Связанно с монетами
Калькулятор цены
История цены
Прогноз цены
Технический анализ
Руководство по покупке монет
Категория криптовалют
Калькулятор прибыли
Курс GPT ProtocolGPT
Валюта котировки:
USD
Что вы думаете о GPT Protocol сегодня?
ХорошоПлохо
Примечание: данная информация носит исключительно справочный характер.
Цена GPT Protocol на сегодня
Актуальная цена GPT Protocol на сегодня составляет $0.003368 за (GPT / USD) с текущей капитализацией $0.00 USD. Торговый объем за 24 ч. составляет $23,400.76 USD. Цена GPT в USD обновляется в режиме реального времени. Изменение цены GPT Protocol: 3.20% за последние 24 ч. Объем в обращении составляет 0 .
Какова наибольшая цена GPT?
GPT имеет исторический максимум (ATH) $0.03317, зафиксированный 2024-04-22.
Какова наименьшая цена GPT?
Исторический минимум GPT (ATL): $0.002960, зафиксированный 2024-11-22.
Прогноз цен на GPT Protocol
Какой будет цена GPT в 2025?
Основываясь на модели прогнозирования исторических показателей GPT, цена GPT может достигнуть $0.008256 в 2025 г.
Какой будет цена GPT в 2030?
Ожидается, что в 2030 году цена GPT изменится на -9.00%. По прогнозам, к концу 2030 года цена GPT достигнет $0.01634, а совокупный ROI составит +366.33%.
История цен GPT Protocol (USD)
Цена GPT Protocol изменилась на -88.07% за последний год. Самая высокая цена в USD за последний год составила $0.03317, а самая низкая цена в USD за последний год составила $0.002960.
ВремяИзменение цены (%)Самая низкая ценаСамая высокая цена
24h+3.20%$0.003251$0.003504
7d+2.97%$0.002960$0.003518
30d-18.78%$0.002960$0.004857
90d-50.07%$0.002960$0.01041
1y-88.07%$0.002960$0.03317
Все время-87.89%$0.002960(2024-11-22, 6 дней назад )$0.03317(2024-04-22, 220 дней назад )
Информация о рынке криптовалют
Рыночная капитализация
--
+3.20%
Полностью разводненная рыночная капитализация
$3,367,872.83
+3.20%
24 ч. объем
$23,400.76
-44.68%
Рыночные рейтинги
Скорость обращения
0.00%
Объем за 24 часа / рыночная капитализация
0.00%
Объем в обращении
0 GPT
Общий запас / Максимальный запас
1,000,000,000 GPT
1,000,000,000 GPT
Рейтинг GPT Protocol
Средний рейтинг от сообщества
4.6
Содержимое страницы представлено только в ознакомительных целях.
GPT в местную валюту
1 GPT в MXN$0.071 GPT в GTQQ0.031 GPT в CLP$3.291 GPT в HNLL0.081 GPT в UGXSh12.391 GPT в ZARR0.061 GPT в TNDد.ت0.011 GPT в IQDع.د4.41 GPT в TWDNT$0.111 GPT в RSDдин.0.371 GPT в DOP$0.21 GPT в MYRRM0.011 GPT в GEL₾0.011 GPT в UYU$0.141 GPT в MADد.م.0.031 GPT в AZN₼0.011 GPT в OMRر.ع.01 GPT в KESSh0.441 GPT в SEKkr0.041 GPT в UAH₴0.14
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Последнее обновление 2024-11-28 11:24:07(UTC+0)
Как купить GPT Protocol(GPT)
Создайте бесплатный аккаунт на Bitget
Зарегистрируйтесь на Bitget, указав свой адрес электронной почты/номер мобильного телефона, и придумайте надежный пароль для защиты учетной записи.
Верификация учетной записи
Подтвердите свою личность, введя персональные данные и загрузив действительное удостоверение личности с фотографией.
