loading Please wait. Data is being processed...

News of Inventions: 303779
Files of Inventions: 220
Groups of Inventors: 50

Friend Requests:
Private Messages:

Today's News: 14
Yesterday's News: 292

Today: 20 October 2020, Tuesday.

All latest news of Inventions in one place

Listed news of Inventions: 32 from total 303779

Filters


Welcome, Guest

News for Artificial intelligence





#1

 

View it
Name
Description As more and more people work from home and at times have no control over jarring sounds in the background, Microsoft is set to release a new Artificial Intelligence (AI)-based noise suppression tool in its video conferencing app Teams.

#Artificial Intelligence
Field # Artificial Intelligence
Updated 20 October 2020

#2

 

View it
Name
Description Facebook on Monday unveiled software based on machine learning which the company said was the first to be able to translate from any of 100 languages without relying on English.

#Artificial Intelligence
Field # Artificial Intelligence
Updated 19 October 2020

#3

 

View it
Name
Description Biofluid-based metabolomics has the potential to provide highly accurate, minimally invasive diagnostics. Metabolomics studies using mass spectrometry typically reduce the high-dimensional data to only a small number of statistically significant features, that are often chemically identified—where each feature corresponds to a mass-to-charge ratio, retention time, and intensity. This practice may remove a substantial amount of predictive signal. To test the utility of the complete feature set, we train machine learning models for health state-prediction in 35 human metabolomics studies, representing 148 individual data sets. Models trained with all features outperform those using only significant features and frequently provide high predictive performance across nine health state categories, despite disparate experimental and disease contexts. Using only non-significant features it is still often possible to train models and achieve high predictive performance, suggesting useful predictive signal. This work highlights the potential for health state diagnostics using all metabolomics features with data-driven analysis.

#Artificial Intelligence
Field # Artificial Intelligence
Updated 19 October 2020

#4

 

View it
Name
Description The White House Office of Management and Budget (OMB) said Friday that federal agencies will use artificial intelligence to eliminate outdated, obsolete, and inconsistent requirements across tens of pages of government regulations.

#Artificial intelligence
Field # Artificial intelligence
Updated 19 October 2020

#5

 

View it
Name
Description Machine learning algorithms have the potential to provide huge benefits in health care, potentially providing more reliable diagnoses than human doctors in some cases. Yet, many of us are reluctant to entrust our health to an algorithm – especially when even its designers can’t explain exactly how i

#Artificial intelligence
Field # Artificial intelligence
Updated 19 October 2020

#6

 

View it
Name
Description To gain a better understanding of human behavior and cognition, as well as their neural underpinnings, researchers often study other mammals with similar characteristics. One of the most common species examined in these studies ...

#Artificial Intelligence
Field # Artificial Intelligence
Updated 19 October 2020

#7

 

View it
Name
Description Researchers from the Image Processing Laboratory (IPL) of the University of Valencia and the Department of Information and Communication Technologies (DTIC) of the Pompeu Fabra University (UPF) have shown that convolutional ...

#Artificial Intelligence
Field # Artificial Intelligence
Updated 19 October 2020

#8

 

View it
Name
Description Facebook has developed an AI that can translate directly between any pair of 100 languages without having to go through an English translation first, as many existing systems do

#Artificial intelligence
Field # Artificial intelligence
Updated 19 October 2020

#9

 

View it
Name
Description Convened by the MIT Schwarzman College of Computing, the AI Policy Forum is a global collaboration to move AI principles to practice.

#Artificial Intelligence
Field # Artificial Intelligence
Updated 19 October 2020

#10

 

View it
Name
Description Thanks to its ability to process large amounts of data, artificial intelligence (AI) has become an increasingly popular tool for solving complex problems over the past several decades. As understanding of advanced computer algorithms grows, the range of AI-based techniques for the diagnosis, surg

#Artificial intelligence
Field # Artificial intelligence
Updated 18 October 2020

#11

 

View it
Name
Description The White House Office of Management and Budget (OMB) said Friday that federal agencies will use artificial intelligence to eliminate outdated, obsolete, and inconsistent requirements across tens of pages of government regulations.

