No? The human brain has ~100 billion neurons and ~1 trillion connection weights. Google’s PaLM uses ~540 Billion nodes with ~100 trillion connection weights.
And the key point is this- these models are the worst they will ever be, and are gaining size at pace. So even if you we grant the argument that our brains are still a bit more complex, hopefully we can agree that will not be the case in 5 years. Heck, how about 20 years, or 100? Let's be real.
If you took the "no it won't" side of every argument about "how in X number of years, AI is sure to Y", you'd be way ahead.
In any event, raw parameter/weight count to me seems like a very primitive way to judge "complexity" in comparison to the human brain. Looked at most ways, our brains are for more efficient at doing the incredible things they do than LLMs. Consider how little language young children are exposed to in comparison to LLMs given their abilities to figure out how to produce language.
If the brain doesn't work like an LLM, you can expand the size and "complexity" of these models to the moon and they won't outperform the brain. Current models can write impressively well, but they can barely do math. It's clear they don't reason as we do.
nodes and weights are different from neurons and connections. Neurons are also not the only components in the brain which contribute to intelligence.
google recently scanned a 1mm cube of human brain which was 1.5Petabytes of raw data. The AI hardware that Google trains on is multiple racks.
I think a better analogy would be between an entire google datacenter (including all the networking, storage, sensors, processors, and memory) and a human body although even then it's a stretch.
And the key point is this- these models are the worst they will ever be, and are gaining size at pace. So even if you we grant the argument that our brains are still a bit more complex, hopefully we can agree that will not be the case in 5 years. Heck, how about 20 years, or 100? Let's be real.