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Today: 30 October 2020, Friday.

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News for Artificial intelligence





#1

 

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Description A trans-institutional team of Vanderbilt engineering, data science and clinical researchers has developed a novel approach for monitoring bone stress in recreational and professional athletes, with the goal of anticipating and preventing injury. Using machine learning and biomechanical modelling

#Artificial Intelligence
Field # Artificial Intelligence
Updated 29 October 2020

#2

 

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Description Using deep reinforcement learning in robots may be a more efficient alternative to preprogrammed movements and enable robots to perform more challenging tasks. Nevertheless, learning on physical systems must not damage the robot. A recent paper focuses on the task of juggling two balls. It cannot

#Artificial Intelligence
Field # Artificial Intelligence
Updated 29 October 2020

#3

 

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Description TBD

#Artificial intelligence
Field # Artificial intelligence
Updated 29 October 2020

#4

 

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Description Every day we are likely to interact with some form of artificial intelligence (AI). It works behind the scenes in everything from social media and traffic navigation apps to product recommendations and virtual assistants.

#Artificial Intelligence
Field # Artificial Intelligence
Updated 29 October 2020

#5

 

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Description The University of Surrey has unveiled a device with unique functionality that could signal the dawn of a new design philosophy for electronics, including next-generation wearables and eco-disposable sensors.

#Artificial intelligence
Field # Artificial intelligence
Updated 28 October 2020

#6

 

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Description The European Space Agency (ESA), working together with Intel and Ubotica, has launched the world’s first AI-powered PhiSat-1 satellite into Earth’s orbit. The satellite carries Intel’s integrated Movidius Myriad 2 Vision Processing Unit (VPU) which will allow it to process large amounts of data a

#Artificial intelligence
Field # Artificial intelligence
Updated 28 October 2020

#7

 

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Description Apple acquired Vilynx, a startup specialising in advanced artificial intelligence and computer vision technology, that may help the iPhone maker improve its own AI across a number of apps and services.

#Artificial intelligence
Field # Artificial intelligence
Updated 28 October 2020

#8

 

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Description Machine learning and AI in general one day will help us to save time and resources, because computer-controlled decision making processes are going to be that much more efficient. However, machine learning right now itself is very expensive and incredibly time consuming, because in order to train th

#Artificial Intelligence
Field # Artificial Intelligence
Updated 27 October 2020

#9

 

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Description Microsoft has made its Lobe machine learning tool available in public preview for Windows and Mac. The free app lets people apply deep learning and AI models quickly into tools, with image classification support.

#Artificial Intelligence
Field # Artificial Intelligence
Updated 27 October 2020

#10

 

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Description Tzanck smear test is a low-cost, rapid and reliable tool which can be used for the diagnosis of many erosive-vesiculobullous, tumoral and granulomatous diseases. Currently its use is limited mainly due to lack of experience in interpretation of the smears. We developed a deep learning model, TzanckNet, that can identify cells in Tzanck smear test findings. TzanckNet was trained on a retrospective development dataset of 2260 Tzanck smear images collected between December 2006 and December 2019. The finalized model was evaluated using a prospective validation dataset of 359 Tzanck smear images collected from 15 patients during January 2020. It is designed to recognize six cell types (acantholytic cells, eosinophils, hypha, multinucleated giant cells, normal keratinocytes and tadpole cells). For 359 images and 6 cell types, TzanckNet made 2154 predictions. The accuracy was 94.3% (95% CI 93.4–95.3), the sensitivity was 83.7% (95% CI 80.3–87.0) and the specificity was 97.3% (95% CI 96.5–98.1). The area under the receiver operating characteristic curve was 0.974. Our results show that TzanckNet has the potential to lower the experience barrier needed to use this test, broadening its user base, and hence improving patient well-being.

#Artificial intelligence
Field # Artificial intelligence
Updated 27 October 2020

#11

 

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Description Snow avalanche is among the most harmful natural hazards with major socioeconomic and environmental destruction in the cold and mountainous regions. The devastating propagation and accumulation of the snow avalanche debris and mass wasting of surface rocks and vegetation particles threaten human life, transportation networks, built environments, ecosystems, and water resources. Susceptibility assessment of snow avalanche hazardous areas is of utmost importance for mitigation and development of land-use policies. This research evaluates the performance of the well-known machine learning methods, i.e., generalized additive model (GAM), multivariate adaptive regression spline (MARS), boosted regression trees (BRT), and support vector machine (SVM), in modeling the mass wasting hazard induced by snow avalanches. The key features are identified by the recursive feature elimination (RFE) method and used for the model calibration. The results indicated a good performance of the modeling process (Accuracy > 0.88, Kappa > 0.76, Precision > 0.84, Recall > 0.86, and AUC > 0.89), which the SVM model highlighted superior performance than others. Sensitivity analysis demonstrated that the topographic position index (TPI) and distance to stream (DTS) were the most important variables which had more contribution in producing the susceptibility maps.