#Artificial intelligence
Field # Artificial intelligence
Updated 17 October 2020

#12

 

View it
Name
Description Construction sites are vast jigsaws of people and parts that must be pieced together just so at just the right times. As projects get larger, mistakes and delays get more expensive. The consultancy Mckinsey estimates that on-site mismanagement costs the construction industry $1.6 trillion a year. But typically you might only have five managers overseeing…

#Artificial intelligence
Field # Artificial intelligence
Updated 17 October 2020

#13

 

View it
Name
Description Since its inception in 2017, npj Digital Medicine has attracted a disproportionate number of manuscripts reporting on uses of artificial intelligence. This field has matured rapidly in the past several years. There was initial fascination with the algorithms themselves (machine learning, deep learning, convoluted neural networks) and the use of these algorithms to make predictions that often surpassed prevailing benchmarks. As the discipline has matured, individuals have called attention to aberrancies in the output of these algorithms. In particular, criticisms have been widely circulated that algorithmically developed models may have limited generalizability due to overfitting to the training data and may systematically perpetuate various forms of biases inherent in the training data, including race, gender, age, and health state or fitness level (Challen et al. BMJ Qual. Saf. 28:231–237, 2019; O’neil. Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy, Broadway Book, 2016). Given our interest in publishing the highest quality papers and the growing volume of submissions using AI algorithms, we offer a list of criteria that authors should consider before submitting papers to npj Digital Medicine.

#Artificial Intelligence
Field # Artificial Intelligence
Updated 16 October 2020

#14

 

View it
Name
Description This study aimed to verify a deep convolutional neural network (CNN) algorithm to detect intussusception in children using a human-annotated data set of plain abdominal X-rays from affected children. From January 2005 to August 2019, 1449 images were collected from plain abdominal X-rays of patients ≤ 6 years old who were diagnosed with intussusception while 9935 images were collected from patients without intussusception from three tertiary academic hospitals (A, B, and C data sets). Single Shot MultiBox Detector and ResNet were used for abdominal detection and intussusception classification, respectively. The diagnostic performance of the algorithm was analysed using internal and external validation tests. The internal test values after training with two hospital data sets were 0.946 to 0.971 for the area under the receiver operating characteristic curve (AUC), 0.927 to 0.952 for the highest accuracy, and 0.764 to 0.848 for the highest Youden index. The values from external test using the remaining data set were all lower (P-value < 0.001). The mean values of the internal test with all data sets were 0.935 and 0.743 for the AUC and Youden Index, respectively. Detection of intussusception by deep CNN and plain abdominal X-rays could aid in screening for intussusception in children.

#Artificial Intelligence
Field # Artificial Intelligence
Updated 16 October 2020

#15

 

View it
Name
Description The supply chain is an ecosystem that affects businesses around the world, and the COVID-19 pandemic has thrown a monkey…...

#Artificial intelligence
Field # Artificial intelligence
Updated 16 October 2020

#16

 

View it
Name
Description Machine learning is a computational tool used by many biologists to analyze huge amounts of data, helping them to identify potential new drugs. MIT researchers have now incorporated a new feature into these types of machine-learning algorithms, improving their prediction-making ability. Using thi

#Artificial Intelligence
Field # Artificial Intelligence
Updated 16 October 2020

#17

 

View it
Name
Description Google Search has added several new AI-based tools to enhance user experience, the company announced during its Search On livestream on October 15. It brings in a host of improvements that includes better understanding of spelling inputs from users, indexing individual passages from webpages, dividing broader searches into subtopics, dividing videos into segments, and humming to identify songs.

#Artificial intelligence
Field # Artificial intelligence
Updated 16 October 2020

#18

 

View it
Name
Description “Less than one”-shot learning can teach a model to identify more objects than the number of examples it is trained on.

#Artificial intelligence
Field # Artificial intelligence
Updated 16 October 2020

#19

 

View it
Name
Description This paper introduces a study on the classification of aortic stenosis (AS) based on cardio-mechanical signals collected using non-invasive wearable inertial sensors. Measurements were taken from 21 AS patients and 13 non-AS subjects. A feature analysis framework utilizing Elastic Net was implemented to reduce the features generated by continuous wavelet transform (CWT). Performance comparisons were conducted among several machine learning (ML) algorithms, including decision tree, random forest, multi-layer perceptron neural network, and extreme gradient boosting. In addition, a two-dimensional convolutional neural network (2D-CNN) was developed using the CWT coefficients as images. The 2D-CNN was made with a custom-built architecture and a CNN based on Mobile Net via transfer learning. After the reduction of features by 95.47%, the results obtained report 0.87 on accuracy by decision tree, 0.96 by random forest, 0.91 by simple neural network, and 0.95 by XGBoost. Via the 2D-CNN framework, the transfer learning of Mobile Net shows an accuracy of 0.91, while the custom-constructed classifier reveals an accuracy of 0.89. Our results validate the effectiveness of the feature selection and classification framework. They also show a promising potential for the implementation of deep learning tools on the classification of AS.

