260 research outputs found
On The Foundations of Digital Games
Computers have lead to a revolution in the games we play, and, following this, an interest for computer-based games has been sparked in research communities. However, this easily leads to the perception of a one-way direction of influence between that the field of game research and computer science. This historical investigation points towards a deep and intertwined relationship between research on games and the development of computers, giving a richer picture of both fields. While doing so, an overview of early game research is presented and an argument made that the
distinction between digital games and non-digital games may be counter-productive to game research as a whole
Will machines ever think
Artificial Intelligence research has come under fire for failing to fulfill its promises. A growing number of AI researchers are reexamining the bases of AI research and are challenging the assumption that intelligent behavior can be fully explained as manipulation of symbols by algorithms. Three recent books -- Mind over Machine (H. Dreyfus and S. Dreyfus), Understanding Computers and Cognition (T. Winograd and F. Flores), and Brains, Behavior, and Robots (J. Albus) -- explore alternatives and open the door to new architectures that may be able to learn skills
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
Microfunctionalism: Connectionism and the Scientific Explanation of Mental States
My goal in the present treatment is to sketch and compare two scientific approaches to understanding the mind. The first approach, that of classical cognitivism, depicts mind as a manipulator of chunky, quite high-level, symbols. The second approach, that of connectionism (Artificial Neural Networks, Parallel Distributed Processing) depicts mind as a product of the complex interactions between multiple so-called sub-symbolic elements. I shall try to clarify this contrast by associating classical cognitivism with the development of what I shall call semantically transparent systems, and connectionism with the deliberate eschewal of this strategy. Connectionism, I then argue, represents a subtle twist on the standard philosophical view of mental states as functional states. For it suggests a kind of microfunctionalism in which the inner roles do not map neatly onto roles determined by our everyday, contentful, purposive characterizations of the mental. (For the reader unfamiliar
with some of these terms, such as functionalism, sub-symbolic, etc. don't worry: these will be explained as we go along)
Chiasmic Rhetoric: Alan Turing Between Bodies and Words
This Dissertation analyzes the life and writing of inventor and scientist Alan Turing in order to identify and theorize chiasmic relations between bodies and texts. Chiasmic rhetoric, as I develop throughout the Dissertation, is the dynamic processes between materials and discourses that interact to construct powerful rhetorical effect, shape bodies, and also compose new knowledges. My research here extends our knowledge of the rhetoric of science by demonstrating the ways that Alan Turing\u27s embodied experiences shape his rhetoric. Turing is an unusual figure for research on bodily rhetoric and embodied knowledge. He is often associated with disembodied knowledge and as his inventions are said to move intelligence towards greater abstraction and away from human bodies. However, this Dissertation exposes the many ways that bodies are active in shaping and producing knowledge even within Turing\u27s scientific and technical writing. I identify how, in every text that Turing produces, chiasmic interactions between bodies and texts actively compose Turing\u27s scientific knowledge and technical innovations towards digital computation and artificial intelligence. His knowledge, thus, is not composed out of abstract logic, or neutral technological advances. Rather, his knowledge and invention are composed and in through discourses and embodied experiences. Given that bodies and discourses are also composed within social and political power dynamics, then the political, social, and personal embodied experiences that compose Turing\u27s life and his embodiment also compose his texts, rhetoric, inventions, and science. Throughout the Dissertation, I develop chiasmic rhetoric as it develops in the rhetorical figure of chiasmus, as intersecting bodies and discourse, dynamic and productive, and potentially destabilizing. I conclude by proposing a pedagogy of care and disorientation that are attuned to the complex embodiment of students interacting with texts in our technical writing and composition classrooms
Apperceptive patterning: Artefaction, extensional beliefs and cognitive scaffolding
In âPsychopower and Ordinary Madnessâ my ambition, as it relates to Bernard Stieglerâs recent literature, was twofold: 1) critiquing Stieglerâs work on exosomatization and artefactual posthumanismâor, more specifically, nonhumanismâto problematize approaches to media archaeology that rely upon technical exteriorization; 2) challenging how Stiegler engages with Giuseppe Longo and Francis Baillyâs conception of negative entropy. These efforts were directed by a prevalent techno-cultural qualifier: the rise of Synthetic Intelligence (including neural nets, deep learning, predictive processing and Bayesian models of cognition). This paper continues this project but first directs a critical analytic lens at the Derridean practice of the ontologization of grammatization from which Stiegler emerges while also distinguishing how metalanguages operate in relation to object-oriented environmental interaction by way of inferentialism. Stalking continental (Kapp, Simondon, Leroi-Gourhan, etc.) and analytic traditions (e.g., Carnap, Chalmers, Clark, Sutton, Novaes, etc.), we move from artefacts to AI and Predictive Processing so as to link theories related to technicity with philosophy of mind. Simultaneously drawing forth Robert Brandomâs conceptualization of the roles that commitments play in retrospectively reconstructing the social experiences that lead to our endorsement(s) of norms, we compliment this account with Reza Negarestaniâs deprivatized account of intelligence while analyzing the equipollent role between language and media (both digital and analog)
Language Writ Large: LLMs, ChatGPT, Grounding, Meaning and Understanding
Apart from what (little) OpenAI may be concealing from us, we all know
(roughly) how ChatGPT works (its huge text database, its statistics, its vector
representations, and their huge number of parameters, its next-word training,
and so on). But none of us can say (hand on heart) that we are not surprised by
what ChatGPT has proved to be able to do with these resources. This has even
driven some of us to conclude that ChatGPT actually understands. It is not true
that it understands. But it is also not true that we understand how it can do
what it can do. I will suggest some hunches about benign biases: convergent
constraints that emerge at LLM scale that may be helping ChatGPT do so much
better than we would have expected. These biases are inherent in the nature of
language itself, at LLM scale, and they are closely linked to what it is that
ChatGPT lacks, which is direct sensorimotor grounding to connect its words to
their referents and its propositions to their meanings. These convergent biases
are related to (1) the parasitism of indirect verbal grounding on direct
sensorimotor grounding, (2) the circularity of verbal definition, (3) the
mirroring of language production and comprehension, (4) iconicity in
propositions at LLM scale, (5) computational counterparts of human categorical
perception in category learning by neural nets, and perhaps also (6) a
conjecture by Chomsky about the laws of thought. The exposition will be in the
form of a dialogue with ChatGPT-4.Comment: 48 pages, 25 reference
Perspectives on the Neuroscience of Cognition and Consciousness
The origin and current use of the concepts of computation, representation and information in Neuroscience are examined and conceptual flaws are identified which vitiate their usefulness for addressing problems of the neural basis of Cognition and Consciousness. In contrast, a convergence of views is presented to support the characterization of the Nervous System as a complex dynamical system operating in the metastable regime, and capable of evolving to configurations and transitions in phase space with potential relevance for Cognition and Consciousness
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