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artificial general intelligence

Valerie Hazan, Outi Tuomainen, Michèle Pettinato
Purpose: This study investigated the acoustic characteristics of spontaneous speech by talkers aged 9-14 years and their ability to adapt these characteristics to maintain effective communication when intelligibility was artificially degraded for their interlocutor. Method: Recordings were made for 96 children (50 female participants, 46 male participants) engaged in a problem-solving task with a same-sex friend; recordings for 20 adults were used as reference. The task was carried out in good listening conditions (normal transmission) and in degraded transmission conditions...
December 1, 2016: Journal of Speech, Language, and Hearing Research: JSLHR
Chaoxing Wu, Tae Whan Kim, Tailiang Guo, Fushan Li, Dea Uk Lee, J Joshua Yang
The mimicking of classical conditioning, including acquisition, extinction, recovery, and generalization, can be efficiently achieved by using a single flexible memristor. In particular, the experiment of Pavlov's dog is successfully demonstrated. This demonstration paves the way for reproducing advanced neural processes and provides a frontier approach to the design of artificial intelligence systems with dramatically reduced complexity.
December 20, 2016: Advanced Materials
Rajkumar S, Muttan S, Sapthagirivasan V, Jaya V, Vignesh S S
PURPOSE: The anatomy and physiology of the ear is complex in nature, which makes it a challenge for audiologists to prescribe solutions for varied hearing-impaired subjects. There is a need to increase the satisfaction level of hearing-aid users by adopting better strategies that involve modern technological advancements. AIM: To design and develop a decision support Software Intelligent System (SIS) that performs audiological investigations to assess the degree of hearing loss and to suggest appropriate hearing-aid gain values...
January 2017: International Journal of Medical Informatics
Brenden M Lake, Tomer D Ullman, Joshua B Tenenbaum, Samuel J Gershman
Recent progress in artificial intelligence (AI) has renewed interest in building systems that learn and think like people. Many advances have come from using deep neural networks trained end-to-end in tasks such as object recognition, video games, and board games, achieving performance that equals or even beats humans in some respects. Despite their biological inspiration and performance achievements, these systems differ from human intelligence in crucial ways. We review progress in cognitive science suggesting that truly human-like learning and thinking machines will have to reach beyond current engineering trends in both what they learn, and how they learn it...
November 24, 2016: Behavioral and Brain Sciences
A Flahault, A Bar-Hen, N Paragios
OBJECTIVES: The aim of this manuscript is to provide a brief overview of the scientific challenges that should be addressed in order to unlock the full potential of using data from a general point of view, as well as to present some ideas that could help answer specific needs for data understanding in the field of health sciences and epidemiology. METHODS: A survey of uses and challenges of big data analyses for medicine and public health was conducted. The first part of the paper focuses on big data techniques, algorithms, and statistical approaches to identify patterns in data...
November 10, 2016: Yearbook of Medical Informatics
Muhammad Shahin Uddin, Kalyan Kumar Halder, Murat Tahtali, Andrew J Lambert, Mark R Pickering, Margaret Marchese, Iain Stuart
Ultrasound (US) imaging is a widely used clinical diagnostic tool in medical imaging techniques. It is a comparatively safe, economical, painless, portable, and noninvasive real-time tool compared to the other imaging modalities. However, the image quality of US imaging is severely affected by the presence of speckle noise and blur during the acquisition process. In order to ensure a high-quality clinical diagnosis, US images must be restored by reducing their speckle noise and blur. In general, speckle noise is modeled as a multiplicative noise following a Rayleigh distribution and blur as a Gaussian function...
November 1, 2016: Applied Optics
Janna Hastings
As molecular biology has increasingly become a data-intensive discipline, ontologies have emerged as an essential computational tool to assist in the organisation, description and analysis of data. Ontologies describe and classify the entities of interest in a scientific domain in a computationally accessible fashion such that algorithms and tools can be developed around them. The technology that underlies ontologies has its roots in logic-based artificial intelligence, allowing for sophisticated automated inference and error detection...
2017: Methods in Molecular Biology
Golam Sorwar, Mortuza Ali, Md Kamrul Islam, Mohammad Selim Miah
Modern healthcare systems are undergoing a paradigm shift from in-hospital care to in-home monitoring, leveraging the emerging technologies in the area of bio-sensing, wireless communication, mobile computing, and artificial intelligence. In-home monitoring promises to significantly reduce healthcare spending by preventing unnecessary hospital admissions and visits to healthcare professionals. Most of the in-home monitoring systems, proposed in the literature, focus on monitoring a set of specific vital signs...
2016: Studies in Health Technology and Informatics
Link R Swanson
Predictive processing (PP) is a paradigm in computational and cognitive neuroscience that has recently attracted significant attention across domains, including psychology, robotics, artificial intelligence and philosophy. It is often regarded as a fresh and possibly revolutionary paradigm shift, yet a handful of authors have remarked that aspects of PP seem reminiscent of the work of 18th century philosopher Immanuel Kant. To date there have not been any substantive discussions of how exactly PP links back to Kant...
2016: Frontiers in Systems Neuroscience
M Vadiati, A Asghari-Moghaddam, M Nakhaei, J Adamowski, A H Akbarzadeh
Due to inherent uncertainties in measurement and analysis, groundwater quality assessment is a difficult task. Artificial intelligence techniques, specifically fuzzy inference systems, have proven useful in evaluating groundwater quality in uncertain and complex hydrogeological systems. In the present study, a Mamdani fuzzy-logic-based decision-making approach was developed to assess groundwater quality based on relevant indices. In an effort to develop a set of new hybrid fuzzy indices for groundwater quality assessment, a Mamdani fuzzy inference model was developed with widely-accepted groundwater quality indices: the Groundwater Quality Index (GQI), the Water Quality Index (WQI), and the Ground Water Quality Index (GWQI)...
