Why and How We Are Not Zombies

A robot that is functionally indistinguishable from us may or may not be a mindless Zombie. There will never be any way to know, yet its functional principles will be as close as we can ever get to explaining the mind

Alan Turing and the “hard” and “easy” problem of cognition: doing and feeling

The "easy" problem of cognitive science is explaining how and why we can do what we can do. The "hard" problem is explaining how and why we feel. Turing's methodology for cognitive science (the Turing Test) is based on doing: Design a model that can do anything a human can do, indistinguishably from a human, to a human, and you have explained cognition. Searle has shown that the successful model cannot be solely computational. Sensory-motor robotic capacities are necessary to ground some, at least, of the model's words, in what the robot can do with the things in the world that the words are about. But even grounding is not enough to guarantee that -- nor to explain how and why -- the model feels (if it does). That problem is much harder to solve (and perhaps insoluble)

The Annotation Game: On Turing (1950) on Computing, Machinery, and Intelligence

This quote/commented critique of Turing's classical paper suggests that Turing meant -- or should have meant -- the robotic version of the Turing Test (and not just the email version). Moreover, any dynamic system (that we design and understand) can be a candidate, not just a computational one. Turing also dismisses the other-minds problem and the mind/body problem too quickly. They are at the heart of both the problem he is addressing and the solution he is proposing

The role of cardiac troponin T quantity and function in cardiac development and dilated cardiomyopathy

Background: Hypertrophic (HCM) and dilated (DCM) cardiomyopathies results from sarcomeric protein mutations, including cardiac troponin T (cTnT, TNNT2). We determined whether TNNT2 mutations cause cardiomyopathies by altering cTnT function or quantity; whether the severity of DCM is related to the ratio of mutant to wildtype cTnT; whether Ca2+ desensitization occurs in DCM; and whether absence of cTnT impairs early embryonic cardiogenesis. Methods and Findings: We ablated Tnnt2 to produce heterozygous Tnnt2+/ mice, and crossbreeding produced homozygous null Tnnt2-/-embryos. We also generated transgenic mice overexpressing wildtype (TGWT) or DCM mutant (TGK210Δ) Tnnt2. Crossbreeding produced mice lacking one allele of Tnnt2, but carrying wildtype (Tnnt2+/-/TGWT) or mutant (Tnnt2+/-/TGK210Δ) transgenes. Tnnt2+/-mice relative to wildtype had significantly reduced transcript (0.82 ± 0.06 [SD] vs. 1.00 ± 0.12 arbitrary units; p = 0.025), but not protein (1.01 ± 0.20 vs. 1.00 ± 0.13 arbitrary units; p = 0.44). Tnnt2+/-mice had normal hearts (histology, mass, left ventricular end diastolic diameter [LVEDD], fractional shortening [FS]). Moreover, whereas Tnnt2+/-/ TGK210Δ mice had severe DCM, TGK210Δ mice had only mild DCM (FS 18 ± 4 vs. 29 ± 7%; p < 0.01). The difference in severity of DCM may be attributable to a greater ratio of mutant to wildtype Tnnt2 transcript in Tnnt2+/-/TGK210Δ relative to TGK210Δ mice (2.42±0.08, p = 0.03). Tnnt2+/-/TGK210Δ muscle showed Ca2+ desensitization (pCa50 = 5.34 ± 0.08 vs. 5.58 ± 0.03 at sarcomere length 1.9 μm. p<0.01), but no difference in maximum force generation. Day 9.5 Tnnt2-/-embryos had normally looped hearts, but thin ventricular walls, large pericardial effusions, noncontractile hearts, and severely disorganized sarcomeres. Conclusions: Absence of one Tnnt2 allele leads to a mild deficit in transcript but not protein, leading to a normal cardiac phenotype. DCM results from abnormal function of a mutant protein, which is associated with myocyte Ca2+ desensitization. The severity of DCM depends on the ratio of mutant to wildtype Tnnt2 transcript. cTnT is essential for sarcomere formation, but normal embryonic heart looping occurs without contractile activity. © 2008 Ahmad et al

