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I am looking for books or to state of the art papers about current the development trends for a strong-AI.

Please, do not include opinions about the books, just refer the book with a brief description. To emphasize, I am not looking for books on applied AI (e.g. neural networks or the book by Norvig). Furthermore, do not consider AGI proceedings, which contains papers that focus on very concrete aspects. The related Wikipedia describes some active investigation lines about AGI (cognitive, neuroscience, etc.) but can not be considered an educational/introductory resource. Finally, I am not interested in philosophical questions related to AI safety or risks or its morality if they are not related to its development. Development doesn't exclude mathematical foundation about it.

By example, if I look by example at this list "https://bigthink.com/mike-colagrossi/the-10-best-books-on-ai", the final candidates list became empty.

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There is actually a book called Artificial General Intelligence by Ben Goertzel and Cassio Pennachin. It's a bit out of date (from 2008), and published as a Springer-Verlag monograph (which tends to have fairly low editorial standards). This one is also an anthology, with each chapter written by a different author. It's probably not suitable as an undergraduate level book, but it does seem to contain something like the information that's wanted.

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The paper Artificial General Intelligence: Concept, State of the Art, and Future Prospects (2014), by Ben Goertzel (one of the people that are really still very interested in AGI), surveys the field of artificial general intelligence (AGI), its progress, approaches, mathematical formalisms, engineering, and biology-inspired perspectives, and metrics for assessing AGI.

Just to give a little bit more context and whet your appetite, let me briefly describe the different approaches to AGI (section 3, p. 14).

  • symbolic approach (which is based on the Physical Symbol System Hypothesis; examples of this approach are ACT-R or SOAR),

  • emergentist approach (aka sub-symbolic, i.e. the use of neural networks, and similar sub-symbolic models, from which abstract symbolic processing/reasoning can or is expected to emerge; so examples of this approach is deep learning, computational neuroscience, and artificial life),

  • hybrid approach (a combination of the symbolic and sub-symbolic approaches; examples of this approach are CLARION and CogPrime), and

  • universalist approach (examples of this approach are the AIXI and Gödel machine).

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