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The Dragon Hatchling Learns to Fly: Inside AI’s Next Learning Revolution
Manage episode 515066225 series 3474148
This story was originally published on HackerNoon at: https://hackernoon.com/the-dragon-hatchling-learns-to-fly-inside-ais-next-learning-revolution.
Exploring Brain-like Dragon Hatchling (BDH) — a new AI model that learns on the fly, adapts like a brain, and challenges the transformer era.
Check more stories related to machine-learning at: https://hackernoon.com/c/machine-learning. You can also check exclusive content about #neural-networks, #bdh-neural-architecture, #brain-like-dragon-hatchling, #inference-time-learning, #hebbian-learning-in-ai, #interpretable-ai, #modular-model-merging, #hackernoon-top-story, and more.
This story was written by: @zhukmax. Learn more about this writer by checking @zhukmax's about page, and for more stories, please visit hackernoon.com.
This article demystifies the Brain-like Dragon Hatchling (BDH), a neural architecture that keeps learning during inference using Hebbian “fast memory” while retaining pre-trained “slow” weights. BDH aims for interpretable reasoning, stable long-range behavior, modular model merging without catastrophic forgetting, and efficiency suited to GPUs and neuromorphic chips. A minimal Rust+tch proof-of-concept (XOR) illustrates the mechanics and why σ (fast memory) shines on sequence/context tasks, pointing toward practical lifelong learning systems.
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Manage episode 515066225 series 3474148
This story was originally published on HackerNoon at: https://hackernoon.com/the-dragon-hatchling-learns-to-fly-inside-ais-next-learning-revolution.
Exploring Brain-like Dragon Hatchling (BDH) — a new AI model that learns on the fly, adapts like a brain, and challenges the transformer era.
Check more stories related to machine-learning at: https://hackernoon.com/c/machine-learning. You can also check exclusive content about #neural-networks, #bdh-neural-architecture, #brain-like-dragon-hatchling, #inference-time-learning, #hebbian-learning-in-ai, #interpretable-ai, #modular-model-merging, #hackernoon-top-story, and more.
This story was written by: @zhukmax. Learn more about this writer by checking @zhukmax's about page, and for more stories, please visit hackernoon.com.
This article demystifies the Brain-like Dragon Hatchling (BDH), a neural architecture that keeps learning during inference using Hebbian “fast memory” while retaining pre-trained “slow” weights. BDH aims for interpretable reasoning, stable long-range behavior, modular model merging without catastrophic forgetting, and efficiency suited to GPUs and neuromorphic chips. A minimal Rust+tch proof-of-concept (XOR) illustrates the mechanics and why σ (fast memory) shines on sequence/context tasks, pointing toward practical lifelong learning systems.
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