Replicode: A Constructivist Programming Paradigm and Language

Replicode is a language designed to encode short parallel programs and executable models, and is centered on the notions of extensive pattern-matching and dynamic code production. The language is domain independent and has been designed to build systems that are modelbased and model-driven, as production systems that can modify their own code. More over, Replicode supports the distribution of knowledge and computation across clusters of computing nodes. This document describes Replicode and its executive, i.e. the system that executes Replicode constructions. The Replicode executive is meant to run on Linux 64 bits and Windows 7 32/64 bits platforms and interoperate with custom C++ code. The motivations for the Replicode language, the constructivist paradigm it rests on, and the higher-level AI goals targeted by its construction, are described by Thórisson (2012), Nivel and Thórisson (2009), and Thórisson and Nivel (2009a, 2009b). An overview presents the main concepts of the language. Section 3 describes the general structure of Replicode objects and describes pattern matching. Section 4 describes the execution model of Replicode and section 5 describes how computation and knowledge are structured and controlled. Section 6 describes the high-level reasoning facilities offered by the system. Finally, section 7 describes how the computation is distributed over a cluster of computing nodes. Consult Annex 1 for a formal definition of Replicode, Annex 2 for a specification of the executive, Annex 3 for the specification of the executable code format (r-code) and its C++ API, and Annex 4 for the definition of the Replicode Extension C++ API

Autonomous Acquisition of Natural Situated Communication

An important part of human intelligence, both historically and operationally, is our ability to communicate. We learn how to communicate, and maintain our communicative skills, in a society of communicators – a highly effective way to reach and maintain proficiency in this complex skill. Principles that might allow artificial agents to learn language this way are in completely known at present – the multi-dimensional nature of socio-communicative skills are beyond every machine learning framework so far proposed. Our work begins to address the challenge of proposing a way for observation-based machine learning of natural language and communication. Our framework can learn complex communicative skills with minimal up-front knowledge. The system learns by incrementally producing predictive models of causal relationships in observed data, guided by goal-inference and reasoning using forward-inverse models. We present results from two experiments where our S1 agent learns human communication by observing two humans interacting in a realtime TV-style interview, using multimodal communicative gesture and situated language to talk about recycling of various materials and objects. S1 can learn multimodal complex language and multimodal communicative acts, a vocabulary of 100 words forming natural sentences with relatively complex sentence structure, including manual deictic reference and anaphora. S1 is seeded only with high-level information about goals of the interviewer and interviewee, and a small ontology; no grammar or other information is provided to S1 a priori. The agent learns the pragmatics, semantics, and syntax of complex utterances spoken and gestures from scratch, by observing the humans compare and contrast the cost and pollution related to recycling aluminum cans, glass bottles, newspaper, plastic, and wood. After 20 hours of observation S1 can perform an unscripted TV interview with a human, in the same style, without making mistakes

Autonomous Acquisition of Natural Language

An important part of human intelligence is the ability to use language. Humans learn how to use language in a society of language users, which is probably the most effective way to learn a language from the ground up. Principles that might allow an artificial agents to learn language this way are not known at present. Here we present a framework which begins to address this challenge. Our auto-catalytic, endogenous, reflective architecture (AERA) supports the creation of agents that can learn natural language by observation. We present results from two experiments where our S1 agent learns human communication by observing two humans interacting in a realtime mock television interview, using gesture and situated language. Results show that S1 can learn multimodal complex language and multimodal communicative acts, using a vocabulary of 100 words with numerous sentence formats, by observing unscripted interaction between the humans, with no grammar being provided to it a priori, and only high-level information about the format of the human interaction in the form of high-level goals of the interviewer and interviewee and a small ontology. The agent learns both the pragmatics, semantics, and syntax of complex sentences spoken by the human subjects on the topic of recycling of objects such as aluminum cans, glass bottles, plastic, and wood, as well as use of manual deictic reference and anaphora

Outpatient costs in pharmaceutically treated diabetes patients with and without a diagnosis of depression in a Dutch primary care setting

