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[Перевод] ИИ-модели ломаются при обучении на рекурсивно сгенерированных данных

Ранее мы разбирали документ о неизбежности AGI от Леопольда Ашенбреннера, в котором говорилось о «Стене данных». Проблема заключается в том, что данные для обучения новых ИИ-моделей в интернете не бесконечны. Один из подходов для обхода этой стены — создание синтетических данных, т.е сгенерированных ИИ. Ученые из Оксфордского и Кембриджского университетов опубликовали статью, в которой исследуют возможность использования таких данных при обучении новых моделей. Спойлер: модели сломались.

Используемые термины

Выразительность (expressivity) — широта идей, которые могут быть представлены и переданы. Чем более выразителен язык, тем большее количество идей он может использовать для представления. Иными словами, выразительная сила языка определяет, насколько хорошо он подходит для описания различных концепций и понятий. Язык с высокой выразительностью позволяет ясно и точно выражать сложные идеи, в то время как менее выразительные языки могут быть ограничены в своих возможностях передачи некоторых мыслей или требовать более многословных конструкций для достижения той же цели.

Аппроксимация (approximation) — это процесс оценки значения функции для заданного входного параметра с помощью более простой функции, которая близко напоминает исходную. Другими словами, когда точное значение функции сложно или невозможно вычислить напрямую, мы можем попытаться найти приближенное решение, используя другую, более простую функцию. Эта упрощенная функция должна вести себя похожим образом и давать результаты, близкие к оригинальной функции.

Аннотация

Модель Stable diffusion стала революционной в создании изображений из описательного текста. Модели GPT-3.5 и GPT-4 продемонстрировали хорошую производительность в широком спектре задач. Когда мы только познакомились с этими моделями, стало ясно, что генеративный искусственный интеллект — это не временное явление. Он существенно изменит (и уже изменил) подход к созданию текстов и картинок.

В исследовании мы рассмотрим, что произойдет с генеративными моделями, когда LLM будут использовать для обучения данные, сгенерированные самими же моделями. Мы обнаружили, что неизбирательное использование таких данных в конечном итоге приводит к необратимым дефектам. Этот эффект мы назвали «Коллапсом модели» и выяснили, что он может возникать как и в LLM, так в VAE и GMM. Это явление встречается среди всех обученных генеративных моделей, и мы хотим продемонстрировать, что к данной проблеме нужно отнестись серьезно.

Основная часть

Разработка LLM — это сложный процесс, требующий огромного количества данных для обучения. Хотя современные LLM были обучены преимущественно на текстах, созданных людьми, в будущем это может измениться. Если данные для обучения будущих моделей также будут собираться из интернета, то определенный процент из этих данных будет контентом, сгенерированным ИИ. В этой статье мы исследуем, что произойдет, если текст, сгенерированный, например, ChatGPT, составит большую часть обучающего набора данных для последующих моделей. Что в таком случае будет происходить с GPT-{n} по мере увеличения n?

Мы обнаружили, что неизбирательное обучение на данных, созданных другими моделями, приводит к «коллапсу модели» — дегенеративному процессу, при котором модель со временем забывает истинное распределение данных, даже при отсутствии сдвига в распределении. Со временем модель начинает терять информацию об истинном распределении, что сначала проявляется в исчезновении «хвостов», а выученное поведение сходится от поколения к поколению к точечной оценке с очень малой дисперсией. Кроме того, мы выяснили, что этот процесс неизбежен даже в случаях с почти идеальными условиями для долгосрочного обучения, то есть при отсутствии ошибки оценки функции.

Также существует две близкие концепции к коллапсу модели из уже существующей литературы: катастрофическое забывание, возникающее в рамках непрерывного обучения без задач, и отравление данных, злонамеренно приводящее к непреднамеренному поведению. Ни одна из них не может полностью объяснить феномен коллапса модели, поскольку условия принципиально отличаются. Но, все же они дают другой взгляд на наблюдаемое явление. Мы хотим отметить, что доступ к исходному распределению данных имеет решающее значение: в задачах, в которых важны «хвосты» базового распределения, необходим доступ к реальным данным, созданным человеком. Другими словами, повсеместное использование LLM для публикации контента в Интернете со временем неизбежно загрязнит набор данных для обучения их преемников.

Что такое коллапс модели?