Купить GPT Protocol (GPT)
Чтобы купить GPT Protocol на Bitget, используйте различные способы оплаты. Мы покажем вам, как это сделать.
Присоединяйтесь к копированию сделок с GPT, подписываясь на элитных трейдеров.
После регистрации на Bitget и покупки USDT или GPT вы также можете начать копировать сделки, подписавшись на элитных трейдеров.
Новости о GPT Protocol
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Новые листинги на Bitget
Новые листинги
Купить больше
ЧАСТО ЗАДАВАЕМЫЕ ВОПРОСЫ
Какова текущая цена GPT Protocol?
Актуальная цена GPT Protocol составляет $0 за (GPT/USD) с текущей рыночной капитализацией $0 USD. Стоимость GPT Protocol подвержена частым колебаниям из-за постоянной круглосуточной активности на криптовалютном рынке. Текущая цена GPT Protocol в реальном времени и ее исторические данные доступны на Bitget.
Каков торговый объем GPT Protocol за 24 часа?
За последние 24 часа торговый объем GPT Protocol составил $23,400.76.
Какая рекордная цена GPT Protocol?
Рекордная цена GPT Protocol составляет $0.03317. Это самая высокая цена GPT Protocol с момента запуска.
Могу ли я купить GPT Protocol на Bitget?
Можете. GPT Protocol представлен на централизованной бирже Bitget. Более подробную инструкцию можно найти в полезном гайде Как купить .
Могу ли я получать стабильный доход от инвестиций в GPT Protocol?
Конечно, Bitget предоставляет платформа для стратегического трейдинга с интеллектуальными торговыми ботами для автоматизации ваших сделок и получения прибыли.
Где я могу купить GPT Protocol по самой низкой цене?
Мы рады сообщить, что платформа для стратегического трейдинга теперь доступен на бирже Bitget. Bitget предлагает лучшие в отрасли торговые сборы и глубину для обеспечения прибыльных инвестиций для трейдеров.
Где можно купить GPT Protocol (GPT)?
Раздел с видео – быстрая верификация, быстрая торговля
Как пройти верификацию личности на Bitget и защитить себя от мошенничества
1. Войдите в ваш аккаунт Bitget.
2. Если вы новичок на Bitget, ознакомьтесь с нашим руководством по созданию аккаунта.
3. Наведите курсор на значок профиля, нажмите на «Не верифицирован» и нажмите «Верифицировать».
4. Выберите страну или регион выдачи и тип документа, а затем следуйте инструкциям.
5. Выберите «Верификация по мобильному» или «ПК» в зависимости от ваших предпочтений.
6. Введите свои данные, предоставьте копию вашего удостоверения личности и сделайте селфи.
7. Отправьте вашу заявку, и вуаля, вы прошли верификацию личности!
Инвестирование в криптовалюты, включая покупку GPT Protocol онлайн на Bitget, подразумевает риски. Bitget предлагает легкие и удобные способы покупки GPT Protocol и делает все возможное, чтобы предоставить полную информацию о криптовалюте, представленной на бирже. Однако платформа не несет ответственность за последствия вашей покупки GPT Protocol. Вся представленная информация не является рекомендацией покупки.
Bitget Идеи
sanajan
2дн.
Predictive Analytics: The Core of $ASI’s AI Strategy
Predictive analytics forms the backbone of $ASI’s AI-powered trading strategy, enabling the generation of actionable insights for traders. By leveraging data-driven forecasting models, $ASI delivers cutting-edge solutions that help users anticipate market trends and optimize trading decisions. Here’s how predictive analytics drives $ASI’s AI strategy:
1. What is Predictive Analytics?
Predictive analytics involves using statistical algorithms, machine learning (ML), and historical data to identify patterns and forecast future outcomes. In the context of $ASI, this means predicting market movements, price trends, and trading opportunities with high precision.
