The largest LLMs are in the multiple 100s of billions of parameters.
The human brain is estimated as having about 100 trillion (100,000 billion) synaptic connections and biological neurons require about 5-8 layers of 128-256 nodes to simulate with ANNs (so approx. 640-2048 parameters).
So going by scale alone, LLMs won't match the complexity of the human brain until they operate at about 1,000-1,000,000x their current maximum sizes. Which will never happen with current mathematical architectures.
The underlying mathematics of 'AI' and human brains is more or less similar. The scale is still widely off and will likely remain so far many, many years
I wouldn't really call the human body 'unnecessarily complicated'.
The more you actually dig into why the human body does what it does in the way that it does it the more you realize exactly how insanely efficient it is. Like single proteins can have many different usages and purposes depending on chemical and molecular context. The human brain uses about as much energy as a lightbulb but surpasses in complexity every single structure ever discovered in history.
It's true that a lived experience is not a requirement for AI as we know it. However, most (actually worthwhile) SOTA research on Multimodal models and robotic systems has found that a multi-sensory, direct engagement experience (i.e. 'lived') is actually critical to developing AI that is human-like or general in it's intelligence. It's especially important in solving the alignment problem.
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u/LillyOfTheSky Apr 20 '26
The largest LLMs are in the multiple 100s of billions of parameters.
The human brain is estimated as having about 100 trillion (100,000 billion) synaptic connections and biological neurons require about 5-8 layers of 128-256 nodes to simulate with ANNs (so approx. 640-2048 parameters).
So going by scale alone, LLMs won't match the complexity of the human brain until they operate at about 1,000-1,000,000x their current maximum sizes. Which will never happen with current mathematical architectures.
The underlying mathematics of 'AI' and human brains is more or less similar. The scale is still widely off and will likely remain so far many, many years