Development of a Large-Scale Integrated Neurocognitive Architecture Part 1: Conceptual Framework

The idea of creating a general purpose machine intelligence that captures many of the features of human cognition goes back at least to the earliest days of artificial intelligence and neural computation. In spite of more than a half-century of research on this issue, there is currently no existing approach to machine intelligence that comes close to providing a powerful, general-purpose human-level intelligence. However, substantial progress made during recent years in neural computation, high performance computing, neuroscience and cognitive science suggests that a renewed effort to produce a general purpose and adaptive machine intelligence is timely, likely to yield qualitatively more powerful approaches to machine intelligence than those currently existing, and certain to lead to substantial progress in cognitive science, AI and neural computation. In this report, we outline a conceptual framework for the long-term development of a large-scale machine intelligence that is based on the modular organization, dynamics and plasticity of the human brain. Some basic design principles are presented along with a review of some of the relevant existing knowledge about the neurobiological basis of cognition. Three intermediate-scale prototypes for parts of a larger system are successfully implemented, providing support for the effectiveness of several of the principles in our framework. We conclude that a human-competitive neuromorphic system for machine intelligence is a viable long- term goal, but that for the short term, substantial integration with more standard symbolic methods as well as substantial research will be needed to make this goal achievable

Technological elites, the meritocracy, and postracial myths in Silicon Valley

Entre as modernas elites tecnológicas digitais, os mitos da meritocracia e da façanha intelectual são usados como marcadores de raça e gênero por uma supremacia branca masculina que consolida recursos de forma desproporcional em relação a pessoas não brancas, principalmente negros, latinos e indígenas. Os investimentos em mitos meritocráticos suprimem os questionamentos de racismo e discriminação, mesmo quando os produtos das elites digitais são infundidos com marcadores de raça, classe e gênero. As lutas históricas por inclusão social, política e econômica de negros, mulheres e outras classes desprotegidas têm implicado no reconhecimento da exclusão sistêmica, do trabalho forçado e da privação de direitos estruturais, além de compromissos com políticas públicas dos EUA, como as ações afirmativas, que foram igualmente fundamentais para reformas políticas voltadas para participação e oportunidades econômicas. A ascensão da tecnocracia digital tem sido, em muitos aspectos, antitética a esses esforços no sentido de reconhecer raça e gênero como fatores cruciais para inclusão e oportunidades tecnocráticas. Este artigo explora algumas das formas pelas quais os discursos das elites tecnocráticas do Vale do Silício reforçam os investimentos no pós racialismo como um pretexto para a re-consolidação do capital em oposição às políticas públicas que prometem acabar com práticas discriminatórias no mundo do trabalho. Por meio de uma análise cuidadosa do surgimento de empresas de tecnologias digitais e de uma discussão sobre como as elites tecnológicas trabalham para mascarar tudo, como inscrições algorítmicas e genéticas de raça incorporadas em seus produtos, mostramos como as elites digitais omitem a sua responsabilidade por suas reinscrições pós raciais de (in)visibilidades raciais. A partir do uso de análise histórica e crítica do discurso, o artigo revela como os mitos de uma meritocracia digital baseados em um “daltonismo racial” tecnocrático emergem como chave para a manutenção de exclusões de gênero e raça.Palavras-chave: Tecnologia. Raça. Gênero.Among modern digital technology elites, myths of meritocracy and intellectual prowess are used as racial and gender markers of white male supremacy that disproportionately consolidate resources away from people of color, particularly African Americans, Latino/as and Native Americans. Investments in meritocratic myths suppress interrogations of racism and discrimination even as the products of digital elites are infused with racial, class, and gender markers. Longstanding struggles for social, political, and economic inclusion for African Americans, women, and other legally protected classes have been predicated upon the recognition of systemic exclusion, forced labor, and structural disenfranchisement, and commitments to US public policies like affirmative action have, likewise, been fundamental to political reforms geared to economic opportunity and participation. The rise of the digital technocracy has, in many ways, been antithetical to these sustained efforts to recognize race and gender as salient factors structuring technocratic opportunity and inclusion. This paper explores some of the ways in which discourses of Silicon Valley technocratic elites bolster investments in post-racialism as a pretext for re-consolidations of capital, in opposition to public policy commitments to end discriminatory labor practices. Through a careful analysis of the rise of digital technology companies, and a discussion of how technology elites work to mask everything from algorithmic to genetic inscriptions of race embedded in their products, we show how digital elites elide responsibility for their post-racial re-inscriptions of racial visibilities (and invisibilities). Using historical and critical discourse analysis, the paper reveals how myths of a digital meritocracy premised on a technocratic colorblindness emerge key to perpetuating gender and racial exclusions.Keywords: Technology. Race. Gender

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Draft copy – not for dissemination 2 Two findings serve as the hallmark for hemispheric specialization during lateralized lexical decision. First is an overall word advantage, with words being recognized more quickly and accurately than non-words (the effect being stronger in response latency). Second, a right visual field advantage is observed for words, with little or no hemispheric differences in the ability to identify non-words. Several theories have been proposed to account for this difference in word and non-word recognition, some by suggesting dual routes of lexical access and others by incorporating separate, and potentially independent, word and non-word detection mechanisms. We compare three previously proposed cognitive theories of hemispheric interactions (callosal relay, direct access, and cooperative hemispheres) through neural network modeling, with each network incorporating different means of interhemispheric communication. When parameters were varied to simulate left hemisphere specialization for lexical decision, only the cooperative hemispheres model showed both a consistent left hemisphere advantage for word recognition but not non-word recognition, as well as an overall word advantage. These results support the theory that neural representations of words are more strongly established in the left hemisphere through prior learning, despite open communication between the hemispheres during both learning and recall