\n| Geli\u015ftirme Yakla\u015f\u0131m\u0131<\/td>\n | Kod tabanl\u0131 m\u00fchendislik<\/td>\n | Veri tabanl\u0131 m\u00fchendislik, MLOps<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n4. Kar\u015f\u0131la\u015f\u0131lan Zorluklar ve Riskler<\/h3>\nHer ne kadar “Model Makine Oldu\u011funda” yakla\u015f\u0131m\u0131 \u00f6nemli avantajlar sunsa da, beraberinde baz\u0131 ciddi zorluklar\u0131 ve riskleri de getirmektedir. Bu zorluklar\u0131n \u00fcstesinden gelmek, bu sistemlerin ba\u015far\u0131l\u0131 bir \u015fekilde uygulanmas\u0131 i\u00e7in kritik \u00f6neme sahiptir.<\/p>\n 4.1. G\u00fcvenlik ve Gizlilik Endi\u015feleri<\/h4>\nOtonom sistemler, siber sald\u0131r\u0131lara kar\u015f\u0131 daha savunmas\u0131z olabilir. K\u00f6t\u00fc niyetli akt\u00f6rler, modeli manip\u00fcle ederek veya zehirleyerek sistemin yanl\u0131\u015f kararlar almas\u0131na neden olabilir. Ayr\u0131ca, modelin i\u015fledi\u011fi hassas verilerin gizlili\u011fi de b\u00fcy\u00fck bir endi\u015fe kayna\u011f\u0131d\u0131r. Veri \u015fifreleme, eri\u015fim kontrol\u00fc ve model b\u00fct\u00fcnl\u00fc\u011f\u00fc denetimleri bu riskleri azaltmak i\u00e7in gereklidir.<\/p>\n 4.2. A\u00e7\u0131klanabilirlik ve \u015eeffafl\u0131k Sorunlar\u0131<\/h4>\nDerin \u00f6\u011frenme modelleri gibi karma\u015f\u0131k yap\u0131lar, genellikle “kara kutu” olarak adland\u0131r\u0131l\u0131r. Bir modelin neden belirli bir karar ald\u0131\u011f\u0131n\u0131 anlamak veya a\u00e7\u0131klamak zor olabilir. Bu durum, \u00f6zellikle finans, sa\u011fl\u0131k veya hukuk gibi kritik sekt\u00f6rlerde g\u00fcven sorunlar\u0131na yol a\u00e7abilir. A\u00e7\u0131klanabilir Yapay Zeka (XAI) teknikleri, bu sorunu hafifletmeye \u00e7al\u0131\u015fmaktad\u0131r.<\/p>\n 4.3. Etik ve Sosyal Yans\u0131malar<\/h4>\nModelin otonom kararlar almas\u0131, etik ikilemleri beraberinde getirir. \u00d6rne\u011fin, bir otonom arac\u0131n kaza an\u0131nda kimin hayat\u0131n\u0131 kurtaraca\u011f\u0131na dair verece\u011fi karar gibi. Ayr\u0131ca, bu sistemlerin i\u015fg\u00fcc\u00fc piyasas\u0131 \u00fczerindeki etkileri, ayr\u0131mc\u0131l\u0131k potansiyeli ve sosyal adalet gibi konular da ciddi etik tart\u0131\u015fmalar\u0131 tetiklemektedir.<\/p>\n 4.4. Bak\u0131m ve S\u00fcrd\u00fcr\u00fclebilirlik<\/h4>\nModelin s\u00fcrekli \u00f6\u011frenmesi ve adapte olmas\u0131, modelin ya\u015fam d\u00f6ng\u00fcs\u00fc boyunca s\u00fcrekli izlenmesini, g\u00fcncellenmesini ve yeniden e\u011fitilmesini gerektirir. Model kaymas\u0131 (model drift) gibi sorunlar, modelin zamanla performans\u0131n\u0131 d\u00fc\u015f\u00fcrebilir. Bu durum, sa\u011flam MLOps altyap\u0131lar\u0131 ve s\u00fcrekli entegrasyon\/s\u00fcrekli da\u011f\u0131t\u0131m (CI\/CD) s\u00fcre\u00e7lerinin \u00f6nemini art\u0131r\u0131r.