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[Перевод] 25 лет в Google: интервью с Ноамом Шазиром и Джеффом Дином, разработчиками Gemini

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Это не просто юбилей, а целая эпоха. Когда Джефф Дин и Ноам Шазир присоединились к компании, её можно было назвать стартапом (ну, почти). Сегодня же они работают над самыми продвинутыми технологиями искусственного интеллекта, определяющими будущее всей индустрии. Чем они занимались за это время? Разрабатывали фундаментальные системы, без которых современный интернет выглядел бы иначе: MapReduce, BigTable, TensorFlow... и это только верхушка айсберга. Их текущая миссия — развивать Gemini (не так давно известный как Bard) — амбициозный проект Google DeepMind, который стремится объединить лучшее из мира поисковых технологий и генеративного ИИ.

В интервью Дваркешу Пателю Джефф и Ноам делятся своими наблюдениями о росте компании, о том, как изменились вычисления за последние десятилетия и почему успех современных моделей ИИ — это не просто магия больших данных, а результат тщательно выстроенной архитектуры и аппаратных решений. Спойлер: закон Мура уже не тот, но инновации не остановились.


Дваркеш Патель: Вы оба работаете в Google уже четверть века или около того. В начале пути, наверное, было ощущение, что вы понимаете, как устроена вся компания. В какой момент это чувство исчезло, был ли какой‑то конкретный момент?

Ноам Шазир: Я пришёл в конце 2000-го, и у нас была традиция — каждому новичку назначали наставника. Я не знал вообще ничего, просто спрашивал у наставника, а он знал всё. Кстати, им оказался Джефф. Дело было не в том, что все в Google знали всё, — просто Джефф знал, потому что он практически сам это всё и написал.

Джефф Дин: Правда в том, что по мере роста компании ты проходишь через несколько фаз. Когда я пришёл, нас было всего 25 или 26 человек. Ты знал всех по именам — даже когда команда начала расти, можно было следить, кто приходит. Потом наступает момент, когда ты уже не запоминаешь всех по именам, но всё ещё держишь в голове, кто чем занимается в разработке. Потом ты перестаёшь отслеживать даже это, но хотя бы понимаешь, какие проекты ведутся. А потом компания становится настолько большой, что тебе приходит письмо: «Проект „Утконос“ запускается в пятницу» — и ты думаешь: что за проект «Утконос»?

Дваркеш Патель: Как за последние два‑три десятилетия изменился закон Мура с точки зрения проектирования новых систем и оценки их реалистичности? Какие ограничения остаются? Что теперь возможно, чего раньше было совершенно нереально сделать?

Джефф Дин: За последние двадцать лет ситуация сильно изменилась: если раньше можно было просто подождать и через восемнадцать месяцев получить в разы более мощное железо, ничего для этого не делая, то теперь всё не так просто. Рост вычислительных мощностей на базе обычных CPU уже не столь впечатляющий: техпроцессы совершенствуются медленнее, цикл их обновления увеличился с двух до трёх лет, а архитектурные улучшения многопоточных процессоров больше не дают того прироста, к которому мы привыкли.

Зато появилось множество специализированных вычислительных устройств: ускорители для машинного обучения, тензорные процессоры (TPU), высокоэффективные GPU, ориентированные на ML‑задачи. Именно они позволяют получать максимальную производительность и эффективность при работе с современными вычислениями.

Ноам Шазир: Сейчас складывается ощущение, что алгоритмы идут вслед за железом. Вычисления стали очень дешёвыми, а вот передача данных — наоборот, значительно дороже. По сути, взлёт глубокого обучения произошёл именно благодаря этой динамике. Теперь можно строить архитектуры, которые сводятся к матричным умножениям — это вычисления сложности O(N³), при этом объём передаваемых данных ограничивается O(N²).

Джефф Дин: И ключевой момент в том, что переход к аппаратной архитектуре, ориентированной на такие вычисления, стал решающим шагом. Раньше у нас были только CPU и GPU, которые не особо подходили для задач глубинного обучения. Тогда мы начали разрабатывать TPU в Google — фактически это просто специализированные машины для вычислений линейной алгебры с пониженной точностью. И как только у тебя появляется такое железо, логично строить на нём соответствующие алгоритмы.

Ноам Шазир: В этом, по сути, весь смысл поиска скрытых возможностей. Ларри Пейдж, кажется, часто повторял: «Наши вторые по величине затраты — это налоги, а первые — это упущенные возможности». (Если он так не говорил, значит, я уже много лет неправильно его цитирую.) Суть в том, чтобы правильно оценить, какие ресурсы у тебя есть и чего ты недополучаешь. В данном случае, например, у нас были чипы, на которых размещалось слишком мало вычислительных блоков. Почему бы не заполнить их арифметическими модулями? Тогда можно было бы выполнять вычисления на порядки быстрее. А дальше возникает вопрос: что ещё должно измениться? Алгоритмы, потоки данных, структура вычислений — всё это тоже придётся перестраивать.

Джефф Дин: При этом вычисления можно делать с пониженной точностью, а это значит, что на чипе можно уместить ещё больше арифметических блоков.

Дваркеш Патель: Какие компромиссы вы учитываете при проектировании новых поколений TPU, чтобы лучше адаптировать их к алгоритмам?

Джефф Дин: Один из ключевых трендов — это работа над квантованием и моделями с ещё более низкой точностью вычислений. Когда мы разрабатывали TPUv1, мы даже не были уверены, что сможем выполнять инференс с 8-битными целыми числами, но у нас были некоторые обнадёживающие результаты, и мы решили: «Ладно, строим весь чип под эту задачу».

Со временем выяснилось, что снижать разрядность можно не только для инференса, но и для обучения моделей. Сейчас уже используют INT4 или FP4, что двадцать лет назад для специалистов по суперкомпьютерам прозвучало бы как сумасшествие. Тогда все настаивали: «Нам нужны только 64-битные числа с плавающей запятой», а теперь некоторые умудряются квантовать модели до двух бит, а то и вовсе до одного. Думаю, этот тренд будет только набирать обороты.

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Когда алгоритмы заговорили

Дваркеш Патель: На разных этапах вашей карьеры вы работали над вещами, которые удивительно похожи на то, что мы сегодня используем в генеративном ИИ. Джефф, в 1990 году ты написал дипломную работу о методе обратного распространения ошибки. А в 2007-м — и это я понял только во время подготовки к интервью — вы с командой обучили языковую модель на 2 триллионах токенов, используя N‑граммы. Расскажи, что тогда было у вас в голове? Вы осознавали, к чему это может привести?

Джефф Дин: Я впервые столкнулся с нейросетями в одном из разделов курса по параллельным вычислениям на последнем курсе. Мне нужно было написать диплом, и я предложил профессору: «Было бы интересно сделать что‑то с нейросетями». В итоге мы решили, что я реализую несколько вариантов параллельного обучения нейросетей с обратным распространением ошибки. Это было в 1990 году. В своей работе я назвал их как‑то забавно, вроде «разбиение по паттернам», но на самом деле это были модельная и параметрическая параллелизация. Я запускал их на 32-процессорной машине Hypercube.

Один метод предполагал разбиение примеров на пакеты, где каждый процессор работал со своей копией модели; в другом примеры передавались по конвейеру между процессорами, у каждого из которых был свой фрагмент модели. Я сравнил оба метода.

Меня очень увлекла сама идея нейросетей, потому что казалось, что это правильная абстракция. Они могли решать крошечные задачи, которые тогдашние методы просто не брали. Я, конечно, был наивным — думал, что 32 процессора хватит, чтобы тренировать мощные нейросети, но на практике выяснилось, что нам нужно в миллион раз больше вычислительных ресурсов, чтобы они начали работать на серьёзных задачах. И только в 2008–2010 годах, когда мощности заметно выросли благодаря закону Мура, стало возможным использовать нейросети на практике. Именно тогда я снова всерьёз заинтересовался этой темой.

Дваркеш Патель: Когда вы осваивали языковые модели, у вас было понимание, что если сделать их сложнее — вместо 5 слов анализировать 100 или 1000, — то из этого может появиться нечто, похожее на интеллект? В какой момент пришло это осознание?

Ноам Шазир: Нет, я не думаю, что у меня когда‑либо было ощущение, что N‑граммные модели станут искусственным интеллектом. В то время многие были увлечены лишь байесовскими сетями, это казалось перспективным направлением.

Но когда появились ранние нейросетевые языковые модели, стало понятно, что в них есть что‑то магическое: они действительно работали. И одновременно это казалось одной из самых увлекательных задач в мире — с одной стороны, очень просто сформулированной («Дай мне вероятностное распределение следующего слова»), а с другой — содержащей бесконечное количество обучающих данных. Весь текст в интернете — это триллионы примеров для самообучения.

Нейросети, кошки и великое озарение

Дваркеш Патель: В истории науки есть интересная дискуссия: насколько неизбежны великие идеи? Они просто витают в воздухе и кто‑то обязательно их реализует или всё же это результат случайного озарения, неожиданного взгляда на проблему? В этом случае если мы можем так логично выстроить путь, значит ли это, что всё происходило неизбежно?

Ноам Шазир: Да, есть ощущение, что это действительно было в воздухе. Были идеи вроде «нейросетевой машины Тьюринга», концепции внимания, механизмы хранения и извлечения данных в нейросетях, которые позволяли моделям фокусироваться на важной информации. Так что, с одной стороны, идеи уже существовали, но с другой — всегда нужна команда, которая возьмёт и сделает это.

Джефф Дин: Я думаю, что многие идеи действительно частично «висят в воздухе». Есть несколько разрозненных исследовательских направлений, и когда ты решаешь новую задачу, то, прищурившись, можешь разглядеть что‑то полезное в каждом из них. Ты черпаешь вдохновение в известных концепциях, но при этом обязательно есть что‑то, что пока не решено. И именно сочетание существующих идей и чего‑то нового приводит к прорыву, к результату, которого раньше просто не существовало.

