Academic literature on the topic 'AI Observer'
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Journal articles on the topic "AI Observer"
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Straeubig, Michael. "Games, AI and Systems." Eludamos: Journal for Computer Game Culture 10, no. 1 (April 21, 2020): 141–60. http://dx.doi.org/10.7557/23.6176.
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In recent years, we have observed impressive advancements at the intersection of games and artificial intelligence. Often these developments are described in terms of technological progress, while public discourses on their cultural, social and political impact are largely decoupled. I present an alternative rhetoric by speculating about the emergence of AI within social systems. In a radical departure from the dominant discourse, I describe seven roles - Mechanic, Alter/Ego, Observer, Protector, Player, Creator and God - that an AI may assume in the environment of videogames. I reflect on the ramifications of these roles for the idea of an artificial general intelligence (AGI), mainly hoping to irritate the prevailing discussion.
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Salinel, Brandon, Matthew Grudza, Sarah Zeien, Matthew Murphy, Jake Adkins, Corey Jensen, Curt Bay, et al. "Comparison of segmentation methods to improve throughput in annotating AI-observer for detecting colorectal cancer." Journal of Clinical Oncology 40, no. 4_suppl (February 1, 2022): 142. http://dx.doi.org/10.1200/jco.2022.40.4_suppl.142.
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142 Background: Colorectal cancer (CRC) is the second leading cause of cancer-related deaths, and its outcome can be improved with better detection of incidental early CRC on routine CT of the abdomen and pelvis (CTAP). AI-second observer (AI) has the potential as shown in our companion abstract. The bottleneck in training AI is the time required for radiologists to segment the CRC. We compared two techniques for accelerating the segmentation process: 1) Sparse annotation (annotating some of the CT slice containing CRC instead of every slice); 2) Allowing AI to perform initial segmentation followed by human adjustment. Methods: 2D U-Net convolutional neural network (CNN) containing 31 million trainable parameters was trained with 58 CRC CT images from Banner MD Anderson (AZ) and MD Anderson Cancer Center (TX) (51 used for training and 7 for validation) and 59 normal CT scans from Banner MD Anderson Cancer Center. Twenty of the 25 CRC cases from public domain data (The Cancer Genome Atlas) were used to evaluate the performance of the models. The CRC was segmented using ITK-SNAP open-source software (v. 3.8). For the first objective, 3 separate models were trained (fully annotated CRC, every other slice, and every third slice). The AI-annotation on the TCGA dataset was analyzed by the percentage of correct detection of CRC, the number of false positives, and the Dice similarity coefficient (DSC). If parts of the CRC were flagged by AI, then it was considered correct. A detection was considered false positive if the marked lesion did not overlap with CRC; contiguous false positives across different slices of CT image were considered a single false positive. DSC measures the quality of the segmentation by measuring the overlap between the ground-truth and AI detected lesion. For the second objective, the time required to adjust the AI-produced annotation was compared to the time required for annotating the entire CRC without AI assistance. The AI-models were trained using ensemble learning (see our companion abstract for details of the techniques). Results: Our results showed that skipping slices of tumor in training did not alter the accuracy, false positives, or DSC classification of the model. When adjusting the AI-observer segmentation, there was a trend toward decreasing the time required to adjust the annotation compared to full manual segmentation, but the difference was not statistically significant (Table; p=0.121). Conclusions: Our results show that both skipping slices of tumor as well as starting with AI-produced annotation can potentially decrease the effort required to produce high-quality ground truth without compromising the performance of AI. These techniques can help improve the throughput to obtain a large volume of cases to train AI for detecting CRC.[Table: see text]
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Smith, Andrew Dennis, Brian C. Allen, Asser Abou Elkassem, Rafah Mresh, Seth T. Lirette, Yujan Shrestha, J. David Giese, et al. "Multi-institutional comparative effectiveness of advanced cancer longitudinal imaging response evaluation methods: Current practice versus artificial intelligence-assisted." Journal of Clinical Oncology 38, no. 15_suppl (May 20, 2020): 2010. http://dx.doi.org/10.1200/jco.2020.38.15_suppl.2010.
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2010 Background: Current-practice methods to evaluate advanced cancer longitudinal tumor response include manual measurements on digital medical images and dictation of text-based reports that are prone to errors, inefficient, and associated with low inter-observer agreement. The purpose of this study is to compare the effectiveness of advanced cancer longitudinal imaging response evaluation using current practice versus artificial intelligence (AI)-assisted methods. Methods: For this multi-institutional longitudinal retrospective study, body CT images from 120 consecutive patients with multiple serial imaging exams and advanced cancer treated with systemic therapy were independently evaluated by 24 radiologists using current-practice versus AI-assisted methods. For the current practice method, radiologists dictated text-based reports and separately categorized response (CR, PR, SD, and PD). For the AI-assisted method, custom software included AI algorithms for tumor measurement, target and non-target location labelling, and tumor localization at follow up. The AI-assisted software automatically categorized tumor response per RECIST 1.1 calculations and displayed longitudinal data in the form of a graph, table, and key images. All studies were read independently in triplicate for assessment of inter-observer agreement. Comparative effectiveness metrics included: major errors, time of image interpretation, and inter-observer agreement for final response category. Results: Major errors were found in 27.5% (99/360) for current-practice versus 0.3% (1/360) for AI-assisted methods (p < 0.001), corresponding to a 99% reduction in major errors. Average time of interpretation by radiologists was 18.7 min for current-practice versus 9.8 min for AI-assisted method (p < 0.001), with the AI-assisted method being nearly twice as fast. Total inter-observer agreement on final response categorization for radiologists was 52% (62/120) for current-practice versus 75% (90/120) for AI-assisted method (p < 0.001), corresponding to a 45% increase in total inter-observer agreement. Conclusion: In a large multi-institutional study, AI-assisted advanced cancer longitudinal imaging response evaluation significantly reduced major errors, was nearly twice as fast, and increased inter-observer agreement relative to the current-practice method, thereby establishing a new and improved standard of care.
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Gürsoy Çoruh, Ayşegül, Bülent Yenigün, Çağlar Uzun, Yusuf Kahya, Emre Utkan Büyükceran, Atilla Elhan, Kaan Orhan, and Ayten Kayı Cangır. "A comparison of the fusion model of deep learning neural networks with human observation for lung nodule detection and classification." British Journal of Radiology 94, no. 1123 (July 1, 2021): 20210222. http://dx.doi.org/10.1259/bjr.20210222.
