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Today: 26 January 2021, Tuesday.

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News for Artificial intelligence





#1

 

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Description This AI robot dog not only climbs stairs but also interacts socially with its human owners.

#Artificial intelligence
Field # Artificial intelligence
Updated 25 January 2021

#2

 

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Description High-performance, high-quality Ni-Co-based superalloy powders are promising aircraft engine raw materials. Using machine learning, a NIMS team has succeeded in speedily determining the optimum parameters for manufacturing ...

#Artificial Intelligence
Field # Artificial Intelligence
Updated 25 January 2021

#3

 

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Description The United States is leading rivals in development and use of artificial intelligence while China is rising quickly and the European Union is lagging, a research report showed Monday.

#Artificial Intelligence
Field # Artificial Intelligence
Updated 25 January 2021

#4

 

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Description Although electric vehicles that reduce greenhouse gas emissions attract many drivers, the lack of confidence in charging services deters others. Building a reliable network of charging stations is difficult in part because ...

#Artificial intelligence
Field # Artificial intelligence
Updated 22 January 2021

#5

 

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Description Facebook has announced new improvements in its artificial intelligence (AI) technology to generate descriptions of photos posted on its platforms including Instagram for the visually-impaired users.

#Artificial intelligence
Field # Artificial intelligence
Updated 22 January 2021

#6

 

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Description Google has suspended an artificial intelligence (AI) ethics researcher weeks after dismissing another member of the team, Timnit Gebru, a recently formed union said.

#Artificial intelligence
Field # Artificial intelligence
Updated 22 January 2021

#7

 

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Description Some scientists are now concerned that a day will come with super-intelligent AI programs will take on an automous life of their own. Already there are AI programs that perform learned tasks without the programmers understanding how it arrived at that state.

#Artificial intelligence
Field # Artificial intelligence
Updated 22 January 2021

#8

 

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Description An artificial intelligence that can grade the skill of a pianist with near-human accuracy could be used in online music tutoring

#Artificial intelligence
Field # Artificial intelligence
Updated 22 January 2021

#9

 

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Description A sensor network powered by an artificial intelligence (AI) algorithm developed by scientists from Nanyang Technological University, Singapore (NTU Singapore) can accurately detect, in real-time, gas leaks and unwanted water ...

#Artificial intelligence
Field # Artificial intelligence
Updated 21 January 2021

#10

 

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Description Google has suspended an artificial intelligence ethics researcher weeks after dismissing another member of the team, a recently formed union said.

#Artificial intelligence
Field # Artificial intelligence
Updated 21 January 2021

#11

 

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Description A new method to reason about uncertainty might help artificial intelligence to find safer options faster, for example in self-driving cars, according to a new study to be published shortly in AAAI involving researchers at ...

#Artificial intelligence
Field # Artificial intelligence
Updated 21 January 2021

#12

 

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Description Warnings about misinformation are now regularly posted on Twitter, Facebook, and other social media platforms, but not all of these cautions are created equal. New research from Rensselaer Polytechnic Institute shows that ...

#Artificial intelligence
Field # Artificial intelligence
Updated 21 January 2021

#13

 

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Description Visually impaired and blind people due to diabetic retinopathy were 2.6 million in 2015 and estimated to be 3.2 million in 2020 globally. Though the incidence of diabetic retinopathy is expected to decrease for high-income countries, detection and treatment of it in the early stages are crucial for low-income and middle-income countries. Due to the recent advancement of deep learning technologies, researchers showed that automated screening and grading of diabetic retinopathy are efficient in saving time and workforce. However, most automatic systems utilize conventional fundus photography, despite ultra-wide-field fundus photography provides up to 82% of the retinal surface. In this study, we present a diabetic retinopathy detection system based on ultra-wide-field fundus photography and deep learning. In experiments, we show that the use of early treatment diabetic retinopathy study 7-standard field image extracted from ultra-wide-field fundus photography outperforms that of the optic disc and macula centered image in a statistical sense.

