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Öğrenme","list":{"type":"unordered","items":["Bahisçi öğrenme özellikle Türkiye'de yaygın değildir, ancak Avrupa ve Amerika'da köklü üniversitelerde lisans, yüksek lisans ve doktora seviyelerinde temel bir derstir.","Frekanslı yaklaşımda dağılım özgü parametrelerin bilindiği varsayılmakta, ancak bahisçi yaklaşımda kitle parametrelerine ilişkin kestirimler koşullu dağılımlar sayesinde gerçekleştirilebilmektedir.","2018'de bir konferansta Alibaba, Amazon ve Google'nin AI takımlarının bu yaklaşımları sistemlerinde kullandıkları ve Monte Carlo simülasyonları ile entegre ettikleri görülmüştür."]},"beginTime":1831,"endTime":2080,"href":"/video/preview/7045146616548005910?parent-reqid=1773337802877880-5134886574411016539-balancer-l7leveler-kubr-yp-sas-140-BAL&text=TechAI+Hub&t=1831&ask_summarization=1"},{"index":13,"title":"Bahisçi Yaklaşımın Avantajları","list":{"type":"unordered","items":["Bahisçi yaklaşımda önsel ve sonsal dağılımlar hakkında bilgi sağlandığı ve dağılıma yönelik sonuçlar elde edildiği için daha iyi kestirimler bulunabilmektedir.","Bahisçi yaklaşımlar özellikle sosyal ve davranış bilimlerinde daha çok kullanılmakta ve tahminlerde daha faydalı sonuçlar vermektedir.","Bahisçi network'leri cloud sistemlerinde, deprem tahminleri ve komik hastalığına yönelik classification network'leri gibi alanlarda kullanılmaktadır."]},"beginTime":2080,"endTime":2221,"href":"/video/preview/7045146616548005910?parent-reqid=1773337802877880-5134886574411016539-balancer-l7leveler-kubr-yp-sas-140-BAL&text=TechAI+Hub&t=2080&ask_summarization=1"},{"index":14,"title":"Yapay Zeka ve Bulanık Mantık Sistemleri","list":{"type":"unordered","items":["Cloud sisteminde Microsoft, IBM, Amazon ve Google Cloud gibi şirketlerin kullandığı linkler ve uygulamalar karşılaştırılabilir.","Bulanık mantık yaklaşımla, IBM tarafından kullanılan network ve diğer araştırmacıların geliştirdiği uygulamalar incelenebilir.","Şirketler arasında kullanılan model tipleri ve uygulamalar programların karşılaştırılması yapay zekadaki gelişmeleri incelemek için kullanılabilir."]},"beginTime":2233,"endTime":2325,"href":"/video/preview/7045146616548005910?parent-reqid=1773337802877880-5134886574411016539-balancer-l7leveler-kubr-yp-sas-140-BAL&text=TechAI+Hub&t=2233&ask_summarization=1"},{"index":15,"title":"Frekansçı ve Bulanık Mantık Yaklaşımlarının Karşılaştırılması","list":{"type":"unordered","items":["Para atma deneyinde frekansçı yaklaşımda yazı ya da tura gelme olasılığı 1/2'dir, ancak önsel bilgiye dayanarak daha iyi kestirimler bulunabilir.","Binom veya Bernoulli dağılımı bulunmadan önce yapılan kestirimler, önsel bilgi olmadan daha az doğrudur.","Bulanık mantık yaklaşımı, frekansçı yaklaşıma göre daha iyi tahminlerde bulunarak bilime ve yapay zekaya daha iyi seviyelere taşıma potansiyeline sahiptir."]