Коллапс модели — это дегенеративный, влияющий на поколения генеративных моделей процесс, при котором генерируемые данные в конечном итоге загрязняют обучающий набор следующего поколения. Будучи обученными на загрязненных данных, они неправильно воспринимают реальность. Этот процесс изображен на рисунке ниже. Мы выделяем два особых случая: ранний коллапс модели и поздний коллапс модели. При раннем коллапсе модели она начинает терять информацию о хвостах распределения; при позднем коллапсе модели она сходится к распределению, которое мало похоже на исходное, часто со значительно уменьшенной дисперсией.

Коллапс модели

Этот процесс происходит из-за трех конкретных источников ошибок, накапливающихся из поколения в поколение и вызывающих отклонение от исходной модели:

Statistical approximation error. Это основной тип ошибки, возникающий из-за того, что количество выборок конечно, и исчезающий при стремлении количества выборок к бесконечности. Это происходит из-за ненулевой вероятности потери информации на каждом шаге повторной выборки.

Functional expressivity error. Это вторичный тип ошибки, возникающий из-за ограниченной выразительности аппроксиматора функции. В частности, нейронные сети являются универсальными аппроксиматорами только при стремлении их размера к бесконечности. Простым примером ошибки выразительности является попытка аппроксимировать смесь двух гауссовых распределений одним гауссовым распределением. Даже если у нас есть идеальная информация о распределении данных (то есть бесконечное количество выборок), ошибки модели неизбежны. Однако при отсутствии двух других типов ошибок это может произойти только в первом поколении.

Functional approximation error. Это вторичный тип ошибки, возникающий в основном из-за ограничений процедур обучения, например, структурной предвзятости стохастического градиентного спуска или выбора целевой функции. Эту ошибку можно рассматривать как возникающую в пределе бесконечных данных и идеальной выразительности на каждом поколении.

Каждая из вышеперечисленных ошибок может привести к коллапсу модели в той или иной степени. Стоит отметить, что существуют и другие типы ошибок. Например, на практике компьютеры имеют ограниченную точность. Теперь попробуем объяснить, как вышеперечисленные факторы приводят к наблюдаемым ошибкам, как различные источники могут накапливаться и как мы можем количественно оценить среднее отклонение модели.

Математическое обоснование

В этом разделе мы предоставим теоретическое обоснование феномена коллапса модели. Мы утверждаем, что процесс коллапса модели является универсальным для генеративных моделей, которые рекурсивно обучаются на данных, сгенерированных предыдущими поколениями. Мы количественно оценим источники ошибок, обсуждаемых в предыдущем разделе, исследуя две математические модели, которые оказываются достаточно простыми, чтобы обеспечить аналитические выражения для интересующих нас величин, но также отражают феномен коллапса модели: дискретное распределение в отсутствие функциональной выразительности и ошибок аппроксимации, а также многомерное гауссовское приближение, отражающее совместные функциональные выразительные и статистические ошибки.

Общий стохастический процесс, который мы рассматриваем и называем обучением с использованием данных поколений, выглядит следующим образом. Набор данных в поколении — это ${{\mathcal{D}}}_{i}$ , состоящий из независимых и одинаково распределенных случайных переменных ${X}_{j}^{i}$ с распределением p_i, j ∈ {1,…, M_i} . Он обозначает размер набора данных. Переходя от поколения к поколению i+1 , мы стремимся оценить распределение выбросок в ${{\mathcal{D}}}_{i}$ , с аппроксимацией ${p}_{{\theta }_{i+1}}={{\mathcal{F}}}_{\theta }({p}_{i})$ . Этот шаг мы называем функциональной аппроксимацией, ${p}_{{\theta }_{i+1}}={{\mathcal{F}}}_{\theta }({p}_{i})$ . Затем набор данных ${{\mathcal{D}}}_{i+1}$ генерируется путем выборки из ${p}_{i+1}={\alpha }_{i}{p}_{{\theta }_{i+1}}+{\beta }_{i}{p}_{i}+{\gamma }_{i}{p}_{0}$ , с неотрицательными параметрами αi, βi, γi, сумма которых равна 1. То есть, они представляют собой доли данных, используемых в разных поколениях. Это соответствует смешиванию данных, поступающих из исходного распределения (γi), данных, используемых предыдущим поколением (βi), и данных, сгенерированных новой моделью (αi). Мы называем это шагом выборки. Для представленных далее математических моделей мы рассматриваем αi = γi = 0, то есть используются данные только одного шага, тогда как численные эксперименты проводятся на более реалистичных вариантах параметров.