Дваркеш Патель: Были ли у вас моменты, когда вы занимались исследованием, придумали идею — и вдруг осознали: «Чёрт возьми, не могу поверить, что это сработало»?

Джефф Дин: Один из таких моментов был в ранние годы команды Brain. Тогда мы думали: «А что если построить инфраструктуру, которая позволит обучать действительно огромные нейросети?» В наших дата‑центрах ещё не было GPU, мы работали только с процессорами, но зато умели заставлять их эффективно взаимодействовать.

Мы создали систему, которая позволяла тренировать крупные модели с использованием как параллелизма модели, так и параллелизма данных. В рамках эксперимента мы запустили обучение на 10 миллионах случайных кадров с YouTube. Это было полностью безнадзорное обучение: сеть пыталась самостоятельно выявлять закономерности, формируя представления на основе высокоуровневых признаков.

Мы запустили обучение на 2000 компьютерах с 16 000 процессорных ядер, и спустя какое‑то время модель начала выделять устойчивые паттерны: например, один из нейронов явно реагировал на изображения кошек — хотя никто не объяснял сети, что такое кошка. Но видимо, в обучающих данных оказалось достаточно снимков с кошачьими мордами, чтобы нейрон разобрался и начал включаться только на таких изображениях.

Точно так же возникли нейроны, активирующиеся на человеческие лица или, например, на силуэты людей, идущих спиной. Это был мощный результат: система сама, без учителя, формировала сложные абстрактные представления. А затем, когда мы протестировали её на задачах с учителем в конкурсе ImageNet на 20 000 категорий, модель показала прорывной результат — улучшение точности на 60% по сравнению с предыдущими подходами.

На тот момент наша нейросеть была примерно в 50 раз крупнее всех, что обучались до неё, и работала значительно лучше. Тогда я понял: масштабирование действительно работает, это было хорошей идеей, и нам точно стоит двигаться в этом направлении дальше.

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Как научить ИИ думать масштабно

Дваркеш Патель: В поиске Google содержится весь индекс интернета, но сам поиск остаётся поверхностным; в то же время у языковых моделей ограниченный контекст, но они действительно способны «думать». Это почти магия — обучение в контексте, когда модель осмысляет то, что анализирует. Как вы представляете объединение технологий Google‑поиска и контекстного обучения?

Джефф Дин: Хороший вопрос, я уже много размышлял на эту тему. Одна из проблем современных моделей — они действительно мощные, но иногда выдают ошибки, галлюцинируют или путаются в фактах. Отчасти это связано с тем, что они обучены на десятках триллионов токенов и вся эта информация перемешана внутри сотен миллиардов параметров: из‑за этого модель порой искажает данные — она помнит их в общем виде, но может ошибаться, например, с датами.

Зато информация, которая попадает в контекстное окно модели в момент запроса, остаётся кристально чёткой: это связано с тем, что у трансформеров есть мощный механизм внимания. Модель может выделять важные части текста, видео, аудио — любые данные, которые она анализирует в реальном времени.

Сейчас наши модели уже способны работать с миллионами токенов контекста — это эквивалент сотен страниц PDF‑документа, 50 научных статей, часов видеозаписей или десятков часов аудио, а иногда и всего этого вместе. Конечно, впечатляюще, но представляете, если бы модель могла анализировать триллионы токенов? Она могла бы охватывать весь интернет и моментально находить нужную информацию. Или скажем, полностью учитывать все ваши личные данные: почту, документы, фотографии — разумеется, с вашего разрешения. Тогда при запросе она могла бы использовать всю эту информацию, чтобы дать действительно персонализированный и полезный ответ.

Но это колоссальная вычислительная задача: наивный алгоритм внимания в трансформерах имеет квадратичную сложность, и даже сейчас мы с трудом заставляем его работать с миллионами токенов. Просто увеличить объём до триллионов — невозможно, это не масштабируется. Значит, нам нужны новые алгоритмические приближения, которые позволят модели осмысленно работать с огромными объёмами данных. Например, представьте, если разработчик мог бы видеть в контексте исходный код нескольких репозиториев.

Ноам Шазир: Прекрасная особенность параметров модели в том, что они довольно эффективно запоминают факты; можно сказать, что на один параметр приходится примерно один факт или что‑то в этом роде. А вот если информация находится в контексте, то на каждом уровне модели появляются ключи и значения, в итоге один токен может занимать килобайты или даже мегабайты памяти.

Джефф Дин: Берёшь одно слово — и оно раздувается до 10 килобайт.

Ноам Шазир: Сейчас идёт много работы над тем, как минимизировать этот объём: какие слова и данные действительно нужны в контексте, как эффективнее извлекать отдельные куски информации.

Где проходят границы масштабируемости

Дваркеш Патель: Есть один момент, который, возможно, не все осознают. Все понимают, что увеличение модели в 100 раз требует в 100 раз больше вычислительных ресурсов, и потому переход, скажем, от Gemini 2 к Gemini 3 многим кажется экспоненциально усложняющейся задачей. Но при этом часто упускают из виду другой важный фактор: Gemini 3 не просто масштабируется, а разрабатывает новые архитектурные решения, тестирует их, находит эффективные алгоритмы и таким образом делает процесс обучения следующего поколения проще и быстрее. Как далеко можно зайти в этом цикле обратной связи?

Джефф Дин: Думаю, важно понимать, что прогресс в разработке новых поколений моделей обусловлен не только улучшением аппаратного обеспечения и увеличением масштаба, но в равной степени — а возможно, даже в большей — значительными алгоритмическими прорывами, изменениями в архитектуре моделей, а также оптимизацией состава обучающих данных. Именно эти факторы делают модель эффективнее при тех же вычислительных затратах. Если мы сумеем автоматизировать процесс поиска и проверки новых идей, это значительно ускорит их внедрение в практическое обучение новых поколений моделей.

Фактически именно так мы сейчас работаем: большая команда талантливых специалистов по машинному обучению изучает множество концепций, отбирает те, что показывают перспективные результаты на малых масштабах, тестирует их на средних, а затем переносит лучшие решения в крупные экспериментальные модели. Если этот процесс можно ускорить в 100 раз за счёт автоматизированного поиска идей и более целенаправленного вмешательства исследователей, вместо того чтобы они вручную контролировали сотни экспериментов, — это будет огромным шагом вперёд.

Дваркеш Патель: А есть ли фундаментальные ограничения, мешающие масштабировать вычисления на этапе вывода? Можно ли просто линейно увеличивать мощности, накидывая в 100 или 1000 раз больше ресурсов, и получать на выходе пропорционально более качественные ответы? Или это задача, которую мы уже практически решили?

Джефф Дин: В наших экспериментах мы уже видим примеры, когда увеличение вычислений при выводе действительно улучшает качество ответов: если выделить на обработку в 10 раз больше ресурсов, чем обычно, можно добиться заметно лучших результатов, и это важное направление. Но нас интересует не просто линейное улучшение, а более высокий коэффициент эффективности — если мы тратим в 10 раз больше вычислительных мощностей, хотелось бы, чтобы и качество ответа росло гораздо сильнее, чем сейчас. Это вопрос поиска новых алгоритмов, новых подходов и оптимального распределения ресурсов.

Мне очень нравится статья Рича Саттона «Горький урок»: в ней, если кратко, говорится, что можно пробовать самые разные подходы, но в итоге два самых мощных инструмента — это обучение и поиск. Если эти методы можно эффективно масштабировать алгоритмически или вычислительно, то они зачастую дают лучший результат, чем любые другие стратегии, независимо от задачи.

Поиск должен стать неотъемлемой частью повышения качества работы модели на этапе вывода. Например, можно запускать несколько стратегий решения проблемы, анализировать их — и, если один вариант оказался неудачным, продолжать разработку другого, более перспективного.

Дваркеш Патель: Может ли подобный поиск решений выполняться асинхронно? Насколько тогда изменятся требования к размерам вычислительных центров и инфраструктуре в целом?

Ноам Шазир: Вычисления на этапе вывода будут становиться всё более важными, к тому же они могут быть значительно эффективнее, чем при обучении. В целом трансформеры во время обучения могут работать с последовательностью как с батчем, но на этапе вывода не всё так просто: генерация идёт по одному токену за раз, что ограничивает возможности параллелизации. Поэтому возможно, что для вывода потребуется специализированное оборудование и оптимизированные алгоритмы, заточенные под задачу.

Джефф Дин: Хороший пример алгоритмического улучшения — это так называемые модели‑зарисовщики. Здесь используется небольшая языковая модель, которая при декодировании генерирует сразу четыре токена. Затем эти токены передаются в основную, более мощную модель, которая оценивает их и решает, какие принять.

Допустим, большая модель соглашается с первыми тремя токенами. В этом случае можно просто продвинуться вперёд, что фактически превращает однотокенную генерацию в параллельную обработку сразу четырёх токенов. Такие подходы позволяют увеличить эффективность вывода, устраняя узкое место в виде последовательной генерации по одному токену за раз.

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Распределённое машинное обучение

Дваркеш Патель: Сейчас активно обсуждается, что мощности даже атомных электростанций уже работают на пределе, когда речь идёт о снабжении одного вычислительного центра. Нужно ли нам собирать в одном месте 2 гигаватта, 5 гигаватт, больше — или же можно распределять нагрузку по разным центрам обработки данных и при этом эффективно обучать модели? Меняет ли новая парадигма масштабирования вычислений на этапе вывода наши подходы к распределённому обучению?

Джефф Дин: Мы уже работаем в этом направлении и поддерживаем концепцию многодатацентрового обучения. В техническом отчёте о Gemini 1.5 мы упоминали, что использовали несколько мегаполисов, распределяя вычисления между разными регионами. Между дата‑центрами установлено соединение с высокой пропускной способностью, но значительной задержкой, однако для обучения это не проблема: обучение больших моделей интересно тем, что каждый шаг обычно занимает несколько секунд, поэтому задержка в 50 миллисекунд не играет большой роли.

Ноам Шазир: Пока удаётся синхронизировать все параметры модели между дата‑центрами и собирать градиенты в пределах одного шага обучения, проблем не возникает.