},"beginTime":2325,"endTime":2501,"href":"/video/preview/7045146616548005910?parent-reqid=1773337802877880-5134886574411016539-balancer-l7leveler-kubr-yp-sas-140-BAL&text=TechAI+Hub&t=2325&ask_summarization=1"},{"index":16,"title":"Epilepsi Tespiti Uygulaması","list":{"type":"unordered","items":["Uygulamanın amacı, epileptik hastaların sosyal aktivitelere katılabilmeleri için gerçek zamanlı epilepsi tespiti yapmaktır.","Klinik ortamda epileptik hastaların EEG sinyalleri alınabilirken, ev ortamında veya dışarıda sinyallerin taşınabilir mobil cihazlarla alınması ve epileptik olup olmadığı tespiti yapılabilir.","Epileptik sinyallerin orijinine bağlı olarak farklı sınıflandırmalar yapılabilir ve hastaların sosyal aktivitelere katıldığında takip edilmesi amaçlanmaktadır."]},"beginTime":2501,"endTime":2689,"href":"/video/preview/7045146616548005910?parent-reqid=1773337802877880-5134886574411016539-balancer-l7leveler-kubr-yp-sas-140-BAL&text=TechAI+Hub&t=2501&ask_summarization=1"},{"index":17,"title":"Epilepsi Tespiti Sisteminin Önemi","list":{"type":"unordered","items":["Sistem, epilepsi hastasına anlık uyarı göndererek araç kullanırken veya spor yaparken nöbet gelmesi durumunda önlem almasını sağlar.","Türkiye'de 5 yıl içerisinde nöbet geçirmeyen ve ilaç kullanmayan epilepsi hastalarına ehliyet verilebilmektedir.","Nöbet geçiren bir kişinin araç kullanması ciddi yaralanmalara veya ölümlere yol açabilir, bu nedenle bu tür sistemlerin tasarlanması önemlidir."]},"beginTime":2689,"endTime":2803,"href":"/video/preview/7045146616548005910?parent-reqid=1773337802877880-5134886574411016539-balancer-l7leveler-kubr-yp-sas-140-BAL&text=TechAI+Hub&t=2689&ask_summarization=1"},{"index":18,"title":"Epilepsi Tespiti Sisteminin Teknik Detayları","list":{"type":"unordered","items":["Sistemin başarısı, EEG sinyallerinin doğru ölçülmesi, işlenmesi ve modellenmesine bağlıdır.","Verinin kalitesi, yüksek frekanslarda ve art efektli sinyallerin doğru ölçülmesi önemlidir.","Epileptik sinyallerin pre-ictal (nöbet öncesi) ve post-ictal (nöbet sonrası) dönemlerindeki ayrıştırılması için sınıflandırma algoritmaları geliştirilebilir."]},"beginTime":2803,"endTime":3029,"href":"/video/preview/7045146616548005910?parent-reqid=1773337802877880-5134886574411016539-balancer-l7leveler-kubr-yp-sas-140-BAL&text=TechAI+Hub&t=2803&ask_summarization=1"},{"index":19,"title":"Epilepsi Veri Setleri ve Özellikleri","list":{"type":"unordered","items":["Epileptik veri setleri artık fiks bir veri seti olarak algılanıyor ve MIT ve Koç Üniversitesi'nden sağlanan veri setleri üzerinden analizler gerçekleştiriliyor.","BOND data seti beş gruba ayrılmış: A grubunda gözleri kapalı ve açık durumda sağlıklı insanların EEG verileri, B grubunda hipokampüsünden nöbet öncesi EEG sinyalleri, C grubunda epileptik zonda ve nöbet öncesi EEG sinyalleri, E grubunda ise nöbet esnasında EEG sinyalleri bulunuyor.","Bu veri setlerinin frekansı çok düşük olduğu için klinik araştırmacılar için değer içermediği belirtiliyor."]},"beginTime":3033,"endTime":3162,"href":"/video/preview/7045146616548005910?parent-reqid=1773337802877880-5134886574411016539-balancer-l7leveler-kubr-yp-sas-140-BAL&text=TechAI+Hub&t=3033&ask_summarization=1"},{"index":20,"title":"Epilepsi Nöbetinin Tanımlanması","list":{"type":"unordered","items":["Klinik araştırmacılar epilepsi nöbetinin offline olarak tanımlanmasından ziyade paternleri ve nereden orijine ettiği ile ilgileniyor.","Son yapılan çalışmalarda ripples ve fast ripples'ların varlığının özellikle interiktonal dönemde epilepsi nöbetinin bir göstergesi olduğu ve nöbetin habercisi olduğu gösterildi.","Bu tür çalışmaları gerçekleştirmek için yüksek frekanslarda ölçülmüş EEG sinyallerinin tercih edilmesi öneriliyor."]