The AionMind models are built upon our expertise on the physics of disordered and frustrated networks, and in particular, catastrophic dynamics. See, for example, sample prior works on the subject in various applications here or here. It is a basic valid hypothesis that catastrophic dynamics (or avalanches) during training/learning in neural networks is the ultimate reason behind catastrophic forgetting in neural networks.
In the models developed at AionMind, and in the context of sequential learning, a fixed neural network core acts as a task-agnostic hub, where neuron count sets a hard capacity boundary — the number of neurons in the last hidden layer (in a 3-layer setting with prior of equal or higher size) is equal to the number of classes that can be incrementally learned without accuracy loss, carving out interference-free subspaces that exactly match the knowledge load. Beyond that, the small net can’t “stretch” without degradation (hence identical accuracy up to the limit). Moving to larger number of neurons unlocks more orthogonal subspaces, letting it absorb equally more classes with virtually the same per-task fidelity. This isn’t probabilistic luck (à la Lottery Ticket Hypothesis); it’s engineered geometry— the physics of the neural networks (driven by proprietary regularization constraints) drive the growing heads to plug in like LEGO bricks without destabilizing the backbone. Further, overfitting becomes virtually impossible due to the stiffness of the neural network core.
The approach used in the models of AionMind has drastic implications: Minimal resources are needed and training epochs are completely unconstrained.
The models at AionMind are application-agnostic, namely they work on features provided by frozen pre-trained models “Readers” that process the original inputs into structured representations. These models, on their own, perform quite poorly on the learning tasks. For example, in the case of the Omniglot Benchmark study , the Reader used is the well known model ResNet-18 which is pretrained on large datasets of images, but it is not equipped at all to recognize different letters of randomly selected alphabets, randomly guessing in classification on Omniglot images (without any prior fine-tuning). Prior fine-tuning may only slightly improve performance for a large number of letter classes.
Core Principles of AionMind Approach:
- No Task-Related Pretraining: Our systems require no task-specific initialization, enabling rapid deployment across diverse domains without costly setup.
- Minimal Resources: Using minimal memory (5 images/class storage) and small networks (~100 neurons/layer), we achieve high performance with low computational cost for 100 distinct classes, learned across 20+ sequential tasks.
- Long-Term Stability: Our proprietary approach ensures stability over thousands of training epochs using common learning rates for neural net optimizers, supporting lifelong learning applications.
- Scalability and Robustness: Linear scaling in terms of number of classes learning without catastrophic forgetting, with respect to the number of neurons in individual hidden layers. The typical application of 3-hidden-layer NxNxN neural networks, we find the unprecedented one-class-per-neuron (see Omniglot Benchmark for more details). The model also displays extreme noise robustness, making our AI versatile for real-world challenges.
- Proprietary Innovation: Our unique regularization and optimization strategies set a new standard for continual learning, delivering unmatched efficiency.
Commercial Impact:
- From autonomous systems to personalized AI, our technology reduces costs, enhances scalability, and ensures reliability.
- Our proprietary IP offers a competitive edge for companies seeking cutting-edge AI solutions.