Дискретные распределения с точной аппроксимацией

В этом подразделе мы рассматриваем дискретное вероятностное распределение при отсутствии ошибок функциональной аппроксимации и выразительности, то есть ${\mathcal{F}}(p)=p$ . В этом случае коллапс модели возникает только из-за статистических ошибок на этапе выборки. Сначала хвосты (маловероятные события) начинают исчезать в результате низкой вероятности их выборки, и со временем носитель распределения сжимается. Обозначая размер выборки как , если мы рассмотрим состояние с вероятностью $q\le \frac{1}{M}$ , ожидаемое количество выборок со значением , исходящих из этих событий, будет меньше 1. На практике это будет означать, что мы теряем о них информацию. Рассматривая более общее состояние с вероятностью , используя стандартную условную вероятность, мы можем показать, что вероятность потери информации (то есть отсутствия данных на каком-либо поколении) равна 1 - q , что подразумевает, что распределение должно сходиться к дельта-функции, расположенной в некотором состоянии, с вероятностью оказаться в определенном состоянии, равной вероятности выборки указанного состояния из исходного распределения.

Это можно показать напрямую, рассматривая процесс ${{\bf{X}}}^{i}\to {\mathcal{F}}\,\to {p}_{i+1}\to {{\bf{X}}}^{i+1}$ как цепь Маркова, поскольку $X^{i+1}$ зависит только от X^i . Кроме того, если все X_j^i имеют одинаковое значение, то на следующем поколении аппроксимированное распределение будет в точности дельта-функцией, и, следовательно, все ${X}_{j}^{i+1}$ также будут иметь одинаковое значение. Это означает, что цепь Маркова содержит по крайней мере одно поглощающее состояние и, следовательно, с вероятностью 1, сойдется к одному из поглощающих состояний. Для этой цепи единственными поглощающими состояниями являются те, которые соответствуют дельта-функциям. В результате, когда мы следим за прогрессом коллапса модели, мы гарантированно окажемся в постоянном состоянии, потеряв всю информацию об исходном распределении, когда цепь поглощается. Этот аргумент также работает в общем случае из-за того, что представления с плавающей запятой дискретны, что делает цепь Маркова над параметрами модели дискретной. Таким образом, до тех пор, пока параметризация модели допускает дельта-функции, мы придем к ней, потому что из-за ошибок выборки единственными возможными поглощающими состояниями являются дельта-функции. На основе приведенного выше обсуждения мы видим, как ранний коллапс модели, при котором отсекаются только маловероятные события, и поздний коллапс модели, при котором процесс начинает сворачиваться в один режим, должны возникать в случае дискретных распределений с идеальной функциональной аппроксимацией.

Коллапс гауссовой модели

Предположим, что исходные данные извлечены из распределения D_o (не обязательно гауссова) с ненулевой выборочной дисперсией. Предположим, что X_n рекурсивно подгоняются с использованием несмещенных оценок выборочного среднего и дисперсии из предыдущего поколения, ${X}_{j}^{n}| {\mu }_{n},{\Sigma }_{n} \sim {\mathcal{N}}({\mu }_{n},{\Sigma }_{n})$ , с фиксированным размером выборки. Тогда,

${\mathbb{E}}[{{\mathbb{W}}}_{2}^{2}({\mathcal{N}}({\mu }_{n},{\Sigma }_{n}),{{\mathcal{D}}}_{0})]\to \infty ;\,{\Sigma }_{n}\,\mathop{\to }\limits^{{\rm{a}}.{\rm{s}}.}\,0\,\,{\rm{a}}{\rm{s}}\,\,n\to \infty ,$

где ${{\mathbb{W}}}_{2}$ обозначает расстояние Вассерштейна между истинным распределением и его приближением на поколении .

Другими словами, это означает, что не только приближение n-го поколения произвольно далеко отклоняется от исходного, но и с вероятностью 1 сжимается до нулевой дисперсии при увеличении числа поколений. Результаты очень похожи на те, что наблюдались в дискретном случае, причем эта теорема иллюстрирует эффект позднего коллапса модели, когда процесс начинает сворачиваться до нулевой дисперсии. Ранний коллапс модели также можно увидеть, и заинтересованному читателю рекомендуется обратиться к дополнительным материалам для более подробного обсуждения.