},"beginTime":3162,"endTime":3236,"href":"/video/preview/7045146616548005910?parent-reqid=1773337802877880-5134886574411016539-balancer-l7leveler-kubr-yp-sas-140-BAL&text=TechAI+Hub&t=3162&ask_summarization=1"},{"index":21,"title":"Veri Setinin İşlenmesi","list":{"type":"unordered","items":["Veri setinde placement system kullanılıyor ve her hastanın tek bir kanalından alınan veriler inceleniyor.","Veri setinde her kanalda 4096 kesikli gözlem bulunuyor ve bu veriler küçük parçalara ayrılıyor.","Küçük parçalara ayrılan verilerde wavelet transformasyon gerçekleştiriliyor ve bu dönüşümler sayesinde farklı bantlarda detaylar elde ediliyor."]},"beginTime":3236,"endTime":3362,"href":"/video/preview/7045146616548005910?parent-reqid=1773337802877880-5134886574411016539-balancer-l7leveler-kubr-yp-sas-140-BAL&text=TechAI+Hub&t=3236&ask_summarization=1"},{"index":22,"title":"Feature Extraction ve Sınıflandırma","list":{"type":"unordered","items":["Wavelet transformasyon sonrası elde edilen bantlar sayesinde epileptik paternler belirleniyor ve bu paternler fitch extraction yöntemleri ile değişkenlere atılıyor.","Elde edilen değişkenler modellemeye input olarak kullanılıyor ve A, B, C, D, E grupları için tipik sınıflandırma problemi olarak ele alınıyor.","Yeni başlayanlar basit bir veri seti oluşturup bağımlı değişkeni label olarak atayarak sınıflandırma modellemeleri gerçekleştirebilirler."]},"beginTime":3362,"endTime":3590,"href":"/video/preview/7045146616548005910?parent-reqid=1773337802877880-5134886574411016539-balancer-l7leveler-kubr-yp-sas-140-BAL&text=TechAI+Hub&t=3362&ask_summarization=1"},{"index":23,"title":"Epilepsi Çalışmalarının Önemi","list":{"type":"unordered","items":["Epilepsi doğru tanı konulduğunda tedavi edilebilen bir hastalık olup, 270'e kadar nöbetsiz yaşayabiliyor insanlar.","2016 yılında yapılan bir araştırmada %71'e kadar yanlış tanı ve doktor hatası gibi nedenlerle hatalı sonuçlar ortaya çıkmış.","Epilepsi nöbetlerinin önceden tahmin edilmesi ve ne zaman geleceğini belirlemek, hastaların güvenli bir yere götürelmesi için önemli bir motivasyon kaynağı."]},"beginTime":3590,"endTime":3661,"href":"/video/preview/7045146616548005910?parent-reqid=1773337802877880-5134886574411016539-balancer-l7leveler-kubr-yp-sas-140-BAL&text=TechAI+Hub&t=3590&ask_summarization=1"},{"index":24,"title":"Veri Ön İşleme ve Sinyal İşleme","list":{"type":"unordered","items":["EEG verilerini analiz etmek için machine learning ve deep learning kullanmak heyecan verici olabilir, ancak veri ön işleme kısmında iyi işler yapmak modelin daha az veriden bağımlı ve daha genel olmasını sağlıyor.","Farklı epilepsi türleri farklı modeller gerektirirse, her aşamada yeniden işlem yapmak gerekecek ve bu yorucu olabilir.","Sinyal işleme kısmında veriyi güzel bir şekilde işleyip inputu hazırlamak, daha sonra deep learning gibi teknikler kullanırken işi kolaylaştırıyor."]