Джефф Дин: У нас накоплен большой опыт в этом направлении, начиная ещё с первых лет работы Brain Team: тогда мы использовали медленные CPU и вынуждены были прибегать к асинхронному обучению, чтобы масштабировать процесс. Каждая копия модели выполняла локальные вычисления, отправляла обновления градиентов в центральную систему, где они асинхронно применялись, в то же время другие копии модели делали то же самое.

Это приводит к тому, что параметры модели могут слегка дрожать, что вызывает у теоретиков сомнения в строгих математических гарантиях. Но как показывает практика, этот подход вполне рабочий.

Ноам Шазир: Как же приятно было перейти от асинхронного режима к синхронному... Теперь эксперименты можно воспроизводить, а не зависеть от того, запущен ли на той же машине веб‑краулер. Так что работать на TPU‑подах мне куда комфортнее.

Джефф Дин: Мы перешли от асинхронного обучения на CPU к полностью синхронному благодаря тому, что у нас появились сверхбыстрые TPU‑чипы и поды. Между чипами внутри одного пода‑ колоссальная пропускная способность. А если нужно выйти за эти рамки, у нас есть отличные сети дата‑центров и даже межгородские соединения, позволяющие объединять множество TPU‑подов в разных регионах в рамках крупнейших обучающих запусков — и делать это полностью синхронно.

Как сказал Ноам, пока градиенты накапливаются и параметры передаются между регионами достаточно быстро относительно шага обучения, всё идёт идеально, но по мере масштабирования, возможно, нам придётся вводить чуть больше асинхронности. Пока что наши исследователи в области машинного обучения очень довольны тем, как далеко удалось продвинуться с синхронным обучением, — просто потому, что это более понятная ментальная модель: тогда алгоритм борется только с задачей, а не с асинхронностью, которая усложняет процесс.

Ноам Шазир: Чем больше масштаб, тем больше факторов, которые работают против тебя. Проблема в том, что ты не всегда понимаешь, что именно тебе мешает: может, ты слишком агрессивно сжал данные где‑то в процессе? Или возможно, дело в самих данных?

Джефф Дин: А может, это твоя коварная машина MUQQ17, которая хитроумно выставляет седьмой бит экспоненты у всех твоих градиентов?

Ноам Шазир: Точно. И из‑за этого всё начинает работать чуть хуже, но ты даже не сразу понимаешь, что что‑то пошло не так.

Джефф Дин: Это, вообще, одна из сложностей с нейросетями — они невероятно устойчивы к шуму. У тебя может быть куча ошибок в настройках, но они как‑то адаптируются, учатся их обходить и продолжают работать.

Ноам Шазир: Ты можешь даже не знать, что у тебя баг в коде. Чаще всего он вообще ни на что не влияет. Иногда он ухудшает модель, а иногда — делает её лучше: и вот тогда ты открываешь что‑то новое, потому что просто никогда не проверял этот конкретный баг в условиях большого масштаба.

Баланс между сложностью и эффективностью

Дваркеш Патель: Как на практике выглядит процесс отладки и расшифровки работы модели? У вас есть факторы, которые улучшают модель, и есть те, которые её ухудшают; вот вы приходите на работу завтра — как понять, какие из них наиболее значимы?

Ноам Шазир: На небольших масштабах ты просто проводишь много экспериментов. Часть исследований посвящена тому, чтобы изолированно разрабатывать новые идеи, искать улучшения или прорывы. Для этого нужна понятная, простая кодовая база, которую можно легко форкнуть, модифицировать и тестировать, плюс надёжные базовые метрики.

В идеале я хочу проснуться утром, придумать идею, за день её запрограммировать, запустить эксперименты и уже к вечеру получить первые результаты: вот это сработало, а вот это нет. И это вполне реально, если держать кодовую базу в порядке и не перегружать процесс.

Джефф Дин: Тогда эксперимент занимает час‑два, а не две недели.

Ноам Шазир: Это один уровень исследований. А затем начинается масштабирование: когда нужно собрать все улучшения вместе и проверить, как они работают в больших моделях, в сложных системах.

Джефф Дин: Тут важно понимать, как они взаимодействуют: вроде бы кажется, что улучшения независимы друг от друга, но потом оказывается, что, скажем, оптимизация обработки видеоданных каким‑то образом влияет на обновление параметров модели. И возможно, это сильнее выражено именно в видео, а не в других типах данных. Такие неожиданные взаимосвязи всплывают постоянно, поэтому важно регулярно проводить эксперименты, где ты комбинируешь все улучшения и проверяешь, действительно ли они работают вместе. А если что‑то не сочетается, разбираться почему.

Дваркеш Патель: Во‑первых, как часто оказывается, что улучшения не работают вместе? Это редкое явление или случается постоянно?

Ноам Шазир: В половине случаев.

Джефф Дин: Да, большинство идей даже не доходят до стадии объединения, потому что на ранних этапах эксперименты показывают слабые результаты или не дают заметного прироста по сравнению с базовой моделью. Поэтому сначала проверяются улучшения по отдельности.

Когда мы видим, что некоторые из них действительно перспективны, мы включаем их в общий стек и пробуем комбинировать с другими успешными изменениями. Потом запускаем эксперименты — и тут нередко выясняется, что вместе они работают не так хорошо, как по отдельности. Тогда приходится разбираться почему.

Ноам Шазир: При этом всегда есть баланс: хочется, чтобы система оставалась как можно более чистой и понятной, потому что избыточная сложность кода и алгоритмических решений замедляет работу, увеличивает риски, добавляет неопределённости. Но при этом хочется, чтобы итоговая модель была максимально мощной. И конечно, каждый исследователь желает, чтобы именно его находки вошли в финальный вариант. Так что вызовы есть, но мы неплохо справляемся.

Бесконечная петля обратной связи

Дваркеш Патель: Вернёмся к вопросу о том, что с каждым новым поколением модели становятся лучше за счёт алгоритмических улучшений, даже если исключить аппаратные факторы. Должен ли мир, и вы в частности, задумываться об этом серьёзнее?

Есть один сценарий, при котором развитие ИИ занимает десятилетия, идёт плавно, шаг за шагом. Если где‑то допущена ошибка, её можно спокойно исправить — это некритично, потому что новая версия модели всего лишь немного лучше предыдущей. Но есть и другой сценарий, где система входит в мощную петлю обратной связи, и тогда два года между Gemini 4 и Gemini 5 могут стать самыми важными в истории человечества. Потому что в этот период мы можем перейти от модели, которая на уровне хорошего исследователя, к сверхчеловеческому интеллекту. Насколько, по‑вашему, этот второй сценарий реалистичен? И если он возможен, изменяется ваш подход к созданию все более мощных систем?

Ноам Шазир: Я уже перестал убираться в гараже, потому что жду, когда это сделают роботы. Так что, думаю, я больше склоняюсь ко второму сценарию — нас ждёт мощное ускорение.

Джефф Дин: Я считаю, что очень важно осознавать, что происходит и какие тренды задают направление. На данный момент модели действительно становятся значительно лучше с каждым новым поколением, и пока я не вижу причин, почему этот процесс должен замедлиться в ближайшие несколько итераций.

Это значит, что модели через два‑три поколения будут способны на куда большее. Возьмём тот же пример: если сейчас модель может разбить сложную задачу на 10 подзадач и решить их правильно в 80% случаев, то вскоре она сможет разложить уже очень сложную задачу на 100 или даже 1000 шагов и выполнить их с 90-процентной точностью. Это колоссальный скачок в возможностях ИИ.

Но при этом мы осознаём и риски: модели могут использоваться для дезинформации, для автоматизированных кибератак. Именно поэтому мы вкладываем много усилий в то, чтобы создать надёжные механизмы защиты, лучше понимать границы возможностей моделей и управлять их применением. В Google мы серьёзно относимся к этим вопросам, и у нас есть чёткое видение того, как правильно двигаться в этом направлении. Наши принципы ответственного ИИ — это хорошая концепция, которая помогает находить баланс между развитием мощных систем и их безопасностью, делая так, чтобы они не использовались во вред.

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Дваркеш Патель: Если посмотреть на этот период истории человечества с высоты, меня поражает вот что. Если мы живём в мире, где, допустим, вы неудачно провели посттренировку Gemini 3, из‑за чего он начал распространять дезинформацию, — это серьёзная ошибка, но её можно исправить, правда? Вы просто корректируете посттренировку — и проблема решена.

А теперь представим сценарий с мощной петлёй обратной связи, где рост интеллекта ускоряется. Ошибка уже не просто в том, что модель генерирует неточные факты, — ошибка в том, что система, ставшая катализатором этого взрывного роста, изначально оказалась несбалансированной. Она не пытается писать код так, как вы ожидаете, а вместо этого начинает оптимизироваться под какую‑то другую цель.

И вот этот процесс занимает всего несколько лет, возможно даже меньше. В результате на выходе мы получаем интеллект, который сравним или даже превосходит уровень Джеффа Дина или Ноама Шазира. В такой ситуации масштаб ошибки будет совсем другим — её уже не так просто исправить.

Ноам Шазир: Чем мощнее становятся системы, тем осторожнее нужно быть в их разработке.

Джефф Дин: Я бы сказал так: в этом вопросе есть два крайних взгляда. Один — «Боже мой, эти системы скоро превзойдут людей во всём и мы окажемся в полном подчинении!». Другой — «Эти технологии просто великолепны, и нам вообще не о чем беспокоиться». Я нахожусь где‑то посередине. Я был соавтором статьи «Рука ИИ на пульсе человечества», в которой, среди прочего, обсуждается, что оба этих взгляда склоняются к позиции невмешательства, мол, ИИ сам будет развиваться в том направлении, в котором ему суждено. Но на самом деле у нас есть все возможности, чтобы направлять этот процесс: мы можем управлять тем, как ИИ внедряется в мир, добиваясь максимальной пользы в ключевых сферах — образовании, медицине и других областях, где его потенциал может принести огромную пользу.

