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Ben Goertzel - One of the best experts on this subject based on the ideXlab platform.

  • AGI - Probabilistic Growth and Mining of Combinations: A Unifying Meta-Algorithm for Practical General Intelligence
    Artificial General Intelligence, 2016
    Co-Authors: Ben Goertzel
    Abstract:

    A new conceptual framing of the notion of the General Intelligence is outlined, in the form of a universal learning meta-algorithm called Probabilistic Growth and Mining of Combinations (PGMC). Incorporating ideas from logical inference systems, Solomonoff induction and probabilistic programming, PGMC is a probabilistic inference based framework which reflects processes broadly occurring in the natural world, is theoretically capable of arbitrarily powerful Generally intelligent reasoning, and encompasses a variety of existing practical AI algorithms as special cases. Several ways of manifesting PGMC using the OpenCog AI framework are described. It is proposed that PGMC can be viewed as a core learning process serving as the central dynamic of real-world General Intelligence; but that to achieve high levels of General Intelligence using limited computational resources, it may be necessary for cognitive systems to incorporate multiple distinct structures and dynamics, each of which realizes this core PGMC process in a different way (optimized for some particular sort of sub-problem).

  • Artificial General Intelligence - Artificial General Intelligence
    Scholarpedia, 2015
    Co-Authors: Ben Goertzel
    Abstract:

    This edited volume gives the first-ever book-length presentation of contemporary research in the domain of Artificial General Intelligence (AGI). What distinguishes AGI research from run-of-the-mill AI research is that it is explicitly focused on engineering General Intelligence ? autonomous, self-reflective, self-improving, commonsensical Intelligence ? in the short term. Bringing the diverse body of AGI research together in a single volume reveals the common themes among various researchers? work, and makes clear what the big open questions are in this vital and critical area of research, as well as highlighting relationships between AI and related fields such as philosophy, neuroscience, linguistics, psychology, biology, sociology, anthropology and engineering.

  • engineering General Intelligence part 2 the cogprime architecture for integrative embodied agi
    2014
    Co-Authors: Ben Goertzel, Cassio Pennachin, Nil Geisweiller
    Abstract:

    The work outlines a detailed blueprint for the creation of an Artificial General Intelligence system with capability at the human level and ultimately beyond, according to the Cog Prime AGI design and the Open Cog software architecture.

  • General Intelligence in the Everyday Human World
    Engineering General Intelligence Part 1, 2014
    Co-Authors: Ben Goertzel, Cassio Pennachin, Nil Geisweiller
    Abstract:

    Intelligence is not just about what happens inside a system, but also about what happens outside that system, and how the system interacts with its environment. Real-world General Intelligence is about Intelligence relative to some particular class of environments, and human-like General Intelligence is about Intelligence relative to the particular class of environments that humans evolved in (which in recent millennia has included environments humans have created using their Intelligence). In Chap. 4, we reviewed some specific capabilities characterizing human-like General Intelligence; to connect these with the General theory of General Intelligence from the last few chapters, we need to explain what aspects of human-relevant environments correspond to these human-like intelligent capabilities. We begin with aspects of the environment related to communication, which turn out to tie in closely with cognitive synergy. Then we turn to physical aspects of the environment, which we suspect also connect closely with various human cognitive capabilities. Finally we turn to physical aspects of the human body and their relevance to the human mind. In the following chapter we present a deeper, more abstract theoretical framework encompassing these ideas.

  • What is Human-Like General Intelligence?
    Engineering General Intelligence Part 1, 2014
    Co-Authors: Ben Goertzel, Cassio Pennachin, Nil Geisweiller
    Abstract:

    CogPrime, the AGI architecture on which the bulk of this book focuses, is aimed at the creation of artificial General Intelligence that is vaguely human-like in nature, and possesses capabilities at the human level and ultimately beyond.

Richard J Haier - One of the best experts on this subject based on the ideXlab platform.

  • General Intelligence and memory span: Evidence for a common neuroanatomic framework
    Cognitive neuropsychology, 2007
    Co-Authors: Roberto Colom, Rex E Jung, Richard J Haier
    Abstract:

    General Intelligence (g) is highly correlated with working-memory capacity (WMC). It has been argued that these central psychological constructs should share common neural systems. The present study examines this hypothesis using structural magnetic resonance imaging to determine any overlap in brain areas where regional grey matter volumes are correlated to measures of General Intelligence and to memory span. In normal volunteers (N = 48) the results (p < .05, corrected for multiple comparisons) indicate that a common anatomic framework for these constructs implicates mainly frontal grey matter regions belonging to Brodmann area (BA) 10 (right superior frontal gyrus and left middle frontal gyrus) and, to a lesser degree, the right inferior parietal lobule (BA 40). These findings support the nuclear role of a discrete parieto-frontal network.

