5,596 research outputs found
National Pastime(s)
In his new book, Baseball as a Road to God, New York University President and Professor of Law John Sexton submits that baseball can serve as a vehicle for living a more conscious life that elevates the human experience for lawyers and non-lawyers. This Essay examines the credibility of the bookâs thesis in a world where human intelligence, human deliberation, and human action is being replaced by artificial intelligence, mathematical models, and mechanical automation. It uses the preeminent national pastime of baseball, and the less eminent pastimes of law and finance as case studies for the bookâs thesis. It concludes that a more conscious and meaningful life is much harder to foster, but also much more important to cultivate in light of modern advances. This Essay ultimately offers a different narrative for lawyers and non-lawyers to think anew about modern law and society in light of ongoing changes in baseball, law, finance, and beyond
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-2017â33022). Autonomous Government of Andalusia (Spain) projects: UMA18-FEDERJA-084. ConsellerĂa de Cultura, EducaciĂłn e OrdenaciĂłn Universitaria of Galicia: ED431C2017/12, accreditation 2016â2019, ED431G/08, ED431C2018/29, Comunidad de Madrid, Y2018/EMT-5062 and grant ED431F2018/02.
PPMI â a public â private partnership â is funded by The Michael J. Fox Foundation for Parkinsonâs Research and funding partners, including Abbott, Biogen Idec, F. Hoffman-La Roche Ltd., GE Healthcare, Genentech and Pfizer Inc
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Transition expertise: Cognitive factors and developmental processes that contribute to repeated successful career transitions amongst elite athletes, musicians and business people
This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.This thesis examines the nature of transition expertise which enables individuals to make repeated successful transitions over the course of their career. It addressed four areas that contribute to transition expertise: 1) cognitive flexibility that enables the generalisation of expert knowledge and processes; 2) inferential and inductive cognitive mechanisms that enable expertise to be generalised; 3) personal intelligences that are used to support transitions; and 4) practical intelligence as it supports performance contextually during transitions.
The study used retrospective interviews to gather data from elite performers in three fields who had made successful career transitions: sports people who become national coaches or heads of national bodies; successful musicians who become heads of faculty or principals of a conservatoire; successful business people who become senior vice presidents or CEOs.
Participants were able to generalise expert knowledge and processes beyond their primary domains, contrary to widely held views about the domain specificity of expertise. Cognitive flexibility enabled this generalisation and was developed through broad based training, early exposure to multiple domains and the early use of generative cognitive processes during the development of primary domain expertise. Inductive, inferential and analogical cognitive mechanisms were the main tools through which expertise was generalised during transitions. Personal intelligence contributed to transition expertise. Intrapersonal intelligence enabled individuals to understand how their abilities, values and motivations shaped their career progression. Interpersonal intelligence enabled individuals to respond effectively to the requirements of their peers, direct reports, stakeholders and organisational context. Contrary to expectations, self regulatory processes did not play a central role in the management of transitions. Practical intelligence enabled transition expertise. It involved more than applying subject-area and tacit knowledge. It encompassed the abilities to: identify and resolve problems; manipulate environmental objects in the form of administrative tasks, schedules and plans; utilise resources in terms of people and materials; and shape their environment, corporate structures and culture.
Transition expertise develops and evolves over the course of a career as it uses convergent and divergent cognitive processes, inductive mechanisms, personal awareness and cognitive pragmatics to address issues of increasing scope and implication. While motivational factors, self belief and personality resiliency are important contributors to transition expertise they did not form part of this study
Eye quietness and quiet eye in expert and novice golf performance: an electrooculographic analysis
Quiet eye (QE) is the final ocular fixation on the target of an action (e.g., the ball in golf putting). Camerabased eye-tracking studies have consistently found longer QE durations in experts than novices; however, mechanisms underlying QE are not known. To offer a new perspective we examined the feasibility of measuring the QE using electrooculography (EOG) and developed an index to assess ocular activity across time: eye quietness (EQ). Ten expert and ten novice golfers putted 60 balls to a 2.4 m distant hole. Horizontal EOG (2ms resolution) was recorded from two electrodes placed on the outer sides of the eyes. QE duration was measured using a EOG voltage threshold and comprised the sum of the pre-movement and post-movement initiation components. EQ was computed as the standard deviation of the EOG in 0.5 s bins from â4 to +2 s, relative to backswing initiation: lower values indicate less movement of the eyes, hence greater quietness. Finally, we measured club-ball address and swing durations. T-tests showed that total QE did not differ between groups (p = .31); however, experts had marginally shorter pre-movement QE (p = .08) and longer post-movement QE (p < .001) than novices. A group Ă time ANOVA revealed that experts had less EQ before
backswing initiation and greater EQ after backswing initiation (p = .002). QE durations were inversely correlated with EQ from â1.5 to 1 s (rs = â.48 - â.90, ps = .03 - .001). Experts had longer swing durations than novices (p = .01) and, importantly, swing durations correlated positively with post-movement QE (r = .52, p = .02) and negatively with EQ from 0.5 to 1s (r = â.63, p = .003). This study demonstrates the feasibility of measuring ocular activity using EOG and validates EQ as an index of ocular activity. Its findings challenge the dominant perspective on QE and provide new evidence that expert-novice differences in ocular activity may reflect differences in the kinematics of how experts and novices execute skills
Autonomous Decision-Making based on Biological Adaptive Processes for Intelligent Social Robots
MenciĂłn Internacional en el tĂtulo de doctorThe unceasing development of autonomous robots in many different scenarios drives a
new revolution to improve our quality of life. Recent advances in human-robot interaction
and machine learning extend robots to social scenarios, where these systems pretend
to assist humans in diverse tasks. Thus, social robots are nowadays becoming real in
many applications like education, healthcare, entertainment, or assistance. Complex
environments demand that social robots present adaptive mechanisms to overcome
different situations and successfully execute their tasks. Thus, considering the previous
ideas, making autonomous and appropriate decisions is essential to exhibit reasonable
behaviour and operate well in dynamic scenarios.