},"beginTime":3661,"endTime":3741,"href":"/video/preview/7045146616548005910?parent-reqid=1773337802877880-5134886574411016539-balancer-l7leveler-kubr-yp-sas-140-BAL&text=TechAI+Hub&t=3661&ask_summarization=1"},{"index":25,"title":"Boston Çocuk Hastanesi Veri Seti","list":{"type":"unordered","items":["Boston Çocuk Hastanesi ve işbirliği ile kaydedilmiş veri setinde 198 nöbet ve 26 farklı hasta bulunuyor.","Veri seti bir saatlik veya üç saatlik farklı ölçümler içeriyor ve .edf formatında internette bulunabilir.","Bu veri seti yeni başlayanlar için test amaçlı kullanılabilecek çok fazla veri içeriyor."]},"beginTime":3743,"endTime":3771,"href":"/video/preview/7045146616548005910?parent-reqid=1773337802877880-5134886574411016539-balancer-l7leveler-kubr-yp-sas-140-BAL&text=TechAI+Hub&t=3743&ask_summarization=1"},{"index":26,"title":"Veri Seti Zorlukları","list":{"type":"unordered","items":["Veri setinde örnekleme frekansı değiştirilemez ve çok geniş bir veri seti bulunuyor.","Bir saatlik veri seti 60x60x56 boyutunda büyük bir alan oluşturuyor ve bu bilgisayarı yoruyor.","Prediction yaparken zamanla yarışmak gerekiyor ve algoritmanın hızlı olması, veriyi iyi kullanmak önemli."]},"beginTime":3771,"endTime":3815,"href":"/video/preview/7045146616548005910?parent-reqid=1773337802877880-5134886574411016539-balancer-l7leveler-kubr-yp-sas-140-BAL&text=TechAI+Hub&t=3771&ask_summarization=1"},{"index":27,"title":"Veri Seti İşleme","list":{"type":"unordered","items":["Bir saatlik veri setinden nöbet öncesi ve sonrası kısımlarının seçilmesi önemlidir.","Nöbet öncesi (preictal) kısım, nöbetin başlamasından önceki zaman dilimini ifade eder.","Literatürdeki çalışmalar 10 dakikalık bir süre için yeterli olduğu kabul edilir."]},"beginTime":3815,"endTime":3895,"href":"/video/preview/7045146616548005910?parent-reqid=1773337802877880-5134886574411016539-balancer-l7leveler-kubr-yp-sas-140-BAL&text=TechAI+Hub&t=3815&ask_summarization=1"},{"index":28,"title":"Nöbet Zamanlaması","list":{"type":"unordered","items":["Nöbetin başlangıç ve bitiş zamanları saniye cinsinden girilmelidir.","Nöbet sonrası salınımlar ve frekans değişimlerini görmek için de bir dakikalık süreç alınabilir.","Maksimum örnekleme frekansı, orijinal frekansın yarısı kadar alınamaktadır."]},"beginTime":3895,"endTime":3953,"href":"/video/preview/7045146616548005910?parent-reqid=1773337802877880-5134886574411016539-balancer-l7leveler-kubr-yp-sas-140-BAL&text=TechAI+Hub&t=3895&ask_summarization=1"},{"index":29,"title":"Veri Dönüşümü","list":{"type":"unordered","items":["Veriyi algoritmaya koyabilmek için sinyal dönüştürülmesi gerekiyor.","Wavelet transformları beyin sinyallerinden esinlenerek geliştirilmiş fonksiyonlardır.","Veriyi benzer bir sistemle dönüştürmek, başlangıçtaki veriye benzer özelliklere sahip sonuçlar elde etmeye yardımcı olur."]},"beginTime":3953,"endTime":4005,"href":"/video/preview/7045146616548005910?parent-reqid=1773337802877880-5134886574411016539-balancer-l7leveler-kubr-yp-sas-140-BAL&text=TechAI+Hub&t=3953&ask_summarization=1"},{"index":30,"title":"Wavelet Dönüşümü Avantajları","list":{"type":"unordered","items":["Wavelet dönüşümü, seçilen fonksiyon ile mevcut sinyal arasındaki korelasyonu tespit eder.","Seçilen fonksiyonun mevcut sinyal ile benzerliği varsa, model daha doğru sonuçlar verecektir.","Wavelet dönüşümü frekans domaininde çözünürlük sağlayarak veriyi işler."]