tx · HNeTMBXfdabHeNZzG2rYW4N6PDQrdabkvxa2v6Jc5wKq

3N9ttyLcRwDo7L4EmJkbS3ZFuQJygivupsL:  -0.00500000 Waves

2023.07.31 16:25 [2690115] invoke 3N9ttyLcRwDo7L4EmJkbS3ZFuQJygivupsL > 3N9tKixzqTYWnEXQxrDQ5pBTGvQd6sFsvmV commitChatGPTTask()

{ "type": 16, "id": "HNeTMBXfdabHeNZzG2rYW4N6PDQrdabkvxa2v6Jc5wKq", "fee": 500000, "feeAssetId": null, "timestamp": 1690809971381, "version": 2, "chainId": 84, "sender": "3N9ttyLcRwDo7L4EmJkbS3ZFuQJygivupsL", "senderPublicKey": "92ovWCy1Zf8CSsTLLLssC74m8yn5yPMqVp9fmVacou97", "proofs": [ "66cYASZSF1U4LURgw9GRSZVsuH2jciNkZbuUBveYFXkTJt9rBUuaDcmqkAS2zA2Sst534j3pTegdF37V6XChLwjD" ], "dApp": "3N9tKixzqTYWnEXQxrDQ5pBTGvQd6sFsvmV", "payment": [], "call": { "function": "commitChatGPTTask", "args": [ { "type": "string", "value": "6RKUTM3V7phJHQp4S8CMt1AGkeFQT4gzzHWnugfxBGwY_92ovWCy1Zf8CSsTLLLssC74m8yn5yPMqVp9fmVacou97" }, { "type": "string", "value": "There have been several significant research advancements in the field of neural networks over the last ten years. Some of the most impressive ones include:\n\n1. Breakthroughs in deep learning: One important milestone was the development of deep neural networks, which are capable of learning hierarchical representations from vast amounts of data. This led to significant advancements in various machine learning tasks such as image classification, natural language processing, and speech recognition.\n\n2. Image recognition and object detection: Convolutional neural networks (CNNs) have greatly improved image recognition and object detection capabilities. The introduction of architectures such as AlexNet, GoogLeNet, and ResNet have achieved unprecedented performance in image classification tasks, enabling algorithms to recognize objects and scenes with high accuracy.\n\n3. Natural language processing: Recurrent neural networks (RNNs) and their variants, such as long short-term memory (LSTM) networks and transformer models, have revolutionized natural language processing. These models have achieved state-of-the-art performance in tasks like machine translation, sentiment analysis, question answering, and text generation.\n\n4. Reinforcement learning breakthroughs: Advances in deep reinforcement learning have led to significant breakthroughs in autonomous decision-making. Algorithms such as Deep Q-Networks (DQN) and Proximal Policy Optimization (PPO) have allowed agents to learn complex tasks in environments with minimal human intervention, surpassing human-level performance in games like Go, chess, and more recently, Dota 2.\n\n5. Generative models: The development of generative adversarial networks (GANs) has unlocked the potential for realistic image synthesis. GANs allow the generation of new content that is visually convincing and indistinguishable from real images, transforming various areas such as art, entertainment, and data augmentation.\n\n6. Transfer learning and pre-training: The use of pre-training techniques, such as transfer learning and self-supervised learning, has become increasingly popular and effective. By pre-training neural networks on large-scale datasets, models can be fine-tuned on specific tasks with a smaller amount of labeled data, improving performance and reducing training time.\n\nThese advancements, among many others, have had a profound impact on various fields, paving the way for remarkable applications in areas like healthcare, robotics, autonomous vehicles, and more." } ] }, "height": 2690115, "applicationStatus": "succeeded", "spentComplexity": 28, "stateChanges": { "data": [ { "key": "6RKUTM3V7phJHQp4S8CMt1AGkeFQT4gzzHWnugfxBGwY_92ovWCy1Zf8CSsTLLLssC74m8yn5yPMqVp9fmVacou97_status", "type": "string", "value": "done" }, { "key": "6RKUTM3V7phJHQp4S8CMt1AGkeFQT4gzzHWnugfxBGwY_92ovWCy1Zf8CSsTLLLssC74m8yn5yPMqVp9fmVacou97_result", "type": "string", "value": "There have been several significant research advancements in the field of neural networks over the last ten years. Some of the most impressive ones include:\n\n1. 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These models have achieved state-of-the-art performance in tasks like machine translation, sentiment analysis, question answering, and text generation.\n\n4. Reinforcement learning breakthroughs: Advances in deep reinforcement learning have led to significant breakthroughs in autonomous decision-making. Algorithms such as Deep Q-Networks (DQN) and Proximal Policy Optimization (PPO) have allowed agents to learn complex tasks in environments with minimal human intervention, surpassing human-level performance in games like Go, chess, and more recently, Dota 2.\n\n5. Generative models: The development of generative adversarial networks (GANs) has unlocked the potential for realistic image synthesis. GANs allow the generation of new content that is visually convincing and indistinguishable from real images, transforming various areas such as art, entertainment, and data augmentation.\n\n6. Transfer learning and pre-training: The use of pre-training techniques, such as transfer learning and self-supervised learning, has become increasingly popular and effective. By pre-training neural networks on large-scale datasets, models can be fine-tuned on specific tasks with a smaller amount of labeled data, improving performance and reducing training time.\n\nThese advancements, among many others, have had a profound impact on various fields, paving the way for remarkable applications in areas like healthcare, robotics, autonomous vehicles, and more." }, { "key": "6RKUTM3V7phJHQp4S8CMt1AGkeFQT4gzzHWnugfxBGwY_92ovWCy1Zf8CSsTLLLssC74m8yn5yPMqVp9fmVacou97_commit_height", "type": "integer", "value": 2690115 }, { "key": "6RKUTM3V7phJHQp4S8CMt1AGkeFQT4gzzHWnugfxBGwY_92ovWCy1Zf8CSsTLLLssC74m8yn5yPMqVp9fmVacou97_commit_timestamp", "type": "integer", "value": 1690809945867 } ], "transfers": [], "issues": [], "reissues": [], "burns": [], "sponsorFees": [], "leases": [], "leaseCancels": [], "invokes": [] } }

github/deemru/w8io/3ef1775 
74.80 ms