A commonly shared image online (not attached here) compares the number of parameters in a large language model to the number of neurons in the human brain, i.e. 86 billion. The intent is probably to illustrate scale, but the analogy is broken at the root, let me tell you why
A parameter, which is a connection weight is a static number, frozen the moment inference from a model begins while a neuron is a computational device in itself, connected to thousands of other neurons. Each neuron integrates all its inputs, computes across time, has memory and contains dendrites that act as sub-processors. So the map isn’t just off by a scaling factor, it’s broken at the root as I said earlier, because it compares a living i.e. changing thing to a fixed one. A more careful version of the comparison would map parameters to synapses instead, and even that fails, because a biological synapse is plastic and dynamic. It strengthens, weakens and rewires as the brain computes. A parameter? Just a number at inference
The cleaner analogy is between silicon and biology at the level of their atomic units. The transistor is the fundamental unit of a CPU, a switch either on or off. The neuron is the fundamental unit of the brain. But the atoms are not comparable. A transistor is wired to a handful of others in a fixed circuit, while a single neuron connects to thousands. A neuron is far closer to a whole microprocessor than to a switch. Research labs have trained deep networks to reproduce the input output behavior of a single neuron, and needed a network several layers deep to do it
One neuron is already a small deep network and millions of transistors would be required to replicate what one neuron does naturally, on its own. So, the widely used comparision o social media and AI presentations at workplace is flawed at it’s core