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Objectives: To compare the diagnostic performance of a newly developed artificial intelligence (AI) algorithm derived from the fusion of convolution neural networks (CNN) versus human observers in the estimation of malignancy risk in pulmonary nodules. Methods: The study population consists of 158 nodules from 158 patients. All nodules (81 benign and 77 malignant) were determined to be malignant or benign by a radiologist based on pathologic assessment and/or follow-up imaging. Two radiologists and an AI platform analyzed the nodules based on the Lung-RADS classification. The two observers also noted the size, location, and morphologic features of the nodules. An intraclass correlation coefficient was calculated for both observers and the AI; ROC curve analysis was performed to determine diagnostic performances. Results: Nodule size, presence of spiculation, and presence of fat were significantly different between the malignant and benign nodules (p < 0.001, for all three). Eighteen (11.3%) nodules were not detected and analyzed by the AI. Observer 1, observer 2, and the AI had an AUC of 0.917 ± 0.023, 0.870 ± 0.033, and 0.790 ± 0.037 in the ROC analysis of malignity probability, respectively. The observers were in almost perfect agreement for localization, nodule size, and lung-RADS classification [κ (95% CI)=0.984 (0.961–1.000), 0.978 (0.970–0.984), and 0.924 (0.878–0.970), respectively]. Conclusion: The performance of the fusion AI algorithm in estimating the risk of malignancy was slightly lower than the performance of the observers. Fusion AI algorithms might be applied in an assisting role, especially for inexperienced radiologists. Advances in knowledge: In this study, we proposed a fusion model using four state-of-art object detectors for lung nodule detection and discrimination. The use of fusion of deep learning neural networks might be used in a supportive role for radiologists when interpreting lung nodule discrimination.
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M. Lazim, Izzuddin, Abdul Rashid Husain, Nurul Adilla Mohd Subha, and Mohd Ariffanan Mohd Basri. "Intelligent Observer-Based Feedback Linearization for Autonomous Quadrotor Control." International Journal of Engineering & Technology 7, no. 4.35 (November 30, 2018): 904. http://dx.doi.org/10.14419/ijet.v7i4.35.26280.
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The presence of disturbances can cause instability to the quadrotor flight and can be dangerous especially when operating near obstacles or other aerial vehicles. In this paper, a hybrid controller called state feedback with intelligent disturbance observer-based control (SF-iDOBC) is developed for trajectory tracking of quadrotor in the presence of time-varying disturbances, e.g. wind. This is achieved by integrating artificial intelligence (AI) technique with disturbance observer-based feedback linearization to achieve a better disturbance rejection capability. Here, the observer estimates the disturbances acting on the quadrotor, while AI technique using the radial basis function neural network (RBFNN) compensates the disturbance estimation error. To improve the error compensation of RBFNN, the k-means clustering method is used to find the optimal centers of the Gaussian activation function. In addition, the weights of the RBFNN are tuned online using the derived adaptation law based on the Lyapunov method, which eliminates the offline training. In the simulation experiment conducted, a total of four input nodes and five hidden neurons are used to compensate for the error. The results obtained demonstrate the effectiveness and merits of the theoretical development.
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Salinel, Brandon, Matthew Grudza, Sarah Zeien, Matthew Murphy, Jake Adkins, Corey Jensen, Curt Bay, et al. "Ensemble voting decreases false positives in AI second-observer reads for detecting colorectal cancer." Journal of Clinical Oncology 40, no. 4_suppl (February 1, 2022): 141. http://dx.doi.org/10.1200/jco.2022.40.4_suppl.141.
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141 Background: Colorectal cancer (CRC) is the second leading cause of cancer-related deaths, and survival can be improved if early, suspect imaging features on CT of the abdomen and pelvis (CTAP) can be routinely identified. At present, up to 40% of these features are undiagnosed on routine CTAP, but this can be improved with a second observer. In this study, we developed a deep ensemble learning method for detecting CRC on CTAP to determine if increasing agreement between ensemble models can decrease the false positives detected by artificial intelligence (AI) second-observer. Methods: 2D U-Net convolutional neural network (CNN) containing 31 million trainable parameters was trained with 58 CRC CT images from Banner MD Anderson (AZ) and MD Anderson Cancer Center (TX) (51 used for training and 7 for validation) and 59 normal CT scans from Banner MD Anderson Cancer Center. 20 of the 25 CRC cases from public domain data (The Cancer Genome Atlas) were used to evaluate the performance of the models. The CRC was segmented using ITK-SNAP open-source software (v. 3.8). To apply the deep ensemble approach, five CNN models were trained independently with random initialization using the same U-Net architect and the same training data. Given a testing CT scan, each of the five trained CNN models was applied to produce tumor segmentation for the testing CT scan. The tumor segmentation results produced by the trained CNN models were then fused using a simple majority voting rule to produce consensus tumor segmentation results. The segmentation was analyzed by the percentage of correct detection, the number of false positives per case, and the Dice similarity coefficient (DSC). If parts of the CRC were flagged by AI, then it was considered correct. A detection was considered false positive if the marked lesion did not overlap with any CRC; contiguous false positives across different slices of CT image were considered a single false positive. DSC measures the quality of the segmentation by measuring the overlap between the ground-truth and AI detected lesion. Results: Our results showed that increasing the agreement between the 5 models dramatically decreases the number of false positives per CT at the expense of slight decrease in accuracy and DSC. This is described in the table. Conclusions: Our results show that AI-based second observer can potentially detect CRC on routine CTAP. Although the initial result yields high false positives per case, ensemble voting is an effective method for decreasing the false positives with a slight decrease in accuracy. This technique can be further improved for eventual clinical application.[Table: see text]
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Palm, Christiane, Catherine E. Connolly, Regina Masser, Barbara Padberg Sgier, Eva Karamitopoulou, Quentin Simon, Beata Bode, and Marianne Tinguely. "Determining HER2 Status by Artificial Intelligence: An Investigation of Primary, Metastatic, and HER2 Low Breast Tumors." Diagnostics 13, no. 1 (January 3, 2023): 168. http://dx.doi.org/10.3390/diagnostics13010168.
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The expression of human epidermal growth factor receptor 2 (HER2) protein or gene transcripts is critical for therapeutic decision making in breast cancer. We examined the performance of a digitalized and artificial intelligence (AI)-assisted workflow for HER2 status determination in accordance with the American Society of Clinical Oncology (ASCO)/College of Pathologists (CAP) guidelines. Our preliminary cohort consisted of 495 primary breast carcinomas, and our study cohort included 67 primary breast carcinomas and 30 metastatic deposits, which were evaluated for HER2 status by immunohistochemistry (IHC) and in situ hybridization (ISH). Three practicing breast pathologists independently assessed and scored slides, building the ground truth. Following a washout period, pathologists were provided with the results of the AI digital image analysis (DIA) and asked to reassess the slides. Both rounds of assessment from the pathologists were compared to the AI results and ground truth for each slide. We observed an overall HER2 positivity rate of 15% in our study cohort. Moderate agreement (Cohen’s κ 0.59) was observed between the ground truth and AI on IHC, with most discrepancies occurring between 0 and 1+ scores. Inter-observer agreement amongst pathologists was substantial (Fleiss´ κ 0.77) and pathologists’ agreement with AI scores was 80.6%. Substantial agreement of the AI with the ground truth (Cohen´s κ 0.80) was detected on ISH-stained slides, and the accuracy of AI was similar for the primary and metastatic tumors. We demonstrated the feasibility of a combined HER2 IHC and ISH AI workflow, with a Cohen’s κ of 0.94 when assessed in accordance with the ASCO/CAP recommendations.