Minds, Brains and Turing

Turing set the agenda for (what would eventually be called) the cognitive sciences. He said, essentially, that cognition is as cognition does (or, more accurately, as cognition is capable of doing): Explain the causal basis of cognitive capacity and you’ve explained cognition. Test your explanation by designing a machine that can do everything a normal human cognizer can do – and do it so veridically that human cognizers cannot tell its performance apart from a real human cognizer’s – and you really cannot ask for anything more. Or can you? Neither Turing modelling nor any other kind of computational r dynamical modelling will explain how or why cognizers feel

Doing, Feeling, Meaning And Explaining

It is “easy” to explain doing, “hard” to explain feeling. Turing has set the agenda for the easy explanation (though it will be a long time coming). I will try to explain why and how explaining feeling will not only be hard, but impossible. Explaining meaning will prove almost as hard because meaning is a hybrid of know-how and what it feels like to know how

On Fodor on Darwin on Evolution

Jerry Fodor argues that Darwin was wrong about "natural selection" because (1) it is only a tautology rather than a scientific law that can support counterfactuals ("If X had happened, Y would have happened") and because (2) only minds can select. Hence Darwin's analogy with "artificial selection" by animal breeders was misleading and evolutionary explanation is nothing but post-hoc historical narrative. I argue that Darwin was right on all counts. Until Darwin's "tautology," it had been believed that either (a) God had created all organisms as they are, or (b) organisms had always been as they are. Darwin revealed instead that (c) organisms have heritable traits that evolved across time through random variation, with survival and reproduction in (changing) environments determining (mindlessly) which variants were successfully transmitted to the next generation. This not only provided the (true) alternative (c), but also the methodology for investigating which traits had been adaptive, how and why; it also led to the discovery of the genetic mechanism of the encoding, variation and evolution of heritable traits. Fodor also draws erroneous conclusions from the analogy between Darwinian evolution and Skinnerian reinforcement learning. Fodor’s skepticism about both evolution and learning may be motivated by an overgeneralization of Chomsky’s “poverty of the stimulus argument” -- from the origin of Universal Grammar (UG) to the origin of the “concepts” underlying word meaning, which, Fodor thinks, must be “endogenous,” rather than evolved or learned

Explorative Synthetic Biology in AI: Criteria of Relevance and a Taxonomy for Synthetic Models of Living and Cognitive Processes

This article tackles the topic of the special issue “Biology in AI: New Frontiers in Hardware, Software and Wetware Modeling of Cognition” in two ways. It addresses the problem of the relevance of hardware, software, and wetware models for the scientific understanding of biological cognition, and it clarifies the contributions that synthetic biology, construed as the synthetic exploration of cognition, can offer to artificial intelligence (AI). The research work proposed in this article is based on the idea that the relevance of hardware, software, and wetware models of biological and cognitive processes—that is, the concrete contribution that these models can make to the scientific understanding of life and cognition—is still unclear, mainly because of the lack of explicit criteria to assess in what ways synthetic models can support the experimental exploration of biological and cognitive phenomena. Our article draws on elements from cybernetic and autopoietic epistemology to define a framework of reference, for the synthetic study of life and cognition, capable of generating a set of assessment criteria and a classification of forms of relevance, for synthetic models, able to overcome the sterile, traditional polarization of their evaluation between mere imitation and full reproduction of the target processes. On the basis of these tools, we tentatively map the forms of relevance characterizing wetware models of living and cognitive processes that synthetic biology can produce and outline a programmatic direction for the development of “organizationally relevant approaches” applying synthetic biology techniques to the investigative field of (embodied) AI

Systems Biology of Cancer: A Challenging Expedition for Clinical and Quantitative Biologists

A systems-biology approach to complex disease (such as cancer) is now complementing traditional experience-based approaches, which have typically been invasive and expensive. The rapid progress in biomedical knowledge is enabling the targeting of disease with therapies that are precise, proactive, preventive, and personalized. In this paper, we summarize and classify models of systems biology and model checking tools, which have been used to great success in computational biology and related fields. We demonstrate how these models and tools have been used to study some of the twelve biochemical pathways implicated in but not unique to pancreatic cancer, and conclude that the resulting mechanistic models will need to be further enhanced by various abstraction techniques to interpret phenomenological models of cancer progression