},"beginTime":4005,"endTime":4077,"href":"/video/preview/7045146616548005910?parent-reqid=1773337802877880-5134886574411016539-balancer-l7leveler-kubr-yp-sas-140-BAL&text=TechAI+Hub&t=4005&ask_summarization=1"},{"index":31,"title":"Wavelet Derecesi Seçimi","list":{"type":"unordered","items":["Veri setinde db15 seçilmiş ancak bu tamamen verinin türüne bağlıdır.","Literatür çalışmalarında db5, db7 ve db8'in de başarılı sonuçlar verdiği belirtilmiştir.","Veri seti değişince kullanılan wavelet derecesi de değişebilir."]},"beginTime":4077,"endTime":4141,"href":"/video/preview/7045146616548005910?parent-reqid=1773337802877880-5134886574411016539-balancer-l7leveler-kubr-yp-sas-140-BAL&text=TechAI+Hub&t=4077&ask_summarization=1"},{"index":32,"title":"Ford Transform ve Enerji Değişimi","list":{"type":"unordered","items":["Ford transform işlemi, ege sinyallerinin analizinde kullanılır ve bu işlem zaman alıcıdır; özellikle uzun sinyalleri analiz etmek için güçlü bir prosesöre ihtiyaç vardır.","Orjinal ve filtrelenmiş kanalların karşılaştırılması, nöbetlerin hangi zondan veya genel olarak başladığını belirlemek için kullanılır.","Klinik araştırmacılar görsel testlerde ham verilere bakarak analiz yapabilirler, ancak bu zaman alıcıdır."]},"beginTime":4156,"endTime":4305,"href":"/video/preview/7045146616548005910?parent-reqid=1773337802877880-5134886574411016539-balancer-l7leveler-kubr-yp-sas-140-BAL&text=TechAI+Hub&t=4156&ask_summarization=1"},{"index":33,"title":"Yapay Zeka ve Ege Sinyalleri","list":{"type":"unordered","items":["Klinik ortamda ege sinyallerini gözle analiz etmek zaman alıcı olduğundan, offline olarak analiz edilir ve nöbet geçiren kişilerin raporları yazılır.","Yapay zeka ile ege sinyallerinin online analizi yapılarak, akan verilerden epileptik nöbetler ve öncesindeki patenler tespit edilmeye çalışılmaktadır.","Video ege sistemleri, verinin bir yerden bir yere taşınması ve işlenmesi nedeniyle zaman alıcı olduğundan, daha eski yöntemler hala revaçta."]},"beginTime":4305,"endTime":4470,"href":"/video/preview/7045146616548005910?parent-reqid=1773337802877880-5134886574411016539-balancer-l7leveler-kubr-yp-sas-140-BAL&text=TechAI+Hub&t=4305&ask_summarization=1"},{"index":34,"title":"Nöbet Dönemleri ve Enerji Değişimi","list":{"type":"unordered","items":["Kırmızı çizgi orijinal sinyal, yeşil çizgi ise dönüştürme sonucu oluşan sinyaldir; nöbet başlangıcı, bitişi, preda ve pozitif iptal dönemleri belirlenir.","Nöbet dönemlerinin belirlenmesi doktorlarla birlikte yapılır ve raporlara göre hesaplanır, birkaç saniyelik hatalar olabilir.","Yapay zeka algoritmaları, nöbetin başlangıcını ve bitişi bilinmeden tespit etmeyi hedeflerken, yapılan çalışmaların %90'ından fazlası offline olarak yapılır."]