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Ajmera, Pranav, Amit Kharat, Tanveer Gupte, Richa Pant, Viraj Kulkarni, Vinay Duddalwar, and Purnachandra Lamghare. "Observer performance evaluation of the feasibility of a deep learning model to detect cardiomegaly on chest radiographs." Acta Radiologica Open 11, no. 7 (July 2022): 205846012211073. http://dx.doi.org/10.1177/20584601221107345.
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Background Cardiothoracic ratio (CTR) is the ratio of the diameter of the heart to the diameter of the thorax. An abnormal CTR (>0.55) is often an indicator of an underlying pathological condition. The accurate prediction of an abnormal CTR chest X-rays (CXRs) aids in the early diagnosis of clinical conditions. Purpose We propose a deep learning (DL)-based model for automatic CTR calculation to assist radiologists with rapid diagnosis of cardiomegaly and thus optimise the radiology flow. Material and Methods The study population included 1012 posteroanterior CXRs from a single institution. The Attention U-Net DL architecture was used for the automatic calculation of CTR. An observer performance test was conducted to assess the radiologist’s performance in diagnosing cardiomegaly with and without artificial intelligence assistance. Results U-Net model exhibited a sensitivity of 0.80 [95% CI: 0.75, 0.85], specificity >99%, precision of 0.99 [95% CI: 0.98, 1], and a F1 score of 0.88 [95% CI: 0.85, 0.91]. Furthermore, the sensitivity of the reviewing radiologist in identifying cardiomegaly increased from 40.50% to 88.4% when aided by the AI-generated CTR. Conclusion Our segmentation-based AI model demonstrated high specificity (>99%) and sensitivity (80%) for CTR calculation. The performance of the radiologist on the observer performance test improved significantly with provision of AI assistance. A DL-based segmentation model for rapid quantification of CTR can therefore have significant potential to be used in clinical workflows by reducing radiologists’ burden and alerting to an abnormal enlarged heart early on.
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Al-Hammadi, Noora, Palmira Caparrotti, Saju Divakar, Mohamed Riyas, Suparna Halsnad Chandramouli, Rabih Hammoud, Jillian Hayes, Maeve Mc Garry, Satheesh Prasad Paloor, and Primoz Petric. "MRI reduces variation of contouring for boost clinical target volume in breast cancer patients without surgical clips in the tumour bed." Radiology and Oncology 51, no. 2 (May 24, 2017): 160–68. http://dx.doi.org/10.1515/raon-2017-0014.
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Abstract Background Omitting the placement of clips inside tumour bed during breast cancer surgery poses a challenge for delineation of lumpectomy cavity clinical target volume (CTVLC). We aimed to quantify inter-observer variation and accuracy for CT- and MRI-based segmentation of CTVLC in patients without clips. Patients and methods CT- and MRI-simulator images of 12 breast cancer patients, treated by breast conserving surgery and radiotherapy, were included in this study. Five radiation oncologists recorded the cavity visualization score (CVS) and delineated CTVLC on both modalities. Expert-consensus (EC) contours were delineated by a senior radiation oncologist, respecting opinions of all observers. Inter-observer volumetric variation and generalized conformity index (CIgen) were calculated. Deviations from EC contour were quantified by the accuracy index (AI) and inter-delineation distances (IDD). Results Mean CVS was 3.88 +/− 0.99 and 3.05 +/− 1.07 for MRI and CT, respectively (p = 0.001). Mean volumes of CTVLC were similar: 154 +/− 26 cm3 on CT and 152 +/− 19 cm3 on MRI. Mean CIgen and AI were superior for MRI when compared with CT (CIgen: 0.74 +/− 0.07 vs. 0.67 +/− 0.12, p = 0.007; AI: 0.81 +/− 0.04 vs. 0.76 +/− 0.07; p = 0.004). CIgen and AI increased with increasing CVS. Mean IDD was 3 mm +/− 1.5 mm and 3.6 mm +/− 2.3 mm for MRI and CT, respectively (p = 0.017). Conclusions When compared with CT, MRI improved visualization of post-lumpectomy changes, reduced interobserver variation and improved the accuracy of CTVLC contouring in patients without clips in the tumour bed. Further studies with bigger sample sizes are needed to confirm our findings.
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Hameed, B. M. Zeeshan, Milap Shah, Nithesh Naik, Sufyan Ibrahim, Bhaskar Somani, Patrick Rice, Naeem Soomro, and Bhavan Prasad Rai. "Contemporary application of artificial intelligence in prostate cancer: an i-TRUE study." Therapeutic Advances in Urology 13 (January 2021): 175628722098664. http://dx.doi.org/10.1177/1756287220986640.
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Artificial intelligence (AI) involves technology that is able to emulate tasks previously carried out by humans. The growing incidence, novel diagnostic strategies and newer available therapeutic options have had resource and economic impacts on the healthcare organizations providing prostate cancer care. AI has the potential to be an adjunct to and, in certain cases, a replacement for human input in prostate cancer care delivery. Automation can also address issues such as inter- and intra-observer variability and has the ability to deliver analysis of large volume datasets quickly and accurately. The continuous training and testing of AI algorithms will facilitate development of futuristic AI models that will have integral roles to play in diagnostics, enhanced training and surgical outcomes and developments of prostate cancer predictive tools. These AI related innovations will enable clinicians to provide individualized care. Despite its potential benefits, it is vital that governance with AI related care is maintained and responsible adoption is achieved.
Books on the topic "AI Observer"
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Enemark, Christian, ed. Ethics of Drone Strikes. Edinburgh University Press, 2021. http://dx.doi.org/10.3366/edinburgh/9781474483575.001.0001.
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This collection of essays explores a variety of ways of thinking ethically about drone violence. The violent use of armed, unmanned aircraft (‘drones’) is increasing worldwide, but uncertainty persists about the moral status of remote-control killing and why it should be restrained. Practitioners, observers and potential victims of such violence often struggle to reconcile it with traditional expectations about the nature of war and the risk to combatants. Addressing the ongoing policy concern that the state use of drone violence is sometimes poorly understood and inadequately governed, the book’s ethical assessments are not restricted to the application of traditional Just War principles. They also consider the ethics of artificial intelligence (AI), virtue ethics, and guiding principles for forceful law-enforcement. The collection brings together nine original contributions by established and emerging scholars, incorporating expertise in military ethics, critical military studies, gender, history, international law and international relations, in order to better assess the multi-faceted relationship between drone violence and justice.
