All2General

All2General is a theoretical framework in artificial intelligence that seeks to enable models to generalize across tasks by mapping inputs from diverse domains into a common generalized latent space. In this approach, task-specific performance is achieved by lightweight heads trained atop a universal representation.

In typical All2General designs, a shared encoder processes data from multiple modalities—such as text, images, audio, or structured data—and outputs a unified latent representation. Task-specific heads then produce predictions for different objectives. Training relies on multi-task losses, often augmented with contrastive objectives to encourage cross-modal alignment and robustness to distribution shifts.

All2General is not a single implementation but a family of approaches related to universal representation learning and foundation-model concepts. It relates to multi-task learning, meta-learning, and cross-domain transfer, and is frequently discussed as an aspirational goal rather than a concrete product. Real-world work often centers on large-scale pretraining and unified encoders.

Its applications include zero-shot or few-shot transfer to new tasks, cross-domain reasoning, and data-efficient learning in fields such as natural language processing, computer vision, and robotics. Variants differ in whether they assume explicit modality alignment, how the latent space is structured, and whether training is supervised or self-supervised.

Limitations and challenges include substantial computational cost, potential negative transfer, difficulties in evaluating true generality, and safety and ethical considerations. Ongoing research aims to improve scalability, evaluation methodology, and guarantees on generalization performance.