki\u015fi i\u00e7in gizemini koruyor. Bu makale, \u00dcYAZ geli\u015ftirmenin ard\u0131ndaki temel ad\u0131mlar\u0131, kar\u015f\u0131la\u015faca\u011f\u0131n\u0131z zorluklar\u0131 ve ba\u015far\u0131l\u0131 bir \u00dcYAZ projesi i\u00e7in gerekli olan teknik becerileri ad\u0131m ad\u0131m a\u00e7\u0131klayarak, bu gizemi \u00e7\u00f6zmenize yard\u0131mc\u0131 olacakt\u0131r. Yeni ba\u015flayanlardan deneyimli geli\u015ftiricilere kadar herkes i\u00e7in faydal\u0131 bilgiler i\u00e7ermektedir.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/fatihsoysal.com\/blog\/uretim-yapay-zekasinin-ai-gercekten-nasil-yapildigini-ogrenmek\/\" \/>\n<meta property=\"og:locale\" content=\"tr_TR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"\u00dcretim Yapay Zek\u00e2s\u0131n\u0131n (AI) Ger\u00e7ekten Nas\u0131l Yap\u0131ld\u0131\u011f\u0131n\u0131 \u00d6\u011frenmek\" \/>\n<meta property=\"og:description\" content=\"\u00dcretim yapay zek\u00e2s\u0131 (\u00dcYAZ) g\u00fcn\u00fcm\u00fczde bir\u00e7ok sekt\u00f6rde devrim yarat\u0131yor, ancak bu karma\u015f\u0131k sistemlerin nas\u0131l in\u015fa edildi\u011fi \u00e7o\u011fu ki\u015fi i\u00e7in gizemini koruyor. 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