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News for Artificial intelligence





#1

 

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Description We aimed to develop machine learning models to accurately predict bronchiolitis severity, and to compare their predictive performance with a conventional scoring (reference) model. In a 17-center prospective study of infants (aged < 1 year) hospitalized for bronchiolitis, by using routinely-available pre-hospitalization data as predictors, we developed four machine learning models: Lasso regression, elastic net regression, random forest, and gradient boosted decision tree. We compared their predictive performance—e.g., area-under-the-curve (AUC), sensitivity, specificity, and net benefit (decision curves)—using a cross-validation method, with that of the reference model. The outcomes were positive pressure ventilation use and intensive treatment (admission to intensive care unit and/or positive pressure ventilation use). Of 1,016 infants, 5.4% underwent positive pressure ventilation and 16.0% had intensive treatment. For the positive pressure ventilation outcome, machine learning models outperformed reference model (e.g., AUC 0.88 [95% CI 0.84–0.93] in gradient boosted decision tree vs 0.62 [95% CI 0.53–0.70] in reference model), with higher sensitivity (0.89 [95% CI 0.80–0.96] vs. 0.62 [95% CI 0.49–0.75]) and specificity (0.77 [95% CI 0.75–0.80] vs. 0.57 [95% CI 0.54–0.60]). The machine learning models also achieved a greater net benefit over ranges of clinical thresholds. Machine learning models consistently demonstrated a superior ability to predict acute severity and achieved greater net benefit.

#Artificial Intelligence
Field # Artificial Intelligence
Updated 03 July 2020

#2

 

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Description Processing data from surveys using photos or videos remains a major bottleneck in ecology. Deep Learning Algorithms (DLAs) have been increasingly used to automatically identify organisms on images. However, despite recent advances, it remains difficult to control the error rate of such methods. Here, we proposed a new framework to control the error rate of DLAs. More precisely, for each species, a confidence threshold was automatically computed using a training dataset independent from the one used to train the DLAs. These species-specific thresholds were then used to post-process the outputs of the DLAs, assigning classification scores to each class for a given image including a new class called “unsure”. We applied this framework to a study case identifying 20 fish species from 13,232 underwater images on coral reefs. The overall rate of species misclassification decreased from 22% with the raw DLAs to 2.98% after post-processing using the thresholds defined to minimize the risk of misclassification. This new framework has the potential to unclog the bottleneck of information extraction from massive digital data while ensuring a high level of accuracy in biodiversity assessment.

#Artificial Intelligence
Field # Artificial Intelligence
Updated 03 July 2020

#3

 

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Description The scalability, error correction and practical problem solving are important challenges for quantum computing (QC) as more emphasized by quantum supremacy (QS) experiments. Quantum path computing (QPC), recently introduced for linear optic based QCs as an unconventional design, targets to obtain scalability and practical problem solving. It samples the intensity from the interference of exponentially increasing number of propagation paths obtained in multi-plane diffraction (MPD) of classical particle sources. QPC exploits MPD based quantum temporal correlations of the paths and freely entangled projections at different time instants, for the first time, with the classical light source and intensity measurement while not requiring photon interactions or single photon sources and receivers. In this article, photonic QPC is defined, theoretically modeled and numerically analyzed for arbitrary Fourier optical or quadratic phase set-ups while utilizing both Gaussian and Hermite-Gaussian source laser modes. Problem solving capabilities already including partial sum of Riemann theta functions are extended. Important future applications, implementation challenges and open issues such as universal computation and quantum circuit implementations determining the scope of QC capabilities are discussed. The applications include QS experiments reaching more than $$2^{100}$$ Feynman paths, quantum neuron implementations and solutions of nonlinear Schrödinger equation.

#Artificial intelligence
Field # Artificial intelligence
Updated 03 July 2020

#4

 

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Description When you are aiming to master machine learning algorithms, it is always useful to know how the systems, frameworks and formalization rules really work, and what is going on behind the scenes. Revisiting some calculus-related topics is one of those critical points needed to understand the specifics o

#Artificial Intelligence
Field # Artificial Intelligence
Updated 03 July 2020

#5

 

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Description Suggested replies will include emojis and ASCII art to better reflect how people use language on the platform.

#Artificial intelligence
Field # Artificial intelligence
Updated 03 July 2020

#6

 

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Description It will even auto-suggest emojis.