  • the neuroanatomy of General Intelligence sex matters
    NeuroImage, 2005
    Co-Authors: Richard J Haier, Rex E Jung, Ronald A Yeo, Kevin Head, Michael T Alkire
    Abstract:

    We examined the relationship between structural brain variation and General Intelligence using voxel-based morphometric analysis of MRI data in men and women with equivalent IQ scores. Compared to men, women show more white matter and fewer gray matter areas related to Intelligence. In men IQ/gray matter correlations are strongest in frontal and parietal lobes (BA 8, 9, 39, 40), whereas the strongest correlations in women are in the frontal lobe (BA10) along with Broca's area. Men and women apparently achieve similar IQ results with different brain regions, suggesting that there is no singular underlying neuroanatomical structure to General Intelligence and that different types of brain designs may manifest equivalent intellectual performance.

  • structural brain variation and General Intelligence
    NeuroImage, 2004
    Co-Authors: Richard J Haier, Rex E Jung, Ronald A Yeo, Kevin Head, Michael T Alkire
    Abstract:

    Total brain volume accounts for about 16% of the variance in General Intelligence scores (IQ), but how volumes of specific regions-of-interest (ROIs) relate to IQ is not known. We used voxel-based morphometry (VBM) in two independent samples to identify substantial gray matter (GM) correlates of IQ. Based on statistical conjunction of both samples (N = 47; P < 0.05 corrected for multiple comparisons), more gray matter is associated with higher IQ in discrete Brodmann areas (BA) including frontal (BA 10, 46, 9), temporal (BA 21, 37, 22, 42), parietal (BA 43 and 3), and occipital (BA 19) lobes and near BA 39 for white matter (WM). These results underscore the distributed neural basis of Intelligence and suggest a developmental course for volume–IQ relationships in adulthood.

Ian J. Deary - One of the best experts on this subject based on the ideXlab platform.

  • Brain white matter tract integrity as a neural foundation for General Intelligence
    Molecular psychiatry, 2012
    Co-Authors: Lars Penke, Mark E. Bastin, S. Muñoz Maniega, M.c. Valdés Hernández, Catherine Murray, Natalie A. Royle, John M. Starr, Joanna M. Wardlaw, Ian J. Deary
    Abstract:

    General Intelligence is a robust predictor of important life outcomes, including educational and occupational attainment, successfully managing everyday life situations, good health and longevity. Some neuronal correlates of Intelligence have been discovered, mainly indicating that larger cortices in widespread parieto-frontal brain networks and efficient neuronal information processing support higher Intelligence. However, there is a lack of established associations between General Intelligence and any basic structural brain parameters that have a clear functional meaning. Here, we provide evidence that lower brain-wide white matter tract integrity exerts a substantial negative effect on General Intelligence through reduced information-processing speed. Structural brain magnetic resonance imaging scans were acquired from 420 older adults in their early 70s. Using quantitative tractography, we measured fractional anisotropy and two white matter integrity biomarkers that are novel to the study of Intelligence: longitudinal relaxation time (T1) and magnetisation transfer ratio. Substantial correlations among 12 major white matter tracts studied allowed the extraction of three General factors of biomarker-specific brain-wide white matter tract integrity. Each was independently associated with General Intelligence, together explaining 10% of the variance, and their effect was completely mediated by information-processing speed. Unlike most previously established neurostructural correlates of Intelligence, these findings suggest a functionally plausible model of Intelligence, where structurally intact axonal fibres across the brain provide the neuroanatomical infrastructure for fast information processing within widespread brain networks, supporting General Intelligence.

  • A role for the X chromosome in sex differences in variability in General Intelligence
    Perspectives on psychological science : a journal of the Association for Psychological Science, 2009
    Co-Authors: Wendy Johnson, Andrew D. Carothers, Ian J. Deary
    Abstract:

    There is substantial evidence that males are more variable than females in General Intelligence. In recent years, researchers have presented this as a reason that, although there is little, if any, mean sex difference in General Intelligence, males tend to be overrepresented at both ends of its overall distribution. Part of the explanation could be the presence of genes on the X chromosome related both to syndromal disorders involving mental retardation and to population variation in General Intelligence occurring normally. Genes on the X chromosome appear overrepresented among genes with known involvement in mental retardation, which is consistent with a model we developed of the population distribution of General Intelligence as a mixture of two normal distributions. Using this model, we explored the expected ratios of males to females at various points in the distribution and estimated the proportion of variance in General Intelligence potentially due to genes on the X chromosome. These estimates provide clues to the extent to which biologically based sex differences could be manifested in the environment as sex differences in displayed intellectual abilities. We discuss these observations in the context of sex differences in specific cognitive abilities and evolutionary theories of sexual selection.

