Turing's Test and Conscious Thought

Over 40 years ago, A. M. Turing proposed a test for intelligence in machines. Based as it is, solely on an examinee's verbal responses, the Test misses some important components of human thinking. To bring these manifestations within its scope, the Turing Test would require substantial extension. Advances in the application of AI methods in the design of improved human-computer interfaces are now focusing attention on machine models of thought and knowledge from the altered standpoint of practical utility

Seeing things as people: anthropomorphism and common-sense psychology

This thesis is about common-sense psychology and its role in cognitive science. Put simply, the argument is that common-sense psychology is important because it offers clues to some complex problems in cognitive science, and because common-sense psychology has significant effects on our intuitions, both in science and on an everyday level. The thesis develops a theory of anthropomorphism in common-sense psychology. Anthropomorphism, the natural human tendency to ascribe human characteristics (and especially human mental characteristics) to things that aren't human, is an important theme in the thesis. Anthropomorphism reveals an endemic anthropocentricity that deeply influences our thinking about other minds. The thesis then constructs a descriptive model of anthropomorphism in common-sense psychology, and uses it to analyse two studies of the ascription of mental states. The first, Baron- Cohen et al. 's (1985) false belief test, shows how cognitive modelling can be used to compare different theories of common-sense psychology. The second study, Searle's (1980) `Chinese Room', shows 'that this same model can reproduce the patterns of scientific intuitions taken to systems which pass the Turing test (Turing, 1950), suggesting that it is best seen as a common-sense test for a mind, not a scientific one. Finally, the thesis argues that scientific theories involving the ascription of mentality through a model or a metaphor are partly dependent on each individual scientist's common-sense psychology. To conclude, this thesis develops an interdisciplinary study of common-sense psychology and shows that its effects are more wide ranging than is commonly thought. This means that it affects science more than might be expected, but that careful study can help us to become mindful of these effects. Within this new framework, a proper understanding of common-sense psychology could lay important new foundations for the future of cognitive science

Turingův test: filozofické aspekty umělé inteligence

Disertační práce se zabývá problematikou připisování myšlení jiným entitám, a to pomocí imitační hry navržené v roce 1950 britským filosofem Alanem Turingem. Jeho kritérium, známé v dějinách filosofie jako Turingův test, je podrobeno detailní analýze. Práce popisuje nejen původní námitky samotného Turinga, ale především pozdější diskuse v druhé polovině 20. století. Největší pozornost je věnována těmto kritikám: Lucasova matematická námitka využívající Gödelovu větu o neúplnosti, Searlův argument čínského pokoje konstatující nedostatečnost syntaxe pro sémantiku, Blockův návrh na použití brutální síly pro řešení imitační hry, Frenchova teorie subkognitivních informací a Michieho skepticismus ohledně možnosti umělého vědomí. Závěr práce zachycuje současný stav recepce Turingova testu a představuje pokusy o jeho praktickou realizaci, například v každoroční soutěži o Loebnerovu cenu. Autor práce zastává názor, že ani po více než šedesáti letech od uveřejnění Turingova paradigmatického eseje stále neexistují žádné vážné důvody pro zamítnutí jeho tvrzení. Tradiční komputační funkcionalismus možná není ideální teorií vysvětlující činnost myslí a jako slibnější se může jevit vývoj v neurálních vědách, ale Turingův test je přesto užitečným a snad i jediným nástrojem pro detekci inteligence u lidmi vytvořených strojů

Autopoietic-extended architecture: can buildings think?

To incorporate bioremedial functions into the performance of buildings and to balance generative architecture's dominant focus on computational programming and digital fabrication, this thesis first hybridizes theories of autopoiesis into extended cognition in order to research biological domains that include synthetic biology and biocomputation. Under the rubric of living technology I survey multidisciplinary fields to gather perspective for student design of bioremedial and/or metabolic components in generative architecture where generative not only denotes the use of computation but also includes biochemical, biomechanical, and metabolic functions. I trace computation and digital simulations back to Alan Turing's early 1950s Morphogenetic drawings, reaction-diffusion algorithms, and pioneering artificial intelligence (AI) in order to establish generative architecture's point of origin. I ask provocatively: Can buildings think? as a question echoing Turing's own "Can machines think?" Thereafter, I anticipate not only future bioperformative materials but also theories capable of underpinning strains of metabolic intelligences made possible via AI, synthetic biology, and living technology. I do not imply that metabolic architectural intelligence will be like human cognition. I suggest, rather, that new research and pedagogies involving the intelligence of bacteria, plants, synthetic biology, and algorithms define approaches that generative architecture should take in order to source new forms of autonomous life that will be deployable as corrective environmental interfaces. I call the research protocol autopoietic-extended design, theorizing it as an operating system (OS), a research methodology, and an app schematic for design studios and distance learning that makes use of in-field, e-, and m-learning technologies. A quest of this complexity requires scaffolding for coordinating theory-driven teaching with practice-oriented learning. Accordingly, I fuse Maturana and Varela's biological autopoiesis and its definitions of minimal biological life with Andy Clark's hypothesis of extended cognition and its cognition-to-environment linkages. I articulate a generative design strategy and student research method explained via architectural history interpreted from Louis Sullivan's 1924 pedagogical drawing system, Le Corbusier's Modernist pronouncements, and Greg Lynn's Animate Form. Thus, autopoietic-extended design organizes thinking about the generation of ideas for design prior to computational production and fabrication, necessitating a fresh relationship between nature/science/technology and design cognition. To systematize such a program requires the avoidance of simple binaries (mind/body, mind/nature) as well as the stationing of tool making, technology, and architecture within the ream of nature. Hence, I argue, in relation to extended phenotypes, plant-neurobiology, and recent genetic research: Consequently, autopoietic-extended design advances design protocols grounded in morphology, anatomy, cognition, biology, and technology in order to appropriate metabolic and intelligent properties for sensory/response duty in buildings. At m-learning levels smartphones, social media, and design apps source data from nature for students to mediate on-site research by extending 3D pedagogical reach into new university design programs. I intend the creation of a dialectical investigation of animal/human architecture and computational history augmented by theory relevant to current algorithmic design and fablab production. The autopoietic-extended design dialectic sets out ways to articulate opposition/differences outside the Cartesian either/or philosophy in order to prototype metabolic architecture, while dialectically maintaining: Buildings can think