A type of AI in which systems learn patterns and improve: Among the generally accepted definitions and types

Machine learning is AI that learns patterns from data and experience rather than relying solely on explicitly programmed rules.

The question

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1. A type of AI in which systems learn patterns and improve from data and experience rather than following only explicitly hand-coded rules for every case. ✓

   Correct: machine learning derives behavior from data-driven pattern learning instead of exhaustive explicit programming.
2. A type of AI in which every decision is produced strictly by manually written if-then rules, with no capacity to generalize from examples or data at all.

   Plausible but wrong: this describes classic rule-based expert systems, not machine learning.
3. A type of computing hardware that accelerates numerical operations, defined by its physical processors rather than by any particular learning method or algorithm.

   Plausible but wrong: hardware acceleration is infrastructure, not the definition of machine learning.
4. A type of AI that has achieved human-level general intelligence across all domains, matching or exceeding people on essentially every cognitive task they perform.

   Plausible but wrong: that describes hypothetical artificial general intelligence, not machine learning.

How to remember it

Machine learning is AI that learns patterns from data and experience rather than relying solely on explicitly programmed rules.

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