1) What that giant paragraph is actually saying (step‑by‑step, kid‑friendly)
- Tiny recipes everywhere. — Everything you do or think can be broken down into loads of very small, super‑simple “recipes” (tiny programs).
- Brute‑force treasure hunt. — The best way to find the winning recipes is to try zillions of them, keep the ones that work a bit, tweak them, and repeat—like hot‑and‑cold until you’re really warm.
- Big sloppy nets help the hunt. — If you build a huge web of pretend “neurons” and let the treasure hunt run inside it, you eventually stumble on little sub‑webs that are especially good (the “lottery tickets”).
- Average the winners. — Instead of picking one champion, you blend a bunch of near‑winners together; the blend behaves almost like the mathematically perfect answer‑picker (Bayes‑optimal).
- From copying to understanding. — At first the web mostly memorises examples (like a parrot). As it sees more and more, it starts spotting deeper patterns and inventing short‑cut rules, so the recipes get simpler but more powerful.
- Your brain can do the same trick. — Billions of almost‑identical biological neurons, nudged by rough versions of back‑prop, can discover those tiny recipes too.
- Why brains scale with body size. — Bigger animals still have to react fast, so they use wider, shallower “webs” (more neurons in parallel) to keep reaction time low, even if that isn’t the most neuron‑efficient design.
- What IQ (“g”) really is. — It’s not a special thinking potion; it’s just the total pool of healthy brain parts you have left over after running the basic life‑support jobs. The better your overall body build and upkeep, the more spare brainpower you can point at puzzles and tests.
2) If this picture is right, what’s the smartest way to learn something new?Collect mountains of examples.
The bigger and more varied your personal “training set” (problems, worked‑out solutions, real‑world cases), the more raw material your brain has for hunting good mini‑recipes.Cycle through, don’t cram.
Frequent, spaced, mixed practice forces your brain to keep re‑searching and averaging rather than just memorising a single run‑through.Look for the childishly simple rule underneath.
After each study session, ask: “Could I explain the core trick in two sentences?” You’re compressing the examples into a tiny, reusable program.Blend perspectives.
Read multiple authors, solve problems in different ways, teach others. This ensembles several near‑recipes and usually beats any single approach.Push past the “I get it” ceiling.
Once a task feels easy, switch to problems that lean on different sub‑skills; that’s where g stops helping and specialised practice matters.Keep the hardware in shape.
Because spare cognitive fuel comes from whole‑body health, protect sleep, exercise, diet, and avoid chronic stress.
3) The strongest case against this view of intelligence
- Symbols & logic matter.
Humans juggle discrete symbols (“if‑then”, “causality”, “language syntax”) that don’t fit neatly into smooth loss landscapes. Pure recipe‑hunting might never rediscover formal reasoning without extra scaffolding. - Real brains don’t back‑prop, evolution did.
Our wiring was sculpted over millions of years, not by gradient descent during one lifetime. Developmental blueprints, innate priors, and reward systems could be doing most of the heavy lifting. - Data hunger vs. sample efficiency.
Children infer grammar, physics, and social rules from tiny sample sizes. Over‑parameterised interpolation needs absurd data compared with that human feat. - Brittleness & out‑of‑distribution failure.
Networks that lean on nearest‑neighbour tricks crack when the world shifts. Robust generalisation may require causal models, not just ever‑larger ensembles. - g might be more than leftover neurons.
Studies link g to working‑memory control, strategic search, and metacognition—abilities that look like specialised mental “executives,” not just spare capacity. - The size‑latency argument is incomplete.
Bigger animals’ extra neurons also handle longer nerves, larger sensory ranges, and stronger muscles. Width‑vs‑depth trade‑offs alone can’t explain allometric scaling. In short, the “master synthesis” captures an important slice of how pattern‑spotting systems get smart with scale, but it may downplay symbol handling, evolutionary hard‑wiring, and true causal understanding—leaving room for other, possibly richer, pictures of intelligence.