Artificial NNs are simulations (not "abstractions") of animal, and human, neural networks... so, by definition, humans are not more than a neural network.

simple number 0 through 1

Not how it works.

Animal neurons respond as a clamping function, with a constant 0 output up to some threshold, where they start outputting neurotransmitters as a function of the input values. Artificial NNs have been able to simulate that for a while.

Still, for a long time it used to be thought that copying the human connectome and simulating it, would be required to start showing human-like behaviors.

Then, some big surprises came from a few realizations:

1. You don't need to simulate the neurons, just the relationship between inputs and outputs (each one can be seen as the level of some neurotransmitter in some synapse).
2. A grid of values, can represent the connections of more neurons than you might think (most neurons are not connected to most others, the neurotransmitters don't travel too far, they get reabsorbed, and so on).
3. You don't need to think "too much" about the structure of the network; add a few extra trillion connections to a relatively simple stack, and the network can start passing the Turing test.
4. The values don't need to be 16bit floats, NNs quantized to as little as 4bit (0 through 16) can still show pretty much the same behavior.

There are still a couple things to tackle:

1. The lifetime of a neurotransmitter in a synapse.
2. Neuroplasticity.

The first one is kind of getting solved by attention heads and self-reflection, but I'd imagine adding extra layers that "surface" deeper states into shallower ones, might be a closer approach.

The second one... right now we have LoRAs, which are more like psychedelics or psychoactive drugs, working in a "bulk" kind of way... with surprisingly good results, but still.

Where it really will start getting solved, is with massive scale neuromorphic hardware accelerators the size of a 1TB microSD card (proof of concept is already here: https://www.science.org/doi/10.1126/science.ade3483 ), which could cut down training times by 10 orders of magnitude. Shoving those into a billion smartphones, then into some humanoid robots, is when the NN age will really get started.

Whether that's going to take more or less than 5 years, it's hard to say, but surely everyone is trying as hard as possible to make it less.

Then, imagine a "trainee" humanoid robot, with maybe 1000 accelerators of those, that once it trains a NN for whatever task, can be copied over to as many simple "worker" robots as needed. Imagine a company spending a few billion USD on training a wide range of those NNs, then offering a per-core subscription to other companies... at a fraction of the cost of similarly trained humans.

TL;DR: we haven't seen nothing yet.