#Artificial intelligence
Field # Artificial intelligence
Updated 03 July 2020

#7

 

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Description Nilekani, the former chairman of the Unique Identification Authority of India (UIDAI), was speaking at an event on the fifth anniversary of Digital India. “We have completed one part of the journey, but we have to continue applying technology to governance,” he said.

#Artificial intelligence
Field # Artificial intelligence
Updated 02 July 2020

#8

 

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Description Things are different on the other side of the mirror.

#Artificial intelligence
Field # Artificial intelligence
Updated 02 July 2020

#9

 

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Description TBD

#Artificial intelligence
Field # Artificial intelligence
Updated 02 July 2020

#10

 

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Description Known as “amica”, the service lets soon-to-be-exes “make parenting arrangements” and “divide their money and property”.

#Artificial intelligence
Field # Artificial intelligence
Updated 02 July 2020

#11

 

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Description

#Artificial intelligence
Field # Artificial intelligence
Updated 02 July 2020

#12

 

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Description Earlier this week, MIT permanently pulled its 80 Million Tiny Images dataset due to its use of offensive terms to label photos.

#Artificial intelligence
Field # Artificial intelligence
Updated 02 July 2020

#13

 

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Description Machine learning has considerably improved medical image analysis in the past years. Although data-driven approaches are intrinsically adaptive and thus, generic, they often do not perform the same way on data from different imaging modalities. In particular computed tomography (CT) data poses many challenges to medical image segmentation based on convolutional neural networks (CNNs), mostly due to the broad dynamic range of intensities and the varying number of recorded slices of CT volumes. In this paper, we address these issues with a framework that adds domain-specific data preprocessing and augmentation to state-of-the-art CNN architectures. Our major focus is to stabilise the prediction performance over samples as a mandatory requirement for use in automated and semi-automated workflows in the clinical environment. To validate the architecture-independent effects of our approach we compare a neural architecture based on dilated convolutions for parallel multi-scale processing (a modified Mixed-Scale Dense Network: MS-D Net) to traditional scaling operations (a modified U-Net). Finally, we show that an ensemble model combines the strengths across different individual methods. Our framework is simple to implement into existing deep learning pipelines for CT analysis. It performs well on a range of tasks such as liver and kidney segmentation, without significant differences in prediction performance on strongly differing volume sizes and varying slice thickness. Thus our framework is an essential step towards performing robust segmentation of unknown real-world samples.

#Artificial Intelligence
Field # Artificial Intelligence
Updated 01 July 2020

#14

 

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Description In one second, the human eye can only scan through a few photographs. Computers, on the other hand, are capable of performing billions of calculations in the same amount of time. With the explosion of social media, images ...

#Artificial intelligence
Field # Artificial intelligence
Updated 01 July 2020

#15

 

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Description Over the past decade or so, researchers have been developing increasingly advanced artificial intelligence (AI) systems for a wide range of applications. This includes computational techniques that can interact with humans, ...

#Artificial intelligence
Field # Artificial intelligence
Updated 01 July 2020

#16

 

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Description Scientists from the University of Tokyo have created a new design for AI circuits that may bring some needed improvements. The device arranges a stack of memory modules in a spiral, a novel configuration. Based on their research, this new design can make AI systems both faster and more energy-effici

#Artificial intelligence
Field # Artificial intelligence
Updated 01 July 2020

#17

 

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Description Our editorial team has recently found a very interesting presentation of NASA case studies involving applications of machine learning techniques in the field of aviation operations. The author presents the overview of possible applications of machine learning techniques to solve critically im

#Artificial Intelligence
Field # Artificial Intelligence
Updated 01 July 2020

#18

 

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Description Improving Non-Semantic Representation in Speech Recognition Actions speak louder than words, and many times speech recognition does not catch the context or the meaning of what you attempt to convey. Taking the wrong actions based on semantic or non-semantic context may let you down in ca

#Artificial intelligence
Field # Artificial intelligence
Updated 01 July 2020

#19

 

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Description Bottom Line: Barclays’ and Kount’s co-developed new product, Barclays Transact reflects the future of how companies will innovate together to…...

#Artificial intelligence
Field # Artificial intelligence
Updated 01 July 2020

#20

 

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Description A programmer named Aldo Cortesi created an algorithm that draws nonexistent animals, some of which look plausible and others which look like bizarre.

#Artificial intelligence
Field # Artificial intelligence
Updated 01 July 2020

#21

 

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Description No more dick pics in your inbox.