October 6, 2016: Journal of Environmental Management
Vedran Dunjko, Jacob M Taylor, Hans J Briegel
The emerging field of quantum machine learning has the potential to substantially aid in the problems and scope of artificial intelligence. This is only enhanced by recent successes in the field of classical machine learning. In this work we propose an approach for the systematic treatment of machine learning, from the perspective of quantum information. Our approach is general and covers all three main branches of machine learning: supervised, unsupervised, and reinforcement learning. While quantum improvements in supervised and unsupervised learning have been reported, reinforcement learning has received much less attention...
September 23, 2016: Physical Review Letters
Alessio Bottrighi, Paolo Terenziani
CONTEXT: Several different computer-assisted management systems of computer interpretable guidelines (CIGs) have been developed by the Artificial Intelligence in Medicine community. Each CIG system is characterized by a specific formalism to represent CIGs, and usually provides a manager to acquire, consult and execute them. Though there are several commonalities between most formalisms in the literature, each formalism has its own peculiarities. OBJECTIVE: The goal of our work is to provide a flexible support to the extension or definition of CIGs formalisms, and of their acquisition and execution engines...
September 2016: Artificial Intelligence in Medicine
Giorgio Gemignani, Alessandro Rozza
Motion detection in video streams is a challenging task for several computer vision applications. Indeed, segmentation of moving and static elements in the scene allows to increase the efficiency of several challenging tasks, such as human-computer interface, robot visions, and intelligent surveillance systems. In this paper, we approach motion detection through a multi-layered artificial neural network, which is able to build for each background pixel a multi-modal color distribution evolving over time through self-organization...
November 2016: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
Christopher Stanton, Jeff Clune
Natural animals are renowned for their ability to acquire a diverse and general skill set over the course of their lifetime. However, research in artificial intelligence has yet to produce agents that acquire all or even most of the available skills in non-trivial environments. One candidate algorithm for encouraging the production of such individuals is Novelty Search, which pressures organisms to exhibit different behaviors from other individuals. However, we hypothesized that Novelty Search would produce sub-populations of specialists, in which each individual possesses a subset of skills, but no one organism acquires all or most of the skills...
2016: PloS One
Henrik Erikson, Martin Salzmann-Erikson
It is highly likely that artificial intelligence (AI) will be implemented in nursing robotics in various forms, both in medical and surgical robotic instruments, but also as different types of droids and humanoids, physical reinforcements, and also animal/pet robots. Exploring and discussing AI and robotics in nursing and health care before these tools become commonplace is of great importance. We propose that monsters in popular culture might be studied with the hope of learning about situations and relationships that generate empathic capacities in their monstrous existences...
December 0: Permanente Journal
Dharshan Kumaran, Demis Hassabis, James L McClelland
We update complementary learning systems (CLS) theory, which holds that intelligent agents must possess two learning systems, instantiated in mammalians in neocortex and hippocampus. The first gradually acquires structured knowledge representations while the second quickly learns the specifics of individual experiences. We broaden the role of replay of hippocampal memories in the theory, noting that replay allows goal-dependent weighting of experience statistics. We also address recent challenges to the theory and extend it by showing that recurrent activation of hippocampal traces can support some forms of generalization and that neocortical learning can be rapid for information that is consistent with known structure...
July 2016: Trends in Cognitive Sciences
Hossam M Zawbaa, Jakub Szlȩk, Crina Grosan, Renata Jachowicz, Aleksander Mendyk
Poly-lactide-co-glycolide (PLGA) is a copolymer of lactic and glycolic acid. Drug release from PLGA microspheres depends not only on polymer properties but also on drug type, particle size, morphology of microspheres, release conditions, etc. Selecting a subset of relevant properties for PLGA is a challenging machine learning task as there are over three hundred features to consider. In this work, we formulate the selection of critical attributes for PLGA as a multiobjective optimization problem with the aim of minimizing the error of predicting the dissolution profile while reducing the number of attributes selected...
2016: PloS One
Michael S C Thomas
Twin studies indicate that the heritability of general cognitive ability - the genetic contribution to individual differences - increases with age. Brant et al. (2013) reported that this increase in heritability occurs earlier in development for low ability children than high ability children. Allied with structural brain imaging results that indicate faster thickening and thinning of cortex for high ability children (Shaw et al., 2006), Brant and colleagues argued higher cognitive ability represents an extended sensitive period for brain development...
June 2016: Developmental Cognitive Neuroscience
A M Corrêa, P E Teodoro, M C Gonçalves, L M A Barroso, M Nascimento, A Santos, F E Torres
Artificial neural networks have been used for various purposes in plant breeding, including use in the investigation of genotype x environment interactions. The aim of this study was to use artificial neural networks in the selection of common bean genotypes with high phenotypic adaptability and stability, and to verify their consistency with the Eberhart and Russell method. Six trials were conducted using 13 genotypes of common bean between 2002 and 2006 in the municipalities of Aquidauana and Dourados. The experimental design was a randomized block with three replicates...
April 28, 2016: Genetics and Molecular Research: GMR
Randal S Olson, David B Knoester, Christoph Adami
Animal grouping behaviors have been widely studied due to their implications for understanding social intelligence, collective cognition, and potential applications in engineering, artificial intelligence, and robotics. An important biological aspect of these studies is discerning which selection pressures favor the evolution of grouping behavior. In the past decade, researchers have begun using evolutionary computation to study the evolutionary effects of these selection pressures in predator-prey models. The selfish herd hypothesis states that concentrated groups arise because prey selfishly attempt to place their conspecifics between themselves and the predator, thus causing an endless cycle of movement toward the center of the group...
2016: Artificial Life
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