#Artificial Intelligence
Field # Artificial Intelligence
Updated 27 October 2020

#12

 

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Description Diagnosis of endoleak following endovascular aortic repair (EVAR) relies on manual review of multi-slice CT angiography (CTA) by physicians which is a tedious and time-consuming process that is susceptible to error. We evaluate the use of a deep neural network for the detection of endoleak on CTA for post-EVAR patients using a novel data efficient training approach. 50 CTAs and 20 CTAs with and without endoleak respectively were identified based on gold standard interpretation by a cardiovascular subspecialty radiologist. The Endoleak Augmentor, a custom designed augmentation method, provided robust training for the machine learning (ML) model. Predicted segmentation maps underwent post-processing to determine the presence of endoleak. The model was tested against 3 blinded general radiologists and 1 blinded subspecialist using a held-out subset (10 positive endoleak CTAs, 10 control CTAs). Model accuracy, precision and recall for endoleak diagnosis were 95%, 90% and 100% relative to reference subspecialist interpretation (AUC = 0.99). Accuracy, precision and recall was 70/70/70% for generalist1, 50/50/90% for generalist2, and 90/83/100% for generalist3. The blinded subspecialist had concordant interpretations for all test cases compared with the reference. In conclusion, our ML-based approach has similar performance for endoleak diagnosis relative to subspecialists and superior performance compared with generalists.

#Artificial Intelligence
Field # Artificial Intelligence
Updated 27 October 2020

#13

 

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Description Humans have been teaming up with machines throughout history to achieve goals, be it by using simple machines to move materials or complex machines to travel in space. But advances in artificial intelligence today bring possibilities ...

#Artificial intelligence
Field # Artificial intelligence
Updated 27 October 2020

#14

 

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Description More than one million American adults use wheelchairs fitted with robot arms to help them perform everyday tasks such as dressing, brushing their teeth, and eating. But the robotic devices now on the market can be hard to control. Removing a food container from a refrigerator or opening a cabinet do

#Artificial intelligence
Field # Artificial intelligence
Updated 26 October 2020

#15

 

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Description More than one million American adults use wheelchairs fitted with robot arms to help them perform everyday tasks such as dressing, brushing their teeth, and eating. But the robotic devices now on the market can be hard to ...

#Artificial intelligence
Field # Artificial intelligence
Updated 26 October 2020

#16

 

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Description One of the most interesting parts of Dell World is the session on the future. This year, they spoke on a new branch of engineering that is solely AI-focused, the blended technology revolution surrounding food production, how AIs were intentionally corrupted, and how music, math, and the Internet create new entertainment types.

#Artificial Intelligence
Field # Artificial Intelligence
Updated 26 October 2020

#17

 

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Description TBD

#Artificial Intelligence
Field # Artificial Intelligence
Updated 24 October 2020

#18

 

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Description Five emerging artificial intelligence (AI) and machine learning trends include the use of AI and ML in hyperautomation, cybersecurity and Internet of Things (IoT).

#Artificial intelligence
Field # Artificial intelligence
Updated 23 October 2020

#19

 

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Description The Saudi Authority for Data and Artificial Intelligence on Thursday signed memorandums of understanding with IBM, Alibaba and Huawei in areas of artificial intelligence (AI) at a summit in the kingdom, state news agency SPA said.

#Artificial intelligence
Field # Artificial intelligence
Updated 23 October 2020

#20

 

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Description Residual Oil Zones (ROZs) become potential formations for Carbon Capture, Utilization, and Storage (CCUS). Although the growing attention in ROZs, there is a lack of studies to propose the fast tool for evaluating the performance of a CO2 injection process. In this paper, we introduce the application of artificial neural network (ANN) for predicting the oil recovery and CO2 storage capacity in ROZs. The uncertainties parameters, including the geological factors and well operations, were used for generating the training database. Then, a total of 351 numerical samples were simulated and created the Cumulative oil production, Cumulative CO2 storage, and Cumulative CO2 retained. The results indicated that the developed ANN model had an excellent prediction performance with a high correlation coefficient (R2) was over 0.98 on comparing with objective values, and the total root mean square error of less than 2%. Also, the accuracy and stability of ANN models were validated for five real ROZs in the Permian Basin. The predictive results were an excellent agreement between ANN predictions and field report data. These results indicated that the ANN model could predict the CO2 storage and oil recovery with high accuracy, and it can be applied as a robust tool to determine the feasibility in the early stage of CCUS in ROZs. Finally, the prospective application of the developed ANN model was assessed by optimization CO2-EOR and storage projects. The developed ANN models reduced the computational time for the optimization process in ROZs.

#Artificial intelligence
Field # Artificial intelligence
Updated 23 October 2020

#21

 

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Description A pair of statisticians at the University of Waterloo has proposed a math process idea that might allow for teaching AI systems without the need for a large dataset. Ilia Sucholutsky and Matthias Schonlau have written a paper ...

#Artificial intelligence
Field # Artificial intelligence
Updated 23 October 2020