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Moore's law

As explained in Inside AI

Moore's Law is the empirical observation that the number of transistors on an integrated circuit tends to increase exponentially, historically doubling about every two years.

Moore’s Law is the observation that computing power tends to double approximately every two years. It has helped motivate predictions about rapidly advancing computers, but increasing power does not automatically produce intelligence. section 9.3

Why the pattern attracts bold predictions

Computers defeating chess champions can make continued hardware progress seem like a path toward human-level abilities. That inference remains limited: machines execute human-coded instructions, while human intelligence involves biological, psychological, and neurological complexity. section 9.3

Treat Moore’s Law as a useful trend with important constraints:

  • Transistor density is approaching physical limits on microchips. section 9.3
  • Heat dissipation becomes difficult as devices grow highly compact. section 9.3
  • Faster, smaller, and cheaper computers still do not equal more sophisticated intelligence. section 9.3

How Inside AI covers it

How deep it goes
Defines the observed doubling trend and explains physical limits, but does not examine transistor engineering in detail.
Examples
Uses chess computers, transistor density, and heat dissipation to illustrate progress and constraints.
What you can do after
Evaluate claims that increasing computing power necessarily produces human-like intelligence.
How it connects
Connects Moore’s Law to computing power, artificial intelligence, neural networks, and technological singularity.

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This explainer was generated by Manning's knowledge graph from the text of Inside AI. The section links point to the parts of the book it draws on.