Apple details the Apple intelligence training system using user data

Last month, Apple delayed The deployment of its more personal and powerful Siri characteristics. While trying to update the ship for future Apple intelligence updates, Bloomberg Underlines a change that Apple makes in the way he forms his artificial intelligence models.

The report highlights a blog article from Apple automatic learning research websiteExplaining how Apple generally uses synthetic data to form its AI models. However, there are limits to this strategy, including the fact that it is difficult for synthetic data to “understand trends” of functionalities such as summary or writing tools that work on longer sentences or whole emails.

To approach this limitation, Apple highlights a new technology that it will soon start to use which compares synthetic data to a small sample of recent user emails, but without compromising the confidentiality of users:

To improve our models, we must generate a set of many emails that cover the most frequent subjects in messages. To organize a representative set of synthetic emails, we start by creating a large set of synthetic messages on a variety of subjects. For example, we could create a synthetic message: “Would you like to play tennis tomorrow at 11:30 am?”

This is done without any knowledge of individual user emails. We then derive a representation, called integration, of each synthetic message which captures some of the key dimensions of the message such as language, subject and length. These interests are then sent to a small number of user devices which have opted for the analysis of peripherals.

The participating devices select then select a small sample of recent user emails and calculate their interests. Each device then decides which synthetic incorporations is closest to these samples. Using differential confidentiality, Apple can then learn the most selected synthetic incorporations on all devices, without learning which synthetic integration was selected on a given device.

These most selected synthetic incorporations can then be used to generate training or test data, or we can perform additional stages of retention to further refine the data set. For example, if the message on reading tennis is one of the best interests, a similar message replacing “tennis” with “football” or another sport could be generated and added to the next conservation cycle (see Figure 1). This process allows us to improve the subjects and language of our synthetic emails, which helps us to train our models to create better text outlets in functionalities such as messaging summaries, while protecting confidentiality.

You can read Apple’s Full blog article for more details.

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