Decision-making systems provide artificial agents with the capacity of making
decisions about how to behave depending on input information from the environment.
In the last decades, human decision-making has served researchers as an inspiration to
endow robots with similar deliberation. Especially in social robotics, where people expect
to interact with machines with human-like capabilities, biologically inspired decisionmaking
systems have demonstrated great potential and interest. Thereby, it is expected
that these systems will continue providing a solid biological background and improve the
naturalness of the human-robot interaction, usability, and the acceptance of social robots
in the following years.
This thesis presents a decision-making system for social robots acting in healthcare,
entertainment, and assistance with autonomous behaviour. The systemâs goal is to
provide robots with natural and fluid human-robot interaction during the realisation of
their tasks. The decision-making system integrates into an already existing software
architecture with different modules that manage human-robot interaction, perception,
or expressiveness. Inside this architecture, the decision-making system decides which
behaviour the robot has to execute after evaluating information received from different
modules in the architecture. These modules provide structured data about planned
activities, perceptions, and artificial biological processes that evolve with time that are the
basis for natural behaviour. The natural behaviour of the robot comes from the evolution
of biological variables that emulate biological processes occurring in humans. We also
propose a Motivational model, a module that emulates biological processes in humans for
generating an artificial physiological and psychological state that influences the robotâs
decision-making. These processes emulate the natural biological rhythms of the human organism to produce biologically inspired decisions that improve the naturalness exhibited
by the robot during human-robot interactions. The robotâs decisions also depend on what
the robot perceives from the environment, planned events listed in the robotâs agenda, and
the unique features of the user interacting with the robot.
The robotâs decisions depend on many internal and external factors that influence how
the robot behaves. Users are the most critical stimuli the robot perceives since they are
the cornerstone of interaction. Social robots have to focus on assisting people in their
daily tasks, considering that each person has different features and preferences. Thus,
a robot devised for social interaction has to adapt its decisions to people that aim at
interacting with it. The first step towards adapting to different users is identifying the user
it interacts with. Then, it has to gather as much information as possible and personalise
the interaction. The information about each user has to be actively updated if necessary
since outdated information may lead the user to refuse the robot. Considering these facts,
this work tackles the user adaptation in three different ways.
âą The robot incorporates user profiling methods to continuously gather information
from the user using direct and indirect feedback methods.
âą The robot has a Preference Learning System that predicts and adjusts the userâs
preferences to the robotâs activities during the interaction.
âą An Action-based Learning System grounded on Reinforcement Learning is
introduced as the origin of motivated behaviour.
The functionalities mentioned above define the inputs received by the decisionmaking
system for adapting its behaviour. Our decision-making system has been designed
for being integrated into different robotic platforms due to its flexibility and modularity.
Finally, we carried out several experiments to evaluate the architectureâs functionalities
during real human-robot interaction scenarios. In these experiments, we assessed:
âą How to endow social robots with adaptive affective mechanisms to overcome
interaction limitations.
âą Active user profiling using face recognition and human-robot interaction.
âą A Preference Learning System we designed to predict and adapt the user
preferences towards the robotâs entertainment activities for adapting the interaction.
âą A Behaviour-based Reinforcement Learning System that allows the robot to learn
the effects of its actions to behave appropriately in each situation.
âą The biologically inspired robot behaviour using emulated biological processes and
how the robot creates social bonds with each user.
âą The robotâs expressiveness in affect (emotion and mood) and autonomic functions
such as heart rate or blinking frequency.Programa de Doctorado en IngenierĂa ElĂ©ctrica, ElectrĂłnica y AutomĂĄtica por la Universidad Carlos III de MadridPresidente: Richard J. Duro FernĂĄndez.- Secretaria: ConcepciĂłn Alicia Monje Micharet.- Vocal: Silvia Ross
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