Brain Efficiency: Much More than You Wanted to Know
What if the brain is highly efficient? To be more specific, there are several interconnected key measures of efficiency for physical learning machines: * energy efficiency in ops/J * spatial efficiency in ops/mm^2 or ops/mm^3 * speed efficiency in time/delay for key learned tasks * circuit/compute efficiency in size and steps for key low level algorithmic tasks [1] * learning/data efficiency in samples/observations/bits required to achieve a level of circuit efficiency, or per unit thereof * software efficiency in suitability of learned algorithms to important tasks, is not directly addressed in this article[2] Why should we care? Brain efficiency matters a great deal for AGI timelines and takeoff speeds, as AGI is implicitly/explicitly defined in terms of brain parity. If the brain is about 6 OOM away from the practical physical limits of energy efficiency, then roughly speaking we should expect about 6 OOM of further Moore's Law hardware improvement past the point of brain parity: perhaps two decades of progress at current rates, which could be compressed into a much shorter time period by an intelligence explosion - a hard takeoff. But if the brain is already near said practical physical limits, then merely achieving brain parity in AGI at all will already require using up most of the optimizational slack, leaving not much left for a hard takeoff - thus a slower takeoff. In worlds where brains are efficient, AGI is first feasible only near the end of Moore's Law (for non-exotic, irreversible computers), whereas in worlds where brains are highly inefficient, AGI's arrival is more decorrelated, but would probably come well before any Moore's Law slowdown. In worlds where brains are ultra-efficient, AGI necessarily becomes neuromorphic or brain-like, as brains are then simply what economically efficient intelligence looks like in practice, as constrained by physics. This has important implications for AI-safety: it predicts/postdicts the success of AI app
Yep - this is also my current mental model for agent vs human performance.
But why would we expect this?
Probably because LLMs train vastly longer - they have several OOM more experience than a human, but almost entirely on short term tasks (easier to acquire datasets for) well below one context window (similar to the one-day 500k ish token equivalent context window of the hippocampus wake cycle). This is a side effect of their current data-inefficient training/learning process/algorithms, which itself is a consequence of their unique compute economics (near zero cost to copy, so naturally the first economically viable AI to match/surpass humans will be vastly more expensive to train, because you can amortize that cost).