Artificial intelligence within the interplay between natural and artificial computation:Advances in data science, trends and applications

Artificial intelligence and all its supporting tools, e.g. machine and deep learning in computational intelligence-based systems, are rebuilding our society (economy, education, life-style, etc.) and promising a new era for the social welfare state. In this paper we summarize recent advances in data science and artificial intelligence within the interplay between natural and artificial computation. A review of recent works published in the latter field and the state the art are summarized in a comprehensive and self-contained way to provide a baseline framework for the international community in artificial intelligence. Moreover, this paper aims to provide a complete analysis and some relevant discussions of the current trends and insights within several theoretical and application fields covered in the essay, from theoretical models in artificial intelligence and machine learning to the most prospective applications in robotics, neuroscience, brain computer interfaces, medicine and society, in general.BMS - Pfizer(U01 AG024904). Spanish Ministry of Science, projects: TIN2017-85827-P, RTI2018-098913-B-I00, PSI2015-65848-R, PGC2018-098813-B-C31, PGC2018-098813-B-C32, RTI2018-101114-B-I, TIN2017-90135-R, RTI2018-098743-B-I00 and RTI2018-094645-B-I00; the FPU program (FPU15/06512, FPU17/04154) and Juan de la Cierva (FJCI-2017–33022). Autonomous Government of Andalusia (Spain) projects: UMA18-FEDERJA-084. Consellería de Cultura, Educación e Ordenación Universitaria of Galicia: ED431C2017/12, accreditation 2016–2019, ED431G/08, ED431C2018/29, Comunidad de Madrid, Y2018/EMT-5062 and grant ED431F2018/02. PPMI – a public – private partnership – is funded by The Michael J. Fox Foundation for Parkinson’s Research and funding partners, including Abbott, Biogen Idec, F. Hoffman-La Roche Ltd., GE Healthcare, Genentech and Pfizer Inc

Machine Learning Based Classification of Textual Stimuli to Promote Ideation in Bioinspired Design

Bioinspired design uses biological systems to inspire engineering designs. One of bioinspired design’s challenges is identifying relevant information sources in biology for an engineering design task. Currently information can be retrieved by searching biology texts or journals using biology-focused keywords that map to engineering functions. However, this search technique can overwhelm designers with unusable results. This work explores the use of text classification tools to identify relevant biology passages for design. Further, this research examines the effects of using biology passages as stimuli during idea generation. Four human-subjects studies are examined in this work. Two surveys are performed in which participants evaluate sentences from a biology corpus and indicate whether each sentence prompts an idea for solving a specific design problem. The surveys are used to develop and evaluate text classification tools. Two idea generation studies are performed in which participants generate and record solutions for designing a corn shucker using either different sets of biology passages as design stimuli, or no stimuli. Based 286 sentences from the surveys, a k Nearest Neighbor classifier is developed that is able to identify helpful sentences relating to the function “separate” with a precision of 0.62 and recall of 0.48. This classifier could potentially double the number of helpful results found using a keyword search. The developed classifier is specific to the function “separate” and performs poorly when used for another function. Classifiers developed using all sentences and participant responses from the surveys are not able to reliably identify helpful sentences. From the idea generation studies, we determine that using any biology passages as design stimuli increases the quantity and variety of participant solutions. Solution quantity and variety are also significantly increased when biology passages are presented one at a time instead of all at once. Quality and variety are not significantly affected by the presence of design stimuli. Biological stimuli are also found to lead designers to types of solution that are not typically produced otherwise. This work develops a means for designers to find more useful information when searching biology and demonstrates several ways that biology passages can improve ideation

Detecting dynamic domains and local fluctuations in complex molecular systems via timelapse neighbors shuffling

Many complex molecular systems owe their properties to local dynamic rearrangements or fluctuations that, despite the rise of machine learning (ML) and sophisticated structural descriptors, remain often difficult to detect. Here we show an ML framework based on a new descriptor, named Local Environments and Neighbors Shuffling (LENS), which allows identifying dynamic domains and detecting local fluctuations in a variety of systems via tracking how much the surrounding of each molecular unit changes over time in terms of neighbor individuals. Statistical analysis of the LENS time-series data allows to blindly detect different dynamic domains within various types of molecular systems with, e.g., liquid-like, solid-like, or diverse dynamics, and to track local fluctuations emerging within them in an efficient way. The approach is found robust, versatile, and, given the abstract definition of the LENS descriptor, capable of shedding light on the dynamic complexity of a variety of (not necessarily molecular) systems