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RUNCAN, PATRICIA. Copilărie, consiliere și parentalitate cu impact. Vol. 1. Ediție revizuită. Seria AUTENTIC. EDITURA DE VEST, 2021. http://dx.doi.org/10.51820/autentic.2021.vol.1.editie_revizuita.
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"PREFAȚĂ INCITANT ȘI INFORMATIV TOTODATĂ, titlul volumului Copilărie, consiliere și parentalitate cu impact pune „în joc” trei cuvinte-cheie pentru problematica circumscrisă, gata să stimuleze curiozitatea, indiferent de vârsta, ocupația, statutul cititorului. „Paradisul pierdut al părinților” (copilăria), „misiunea” parentală – onorată sau nu, conștientizată ori ba – și, în fine, consecințele oricăruia dintre stiluri (exigent, autoritar, permisiv, neimplicat), urmări pe care le-am putea „boteza” drept amprentă fastă, „stâlp” al succesului personal și social, „marcă” a nereușitei, „dâră” adâncă în psihicul suferind al copilului, până departe, în viața de adult…, toate acestea „conviețuiesc” într-o „formulă” persuasivă care trimite la necesitatea abordării unei astfel de teme, la urgența dezbaterii ei (și sub formă editorială), la efectele (rezultatele) neapărat trăite de copil, sigur constatate de el însuși, de psiholog, învățător, medic etc., la o constantă preocupare de ordin științific, de ce nu, la un semnal de alarmă vizând decidenți de toate rangurile și responsabilitățile. „Pariul” asumat de coordonator (conf. univ. dr. Patricia Runcan) este autenticitatea, concept cu deschideri hermeneutice (din perspectivă teoretică) și provocări-capcane în privința trăirilor – mărturisirilor fiecărui subiect – actor al scrierii de față. Structurată pe trei secțiuni (părți), cartea debutează consistent prin 8 Interviuri cu profesioniști și oameni autentici, continuă cu 7 Eseuri descriptive aparținând unor masteranzi netemători de vulnerabilitatea asociată autodezvăluirii și se încheie cu Abordări teoretice și practice. Cercetări academice aplicate (rod al colaborării dintre un profesor și două absolvente cu disertații masterale pe tema tratată în Copilărie, consiliere și parentalitate cu impact). „Misterul” aparent al preferinței „omului cu microfon” pentru anumiți interlocutori autentici – în pofida multor altora este măcar parțial „descâlcit” de atribute precum: deschiderea la conversații pe teme provocatoare, delicate, „spinoase”; experiența profesională de învățători, directori de școli, medici neurologi, psihoterapeuți, oameni ai bisericii, universitari; notorietatea și reputația în spațiul public; regăsirea unor valori comune (sau asemănătoare) în copilăria fiecăruia, dar și aplicate, mai apoi, în viața de părinte, profesor, terapeut, formator de opinii etc. Întrebările prestabilite (dar formulate nuanțat, pliindu-se pe structura de personalitate a „adversarului”), răspunsurile (mai concise sau mai ample), „trădând” copilării „zugrăvite” în multe culori, formulări confesive, dar și cu rol de exemplu, îndemn, dovadă, ilustrare, oglindire, lecție de viață, poveste cu tâlc, motto-ul – unul mai bine căutat decât altul – și, deloc de ignorat, ecoul fiecărei „întâlniri admirabile” în ființa „cetitorului”, laolaltă creează un „spectacol” aparte, demn de analiză. Ce este autenticitatea? (Cum să fii autentic ?) adună, din „zona” profesioniștilor, „definiții” într-un veritabil mozaic: „cu autoritate”, „stăpân pe sine”, „care acționează cum îi este sieși propriu”, „autenticitatea este grea; e singura cale de împăcare cu sine”, „în absența onestității nu există autentic”, „autenticitatea nu se determină pe axa natură-cultură”, „să fii adevărat”, „să fii original”, „să fii creativ”, „să fii valoros”. Într-un registru asemănător răspund masteranzii, formulările lor variind între: „a fi TU”, „a nu te preface”, „a-ți susține convingerile”, „a-ți cere drepturile”, „a nu-ți judeca părinții”, „a te accepta așa cum ești”, „a fi sincer mereu”, „a fi om serios”, „a inspira încredere”, „a trăi în scopul vieții, care e veșnicia”, „a fi ca mama”, „a fi același în viața privată și în cea publică”, „autenticitatea este comuniunea dintre gânduri, vorbe și fapte.” La rându-i, definirea copilăriei comportă sensibile diferențe de nuanță, esența rămânând aceeași. Dacă maturii oscilează între „copilăria este familie”, „bucurie, joacă în aer liber”, mediul în care nu trăim „hie încătro” (adică în lipsa rânduielii și a ritualurilor), „paradisul pierdut al părinților”, „poligon de încercare a duplicității, ipocriziei, snobismului, imposturii, lipsei de responsabilitate”, „poveste”, masteranzii numesc propria copilărie: „speranță”, „Nu există nu pot!”, „oglinda prin fața căreia treci ca să mai descoperi ceva din (în) tine”, „binecuvântare”, „liniște”, „colțul de rai pe pămînt”, „fundația vieții”, „o călătorie fericită într-o țară încărcată de emoție și culoare”… Observăm cu îngândurare că în discursul adulților experți – predomină bucuria, joaca, libertatea, în vreme ce o notă accentuată în „confesiunile” tinerilor masteranzi este reprezentată de maturizarea precoce, cu precădere în familiile „cu multe probleme”: părinți conflictuali, alcoolici, abuzivi, cu educație precară, indiferenți la nevoia de afectivitate și reguli rezonabile pentru buna alcătuire a caracterului copilului. Este și motivul pentru care autorul acestei prefețe nu se arată bucuros de confirmarea, în prima cercetare din finalul volumului Copilărie și parentalitate cu impact, a „armoniei” dintre stilul parental aplicat în copilăria cuiva și stilul parental internalizat de subiectul educației și exersat, la rându-i, asupra propriului descendent, după cum, nici de concluzia (ca validare a ipotezei celei din urmă cercetări) conform căreia relația de atașament din copilărie influențează în mare măsură relația de atașament a unei mame cu copilul său. Combinația fericită dintre parentalitatea autentică și copilăria ziditoare nu se constată la tot pasul. Chiar dacă valorile cultivate de familiile-nucleu sau extinse sunt dispuse într-un registru pozitiv generos (bunătate, siguranță, solidaritate, încredere, libertate, dragoste și justiție, limite raționale, principialitate, corectitudine, disciplină, partajare frățească, cinste, respect, bun simț, iubirea față de semeni, răbdare, adevăr, modestie, mediu creștin ș.a. – cum se vede în mărturiile din cartea în discuție) prea adesea, întâlnim fractura între ce e bine de făcut și răul care se petrece în relațiile interpersonale din universul familiei. Uneori, neștiința este cea dintâi cauză. Alteori, o „arhitectură” defectă a personalității părintelui. Nu rareori, influențe nepotrivite din afara mediului educațional primar. Traumele din copilărie ale genitorilor, nerezolvate la vreme, reprezintă un teribil element perturbator pentru copilăria și viitoarea viață de adult a celor mici. Incidența tot mai accentuată a familiilor disfuncționale face tot mai rar valabil adevărul: „Din familii rele ies copii buni” (însă cu o dublă condiție, echilibrul și selectivitatea dovedite de copil). Desigur, teoreticienii și practicienii avertizează și „repară”, colaborează și se luptă cu varii ipostaze ale ignoranței și stricăciunilor avându-i ca autori pe părinți. Nu-i de ajuns. Nu-i mereu la timp. Nu e pentru „toată lumea” copiilor afectați. Nu e cu asumarea și sprijinul tuturor părinților. Nu e, în toată clipa, cu toate resursele (profesioniști, instrumente, bani) trebuincioase celor mai bune terapii și rezultate. Din păcate, în lumea reală, atât de multe nu se pot! Conferențiar universitar dr. ȘTEFANIA BEJAN Universitatea „Alexandru Ioan Cuza” din Iași"
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Runcan, Patricia. Copilărie și parentalitate cu impact. Editura de Vest, 2020. http://dx.doi.org/10.51820/autentic.2020.vol.1.