2. Data-Driven Decision Making
At the core of $ASI’s strategy is the ability to process vast datasets to uncover meaningful insights. Key data inputs include:
Historical Price Movements: Identifies long-term and short-term trends.
Real-Time Market Data: Analyzes price changes, trading volumes, and order books.
Sentiment Analysis: Uses natural language processing (NLP) to gauge investor sentiment from news and social media.
Macroeconomic Factors: Incorporates global financial indicators to provide a holistic view of market dynamics.
3. Core Components of $ASI’s Predictive Models
The $ASI AI platform integrates several technologies to enhance its predictive capabilities:
Machine Learning Algorithms: Adaptive models learn from historical data and refine predictions as new data emerges.
Deep Learning Neural Networks: Processes complex relationships within data for advanced pattern recognition.
Statistical Techniques: Ensures robust and reliable predictions through regression, time-series analysis, and probabilistic models.
Reinforcement Learning: Improves predictions through iterative testing and feedback from past outcomes.
4. Application in Trading
Predictive analytics enables traders to capitalize on opportunities and manage risks effectively:
Trend Forecasting: Anticipates upward or downward price trends across assets.
Volatility Predictions: Alerts traders to potential price fluctuations, helping them prepare for market swings.
Entry and Exit Points: Suggests optimal times to buy or sell assets for maximum profitability.
Risk Mitigation: Provides warnings about adverse market conditions or potential losses.
5. Key Benefits of Predictive Analytics for $ASI Users
Improved Accuracy: Delivers precise signals based on in-depth data analysis.
Faster Decision-Making: Processes data in real-time, enabling traders to react promptly to market changes.
Emotion-Free Trading: Replaces subjective decision-making with objective, data-driven recommendations.
Scalability: Supports trading across multiple markets and assets simultaneously.
6. AI Models in Predictive Analytics
The $ASI AI strategy employs state-of-the-art models to ensure accuracy and reliability:
Time-Series Forecasting Models: ARIMA and LSTM networks for sequential data analysis.
Classification Models: Decision trees and support vector machines (SVMs) for event prediction (e.g., bullish/bearish trends).
Sentiment Models: NLP algorithms, such as BERT, to analyze sentiment data from text sources.
7. Integration with the $ASI Ecosystem
Predictive analytics enhances the overall utility and value of $ASI within the AltSignals ecosystem:
ActualizeAI: Provides predictive insights directly to users, improving their trading outcomes.
DeFi Integration: Helps users optimize liquidity provision and yield farming by predicting market conditions.
Token Value: Drives demand for $ASI tokens, as they are required to access premium AI-powered features.
8. Continuous Improvement Through Feedback Loops
$ASI’s AI models evolve over time, ensuring the platform remains effective in changing market conditions:
Performance Monitoring: Tracks the accuracy of predictions and adjusts models as needed.
User Feedback: Incorporates trader experiences to refine signal generation.
Adaptive Algorithms: Learns from new data to stay ahead of market trends.
9. Challenges in Predictive Analytics and Solutions
Market Noise: Predictive models may struggle with erratic price movements. $ASI mitigates this by combining multiple data sources for robust predictions.
Overfitting Risks: Regular model testing and validation ensure predictions remain generalized and accurate.
Data Integrity: Ensures high-quality data inputs by using verified sources and advanced preprocessing techniques.
10. The Future of Predictive Analytics in $ASI’s Strategy
Enhanced Models: Adoption of advanced AI technologies like GPT-style models for even more sophisticated predictions.
Cross-Market Predictions: Expanding beyond crypto to predict trends in traditional markets and other financial assets.
Automated Execution: Integrating predictive analytics with automated trading systems for hands-free, optimized trades.
Conclusion
Predictive analytics is the cornerstone of $ASI’s AI strategy, equipping traders with the tools they need to navigate complex and volatile markets. By combining cutting-edge technology with real-time insights, $ASI ensures its users stay ahead of the curve, making predictive analytics an indispensable part of the future of trading.