#Artificial Intelligence
Field # Artificial Intelligence
Updated 21 January 2021

#14

 

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Description To understand impact of input and output parameters during optimization and degree of complexity, in the current study we numerically designed a bubble column reactor with a single sparger in the middle of the reactor. After that, some input and output parameters were selected in the post-processing of the numerical method, and then the machine learning observation started to investigate the level of complexity and impact of each input on output parameters. The adaptive neuro-fuzzy inference system (ANFIS) method was exploited as a machine learning approach to analyze the gas–liquid flow in the reactor. The ANFIS method was used as a machine learning approach to simulate the flow of a 3D (three-dimensional) bubble column reactor. This model was also used to analyze the influence of input and output parameters together. More specifically, by analyzing the degree of membership functions as a function of each input, the level of complexity of gas fraction was investigated as a function of computing nodes (X, Y, and Z directions). The results showed that a higher number of membership functions results in a better understanding of the process and higher model accuracy and prediction capability. X and Y computing nodes have a similar impact on the gas fraction, while Z computing points (height of reactor) have a uniform distribution of membership function across the column. Four membership functions (MFs) in each input parameter are insufficient to predict the gas fraction in the 3D bubble column reactor. However, by adding two membership functions, all features of gas fraction in the 3D reactor can be captured by the machine learning algorithm. Indeed, the degree of MFs was considered as a function of each input parameter and the effective parameter was found based on the impact of MFs on the output.

#Artificial Intelligence
Field # Artificial Intelligence
Updated 21 January 2021

#15

 

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Description This paper proposes a convolutional neural network (CNN)-based deep learning model for predicting the difficulty of extracting a mandibular third molar using a panoramic radiographic image. The applied dataset includes a total of 1053 mandibular third molars from 600 preoperative panoramic radiographic images. The extraction difficulty was evaluated based on the consensus of three human observers using the Pederson difficulty score (PDS). The classification model used a ResNet-34 pretrained on the ImageNet dataset. The correlation between the PDS values determined by the proposed model and those measured by the experts was calculated. The prediction accuracies for C1 (depth), C2 (ramal relationship), and C3 (angulation) were 78.91%, 82.03%, and 90.23%, respectively. The results confirm that the proposed CNN-based deep learning model could be used to predict the difficulty of extracting a mandibular third molar using a panoramic radiographic image.

#Artificial Intelligence
Field # Artificial Intelligence
Updated 21 January 2021

#16

 

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Description The purpose of this study is to develop a method for recognizing dental prostheses and restorations of teeth using a deep learning. A dataset of 1904 oral photographic images of dental arches (maxilla: 1084 images; mandible: 820 images) was used in the study. A deep-learning method to recognize the 11 types of dental prostheses and restorations was developed using TensorFlow and Keras deep learning libraries. After completion of the learning procedure, the average precision of each prosthesis, mean average precision, and mean intersection over union were used to evaluate learning performance. The average precision of each prosthesis varies from 0.59 to 0.93. The mean average precision and mean intersection over union of this system were 0.80 and 0.76, respectively. More than 80% of metallic dental prostheses were detected correctly, but only 60% of tooth-colored prostheses were detected. The results of this study suggest that dental prostheses and restorations that are metallic in color can be recognized and predicted with high accuracy using deep learning; however, those with tooth color are recognized with moderate accuracy.

#Artificial Intelligence
Field # Artificial Intelligence
Updated 21 January 2021

#17

 

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Description Artificial intelligence models match or exceed dermatologists in melanoma image classification. Less is known about their robustness against real-world variations, and clinicians may incorrectly assume that a model with an acceptable area under the receiver operating characteristic curve or related performance metric is ready for clinical use. Here, we systematically assessed the performance of dermatologist-level convolutional neural networks (CNNs) on real-world non-curated images by applying computational “stress tests”. Our goal was to create a proxy environment in which to comprehensively test the generalizability of off-the-shelf CNNs developed without training or evaluation protocols specific to individual clinics. We found inconsistent predictions on images captured repeatedly in the same setting or subjected to simple transformations (e.g., rotation). Such transformations resulted in false positive or negative predictions for 6.5–22% of skin lesions across test datasets. Our findings indicate that models meeting conventionally reported metrics need further validation with computational stress tests to assess clinic readiness.

#Artificial Intelligence
Field # Artificial Intelligence
Updated 21 January 2021

#18

 

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Description Recurrence risk stratification of patients undergoing primary surgical resection for hepatocellular carcinoma (HCC) is an area of active investigation, and several staging systems have been proposed to optimize treatment strategies. However, as many as 70% of patients still experience tumor recurrence at 5 years post-surgery. We developed and validated a deep learning-based system (HCC-SurvNet) that provides risk scores for disease recurrence after primary resection, directly from hematoxylin and eosin-stained digital whole-slide images of formalin-fixed, paraffin embedded liver resections. Our model achieved concordance indices of 0.724 and 0.683 on the internal and external test cohorts, respectively, exceeding the performance of the standard Tumor-Node-Metastasis classification system. The model’s risk score stratified patients into low- and high-risk subgroups with statistically significant differences in their survival distributions, and was an independent risk factor for post-surgical recurrence in both test cohorts. Our results suggest that deep learning-based models can provide recurrence risk scores which may augment current patient stratification methods and help refine the clinical management of patients undergoing primary surgical resection for HCC.

