These exercises are fun and may shed some light on comparable complexity of machines vs animals, but they make the (unrealistic) assumption that the computation

These exercises are fun and may shed some light on comparable complexity of machines vs animals, but they make the (unrealistic) assumption that the computational architectures are comparable. Brains (and the nervous system and body in general) have many crucial differences from the (current mainstream) ANNs, beyond the Assumptions listed in TFA. Brain connections are higly recurrent at multiple levels, they are magnitudes slower than GPUs but they are dynamical in the state (e.g. temporal summation), there's a lot of information encoded outside the synaptic connections (e.g. concentration of transmitters/modulators in intercellular fluid), the synaptic connections are more complicated than just a scalar weight (modulated by transmitters etc), etc etc. Also all the rage in contemporary views of animal intelligence is "embodiment" that stresses that there's more to animal intelligence than the brain. Similar, quite apples to oranges, comparisons were made to von Neuman computation architectures (CPU FLOPS and hard drive/memory bits) when the "computer metaphor" of the brain/mind was the trend. In general there's a tendency to compare human intelligence to whatever is seen as the state-of-the-art in machines (e.g. clockworks and telephone networks back in their respective days). These probably give some intuition, but shouldn't be taken too seriously for e.g. projecting when machines and humans are at parity.