<\/p>\n 5. Uygulama Stratejileri ve Teknolojiler<\/h3>\n“Model Makine Oldu\u011funda” paradigmas\u0131n\u0131 hayata ge\u00e7irmek i\u00e7in belirli stratejiler ve teknolojik yakla\u015f\u0131mlar gereklidir. Bu, sadece modelin kendisini geli\u015ftirmekle kalmaz, ayn\u0131 zamanda onun da\u011f\u0131t\u0131m\u0131n\u0131, y\u00f6netimini ve s\u00fcrd\u00fcr\u00fclebilirli\u011fini de kapsar.<\/p>\n 5.1. MLOps ve Model Y\u00f6netimi<\/h4>\nMLOps (Makine \u00d6\u011frenimi Operasyonlar\u0131), modelin ya\u015fam d\u00f6ng\u00fcs\u00fcn\u00fc otomatikle\u015ftiren ve y\u00f6neten bir dizi uygulamad\u0131r. Modelin e\u011fitimi, versiyonlamas\u0131, da\u011f\u0131t\u0131m\u0131, izlenmesi ve yeniden e\u011fitimi gibi s\u00fcre\u00e7leri kapsar. Etkili bir MLOps stratejisi, modelin bir makine gibi otonom \u00e7al\u0131\u015fmas\u0131n\u0131 sa\u011flamak i\u00e7in kritik \u00f6neme sahiptir. Modelin performans\u0131n\u0131 s\u00fcrekli izlemek ve olas\u0131 sapmalar\u0131 tespit etmek i\u00e7in metrikler ve uyar\u0131 sistemleri kurulmal\u0131d\u0131r.<\/p>\n \u00d6rnek bir MLOps ak\u0131\u015f\u0131:<\/p>\n \n# Basit bir model da\u011f\u0131t\u0131m ve izleme senaryosu (konsept)\nimport pandas as pd\nfrom sklearn.ensemble import RandomForestClassifier\nfrom joblib import dump, load\nimport logging\n\nlogging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')\n\nclass ModelAsMachine:\n def __init__(self, model_path=\"model.joblib\"):\n self.model = self._load_model(model_path)\n logging.info(f\"Model {model_path} y\u00fcklendi.\")\n\n def _load_model(self, path):\n try:\n return load(path)\n except FileNotFoundError:\n logging.error(f\"Model dosyas\u0131 bulunamad\u0131: {path}. Yeni model e\u011fitiliyor.\")\n # Ger\u00e7ek senaryoda burada bir e\u011fitim pipeline'\u0131 tetiklenir\n return self._train_new_model()\n\n def _train_new_model(self):\n # Basit bir \u00f6rnek veri seti\n X = pd.DataFrame([[1, 2], [3, 4], [5, 6], [7, 8], [9, 10]])\n y = [0, 0, 1, 1, 1]\n new_model = RandomForestClassifier(random_state=42)\n new_model.fit(X, y)\n dump(new_model, \"model.joblib\")\n logging.info(\"Yeni model e\u011fitildi ve kaydedildi.\")\n return new_model\n\n def make_decision(self, data):\n prediction = self.model.predict(data)\n logging.info(f\"Girdi: {data.values.tolist()}, Tahmin: {prediction[0]}\")\n # Burada modelin tahminine g\u00f6re otonom bir eylem tetiklenebilir\n if prediction[0] == 1:\n logging.info(\"Eylem: Onayland\u0131.\")\n return \"Onayland\u0131\"\n else:\n logging.info(\"Eylem: Reddedildi.\")\n return \"Reddedildi\"\n\n def monitor_performance(self, real_data, real_labels):\n # Ger\u00e7ek zamanl\u0131 performans izleme ve yeniden e\u011fitim tetikleme\n predictions = self.model.predict(real_data)\n accuracy = (predictions == real_labels).mean()\n logging.info(f\"Mevcut model do\u011frulu\u011fu: {accuracy:.2f}\")\n if accuracy < 0.8: # Belirli bir e\u015fi\u011fin alt\u0131na d\u00fc\u015ferse\n logging.warning(\"Model performans\u0131 d\u00fc\u015ft\u00fc. Yeniden e\u011fitim gerekiyor.