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2. Artificial grammar learning - Wikipedia

   en.wikipedia.org/wiki/Artificial_grammar_learning

   The results of the experiment support the dual factor approach to artificial grammar learning in that people use abstract information to learn rules for grammars and use concrete, exemplar-specific memory for chunks. Since the amnesiacs were unable to store specific "chunks" in memory, they completed the task using an abstract set of rules.

3. Grammar induction - Wikipedia

   en.wikipedia.org/wiki/Grammar_induction

   Grammar induction (or grammatical inference) [1] is the process in machine learning of learning a formal grammar (usually as a collection of re-write rules or productions or alternatively as a finite-state machine or automaton of some kind) from a set of observations, thus constructing a model which accounts for the characteristics of the observed objects.

4. LALR parser - Wikipedia

   en.wikipedia.org/wiki/LALR_parser

   Depending on the presence of empty derivations, a LL(1) grammar can be equal to a SLR(1) or a LALR(1) grammar. If the LL(1) grammar has no empty derivations it is SLR(1) and if all symbols with empty derivations have non-empty derivations it is LALR(1). If symbols having only an empty derivation exist, the grammar may or may not be LALR(1). [12]

5. Workers using AI effectively turn off part of their brains ...

   www.aol.com/finance/workers-using-ai-effectively...

   Surveying 319 knowledge workers through 936 first-hand self-reported examples of using generative AI at work, the authors attempted to gauge the perceived enactment of critical thought and how ...

6. Generative grammar - Wikipedia

   en.wikipedia.org/wiki/Generative_grammar

   Generative grammar generally distinguishes linguistic competence and linguistic performance. [11] Competence is the collection of subconscious rules that one knows when one knows a language; performance is the system which puts these rules to use.

7. Language model - Wikipedia

   en.wikipedia.org/wiki/Language_model

   A language model is a model of natural language. [1] Language models are useful for a variety of tasks, including speech recognition, [2] machine translation, [3] natural language generation (generating more human-like text), optical character recognition, route optimization, [4] handwriting recognition, [5] grammar induction, [6] and information retrieval.