#Artificial Intelligence
Field # Artificial Intelligence
Updated 16 October 2020

#20

 

View it
Name
Description We investigated the ability of machine-learning classifiers on radiomics from pre-treatment multiparametric magnetic resonance imaging (MRI) to accurately predict human papillomavirus (HPV) status in patients with oropharyngeal squamous cell carcinoma (OPSCC). This retrospective study collected data of 60 patients (48 HPV-positive and 12 HPV-negative) with newly diagnosed histopathologically proved OPSCC, who underwent head and neck MRIs consisting of axial T1WI, T2WI, CE-T1WI, and apparent diffusion coefficient (ADC) maps from diffusion-weighted imaging (DWI). The median age was 59 years (the range being 35 to 85 years), and 83.3% of patients were male. The imaging data were randomised into a training set (32 HPV-positive and 8 HPV-negative OPSCC) and a test set (16 HPV-positive and 4 HPV-negative OPSCC) in each fold. 1618 quantitative features were extracted from manually delineated regions-of-interest of primary tumour and one definite lymph node in each sequence. After feature selection by using the least absolute shrinkage and selection operator (LASSO), three different machine-learning classifiers (logistic regression, random forest, and XG boost) were trained and compared in the setting of various combinations between four sequences. The highest diagnostic accuracies were achieved when using all sequences, and the difference was significant only when the combination did not include the ADC map. Using all sequences, logistic regression and the random forest classifier yielded higher accuracy compared with the that of the XG boost classifier, with mean area under curve (AUC) values of 0.77, 0.76, and 0.71, respectively. The machine-learning classifier of non-invasive and quantitative radiomics signature could guide the classification of the HPV status.

#Artificial Intelligence
Field # Artificial Intelligence
Updated 16 October 2020

#21

 

View it
Name
Description Tech giant Google is adding new AI features to help refine your search further.

#Artificial intelligence
Field # Artificial intelligence
Updated 16 October 2020

#22

 

View it
Name
Description We evaluated the reproducibility of computer-aided detections (CADs) with a convolutional neural network (CNN) on chest radiographs (CXRs) of abnormal pulmonary patterns in patients, acquired within a short-term interval. Anonymized CXRs (n = 9792) obtained from 2010 to 2016 and comprising five types of disease patterns, including the nodule (N), consolidation (C), interstitial opacity (IO), pleural effusion (PLE), and pneumothorax (PN), were included. The number of normal and abnormal CXRs was 6068 and 3724, respectively. The number of CXRs (region of interests, ROIs) of N, C, IO, PLE, and PN was 944 (1092), 550 (721), 280 (538), 1361 (1661), and 589 (622), respectively. CXRs were randomly allocated to training, tuning, and test sets in 70:10:20 ratios. Two thoracic radiologists labeled and delineated the ROIs of each disease pattern. The CAD system was developed using eDenseYOLO. For the reproducibility evaluation of developed CAD, paired CXRs of various diseases (N = 121, C = 28, IO = 12, PLE = 67, and PN = 20), acquired within a short-term interval from the test sets without any changes confirmed by thoracic radiologists, were used to evaluate CAD reproducibility. Percent positive agreement (PPAs) and Chamberlain’s percent positive agreement (CPPAs) were used to evaluate CAD reproducibility. The figure of merit (FOM) of five classes based on eDenseYOLO showed N-0.72 (0.68–0.75), C-0.41 (0.33–0.43), IO-0.97 (0.96–0.98), PLE-0.94 (0.92–95), and PN-0.87 (0.76–0.93). The PPAs of the five disease patterns including N, C, IO, PLE, and PN were 83.39%, 74.14%, 95.12%, 96.84%, and 84.58%, respectively, whereas the values of CPPAs were 71.70%, 59.13%, 91.16%, 93.91%, and 74.17%, respectively. The reproducibility of abnormal pulmonary patterns from CXRs, based on deep learning-based CAD, showed different results; this is important for assessing the reproducible performance of CAD in clinical settings.