},"beginTime":4470,"endTime":4572,"href":"/video/preview/7045146616548005910?parent-reqid=1773337802877880-5134886574411016539-balancer-l7leveler-kubr-yp-sas-140-BAL&text=TechAI+Hub&t=4470&ask_summarization=1"},{"index":35,"title":"Enerji Bandları ve Nöbet İlişkisi","list":{"type":"unordered","items":["Nöbet sırasında teta bandında (4 Hz arası) yoğun enerji görülürken, preda dönemde de benzer bir enerji yükselişi tespit edilir.","Nöbet başlamadan önceki bandlardaki enerji geçişleri, nöbetle ilgili bilgi verir ve tıbbi bilgileri doğrulamak için kullanılır.","Frekans bilgisinde dik dik gelen çizgiler, 15-18 arası frekanslarda enerji birikimleri gösterir ve bu slip spider (artefakt) olduğunu gösterir."]},"beginTime":4572,"endTime":4710,"href":"/video/preview/7045146616548005910?parent-reqid=1773337802877880-5134886574411016539-balancer-l7leveler-kubr-yp-sas-140-BAL&text=TechAI+Hub&t=4572&ask_summarization=1"},{"index":36,"title":"Nöbet Süreçlerinde Enerji Değişimleri","list":{"type":"unordered","items":["Nöbet öncesi, nöbet esnasında ve nöbet sonrası enerji değişimlerini anlamak için yapay zekaya güvenmek gerekiyor.","Amacımız anlamlı değişkenler elde edebilmek ve bu değişimleri modelleyebilmek.","Entropi kavramı kullanılarak farklı entropi türleriyle (faz entropi, şanon entropi, koşullu entropi, permutasyon entropi) değişimler saptanabiliyor."]},"beginTime":4719,"endTime":4881,"href":"/video/preview/7045146616548005910?parent-reqid=1773337802877880-5134886574411016539-balancer-l7leveler-kubr-yp-sas-140-BAL&text=TechAI+Hub&t=4719&ask_summarization=1"},{"index":37,"title":"Entropi Ölçütleri ve Önemi","list":{"type":"unordered","items":["Farklı entropi ölçütleri (aprosonel entropi, semple entropi, faz entropi, wave entropi, şanon entropi, koşullu entropi, permutasyon entropi) kullanılmış.","Entropi sinyal işlemede değerli çünkü sinyalin rastlantısal bir halini ölçmek zorunda kalıyoruz.","Farklı nöbetlerde denemeler yapılarak hangi entropi türünün uygun olduğu belirlenmiş."]},"beginTime":4881,"endTime":4934,"href":"/video/preview/7045146616548005910?parent-reqid=1773337802877880-5134886574411016539-balancer-l7leveler-kubr-yp-sas-140-BAL&text=TechAI+Hub&t=4881&ask_summarization=1"},{"index":38,"title":"Entropi Sonuçları ve Değişken Seçimi","list":{"type":"unordered","items":["İlk üç entropi türü birbirine benzer paternler gösteriyor: nöbet öncesi ve sonrası enerji azalması var.","Wave entropi diğerlerinden çok farklı olmasına rağmen güvenilir sonuçlar verdiği için tercih edilmiş.","Devlet transformasyon kullanıldığında her seviye için ayrı değişken kümesi oluştuğu için değişken seçimi yapılması gerekiyor."]},"beginTime":4934,"endTime":5031,"href":"/video/preview/7045146616548005910?parent-reqid=1773337802877880-5134886574411016539-balancer-l7leveler-kubr-yp-sas-140-BAL&text=TechAI+Hub&t=4934&ask_summarization=1"},{"index":39,"title":"Çalışmanın Detayları","list":{"type":"unordered","items":["Çalışmada devlet transformasyonlar sayesinde kırkdokuz değişken elde edilmiş.","Bu kırkdokuz değişken daha sonra yapay zeka sistemlerine input olarak gönderilmiştir.","Karşılaştırma için birçok sınıflandırıcı yöntem ve karar destek sistemleri 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Hangi disiplinler bir araya gelerek robot geliştirmeli? Robotlar insanların yerini almak zorunda mı yoksa onlardan fayda sağlayabilmek için