107 research outputs found
Big Data Meet ML and AI for Decision Superiority at the Tactical Edge – Algorithm Design, Demonstrate and Concept Model
NPS NRP Executive SummaryBig Data Meet ML and AI for Decision Superiority at the Tactical Edge – Algorithm Design, Demonstrate and Concept ModelN2/N6 - Information WarfareThis research is supported by funding from the Naval Postgraduate School, Naval Research Program (PE 0605853N/2098). https://nps.edu/nrpChief of Naval Operations (CNO)Approved for public release. Distribution is unlimited.
Causality-Based Feature Importance Quantifying Methods: PN-FI, PS-FI and PNS-FI
In the current ML field models are getting larger and more complex, and data
used for model training are also getting larger in quantity and higher in
dimensions. Therefore, in order to train better models, and save training time
and computational resources, a good Feature Selection (FS) method in the
preprocessing stage is necessary. Feature importance (FI) is of great
importance since it is the basis of feature selection. Therefore, this paper
creatively introduces the calculation of PN (the probability of Necessity), PN
(the probability of Sufficiency), and PNS (the probability of Necessity and
Sufficiency) of Causality into quantifying feature importance and creates 3 new
FI measuring methods, PN-FI, which means how much importance a feature has in
image recognition tasks, PS-FI that means how much importance a feature has in
image generating tasks, and PNS-FI which measures both. The main body of this
paper is three RCTs, with whose results we show how PS-FI, PN-FI, and PNS-FI of
3 features, dog nose, dog eyes, and dog mouth are calculated. The experiments
show that firstly, FI values are intervals with tight upper and lower bounds.
Secondly, the feature dog eyes has the most importance while the other two have
almost the same. Thirdly, the bounds of PNS and PN are tighter than the bounds
of PS.Comment: 7 page
Pengendalian Kualitas Produk Batu Nisan di Kota Gresik dengan Pendekatan Seven Tools
In the Industrial Age 4.0, when business competition is increasing and growing, business people are also required to be able to maintain and increase their existence in products, This tombstone is one of the product needs when approaching the Idhul Fitri holiday because many people will replace the tombstones that have died or if a relative has passed away. Sadar Gresik UMKM produces tombstones and there is a defect problem that occurs, namely experiencing thin cracks and chipped corners of tombstones in April 2022 Sadar UMKM can produce an average of 41 gravestones in one day and for an average defect in 1 month 11 tombstones. In this study, aims to find out the quality control needed so that the tombstone products can control defects, by using the seven tools to find out the factors that cause defects and can control the defects by knowing the NP line on the control chart. Seven tools can also identify the causes and effects of defects and with the addition of 5W+1H recommendations will be made regarding the problems encountered. factors from the results of fishbone Diagrams that require evaluation which include human factors, materials, methods, environment where these factors cause the number of tombstone defects to be 174 thin cracks and tombstone corners to chip 121. To control defects in tombstone products so that at the midpoint of NP 11.8 is right at the midpoint of the control chart. From several factors that are already known in thin cracked tombstones, namely carrying out quality control of 5W + 1H tombstones, so that they can control these defects in accordance with predetermined product specifications/standards, the process of making tombstone products, employees who work on these have responsibilities, the need for training/skill training for the process of making the tombstones and monitoring the evaluation of the workers, the angle of the tombstones, namely carrying out quality control so as to minimize product defects, so that the quality produced is maintained according to the specified standards, the process of making tombstone products, employees those who do it have the responsibility, make repairs on a regular basis
Empathy and the Right to Be an Exception: What LLMs Can and Cannot Do
Advances in the performance of large language models (LLMs) have led some
researchers to propose the emergence of theory of mind (ToM) in artificial
intelligence (AI). LLMs can attribute beliefs, desires, intentions, and
emotions, and they will improve in their accuracy. Rather than employing the
characteristically human method of empathy, they learn to attribute mental
states by recognizing linguistic patterns in a dataset that typically do not
include that individual. We ask whether LLMs' inability to empathize precludes
them from honoring an individual's right to be an exception, that is, from
making assessments of character and predictions of behavior that reflect
appropriate sensitivity to a person's individuality. Can LLMs seriously
consider an individual's claim that their case is different based on internal
mental states like beliefs, desires, and intentions, or are they limited to
judging that case based on its similarities to others? We propose that the
method of empathy has special significance for honoring the right to be an
exception that is distinct from the value of predictive accuracy, at which LLMs
excel. We conclude by considering whether using empathy to consider exceptional
cases has intrinsic or merely practical value and we introduce conceptual and
empirical avenues for advancing this investigation
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