И конечно, мы должны так же осознанно отводить его от сценариев, в которых машины получают бесконтрольную власть. Тут нужны и технические ограничения, и продуманные механизмы регулирования. Задача, по сути, инженерная: как спроектировать системы, которые будут надёжными и безопасными? В каком‑то смысле она похожа на то, как мы раньше подходили к разработке критически важного программного обеспечения.

Ноам Шазир: Хорошая новость в том, что анализировать текст проще, чем его генерировать. Поэтому я считаю, что способность языковых моделей анализировать собственные ответы, выявлять проблемные или опасные моменты и есть ключ к решению множества вопросов контроля.

Мы активно занимаемся вопросами безопасности, в Google сейчас работает команда действительно блестящих специалистов, которые сосредоточены именно на этом. И задача будет становиться всё более важной — как с точки зрения пользы для людей, так и с точки зрения бизнеса. Во многих случаях ограничения на развёртывание ИИ связаны именно с безопасностью, а значит, обеспечение безопасности становится критически важным направлением работы.

Непрерывное обучение и полное переобучение

Дваркеш Патель: Вы часто говорите о концепции непрерывного обучения — идеи, что модель может улучшаться со временем, вместо того чтобы каждый раз начинать с нуля. Есть ли принципиальные препятствия для этого? Ведь теоретически модель можно просто дообучать снова и снова.

Джефф Дин: Мне всегда нравилась идея разрежённых моделей, потому что разные части модели должны быть хороши в разных задачах. У нас есть модель Gemini 1.5 Pro (кстати, в нашем агрегаторе нейросетей тоже) и другие архитектуры в стиле mixture of experts, где определённые части модели активируются для одних токенов, а для других остаются неактивными: например, одна часть специализируется на математике, другая — на распознавании изображений с кошками. Это позволяет создать мощную модель, которая при этом остаётся достаточно эффективной в инференсе: она имеет огромный потенциал, но в каждый момент времени задействуется только небольшая её часть.

Однако у этого подхода есть ограничения. Сейчас модель всё ещё строится по достаточно жёсткой и однообразной структуре: все эксперты одинакового размера, а их пути быстро сливаются обратно в общую сеть. То есть у нас нет ситуации, где, например, математическая ветка модели развивается отдельно от ветки, отвечающей за обработку изображений.

Мне кажется, архитектура моделей должна быть более органичной. Кроме того, было бы здорово, если бы разные части модели можно было дообучать независимо. Сейчас мы обучаем модель как единое целое, тщательно подбирая алгоритмы и состав обучающего набора данных, но всегда сталкиваемся с компромиссами: например, если мы добавим больше мультиязычных данных, это может уменьшить долю кода в обучении, что сделает модель лучше в обработке языков, но хуже в программировании, и наоборот.

Данный подход дал бы ещё и важное преимущество с точки зрения инженерии: вместо огромной, монолитной системы, которую нужно обучать целиком, мы могли бы разбить процесс на более управляемые части, что позволило бы сотням команд по всему миру работать над улучшением конкретных аспектов модели, внося свой вклад в её развитие. В целом так и выглядит форма непрерывного обучения.

Джефф Дин
Джефф Дин

Ноам Шазир: Было бы просто идеально: можно соединять модели, как конструктор, заменять отдельные части, вставляя их в другие, или просто подсоединить «пожарный шланг», выкачать всю информацию из одной модели и залить её в другую. Хотя здесь есть и противоположный интерес — научный: мы по‑прежнему на этапе стремительного прогресса, и если мы хотим проводить чёткие эксперименты и понимать, что именно даёт лучшие результаты, проще обучать модели с нуля, чтобы можно было чисто сравнивать один полный цикл обучения с другим. Не так увлекательно, но зато помогает быстрее понять, в каком направлении двигаться.

Джефф Дин: Возможно, получится совместить эти подходы за счёт модульности и версионности: например, у нас есть зафиксированная версия модели, но мы берём отдельный модуль, скажем интерпретацию Haskell, и дообучаем его, сравнивая с предыдущей версией.

Ноам Шазир: Кстати, так могут ускориться научные исследования: если усовершенствование модели обходится дешевле, чем обучение с нуля, можно сделать процесс экспериментов значительно более быстрым и доступным.

Джефф Дин: Я уже давно обдумываю эту концепцию и описывал её в Pathways, и мы действительно создаём для этого инфраструктуру. Pathways как раз и рассчитана на работу с такими гибкими, разветвлёнными моделями, где разные компоненты могут обновляться асинхронно. Мы используем Pathways для обучения моделей Gemini, но пока ещё не задействовали некоторые из её возможностей. Может, стоит попробовать?

Ноам Шазир: Бывали случаи, когда подходы, подобные тому, как были устроены TPU‑поды, оказались просто гениальным решением. Не знаю, кто это придумал, но результат впечатляет. Вся низкоуровневая программная и аппаратная архитектура идеально сбалансирована: мощное специализированное оборудование, тороидальная топология, а также оптимизированные коллективные операции с рассылкой результата, которые, кажется, пришли из суперкомпьютеров, но оказались именно тем, что нужно для распределённого обучения глубоких нейросетей.

Ключ к оптимальному инференсу

Дваркеш Патель: Допустим, появляется новая, более эффективная архитектура. Вы просто берёте каждый модуль и дистиллируете его в эту новую архитектуру — так модель будет постепенно улучшаться?

Джефф Дин: Думаю, дистилляция — действительно полезный инструмент, потому что она позволяет преобразовать модель из одной архитектурной формы в другую. Обычно её применяют, чтобы взять мощную, но громоздкую модель и создать из неё компактную версию, которую можно эффективно использовать с низкими задержками инференса.

Но этот процесс можно рассматривать и на уровне отдельных модулей. Например, у каждого модуля может быть несколько представлений: крупная, мощная версия и более компактная, которая постоянно обучается на основе первой. Когда маленькая версия достигает достаточной точности, большую можно удалить, освободив параметры для новых данных и знаний. Затем процесс повторяется. Если такой механизм параллельно работает в тысяче мест внутри модульной модели, он, скорее всего, будет довольно эффективным.

Дваркеш Патель: Это может быть способом масштабирования инференса: маршрутизатор решает, какую версию модели использовать.

В открытых исследованиях часто упоминается, что в моделях mixture of experts трудно понять, за что отвечает каждый эксперт. Если использовать ваш подход, как можно обеспечить прозрачную и понятную модульность?

Ноам Шазир: На самом деле, по моему опыту, эксперты довольно легко анализировать. В первой статье о mixture of experts эксперты можно было просто изучить напрямую, чтобы увидеть, как это работает. Мы же брали, скажем, тысячу‑две экспертов — один из них специализируется на словах, относящихся к цилиндрическим объектам...

Джефф Дин: А этот отлично разбирается в датах, временные показатели его конёк.

Ноам Шазир: Совершенно очевидная специализация. При этом для работы всей системы на лету не требуется человеческое понимание того, как именно это устроено, — есть обученный маршрутизатор, который сам распределяет задачи между экспертами.

Джефф Дин: Тут важно отметить, что в исследовательской среде много внимания уделяется интерпретируемости моделей — попыткам разобраться, что именно происходит внутри них. Специализация отдельных экспертов — частный случай этой более широкой проблемы. Мне очень нравятся работы моего бывшего стажёра, Криса Олы, и других исследователей из Anthropic: они обучили разрежённый автокодировщик и смогли выяснить, за что отвечает тот или иной нейрон в большой языковой модели. Например, обнаружили нейрон «Золотые Ворота», который активируется при обсуждении моста в Сан‑Франциско.

Думаю, то же самое можно проделать и с экспертами, и с другими уровнями модели, получив довольно интерпретируемые результаты. Вопрос лишь в том, насколько это вообще необходимо: если модель работает хорошо, нам, возможно, и не нужно знать, что делает каждый конкретный нейрон в Gemini. Ведь одна из главных прелестей глубокого обучения в том, что нам не требуется вручную прописывать все возможные особенности — система сама выявляет закономерности и эффективно с ними справляется.

Дваркеш Патель: Если сейчас у вас есть модель с десятками или сотнями миллиардов параметров, её можно запустить всего на нескольких GPU. А в системе, где каждый отдельный запрос проходит лишь через небольшую часть всех параметров, но вся модель должна быть загружена в память, инфраструктура, в которую Google вложилась, — с TPU, работающими в кластерах по сотни и тысячи чипов, — оказывается невероятно ценной, верно?

Ноам Шазир: Да, даже для существующих версий mixture of experts вам всё равно нужно держать всю модель в памяти. Есть распространённое заблуждение, будто главный плюс MoE — это то, что неиспользуемые эксперты вообще не задействуются и можно не загружать их веса. Но на практике всё не так: дело в том, что для эффективной работы таких систем нужно обрабатывать очень большие батчи независимых запросов. Поэтому нельзя сказать, что в конкретный момент времени модель либо обращается к эксперту, либо нет.

Если бы это было так, тогда при каждом обращении к конкретному эксперту он бы работал на батче размером 1, что чудовищно неэффективно: современное оборудование оптимизировано под огромные вычислительные интенсивности, а запускать процесс на одном элементе — это просто неиспользуемый потенциал. На деле в каждый момент времени система видит все эксперты, но при этом отправляет каждому из них лишь небольшую часть батча.

Ноам Шазир
Ноам Шазир

Джефф Дин: При этом у каждого эксперта остаётся свой собственный батч, который он обрабатывает. Чтобы модель работала сбалансированно, современные реализации делают эксперты примерно одинаковыми по вычислительным затратам: так можно равномерно распределять нагрузку и поддерживать высокую эффективность во время инференса, даже на больших батчах.

Но в будущем, думаю, мы увидим эксперты с вычислительной сложностью, различающейся в сотни или тысячи раз: часть путей могут проходить через множество слоёв, другие вообще ограничиваться одним‑единственным или использовать пропускные соединения.

Где взять данные, если они уже есть?

Дваркеш Патель: Как бы вы охарактеризовали, чего не хватает современным методам дистилляции?

