The goal of Transhumanism is to change the human condition through radical enhancement of its positive traits and through AI (Artificial Intelligence). Among these traits the transhumanists highlight creativity. Here we first describe human creativity at more fundamental levels than those related to the arts and sciences when, for example, childhood is taken into account. We then admit that creativity is experienced on both its bright and dark sides. In a second moment we describe attempts to improve creativity both at (...) the bodily level and in the emulation of human behavior in AI. These developments are presented both as effective results in laboratories and as transhumanist proposals emerging from them. Third, we have introduced the work of some theologians and ethicists, who study such proposals through the lens of the "created co-creator" metaphor. Finally, we analyze these developments through a reflection on the nature of creativity. We identify three problems in the current ideas about enhancement: first, they are bound to Western standards of creativity; second, the tempo and mode of evolution (involving an extended childhood) are not taken into account; third, the enhancement of creativity develops both its healthy and the perverse side. Proponents of the created co-creator metaphor also have difficulties with the problems pointed out. Concerning AI, there is a promising perspective of approaching human creativity, but there are some intrinsic limits: Human emotions are ambiguous, contradictory and far from rational control; human creativity is iconoclastic, thus highlighting the importance of youth and new generations; we do not expect from AI beings to present the perverse side of creativity. Our conclusions are: First, procreation and the new generations are essential to human creativity; in the same way, the wheat does not grow without the chaff; and finally, that birth breaks with causal connections and allows us to act forgiving--at birth, God's creative act is re-enacted. (shrink)

This paper distinguishes two versions of Ryle's notion of 'logical geography'. Logical geography: The network of relationships between current uses of a collection of concepts. (Probably what Ryle meant by the term.) Logical topography Features of the portion of reality, or types of portions of reality, related to a given set of concepts, where the reality may be capable of being divided up in different ways using different networks of relationships between concepts. -/- Studying/analysing logical topography includes evaluating the alternative (...) divisions in terms of the theories or applications that are supported by the different sets of concepts, or perhaps some other uses of the concepts. -/- On re-reading Appendices III and IV of my Oxford DPhil thesis "Knowing and Understanding Relations between meaning and truth, meaning and necessary truth, meaning and synthetic necessary truth",, online here, I found to my surprise that several of the topics discussed below had been discussed in those two Appendices, and that the distinction between logical topography and logical geography was made in Appendix IV, discussing conceptual analysis, even though I did not use these words. (shrink)

The target article sketched and explored a mechanism (action-oriented predictive processing) most plausibly associated with core forms of cortical processing. In assessing the attractions and pitfalls of the proposal we should keep that element distinct from larger, though interlocking, issues concerning the nature of adaptive organization in general.

To perceive an affordance is to perceive an object or situation as presenting an opportunity for action. The concept of affordances has been taken up across wide range of disciplines, including AI. I explore an interesting extension of the concept of affordances in robotics. Among the affordances that artificial systems have been engineered to detect are affordances to deliberate. In psychology, affordances are typically limited to bodily action, so the it is noteworthy that AI researchers have found it helpful to (...) extend the concept to encompass mental actions. I propose that psychologists can learn from this extension, and argue that human subjects can perceive mental affordances, such as affordances to attend, affordances to imagine and affordances to count. (shrink)

Often, when people discuss the role of simplicity in science, they do not notice the trade-off between simplicity of ontology and simplicity of theory using an ontology. Einstein appears to have been emphasising simplicity of ontology (basic elements), though he might have included theory as well (basic axioms/assumptions).

It may be of interest to see what can be done by giving a robot no innate knowledge about its environment or its sensors or effectors and only a totally general learning mechanism, such as reinforcement learning, or some information-reduction algorithm, to see what it can learn in various environments. However, it is clear that that is not how biological evolution designs animals, as McCarthy states.

[Przekład] To, czego w ciągu ostatnich sześciu lub siedmiu tego nauczyliśmy się na temat wirtualnej maszynerii w wyniku dużego postępu nauki i techniki, umożliwia nam zaoferowanie stanowisku darwinowskiemu nowej obrony przeciw krytykom, którzy twierdzili, że jedynie forma fizyczna – a nie zdolności umysłowe czy świadomość – może być produktem ewolucji poprzez dobór naturalny. Obrona ta porównuje zjawiska umysłowe, wspominane przez przeciwników Darwina, z treściami maszynerii wirtualnej w systemach obliczeniowych. Obiekty, stany, zdarzenia i procesy w owej maszynerii, które dopiero od niedawna (...) umiemy projektować i konstruować, a które nie mogły być nawet przedmiotem rozmyślań w czasach Darwina, mogą współdziałać z maszynerią fizyczną, w której są zaimplementowane, nie będąc identycznymi ze swoją fizyczną implementacją, ani też nie będąc jedynie agregatami fizycznych struktur i procesów. Istnienie różnych rodzajów maszynerii wirtualnej (w tym zarówno „platformowych” maszyn wirtualnych, mogących hostować inne takie maszyny, na przykład systemy operacyjne, jak i „aplikacyjnych” maszyn wirtualnych, na przykład korektorów pisowni i gier komputerowych) zależy od skomplikowanych sieci połączeń przyczynowych związanych ze strukturami hardware’u i software’u, zdarzeniami i procesami, gdzie specyfikacja takich sieci przyczynowych wymaga konceptów, które nie mogą być zdefiniowane w kategoriach konceptów nauk fizycznych. Ta niedefiniowalność, a także możliwość zachodzenia różnych rodzajów automonitorowania w maszynerii wirtualnej, wydaje się wyjaśniać niektóre z rzekomo tajemniczych i nieredukowalnych cech świadomości, które motywowały krytyków Darwina, a także bardziej współczesnych filozofów, krytykujących sztuczną inteligencję. Wynikają z tego konsekwencje dla filozofii, psychologii, neurobiologii i robotyki. (shrink)