Коллапс моделей в языковых моделях

Коллапс модели универсален для различных семейств моделей машинного обучения. Однако, если небольшие модели, такие как GMM и VAE, обычно обучаются с нуля, то с большими языковыми моделями (LLM) дело обстоит иначе. Они настолько дороги в переобучении с нуля, что обычно инициализируются с помощью предварительно обученных моделей, таких как BERT4, RoBERTa5 или GPT-2, которые обучаются на больших текстовых массивах. Затем они дообучаются для различных прикладных задач.

Мы исследовали, что происходит с языковыми моделями, когда они последовательно дообучаются на данных, сгенерированных другими моделями. Мы можем легко воспроизвести все эксперименты, описанные в этой статье, с более крупными языковыми моделями в режиме обучения с нуля, чтобы продемонстрировать коллапс модели. Учитывая, что обучение одной умеренно большой модели производит в два раза больше CO2, чем один человек за всю свою жизнь, мы решили не проводить такой эксперимент, а вместо этого сосредоточиться на более реалистичной постановке задачи для последующего доказательства. Отметим, что даже описанные в этой статье языковые эксперименты заняли несколько недель.

Мы будем оценивать наиболее распространенную схему обучения языковой модели — дообучение. В этой схеме каждый цикл обучения начинается с предварительно обученной модели на недавних данных. Данные поступают от другой предварительно обученной модели. Поскольку обучение ограничено получением моделей, близких к исходной предварительно обученной модели, а точки данных, сгенерированные моделями, обычно будут давать очень небольшие градиенты, здесь можно ожидать, что модель после дообучения должна измениться лишь умеренно. Мы решили дообучить языковую модель OPT-125m, которая была выпущена Meta через Hugging Face6.

Мы дообучили ее на наборе данных wikitext216. Для генерации данных из обученных моделей мы использовали лучевой поиск с пятью лучами. Мы ограничили длину последовательностей для обучения 64 токенами. Затем, для каждой последовательности токенов в обучающем наборе, мы просили модель предсказать следующие 64 токена. Мы также прошли через весь исходный обучающий набор данных и создали искусственный набор данных того же размера. Каждый эксперимент проводился пять раз, и результаты показаны как пять отдельных прогонов с разными значениями случайных начальных условий. Исходная модель, дообученная на реальных данных wikitext2, получает среднюю перплексию 34 при базовом значении и 115 при нулевом обучении, то есть она успешно обучается задаче. Далее мы рассматривали два варианта настройки:

Пять эпох, без исходных обучающих данных. Здесь модель обучается в течение пяти эпох, начиная с исходного набора данных, но без сохранения исходных данных для последующих прогонов. Общая производительность исходной задачи представлена на рисунке ниже. Мы обнаружили, что обучение на сгенерированных данных позволяет адаптироваться к базовой задаче, теряя некоторую производительность, от 20 до 28 баллов перплексии.

Десять эпох, сохранено 10% исходных обучающих данных. Здесь модель обучается в течение десяти эпох на исходном наборе данных, и при каждой новой генерации обучения случайным образом выбирается 10% исходных точек данных. Общая производительность исходной задачи представлена на рисунке ниже. Мы обнаружили, что сохранение исходных данных позволяет лучше дообучать модель и приводит лишь к незначительному ухудшению производительности.

Оба режима обучения привели к ухудшению производительности наших моделей, однако мы обнаружили, что обучение на сгенерированных данных возможно, и модели могут успешно обучаться (некоторым) базовым задачам. Важно отметить, что наблюдаемое поведение согласуется с теоретическим обоснованием из раздела «Математическое обоснование».

Пример

Ниже мы привели пример того, как ответы модели OPT-125m, подверженной коллапсу модели, ухудшаются от поколения к поколению.