#Artificial intelligence
Field # Artificial intelligence
Updated 15 October 2020

#23

 

View it
Name
Description Nondestructive techniques for estimating nitrogen (N) status are essential tools for optimizing N fertilization input and reducing the environmental impact of agricultural N management, especially in green tea cultivation, which is notably problematic. Previously, hyperspectral indices for chlorophyll (Chl) estimation, namely a green peak and red edge in the visible region, have been identified and used for N estimation because leaf N content closely related to Chl content in green leaves. Herein, datasets of N and Chl contents, and visible and near-infrared hyperspectral reflectance, derived from green leaves under various N nutrient conditions and albino yellow leaves were obtained. A regression model was then constructed using several machine learning algorithms and preprocessing techniques. Machine learning algorithms achieved high-performance models for N and Chl content, ensuring an accuracy threshold of 1.4 or 2.0 based on the ratio of performance to deviation values. Data-based sensitivity analysis through integration of the green and yellow leaves datasets identified clear differences in reflectance to estimate N and Chl contents, especially at 1325–1575 nm, suggesting an N content-specific region. These findings will enable the nondestructive estimation of leaf N content in tea plants and contribute advanced indices for nondestructive tracking of N status in crops.

#Artificial Intelligence
Field # Artificial Intelligence
Updated 15 October 2020

#24

 

View it
Name
Description Chip giant Intel on Wednesday said that Artificial Intelligence (AI) is essential to the future of work in the post-pandemic world, with over 90 per cent respondents in a survey considering AI to be essential or highly relevant to their businesses in India.

#Artificial intelligence
Field # Artificial intelligence
Updated 15 October 2020

#25

 

View it
Name
Description Machine learning is a computational tool used by many biologists to analyze huge amounts of data, helping them to identify potential new drugs. MIT researchers have now incorporated a new feature into these types of machine-learning ...

#Artificial Intelligence
Field # Artificial Intelligence
Updated 15 October 2020

#26

 

View it
Name
Description The newly-created National Security Commission on Artificial Intelligence in its report on Tuesday said the Department of State and the Department of Defence should negotiate formal AI cooperation agreements with India, Australia, Japan, New Zealand, South Korea and Vietnam.

#Artificial intelligence
Field # Artificial intelligence
Updated 14 October 2020

#27

 

View it
Name
Description PNNL researchers peer into water clusters database, train network to predict energy landscapes Machine learning algorithms, the basis of neural networks, are opening doors to new discoveries—or at least offering tantalizing clues—one massive database at a time. Case in point: Pacific Northwest Na

#Artificial intelligence
Field # Artificial intelligence
Updated 14 October 2020

#28

 

View it
Name
Description One year ago, Maneesh Agrawala of Stanford helped develop a lip-sync technology that allowed video editors to almost undetectably modify speakers’ words. The tool could seamlessly insert words that a person never said, even mid-sentence, or eliminate words she had said. To the naked eye, and even to

#Artificial intelligence
Field # Artificial intelligence
Updated 14 October 2020

#29

 

View it
Name
Description In a proof-of-concept study, education and artificial intelligence researchers have demonstrated the use of a machine-learning model to predict how long individual museum visitors will engage with a given exhibit. The finding ...

#Artificial Intelligence
Field # Artificial Intelligence
Updated 13 October 2020

#30

 

View it
Name
Description Artificial intelligence has arrived in our everyday lives—from search engines to self-driving cars. This has to do with the enormous computing power that has become available in recent years. But new results from AI research ...

#Artificial Intelligence
Field # Artificial Intelligence
Updated 13 October 2020

#31

 

View it
Name
Description The new device will go on sale in mid-November, starting at £11.50.

#Artificial intelligence
Field # Artificial intelligence
Updated 13 October 2020

#32

 

View it
Name
Description The demand for mobile applications across all the industries has gone through the roof over the past few years. In…...

#Artificial intelligence
Field # Artificial intelligence
Updated 13 October 2020