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TensorFlow'u kullanarak Sorumlu Yapay Zeka uygulamalarını makine öğrenimi iş akışınıza nasıl entegre edeceğinizi öğrenin

TensorFlow, bir kaynak ve araç koleksiyonunu makine öğrenimi topluluğuyla paylaşarak yapay zekanın sorumlu bir şekilde geliştirilmesinde ilerleme kaydedilmesine yardımcı olmaya kararlıdır.

Sorumlu Yapay Zeka Nedir?

Yapay zekanın gelişimi, zorlu, gerçek dünya sorunlarını çözmek için yeni fırsatlar yaratıyor. Aynı zamanda herkese fayda sağlayacak yapay zeka sistemleri oluşturmanın en iyi yolu hakkında yeni soruları da gündeme getiriyor.

Yapay zeka için önerilen en iyi uygulamalar

Yapay zeka sistemlerinin tasarlanması, insan odaklı bir yaklaşımla yazılım geliştirmenin en iyi uygulamalarını takip etmelidir.
ML'ye yaklaşım

Adalet

Yapay zekanın sektörler ve toplumlar üzerindeki etkisi arttıkça, herkes için adil ve kapsayıcı sistemler üzerinde çalışmak kritik önem taşıyor

Yorumlanabilirlik

Yapay zeka sistemlerini anlamak ve onlara güvenmek, onların amaçlandığı gibi çalışmasını sağlamak açısından önemlidir

Mahremiyet

Hassas verilerden yararlanan eğitim modelleri, gizliliği koruyan önlemlere ihtiyaç duyar

Güvenlik

Potansiyel tehditlerin belirlenmesi yapay zeka sistemlerinin emniyette ve emniyette tutulmasına yardımcı olabilir

Google'ın Sorumlu Yapay Zeka Uygulamaları hakkında daha fazla bilgi edinin

Makine öğrenimi iş akışınızda sorumlu yapay zeka

Sorumlu yapay zeka uygulamaları makine öğrenimi iş akışının her adımına dahil edilebilir. İşte her aşamada dikkate alınması gereken bazı önemli sorular.

ML sistemim kimin için?

Gerçek kullanıcıların sisteminizi deneyimleme şekli, tahminlerin, önerilerin ve kararların gerçek etkisini değerlendirmek için çok önemlidir. Geliştirme sürecinizin başlarında çeşitli kullanıcı gruplarından girdi aldığınızdan emin olun.

Temsili bir veri kümesi mi kullanıyorum?

Verileriniz kullanıcılarınızı (örneğin her yaş için kullanılacak, ancak yalnızca yaşlı vatandaşlara ait eğitim verileriniz olacak) ve gerçek dünya ortamını (örneğin yıl boyunca kullanılacak ancak yalnızca eğitim verileriniz olacak) temsil edecek şekilde örneklenmiş mi? yaz verileri)?

Verilerimde gerçek dünya/insan önyargısı var mı?

Verilerde yatan önyargılar, mevcut stereotipleri güçlendiren karmaşık geri bildirim döngülerine katkıda bulunabilir.

Modelimi eğitmek için hangi yöntemleri kullanmalıyım?

Modele adalet, yorumlanabilirlik, gizlilik ve güvenlik katan eğitim yöntemlerini kullanın.

Modelimin performansı nasıl?

Geniş bir kullanıcı yelpazesi, kullanım senaryoları ve kullanım bağlamlarında kullanıcı deneyimini gerçek dünya senaryolarında değerlendirin. İlk olarak test sürümünde test edin ve yineleyin, ardından lansmandan sonra testlere devam edin.

Karmaşık geri bildirim döngüleri var mı?

Genel sistem tasarımındaki her şey dikkatle hazırlanmış olsa bile, ML tabanlı modeller gerçek, canlı verilere uygulandığında nadiren 0 mükemmellikle çalışır. Yayındaki bir üründe bir sorun ortaya çıktığında, bunun mevcut toplumsal dezavantajlarla uyumlu olup olmadığını ve hem kısa hem de uzun vadeli çözümlerden nasıl etkileneceğini değerlendirin.

TensorFlow için sorumlu yapay zeka araçları

TensorFlow ekosistemi, yukarıdaki soruların bazılarının üstesinden gelmeye yardımcı olacak bir araç ve kaynak paketine sahiptir.

Aşama 1

Sorunu tanımlayın

Sorumlu yapay zekayı göz önünde bulundurarak modeller tasarlamak için aşağıdaki kaynakları kullanın.

İnsanlar Yapay Zeka Araştırması (PAIR) Kılavuzu

Yapay zeka geliştirme süreci ve önemli hususlar hakkında daha fazla bilgi edinin.

Daha fazla bilgi edin
EŞLEŞTİRİLEBİLİRLER

Etkileşimli görselleştirmeler aracılığıyla Sorumlu Yapay Zeka alanındaki temel soruları ve kavramları keşfedin.