<p>Abstract</p> <p>Background</p> <p>To assess differences in outpatient costs among pharmaceutically treated diabetes patients with and without a diagnosis of depression in a Dutch primary care setting.</p> <p>Methods</p> <p>A retrospective case control study over 3 years (2002-2004). Data on 7128 depressed patients and 23772 non-depressed matched controls were available from the electronic medical record system of 20 general practices organized in one large primary care organization in the Netherlands. A total of 393 depressed patients with diabetes and 494 non-depressed patients with diabetes were identified in these records. The data that were extracted from the medical record system concerned only outpatient costs, which included GP care, referrals, and medication.</p> <p>Results</p> <p>Mean total outpatient costs per year in depressed diabetes patients were €1039 (SD 743) in the period 2002-2004, which was more than two times as high as in non-depressed diabetes patients (€492, SD 434). After correction for age, sex, type of insurance, diabetes treatment, and comorbidity, the difference in total annual costs between depressed and non-depressed diabetes patients changed from €408 (uncorrected) to €463 (corrected) in multilevel analyses. Correction for comorbidity had the largest impact on the difference in costs between both groups.</p> <p>Conclusions</p> <p>Outpatient costs in depressed patients with diabetes are substantially higher than in non-depressed patients with diabetes even after adjusting for confounders. Future research should investigate whether effective treatment of depression among diabetes patients can reduce health care costs in the long term.</p

The Road to General Intelligence

Humans have always dreamed of automating laborious physical and intellectual tasks, but the latter has proved more elusive than naively suspected. Seven decades of systematic study of Artificial Intelligence have witnessed cycles of hubris and despair. The successful realization of General Intelligence (evidenced by the kind of cross-domain flexibility enjoyed by humans) will spawn an industry worth billions and transform the range of viable automation tasks.The recent notable successes of Machine Learning has lead to conjecture that it might be the appropriate technology for delivering General Intelligence. In this book, we argue that the framework of machine learning is fundamentally at odds with any reasonable notion of intelligence and that essential insights from previous decades of AI research are being forgotten. We claim that a fundamental change in perspective is required, mirroring that which took place in the philosophy of science in the mid 20th century. We propose a framework for General Intelligence, together with a reference architecture that emphasizes the need for anytime bounded rationality and a situated denotational semantics. We given necessary emphasis to compositional reasoning, with the required compositionality being provided via principled symbolic-numeric inference mechanisms based on universal constructions from category theory. • Details the pragmatic requirements for real-world General Intelligence. • Describes how machine learning fails to meet these requirements. • Provides a philosophical basis for the proposed approach. • Provides mathematical detail for a reference architecture. • Describes a research program intended to address issues of concern in contemporary AI. The book includes an extensive bibliography, with ~400 entries covering the history of AI and many related areas of computer science and mathematics.The target audience is the entire gamut of Artificial Intelligence/Machine Learning researchers and industrial practitioners. There are a mixture of descriptive and rigorous sections, according to the nature of the topic. Undergraduate mathematics is in general sufficient. Familiarity with category theory is advantageous for a complete understanding of the more advanced sections, but these may be skipped by the reader who desires an overall picture of the essential concepts This is an open access book

La política nacional de integridad y lucha contra la corrupción

La corrupción es un fenómeno que afecta la gobernabilidad del país, la confianza en las instituciones y los derechos de las personas, tiene además diferentes manifestaciones y aparece de manera diversa en el escenario social, político y económico. Sus efectos negativos llegan a trascender inclusive fronteras, valiéndose de redes sofisticadas delictivas que aprovechan la debilidad institucional y sectores vulnerables para capturar la toma de decisiones. Por ello, la Comisión de Alto Nivel Anticorrupción como espacio de coordinación y articulación entre las principales entidades públicas, sector empresarial y la sociedad civil, en cumplimiento de su función principal de proponer las políticas de corto, mediano y largo plazo para la prevención y lucha contra la corrupción de manera intersectorial e intergubernamental; elaboró y aprobó la presente propuesta de Política Nacional de Integridad y Lucha contra la Corrupción, la cual, estando próximo a celebrar el bicentenario de nuestra independencia, resulta fundamental e impostergable para lograr un Estado integro, inclusivo y eficiente al servicio del ciudadano

Historia, Geografía y Ciencias Sociales: Módulo didáctico de la enseñanza y aprendizaje en escuelas rurales multigrado. 4to básico

Historia I 4º Básico: Clases 1 a

Historia, Geografía y Ciencias Sociales: Módulo didáctico de la enseñanza y aprendizaje en escuelas rurales multigrado. 2to básico

Historia II 2º Básico: Clases 1 a