216,028 research outputs found
Investigating changing work and economic cultures through the lens of youth employment : a case study from a psychosocial perspective in Italy
Changes in the forms and cultural meanings of work have gone deep during the last decades, with the transient nature of work becoming the norm rather than the exception. This is impacting particularly on youth employment, as Italy’s case epitomizes. Based on interview and focus group data, our study provides a multidimensional model to read and map the multiple tensions young people experience, at an emotional level, on entering today’s corporations. Our findings show, on the one hand, that young professionals’ expectation of work as a place of social learning and exchange clashes with the corporate focus on assimilating young people into target-oriented environments. On the other hand, both in younger and older workers, we found the experience of labour relationships that struggle to direct themselves towards a creative purpose and a developmental prospect, while tending to collapse emotionally inwards, in a fight for security
Building Machines That Learn and Think Like People
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.
Specifically, we argue that these machines should (a) build causal models of
the world that support explanation and understanding, rather than merely
solving pattern recognition problems; (b) ground learning in intuitive theories
of physics and psychology, to support and enrich the knowledge that is learned;
and (c) harness compositionality and learning-to-learn to rapidly acquire and
generalize knowledge to new tasks and situations. We suggest concrete
challenges and promising routes towards these goals that can combine the
strengths of recent neural network advances with more structured cognitive
models.Comment: In press at Behavioral and Brain Sciences. Open call for commentary
proposals (until Nov. 22, 2016).
https://www.cambridge.org/core/journals/behavioral-and-brain-sciences/information/calls-for-commentary/open-calls-for-commentar
Automatic Curriculum Learning For Deep RL: A Short Survey
Automatic Curriculum Learning (ACL) has become a cornerstone of recent
successes in Deep Reinforcement Learning (DRL).These methods shape the learning
trajectories of agents by challenging them with tasks adapted to their
capacities. In recent years, they have been used to improve sample efficiency
and asymptotic performance, to organize exploration, to encourage
generalization or to solve sparse reward problems, among others. The ambition
of this work is dual: 1) to present a compact and accessible introduction to
the Automatic Curriculum Learning literature and 2) to draw a bigger picture of
the current state of the art in ACL to encourage the cross-breeding of existing
concepts and the emergence of new ideas.Comment: Accepted at IJCAI202
Symbol Emergence in Robotics: A Survey
Humans can learn the use of language through physical interaction with their
environment and semiotic communication with other people. It is very important
to obtain a computational understanding of how humans can form a symbol system
and obtain semiotic skills through their autonomous mental development.
Recently, many studies have been conducted on the construction of robotic
systems and machine-learning methods that can learn the use of language through
embodied multimodal interaction with their environment and other systems.
Understanding human social interactions and developing a robot that can
smoothly communicate with human users in the long term, requires an
understanding of the dynamics of symbol systems and is crucially important. The
embodied cognition and social interaction of participants gradually change a
symbol system in a constructive manner. In this paper, we introduce a field of
research called symbol emergence in robotics (SER). SER is a constructive
approach towards an emergent symbol system. The emergent symbol system is
socially self-organized through both semiotic communications and physical
interactions with autonomous cognitive developmental agents, i.e., humans and
developmental robots. Specifically, we describe some state-of-art research
topics concerning SER, e.g., multimodal categorization, word discovery, and a
double articulation analysis, that enable a robot to obtain words and their
embodied meanings from raw sensory--motor information, including visual
information, haptic information, auditory information, and acoustic speech
signals, in a totally unsupervised manner. Finally, we suggest future
directions of research in SER.Comment: submitted to Advanced Robotic
Convolutional Neural Network for Stereotypical Motor Movement Detection in Autism
Autism Spectrum Disorders (ASDs) are often associated with specific atypical
postural or motor behaviors, of which Stereotypical Motor Movements (SMMs) have
a specific visibility. While the identification and the quantification of SMM
patterns remain complex, its automation would provide support to accurate
tuning of the intervention in the therapy of autism. Therefore, it is essential
to develop automatic SMM detection systems in a real world setting, taking care
of strong inter-subject and intra-subject variability. Wireless accelerometer
sensing technology can provide a valid infrastructure for real-time SMM
detection, however such variability remains a problem also for machine learning
methods, in particular whenever handcrafted features extracted from
accelerometer signal are considered. Here, we propose to employ the deep
learning paradigm in order to learn discriminating features from multi-sensor
accelerometer signals. Our results provide preliminary evidence that feature
learning and transfer learning embedded in the deep architecture achieve higher
accurate SMM detectors in longitudinal scenarios.Comment: Presented at 5th NIPS Workshop on Machine Learning and Interpretation
in Neuroimaging (MLINI), 2015, (http://arxiv.org/html/1605.04435), Report-no:
MLINI/2015/1
CURIOUS: Intrinsically Motivated Modular Multi-Goal Reinforcement Learning
In open-ended environments, autonomous learning agents must set their own
goals and build their own curriculum through an intrinsically motivated
exploration. They may consider a large diversity of goals, aiming to discover
what is controllable in their environments, and what is not. Because some goals
might prove easy and some impossible, agents must actively select which goal to
practice at any moment, to maximize their overall mastery on the set of
learnable goals. This paper proposes CURIOUS, an algorithm that leverages 1) a
modular Universal Value Function Approximator with hindsight learning to
achieve a diversity of goals of different kinds within a unique policy and 2)
an automated curriculum learning mechanism that biases the attention of the
agent towards goals maximizing the absolute learning progress. Agents focus
sequentially on goals of increasing complexity, and focus back on goals that
are being forgotten. Experiments conducted in a new modular-goal robotic
environment show the resulting developmental self-organization of a learning
curriculum, and demonstrate properties of robustness to distracting goals,
forgetting and changes in body properties.Comment: Accepted at ICML 201
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