Daha fazla bilgi edin
Adım 2

Verileri oluşturma ve hazırlama

Verileri olası önyargılara karşı incelemek için aşağıdaki araçları kullanın.

Verilerinizi Bilin (Beta)

Veri kalitesini artırmak ve adalet ve önyargı sorunlarını azaltmak için veri kümenizi etkileşimli olarak araştırın.

Daha fazla bilgi edin
TF Veri Doğrulaması

Sorunları tespit etmek ve daha etkili özellik setleri oluşturmak için verileri analiz edin ve dönüştürün.

Daha fazla bilgi edin
Veri Kartları

Veri kümeniz için bir şeffaflık raporu oluşturun.

Daha fazla bilgi edin
Keşiş Cilt Tonu Ölçeği (MST)

Veri toplama ve model oluşturma ihtiyaçlarınızı daha sağlam ve kapsayıcı hale getirmek için açık lisanslı, daha kapsayıcı bir cilt tonu ölçeği.

Daha fazla bilgi edin
Aşama 3

Modeli oluşturun ve eğitin

Gizliliği koruyan, yorumlanabilir teknikler ve daha fazlasını kullanarak modelleri eğitmek için aşağıdaki araçları kullanın.

TF Modeli Düzeltme

Daha adil sonuçlar elde etmek için makine öğrenimi modellerini eğitin.

Daha fazla bilgi edin
TF Gizliliği

Makine öğrenimi modellerini gizlilikle eğitin.

Daha fazla bilgi edin
TF Federasyon

Birleştirilmiş öğrenme tekniklerini kullanarak makine öğrenimi modellerini eğitin.

Daha fazla bilgi edin
TF Kısıtlı Optimizasyon

Eşitsizlikle kısıtlanan sorunları optimize edin.

Daha fazla bilgi edin
TF Kafes

Esnek, kontrollü ve yorumlanabilir kafes tabanlı modeller uygulayın.

Daha fazla bilgi edin
4. Adım

Modeli değerlendir

Aşağıdaki araçları kullanarak model performansında hata ayıklayın, değerlendirin ve görselleştirin.

Adillik Göstergeleri

İkili ve çok sınıflı sınıflandırıcılar için yaygın olarak tanımlanan adalet ölçümlerini değerlendirin.

Daha fazla bilgi edin
TF Model Analizi

Modelleri dağıtılmış bir şekilde değerlendirin ve farklı veri dilimleri üzerinden hesaplama yapın.

Daha fazla bilgi edin
Ne Olursa Aracı

Makine öğrenimi modellerini inceleyin, değerlendirin ve karşılaştırın.

Daha fazla bilgi edin
Dil Yorumlanabilirlik Aracı

NLP modellerini görselleştirin ve anlayın.

Daha fazla bilgi edin
Açıklanabilir Yapay Zeka

Yorumlanabilir ve kapsayıcı makine öğrenimi modelleri geliştirin.

Daha fazla bilgi edin
TF Gizlilik Testleri

Sınıflandırma modellerinin gizlilik özelliklerini değerlendirin.

Daha fazla bilgi edin
Tensör Kartı

Makine öğrenimi iş akışını ölçün ve görselleştirin.

Daha fazla bilgi edin
Adım 5

Dağıtın ve izleyin

Model bağlamını ve ayrıntılarını takip etmek ve bunlarla ilgili iletişim kurmak için aşağıdaki araçları kullanın.

Model Kartı Araç Seti

Model Kartı araç setini kullanarak model kartlarını kolaylıkla oluşturun.

Daha fazla bilgi edin
ML Meta Verileri

Makine öğrenimi geliştiricisi ve veri bilimcisi iş akışlarıyla ilişkili meta verileri kaydedin ve alın.

Daha fazla bilgi edin
Model Kartları

Makine öğreniminin temel gerçeklerini yapılandırılmış bir şekilde düzenleyin.

Daha fazla bilgi edin

Topluluk kaynakları

Topluluğun neler yaptığını öğrenin ve dahil olmanın yollarını keşfedin.

Google'dan kitle kaynağı

Google ürünlerinin daha kapsayıcı olmasına ve dilinizi, bölgenizi ve kültürünüzü temsil etmesine yardımcı olun.

Daha fazla bilgi edin
Sorumlu Yapay Zeka DevPost Yarışması

Katılımcılardan Sorumlu Yapay Zeka ilkelerini göz önünde bulundurarak bir model veya uygulama oluşturmak için TensorFlow 2.2'yi kullanmalarını istedik. Kazananları ve diğer harika projeleri görmek için galeriye göz atın.

Daha fazla bilgi edin
TensorFlow ile Sorumlu Yapay Zeka (TF Geliştirici Zirvesi '20)

ML, adalet ve gizlilik hakkında düşünmek için bir çerçeve sunuyoruz.

Videoyu izle

AI/ML yolculuğunuza rehberlik edecek Google AI kaynaklarını keşfedin

Yapay zeka ilkelerine bakın