BOOKS - Artificial Intelligence Advances, Ethics, and Strategies
Artificial Intelligence Advances, Ethics, and Strategies - James M. Nichols 2024 PDF Nova Science Publishers BOOKS
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Artificial Intelligence Advances, Ethics, and Strategies
Author: James M. Nichols
Year: 2024
Format: PDF
File size: 25.9 MB
Language: ENG

Artificial Intelligence Advances Ethics and Strategies The field of Artificial Intelligence (AI) is rapidly evolving, and one of the most exciting developments in recent years has been the emergence of generative models. These models have shown the ability to produce human-like language and even generate images, videos, and music. While the potential applications of generative models are vast and impressive, there are also serious concerns about the ethical implications of their use. As the potential of AI and generative models is explored, it is essential to consider the impact they may have on society. Generative Artificial Intelligence (GenAI) refers to AI systems, in particular those using Machine Learning (ML) and trained on large volumes of data, that are able to generate new content. In contrast, other AI systems may have a primary goal of classifying data, such as facial recognition, image data, or making decisions, such as those used in autonomous vehicles.
Искусственный интеллект продвигает этику и стратегии Область искусственного интеллекта (ИИ) быстро развивается, и одним из самых захватывающих событий последних лет стало появление генеративных моделей. Эти модели показали способность производить человекоподобный язык и даже генерировать изображения, видео и музыку. Хотя потенциальные применения генеративных моделей обширны и впечатляют, существуют также серьезные опасения по поводу этических последствий их использования. По мере изучения потенциала ИИ и генеративных моделей важно учитывать влияние, которое они могут оказать на общество. Генеративный искусственный интеллект (Generative Artificial Intelligence, GenAI) относится к системам ИИ, в частности, использующим машинное обучение (Machine arning, ML) и обученным на больших объемах данных, которые способны генерировать новый контент. Напротив, другие системы искусственного интеллекта могут иметь основную цель классификации данных, таких как распознавание лиц, данные изображений или принятие решений, таких как те, которые используются в автономных транспортных средствах.
Künstliche Intelligenz treibt Ethik und Strategien voran Der Bereich der Künstlichen Intelligenz (KI) entwickelt sich rasant und eine der spannendsten Entwicklungen der letzten Jahre war die Entstehung generativer Modelle. Diese Modelle zeigten die Fähigkeit, menschenähnliche Sprache zu erzeugen und sogar Bilder, Videos und Musik zu erzeugen. Während die möglichen Anwendungen generativer Modelle umfangreich und beeindruckend sind, gibt es auch ernsthafte Bedenken hinsichtlich der ethischen Auswirkungen ihrer Verwendung. Während wir das Potenzial von KI und generativen Modellen untersuchen, ist es wichtig, die Auswirkungen zu berücksichtigen, die sie auf die Gesellschaft haben können. Generative Artificial Intelligence (GenAI) bezieht sich auf KI-Systeme, insbesondere solche, die maschinelles rnen (Machine arning, ML) verwenden und auf große Datenmengen trainiert sind, die in der Lage sind, neue Inhalte zu generieren. Im Gegensatz dazu können andere KI-Systeme den Hauptzweck der Datenklassifizierung haben, wie Gesichtserkennung, Bilddaten oder Entscheidungsfindung, wie sie in autonomen Fahrzeugen verwendet werden.
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الذكاء الاصطناعي يروج للأخلاق والاستراتيجيات يتطور مجال الذكاء الاصطناعي (AI) بسرعة، وكان أحد أكثر الأحداث إثارة في السنوات الأخيرة هو ظهور نماذج توليدية. أظهرت هذه النماذج القدرة على إنتاج لغة بشرية وحتى إنشاء صور ومقاطع فيديو وموسيقى. وفي حين أن التطبيقات المحتملة للنماذج التوليدية واسعة النطاق ومثيرة للإعجاب، فإن هناك أيضا شواغل جدية بشأن الآثار الأخلاقية لاستخدامها. بينما نستكشف إمكانات الذكاء الاصطناعي والنماذج التوليدية، من المهم النظر في التأثير الذي يمكن أن تحدثه على المجتمع. يشير الذكاء الاصطناعي المولد (GenAI) إلى أنظمة الذكاء الاصطناعي، ولا سيما تلك التي تستخدم التعلم الآلي (ML) وتم تدريبها على كميات كبيرة من البيانات القادرة على توليد محتوى جديد. في المقابل، قد يكون لأنظمة الذكاء الاصطناعي الأخرى الغرض الأساسي من تصنيف البيانات مثل التعرف على الوجه أو بيانات الصورة أو اتخاذ قرارات مثل تلك المستخدمة في المركبات ذاتية القيادة.

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