AGI and the Knight-Darwin Law: why idealized AGI reproduction requires collaboration

Can an AGI create a more intelligent AGI? Under idealized assumptions, for a certain theoretical type of intelligence, our answer is: “Not without outside help”. This is a paper on the mathematical structure of AGI populations when parent AGIs create child AGIs. We argue that such populations satisfy a certain biological law. Motivated by observations of sexual reproduction in seemingly-asexual species, the Knight-Darwin Law states that it is impossible for one organism to asexually produce another, which asexually produces another, and so on forever: that any sequence of organisms (each one a child of the previous) must contain occasional multi-parent organisms, or must terminate. By proving that a certain measure (arguably an intelligence measure) decreases when an idealized parent AGI single-handedly creates a child AGI, we argue that a similar Law holds for AGIs

Psoriasis prediction from genome-wide SNP profiles

<p>Abstract</p> <p>Background</p> <p>With the availability of large-scale genome-wide association study (GWAS) data, choosing an optimal set of SNPs for disease susceptibility prediction is a challenging task. This study aimed to use single nucleotide polymorphisms (SNPs) to predict psoriasis from searching GWAS data.</p> <p>Methods</p> <p>Totally we had 2,798 samples and 451,724 SNPs. Process for searching a set of SNPs to predict susceptibility for psoriasis consisted of two steps. The first one was to search top 1,000 SNPs with high accuracy for prediction of psoriasis from GWAS dataset. The second one was to search for an optimal SNP subset for predicting psoriasis. The sequential information bottleneck (sIB) method was compared with classical linear discriminant analysis(LDA) for classification performance.</p> <p>Results</p> <p>The best test harmonic mean of sensitivity and specificity for predicting psoriasis by sIB was 0.674(95% CI: 0.650-0.698), while only 0.520(95% CI: 0.472-0.524) was reported for predicting disease by LDA. Our results indicate that the new classifier sIB performs better than LDA in the study.</p> <p>Conclusions</p> <p>The fact that a small set of SNPs can predict disease status with average accuracy of 68% makes it possible to use SNP data for psoriasis prediction.</p

Cumulative learning

An important feature of human learning is the ability to continuously accept new information and unify it with existing knowledge, a process that proceeds largely automatically and without catastrophic side-effects. A generally intelligent machine (AGI) should be able to learn a wide range of tasks in a variety of environments. Knowledge acquisition in partially-known and dynamic task-environments cannot happen all-at-once, and AGI-aspiring systems must thus be capable of cumulative learning: efficiently making use of existing knowledge while learning new things, increasing the scope of ability and knowledge incrementally—without catastrophic forgetting or damaging existing skills. Many aspects of such learning have been addressed in artificial intelligence (AI) research, but relatively few examples of cumulative learning have been demonstrated to date and no generally accepted explicit definition exists of this category of learning. Here we provide a general definition of cumulative learning and describe how it relates to other concepts frequently used in the AI literature.</p

Pattern mining for general intelligence: The FISHGRAM algorithm for frequent and interesting subhypergraph mining

Conference Name:5th International Conference on Artificial General Intelligence, AGI 2012. Conference Address: Oxford, United kingdom. Time:December 8, 2012 - December 11, 2012.Oxford University; Future of Humanity Institute; Kurzweil AI; Rick Schwall; Novamente LLCFishgram, a novel algorithm for recognizing frequent or otherwise interesting sub-hypergraphs in large, heterogeneous hypergraphs, is presented. The algorithm's implementation the OpenCog integrative AGI framework is described, and concrete examples are given showing the patterns it recognizes in OpenCog's hypergraph knowledge store when the OpenCog system is used to control a virtual agent in a game world. It is argued that Fishgram is well suited to fill a critical niche in OpenCog and potentially other integrative AGI architectures: scalable recognition of relatively simple patterns in heterogeneous, potentially rapidly-changing data. 漏 2012 Springer-Verlag

The cogprime architecture for embodied Artificial General Intelligence

Conference Name:2013 IEEE Symposium on Computational Intelligence for Human-Like Intelligence, CIHLI 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013. Conference Address: Singapore, Singapore. Time:April 16, 2013 - April 19, 2013.IEEE Computational Intelligence SocietyCogPrime, a comprehensive architecture for embodied Artificial General Intelligence, is reviewed, covering the core architecture and algorithms, the underlying conceptual motivations, and the emergent structures, dynamics and functionalities expected to arise in a completely implemented CogPrime system once it has undergone appropriate experience and education. A qualitative argument is sketched, in favor of the assertion that a completed CogPrime system, given a modest amount of experience in an embodiment enabling it to experience a reasonably rich human-like world, will give rise to human-level general intelligence (with significant difference from humans, and with potential for progress beyond this level). ? 2013 IEEE

Syntax-semantic mapping for general intelligence: Language comprehension as hypergraph homomorphism, language generation as constraint satisfaction

Conference Name:5th International Conference on Artificial General Intelligence, AGI 2012. Conference Address: Oxford, United kingdom. Time:December 8, 2012 - December 11, 2012.Oxford University; Future of Humanity Institute; Kurzweil AI; Rick Schwall; Novamente LLCA new approach to translating between natural language expressions and hypergraph-based semantic knowledge representations is proposed. Language comprehension is formulated in terms of homomorphisms mapping syntactic parse trees into semantic hypergraphs, and language generation as constraint satisfaction based on constraints derived via applying the inverse relations of these homomorphisms. This provides an elegant approach to implementing semantically savvy NLP systems, and also to thinking about the feedbacks between syntactic and semantic processing that are the crux of generally intelligent NLP. A prototype of the approach created using the link parser and the OpenCog Atom semantic representation is described, and initial results presented. Routes to extending this prototype into something useful for aiding generally intelligent dialogue systems are discussed. 漏 2012 Springer-Verlag