[jonnycat]

This kind of unicellular complexity & intelligence has long been my soapbox material in the AGI debate. Even long before the current LLM craze, people were counting neurons in the brain and making bold claims about machine intelligence - in just X years, we'll have a machine with the computational power of the brain!

But of course, every neuron in the brain is bafflingly complex and we still don't know or understand how that complexity manifests itself in thought and intelligence. Given physics and the interactions of "things", every cell in the brain is more complex than the LLMs we're using today. Not to say that every cell is capable of producing the same output as an LLM of course, just that the behavior that it contributes to the overall system is that complex.

[thelastgallon]

> But of course, every neuron in the brain is bafflingly complex and we still don't know or understand how that complexity manifests itself in thought and intelligence.

Indeed.

Biophysics of Computation: Information Processing in Single Neurons challenges this notion, using richly detailed experimental and theoretical findings from cellular biophysics to explain the repertoire of computational functions available to single neurons. The author shows how individual nerve cells can multiply, integrate, or delay synaptic inputs and how information can be encoded in the voltage across the membrane, in the intracellular calcium concentration, or in the timing of individual spikes: https://www.amazon.com/Biophysics-Computation-Information-Co...

[rajnathani]

Though, I feel that ANNs' number of neurons are under-represented is some ways:

- In the example case of a CNN, the total weights of a kernel is of [Input Number of Channels * Input Kernel Size * Number of Filters], which can be a pretty small amount when it comes to for example a 3x3 kernel with 3 channels with 128 filters coming to a total of 3,456 parameters (3 * 3 * 3 * 128), however in the case of an ANN the same filter is strided across the entire 2D input feature map (or 3D for 3D CNNs). If the input image is of HD resolution of 1280 * 720 and the stride is 2 across both dimensions, then the number of strides is 230,400. The effective number of parameter activations is 796,262,400 (3,456 * 230,400). The reason for this example is that it is sort of a known thing for likely decades now that CNNs are inspired in part by the human visual cortex [0]. For the human visual cortex which needs to be fast, there cannot be parameter sharing across a single kernel, and likely the weights would need to be parallelized to an extent, which would theoretically imply duplicating the weights across the human brain. Thus, the advantage out here lies with ANNs.

- The neurons in the human brain would have to have a certain level of redundancy in place due to the constant cellular repair work.

- The neurons in the human brain can seemingly only be updated by Hebbian learning rather than direct updates which is in the case of the computer memory of ANNs.

- Finally, a significant part of the human brain is for non-logical but environmental reasons, such as movement and touch, and non-logical things such as fear, jealousy, lust, etc; parts which ANNs do not need to possess in the same way (eg: the fight-or-flight response of the amygdala part of the brain).

[0] https://msail.github.io/post/cnn_human_visual/

[oefnak]

On the other hand, AI doesn't have to be intelligent to be dangerous. Think of viruses for example.

[tomcam]

In fact we know precious little about the mechanisms inside any given cell. We can describe the outcomes of many processes but we couldn’t replicate them if our lives depended on it.

[krzat]

Brain is basically running a matrix-like simulation with a central person in it, for AGI we just need to simulate the thinking parts which is a simpler problem. But who knows how much simpler...