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