SOCIAL-2.09%
ASI-13.47%
جیک_بیک۔
2дн.
Predictive Analytics: The Core of $ASI’s AI Strategy
Predictive analytics forms the backbone of $ASI’s AI-powered trading strategy, enabling the generation of actionable insights for traders. By leveraging data-driven forecasting models, $ASI delivers cutting-edge solutions that help users anticipate market trends and optimize trading decisions. Here’s how predictive analytics drives $ASI’s AI strategy:
1. What is Predictive Analytics?
Predictive analytics involves using statistical algorithms, machine learning (ML), and historical data to identify patterns and forecast future outcomes. In the context of $ASI, this means predicting market movements, price trends, and trading opportunities with high precision.
2. Data-Driven Decision Making
At the core of $ASI’s strategy is the ability to process vast datasets to uncover meaningful insights. Key data inputs include:
Historical Price Movements: Identifies long-term and short-term trends.
Real-Time Market Data: Analyzes price changes, trading volumes, and order books.
Sentiment Analysis: Uses natural language processing (NLP) to gauge investor sentiment from news and social media.
Macroeconomic Factors: Incorporates global financial indicators to provide a holistic view of market dynamics.
3. Core Components of $ASI’s Predictive Models
The $ASI AI platform integrates several technologies to enhance its predictive capabilities:
Machine Learning Algorithms: Adaptive models learn from historical data and refine predictions as new data emerges.
Deep Learning Neural Networks: Processes complex relationships within data for advanced pattern recognition.
Statistical Techniques: Ensures robust and reliable predictions through regression, time-series analysis, and probabilistic models.
Reinforcement Learning: Improves predictions through iterative testing and feedback from past outcomes.
4. Application in Trading
Predictive analytics enables traders to capitalize on opportunities and manage risks effectively:
Trend Forecasting: Anticipates upward or downward price trends across assets.
Volatility Predictions: Alerts traders to potential price fluctuations, helping them prepare for market swings.
Entry and Exit Points: Suggests optimal times to buy or sell assets for maximum profitability.
Risk Mitigation: Provides warnings about adverse market conditions or potential losses.
5. Key Benefits of Predictive Analytics for $ASI Users
Improved Accuracy: Delivers precise signals based on in-depth data analysis.
Faster Decision-Making: Processes data in real-time, enabling traders to react promptly to market changes.
Emotion-Free Trading: Replaces subjective decision-making with objective, data-driven recommendations.
Scalability: Supports trading across multiple markets and assets simultaneously.
6. AI Models in Predictive Analytics
The $ASI AI strategy employs state-of-the-art models to ensure accuracy and reliability:
Time-Series Forecasting Models: ARIMA and LSTM networks for sequential data analysis.
Classification Models: Decision trees and support vector machines (SVMs) for event prediction (e.g., bullish/bearish trends).
Sentiment Models: NLP algorithms, such as BERT, to analyze sentiment data from text sources.
7. Integration with the $ASI Ecosystem
Predictive analytics enhances the overall utility and value of $ASI within the AltSignals ecosystem:
ActualizeAI: Provides predictive insights directly to users, improving their trading outcomes.
DeFi Integration: Helps users optimize liquidity provision and yield farming by predicting market conditions.
Token Value: Drives demand for $ASI tokens, as they are required to access premium AI-powered features.
8. Continuous Improvement Through Feedback Loops
$ASI’s AI models evolve over time, ensuring the platform remains effective in changing market conditions:
Performance Monitoring: Tracks the accuracy of predictions and adjusts models as needed.
User Feedback: Incorporates trader experiences to refine signal generation.
Adaptive Algorithms: Learns from new data to stay ahead of market trends.
9. Challenges in Predictive Analytics and Solutions
Market Noise: Predictive models may struggle with erratic price movements. $ASI mitigates this by combining multiple data sources for robust predictions.