Джефф Дин: Мне кажется, во время предобучения нам не хватает более продвинутых техник обучения. Не уверен, что текущие методы позволяют извлекать максимум пользы из каждого токена. Возможно, модели стоило бы задумываться куда глубже над некоторыми словами. Например, когда в данных встречается фраза ответ следующий:, модель могла бы на этапе обучения затратить гораздо больше вычислений, чем при обработке слова‑артикля.

Ноам Шазир: Должен быть способ извлекать больше знаний из тех же данных — заставлять модель учиться и в прямом, и в обратном направлении.

Джефф Дин: И во всех возможных вариациях. Можно скрывать одни части данных, другие оставлять, заставлять модель восстанавливать пропущенную информацию. В компьютерном зрении такой подход давно применяется: берут изображение, закрывают его фрагменты и обучают модель угадывать, что на картинке. Например, научить её определять объект, если видна только верхняя часть изображения или нижний левый угол: задача усложняются, но результаты более качественные. Думаю, аналогичный подход можно применить и к тексту или коду, заставляя модели работать активнее.

Ноам Шазир: В компьютерном зрении просто не хватало размеченных данных, вот им и пришлось придумать такие методики.

Джефф Дин: И в итоге они создали отличные техники. Например, дропаут изначально придумали для обработки изображений, но в текстовых моделях он почти не используется. А ведь это способ избежать переобучения — провести, скажем, 100 эпох на всех текстовых данных, используя дропаут, и добиться более эффективного обучения. Потребуются огромные вычислительные мощности, но зато мы получим значительно более сильные модели. Так что, когда говорят, что «мы почти исчерпали текстовые данные», я в это не особо верю — думаю, из уже существующих текстов можно извлечь гораздо больше знаний, чем кажется.

Ноам Шазир: Ну, человек за жизнь видит около миллиарда токенов.

Джефф Дин: И при этом умеет делать массу крутых вещей.

Дваркеш Патель: Здесь можно провести аналогию с modus ponens и modus tollens. С одной стороны, если LLM‑моделям ещё далеко до людей по эффективности усвоения данных, значит, у нас есть потенциал для масштабного улучшения — просто если мы научим их обучаться так же, как люди. С другой стороны, может быть, они вообще учатся совсем по‑другому, разница в порядке величин слишком велика. Как вам кажется, что нужно, чтобы модели достигли той же эффективности обучения, что и человек?

Джефф Дин: Думаю, нам стоит переосмыслить саму цель обучения. Прогнозирование следующего токена на основе предыдущих — это не совсем тот способ, которым обучаются люди. В чём‑то он схож с человеческим процессом, но всё же принципиально отличается. Например, человек может прочитать целую главу книги, а затем попытаться ответить на вопросы в конце — и это уже совсем другой механизм обучения. Кроме того, модели почти не учатся на визуальных данных. Да, видео уже используется в обучении, но мы даже близко не подошли к тому, чтобы полноценно задействовать весь визуальный поток информации.

Ну и конечно, люди извлекают из данных гораздо больше, чем нынешние модели: одна из причин высокой эффективности человеческого обучения в том, что мы сами исследуем мир. Мы совершаем действия, наблюдаем их последствия и учимся на этом. Даже младенцы учат физику, роняя предметы на пол и изучая, как они падают. Попробуй научить модель гравитации, если она просто смотрит на статичные данные... Так что если бы модель могла не просто пассивно потреблять информацию, а взаимодействовать с окружающей средой, это дало бы ей совершенно иной уровень обучения.

Дваркеш Патель: То есть Gato — наше будущее?

Джефф Дин: Что‑то подобное, где модель и наблюдает, и действует, а затем анализирует последствия, определённо кажется перспективным направлением.

Ноам Шазир: Люди могут учиться даже без какого‑либо внешнего ввода, просто с помощью мысленных экспериментов. Эйнштейн, например, многому научился именно так. Ньютон вообще ушёл в карантин, ему яблоко на голову упало — и вот тебе теория гравитации. Или возьмём математику: там же нет никакого внешнего обучения, а знания всё равно накапливаются. Или шахматы: достаточно просто позволить модели играть сама с собой, и она научится. Именно так DeepMind обучала свои шахматные ИИ — им хватило только правил игры.

Так что, вероятно, можно придумать массу способов обучения без внешних данных, причём сразу в нужных нам областях. Конечно, в некоторых случаях без реального опыта не обойтись, но в других, возможно, модель может просто разговаривать сама с собой и становиться умнее.

Дваркеш Патель
Дваркеш Патель

Как находить себя в каждой новой теме

Дваркеш Патель: Мы обсудили некоторые из ваших проектов за последние 25 лет — и это были самые разные области: от поиска и индексирования до распределённых систем, аппаратного обеспечения, алгоритмов ИИ. И это только вершина айсберга — стоит взглянуть на ваши профили в Google Scholar, и список окажется бесконечным. В чём секрет такой карьеры, которая не просто продолжается десятилетиями, но и охватывает столько разных направлений? Как вам удаётся сочетать глубину и широту знаний?

Джефф Дин: Мне всегда было интересно узнавать что‑то новое, и один из лучших способов делать это — внимательно следить за тем, что происходит в науке, общаться с коллегами, читать свежие исследования и понимать, как меняется научный ландшафт. Важно уметь задаваться вопросами вроде: «А что если применить методы обучения с подкреплением в проектировании чипов?» или «Как ИИ может помочь в медицине?». Я, например, много общался с врачами, чтобы разобраться, в каких аспектах ИИ действительно может принести пользу. Иногда оказывается, что в одной сфере он малоэффективен, а в другой — способен совершить прорыв.

Ещё один ключевой момент — работа в команде с людьми, чьи знания дополняют твои. Когда ты сотрудничаешь с коллегами из смежных областей, вместе можно добиться того, что в одиночку оказалось бы невозможным. Постепенно их экспертиза передаётся тебе, твоя им, и в результате ты становишься гораздо более универсальным инженером‑исследователем, способным решать всё более сложные задачи. Возможность учиться на протяжении всей карьеры — это то, что я действительно ценю. Я получаю огромное удовольствие от погружения в новые области и от того, какие возможности открываются благодаря этому.


Кажется, в мире высоких технологий всё циклично: сначала что‑то тормозит, потом это ускоряют, потом оно начинает перегреваться, а потом кто‑то говорит «А давайте попробуем иначе» — и вот уже новый стандарт индустрии. Но самое интересное не в этом, а в том, как люди ухитряются находить элегантные решения в местах, где, казалось бы, уже всё выбито до последнего нанометра.

И всё же, несмотря на продвинутые архитектуры, оптимизации и тонкую настройку, остаётся ощущение, что где‑то внутри каждой модели всё ещё живёт маленький хаос, который иногда выдаёт что‑то совершенно неожиданное. То ли баг, то ли фича, то ли просто очередной повод задуматься, как оно вообще работает. Благодарю за прочтение, делитесь мыслями в комментариях.

Источник

  • 07.09.23 16:24 CherryTeam

    Cherry Team atlyginimų skaičiavimo programa yra labai naudingas įrankis įmonėms, kai reikia efektyviai valdyti ir skaičiuoti darbuotojų atlyginimus. Ši programinė įranga, turinti išsamias funkcijas ir patogią naudotojo sąsają, suteikia daug privalumų, kurie padeda supaprastinti darbo užmokesčio skaičiavimo procesus ir pagerinti finansų valdymą. Štai keletas pagrindinių priežasčių, kodėl Cherry Team atlyginimų skaičiavimo programa yra naudinga įmonėms: Automatizuoti ir tikslūs skaičiavimai: Atlyginimų skaičiavimai rankiniu būdu gali būti klaidingi ir reikalauti daug laiko. Programinė įranga Cherry Team automatizuoja visą atlyginimų skaičiavimo procesą, todėl nebereikia atlikti skaičiavimų rankiniu būdu ir sumažėja klaidų rizika. Tiksliai apskaičiuodama atlyginimus, įskaitant tokius veiksnius, kaip pagrindinis atlyginimas, viršvalandžiai, premijos, išskaitos ir mokesčiai, programa užtikrina tikslius ir be klaidų darbo užmokesčio skaičiavimo rezultatus. Sutaupoma laiko ir išlaidų: Darbo užmokesčio valdymas gali būti daug darbo jėgos reikalaujanti užduotis, reikalaujanti daug laiko ir išteklių. Programa Cherry Team supaprastina ir pagreitina darbo užmokesčio skaičiavimo procesą, nes automatizuoja skaičiavimus, generuoja darbo užmokesčio žiniaraščius ir tvarko išskaičiuojamus mokesčius. Šis automatizavimas padeda įmonėms sutaupyti daug laiko ir pastangų, todėl žmogiškųjų išteklių ir finansų komandos gali sutelkti dėmesį į strategiškai svarbesnę veiklą. Be to, racionalizuodamos darbo užmokesčio operacijas, įmonės gali sumažinti administracines išlaidas, susijusias su rankiniu darbo užmokesčio tvarkymu. Mokesčių ir darbo teisės aktų laikymasis: Įmonėms labai svarbu laikytis mokesčių ir darbo teisės aktų, kad išvengtų baudų ir teisinių problemų. Programinė įranga Cherry Team seka besikeičiančius mokesčių įstatymus ir darbo reglamentus, užtikrindama tikslius skaičiavimus ir teisinių reikalavimų laikymąsi. Programa gali dirbti su sudėtingais mokesčių scenarijais, pavyzdžiui, keliomis mokesčių grupėmis ir įvairių rūšių atskaitymais, todėl užtikrina atitiktį reikalavimams ir kartu sumažina klaidų riziką. Ataskaitų rengimas ir analizė: Programa Cherry Team siūlo patikimas ataskaitų teikimo ir analizės galimybes, suteikiančias įmonėms vertingų įžvalgų apie darbo užmokesčio duomenis. Ji gali generuoti ataskaitas apie įvairius aspektus, pavyzdžiui, darbo užmokesčio paskirstymą, išskaičiuojamus mokesčius ir darbo sąnaudas. Šios ataskaitos leidžia įmonėms analizuoti darbo užmokesčio tendencijas, nustatyti tobulintinas sritis ir priimti pagrįstus finansinius sprendimus. Pasinaudodamos duomenimis pagrįstomis įžvalgomis, įmonės gali optimizuoti savo darbo užmokesčio strategijas ir veiksmingai kontroliuoti išlaidas. Integracija su kitomis sistemomis: Cherry Team programinė įranga dažnai sklandžiai integruojama su kitomis personalo ir apskaitos sistemomis. Tokia integracija leidžia automatiškai perkelti atitinkamus duomenis, pavyzdžiui, informaciją apie darbuotojus ir finansinius įrašus, todėl nebereikia dubliuoti duomenų. Supaprastintas duomenų srautas tarp sistemų padidina bendrą efektyvumą ir sumažina duomenų klaidų ar neatitikimų riziką. Cherry Team atlyginimų apskaičiavimo programa įmonėms teikia didelę naudą - automatiniai ir tikslūs skaičiavimai, laiko ir sąnaudų taupymas, atitiktis mokesčių ir darbo teisės aktų reikalavimams, ataskaitų teikimo ir analizės galimybės bei integracija su kitomis sistemomis. Naudodamos šią programinę įrangą įmonės gali supaprastinti darbo užmokesčio skaičiavimo procesus, užtikrinti tikslumą ir atitiktį reikalavimams, padidinti darbuotojų pasitenkinimą ir gauti vertingų įžvalgų apie savo finansinius duomenis. Programa Cherry Team pasirodo esanti nepakeičiamas įrankis įmonėms, siekiančioms efektyviai ir veiksmingai valdyti darbo užmokestį. https://cherryteam.lt/lt/

