arXiv:2503.21796v2 Announce Type: replace-cross Abstract: Self-supervised learning has become an increasingly important paradigm in the domain of machine intelligence. Furthermore, evidence for self-supervised adaptation, such as contrastive formulations, has emerged in recent computational neuroscience and brain-inspired research. Nevertheless, current work on self-supervised learning relies on biologically implausible credit assignment -- in the form of backpropagation of errors -- and feedforward inference, typically a forward-locked pass. Predictive coding, in its mechanistic form, offers
This paper addresses a fundamental limitation in current AI training methods (backpropagation) by proposing a biologically plausible alternative, indicating a potential evolutionary step for AI models.
Developing neuroscience-informed self-supervised learning could lead to more efficient, robust, and generalizable AI, moving beyond current computational constraints and biological implausibilities.
The paradigm of self-supervised learning could evolve to incorporate more biologically plausible mechanisms, potentially unlocking new architectures and capabilities for AI systems.
- · AI researchers
- · Deep learning frameworks
- · Robotics
- · Neuromorphic computing
- · AI models heavily reliant on backpropagation
- · Compute-intensive AI training methods
New AI models will emerge that are more biologically inspired and potentially more efficient.
This could accelerate the development of AI agents that learn and adapt more like biological systems, reducing the need for massive labeled datasets.
A shift towards more biologically plausible AI could eventually pave the way for AI with a higher degree of common sense and transfer learning capabilities, impacting various industries from healthcare to autonomous systems.
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Read at arXiv cs.LG