Input: some started before 1360 — was typically accomplished by a master mason and a small team of itinerant masons, supplemented by local parish labourers, according to Poyntz Wright. But other authors reject this model, suggesting instead that leading architects designed the parish church towers based on early examples of Perpendicular.
Поколение 0: Revival architecture such as St. John’s Cathedral in London. The earliest surviving example of Perpendicular Revival architecture is found in the 18th @-@ century Church of Our Lady of Guernsey, which dates from the late 19th century. There are two types of perpendicular churches : those.
Поколение 1: architecture such as St. Peter’s Basilica in Rome or St. Peter’s Basilica in Buenos Aires. There is no evidence that any of these buildings were built during the reign of Pope Innocent III, but it is possible that they may have been built during the reign of his successor, Pope Innocent.
Поколение 5: ism, which had been translated into more than 100 languages including English, French, German, Italian, Spanish, Portuguese, Dutch, Swedish, Norwegian, Polish, Hungarian, Slovak, Lithuanian, Estonian, Finnish, Romanian, Bulgarian, Turkish, Croatian, Serbian, Ukrainian, Russian, Kazakh, Kyrgyz.
Поколение 9: architecture. In addition to being home to some of the world’s largest populations of black @-@ tailed jackrabbits, white @-@ tailed jackrabbits, blue @-@ tailed jackrabbits, red @-@ tailed jackrabbits, yellow @-.

Мы обнаружили, что данные, сгенерированные языковыми моделями в наших экспериментах, в конечном итоге содержат большое количество повторяющихся фраз, как в примере выше. Проблема повторения наблюдалась практически во всех генеративных моделях и, чтобы исключить это как причину коллапса модели, мы дополнительно провели эксперименты, когда модели явно поощрялись к созданию неповторяющихся последовательностей со штрафом за повторение 2.0. Мы обнаружили, что это заставило модели выдавать продолжения с меньшим количеством повторов, что, как следствие, привело к ухудшению в работе последующих моделей, в частности к удвоению перплексии по сравнению с исходными результатами. Модели остались такими же восприимчивыми к коллапсу модели, если не более.

Описанный процесс демонстрирует, что тонкая настройка языковых моделей не сдерживает эффекты коллапса модели, и модели, которые подвергаются тонкой настройке, также уязвимы.

Исходный код для всех экспериментов можно посмотреть здесь. А оригинал статьи без перевода можно прочитать тут.

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29 июл. 2024 г.

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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26.06.24 18:46 Jacobethannn098

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04.07.24 04:49 ZionNaomi

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13.07.24 21:13 michaelharrell825

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17.07.24 02:26 thompsonrickey

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02.09.24 20:24 [email protected]

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06.09.24 01:44 Celinagarcia

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16.09.24 00:10 marcusaustin

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16.09.24 00:11 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.
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02.10.24 22:27 Emily Hunter

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31.10.24 00:13 ytre89

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12.11.24 00:50 TERESA

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17.11.24 09:31 Vivianlocke223

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Being a parent is great until your toddler figures out how to use your devices. One afternoon, I left my phone unattended for just a few minutes rookie mistake of the century. I thought I’d take a quick break, but little did I know that my curious little genius was about to embark on a digital adventure. By the time I came back, I was greeted by two shocking revelations: my toddler had somehow managed to buy a $5 dinosaur toy online and, even more alarmingly, had locked me out of my cryptocurrency wallet holding a hefty $75,000. Yes, you heard that right a dinosaur toy was the least of my worries! At first, I laughed it off. I mean, what toddler doesn’t have a penchant for expensive toys? But then reality set in. I stared at my phone in disbelief, desperately trying to guess whatever random string of gibberish my toddler had typed as a new password. Was it “dinosaur”? Or perhaps “sippy cup”? I felt like I was in a bizarre game of Password Gone Wrong. Every attempt led to failure, and soon the laughter faded, replaced by sheer panic. I was in way over my head, and my heart raced as the countdown of time ticked away. That’s when I decided to take action and turned to Digital Tech Guard Recovery, hoping they could solve the mystery that was my toddler’s handiwork. I explained my predicament, half-expecting them to chuckle at my misfortune, but they were incredibly professional and empathetic. Their confidence put me at ease, and I knew I was in good hands. Contact With WhatsApp: +1 (443) 859 - 2886 Email digital tech guard . com Telegram: digital tech guard recovery . com website link :: https : // digital tech guard . com Their team took on the challenge like pros, employing their advanced techniques to unlock my wallet with a level of skill I can only describe as magical. As I paced around, anxiously waiting for updates, I imagined my toddler inadvertently locking away my life savings forever. But lo and behold, it didn’t take long for Digital Tech Guard Recovery to work their magic. Not only did they recover the $75,000, but they also gave me invaluable tips on securing my wallet better like not leaving it accessible to tiny fingers! Who knew parenting could lead to such dramatic situations? Crisis averted, and I learned my lesson: always keep my devices out of reach of little explorers. If you ever find yourself in a similar predicament whether it’s tech-savvy toddlers or other digital disasters don’t hesitate to reach out to Digital Tech Guard Recovery. They saved my funds and my sanity, proving that no challenge is too great, even when it involves a toddler’s mischievous fingers!
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22.11.24 04:41 [email protected]