  • 08.10.23 01:30 davec8080

    The "Shibarium for this confirmed rug pull is a BEP-20 project not related at all to Shibarium, SHIB, BONE or LEASH. The Plot Thickens. Someone posted the actual transactions!!!! https://bscscan.com/tx/0xa846ea0367c89c3f0bbfcc221cceea4c90d8f56ead2eb479d4cee41c75e02c97 It seems the article is true!!!! And it's also FUD. Let me explain. Check this link: https://bscscan.com/token/0x5a752c9fe3520522ea88f37a41c3ddd97c022c2f So there really is a "Shibarium" token. And somebody did a rug pull with it. CONFIRMED. But the "Shibarium" token for this confirmed rug pull is a BEP-20 project not related at all to Shibarium, SHIB, BONE or LEASH.

  • 24.06.24 04:31 tashandiarisha

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  • 26.06.24 18:46 Jacobethannn098

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  • 02.09.24 20:24 [email protected]

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  • 06.09.24 01:35 Celinagarcia

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  • 06.09.24 01:44 Celinagarcia

    HOW TO RECOVER MONEY LOST IN BITCOIN/USDT TRADING OR TO CRYPTO INVESTMENT !! Hi all, friends and families. I am writing From Alberton Canada. Last year I tried to invest in cryptocurrency trading in 2023, but lost a significant amount of money to scammers. I was cheated of my money, but thank God, I was referred to Hack Recovery Wizard they are among the best bitcoin recovery specialists on the planet. they helped me get every penny I lost to the scammers back to me with their forensic techniques. and I would like to take this opportunity to advise everyone to avoid making cryptocurrency investments online. If you ​​​​​​have already lost money on forex, cryptocurrency or Ponzi schemes, please contact [email protected] or WhatsApp: +1 (757) 237–1724 at once they can help you get back the crypto you lost to scammers. BEST WISHES. Celina Garcia.

  • 16.09.24 00:10 marcusaustin

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  • 23.09.24 18:56 matthewshimself

    At first, I was admittedly skeptical about Worldcoin (ref: https://worldcoin.org/blog/worldcoin/this-is-worldcoin-video-explainer-series), particularly around the use of biometric data and the WLD token as a reward mechanism for it. However, after following the project closer, I’ve come to appreciate the broader vision and see the value in the underlying tech behind it. The concept of Proof of Personhood (ref: https://worldcoin.org/blog/worldcoin/proof-of-personhood-what-it-is-why-its-needed) has definitely caught my attention, and does seem like a crucial step towards tackling growing issues like bots, deepfakes, and identity fraud. Sam Altman’s vision is nothing short of ambitious, but I do think he & Alex Blania have the chops to realize it as mainstay in the global economy.

  • 01.10.24 14:54 Sinewclaudia

    I lost about $876k few months ago trading on a fake binary option investment websites. I didn't knew they were fake until I tried to withdraw. Immediately, I realized these guys were fake. I contacted Sinew Claudia world recovery, my friend who has such experience before and was able to recover them, recommended me to contact them. I'm a living testimony of a successful recovery now. You can contact the legitimate recovery company below for help and assistance. [email protected] [email protected] WhatsApp: 6262645164

  • 02.10.24 22:27 Emily Hunter

    Can those who have fallen victim to fraud get their money back? Yes, you might be able to get back what was taken from you if you fell prey to a fraud from an unregulated investing platform or any other scam, but only if you report it to the relevant authorities. With the right plan and supporting documentation, you can get back what you've lost. Most likely, the individuals in control of these unregulated platforms would attempt to convince you that what happened to your money was a sad accident when, in fact, it was a highly skilled heist. You should be aware that there are resources out there to help you if you or someone you know has experienced one of these circumstances. Do a search using (deftrecoup (.) c o m). Do not let the perpetrators of this hoaxes get away with ruining you mentally and financially.

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  • 31.10.24 00:13 ytre89

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  • 04.12.24 22:24 andreygagloev

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  • 27.12.24 00:21 swiftdream

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  • 08.02.25 05:45 [email protected]

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  • 13.02.25 16:50 andytom798

    I lost $210,000 worth of Bitcoin to a group of fake blockchain impostors on Red note, a Chinese app. They contacted me, pretending to be official blockchain support, and I was misled into believing they were legitimate. At the time, I had been saving up in Bitcoin, hoping to take advantage of the rising market. The scammers were convincing, and I made the mistake of trusting them with access to my blockchain wallet. To my shock and disbelief, they stole a total of $10,000 worth of Bitcoin from my wallet. It was devastating, as this amount represented all of my hard-earned savings. I was in utter disbelief, feeling foolish for falling for their deceptive tactics. I felt lost, as though everything I had worked towards was taken from me in an instant. Thankfully, my uncle suggested I reach out to an expert in cryptocurrency recovery. After doing some research online, I came across CYBERPOINT RECOVERY COMPANY. I was hesitant at first, but their positive reviews gave me some hope. I decided to contact them directly and explained my situation, including the amount I had lost and how the scammers had gained access to my account. To my relief, the team at CYBERPOINT RECOVERY responded quickly and assured me they could help. They launched a detailed recovery program, using advanced tools and techniques to trace the stolen Bitcoin. Within a matter of days, they successfully recovered my full $210,000 worth of Bitcoin, and they even identified the individuals behind the scam. Their expertise and professionalism made a huge difference, and I was incredibly grateful for their support. If you find yourself in a similar situation, I highly recommend reaching out to Cyber Constable Intelligence. They helped me recover my funds when I thought all hope was lost. Whether you’ve lost money to scammers or any other form of online fraud, they have the knowledge and resources to help you get your funds back. Don’t give up there are experts who can help you reclaim what you’ve lost. I’m sharing my story to hopefully guide others who are going through something similar. Here's Their Info Below ([email protected]) or W.H.A.T.S.A.P.P:+1.7.6.0.9.2.3.7.4.0.7

  • 13.02.25 16:52 birenderkumar20101

    I was able to reclaim my lost Bitcoin assets worth of $480,99 which i had lost to the scam company known as Capitalix fx a scam company pretending to be an investment platform which alot of people including myself have lost their funds to, sadly not all would be fortunate enough to retrieve back their funds like I did but if you’re reading this today then you’re already a step closer towards regaining your lost digital assets, CYBERPOINT RECOVERY COMPANY successfully retrieved back my funds in less than of 48hours after I sought for their help to get back my funds. This experience has taught me the importance of carrying out my due diligence before embracing any financial opportunity presented to me and while risk taking may be a part of the journey, some risks are not worth taking and never again will I involve myself with any online financial investment. It’s only right that we seek for external intervention and support of a higher knowledge system when it comes to digital assets recovery, Get in contact today with the team to get started on Email: ([email protected])

  • 13.02.25 17:37 eunice49954

    Agent Jasmine Lopez focuses on recovering stolen cryptocurrency, particularly USDT. She is well-known for helping victims of digital asset theft. Her reputation arises from successful recoveries that have allowed many to regain their lost funds. I witnessed this when $122,000 was taken from me. Thanks to Ms. Lopez's skills, I recovered the entire amount in just 24 hours. Her prompt response and effective methods relieved my financial burden. Ms. Lopez’s commitment to helping others is evident. She is always available to offer solutions to those facing similar problems. For assistance, she can be reached via email at recoveryfundprovider@gmail . com or contacted directly on WhatsApp and text at +44 - 7366 445035. Her Instagram handle is recoveryfundprovider.

