When will computer hardware match the human brain? (1998)

Hardware Progress and Moravec’s Predictions

• Many note Moravec’s late‑90s plots still roughly fit modern trends if you count GPUs/FLOPs, not just CPU MIPS.
• MIPS scaling largely stalled, but GPU FLOPs exploded; modern GPUs and supercomputers approach or exceed his “brain-equivalent” ballpark on some metrics.
• Some argue you must normalize by cost ($/compute) as Moravec did, which makes supercomputer comparisons tricky.
• Others point out his scale implies we could simulate a nematode in 1998; since that’s still hard, his simple ops/sec mapping is suspect.

What Sustains Moore’s Law–Like Growth?

• Suggested drivers: huge private investment, positive feedback from using current chips to design the next, and market pressure for yearly performance gains.
• Skeptics say this “the market did it” answer just shifts the question of how we keep finding advances.

Brain vs Computer: Architecture, Energy, and Limits

• Repeated emphasis that brain ≠ von Neumann CPU: massively parallel, largely analog, 3D, with complex cell types (neurons, astrocytes), synapse dynamics, and brain waves.
• The brain runs on ~20 W; GPUs need hundreds of watts, though some argue electricity is cheaper and human “training” takes decades.
• Disagreement over whether we can treat neurons/synapses as simple weights vs needing detailed biophysics. Analog vs digital nature of the brain is called unresolved.

Defining and Detecting Intelligence

• One camp aligns with a performance-based view: intelligence is what systems do, not what they’re made of.
• Others argue internals matter: a lookup table or shallow chatbot can pass a Turing test without “real” intelligence.
• Chollet’s definition (skill acquisition efficiency over many tasks with minimal priors/data) is cited; critics say it misses human-like logical reasoning and “executive” control.
• LLMs are seen by some as powerful yet sample-inefficient, lacking robust reasoning and true memory; others note similarities to predictive brains plus missing working-memory mechanisms.

Do We Already Have Brain-Scale Compute?

• Some rough estimates: billions–trillions of GPU FLOPs may already rival or beat brain-level ops for some abstractions.
• Counterarguments stress missing pieces: unknown brain algorithms, data, memory bandwidth, and realistic neuron/synapse models.
• Back-of-envelope transistor-per-synapse and neuron-count calculations are heavily disputed as oversimplified.

Simulation Efforts and Timelines

• One commenter involved in brain modeling claims a full mouse brain multiscale simulation fits on a modest supercomputer, with room for simplification.
• They hypothesize top supercomputers could simulate a human brain if we had adequate data and theory.
• Others speculate real-time full human-brain analogs might appear late this century, while some insist brains aren’t computers and such equivalence may never be meaningful.