Скачать или смотреть The Brain vs. The GPU: Can We Finally Solve the AI Generalization Crisis?

Does the path to true Artificial General Intelligence lie in bigger GPUs, or in looking back at our own biology?

We exist in an era defined by the Transformer—the engine behind ChatGPT and the modern AI revolution. But we must ask ourselves the difficult questions: Why do these massive systems, despite their brilliance, struggle to maintain a coherent Chain-of-Thought over long horizons? Why does their reasoning crumble when pushed beyond the length of their training data?

In this video, we dismantle the current paradigm to explore a revolutionary new architecture: The Dragon Hatchling (BDH).

Let us explore the following questions together:

1. The Fundamental Disconnect If the human brain is a massive, scale-free, distributed network, why have we spent the last decade building AI based on dense tensor operations that look nothing like it? Alan Turing and modern neuroscientists have long noted this profound dissimilarity. Are we trying to force a square peg into a round hole?

2. The "Missing Link" Hypothesis What if an LLM’s inference dynamics could be rewritten using the "equations of reasoning" found in biological systems? The BDH architecture proposes that the bridge between deep learning and the brain isn't just theoretical—it is mathematical.

3. The Return of Hebbian Learning What governs the Dragon Hatchling? It rejects the standard status quo. Instead, it relies on synaptic plasticity powered by Hebbian Learning. You know the saying: "Neurons that fire together, wire together." But what happens when we apply this biological heuristic to logical inference? We witness the combination of Modus Ponens reasoning with dynamic weight adjustment. Is this the key to an AI that actually thinks rather than just predicts?

4. The Holy Grail: Interpretability We are terrified of "Black Box" AI. But what if the architecture itself guaranteed transparency? BDH allows for Axiomatic AI—where micro-foundations align with macro-behavior. We are seeing monosemantic synapses emerge. Imagine an AI where a specific connection strengthens only when it thinks about a specific concept, like a currency or a country. If we can see the thoughts forming, have we finally solved the alignment problem?

5. Performance vs. Biology Does biological mimicry destroy performance? Surprisingly, no. The BDH-GPU implementation rivals GPT-2 models at equivalent parameter counts (10M to 1B). Furthermore, it scales uniformly. If we can merge models directly through simple geometric operations, are we looking at the future of open-source AI collaboration?

Join me as we dissect the paper that might just be the foundational theory needed to push AI into the "Thermodynamic Limit."

🔍 Verify The Sources (DYOR)
I am an analyst, not an oracle. In the age of AI, verification is your most powerful tool. I strictly encourage you to Do Your Own Research (DYOR) on the claims made in this video. Do not take my word for it—dive into the mathematics and the code yourself.

Primary Research Paper & Technical Breakdown:

The Dragon Hatchling: The Missing Link Between the Transformer and Models of the Brain

Read the Technical Blog: https://pathway.com/research/bdh

Replicate the Results:

Inspect the Code: https://pathway.com/research/bdh (See GitHub link regarding BDH implementation)

Repository: https://github.com/pathwaycom/bdh