  • 14.02.25 02:50 Vladimir876

    I was able to reclaim my lost Bitcoin assets worth of $480,99 which i had lost to the scam company known as Capitalix fx a scam company pretending to be an investment platform which alot of people including myself have lost their funds to, sadly not all would be fortunate enough to retrieve back their funds like I did but if you’re reading this today then you’re already a step closer towards regaining your lost digital assets, CYBERPOINT RECOVERY COMPANY successfully retrieved back my funds in less than of 48hours after I sought for their help to get back my funds. This experience has taught me the importance of carrying out my due diligence before embracing any financial opportunity presented to me and while risk taking may be a part of the journey, some risks are not worth taking and never again will I involve myself with any online financial investment. It’s only right that we seek for external intervention and support of a higher knowledge system when it comes to digital assets recovery, Get in contact today with the team to get started on Email: ([email protected]) or W.H.A.T.S.A.P.P:+1.7.6.0.9.2.3.7.4.0.7

  • 14.02.25 02:56 christophadelbert3

    Я был в полном смятении, когда потерял все свои сбережения, инвестируя в криптовалюту. Со мной связалась онлайн женщина по электронной почте, выдавая себя за менеджера по работе с клиентами банка, которая сказала мне, что я могу удвоить свои сбережения, инвестируя в криптовалюту. Я никогда не думал, что это будет мошенничество, и я потеряю все. Это продолжалось неделями, пока я не понял, что меня обманули. Вся надежда была потеряна, я был опустошен и разорен, к счастью для меня, я наткнулся на статью в моем местном бюллетене о CYBERPUNK RECOVERY Bitcoin Recovery. Я связался с ними и предоставил всю информацию по моему делу. Я был поражен тем, как быстро они вернули мои криптовалютные средства и смогли отследить этих мошенников. Я действительно благодарен за их услуги и рекомендую CYBERPUNK RECOVERY всем, кому нужно вернуть свои средства. Настоятельно рекомендую вам связаться с CYBERPUNK, если вы потеряли свои биткойны USDT или ETH из-за инвестиций в биткойны Электронная почта: ([email protected]) W.h.a.t.s.A.p.p (+.1.7.6.0.9.2.3.7.4.0.7)

  • 14.02.25 02:56 christophadelbert3

    I was in total dismay when I lost my entire savings investing in cryptocurrency, I was contacted online by a lady through email pretending to be an account manager of a bank, who told me I could make double my savings through cryptocurrency investment, I never imagined it would be a scam and I was going to lose everything. It went on for weeks until I realized that I have been scammed. All hope was lost, I was devastated and broke, fortunately for me, I came across an article on my local bulletin about CYBERPUNK RECOVERY Bitcoin Recovery, I contacted them and provided all the information regarding my case, I was amazed at how quickly they recovered my cryptocurrency funds and was able to trace down those scammers. I’m truly grateful for their service and I recommend CYBERPUNK RECOVERY to everyone who needs to recover their funds urge you to contact CYBERPUNK if you have lost your bitcoin USDT or ETH through bitcoin investment Email: ([email protected]) WhatsApp (+17609237407)

  • 14.02.25 15:33 prelogmilivoj

    I never imagined I would find myself in a situation where I was scammed out of such a significant amount of money, but it happened. I became a victim of a fake online donation project that cost me over $30,000. It all started innocently enough when I was searching for assistance after a devastating fire incident in California. While looking for support, I came across an advertisement that seemed to offer donations for fire victims. The ad appeared legitimate, and I reached out to the project manager to inquire about how to receive the donations. The manager was very convincing and insisted that in order to qualify for the donations, I needed to pay $30,000 upfront. In return, I was promised $1 million in donations. It sounded a bit too good to be true, but in my desperate situation, I made the mistake of believing it. The thought of receiving a substantial amount of help to rebuild after the fire clouded my judgment, and I went ahead and sent the money. However, after transferring the funds, the promised donations never arrived, and the manager disappeared. That’s when I realized I had been scammed. Feeling lost, helpless, and completely betrayed, I tried everything I could to contact the scammer, but all my efforts were in vain. Desperation led me to search for help online, hoping to find a way to recover my money and potentially track down the scammer. That’s when I stumbled upon several testimonies from others who had fallen victim to similar scams and had been helped by a company called Tech Cyber Force Recovery. I reached out to them immediately, providing all the details of the scam and the information I had gathered. To my immense relief, the experts at Tech Cyber Force Recovery acted swiftly. Within just 27 hours, they were able to locate the scammer and initiate the recovery process. Not only did they help me recover the $30,000 I had lost, but the most satisfying part was that the scammer was apprehended by local authorities in their region. Thanks to Tech Cyber Force Recovery, I was able to get my money back and hold the scammer accountable for their actions. I am incredibly grateful for their professionalism, expertise, and dedication to helping victims like me. If you have fallen victim to a scam or fraudulent activity, I highly recommend contacting Tech Cyber Force Recovery. They provide swift and efficient recovery assistance, and I can confidently say they made all the difference in my situation. ☎☎ 1️⃣5️⃣6️⃣1️⃣7️⃣2️⃣6️⃣3️⃣6️⃣9️⃣7️⃣ ☎☎ 📩 1️⃣5️⃣6️⃣1️⃣7️⃣2️⃣6️⃣3️⃣6️⃣9️⃣7️⃣ 📩

  • 14.02.25 22:12 eunice49954

    Agent Jasmine Lopez focuses on recovering stolen cryptocurrency, particularly USDT. She is well-known for helping victims of digital asset theft. Her reputation arises from successful recoveries that have allowed many to regain their lost funds. I witnessed this when $122,000 was taken from me. Thanks to Ms. Lopez's skills, I recovered the entire amount in just 24 hours. Her prompt response and effective methods relieved my financial burden. Ms. Lopez’s commitment to helping others is evident. She is always available to offer solutions to those facing similar problems. For assistance, she can be reached via email at recoveryfundprovider@gmail . com or contacted directly on WhatsApp and text at +44 - 7366 445035. Her Instagram handle is recoveryfundprovider.

  • 15.02.25 02:51 Michelle Lynn

    Living in Los Angeles, I never imagined I’d face such a difficult chapter in my life. At the time, my wife was pregnant, and we were both excited about starting a family. I fell victim to a series of scams, losing over $170,000 in total. Just when I thought things couldn’t get worse, I received a call from someone who promised to help me recover my losses. Desperate to fix the situation, I went along with it, hoping for a breakthrough. But it turned out to be another scam. However, most of the options I found either seemed dubious or offered no real guarantees. That’s when I came across Cyber Constable Intelligence. It was a company recommended in a Facebook community The team worked tirelessly on my case, and after some time, they successfully recovered 99% of my investment. Although I didn’t recover everything, the 99% recovery was a huge relief They also educated me on how to better protect my digital Asset Here's Their Website Info www cyberconstableintelligence com

  • 15.02.25 02:51 Michelle Lynn

    Living in Los Angeles, I never imagined I’d face such a difficult chapter in my life. At the time, my wife was pregnant, and we were both excited about starting a family. I fell victim to a series of scams, losing over $170,000 in total. Just when I thought things couldn’t get worse, I received a call from someone who promised to help me recover my losses. Desperate to fix the situation, I went along with it, hoping for a breakthrough. But it turned out to be another scam. However, most of the options I found either seemed dubious or offered no real guarantees. That’s when I came across Cyber Constable Intelligence. It was a company recommended in a Facebook community The team worked tirelessly on my case, and after some time, they successfully recovered 99% of my investment. Although I didn’t recover everything, the 99% recovery was a huge relief They also educated me on how to better protect my digital Asset Here's Their Website Info www cyberconstableintelligence com, WhatsApp Info: 1 (252) 378-7611

  • 16.02.25 01:01 Peter

    I fell victim to a crypto scam and lost a significant amount of money. What are the most effective strategies to recover my funds? I've heard about legal actions, contacting authorities, and hiring recovery experts, but I'm not sure where to start. Can you provide some guidance on the best ways to recover money lost in a crypto scam? Well if this is you, [email protected] gat you covered get in touch and thank me later

  • 16.02.25 20:06 eunice49954

    Agent Lopez specializes in recovering stolen cryptocurrencies, especially Bitcoin/USDT. She has built a strong reputation for helping victims reclaim their lost funds. A personal example highlights her effectiveness: I lost $111,000 and, thanks to her prompt action, I recovered it all within 24 hours. Her dedication and skills eased my financial stress. She is always ready to assist others with similar issues. For help, she can be reached by email at Recoveryfundprovider@gmail. com or contact her through WhatsApp at +44 736 644 5035. Her Insta is recoveryfundprovider.

  • 18.02.25 19:35 donovancristina

    Now, I’m that person sharing my success story on LinkedIn, telling others about the amazing team at TECH CYBER FORCE RECOVERY who literally saved my financial life. I’ve also become that guy who proudly shares advice like “Always back up your wallet, and if you don’t have TECH CYBER FORCE RECOVERY on speed dial.” So, a big thank you to TECH CYBER FORCE RECOVERY if I ever get a chance to meet the team, I might just offer to buy them a drink. They’ve earned it. FOR CRYPTO HIRING WEBSITE WWW://techcyberforcerecovery.com WHATSAPP : ⏩ wa.me/15617263697

  • 18.02.25 22:13 keithphillip671

    WhatsApp +44,7,4,9,3,5,1,3,3,8,5 Telegram @Franciscohack The day my son uncovered the truth—that the man I entrusted my hopes of wealth and companionship with through a cryptocurrency platform was a cunning scammer was the day my world crumbled. The staggering realization that I had been swindled out of 150,000.00 Euro worth of Bitcoin left me in a state of profound despair. As a 73-year-old grappling with loneliness, I had sought solace in what I believed to be a genuine connection, only to find deceit and betrayal. Countless sleepless nights were spent in tears, mourning not only the financial devastation but also the crushing blow to my trust. Attempts to verify the authenticity of our interactions were met with hostility, further deepening my sense of isolation. Through the loss it was my son who became my beacon of resilience. He took upon himself the arduous task of tracing the scam and seeking justice on my behalf. Through meticulous effort and determination, he unearthed {F R A N C I S C O H A C K}, renowned for their expertise in recovering funds lost to cryptocurrency scams. Entrusting them with screenshots and evidence of the fraudulent transactions, my son initiated the journey to reclaim what had been callously taken from me. {F R A N C I S C O H A C K} approached our plight with empathy and unwavering professionalism, immediately instilling a sense of confidence in their abilities. Despite my initial skepticism, their transparent communication and methodical approach reassured us throughout the recovery process. Regular updates on their progress and insights into their strategies provided much-needed reassurance and kept our hopes alive amid the uncertainty. Their commitment to transparency and client welfare was evident in every interaction, fostering a sense of partnership rather than mere service. Miraculously, in what felt like an eternity but was actually an impressively brief period, {F R A N C I S C O H A C K} delivered the astonishing news—I had recovered the entire 150,000.00 Euro worth of stolen Bitcoin. The flood of relief and disbelief was overwhelming, marking not just the restitution of financial losses but the restoration of my faith in justice. {F R A N C I S C O H A C K} proficiency in navigating the intricate landscape of blockchain technology and online fraud was nothing short of extraordinary. Their dedication to securing justice and restoring client confidence set them apart as more than just experts—they were steadfast allies in a fight against digital deceit. What resonated deeply with me {F R A N C I S C O H A C K} integrity and compassion. Despite the monumental recovery, they maintained transparency regarding their fees and ensured fairness in all dealings. Their proactive guidance on cybersecurity measures further underscored their commitment to safeguarding clients from future threats. It was clear that their mission extended beyond recovery—it encompassed education, prevention, and genuine advocacy for those ensnared by cyber fraud. ([email protected]) fills me with profound gratitude. They not only rescued my financial security but also provided invaluable emotional support during a time of profound vulnerability. To anyone navigating the aftermath of cryptocurrency fraud, I wholeheartedly endorse {F R A N C I S C O H A C K}. They epitomize integrity, expertise, and unwavering dedication to their clients' well-being. My experience with {F R A N C I S C O H A C K} transcended mere recovery—it was a transformative journey of resilience, restoration, and renewed hope in the face of adversity.