I never could have imagined the nightmare of losing access to my digital wallet. All of my cryptocurrency holdings were abruptly imprisoned, inaccessible, and appeared to be lost forever following a catastrophic hardware breakdown. Years of meticulous investment and careful saving were reduced to nothing more than strings of code that I could no longer control, and I could feel the dread and sorrow that swept through me at that very instant. Thankfully, during my worst moment, I came into (TRUST GEEKS HACK EXPERT), a professional service devoted to recovering lost or inaccessible digital data. With optimism, I went out to their team of skilled technologists, laying bare the full nature of my issue. What followed was a laborious, multi-step process that required an almost surgical level of digital forensics and Bitcoin skill. In order to create a thorough profile of my wallet's contents and activities, the (TRUST GEEKS HACK EXPERT) team first thoroughly examined the transaction history and metadata connected to it. Next, they implemented a series of advanced recovery techniques, using cutting-edge software tools to bypass the access barriers that had left me locked out. The entire process was shrouded in secrecy and discretion, with the (TRUST GEEKS HACK EXPERT) team working tirelessly to protect the confidentiality of my sensitive financial information. After what felt like an eternity of nervous anticipation, the day finally arrived when I received the triumphant notification – my wallet had been successfully restored, and all of my precious digital assets had been returned to my control. The sense of relief was indescribable, as I could finally breathe easy knowing that the fruits of my financial discipline had been safeguarded. While the experience of losing access to my wallet was undoubtedly traumatic, (TRUST GEEKS HACK EXPERT) intervention allowed me to emerge from the ordeal with my cryptocurrency holdings intact, and a renewed appreciation for the importance of proactive digital asset management. You can contact Them through EMAIL: [email protected] - TELEGRAM: TRUSTGEEKSHACKEXPERT
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22.11.24 23:43 teresaborja

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I never could have imagined the nightmare of losing access to my digital wallet. All of my cryptocurrency holdings were abruptly imprisoned, inaccessible, and appeared to be lost forever following a catastrophic hardware breakdown. Years of meticulous investment and careful saving were reduced to nothing more than strings of code that I could no longer control, and I could feel the dread and sorrow that swept through me at that very instant. Thankfully, during my worst moment, I came into (TRUST GEEKS HACK EXPERT), a professional service devoted to recovering lost or inaccessible digital data. With optimism, I went out to their team of skilled technologists, laying bare the full nature of my issue. What followed was a laborious, multi-step process that required an almost surgical level of digital forensics and Bitcoin skill. In order to create a thorough profile of my wallet's contents and activities, the (TRUST GEEKS HACK EXPERT) team first thoroughly examined the transaction history and metadata connected to it. Next, they implemented a series of advanced recovery techniques, using cutting-edge software tools to bypass the access barriers that had left me locked out. The entire process was shrouded in secrecy and discretion, with the (TRUST GEEKS HACK EXPERT) team working tirelessly to protect the confidentiality of my sensitive financial information. After what felt like an eternity of nervous anticipation, the day finally arrived when I received the triumphant notification – my wallet had been successfully restored, and all of my precious digital assets had been returned to my control. The sense of relief was indescribable, as I could finally breathe easy knowing that the fruits of my financial discipline had been safeguarded. While the experience of losing access to my wallet was undoubtedly traumatic, (TRUST GEEKS HACK EXPERT) intervention allowed me to emerge from the ordeal with my cryptocurrency holdings intact, and a renewed appreciation for the importance of proactive digital asset management. You can contact Them through EMAIL: [email protected] - TELEGRAM: TRUSTGEEKSHACKEXPERT
25.11.24 02:19 briankennedy

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25.11.24 02:20 briankennedy

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26.11.24 23:12 rickrobinson8

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00:39 [email protected]

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