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Именно трансформаторные модели мани х являются основой для систем, таких как ChatGPT или ЯндексGPT. Начальные усилия сформировать искусственный «мозг» начались еще в середине XX века. В пятьдесят восьмом году Френк Розенблатт разработал персептрон – простейшую модель синтетического нейрона. Она способна была идентифицировать крайне элементарные изображения, скажем, разграничивать треугольник от четырехугольника. Чтобы разобрать принцип работы нейросети, визуализируйте систему из большого количества взаимосвязанных точек – это виртуальные «нейроны». Всякий такой нервный элемент мани х казино получает на вход числовое значение (или множество чисел), производит с ними простую операцию (например, складывает их с определенными весами) и отправляет результат далее. Как построена нейросеть: сравнение с головным мозгом Этот процесс повторяется множество раз, применяя инструменты мани х до тех пор, пока сеть не научится предоставлять максимально аккуратные ответы. 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Расчет погрешности Сравниваем пророчество системы с точным результатом (например: система заявила «кот», а на фактически там пес). Заднее распространение погрешности (backpropagation) С применением уникального алгоритма определяется доля каждого коэффициента в ошибку. Обновление нагрузки Массовые множители регулируются так, чтобы ошибка уменьшалась во время следующем прогоне. Впрочем продолжительное время прогресс двигалось постепенно: было недостаточно вычислительных мощностей и учебных данных. Все поменялось со появлением интернета, масштабных информации (Big Data) и высокопроизводительных видеокарт (GPU). В 2012 году, команда Google создала нейросеть, и с помощью платформ мани х она автономно научилась идентифицировать кошек на YouTube-видео – тогда это был настоящий пробив. Образец из реальности Данный подход называют Human-in-the-loop («человек в петле»): окончательное выбор постоянно принимается человеком! 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С тех пор технологии эволюционируют с поразительной скоростью: появляются новые структуры (например, GPT от OpenAI или BERT от Google), а сами сети становятся все глубже и комплекснее. Эти умеют обрабатывать с значительными количествами текста вместе полностью за счет механизма (attention): всякий компонент текста может, «обращать внимание» на другие элементы при обработке информации. CNN – для изображений RNN – для последовательностей Transformer – для трудных текстовых проблем В каких местах мы сталкиваемся с нейросетями каждый день Фильтрация спам-сообщений в электронной почте распознают нежелательные письма. Интерпретаторы автоматически интерпретируют тексты внутри диалектами Фотокамеры мобильных телефонов повышают качество фотографий благодаря AI Музыкальные сервисы советуют свежие треки по вашим вкусам Общественные медиа отображают интересный контент в потоке. Устройства прокладывают наилучшие пути с принимая во внимание пробок Отчего учеба требует столько периода и материалов Манипуляция фотографий нуждается в множества млн параметров Тренировка систем вроде GPT требует недели даже используя высокопроизводительных компьютеров Расходы энергии при подготовки наибольших моделей оценивается в несколько сотен тысяч долларов. Данное дает возможность шаблонам генерировать последовательный текст на тысячи символов длиной либо разбирать значение целых документов в считанные секунды! Проблемы и границы современных сетей. Опора от информации Если натренировать нейросеть только на черно-белых фото кошек из интернета 2010-х годов, она будет плохо работать с цветными изображениями новых пород. Вопрос понятности Нередко затруднительно понять, почему сеть приняла то или иное решение; ее внутренние механизмы остаются черным ящиком даже для разработчиков. Чувствительность к оплошностям Маленькое модификация картинки может ввести в заблуждение алгоритм; существуют примеры нападений «адверсариальными примерами», где ИИ ошибается там, где человек легко справился бы. Неспособность мыслить за

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