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INCITANT ȘI INFORMATIV TOTODATĂ, titlul volumului Copilărie și parentalitate cu impact pune „în joc” trei cuvinte-cheie pentru problematica circumscrisă, gata să stimuleze curiozitatea, indiferent de vârsta, ocupația, statutul cititorului. „Paradisul pierdut al părinților” (copilăria), „misiunea” parentală – onorată sau nu, conștientizată ori ba – și, în fine, consecințele oricăruia dintre stiluri (exigent, autoritar, permisiv, neimplicat), urmări pe care le-am putea „boteza” drept amprentă fastă, „stâlp” al succesului personal și social, „marcă” a nereușitei, „dâră” adâncă în psihicul suferind al copilului, până departe, în viața de adult…, toate acestea „conviețuiesc” într-o „formulă” persuasivă care trimite la necesitatea abordării unei astfel de teme, la urgența dezbaterii ei (și sub formă editorială), la efectele (rezultatele) neapărat trăite de copil, sigur constatate de el însuși, de psiholog, învățător, medic etc., la o constantă preocupare de ordin științific, de ce nu, la un semnal de alarmă vizând decidenți de toate rangurile și responsabilitățile. „Pariul” asumat de coordonator (conf. univ. dr. Patricia Runcan) este autenticitatea, concept cu deschideri hermeneutice (din perspectivă teoretică) și provocări-capcane în privința trăirilor – mărturisirilor fiecărui subiect – actor al scrierii de față. Structurată pe trei secțiuni (părți), cartea debutează consistent prin 8 Interviuri cu profesioniști și oameni autentici, continuă cu 7 Eseuri descriptive aparținând unor masteranzi netemători de vulnerabilitatea asociată autodezvăluirii și se încheie cu Cercetări academice aplicate (rod al colaborării dintre un profesor și două absolvente cu disertații masterale pe tema tratată în Copilărie și parentalitate cu impact). „Misterul” aparent al preferinței „omului cu microfon” pentru anumiți interlocutori autentici – în pofida multor altora este măcar parțial „descâlcit” de atribute precum: deschiderea la conversații pe teme provocatoare, delicate, „spinoase”; experiența profesională de învățători, directori de școli, medici neurologi, psihoterapeuți, oameni ai bisericii, universitari; notorietatea și reputația în spațiul public; regăsirea unor valori comune (sau asemănătoare) în copilăria fiecăruia, dar și aplicate, mai apoi, în viața de părinte, profesor, terapeut, formator de opinii etc. Întrebările prestabilite (dar formulate nuanțat, pliindu-se pe structura de personalitate a „adversarului”), răspunsurile (mai concise sau mai ample), „trădând” copilării „zugrăvite” în multe culori, formulări confesive, dar și cu rol de exemplu, îndemn, dovadă, ilustrare, oglindire, lecție de viață, poveste cu tâlc, motto-ul – unul mai bine căutat decât altul – și, deloc de ignorat, ecoul fiecărei „întâlniri admirabile” în ființa „cetitorului”, laolaltă creează un „spectacol” aparte, demn de analiză. Ce este autenticitatea? (Cum să fii autentic ?) adună, din „zona” profesioniștilor, „definiții” într-un veritabil mozaic: „cu autoritate”, „stăpân pe sine”, „care acționează cum îi este sieși propriu”, „autenticitatea este grea; e singura cale de împăcare cu sine”, „în absența onestității nu există autentic”, „autenticitatea nu se determină pe axa natură-cultură”, „să fii adevărat”, „să fii original”, „să fii creativ”, „să fii valoros”. Într-un registru asemănător răspund masteranzii, formulările lor variind între: „a fi TU”, „a nu te preface”, „a-ți susține convingerile”, „a-ți cere drepturile”, „a nu-ți judeca părinții”, „a te accepta așa cum ești”, „a fi sincer mereu”, „a fi om serios”, „a inspira încredere”, „a trăi în scopul vieții, care e veșnicia”, „a fi ca mama”, „a fi același în viața privată și în cea publică”, „autenticitatea este comuniunea dintre gânduri, vorbe și fapte.” La rându-i, definirea copilăriei comportă sensibile diferențe de nuanță, esența rămânând aceeași. Dacă maturii oscilează între „copilăria este familie”, „bucurie, joacă în aer liber”, mediul în care nu trăim „hie încătro” (adică în lipsa rânduielii și a ritualurilor), „paradisul pierdut al părinților”, „poligon de încercare a duplicității, ipocriziei, snobismului, imposturii, lipsei de responsabilitate”, „poveste”, masteranzii numesc propria copilărie: „speranță”, „Nu există nu pot!”, „oglinda prin fața căreia treci ca să mai descoperi ceva din (în) tine”, „binecuvântare”, „liniște”, „colțul de rai pe pămînt”, „fundația vieții”, „o călătorie fericită într-o țară încărcată de emoție și culoare”… Observăm cu îngândurare că în discursul adulților experți – predomină bucuria, joaca, libertatea, în vreme ce o notă accentuată în „confesiunile” tinerilor masteranzi este reprezentată de maturizarea precoce, cu precădere în familiile „cu multe probleme”: părinți conflictuali, alcoolici, abuzivi, cu educație precară, indiferenți la nevoia de afectivitate și reguli rezonabile pentru buna alcătuire a caracterului copilului. Este și motivul pentru care autorul acestei prefețe nu se arată bucuros de confirmarea, în prima cercetare din finalul volumului Copilărie și parentalitate cu impact, a „armoniei” dintre stilul parental aplicat în copilăria cuiva și stilul parental internalizat de subiectul educației și exersat, la rându-i, asupra propriului descendent, după cum, nici de concluzia (ca validare a ipotezei celei din urmă cercetări) conform căreia relația de atașament din copilărie influențează în mare măsură relația de atașament a unei mame cu copilul său. Combinația fericită dintre parentalitatea autentică și copilăria ziditoare nu se constată la tot pasul. Chiar dacă valorile cultivate de familiile-nucleu sau extinse sunt dispuse într-un registru pozitiv generos (bunătate, siguranță, solidaritate, încredere, libertate, dragoste și justiție, limite raționale, principialitate, corectitudine, disciplină, partajare frățească, cinste, respect, bun simț, iubirea față de semeni, răbdare, adevăr, modestie, mediu creștin ș.a. – cum se vede în mărturiile din cartea în discuție) prea adesea, întâlnim fractura între ce e bine de făcut și răul care se petrece în relațiile interpersonale din universul familiei. Uneori, neștiința este cea dintâi cauză. Alteori, o „arhitectură” defectă a personalității părintelui. Nu rareori, influențe nepotrivite din afara mediului educațional primar. Traumele din copilărie ale genitorilor, nerezolvate la vreme, reprezintă un teribil element perturbator pentru copilăria și viitoarea viață de adult a celor mici. Incidența tot mai accentuată a familiilor disfuncționale face tot mai rar valabil adevărul: „Din familii rele ies copii buni” (însă cu o dublă condiție, echilibrul și selectivitatea dovedite de copil). Desigur, teoreticienii și practicienii avertizează și „repară”, colaborează și se luptă cu varii ipostaze ale ignoranței și stricăciunilor avându-i ca autori pe părinți. Nu-i deajuns. Nu-i mereu la timp. Nu e pentru „toată lumea” copiilor afectați. Nu e cu asumarea și sprijinul tuturor părinților. Nu e, în toată clipa, cu toate resursele (profesioniști, instrumente, bani) trebuincioase celor mai bune terapii și rezultate. Din păcate, în lumea reală, atât de multe nu se pot! Conf. univ. dr. Ștefania Bejan, Universitatea “Al. I. Cuza” din Iași
Book chapters on the topic "AI Observer"
