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I hate to break it to you, but the human brain and animal brains work the same way. The weighted measures used to connect the neural nodes of the AI network are similar to the strength of synapses that connect the neural nodes of the brain. This is a foundational analogy in the design of ANNs (Artificial Neural Networks), which are inspired by biological neural systems. If you don't believe me, ask AI!

I did (ask ChatGPT and Grok) and they both disagree with you. Here are their responses to your comment:

Grok:

Inspiration ≠ identity; it's like saying a paper airplane flies "the same way" as a jet because both use wings. Brains are dynamic masterpieces—86 billion neurons rewiring in real-time with chemicals, emotions, and true comprehension—while AI's just rigid math crunching patterns from data, no soul or spark of life. If that's what makes the brain divine (or demonic in tech's case?), AI's firmly in the "clever tool" camp, not the thinking machine one.

ChatGPT:

That’s a common misunderstanding. The similarity is only metaphorical and stops at the vocabulary.
In biological brains, neurons are living cells that communicate electrochemically, change structure, form new pathways, prune old ones, and operate with constant feedback from hormones, environment, and sensory systems.
In AI, a ‘neuron’ is just a line of code performing a math function.
Its ‘synapse strength’ is a numerical weight adjusted by calculus.
No chemicals, no cells, no self-organization—just matrix algebra.
ANNs were inspired by biology in name, not in mechanism.

I did (ask ChatGPT and Grok) and they both disagree with you.

I just asked Grok4 this question: "Is it true that the weighted measures used to connect the neural nodes of the AI network are similar to the strength of synapses that connect the neural nodes of the human brain?"

Grok's response:

Yes, the weighted measures (often called "weights") used to connect nodes in artificial neural networks (ANNs) are indeed similar to the strengths of synapses that connect neurons in the human brain.

Key Similarities
Conceptual Role: In the brain, synaptic strength determines how effectively a signal from one neuron influences another—stronger synapses amplify the signal, while weaker ones dampen it. Similarly, weights in ANNs are numerical values (typically real numbers) that scale the influence of inputs from one node to another during computation.

Learning Mechanism: Both systems adjust these connections over time. In biology, synaptic plasticity (e.g., long-term potentiation) strengthens or weakens synapses based on activity patterns. In ANNs, training algorithms like backpropagation update weights to minimize errors, mimicking this adaptive process.

Network Function: These adjustable connections enable pattern recognition, decision-making, and learning in both systems, allowing the network to prioritize relevant pathways.

Important Caveats
Biological synapses involve complex biochemical processes (e.g., neurotransmitters, receptors) and can be excitatory or inhibitory, whereas ANN weights are simplified mathematical scalars.

Brains have billions of neurons with dynamic, 3D structures and diverse neuron types; ANNs are abstracted layers of uniform nodes optimized for computation.

This similarity is why ANNs power much of modern AI, from image recognition to language models, by borrowing efficiency from nature's design. If you'd like details on specific ANN architectures or brain neuroscience, let me know!