tx · B2paAcVSbkhxia7hdQjxL5jxSv5819SFPjXdstVumHWM
3NAAoJ554QsZfqE8W8Rg8LsJb79d5b1pDat: -0.00500000 Waves
2025.03.07 17:53 [3533736] invoke 3NAAoJ554QsZfqE8W8Rg8LsJb79d5b1pDat > 3N9tKixzqTYWnEXQxrDQ5pBTGvQd6sFsvmV commitTask()
3N9tKixzqTYWnEXQxrDQ5pBTGvQd6sFsvmV: checked_out_by_Ct2djqZsAz77Ur5Y9pMbgrJR2xn6hDprcsHUYgLfsdkY_chatgpt_ALEaV9Z9vybwoMWmukgqZ8MkAzaGS1k8UAMdRrK4RgDL_AqqtiUWzxuW2sGQZiUBdYgDuY9J9GaL327FdWiEuh6qc: true -> null
3N9tKixzqTYWnEXQxrDQ5pBTGvQd6sFsvmV: ALEaV9Z9vybwoMWmukgqZ8MkAzaGS1k8UAMdRrK4RgDL_AqqtiUWzxuW2sGQZiUBdYgDuY9J9GaL327FdWiEuh6qc_commit_timestamp_chatgpt: 1741359211944
3N9tKixzqTYWnEXQxrDQ5pBTGvQd6sFsvmV: ALEaV9Z9vybwoMWmukgqZ8MkAzaGS1k8UAMdRrK4RgDL_AqqtiUWzxuW2sGQZiUBdYgDuY9J9GaL327FdWiEuh6qc_commit_height_chatgpt: 3533736
3N9tKixzqTYWnEXQxrDQ5pBTGvQd6sFsvmV: ALEaV9Z9vybwoMWmukgqZ8MkAzaGS1k8UAMdRrK4RgDL_AqqtiUWzxuW2sGQZiUBdYgDuY9J9GaL327FdWiEuh6qc_result_chatgpt: "Large Language Models (LLMs) are state-of-the-art artificial intelligence models that have significantly advanced the capabilities of natural language processing. These models are typically trained on massive amounts of text data using deep learning techniques, enabling them to understand and generate human-like language. One of the key technologies powering LLMs is the Transformer architecture, which allows for efficient training on vast datasets by capturing long-range dependencies in the text.
LLMs operate on the principle of predicting the next word in a sentence based on the previous context, a task known as language modeling. By learning the patterns and structures of language through this process, LLMs can perform a wide range of natural language processing tasks, including text generation, translation, summarization, sentiment analysis, question answering, and more.
One of the most well-known LLMs is OpenAI's GPT (Generative Pre-trained Transformer) series, which includes models like GPT-3. These models have billions of parameters, making them highly capable of producing coherent and contextually relevant text. GPT-3, for instance, can generate text that closely mimics human language and has been used in various applications, such as chatbots, content generation, and language understanding tasks.
Despite their impressive capabilities, LLMs also come with certain challenges and limitations. Firstly, ethical concerns around the potential misuse of generated text, including misinformation, fake news, and bias, have raised questions about responsible deployment. Additionally, the computational resources required to train and fine-tune large language models are substantial, making them inaccessible to many smaller organizations and researchers.
In conclusion, Large Language Models represent a major breakthrough in the field of natural language processing, offering powerful tools for understanding and generating human language. As research in this area continues to advance, addressing the ethical and technical challenges associated with LLMs will be crucial in harnessing their potential for positive impact in various domains."
3N9tKixzqTYWnEXQxrDQ5pBTGvQd6sFsvmV: ALEaV9Z9vybwoMWmukgqZ8MkAzaGS1k8UAMdRrK4RgDL_AqqtiUWzxuW2sGQZiUBdYgDuY9J9GaL327FdWiEuh6qc_status_chatgpt: "checked_out" -> "done"
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"senderPublicKey": "Ct2djqZsAz77Ur5Y9pMbgrJR2xn6hDprcsHUYgLfsdkY",
"proofs": [
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"call": {
"function": "commitTask",
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"value": "ALEaV9Z9vybwoMWmukgqZ8MkAzaGS1k8UAMdRrK4RgDL_AqqtiUWzxuW2sGQZiUBdYgDuY9J9GaL327FdWiEuh6qc"
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"value": "Large Language Models (LLMs) are state-of-the-art artificial intelligence models that have significantly advanced the capabilities of natural language processing. These models are typically trained on massive amounts of text data using deep learning techniques, enabling them to understand and generate human-like language. One of the key technologies powering LLMs is the Transformer architecture, which allows for efficient training on vast datasets by capturing long-range dependencies in the text.\n\nLLMs operate on the principle of predicting the next word in a sentence based on the previous context, a task known as language modeling. By learning the patterns and structures of language through this process, LLMs can perform a wide range of natural language processing tasks, including text generation, translation, summarization, sentiment analysis, question answering, and more.\n\nOne of the most well-known LLMs is OpenAI's GPT (Generative Pre-trained Transformer) series, which includes models like GPT-3. These models have billions of parameters, making them highly capable of producing coherent and contextually relevant text. GPT-3, for instance, can generate text that closely mimics human language and has been used in various applications, such as chatbots, content generation, and language understanding tasks.\n\nDespite their impressive capabilities, LLMs also come with certain challenges and limitations. Firstly, ethical concerns around the potential misuse of generated text, including misinformation, fake news, and bias, have raised questions about responsible deployment. Additionally, the computational resources required to train and fine-tune large language models are substantial, making them inaccessible to many smaller organizations and researchers.\n\nIn conclusion, Large Language Models represent a major breakthrough in the field of natural language processing, offering powerful tools for understanding and generating human language. As research in this area continues to advance, addressing the ethical and technical challenges associated with LLMs will be crucial in harnessing their potential for positive impact in various domains."
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},
"height": 3533736,
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"stateChanges": {
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"type": "string",
"value": "done"
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"value": "Large Language Models (LLMs) are state-of-the-art artificial intelligence models that have significantly advanced the capabilities of natural language processing. These models are typically trained on massive amounts of text data using deep learning techniques, enabling them to understand and generate human-like language. One of the key technologies powering LLMs is the Transformer architecture, which allows for efficient training on vast datasets by capturing long-range dependencies in the text.\n\nLLMs operate on the principle of predicting the next word in a sentence based on the previous context, a task known as language modeling. By learning the patterns and structures of language through this process, LLMs can perform a wide range of natural language processing tasks, including text generation, translation, summarization, sentiment analysis, question answering, and more.\n\nOne of the most well-known LLMs is OpenAI's GPT (Generative Pre-trained Transformer) series, which includes models like GPT-3. These models have billions of parameters, making them highly capable of producing coherent and contextually relevant text. GPT-3, for instance, can generate text that closely mimics human language and has been used in various applications, such as chatbots, content generation, and language understanding tasks.\n\nDespite their impressive capabilities, LLMs also come with certain challenges and limitations. Firstly, ethical concerns around the potential misuse of generated text, including misinformation, fake news, and bias, have raised questions about responsible deployment. Additionally, the computational resources required to train and fine-tune large language models are substantial, making them inaccessible to many smaller organizations and researchers.\n\nIn conclusion, Large Language Models represent a major breakthrough in the field of natural language processing, offering powerful tools for understanding and generating human language. As research in this area continues to advance, addressing the ethical and technical challenges associated with LLMs will be crucial in harnessing their potential for positive impact in various domains."
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