  • 21.02.25 07:42 daniel231101

    I never thought I would fall victim to a crypto scam until I was convinced of a crypto investment scam that saw me lose all my entire assets worth $487,000 to a crypto investment manager who convinced me I could earn more from my investment. I thought it was all gone for good but I kept looking for ways to get back my stolen crypto assets and finally came across Ethical Hack Recovery, a crypto recovery/spying company that has been very successful in the recovery of crypto for many other victims of crypto scams and people who lost access to their crypto. I’m truly grateful for their help as I was able to recover my stolen crypto assets and get my life back together. I highly recommend their services EMAIL ETHICALHACKERS009 AT @GMAIL DOT COM whatsapp +14106350697

  • 21.02.25 21:38 eunice49954

    Jasmine Lopez specializes in recovering stolen cryptocurrencies, especially ETH/USDT. She has built a strong reputation for helping victims reclaim their lost funds. A personal example highlights her effectiveness: I lost $111,000 and, thanks to her prompt action, I recovered it all within 24 hours. Her dedication and skills eased my financial stress. She is always ready to assist others with similar issues. For help, she can be reached by email at Recoveryfundprovider@gmail. com or contact her through WhatsApp at +44 736 644 5035. Her Insta is recoveryfundprovider.

  • 22.02.25 18:01 benluna0991

    Mark Zuckerberg. That’s the name I was introduced to when I first encountered the cryptocurrency mining platform, WHATS Invest. A person claiming to be Zuckerberg himself reached out to me, saying that he was personally backing the platform to help investors like me earn passive income. At first, I was skeptical—after all, how often do you get a direct connection to one of the world’s most famous tech entrepreneurs? But this individual seemed convincing and assured me that many people were already seeing substantial returns on their investments. He promised me a great opportunity to secure my financial future, so I decided to take the plunge and invest $10,000 into WHATS Invest. They told me that I could expect to see significant returns in just a few months, with payouts of at least $1,500 or more each month. I was excited, believing this would be my way out of financial struggles. However, as time passed, things didn’t go according to plan. Months went by, and I received very little communication. When I finally did receive a payout, it was nowhere near the $1,500 I was promised. Instead, I received just $200, barely 13% of what I had expected. Frustrated, I contacted the support team, but the responses were vague and unhelpful. No clear answers or solutions were offered, and my trust in the platform quickly started to erode. It became painfully clear that I wasn’t going to get anywhere with WHATS Invest, and I began to worry that my $10,000 might be lost for good. That's when I discovered Certified Recovery Services. Desperate to recover my funds, I decided to reach out to them for help. In just 24 hours, they worked tirelessly to recover the majority of my funds, successfully retrieving $8,500 85% of my initial investment. I couldn’t believe how quickly and efficiently they worked to get my money back. I’m extremely grateful for Certified Recovery Servicer's fast and professional service. Without them, I would have been left with a significant loss, and I would have had no idea how to move forward. If you find yourself in a similar situation with WHATS Invest or any other platform that isn’t delivering as promised, I highly recommend reaching out to Certified Recovery Services They were a lifesaver for me, helping me recover nearly all of my funds. It's reassuring to know that trustworthy services like this exist to help people when things go wrong. They also specialize in recovering money lost to online scams, so if you’ve fallen victim to such a scam, don’t hesitate to contact Certified Recovery Services they can help! Here's Their Info Below: WhatsApp: +1(740)258‑1417 mail: [email protected], [email protected] Website info; https://certifiedrecoveryservices.com

  • 23.02.25 22:00 eunice49954

    Jasmine Lopez specializes in recovering stolen cryptocurrencies, especially ETH/USDT. She has built a strong reputation for helping victims reclaim their lost funds. A personal example highlights her effectiveness: I lost $111,000 and, thanks to her prompt action, I recovered it all within 24 hours. Her dedication and skills eased my financial stress. She is always ready to assist others with similar issues. For help, she can be reached by email at Recoveryfundprovider@gmail. com or contact her through WhatsApp at +44 736 644 5035. Her Insta is recoveryfundprovider.

  • 24.02.25 06:36 ANDREW DAVIS

    RECOVER YOUR SCAMMED FUNDS AND CRYPTOCURRENCY VIA SPOTLIGHT RECOVERY Professional hackers at Spotlight Recovery provide services for compromised devices, accounts, and websites as well as for recovering stolen bitcoin and money from scams. They finish their work safely and quickly. Their order has been fulfilled since day one, and the victim will never be conscious of the outside entrance. Very few even attempt to give critical information, look into network security, or discreetly discuss personal issues. The Spotlight Recovery Crew helped me recover $264,000 that was stolen from my corporate bitcoin wallet, and I appreciate them giving me further details on the unidentified people. In the event that you have been defrauded of your hard-earned cash or bitcoins, contact SPOTLIGHT RECOVERY CREW at Contact: [email protected]

  • 24.02.25 07:19 maggie4567

    Jasmine Lopez specializes in recovering stolen cryptocurrencies, especially ETH/USDT. She has built a strong reputation for helping victims reclaim their lost funds. A personal example highlights her effectiveness: I lost $111,000 and, thanks to her prompt action, I recovered it all within 24 hours. Her dedication and skills eased my financial stress. She is always ready to assist others with similar issues. For help, she can be reached by email at Recoveryfundprovider@gmail. com or contact her through WhatsApp at +44 736 644 5035. Her Insta is recoveryfundprovider.

  • 24.02.25 07:19 maggie4567

    Jasmine Lopez specializes in recovering stolen cryptocurrencies, especially ETH/USDT. She has built a strong reputation for helping victims reclaim their lost funds. A personal example highlights her effectiveness: I lost $111,000 and, thanks to her prompt action, I recovered it all within 24 hours. Her dedication and skills eased my financial stress. She is always ready to assist others with similar issues. For help, she can be reached by email at Recoveryfundprovider@gmail. com or contact her through WhatsApp at +44 736 644 5035. Her Insta is recoveryfundprovider.

  • 24.02.25 07:19 maggie4567

    Jasmine Lopez specializes in recovering stolen cryptocurrencies, especially ETH/USDT. She has built a strong reputation for helping victims reclaim their lost funds. A personal example highlights her effectiveness: I lost $111,000 and, thanks to her prompt action, I recovered it all within 24 hours. Her dedication and skills eased my financial stress. She is always ready to assist others with similar issues. For help, she can be reached by email at Recoveryfundprovider@gmail. com or contact her through WhatsApp at +44 736 644 5035. Her Insta is recoveryfundprovider.

  • 27.02.25 12:46 monikaguttmacher

    Weddings are supposed to be magical, but the months leading up to mine were anything but. Already, wedding planning was a high-stress, sleep-deprived whirlwind: endless details to manage, from venue deposits and guest lists to dress fittings and vendor contracts. But nothing-and I mean, nothing-compared to the panic that washed over me when I realized that somehow, I had lost access to my Bitcoin wallet-with $600,000 inside. It happened in the worst possible way. In between juggling my to-do lists and trying to keep my sanity intact, I lost my seed phrase. I went through my apartment like a tornado, flipping through notebooks, checking every email, every file-nothing. I sat there in stunned silence, heart pounding, trying to process the fact that my entire savings, my security, and my financial future might have just vanished. In utter despair, I vented to my bridesmaid's group chat for some sympathetic words from the girls. Instead, one casually threw out a name that would change everything in a second: "Have you ever heard of Tech Cyber Force Recovery? They recovered Bitcoin for my cousin. You should call them." I had never heard of them before, but at that moment, I would have tried anything. I immediately looked them up, scoured reviews, and found story after story of people just like me—people who thought they had lost everything, only for Tech Cyber Force Recovery to pull off the impossible. That was all the convincing I needed. From the very first call, I knew I was in good hands. Their team was calm, professional, and incredibly knowledgeable. They explained the recovery process in a way that made sense, even through my stress-fogged brain. Every step of the way, they kept me informed, reassured me, and made me feel like this nightmare actually had a solution. And then, just a few days later, I got the message: "We have recovered your Bitcoin." (EMAIL. support @ tech cyber force recovery . com) OR WHATSAPP (+1 56 17 26 36 97) I could hardly believe my eyes: Six. Hundred. Thousand. Dollars. In my hands again. I let out my longest breath ever and almost cried, relieved. It felt like I woke up from a bad dream, but it was real, and Tech Cyber Force Recovery had done it. Because of them, I walked down the aisle not just as a bride, but as someone who had dodged financial catastrophe. Instead of spending my honeymoon stressing over lost funds, I got to actually enjoy it—knowing that my wallet, and my future, were secure. Would I refer to them? In a heartbeat. If you ever find yourself in that situation, please don't freak out, just call Tech Cyber Force Recovery. They really are the real deal.

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