7,326 research outputs found
Measurement with Persons: A European Network
The European ‘Measuring the Impossible’ Network MINET promotes new research activities in measurement dependent on human perception and/or interpretation. This includes the perceived attributes of products and services, such as quality or desirability, and societal parameters such as security and well-being. Work has aimed at consensus about four ‘generic’ metrological issues: (1) Measurement Concepts & Terminology; (2) Measurement Techniques: (3) Measurement Uncertainty; and (4) Decision-making & Impact Assessment, and how these can be applied specificallyto the ‘Measurement of Persons’ in terms of ‘Man as a Measurement Instrument’ and ‘Measuring Man.’ Some of the main achievements of MINET include a research repository with glossary; training course; book; series of workshops;think tanks and study visits, which have brought together a unique constellation of researchers from physics, metrology,physiology, psychophysics, psychology and sociology. Metrology (quality-assured measurement) in this area is relativelyunderdeveloped, despite great potential for innovation, and extends beyond traditional physiological metrology in thatit also deals with measurement with all human senses as well as mental and behavioral processes. This is particularlyrelevant in applications where humans are an important component of critical systems, where for instance health andsafety are at stake
The Precautionary Principle (with Application to the Genetic Modification of Organisms)
We present a non-naive version of the Precautionary (PP) that allows us to
avoid paranoia and paralysis by confining precaution to specific domains and
problems. PP is intended to deal with uncertainty and risk in cases where the
absence of evidence and the incompleteness of scientific knowledge carries
profound implications and in the presence of risks of "black swans", unforeseen
and unforeseable events of extreme consequence. We formalize PP, placing it
within the statistical and probabilistic structure of ruin problems, in which a
system is at risk of total failure, and in place of risk we use a formal
fragility based approach. We make a central distinction between 1) thin and fat
tails, 2) Local and systemic risks and place PP in the joint Fat Tails and
systemic cases. We discuss the implications for GMOs (compared to Nuclear
energy) and show that GMOs represent a public risk of global harm (while harm
from nuclear energy is comparatively limited and better characterized). PP
should be used to prescribe severe limits on GMOs
Knowledge will Propel Machine Understanding of Content: Extrapolating from Current Examples
Machine Learning has been a big success story during the AI resurgence. One
particular stand out success relates to learning from a massive amount of data.
In spite of early assertions of the unreasonable effectiveness of data, there
is increasing recognition for utilizing knowledge whenever it is available or
can be created purposefully. In this paper, we discuss the indispensable role
of knowledge for deeper understanding of content where (i) large amounts of
training data are unavailable, (ii) the objects to be recognized are complex,
(e.g., implicit entities and highly subjective content), and (iii) applications
need to use complementary or related data in multiple modalities/media. What
brings us to the cusp of rapid progress is our ability to (a) create relevant
and reliable knowledge and (b) carefully exploit knowledge to enhance ML/NLP
techniques. Using diverse examples, we seek to foretell unprecedented progress
in our ability for deeper understanding and exploitation of multimodal data and
continued incorporation of knowledge in learning techniques.Comment: Pre-print of the paper accepted at 2017 IEEE/WIC/ACM International
Conference on Web Intelligence (WI). arXiv admin note: substantial text
overlap with arXiv:1610.0770
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