我刚才问了个问题。它死机了。我问的问题是。 为什么你用cross-validation来tuning 模型的参数的时候，要基于testing set呢？ 它就死了。 我又try again。得到如下答案。大家说它回答正确吗？ In step 5 of the machine learning pipeline, the model is fine-tuned using techniques such as cross-validation or hyperparameter optimization. This step is typically performed after the model has been trained and evaluated on the testing set. The performance of the model on the testing set is used as the criterion for selecting the optimal set of hyperparameters. The reason for using the performance on the testing set as the criterion for selecting the optimal set of hyperparameters is to ensure that the model generalizes well to new data. The testing set is a sample of data that the model has not seen during training, and the performance of the model on this unseen data provides an estimate of how the model will perform on new data in the future. Therefore, using the performance on the testing set as the criterion for selecting the optimal set of hyperparameters can help to prevent overfitting and ensure that the model generalizes well to new data. In contrast, using the performance on the training set as the criterion for selecting the optimal set of hyperparameters could result in a model that is overly complex and does not generalize well to new data. This is because the training set is used to fit the model, and the model is likely to have a very low error on the training set. However, this low error may not necessarily be indicative of the model''''s performance on new data, and the model may not generalize well to unseen data. Therefore, it is generally not advisable to use the performance on the training set as the criterion for selecting the optimal set of hyperparameters. 群里的AI大牛们，也来尝试回答一下我的问题。和这个机器人PK一下，看看谁厉害。
我试了几个，效果还挺精彩的。是个有用的工具 ：） 它连商业模式都写出来了，虽然是常识，但还合情合理。 build a business model for a technology called Trusted Execution Environment A Trusted Execution Environment (TEE) is a secure area of a device''s hardware that is isolated from the main operating system and processes. It can be used to securely store and process sensitive data, such as financial information or personal identification. One potential business model for a TEE technology is to offer it as a service to companies and organizations that handle large amounts of sensitive data. For example, a bank could use a TEE to securely process customer transactions, or a healthcare provider could use it to store and access patient records. To monetize the technology, the company could charge a subscription fee to its customers based on the amount of data they need to securely process and store. The company could also offer additional services, such as data migration and integration with existing systems, for an additional fee. In order to drive adoption of the technology, the company could focus on building strong partnerships with key industry players and promoting the security and privacy benefits of TEE. It could also invest in marketing and advertising to raise awareness of the technology among potential customers.
https://chat.openai.com/chat
都是闭门的测试结果，拿一段可以给人看的。那么多年了，哪个网站有实用的让大家和chat bot 试一下多厉害？我试过的都完全不行。给个厉害的chatbot网站link吧。
强词夺理的劲头可以混职场了
not bad. I have tried it.
感受一下人工智能的文学修养，这能把海子顾城气得活过来
以前有人试过， 一天之内被网友 教成了 racist
我刚才问了个问题。它死机了。我问的问题是。
为什么你用cross-validation来tuning 模型的参数的时候，要基于testing set呢？
它就死了。
我又try again。得到如下答案。大家说它回答正确吗？
In step 5 of the machine learning pipeline, the model is fine-tuned using techniques such as cross-validation or hyperparameter optimization. This step is typically performed after the model has been trained and evaluated on the testing set. The performance of the model on the testing set is used as the criterion for selecting the optimal set of hyperparameters. The reason for using the performance on the testing set as the criterion for selecting the optimal set of hyperparameters is to ensure that the model generalizes well to new data. The testing set is a sample of data that the model has not seen during training, and the performance of the model on this unseen data provides an estimate of how the model will perform on new data in the future. Therefore, using the performance on the testing set as the criterion for selecting the optimal set of hyperparameters can help to prevent overfitting and ensure that the model generalizes well to new data. In contrast, using the performance on the training set as the criterion for selecting the optimal set of hyperparameters could result in a model that is overly complex and does not generalize well to new data. This is because the training set is used to fit the model, and the model is likely to have a very low error on the training set. However, this low error may not necessarily be indicative of the model''''s performance on new data, and the model may not generalize well to unseen data. Therefore, it is generally not advisable to use the performance on the training set as the criterion for selecting the optimal set of hyperparameters.
群里的AI大牛们，也来尝试回答一下我的问题。和这个机器人PK一下，看看谁厉害。
build a business model for a technology called Trusted Execution Environment
A Trusted Execution Environment (TEE) is a secure area of a device''s hardware that is isolated from the main operating system and processes. It can be used to securely store and process sensitive data, such as financial information or personal identification. One potential business model for a TEE technology is to offer it as a service to companies and organizations that handle large amounts of sensitive data. For example, a bank could use a TEE to securely process customer transactions, or a healthcare provider could use it to store and access patient records. To monetize the technology, the company could charge a subscription fee to its customers based on the amount of data they need to securely process and store. The company could also offer additional services, such as data migration and integration with existing systems, for an additional fee. In order to drive adoption of the technology, the company could focus on building strong partnerships with key industry players and promoting the security and privacy benefits of TEE. It could also invest in marketing and advertising to raise awareness of the technology among potential customers.
哈哈哈哈哈哈 😄 闪现了某些同事的工作方式
有点儿像我和某些印度“信息咨询专家”的对话。
OpenAI的学习模型还可能真就是印度专家
感受一下人工智能的英语文学修养，比中文高多了。韵脚抓住，关键词码上，就有了大师神韵
你应该关注一下最近 text to image 扩散生成模型的研究进展，图像解析度非常令人惊艳了，前几天还有人抱怨一堆人在图像设计里面提技术的话题，他们不了解现代技术的进展已经超出了他们平时的认知，未来的艺术设计将是技术驱动的