beklentilerimizi...","preview":{"posterSrc":"//avatars.mds.yandex.net/get-vthumb/1582579/2c177d03484fb634fb9c7e8efb131d61/564x318_1","videoSrc":"https://video-preview.s3.yandex.net/ptInCgEAAAA.mp4","videoType":"video/mp4"},"target":"_self","position":"5","reqid":"1773337802877880-5134886574411016539-balancer-l7leveler-kubr-yp-sas-140-BAL","summary":{"isFull":true,"fullTextUrl":"/video/result?ask_summarization=1&numdoc=1&noreask=1&nomisspell=1&parent-reqid=1773337802877880-5134886574411016539-balancer-l7leveler-kubr-yp-sas-140-BAL&text=videoid:3973517140853069242","teaser":[{"list":{"type":"unordered","items":["Bu video, Global Haptan'ın Aslı'nın sunuculuğunda gerçekleşen bir sohbet programıdır. İlk bölümde Humanage Teknoloji kurucusu ve mekatronik mühendisi Fahri Berk Güler, ikinci bölümde ise Ilker Karayım olarak anılan bir uzman konuk olarak yer almaktadır.","Program, yapay zeka ve robotik teknolojileri üzerine odaklanmakta, özellikle insan-robot iletişimini ele almaktadır. Konuşmacılar, yapay zekanın gelişimi, robotların eğitim setleri, doğal dil işleme (NLP) ve reforment learning gibi konuları tartışırken, Nellie adlı bir robot hakkında da bilgi vermektedir.","Videoda ayrıca \"akıllı\" teriminin yanlış kullanımı, teknolojik cihazların gelecekte iletişim kurma becerilerine göre ayrılacağı, yapay zekanın marka kimliği ve dijital iletişimdeki rolü gibi konular da tartışılmaktadır. Program, izleyicilerden gelen sorulara cevap vererek ilerlemektedir."]},"endTime":3589,"title":"Yapay Zeka ve Robotik Üzerine Bir Sohbet","beginTime":0}],"fullResult":[{"index":0,"title":"Giriş ve Konuk Tanıtımı","list":{"type":"unordered","items":["Global Haptan Aslı ve konuğu Humanagechnology kurucusu Fahri Berk Güler, insan-robot yapay zeka iletişimi hakkında konuşuyorlar.","Fahri Berk Güler, 2016 yılında kurulan Humanage Teknoloji şirketinin kurucusu ve mekatonik mühendisi olarak robot tasarımı, yazılımı ve üretimi alanında uzman.","Konuşma, önümüzdeki yıllardaki teknolojide yapay zekanın iletişiminde nereye gideceğimiz ve son kullanıcının yapay zeka ile karşılaştığında beklentilerinin ne olacağını ele alacak."]},"beginTime":4,"endTime":135,"href":"/video/preview/3973517140853069242?parent-reqid=1773337802877880-5134886574411016539-balancer-l7leveler-kubr-yp-sas-140-BAL&text=TechAI+Hub&t=4&ask_summarization=1"},{"index":1,"title":"Yapay Zeka ve Robotların 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şirketlerin pnömatik asansör sistemlerini robot olarak tanımlaması gibi yanlış kavramlar piyasada yaygınlaşmıştır.","\"Akıllı\" kavramının her şeye uygulanması, gerçek akıl ve beceri arasındaki farkı gösterecek doğru kavramların ortaya çıkmasını engellemektedir."]},"beginTime":314,"endTime":525,"href":"/video/preview/3973517140853069242?parent-reqid=1773337802877880-5134886574411016539-balancer-l7leveler-kubr-yp-sas-140-BAL&text=TechAI+Hub&t=314&ask_summarization=1"},{"index":3,"title":"Akıl ve İletişim","list":{"type":"unordered","items":["Akıl, yapay zekanın ilgilendiği ve önümüzdeki yıllarda sıkça duracağı bir alan olacak.","Teknolojik yapılar akıllıysa iletişim kurmalı, iletişim kurmayanlar sadece iyi tasarlanmış ürünler kategorisine girer.","İlerleyen yıllarda teknolojik cihazlar, birbirlerinden ve kullanıcıları anlama becerilerine göre 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