#Artificial Intelligence
Field # Artificial Intelligence
Updated 21 January 2021

#19

 

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Description The Lucknow police is deploying facial recognition technology backed by artificial intelligence-enabled security cameras that will read expressions of women in distress and alert their nearest police station.

#Artificial intelligence
Field # Artificial intelligence
Updated 21 January 2021

#20

 

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#Artificial Intelligence
Field # Artificial Intelligence
Updated 21 January 2021

#21

 

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Description Before he joined the University of Texas at Arlington as an Assistant Professor in the Department of Computer Science and Engineering and founded the Robotic Vision Laboratory there, William Beksi interned at iRobot, the ...

#Artificial intelligence
Field # Artificial intelligence
Updated 20 January 2021

#22

 

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Description Google is investigating a member of its ethical AI team and has locked the corporate account linked to that person after finding that thousands of files were retrieved from its server and shared with external accounts, the company said.

#Artificial intelligence
Field # Artificial intelligence
Updated 20 January 2021

#23

 

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Description Canon may soon announce a new PowerShot digital camera with AI. The camera was first exhibited at The Photography Show in the UK in 2018.

#Artificial intelligence
Field # Artificial intelligence
Updated 20 January 2021

#24

 

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Description This study proposes a new method of visualizing the ambient dose rate distribution using artificial neural networks (ANNs) from airborne radiation monitoring results. The method was applied to the results of the airborne radiation monitoring which was conducted around the Fukushima Daiichi Nuclear Power Plant by an unmanned aerial vehicle. Much of the survey data obtained in the past were used as the training data for building a network. The number of training cases was related to the error between the ground and converted values by the ANN. The quantitative evaluation index (the root-mean-square error) between the ANN-converted value and the ground-based survey result converged at 200 training cases. This number of training case was considered a rough criterion of the required number of training cases. The reliability of the ANN method was evaluated by comparison with the ground-based survey data. The dose rate map created by the ANNs method reproduced ground-based survey results better than traditional methods.

#Artificial intelligence
Field # Artificial intelligence
Updated 20 January 2021

#25

 

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Description A team of researchers at Colorado State University has designed an AI system to train a dog to obey certain oral commands without human assistance. In their paper uploaded to the arXiv preprint server, the researchers describe ...

#Artificial intelligence
Field # Artificial intelligence
Updated 20 January 2021

#26

 

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Description VMware selects AI specialist and venture capitalist Ken Denman to its board as CEO Pat Gelsinger departs for Intel.

#Artificial intelligence
Field # Artificial intelligence
Updated 20 January 2021

#27

 

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Description The B2B model focuses on selling services and products to other companies. More than one person is often involved in…...

#Artificial Intelligence
Field # Artificial Intelligence
Updated 19 January 2021

#28

 

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Description Machines—like people—learn best when tasks are just hard enough

#Artificial Intelligence
Field # Artificial Intelligence
Updated 19 January 2021

#29

 

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Description Air quality has been the main concern worldwide and Nitrous oxide (NO2) is one of the pollutants that have a significant effect on human health and environment. This study was conducted to compare the regression analysis and neural network model for predicting NO2 pollutants in the air of Tehran metropolis. Data has been collected during a year in the urban area of Tehran and was analyzed using multi-linear regression (MLR) and multilayer perceptron (MLP) neural networks. Meteorological parameters, urban traffic data, urban green space information, and time parameters are applied as input to forecast the daily concentration of NO2 in the air. The results demonstrate that artificial neural network modeling (R2 = 0.89, RMSE = 0.32) results in more accurate predictions than MLR analysis (R2 = 0.81, RMSE = 13.151). According to the result of sensitivity analysis of the model, the value of park area, the average of green space area and one-day time delay are the crucial parameters influencing NO2 concentration of air. Artificial neural network models could be a powerful, effective and suitable tool for analysis and modeling complex and non-linear relation of environmental variables such as ability in forecasting air pollution. Green spaces establishment has a significant role in NO2 reduction even more than traffic volume.

#Artificial Intelligence
Field # Artificial Intelligence
Updated 19 January 2021

#30

 

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Description Facebook is publishing a research conducted by its artificial intelligence (AI) unit in an effort to help healthcare providers predict in advance if a COVID-19 patient may need more intensive care solutions and adjust resources accordingly.

#Artificial intelligence
Field # Artificial intelligence
Updated 19 January 2021