#Artificial intelligence
Field # Artificial intelligence
Updated 30 June 2020

#22

 

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Description Scientists have developed a new artificial intelligence method that automates experiments. It autonomously defines and conducts the next step of an experiment without input from human researchers. The method works by creating a model that fits the data from an experiment. It then uses that model as

#Artificial Intelligence
Field # Artificial Intelligence
Updated 30 June 2020

#23

 

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Description Researchers from Washington University in St. Louis’ McKelvey School of Engineering have combined artificial intelligence with systems theory to develop a more efficient way to detect and accurately identify an epileptic seizure in real-time. Their results were published in the journal Scientific

#Artificial Intelligence
Field # Artificial Intelligence
Updated 30 June 2020

#24

 

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Description The selection of free online courses related to machine learning, artificial intelligence, and algorithms in general is getting quite wide: MIT OpenCourseWare has very recently published a new and also free-access online course titled Mathematics of Big Data and Machine Learning. This lecture ser

#Artificial Intelligence
Field # Artificial Intelligence
Updated 30 June 2020

#25

 

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Description In collaboration with Rigshospitalet, researchers from DTU Health Technology have developed a machine learning model that can predict chemotherapy-associated nephrotoxicity, a particularly significant side effect in patients treated with cisplatin. Testicular cancer is the most common cance

#Artificial Intelligence
Field # Artificial Intelligence
Updated 29 June 2020

#26

 

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Description Researchers at the Indian Institute of Technology (IIT), Gandhinagar have developed an artificial intelligence-based deep learning tool for detection of Covid-19 from chest X-ray images.

#Artificial Intelligence
Field # Artificial Intelligence
Updated 29 June 2020

#27

 

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Description When designing new optical devices, many simulations must be conducted to determine the optimal design parameters. Therefore, fast and accurate simulations are essential for designing optical devices. In this work, we introduce a deep learning approach that accelerates a simulator solving frequency-domain Maxwell equations. Our model achieves high accuracy while predicting transmittance per wavelength in 2D slit arrays under certain conditions to achieve 160,000 times faster results than the simulator. We generated a dataset using an open-source simulator and compared its performance with those of other machine learning models. Additionally, we propose a new loss function and performance evaluation method for creating better performance models with multiple regression outputs from one input source. We observed that using a loss function that adds binary cross-entropy loss, which predicts whether the differential of the transmittance is positive or negative at wavelengths adjacent to the root mean-squared error of the transmittance value, is more effective for predicting variations in multiple regression outputs. The simulation results show that a four-layer convolutional neural network model demonstrates the best accuracy (R2 score: 0.86). The overall approach presented here is expected to be useful for simulating and designing optical devices.

#Artificial Intelligence
Field # Artificial Intelligence
Updated 29 June 2020

#28

 

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Description Multiparametric magnetic resonance imaging (mpMRI) has been shown to improve radiologists’ performance in the clinical diagnosis of breast cancer. This machine learning study develops a deep transfer learning computer-aided diagnosis (CADx) methodology to diagnose breast cancer using mpMRI. The retrospective study included clinical MR images of 927 unique lesions from 616 women. Each MR study included a dynamic contrast-enhanced (DCE)-MRI sequence and a T2-weighted (T2w) MRI sequence. A pretrained convolutional neural network (CNN) was used to extract features from the DCE and T2w sequences, and support vector machine classifiers were trained on the CNN features to distinguish between benign and malignant lesions. Three methods that integrate the sequences at different levels (image fusion, feature fusion, and classifier fusion) were investigated. Classification performance was evaluated using the receiver operating characteristic (ROC) curve and compared using the DeLong test. The single-sequence classifiers yielded areas under the ROC curves (AUCs) [95% confidence intervals] of AUCDCE = 0.85 [0.82, 0.88] and AUCT2w = 0.78 [0.75, 0.81]. The multiparametric schemes yielded AUCImageFusion = 0.85 [0.82, 0.88], AUCFeatureFusion = 0.87 [0.84, 0.89], and AUCClassifierFusion = 0.86 [0.83, 0.88]. The feature fusion method statistically significantly outperformed using DCE alone (P < 0.001). In conclusion, the proposed deep transfer learning CADx method for mpMRI may improve diagnostic performance by reducing the false positive rate and improving the positive predictive value in breast imaging interpretation.

#Artificial Intelligence
Field # Artificial Intelligence
Updated 29 June 2020