Apple Creator Studio includes AI-powered features, along with premium content for Keynote, Pages and Numbers. Apple has been putting more onus on its services for the past several years -- the company makes tens of billions of dollars in revenue from that side of the business, which it claimed had a record year in 2025 . Apple is nudging a little more in that direction with a new subscription bundle called Apple Creator Studio . This allows creators to pay a single fee ($13 per month or $129 per year) to use Final Cut Pro, Logic Pro, Pixelmator Pro, Motion, Compressor and MainStage. Subscribers will get access to "premium content" in Pages, Keynote and Numbers (as well as in Freeform later this year).

We target open-world feature extrapolation problem where the feature space of input data goes through expansion and a model trained on partially observed features needs to handle new features in test data without further retraining. The problem is of much significance for dealing with features incrementally collected from different fields. To this end, we propose a new learning paradigm with graph representation and learning. Our framework contains two modules: 1) a backbone network (e.g., feedforward neural nets) as a lower model takes features as input and outputs predicted labels; 2) a graph neural network as an upper model learns to extrapolate embeddings for new features via message passing over a feature-data graph built from observed data. Based on our framework, we design two training strategies, a self-supervised approach and an inductive learning approach, to endow the model with extrapolation ability and alleviate feature-level over-fitting. We also provide theoretical analysis on the generalization error on test data with new features, which dissects the impact of training features and algorithms on generalization performance. Our experiments over several classification datasets and large-scale advertisement click prediction datasets demonstrate that our model can produce effective embeddings for unseen features and significantly outperforms baseline methods that adopt KNN and local aggregation.

A new feature on X revealed that many influential MAGA accounts are not actually based in the US. President Donald Trump has continued sharing their posts anyway. A new feature on X has revealed that a number of major MAGA accounts on the platform are operated by people based overseas. And in the days since these accounts were exposed, President Donald Trump has continued boosting several of them. Many of the accounts, which have large followings and claim to be conservative people based in Texas or "America First" accounts "promoting good resisting evil," are actually operated everywhere from Chile and Nigeria to Russia and across eastern Europe .

Learning with streaming data has attracted much attention during the past few years.Though most studies consider data stream with fixed features, in real practice the features may be evolvable. For example, features of data gathered by limited lifespan sensors will change when these sensors are substituted by new ones. In this paper, we propose a novel learning paradigm: Feature Evolvable Streaming Learning where old features would vanish and new features would occur. Rather than relying on only the current features, we attempt to recover the vanished features and exploit it to improve performance. Specifically, we learn two models from the recovered features and the current features, respectively. To benefit from the recovered features, we develop two ensemble methods. In the first method, we combine the predictions from two models and theoretically show that with the assistance of old features, the performance on new features can be improved. In the second approach, we dynamically select the best single prediction and establish a better performance guarantee when the best model switches.

Learning with streaming data has attracted much attention during the past few years.

The prediction concatenation (PreConc) operation is crucial for the multi-layer feature transformation in deep forest, though little has been known about its theoretical property.

We introduced an adaptive feature bank update scheme to dynamically absorb new features and discard obsolete features.