Overfitting Risks: Regular model testing and validation ensure predictions remain generalized and accurate.
Data Integrity: Ensures high-quality data inputs by using verified sources and advanced preprocessing techniques.
10. The Future of Predictive Analytics in $ASI’s Strategy
Enhanced Models: Adoption of advanced AI technologies like GPT-style models for even more sophisticated predictions.
Cross-Market Predictions: Expanding beyond crypto to predict trends in traditional markets and other financial assets.
Automated Execution: Integrating predictive analytics with automated trading systems for hands-free, optimized trades.
Conclusion
Predictive analytics is the cornerstone of $ASI’s AI strategy, equipping traders with the tools they need to navigate complex and volatile markets. By combining cutting-edge technology with real-time insights, $ASI ensures its users stay ahead of the curve, making predictive analytics an indispensable part of the future of trading.
SOCIAL-2.09%
ASI-13.47%
Crypto-Hania
2дн.
$ASI Predictive Analytics: The Core of $ASI’s AI Strategy
$ASI Predictive analytics forms the backbone of $ASI’s AI-powered trading strategy, enabling the generation of actionable insights for traders. By leveraging data-driven forecasting models, $ASI delivers cutting-edge solutions that help users anticipate market trends and optimize trading decisions. Here’s how predictive analytics drives $ASI’s AI strategy:
$ASI
1. What is Predictive Analytics?
Predictive analytics involves using statistical algorithms, machine learning (ML), and historical data to identify patterns and forecast future outcomes. In the context of $ASI, this means predicting market movements, price trends, and trading opportunities with high precision.
2. Data-Driven Decision Making
At the core of $ASI’s strategy is the ability to process vast datasets to uncover meaningful insights. Key data inputs include:
Historical Price Movements: Identifies long-term and short-term trends.
Real-Time Market Data: Analyzes price changes, trading volumes, and order books.
Sentiment Analysis: Uses natural language processing (NLP) to gauge investor sentiment from news and social media.
Macroeconomic Factors: Incorporates global financial indicators to provide a holistic view of market dynamics.
3. Core Components of $ASI’s Predictive Models
The $ASI AI platform integrates several technologies to enhance its predictive capabilities:
Machine Learning Algorithms: Adaptive models learn from historical data and refine predictions as new data emerges.
Deep Learning Neural Networks: Processes complex relationships within data for advanced pattern recognition.
Statistical Techniques: Ensures robust and reliable predictions through regression, time-series analysis, and probabilistic models.
Reinforcement Learning: Improves predictions through iterative testing and feedback from past outcomes.
4. Application in Trading
Predictive analytics enables traders to capitalize on opportunities and manage risks effectively:
Trend Forecasting: Anticipates upward or downward price trends across assets.
Volatility Predictions: Alerts traders to potential price fluctuations, helping them prepare for market swings.
Entry and Exit Points: Suggests optimal times to buy or sell assets for maximum profitability.
Risk Mitigation: Provides warnings about adverse market conditions or potential losses.
5. Key Benefits of Predictive Analytics for $ASI Users
Improved Accuracy: Delivers precise signals based on in-depth data analysis.
Faster Decision-Making: Processes data in real-time, enabling traders to react promptly to market changes.
Emotion-Free Trading: Replaces subjective decision-making with objective, data-driven recommendations.
Scalability: Supports trading across multiple markets and assets simultaneously.
6. AI Models in Predictive Analytics
The $ASI AI strategy employs state-of-the-art models to ensure accuracy and reliability:
Time-Series Forecasting Models: ARIMA and LSTM networks for sequential data analysis.
Classification Models: Decision trees and support vector machines (SVMs) for event prediction (e.g., bullish/bearish trends).
Sentiment Models: NLP algorithms, such as BERT, to analyze sentiment data from text sources.
