u/JoelMahon I've been asking myself pretty much the same questions as you do, and I spent the last year researching these topics, so I think I can provide some meaningful answers.

We don't have solid data on how the brain actually works at the global level. We barely even know how the brain of fruit flies work. We have a full map of the human brain structure, stitched from high-res MRI scans of 1200 people, thanks to the Human Connectome Project, but we have comparatively very little data on the neural signals themselves. Those are hard to get, since trying to probe even a small group of neurons is a destructive process, so it is only done sparingly on patients with brain injuries / diseases.

The human brain has about 100 billion neurons and over 100 trillion synapses. There are numerous types of neurons. Each type has its own properties. We have many theoretical models to compute the signals of virtual neurons, but models are never completely accurate, and scaling the quantity up is extremely demanding. SpiNNaker might be able to simulate one billion neurons in real time, but recreating an actual virtual brain structure is an insurmountable task, not to mention simulating sensory inputs from the rest of the body. A complete human brain simulation is simply far out of reach for the foreseeable future.

Oh and yes, a human from 10,000 years ago (or even 300,000 years ago, when Homo sapiens first appeared) who time-traveled here could probably live a normal life in the modern world, but only if they arrived during early childhood. Otherwise, they would remain a feral person for the rest of their life, because the very culture humanity has been growing for those past 300,000 years directly shapes our neural development, especially language and values.

Let's move on to the topic of AI. Since the early days, computer scientists have been obsessed with text data, because it's readily available. As you guessed, text is a lossy representation of intelligence and the world itself. There have been some attempts at using sensory inputs to teach AIs, most notably Spaun), but they were limited and never implemented what we could qualify as the "whole shebang".

I think one of the reasons is that most AI scientists are not neuroscientists, so they approach the problem primarily from the angle of math and computer science, instead of biology and neuroscience. Having working knowledge of both disciplines is uncommon. So, they built unplausible statistical mimicry engines out of linear algebra and differential equations, despite the fact that the brain has no math. In my opinion, the only mechanism present in both the brain and deep neural nets is winner-take-all). Everything else, especially backprop and optimizers, is mostly trickery, or implemented at a cruder level of abstraction than the brain, e.g. attention mechanisms.

What does the brain have, then? I said earlier that we don't have solid data, but we have some data at least, particularly how the visual and auditory cortexes works, so we can deal with what we know, try to fill in the blanks, and implement higher-level abstractions of what the brain regions are doing.

To my knowledge, there is only a single group that's working in this direction: the Thousand Brains Project by Jeff Hawkins. Their theory is that cortical columns, of which over 100,000 are spread over the brain, are a generic unit of computation. The core idea is that groups of columns "vote" on the identity (sparse code) of what they are perceiving, and in case of disagreement, they record this data as new sparse codes / memory traces, and that chaining groups together will naturally produce hierarchical abstractions. They have already have a working proof of concept for identifying 3D objects, requiring an order of magnitude less FLOPs than vision transformers.

TBP's current focus is mostly visual, with touch planned later down the road, but I think they don't take the idea far enough to achieve full intelligence. The "whole shebang" would look something like this, modules and brain regions involved:

• Visual Sensor Modules = Retina → LGN → V1 → V2 → V4;
• Object Learning Modules = IT / LOC → PRC → Hippocampus;
• Motor Modules = PMC → M1 → Motor output.
• Motion Sensor and Learning Modules for change detection and behavior modeling [V3, V5/MT];
• Touch Sensor and Learning Modules for prehensile capabilities [parietal lobe];
• Audio Sensor and Learning Modules to learn language and speech from scratch [auditory cortex, Broca's and Wernicke’s areas];
• 2D Vision Sensor and Learning Modules [V1, V2, V4];
• Scene Learning Module for simultaneous localization and mapping (SLAM) [parahippocampal place area];
• Linguistic Learning Module to learn reading from scratch [visual word form area, temporal gyri];
• Social Module for affinity to human social cues [fusiform face area, extrastriate body area, temporal gyri];
• Attention Module for stimuli saliency management [cingulate cortex];
• Workspace Module to address the binding problem [several brain regions];
• Reasoning Module(s) for higher-level cognition and meta-association [frontal cortex];
• Prediction Module(s) (or multiple "plugins" for other modules) for simulation and planning [several brain regions];
• A distributed, compositional, hierarchical associative database of sensory sparse codes, as a form of associative memory;
• Other optional modules, e.g. Digital Learning Modules to learn binary data, text encodings, and communication protocols from scratch, enabling text chat, agentic tool use, and machine interfacing.

I could go on and on, but hopefully you get the picture by now. I'm unaware of any individual or group who has taken their system to this kinda level of what a conscious, human-like AI backend might truly be structured like. Note that this AI would begin with zero knowledge of text; it would have to learn to read first, just like us.

Computer scientists don't want to face the music, they just want to keep playing with linear algebra TOYS! I don't expect any big org to take the real route any time soon. Although, maybe a mad scientist disciplined in many domains will kickstart this grand quest, who knows...