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Cowie, Roddy, Dearbhaile Bradley, and Mark Livingstone. "Using observer-controlled movement and expectations of regularity to recover tridimensional structure." In AI and Cognitive Science ’90, 178–92. London: Springer London, 1991. http://dx.doi.org/10.1007/978-1-4471-3542-5_12.
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Maruyama, Yoshihiro. "AI, Quantum Information, and External Semantic Realism: Searle’s Observer-Relativity and Chinese Room, Revisited." In Fundamental Issues of Artificial Intelligence, 115–27. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-26485-1_8.
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Mamun, Shamim Al, Mohammad Eusuf Daud, Mufti Mahmud, M. Shamim Kaiser, and Andre Luis Debiaso Rossi. "ALO: AI for Least Observed People." In Applied Intelligence and Informatics, 306–17. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-82269-9_24.
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Bragaglia, Stefano, Federico Chesani, Paola Mello, Marco Montali, and Davide Sottara. "Fuzzy Conformance Checking of Observed Behaviour with Expectations." In AI*IA 2011: Artificial Intelligence Around Man and Beyond, 80–91. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23954-0_10.
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Musiolik, Grzegorz. "Predictability of AI Decisions." In Analyzing Future Applications of AI, Sensors, and Robotics in Society, 17–28. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-3499-1.ch002.
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Artificial intelligence evolves rapidly and will have a great impact on the society in the future. One important question which still cannot be addressed with satisfaction is whether the decision of an intelligent agent can be predicted. As a consequence of this, the general question arises if such agents can be controllable and future robotic applications can be safe. This chapter shows that unpredictable systems are very common in mathematics and physics although the underlying mathematical structure can be very simple. It also shows that such unpredictability can also emerge for intelligent agents in reinforcement learning, especially for complex tasks with various input parameters. An observer would not be capable to distinguish this unpredictability from a free will of the agent. This raises ethical questions and safety issues which are briefly presented.
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Natale, Simone. "How to Dispel Magic." In Deceitful Media, 33–49. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780190080365.003.0003.
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This chapter shows that the problem of the observer—that is, the question of how humans respond to witnessing machines that exhibit intelligence—was the subject of substantial reflections in the field of AI in the 1950s and 1960s. As AI developed as a heterogeneous milieu, bringing together multiple disciplinary perspectives and approaches, many acknowledged that users might be deceived in interactions with “intelligent” machines. Most members of the AI community were confident that the deceptive character of AI would be dispelled, similarly to a magic trick, by providing users with a better understanding of computer systems. This approach, however, did not take it into account that deception is not a transitional but a structural component of people’s interactions with computers. The chapter argues that the dream of dispelling the magic aura of computers was superseded by the realization that users’ perceptions of AI systems can be manipulated in order to improve interactions between humans and machines.
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Charles, Darryl, Colin Fyfe, Daniel Livingstone, and Stephen McGlinchey. "Ant Colony Optimisation." In Biologically Inspired Artificial Intelligence for Computer Games, 180–201. IGI Global, 2008. http://dx.doi.org/10.4018/978-1-59140-646-4.ch011.
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Ants are truly amazing creatures. Most species of ant are virtually blind; some of which have no vision at all, yet despite this, they are able to explore and find their way around their environment, discovering and ‘remembering’ routes between their nest and food sources. Ants exhibit complex social behaviours, with different roles assigned to different ants, and they are able to perform organised operations, even, for example, relocating their entire nest. Even a casual observer of an ant colony can see the efficiency and organisation with which they perform tasks such as foraging food. They are able to find and follow shortest paths between locations, negotiating obstacles between them, and this problem is an active area of interest in computer science, particularly in computer game AI.
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Smith, Gary. "If You Torture the Data Long Enough." In The AI Delusion. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198824305.003.0008.
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I recently received an e-mail that offered me a way to automate my research: Dear Professor Smith, We would like to introduce you to [our] brand new research tool . . . , ready to automate your empirical research basing on official statistical time series databases. [Our software] has been designed to explore and discover new exciting economic correlations directly from your desktop. No extra software required, no need to crawl thousands of databases manually. You’ll be up and running in no time your first big data project. The e-mail went on to boast that their software will calculate “correlation coefficients with millions of statistical time series,” “identify unexpected interdependences,” and “find new insights.” The creative grammar was one thing. More disheartening was their assumption that I wanted to sift through literally trillions of correlations looking for unexpected patterns. An unexpected pattern has no logical basis—and I am skeptical of patterns that defy logic. Statistical tests assume that researchers have well-defined theories in mind and gather appropriate data to test their theories. This company assumed that I was eager and willing to pay a substantial amount of money to work the other way around. Look at every possible correlation—not caring whether they made sense or not—and report the correlations that turn out to be the most statistically persuasive. It is a sign of the times, but not an inspiring sign. Many important scientific theories started out as efforts to explain observed patterns. For example, during the 1800s,most biologists believed that parental characteristics were averaged together to determine the characteristics of their offspring. For example, a child’s height is an average of the father’s and mother’s heights, modified by environmental influences. However, Gregor Mendel discovered something quite different in his experiments with pea plants. Mendel was born in Austria in 1822 and grew up on his family’s farm. His parents expected him to take over the farm, but Mendel was an excellent student and became an Augustinian monk at a monastery known for its scientific library and research. Perhaps because of his farming roots, Mendel conducted meticulous studies of tens of thousands of pea plants grown in the monastery’s gardens over an eight-year period.
