The brain's hardware limitations, like slow and stochastic neurons, may actually be advantages. These properties seem perfectly suited for probabilistic inference algorithms that rely on sampling—a task that requires explicit, computationally-intensive random number generation in digital systems. Hardware and algorithm are likely co-designed.

AI errors, or "hallucinations," are analogous to a child's endearing mistakes, like saying "direction" instead of "construction." This reframes flaws not as failures but as a temporary, creative part of a model's development that will disappear as the technology matures.

The hypothesis for ImageNet—that computers could learn to "see" from vast visual data—was sparked by Dr. Li's reading of psychology research on how children learn. This demonstrates that radical innovation often emerges from the cross-pollination of ideas from seemingly unrelated fields.

The term "data labeling" minimizes the complexity of AI training. A better analogy is "raising a child," as the process involves teaching values, creativity, and nuanced judgment. This reframe highlights the deep responsibility of shaping the "objective functions" for future AI.

The popular concept of AGI as a static, all-knowing entity is flawed. A more realistic and powerful model is one analogous to a 'super intelligent 15-year-old'—a system with a foundational capacity for rapid, continual learning. Deployment would involve this AI learning on the job, not arriving with complete knowledge.

Children are more rational Bayesians than scientists because they lack strong pre-existing beliefs (priors). This makes them more open to updating their views based on new, even unusual, evidence. Scientists' extensive experience makes them rationally stubborn, requiring more evidence to change their minds.

To explain the LLM 'temperature' parameter, imagine a claw machine. A low temperature (zero) is a sharp, icy peak where the claw deterministically grabs the top token. A high temperature melts the peak, allowing the claw to grab more creative, varied tokens from a wider, flatter area.

A useful mental model for AGI is child development. Just as a child can be left unsupervised for progressively longer periods, AI agents are seeing their autonomous runtimes increase. AGI arrives when it becomes economically profitable to let an AI work continuously without supervision, much like an independent adult.

Just as crawling is a vital developmental step for babies even though adults don't crawl, some learning processes that AI can automate might be essential for cognitive development. We shouldn't skip steps without understanding their underlying neurological purpose.