7. Integration with the $ASI Ecosystem
Predictive analytics enhances the overall utility and value of $ASI within the AltSignals ecosystem:
ActualizeAI: Provides predictive insights directly to users, improving their trading outcomes.
DeFi Integration: Helps users optimize liquidity provision and yield farming by predicting market conditions.
Token Value: Drives demand for $ASI tokens, as they are required to access premium AI-powered features.
8. Continuous Improvement Through Feedback Loops
$ASI’s AI models evolve over time, ensuring the platform remains effective in changing market conditions:
Performance Monitoring: Tracks the accuracy of predictions and adjusts models as needed.
User Feedback: Incorporates trader experiences to refine signal generation.
Adaptive Algorithms: Learns from new data to stay ahead of market trends.
9. Challenges in Predictive Analytics and Solutions
Market Noise: Predictive models may struggle with erratic price movements. $ASI mitigates this by combining multiple data sources for robust predictions.
Overfitting Risks: Regular model testing and validation ensure predictions remain generalized and accurate.
Data Integrity: Ensures high-quality data inputs by using verified sources and advanced preprocessing techniques.
10. The Future of Predictive Analytics in $ASI’s Strategy
Enhanced Models: Adoption of advanced AI technologies like GPT-style models for even more sophisticated predictions.
Cross-Market Predictions: Expanding beyond crypto to predict trends in traditional markets and other financial assets.
Automated Execution: Integrating predictive analytics with automated trading systems for hands-free, optimized trades.
$ASI Conclusion
Predictive analytics is the cornerstone of $ASI’s AI strategy, equipping traders with the tools they need to navigate complex and volatile markets. By combining cutting-edge technology with real-time insights, $ASI ensures its users stay ahead of the curve, making predictive analytics an indispensable part of the future of trading.$ASI
SOCIAL-2.09%
ASI-13.47%
Robertz
2дн.
Predictive Analytics: The Core of $ASI’s AI Strategy
Predictive Analytics: The Core of $ASI’s AI Strategy
Predictive analytics forms the backbone of $ASI’s AI-powered trading strategy, enabling the generation of actionable insights for traders. By leveraging data-driven forecasting models, $ASI delivers cutting-edge solutions that help users anticipate market trends and optimize trading decisions. Here’s how predictive analytics drives $ASI’s AI strategy:
1. What is Predictive Analytics?
Predictive analytics involves using statistical algorithms, machine learning (ML), and historical data to identify patterns and forecast future outcomes. In the context of $ASI, this means predicting market movements, price trends, and trading opportunities with high precision.
2. Data-Driven Decision Making
At the core of $ASI’s strategy is the ability to process vast datasets to uncover meaningful insights. Key data inputs include:
Historical Price Movements: Identifies long-term and short-term trends.
Real-Time Market Data: Analyzes price changes, trading volumes, and order books.
Sentiment Analysis: Uses natural language processing (NLP) to gauge investor sentiment from news and social media.
Macroeconomic Factors: Incorporates global financial indicators to provide a holistic view of market dynamics.
3. Core Components of $ASI’s Predictive Models
The $ASI AI platform integrates several technologies to enhance its predictive capabilities:
Machine Learning Algorithms: Adaptive models learn from historical data and refine predictions as new data emerges.
Deep Learning Neural Networks: Processes complex relationships within data for advanced pattern recognition.
Statistical Techniques: Ensures robust and reliable predictions through regression, time-series analysis, and probabilistic models.
Reinforcement Learning: Improves predictions through iterative testing and feedback from past outcomes.
4. Application in Trading
Predictive analytics enables traders to capitalize on opportunities and manage risks effectively:
Trend Forecasting: Anticipates upward or downward price trends across assets.
Volatility Predictions: Alerts traders to potential price fluctuations, helping them prepare for market swings.
Entry and Exit Points: Suggests optimal times to buy or sell assets for maximum profitability.
Risk Mitigation: Provides warnings about adverse market conditions or potential losses.