\")\n self.model = self._train_new_model() # Otomatik yeniden e\u011fitim\n logging.info(\"Model otomatik olarak yeniden e\u011fitildi.\")\n\n# Kullan\u0131m \u00f6rne\u011fi\nif __name__ == \"__main__\":\n # \u0130lk \u00e7al\u0131\u015ft\u0131rmada model yoksa e\u011fitilecek\n my_machine = ModelAsMachine()\n\n # Modelin karar vermesi\n test_data_1 = pd.DataFrame([[8, 9]])\n my_machine.make_decision(test_data_1) # \u00c7\u0131kt\u0131: Onayland\u0131\n\n test_data_2 = pd.DataFrame([[2, 1]])\n my_machine.make_decision(test_data_2) # \u00c7\u0131kt\u0131: Reddedildi\n\n # Performans izleme ve gerekirse yeniden e\u011fitim\n # Ger\u00e7ek senaryoda bu, s\u00fcrekli \u00e7al\u0131\u015fan bir s\u00fcre\u00e7 olur\n real_data_for_monitoring = pd.DataFrame([[1,1],[2,2],[9,9],[10,10]])\n real_labels_for_monitoring = [0,0,1,0] # Son etiket yanl\u0131\u015f, modelin d\u00fc\u015f\u00fc\u015f\u00fcn\u00fc sim\u00fcle etmek i\u00e7in\n my_machine.monitor_performance(real_data_for_monitoring, real_labels_for_monitoring)\n<\/pre>\n<\/code><\/p>\n 5.2. Servis Olarak Model (MaaS) Yakla\u015f\u0131m\u0131<\/h4>\nModelin bir makine gibi \u00e7al\u0131\u015fabilmesi i\u00e7in, genellikle bir servis olarak da\u011f\u0131t\u0131lmas\u0131 gerekir. MaaS (Model as a Service), modelin bir API \u00fczerinden eri\u015filebilir olmas\u0131n\u0131 sa\u011flar. Ancak bu paradigmada MaaS, sadece bir tahmin servisi sunmaktan \u00f6te, modelin kendi ba\u015f\u0131na bir i\u015f s\u00fcrecini y\u00f6netebilece\u011fi veya bir cihaz\u0131 kontrol edebilece\u011fi daha kapsaml\u0131 bir hizmet sunar.<\/p>\n 5.3. Konteynerizasyon ve Orkestrasyon<\/h4>\nDocker gibi konteyner teknolojileri, modellerin farkl\u0131 ortamlarda tutarl\u0131 bir \u015fekilde \u00e7al\u0131\u015fmas\u0131n\u0131 sa\u011flar. Kubernetes gibi orkestrasyon ara\u00e7lar\u0131 ise bu konteynerize edilmi\u015f modellerin da\u011f\u0131t\u0131m\u0131n\u0131, \u00f6l\u00e7eklendirilmesini ve y\u00f6netimini otomatikle\u015ftirir. Bu teknolojiler, modelin bir makine gibi davranan \u00f6zerk bir birim olarak kolayca da\u011f\u0131t\u0131lmas\u0131na ve y\u00f6netilmesine olanak tan\u0131r.<\/p>\n 5.4. Kenar Bili\u015fim ve Da\u011f\u0131t\u0131k Sistemler<\/h4>\nBaz\u0131 durumlarda, modelin kararlar\u0131 ger\u00e7ek zamanl\u0131 ve d\u00fc\u015f\u00fck gecikmeyle al\u0131nmas\u0131 gerekti\u011fi i\u00e7in bulut yerine cihaz\u0131n kendisinde (kenarda) \u00e7al\u0131\u015fmas\u0131 gerekebilir. Kenar bili\u015fim (Edge Computing), modelin verilerin topland\u0131\u011f\u0131 yere daha yak\u0131n \u00e7al\u0131\u015fmas\u0131n\u0131 sa\u011flayarak gecikmeyi azalt\u0131r ve gizlilik endi\u015felerini gidermeye yard\u0131mc\u0131 olur. Da\u011f\u0131t\u0131k sistemler ise modelin farkl\u0131 bile\u015fenlerinin farkl\u0131 konumlarda \u00e7al\u0131\u015fmas\u0131na izin verir.