Application of Analogical Reasoning for Use in Visual Knowledge Extraction

There is a continual push to make Artificial Intelligence (AI) as human-like as possible; however, this is a difficult task because of its inability to learn beyond its current comprehension. Analogical reasoning (AR) has been proposed as one method to achieve this goal. Current literature lacks a technical comparison on psychologically-inspired and natural-language-processing-produced AR algorithms with consistent metrics on multiple-choice word-based analogy problems. Assessment is based on “correctness” and “goodness” metrics. There is not a one-size-fits-all algorithm for all textual problems. As contribution in visual AR, a convolutional neural network (CNN) is integrated with the AR vector space model, Global Vectors (GloVe), in the proposed, Image Recognition Through Analogical Reasoning Algorithm (IRTARA). Given images outside of the CNN’s training data, IRTARA produces contextual information by leveraging semantic information from GloVe. IRTARA’s quality of results is measured by definition, AR, and human factors evaluation methods, which saw consistency at the extreme ends. The research shows the potential for AR to facilitate more a human-like AI through its ability to understand concepts beyond its foundational knowledge in both a textual and visual problem space

Clustering and Topological Data Analysis: Comparison and Application

Clustering is common technique used to demonstrate relationships between data and information. Of recent interest is topological data analysis (TDA), which can represent and cluster data through persistent homology. The TDA algorithms used include the Topological Mode Analysis Tool (ToMATo) algorithm, Garin and Tauzin’s TDA Pipeline, and the Mapper algorithm. First, TDA is compared to ten other clustering algorithms on artificial 2D data where it ranked third overall. TDA had the second-highest performance in terms of average accuracy (97.9%); however, its computation-time performance ranked in the middle of the algorithms. TDA ranked fourth on the qualitative “visual trustworthiness” metric. On real-world data, TDA showed promising classification results (accuracy between 80-95%). Overall, this paper shows TDA is a competitive algorithm performance-wise, though computationally expensive. When TDA is used for visualization, the Mapper algorithm allows for unique alternative views especially effective for visualizing highly dimensional data

Integration of Computer Vision with Analogical Reasoning for Characterizing Unknowns

Current state-of-the-art artificial intelligence struggles with accurate interpretation of out-of-library (OOL) objects. One method proposed remedy is analogical reasoning (AR), which utilizes abductive reasoning to draw inferences on an unfamiliar scenario given knowledge about a similar familiar scenario. Currently, applications of visual AR gravitate toward analogy-formatted image problems rather than to computer vision data sets. The Image Recognition Through Analogical Reasoning Algorithm (IRTARA) approach described herein shows how AR can be leveraged to improve computer vision in OOL situations. IRTARA produces a word-based term frequency list that characterizes the OOL object of interest. To evaluate the quality of the results of IRTARA, both quantitative and qualitative assessments are used, including a baseline to compare the automated methods with human-generated results. Fifteen OOL objects were tested using IRTARA, which showed consistent results across all three evaluation methods on the objects that performed exceptionally well or poorly overall

Analogical Reasoning: An Algorithm Comparison for Natural Language Processing

There is a continual push to make Artificial Intelligence (AI) as human-like as possible; however, this is a difficult task. A significant limitation is the inability of AI to learn beyond its current comprehension. Analogical reasoning (AR), whereby learning by analogy occurs, has been proposed as one method to achieve this goal. Current AR models have their roots in symbolist, connectionist, or hybrid approaches which indicate how analogies are evaluated. No current studies have compared psychologically-inspired and natural language processing (NLP)-produced algorithms to one another; this study compares seven AR algorithms from both realms on multiple-choice word-based analogy problems. Assessment is based on selection of the correct answer, “correctness,” and their similarity score prediction compared to the “ideal” score, which is defined as the “goodness” metric. Psychologically-based models have an advantage based on our metrics; however, there is not a clear one-size-fits-all algorithm for all AR problems

Analogical Reasoning: An Algorithm Comparison for Natural Language Processing

There is a continual push to make Artificial Intelligence (AI) as human-like as possible; however, this is a difficult task. A significant limitation is the inability of AI to learn beyond its current comprehension. Analogical reasoning (AR), whereby learning by analogy occurs, has been proposed as one method to achieve this goal. Current AR models have their roots in symbolist, connectionist, or hybrid approaches which indicate how analogies are evaluated. No current studies have compared psychologically-inspired and natural language processing (NLP)-produced algorithms to one another; this study compares seven AR algorithms from both realms on multiple-choice word-based analogy problems. Assessment is based on selection of the correct answer, “correctness,” and their similarity score prediction compared to the “ideal” score, which is defined as the “goodness” metric. Psychologically-based models have an advantage based on our metrics; however, there is not a clear one-size-fits-all algorithm for all AR problems

Application of Analogical Reasoning for Use in Visual Knowledge Extraction

There is a continual push to make Artificial Intelligence (AI) as human-like as possible; however, this is a difficult task because of its inability to learn beyond its current comprehension. Analogical reasoning (AR) has been proposed as one method to achieve this goal. Current literature lacks a technical comparison on psychologically-inspired and natural-language-processing-produced AR algorithms with consistent metrics on multiple-choice word-based analogy problems. Assessment is based on “correctness” and “goodness” metrics. There is not a one-size-fits-all algorithm for all textual problems. As contribution in visual AR, a convolutional neural network (CNN) is integrated with the AR vector space model, Global Vectors (GloVe), in the proposed, Image Recognition Through Analogical Reasoning Algorithm (IRTARA). Given images outside of the CNN’s training data, IRTARA produces contextual information by leveraging semantic information from GloVe. IRTARA’s quality of results is measured by definition, AR, and human factors evaluation methods, which saw consistency at the extreme ends. The research shows the potential for AR to facilitate more a human-like AI through its ability to understand concepts beyond its foundational knowledge in both a textual and visual problem space

Inpatient Discharge-By-Noon: Are Fewer Better than All?

Introduction: To address boarding in hospital emergency departments, discharge-by-noon could free up inpatient beds earlier in the day. However, discharging all patients by noon can heavily burden inpatient units and may not be feasible. In this study, we determine the number of discharges after which the benefits of an additional discharge-by-noon diminish. Methods: We conducted a simulation analysis to quantify how occupancy rate, mean daily number of discharges, and peak discharge time impact upstream boarding time in an inpatient neurology unit at Maine Medical Center. Using a day-of-discharge simulation model with one year of retrospective data, we assessed configurations approximating various inpatient units to increase the number of patients discharged by noon from 1 to all. Measured outcomes included the (1) average upstream boarding time across all patients and (2) average time of day for discharge completion. Results: Units with a higher occupancy rate, later peak time of day for discharge, or more discharges may benefit more from discharge-by-noon initiatives. For any unit configuration studied, approximately 75% of the maximum expected reduction in boarding time (when all-by-noon is implemented) can be achieved by discharging half of the average daily discharges by noon. Discussion: Studies have aimed to achieve all discharges before noon. Our study suggests that although discharging patients by noon reduces upstream boarding time, discharging all by noon does not eliminate upstream boarding. Hospitals may have better outcomes by implementing strategies based on the characteristics of specific units. Conclusions: Although setting a discharge target can help an inpatient unit better achieve earlier discharges and reduce upstream patient boarding, discharge-by-noon does not need to be used in its original form of “all” by noon