5. Key Benefits of Predictive Analytics for $ASI Users
Improved Accuracy: Delivers precise signals based on in-depth data analysis.
Faster Decision-Making: Processes data in real-time, enabling traders to react promptly to market changes.
Emotion-Free Trading: Replaces subjective decision-making with objective, data-driven recommendations.
Scalability: Supports trading across multiple markets and assets simultaneously.
6. AI Models in Predictive Analytics
The $ASI AI strategy employs state-of-the-art models to ensure accuracy and reliability:
Time-Series Forecasting Models: ARIMA and LSTM networks for sequential data analysis.
Classification Models: Decision trees and support vector machines (SVMs) for event prediction (e.g., bullish/bearish trends).
Sentiment Models: NLP algorithms, such as BERT, to analyze sentiment data from text sources.
7. Integration with the $ASI Ecosystem
Predictive analytics enhances the overall utility and value of $ASI within the AltSignals ecosystem:
ActualizeAI: Provides predictive insights directly to users, improving their trading outcomes.
DeFi Integration: Helps users optimize liquidity provision and yield farming by predicting market conditions.
Token Value: Drives demand for $ASI tokens, as they are required to access premium AI-powered features.
8. Continuous Improvement Through Feedback Loops
$ASI’s AI models evolve over time, ensuring the platform remains effective in changing market conditions:
Performance Monitoring: Tracks the accuracy of predictions and adjusts models as needed.
User Feedback: Incorporates trader experiences to refine signal generation.
Adaptive Algorithms: Learns from new data to stay ahead of market trends.
9. Challenges in Predictive Analytics and Solutions
Market Noise: Predictive models may struggle with erratic price movements. $ASI mitigates this by combining multiple data sources for robust predictions.
Overfitting Risks: Regular model testing and validation ensure predictions remain generalized and accurate.
Data Integrity: Ensures high-quality data inputs by using verified sources and advanced preprocessing techniques.
10. The Future of Predictive Analytics in $ASI’s Strategy
Enhanced Models: Adoption of advanced AI technologies like GPT-style models for even more sophisticated predictions.
Cross-Market Predictions: Expanding beyond crypto to predict trends in traditional markets and other financial assets.
Automated Execution: Integrating predictive analytics with automated trading systems for hands-free, optimized trades.
Conclusion
Predictive analytics is the cornerstone of $ASI’s AI strategy, equipping traders with the tools they need to navigate complex and volatile markets. By combining cutting-edge technology with real-time insights, $ASI ensures its users stay ahead of the curve, making predictive analytics an indispensable part of the future of trading.
ASI-13.47%
HIGH-3.25%
holiest
2дн.
ASI Alliance can overcome OpenAI in hardware.
The Artificial Superintelligence (ASI) Alliance, an industry merger aiming to challenge Big Tech dominance in artificial intelligence, has a long path to reach rivals’ computing power. Still, a key alliance member believes it can offer much smarter decentralization solutions.
ASI officially opened voting on bringing the cloud computing and blockchain platform Cudos into its alliance in a move to expand its computing power and AI tools.
The vote allows the community to decide whether Cudos should join and merge their native token, Cudos (CUDOS), with the ASI Alliance, which currently includes SingularityNET, the Ocean Protocol and Fetch.ai.
Cudos’ entrance into the ASI Alliance is expected to scale with the alliance’s growing computing needs on its mission to deploy artificial general intelligence (AGI), which attempts to create software with humanlike intelligence powered by immense computational power.
With the potential Cudos merger, the ASI Alliance could bring $200 million of dedicated computer hardware if token prices remain healthy.
Despite this, the ASI Alliance is still far from achieving the level of computational power of major AI rivals. With firms like OpenAI supposedly using around $1 billion of computing power for GPT-4, one could roughly estimate that the ASI Alliance is 80% far from matching the level of OpenAI’s AI model. $ASI
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