<\/p>\n 6. Kullan\u0131m Alanlar\u0131 ve \u00d6rnekler<\/h3>\n\"Model Makine Oldu\u011funda\" paradigmas\u0131, bir\u00e7ok sekt\u00f6rde devrim niteli\u011finde uygulamalara yol a\u00e7maktad\u0131r. Bu yakla\u015f\u0131m, \u00f6zellikle otonomi ve adaptasyonun kritik oldu\u011fu alanlarda b\u00fcy\u00fck potansiyel ta\u015f\u0131maktad\u0131r.<\/p>\n 6.1. Otonom Ara\u00e7lar ve Robotik<\/h4>\nOtonom ara\u00e7lar, bu paradigman\u0131n en bilinen \u00f6rneklerinden biridir. Ara\u00e7taki yapay zeka modelleri, sens\u00f6r verilerini (kamera, lidar, radar) i\u015fleyerek \u00e7evreyi alg\u0131lar, s\u00fcr\u00fc\u015f kararlar\u0131 al\u0131r ve arac\u0131 do\u011frudan kontrol eder. Robotik sistemlerde de benzer \u015fekilde, robotun \u00e7evresiyle etkile\u015fimini, g\u00f6rev planlamas\u0131n\u0131 ve eylem y\u00fcr\u00fctmesini sa\u011flayan modeller, robotun kendisi haline gelir.<\/p>\n 6.2. Finansal Karar Destek Sistemleri<\/h4>\nFinans sekt\u00f6r\u00fcnde, kredi risk de\u011ferlendirmesi, doland\u0131r\u0131c\u0131l\u0131k tespiti veya algoritmik ticaret gibi alanlarda modeller, sadece tahminler sunmakla kalmaz, ayn\u0131 zamanda do\u011frudan i\u015flem kararlar\u0131 alabilir. \u00d6rne\u011fin, bir algoritmik ticaret modeli, piyasa verilerini analiz ederek otomatik olarak al\u0131m-sat\u0131m emirleri verebilir.<\/p>\n 6.3. Sa\u011fl\u0131k Hizmetlerinde Ki\u015fiselle\u015ftirme<\/h4>\nSa\u011fl\u0131k alan\u0131nda, ki\u015fiselle\u015ftirilmi\u015f tedavi planlar\u0131 veya ila\u00e7 dozaj\u0131 ayarlamalar\u0131 i\u00e7in modeller kullan\u0131labilir. Bir hastan\u0131n genetik verileri, t\u0131bbi ge\u00e7mi\u015fi ve ger\u00e7ek zamanl\u0131 fizyolojik tepkileri temel al\u0131narak, model do\u011frudan tedavi protokollerini adapte edebilir veya ila\u00e7 pompalar\u0131n\u0131 kontrol edebilir.<\/p>\n 6.4. End\u00fcstriyel Otomasyon<\/h4>\n\u00dcretim tesislerinde, kalite kontrol, kestirimci bak\u0131m veya \u00fcretim hatt\u0131 optimizasyonu gibi s\u00fcre\u00e7lerde yapay zeka modelleri, makinelerin ve robotlar\u0131n davran\u0131\u015flar\u0131n\u0131 do\u011frudan y\u00f6netebilir. \u00d6rne\u011fin, bir model, bir makinenin ar\u0131zalanma riskini tespit etti\u011finde, otomatik olarak bak\u0131m s\u00fcrecini ba\u015flatabilir veya \u00fcretim parametrelerini ayarlayabilir.<\/p>\n 7. Gelecek Perspektifi<\/h3>\n\"Model Makine Oldu\u011funda\" yakla\u015f\u0131m\u0131, yapay zekan\u0131n gelecekteki rol\u00fc hakk\u0131nda \u00f6nemli ipu\u00e7lar\u0131 sunmaktad\u0131r. Bu paradigma, teknolojinin sadece ara\u00e7 olmaktan \u00e7\u0131k\u0131p, otonom ve ak\u0131ll\u0131 varl\u0131klar olarak i\u015flev g\u00f6rmeye ba\u015flayaca\u011f\u0131 bir gelece\u011fe i\u015faret ediyor.