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Dasgupta, Tirthankar, Abir Naskar, Lipika Dey, and Mohammad Shakir. "A Joint Model for Detecting Causal Sentences and Cause-Effect Relations from Text." In Towards a Knowledge-Aware AI. IOS Press, 2022. http://dx.doi.org/10.3233/ssw220021.
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Text documents are rich repositories of causal knowledge. While journal publications typically contain analytical explanations of observations on the basis of scientific experiments conducted by researchers, analyst reports, News articles or even consumer generated text contain not only viewpoints of authors, but often contain causal explanations for those viewpoints. As interest in data science shifts towards understanding causality rather than mere correlations, there is also a surging interest in extracting causal constructs from text to provide augmented information for better decision making. Causality extraction from text is viewed as a relation extraction problem which requires identification of causal sentences as well as detection of cause and effect clauses separately. In this paper, we present a joint model for causal sentence classification and extraction of cause and effect clauses, using a sequence-labeling architecture cascaded with fine-tuned Bidirectional Encoder Representations from Transformers (BERT) language model. The cause and effect clauses are further processed to identify named entities and build a causal graph using domain constraints. We have done multiple experiments to assess the generalizability of the model. It is observed that when fine-tuned with sentences from a mixed corpus, and further trained to solve both the tasks correctly, the model learns the nuances of expressing causality independent of the domain. The proposed model has been evaluated against multiple state-of-the-art models proposed in literature and found to outperform them all.
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Pandey, Avinash Kumar, Varsha Singh, and Sachin Jain. "Study and comparative analysis of perturb and observe (P&O) and fuzzy logic based PV-MPPT algorithms." In Applications of AI and IOT in Renewable Energy, 193–209. Elsevier, 2022. http://dx.doi.org/10.1016/b978-0-323-91699-8.00011-5.
Full textConference papers on the topic "AI Observer"
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Barnett, Alina J., Vaibhav Sharma, Neel Gajjar, Jerry D. Fang, Fides Schwartz, Chaofan Chen, Joseph Y. Lo, and Cynthia Rudin. "Interpretable deep learning models for better clinician-AI communication in clinical mammography." In Image Perception, Observer Performance, and Technology Assessment, edited by Claudia R. Mello-Thoms and Sian Taylor-Phillips. SPIE, 2022. http://dx.doi.org/10.1117/12.2612372.
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Giger, Maryellen L. "Towards understanding perception in the latest era of AI in medical imaging (Conference Presentation)." In Image Perception, Observer Performance, and Technology Assessment, edited by Frank W. Samuelson and Sian Taylor-Phillips. SPIE, 2020. http://dx.doi.org/10.1117/12.2556704.
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Šerić, Ljiljana, Darko Stipaničev, and Damir Krstinić. "ML/AI in Intelligent Forest Fire Observer Network." In 3rd EAI International Conference on Management of Manufacturing Systems. EAI, 2018. http://dx.doi.org/10.4108/eai.6-11-2018.2279681.
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Tao, Xuetong, Ziba Gandomkar, Tong Li, Warren M. Reed, and Patrick C. Brennan. "Varying performance levels for diagnosing mammographic images depending on reader nationality have AI and educational implications." In Image Perception, Observer Performance, and Technology Assessment, edited by Claudia R. Mello-Thoms and Sian Taylor-Phillips. SPIE, 2022. http://dx.doi.org/10.1117/12.2611342.
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Whitney, Heather M., Karen Drukker, Hiroyuki Abe, and Maryellen L. Giger. "Case-based repeatability and operating point variability of AI: breast lesion classification based on deep transfer learning." In Image Perception, Observer Performance, and Technology Assessment, edited by Claudia R. Mello-Thoms and Sian Taylor-Phillips. SPIE, 2022. http://dx.doi.org/10.1117/12.2612405.
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Pershin, Ilya, Maksim Kholiavchenko, Bulat Maksudov, Tamerlan Mustafaev, and Bulat Ibragimov. "AI-based analysis of radiologist’s eye movements for fatigue estimation: a pilot study on chest X-rays." In Image Perception, Observer Performance, and Technology Assessment, edited by Claudia R. Mello-Thoms and Sian Taylor-Phillips. SPIE, 2022. http://dx.doi.org/10.1117/12.2612760.
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Byrd, Darrin, Dennis Bontempi, Hao Yang, Hugo Aerts, Binsheng Zhao, Andriy Fedorov, Lawrence Schwartz, Tavis Allison, Chaya Moscowitz, and Paul E. Kinahan. "Using virtual clinical trials to determine the accuracy of AI-based quantitative imaging biomarkers in oncology trials using standard-of-care CT." In Image Perception, Observer Performance, and Technology Assessment, edited by Claudia R. Mello-Thoms and Sian Taylor-Phillips. SPIE, 2022. http://dx.doi.org/10.1117/12.2610980.
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Kammardi Shashiprakash, Avinash, Brendon Lutnick, Brandon Ginley, Darshana Govind, Nicholas Lucarelli, Kuang-Yu Jen, Avi Z. Rosenberg, et al. "A distributed system improves inter-observer and AI concordance in annotating interstitial fibrosis and tubular atrophy." In Digital and Computational Pathology, edited by John E. Tomaszewski and Aaron D. Ward. SPIE, 2021. http://dx.doi.org/10.1117/12.2581789.
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Zhang, Qiang, Konrad Werys, Elena Lukaschuk, Iulia Popescu, Evan Hann, Stefan Neubauer, Vanessa M Ferreira, and Stefan K Piechnik. "3 Train the Ai like a human observer: deep learning with visualisation and guidance on attention in cardiac T1 mapping." In British Society of Cardiovascular Magnetic Resonance 2019 annual meeting, March 26 – 27th, Oxford UK. BMJ Publishing Group Ltd and British Cardiovascular Society, 2019. http://dx.doi.org/10.1136/heartjnl-2019-bscmr.3.
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Zhang, Qiang, Konrad Werys, Elena Lukaschuk, Iulia Popescu, Evan Hann, Stefan Neubauer, Vanessa M Ferreira, and Stefan K Piechnik. "9 Train the Ai like a human observer: deep learning with visualisation and guidance on attention in cardiac T1 mapping." In British Society of Cardiovascular Magnetic Resonance 2019 annual meeting, March 26 – 27th, Oxford UK. BMJ Publishing Group Ltd and British Cardiovascular Society, 2019. http://dx.doi.org/10.1136/heartjnl-2019-bscmr.9.