  • Sex Differences in Variability in General Intelligence: A New Look at the Old Question
    Perspectives on psychological science : a journal of the Association for Psychological Science, 2008
    Co-Authors: Wendy Johnson, Andrew D. Carothers, Ian J. Deary
    Abstract:

    The idea that General Intelligence may be more variable in males than in females has a long history. In recent years it has been presented as a reason that there is little, if any, mean sex difference in General Intelligence, yet males tend to be overrepresented at both the top and bottom ends of its overall, presumably normal, distribution. Clear analysis of the actual distribution of General Intelligence based on large and appropriately population-representative samples is rare, however. Using two population-wide surveys of General Intelligence in 11-year-olds in Scotland, we showed that there were substantial departures from normality in the distribution, with less variability in the higher range than in the lower. Despite mean IQ-scale scores of 100, modal scores were about 105. Even above modal level, males showed more variability than females. This is consistent with a model of the population distribution of General Intelligence as a mixture of two essentially normal distributions, one reflecting no...

Michael T Alkire - One of the best experts on this subject based on the ideXlab platform.

  • the neuroanatomy of General Intelligence sex matters
    NeuroImage, 2005
    Co-Authors: Richard J Haier, Rex E Jung, Ronald A Yeo, Kevin Head, Michael T Alkire
    Abstract:

    We examined the relationship between structural brain variation and General Intelligence using voxel-based morphometric analysis of MRI data in men and women with equivalent IQ scores. Compared to men, women show more white matter and fewer gray matter areas related to Intelligence. In men IQ/gray matter correlations are strongest in frontal and parietal lobes (BA 8, 9, 39, 40), whereas the strongest correlations in women are in the frontal lobe (BA10) along with Broca's area. Men and women apparently achieve similar IQ results with different brain regions, suggesting that there is no singular underlying neuroanatomical structure to General Intelligence and that different types of brain designs may manifest equivalent intellectual performance.

  • structural brain variation and General Intelligence
    NeuroImage, 2004
    Co-Authors: Richard J Haier, Rex E Jung, Ronald A Yeo, Kevin Head, Michael T Alkire
    Abstract:

    Total brain volume accounts for about 16% of the variance in General Intelligence scores (IQ), but how volumes of specific regions-of-interest (ROIs) relate to IQ is not known. We used voxel-based morphometry (VBM) in two independent samples to identify substantial gray matter (GM) correlates of IQ. Based on statistical conjunction of both samples (N = 47; P < 0.05 corrected for multiple comparisons), more gray matter is associated with higher IQ in discrete Brodmann areas (BA) including frontal (BA 10, 46, 9), temporal (BA 21, 37, 22, 42), parietal (BA 43 and 3), and occipital (BA 19) lobes and near BA 39 for white matter (WM). These results underscore the distributed neural basis of Intelligence and suggest a developmental course for volume–IQ relationships in adulthood.

Marcus Hutter - One of the best experts on this subject based on the ideXlab platform.

  • Asymptotically Unambitious Artificial General Intelligence
    Proceedings of the AAAI Conference on Artificial Intelligence, 2020
    Co-Authors: Michael Cohen, Badri Vellambi, Marcus Hutter
    Abstract:

    General Intelligence, the ability to solve arbitrary solvable problems, is supposed by many to be artificially constructible. Narrow Intelligence, the ability to solve a given particularly difficult problem, has seen impressive recent development. Notable examples include self-driving cars, Go engines, image classifiers, and translators. Artificial General Intelligence (AGI) presents dangers that narrow Intelligence does not: if something smarter than us across every domain were indifferent to our concerns, it would be an existential threat to humanity, just as we threaten many species despite no ill will. Even the theory of how to maintain the alignment of an AGI's goals with our own has proven highly elusive. We present the first algorithm we are aware of for asymptotically unambitious AGI, where “unambitiousness” includes not seeking arbitrary power. Thus, we identify an exception to the Instrumental Convergence Thesis, which is roughly that by default, an AGI would seek power, including over us.

  • AAAI - Asymptotically Unambitious Artificial General Intelligence
    2020
    Co-Authors: Michael K. Cohen, Badri Vellambi, Marcus Hutter
    Abstract:

    General Intelligence, the ability to solve arbitrary solvable problems, is supposed by many to be artificially constructible. Narrow Intelligence, the ability to solve a given particularly difficult problem, has seen impressive recent development. Notable examples include self-driving cars, Go engines, image classifiers, and translators. Artificial General Intelligence (AGI) presents dangers that narrow Intelligence does not: if something smarter than us across every domain were indifferent to our concerns, it would be an existential threat to humanity, just as we threaten many species despite no ill will. Even the theory of how to maintain the alignment of an AGI's goals with our own has proven highly elusive. We present the first algorithm we are aware of for asymptotically unambitious AGI, where “unambitiousness” includes not seeking arbitrary power. Thus, we identify an exception to the Instrumental Convergence Thesis, which is roughly that by default, an AGI would seek power, including over us.

  • Report on the third conference on artificial General Intelligence
    AI Magazine, 2010
    Co-Authors: Ben Goertzel, Marcus Hutter
    Abstract:

    During March 5-8, 2010, around 75 researchers from various disciplines converged at the University of Lugano for the Third Conference on Artificial General Intelligence (AGI-10).