<\/p>\n 7.1. Yapay Genel Zeka (AGI) Yolculu\u011fu<\/h4>\nBu yakla\u015f\u0131m, Yapay Genel Zeka (AGI) hedefine do\u011fru at\u0131lan \u00f6nemli bir ad\u0131md\u0131r. AGI, insan benzeri bili\u015fsel yeteneklere sahip, farkl\u0131 g\u00f6revleri \u00f6\u011frenebilen ve uygulayabilen bir yapay zeka anlam\u0131na gelir. Modelin bir makine gibi davranmas\u0131, AGI'nin temelini olu\u015fturan \u00f6zerk \u00f6\u011frenme ve adaptasyon yeteneklerinin geli\u015fmesine katk\u0131da bulunur.<\/p>\n 7.2. \u0130nsan-Model Etkile\u015fiminin Evrimi<\/h4>\nGelecekte, insanlar ve \"model-makineler\" aras\u0131ndaki etkile\u015fim daha karma\u015f\u0131k ve i\u015fbirlik\u00e7i hale gelecektir. \u0130nsanlar, bu otonom sistemlerin denetleyicisi, e\u011fiticisi veya i\u015f orta\u011f\u0131 olarak konumlanacak, rutin g\u00f6revler ise modellere devredilecektir. Bu, yeni beceriler ve i\u015f rolleri gerektirecektir.<\/p>\n 7.3. Yeni \u0130\u015f Modelleri ve Ekonomik Etkiler<\/h4>\nBu paradigmada, MaaS (Model as a Service) kavram\u0131 daha da geni\u015fleyerek, otonom kararlar ve eylemler sunan \"Model as a Business\" veya \"Model as an Agent\" gibi yeni i\u015f modellerini ortaya \u00e7\u0131karabilir. Bu durum, ekonominin bir\u00e7ok sekt\u00f6r\u00fcnde verimlilik art\u0131\u015f\u0131 sa\u011flayacak, ancak ayn\u0131 zamanda i\u015fg\u00fcc\u00fc piyasas\u0131nda d\u00f6n\u00fc\u015f\u00fcmlere yol a\u00e7acakt\u0131r.<\/p>\n Sonu\u00e7<\/h3>\n\"Model Makine Oldu\u011funda\" paradigmas\u0131, yapay zeka ve makine \u00f6\u011frenimi alan\u0131ndaki en heyecan verici ve d\u00f6n\u00fc\u015ft\u00fcr\u00fcc\u00fc geli\u015fmelerden birini temsil etmektedir. Modelin sadece bir ara\u00e7 olmaktan \u00e7\u0131k\u0131p, otonom, adaptif ve karar verici bir \"makine\" haline gelmesi, end\u00fcstriyel otomasyondan sa\u011fl\u0131k hizmetlerine, finanstan ula\u015f\u0131ma kadar bir\u00e7ok alanda devrim niteli\u011finde yeniliklerin kap\u0131s\u0131n\u0131 aralamaktad\u0131r. Bu yakla\u015f\u0131m, \u00f6zerklik, \u00f6l\u00e7eklenebilirlik ve s\u00fcrekli \u00f6\u011frenme gibi \u00f6nemli avantajlar sunarken, g\u00fcvenlik, a\u00e7\u0131klanabilirlik ve etik gibi zorluklar\u0131 da beraberinde getirmektedir. Bu zorluklar\u0131n \u00fcstesinden gelmek i\u00e7in sa\u011flam MLOps stratejileri, \u015feffafl\u0131k odakl\u0131 tasar\u0131m ve etik rehberlik b\u00fcy\u00fck \u00f6nem ta\u015f\u0131maktad\u0131r. Gelecekte, \"model-makinelerin\" insanlarla i\u015fbirli\u011fi i\u00e7inde \u00e7al\u0131\u015ft\u0131\u011f\u0131, daha ak\u0131ll\u0131 ve verimli sistemlerin yayg\u0131nla\u015ft\u0131\u011f\u0131 bir d\u00fcnyaya do\u011fru ilerliyoruz. Bu d\u00f6n\u00fc\u015f\u00fcm, hem teknolojik hem de sosyal a\u00e7\u0131dan derin etkiler yaratmaya devam edecektir.<\/p>\n SSS (S\u0131k Sorulan Sorular)<\/h3>\n\n- Modelin Makine Olmas\u0131 ne anlama geliyor?<\/strong><\/dt>\n