Full textReports on the topic "AI Observer"
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Roschelle, Jeremy, James Lester, and Judi Fusco. AI and the Future of Learning: Expert Panel Report. Digital Promise, November 2020. http://dx.doi.org/10.51388/20.500.12265/106.
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This report is based on the discussion that emerged from a convening of a panel of 22 experts in artificial intelligence (AI) and in learning. It introduces three layers that can frame the meaning of AI for educators. First, AI can be seen as “computational intelligence” and capability can be brought to bear on educational challenges as an additional resource to an educator’s abilities and strengths. Second, AI brings specific, exciting new capabilities to computing, including sensing, recognizing patterns, representing knowledge, making and acting on plans, and supporting naturalistic interactions with people. Third, AI can be used as a toolkit to enable us to imagine, study, and discuss futures for learning that don’t exist today. Experts voiced the opinion that the most impactful uses of AI in education have not yet been invented. The report enumerates important strengths and weaknesses of AI, as well as the respective opportunities and barriers to applying AI to learning. Through discussions among experts about these layers, we observed new design concepts for using AI in learning. The panel also made seven recommendations for future research priorities.
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Borrett, Veronica, Melissa Hanham, Gunnar Jeremias, Jonathan Forman, James Revill, John Borrie, Crister Åstot, et al. Science and Technology for WMD Compliance Monitoring and Investigations. The United Nations Institute for Disarmament Research, December 2020. http://dx.doi.org/10.37559/wmd/20/wmdce11.
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The integration of novel technologies for monitoring and investigating compliance can enhance the effectiveness of regimes related to weapons of mass destruction (WMD). This report looks at the potential role of four novel approaches based on recent technological advances – remote sensing tools; open-source satellite data; open-source trade data; and artificial intelligence (AI) – in monitoring and investigating compliance with WMD treaties. The report consists of short essays from leading experts that introduce particular technologies, discuss their applications in WMD regimes, and consider some of the wider economic and political requirements for their adoption. The growing number of space-based sensors is raising confidence in what open-source satellite systems can observe and record. These systems are being combined with local knowledge and technical expertise through social media platforms, resulting in dramatically improved coverage of the Earth’s surface. These open-source tools can complement and augment existing treaty verification and monitoring capabilities in the nuclear regime. Remote sensing tools, such as uncrewed vehicles, can assist investigators by enabling the remote collection of data and chemical samples. In turn, this data can provide valuable indicators, which, in combination with other data, can inform assessments of compliance with the chemical weapons regime. In addition, remote sensing tools can provide inspectors with real time two- or three-dimensional images of a site prior to entry or at the point of inspection. This can facilitate on-site investigations. In the past, trade data has proven valuable in informing assessments of non-compliance with the biological weapons regime. Today, it is possible to analyse trade data through online, public databases. In combination with other methods, open-source trade data could be used to detect anomalies in the biological weapons regime. AI and the digitization of data create new ways to enhance confidence in compliance with WMD regimes. In the context of the chemical weapons regime, the digitization of the chemical industry as part of a wider shift to Industry 4.0 presents possibilities for streamlining declarations under the Chemical Weapons Convention (CWC) and for facilitating CWC regulatory requirements.
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Daudelin, Francois, Lina Taing, Lucy Chen, Claudia Abreu Lopes, Adeniyi Francis Fagbamigbe, and Hamid Mehmood. Mapping WASH-related disease risk: A review of risk concepts and methods. United Nations University Institute for Water, Environment and Health, December 2021. http://dx.doi.org/10.53328/uxuo4751.
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The report provides a review of how risk is conceived of, modelled, and mapped in studies of infectious water, sanitation, and hygiene (WASH) related diseases. It focuses on spatial epidemiology of cholera, malaria and dengue to offer recommendations for the field of WASH-related disease risk mapping. The report notes a lack of consensus on the definition of disease risk in the literature, which limits the interpretability of the resulting analyses and could affect the quality of the design and direction of public health interventions. In addition, existing risk frameworks that consider disease incidence separately from community vulnerability have conceptual overlap in their components and conflate the probability and severity of disease risk into a single component. The report identifies four methods used to develop risk maps, i) observational, ii) index-based, iii) associative modelling and iv) mechanistic modelling. Observational methods are limited by a lack of historical data sets and their assumption that historical outcomes are representative of current and future risks. The more general index-based methods offer a highly flexible approach based on observed and modelled risks and can be used for partially qualitative or difficult-to-measure indicators, such as socioeconomic vulnerability. For multidimensional risk measures, indices representing different dimensions can be aggregated to form a composite index or be considered jointly without aggregation. The latter approach can distinguish between different types of disease risk such as outbreaks of high frequency/low intensity and low frequency/high intensity. Associative models, including machine learning and artificial intelligence (AI), are commonly used to measure current risk, future risk (short-term for early warning systems) or risk in areas with low data availability, but concerns about bias, privacy, trust, and accountability in algorithms can limit their application. In addition, they typically do not account for gender and demographic variables that allow risk analyses for different vulnerable groups. As an alternative, mechanistic models can be used for similar purposes as well as to create spatial measures of disease transmission efficiency or to model risk outcomes from hypothetical scenarios. Mechanistic models, however, are limited by their inability to capture locally specific transmission dynamics. The report recommends that future WASH-related disease risk mapping research: - Conceptualise risk as a function of the probability and severity of a disease risk event. Probability and severity can be disaggregated into sub-components. For outbreak-prone diseases, probability can be represented by a likelihood component while severity can be disaggregated into transmission and sensitivity sub-components, where sensitivity represents factors affecting health and socioeconomic outcomes of infection. -Employ jointly considered unaggregated indices to map multidimensional risk. Individual indices representing multiple dimensions of risk should be developed using a range of methods to take advantage of their relative strengths. -Develop and apply collaborative approaches with public health officials, development organizations and relevant stakeholders to identify appropriate interventions and priority levels for different types of risk, while ensuring the needs and values of users are met in an ethical and socially responsible manner. -Enhance identification of vulnerable populations by further disaggregating risk estimates and accounting for demographic and behavioural variables and using novel data sources such as big data and citizen science. This review is the first to focus solely on WASH-related disease risk mapping and modelling. The recommendations can be used as a guide for developing spatial epidemiology models in tandem with public health officials and to help detect and develop tailored responses to WASH-related disease outbreaks that meet the needs of vulnerable populations. The report’s main target audience is modellers, public health authorities and partners responsible for co-designing and implementing multi-sectoral health interventions, with a particular emphasis on facilitating the integration of health and WASH services delivery contributing to Sustainable Development Goals (SDG) 3 (good health and well-being) and 6 (clean water and sanitation).