A Theory of Consciousness from a Theoretical Computer Science Perspective: Insights from the Conscious Turing Machine

The quest to understand consciousness, once the purview of philosophers and theologians, is now actively pursued by scientists of many stripes. We examine consciousness from the perspective of theoretical computer science (TCS), a branch of mathematics concerned with understanding the underlying principles of computation and complexity, including the implications and surprising consequences of resource limitations. In the spirit of Alan Turing's simple yet powerful definition of a computer, the Turing Machine (TM), and perspective of computational complexity theory, we formalize a modified version of the Global Workspace Theory (GWT) of consciousness originated by cognitive neuroscientist Bernard Baars and further developed by him, Stanislas Dehaene, Jean-Pierre Changeaux and others. We are not looking for a complex model of the brain nor of cognition, but for a simple computational model of (the admittedly complex concept of) consciousness. We do this by defining the Conscious Turing Machine (CTM), also called a conscious AI, and then we define consciousness and related notions in the CTM. While these are only mathematical (TCS) definitions, we suggest why the CTM has the feeling of consciousness. The TCS perspective provides a simple formal framework to employ tools from computational complexity theory and machine learning to help us understand consciousness and related concepts. Previously we explored high level explanations for the feelings of pain and pleasure in the CTM. Here we consider three examples related to vision (blindsight, inattentional blindness, and change blindness), followed by discussions of dreams, free will, and altered states of consciousness.Comment: arXiv admin note: text overlap with arXiv:2011.0985

A Theoretical Computer Science Perspective on Free Will

We consider the paradoxical concept of free will from the perspective of Theoretical Computer Science (TCS), a branch of mathematics concerned with understanding the underlying principles of computation and complexity, including the implications and surprising consequences of resource limitations.Comment: arXiv admin note: text overlap with arXiv:2107.1370

Towards Human Computable Passwords

An interesting challenge for the cryptography community is to design authentication protocols that are so simple that a human can execute them without relying on a fully trusted computer. We propose several candidate authentication protocols for a setting in which the human user can only receive assistance from a semi-trusted computer --- a computer that stores information and performs computations correctly but does not provide confidentiality. Our schemes use a semi-trusted computer to store and display public challenges $C_i\in[n]^k$. The human user memorizes a random secret mapping $\sigma:[n]\rightarrow\mathbb{Z}_d$ and authenticates by computing responses $f(\sigma(C_i))$ to a sequence of public challenges where $f:\mathbb{Z}_d^k\rightarrow\mathbb{Z}_d$ is a function that is easy for the human to evaluate. We prove that any statistical adversary needs to sample $m=\tilde{\Omega}(n^{s(f)})$ challenge-response pairs to recover $\sigma$, for a security parameter $s(f)$ that depends on two key properties of $f$. To obtain our results, we apply the general hypercontractivity theorem to lower bound the statistical dimension of the distribution over challenge-response pairs induced by $f$ and $\sigma$. Our lower bounds apply to arbitrary functions $f$ (not just to functions that are easy for a human to evaluate), and generalize recent results of Feldman et al. As an application, we propose a family of human computable password functions $f_{k_1,k_2}$ in which the user needs to perform $2k_1+2k_2+1$ primitive operations (e.g., adding two digits or remembering $\sigma(i)$), and we show that $s(f) = \min\{k_1+1, (k_2+1)/2\}$. For these schemes, we prove that forging passwords is equivalent to recovering the secret mapping. Thus, our human computable password schemes can maintain strong security guarantees even after an adversary has observed the user login to many different accounts.Comment: Fixed bug in definition of Q^{f,j} and modified proofs accordingl

Self-testing/correcting with applications to numerical problems

AbstractSuppose someone gives us an extremely fast program P that we can call as a black box to compute a function f. Should we trust that P works correctly? A self-testing/correcting pair for f allows us to: (1) estimate the probability that P(x) ≠ φ(x) when x is randomly chosen; (2) on any input x, compute f(x) correctly as long as P is not too faulty on average. Furthermore, both (1) and (2) take time only slightly more than the original running time of P. We present general techniques for constructing simple to program self-testing/correcting pairs for a variety of numerical functions, including integer multiplication, modular multiplication, matrix multiplication, inverting matrices, computing the determinant of a matrix, computing the rank of a matrix, integer division, modular exponentiation, and polynomial multiplication

Community Practice Social Entrepreneurship: An Interdisciplinary Approach to Graduate Education

The rapidly changing global environment for community practice social workers (CPSWs) has challenged these practitioners to devise innovative intervention strategies. Some practitioners are utilising community organising, community planning, community development and policy practice intervention strategies simultaneously to create sustainable changes and are unwittingly, or purposefully, acting as social entrepreneurs. This article delineates similarities between community practice social work and social entrepreneurship – orientation and behaviours – and introduces the concept of community practice social entrepreneurship (CPSE). The authors propose interdisciplinary venues to teach graduate students in social work and in other disciplines skills for practicing as community practice social entrepreneurs

Diseño de un modelo de gestión del conocimiento para la función de investigación en la universidad Santiago de Cali

Con el propósito de contribuir a la preservación de la memoria institucional, al mejoramiento del aprendizaje organizacional y funcionamiento como una organización efectiva haciendo énfasis en los procesos y actividades de investigación con el fin de colaborar en la construcción de una universidad científica se diseñó un modelo de gestión del conocimiento para la función sustantiva de investigación de la Universidad Santiago de Cali, el cual se desarrolló a partir de: 1) la investigación de las mejores prácticas en modelos de Gestión del Conocimiento en instituciones de educación superior, 2) Diseño de un modelo de gestión de conocimiento para la Universidad Santiago de Cali que facilite la implementación de las mejores prácticas en cuanto a la captura selección, internalización y uso del conocimiento, 3) Realización de un diagnóstico teniendo en cuenta capacidades en talento humano, estructura organizacional, cultura organizacional y tecnologías de información a los grupos de investigación de la Universidad Santiago de Cali para hacer una caracterización de los mismos y 4) validación del modelo en el centro de estudios e investigaciones en ingeniería (CEII).MaestríaMAGISTER EN INGENIERÍA ÉNFASIS INGENIERÍA INDUSTRIA

Construct, Merge, Solve & Adapt A new general algorithm for combinatorial optimization

[EN]This paper describes a general hybrid metaheuristic for combinatorial optimization labelled Construct,Merge, Solve & Adapt. The proposed algorithm is a specific instantiation of a framework known from theliterature as Generate-And-Solve, which is based on the following general idea. First, generate a reducedsub-instance of the original problem instance, in a way such that a solution to the sub-instance is also asolution to the original problem instance. Second, apply an exact solver to the reduced sub-instance inorder to obtain a (possibly) high quality solution to the original problem instance. And third, make use ofthe results of the exact solver as feedback for the next algorithm iteration. The minimum common stringpartition problem and the minimum covering arborescence problem are chosen as test cases in order todemonstrate the application of the proposed algorithm. The obtained results show that the algorithm iscompetitive with the exact solver for small to medium size problem instances, while it significantlyoutperforms the exact solver for larger problem instancesC. Blum was supported by project TIN2012-37930-02 of the Spanish Government. In addition, support is acknowledged from IKERBASQUE (Basque Foundation for Science). J.A. Lozano was partially supported by the IT609-13 program (Basque Government) and project TIN2013-41272P (Spanish Ministry of Science and Innovation)Peer reviewe