- Modelin Makine Olmas\u0131, makine \u00f6\u011frenimi modelinin sadece tahmin yapmakla kalmay\u0131p, ayn\u0131 zamanda otonom kararlar al\u0131p bu kararlar\u0131 do\u011frudan icra eden, kendi ba\u015f\u0131na bir sistem gibi davranan bir varl\u0131k haline gelmesidir. Yani model, bir uygulaman\u0131n sadece bir par\u00e7as\u0131 olmaktan \u00e7\u0131k\u0131p, uygulaman\u0131n \u00e7ekirdek i\u015flevini yerine getiren ana akt\u00f6r olur.<\/dd>\n
- Bu yakla\u015f\u0131m\u0131n geleneksel yaz\u0131l\u0131mdan fark\u0131 nedir?<\/strong><\/dt>\n
- Geleneksel yaz\u0131l\u0131m, \u00f6nceden tan\u0131mlanm\u0131\u015f kat\u0131 kurallar \u00fczerine kuruludur ve de\u011fi\u015fen ko\u015fullara adaptasyonu zordur. Modelin Makine Oldu\u011fu sistemler ise verilerden \u00f6\u011frenir, dinamiktir, \u00e7evresel de\u011fi\u015fikliklere otonom olarak uyum sa\u011flar ve s\u00fcrekli olarak kendini geli\u015ftirir.<\/dd>\n
- G\u00fcvenlik riskleri nas\u0131l y\u00f6netilir?<\/strong><\/dt>\n
- G\u00fcvenlik riskleri; g\u00fc\u00e7l\u00fc veri \u015fifreleme, kat\u0131 eri\u015fim kontrol mekanizmalar\u0131, model b\u00fct\u00fcnl\u00fc\u011f\u00fc denetimleri, adversarial sald\u0131r\u0131lara kar\u015f\u0131 savunma teknikleri ve s\u00fcrekli g\u00fcvenlik izleme ile y\u00f6netilir. Modelin k\u00f6t\u00fc niyetli manip\u00fclasyonlara kar\u015f\u0131 korunmas\u0131 hayati \u00f6nem ta\u015f\u0131r.<\/dd>\n
- Hangi sekt\u00f6rlerde daha \u00e7ok kullan\u0131l\u0131r?<\/strong><\/dt>\n
- Bu paradigma, otonom ara\u00e7lar ve robotik, finans (algoritmik ticaret, doland\u0131r\u0131c\u0131l\u0131k tespiti), sa\u011fl\u0131k (ki\u015fiselle\u015ftirilmi\u015f tedavi), end\u00fcstriyel otomasyon (kestirimci bak\u0131m, kalite kontrol) gibi y\u00fcksek otonomi ve adaptasyon gerektiren sekt\u00f6rlerde yayg\u0131n olarak kullan\u0131lmaktad\u0131r.<\/dd>\n
- Gelecekte bizi neler bekliyor?<\/strong><\/dt>\n
- Gelecekte, Yapay Genel Zeka (AGI) hedefine do\u011fru ilerleme h\u0131zlanacak, insan-model etkile\u015fimleri daha i\u015fbirlik\u00e7i hale gelecek ve \"Model as a Business\" gibi yeni i\u015f modelleri ortaya \u00e7\u0131kacakt\u0131r. Bu durum, i\u015fg\u00fcc\u00fc piyasas\u0131nda ve ekonomide \u00f6nemli d\u00f6n\u00fc\u015f\u00fcmlere yol a\u00e7acakt\u0131r.<\/dd>\n<\/dl>\n","protected":false},"excerpt":{"rendered":"\u201cModel Makine Oldu\u011funda\u201d ifadesi, makine \u00f6\u011frenimi modelinin art\u0131k sadece girdi al\u0131p \u00e7\u0131kt\u0131 \u00fcreten pasif bir algoritma olmaktan \u00e7\u0131k\u0131p, kendi ba\u015f\u0131na bir sistem gibi davranan, \u00e7evresiyle etkile\u015fime giren, kararlar alan ve bu kararlar\u0131 icra eden aktif bir varl\u0131k haline gelmesini tan\u0131mlar.","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-41096","1":"post","2":"type-post","3":"status-publish","4":"format-standard","6":"category-genel","7":"cs-entry","8":"cs-video-wrap"},"yoast_head":"\nModel Makine Oldu\u011funda: Yapay Zeka Sistemlerinin Yeni Paradigmas\u0131 - 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