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3月25日，OpenAI发文证实，部分ChatGPT Plus服务订阅用户可能泄露了部分个人隐私和支付信息。

OpenAI表示，由于开源库中的一个错误，本周早些时候，部分用户可以看到另一个用户的聊天记录标题。发现该问题后，公司将ChatGPT短暂下线并紧急修补了这个bug。

经过更深入的调查，OpenAI还发现，在特定的 9 小时窗口内，同样的漏洞可能导致活跃的ChatGPT Plus用户（占比约1.2%）与支付相关的信息对外可见：

“我们在本周一关闭 ChatGPT 几个小时前，部分用户可能会到另一位活跃成员的姓名、电子邮件地址、支付地址、信用卡的后四位和信用卡到期时间。我们承诺在任何情况下，都不会暴露完整的信用卡号码”。

OpenAI 还表示，已联系到所有可能受到影响的用户，同时向用户和整个ChatGPT社区致歉。

值得一提的是，该公司在公告中还提及，实际看到他人个人和支付信息的ChatGPT Plus用户数量“极低”，主要可能是因为以下两种情况：

用户打开了太平洋时间3月20日星期一凌晨1点到10点之间发送的订阅确认电子邮件 ―― 该窗口期间生成的一些订阅确认电子邮件被发送给了错误的用户。这些电子邮件包含另一个用户信用卡号的最后四位数字，但没有显示完整的信用卡号。在3月20日之前，可能有少量订阅确认电子邮件被错误地处理了，尽管 OpenAI 尚未确认任何此类情况。

在太平洋时间3月20日星期一凌晨1点到10点之间，在ChatGPT 中单击“我的帐户”，然后单击“管理我的订阅”，在此窗口中，另一个活跃的ChatGPT Plus用户的名字、姓氏、电子邮件地址、付款地址、信用卡号码的最后四位和信用卡到期日期可能是可见的。这种情况也是可能发生在3月20日之前，尽管 OpenAI 尚未确认任何此类情况。

本文编选自“华尔街见闻”，作者：高智谋;智通财经编辑：陈筱亦。

标签：

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tensorflow translator tensorflow实现cv操作

tensorboard,tensor core,tensor g2,tensorflow干嘛的

请记住，您是一位NLP领域的专家和优秀的算法工程师。使用带有 tensorflow2.0 subclass api 的 python 从头开始实现 transformer 模型。 全部内容如下：

1. 构建transformer模型架构和依赖层；
2. 生成并预处理一些假样本数据，用于训练上面构建的模型；
3. 上面生成的样本数据的训练模型示例教程；
4. 上面生成的样本数据的预测模型示例教程；
5. 上面生成的示例数据的部署模型示例教程；

最后，您所有的答案都以markdown格式呈现。

You: Remember you’re an expert and excellent algorithm engineer in NLP area. Implement transformer model from scratch using python with tensorflow2.0 subclass api step by step. All content is as follows:

1. Build the transformer model architecture and dependency layers;
2. Generate and preprocess some fake sample data for training the model built above;
3. 【【微信】】e tutorial with sample data generated above;
4. 【【微信】】ple tutorial with sample data generated above;
5. 【【微信】】le tutorial with sample data generated above;

【【微信】】, All of your answers are presented in markdown format.

ChatGPT:

Sure, I’d be happy to help you implement a transformer model from scratch using TensorFlow 2.0’s subclass API. Here are the step-by-step instructions for building, training, and deploying the model:

The transformer model architecture consists of an encoder and a decoder. The encoder processes the input se【【微信】】xed-length representation of it, while the decoder generates the output se【【微信】】’s representation and the pre【【微信】】. Each of the encoder and decoder consists of multiple layers, and each layer has multiple sub-layers, including self-attention, feed-forward neural networks, and layer normalization.

To build the transformer model architecture and dependency layers, you can define them as classes in Python using TensorFlow 2.0’s subclass API. Here’s an example implementation:

In this section, we will generate fake sample data to train our transformer model. We will create a 【【微信】】e some random sentences to be used as input and output pairs for the transformer.

In this section, we will train our transformer model using the sample data generated above. 【【微信】】zer with a custom learning rate scheduler and the sparse categorical cross-entropy loss function.

In this section, we will use our trained transformer model to generate predictions for some input sequences.

In this section, we will use the trained transformer model to generate predictions for new input sequences.

This will output the following:

This concludes the implementation of the Transformer model from scratch using Python and TensorFlow 2.0. We ha【【微信】】nents of the Transformer model, including self-attention, multi-head attention, and positional encoding. We ha【【微信】】w to preprocess data, train the model, and generate predictions for new input sequences. This implementation can be used as a basis for more ad【【微信】】els, such as BERT or GPT.

You:

The first part “Build the transformer model architecture and dependency layers” is malformed. Regenerate it.

ChatGPT:

Sure, here’s the corrected 【【微信】】t:

In this section, we will define the architecture of the Transformer model and its dependency layers.

We will start by importing the necessary libraries:

Next, we will define the layer, which adds positional encoding to the input embeddings.

Next, we will define the layer, which computes the dot product of the 【【微信】】cales the result by the square root of the dimension of the key 【【微信】】, and applies a softmax activation to obtain the attention weights. The attention weights are then multiplied by the 【【微信】】 to obtain the output of the layer.

Next, we will define the layer, which applies multiple instances of the layer in parallel. This is done by splitting the query, key, and 【【微信】】 into sub-【【微信】】, applying the layer to each sub-【【微信】】, and then concatenating the output of each instance of the layer.

Next, we will define the and layers, which represent a single layer in the encoder and decoder blocks of the Transformer model, respectively. 【【微信】】 layer, followed by a dropout and a layer normalization, and a feedforward network consisting of two dense layers with a ReLU acti【【微信】】, followed by another dropout and layer normalization.

Now, we need to generate some fake data to train the transformer model. For this purpose, we will use the library to generate random numbers as input and output sequences.

Now that we have our transformer model and sample data, we can train our model using the method of the class.

Output:

To generate predictions from our transformer model, we can use the method of the class.

Output:

To deploy our transformer model, we can sa【【微信】】 load it for later use.

We can now use the object to generate predictions as before.

In this tutorial, we ha【【微信】】ormer model from scratch using TensorFlow 2.0’s subclassing API. We ha【【微信】】 transformer model architecture and the necessary layers, generate and preprocess fake data, train and e【【微信】】, generate predictions, and deploy the model for later use.

While our example was relati【【微信】】, the transformer model is a powerful tool for natural language processing tasks, and can be extended to a wide range of applications. By modifying the model architecture, loss function, and hyperparameters, we can train a transformer model to perform a 【【微信】】lated tasks, such as text classification, machine translation, and text generation.