Unlike other species, humans are born with "half-baked" brains that wire themselves based on the culture, language, and knowledge accumulated by all previous generations. This cumulative learning, not just individual experience, is the key to our rapid advancement as a species.
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The human brain contains more potential connections than there are atoms in the universe. This immense, dynamic 'configurational space' is the source of its power, not raw processing speed. Silicon chips are fundamentally different and cannot replicate this morphing, high-dimensional architecture.
Intelligence is not a single trait but the culmination of a causal chain. The sequence begins with evolution enabling sensing, which necessitates memory. This leads to consciousness and imagination, which finally allows for free will — the sum total of which is intelligence.
The small size of the human genome is a puzzle. The solution may be that evolution doesn't store a large "pre-trained model." Instead, it uses the limited genomic space to encode a complex set of reward and loss functions, which is a far more compact way to guide a powerful learning algorithm.
While geological and biological evolution are slow, cultural evolution—the transmission and updating of knowledge—is incredibly fast. Humans' success stems from shifting to this faster clock. AI and LLMs are tools that dramatically accelerate this process, acting as a force multiplier for cultural evolution.
We often think of "human nature" as fixed, but it's constantly redefined by our tools. Technologies like eyeglasses and literacy fundamentally changed our perception and cognition. AI is not an external force but the next step in this co-evolution, augmenting what it means to be human.
The idea that language creates thought is backwards. Pre-linguistic infants already have a sophisticated understanding of the world (e.g., cause and effect). They learn language by shrewdly guessing a speaker's intent and mapping the sounds they hear onto thoughts they already possess.
Andre Karpathy argues that comparing AI to animal learning is flawed because animal brains possess powerful initializations encoded in DNA via evolution. This allows complex behaviors almost instantly (e.g., a newborn zebra running), which contradicts the 'tabula rasa' or 'blank slate' approach of many AI models.
Our sense of self isn't an innate property but an emergent phenomenon formed from the interaction between our internal consciousness and the external language of our community (the "supermind"). This implies our identity is primarily shaped not by DNA or our individual brain, but by the collective minds and ideas we are immersed in.
The Fetus GPT experiment reveals that while its model struggles with just 15MB of text, a human child learns language and complex concepts from a similarly small dataset. This highlights the incredible data and energy efficiency of the human brain compared to large language models.
AI models use simple, mathematically clean loss functions. The human brain's superior learning efficiency might stem from evolution hard-coding numerous, complex, and context-specific loss functions that activate at different developmental stages, creating a sophisticated learning curriculum.