Machines use Google-type algorithms on biopsy images to help children get treatment faster. A study published in the open access journal JAMA Open Network today by scientists at the University of Virginia schools of Engineering and Medicine says machine learning algorithms applied to biopsy images can shorten the time for diagnosing and treating a gut disease that often causes permanent physical and cognitive damage in children from impoverished areas. In places where sanitation, potable water and food are scarce, there are high rates of children suffering from environmental enteric dysfunction, a disease that limits the gut's ability to absorb essential nutrients and can lead to stunted growth, impaired brain development and even death. The disease affects 20 percent of children under the age of 5 in low- and middle-income countries, such as Bangladesh, Zambia and Pakistan, but it also affects some children in rural Virginia. For Dr. Sana Syed, an assistant professor of pediatrics in the UVA School of Medicine, this project is an example of why she got into medicine.

With the unprecedented success of deep convolutional neural networks came the quest for training always deeper networks. However, while deeper neural networks give better performance when trained appropriately, that depth also translates in memory and computation heavy models, typically with tens of millions of parameters. Several methods have been proposed to leverage redundancies in the network to alleviate this complexity. Either a pretrained network is compressed, e.g. using a low-rank tensor decomposition, or the architecture of the network is directly modified to be more effective. In this paper, we study both approaches in a unified framework, under the lens of tensor decompositions. We show how tensor decomposition applied to the convolutional kernel relates to efficient architectures such as MobileNet. Moreover, we propose a tensor-based method for efficient higher order convolutions, which can be used as a plugin replacement for N-dimensional convolutions. We demonstrate their advantageous properties both theoretically and empirically for image classification, for both 2D and 3D convolutional networks.

In this paper we propose a method of obtaining points of extreme overfitting - parameters of modern neural networks, at which they demonstrate close to 100 % training accuracy, simultaneously with almost zero accuracy on the test sample. Despite the widespread opinion that the overwhelming majority of critical points of the loss function of a neural network have equally good generalizing ability, such points have a huge generalization error. The paper studies the properties of such points and their location on the surface of the loss function of modern neural networks.

Providing users with alternatives to choose from is an essential component in many online platforms, making the accurate prediction of choice vital to their success. A renewed interest in learning choice models has led to significant progress in modeling power, but most current methods are either limited in the types of choice behavior they capture, cannot be applied to large-scale data, or both. Here we propose a learning framework for predicting choice that is accurate, versatile, theoretically grounded, and scales well. Our key modeling point is that to account for how humans choose, predictive models must capture certain set-related invariances. Building on recent results in economics, we derive a class of models that can express any behavioral choice pattern, enjoy favorable sample complexity guarantees, and can be efficiently trained end-to-end. Experiments on three large choice datasets demonstrate the utility of our approach.

African languages are numerous, complex and low-resourced. The datasets required for machine translation are difficult to discover, and existing research is hard to reproduce. Minimal attention has been given to machine translation for African languages so there is scant research regarding the problems that arise when using machine translation techniques. To begin addressing these problems, we trained models to translate English to five of the official South African languages (Afrikaans, isiZulu, Northern Sotho, Setswana, Xitsonga), making use of modern neural machine translation techniques. The results obtained show the promise of using neural machine translation techniques for African languages. By providing reproducible publicly-available data, code and results, this research aims to provide a starting point for other researchers in African machine translation to compare to and build upon.

Credit risk modelling is an integral part of the global financial system. While there has been great attention paid to neural network models for credit default prediction, such models often lack the required interpretation mechanisms and measures of the uncertainty around their predictions. This work develops and compares Bayesian Neural Networks(BNNs) for credit card default modelling. This includes a BNNs trained by Gaussian approximation and the first implementation of BNNs trained by Hybrid Monte Carlo(HMC) in credit risk modelling. The results on the Taiwan Credit Dataset show that BNNs with Automatic Relevance Determination(ARD) outperform normal BNNs without ARD. The results also show that BNNs trained by Gaussian approximation display similar predictive performance to those trained by the HMC. The results further show that BNN with ARD can be used to draw inferences about the relative importance of different features thus critically aiding decision makers in explaining model output to consumers. The robustness of this result is reinforced by high levels of congruence between the features identified as important using the two different approaches for training BNNs.

According to the National Academy of Sciences, a sixth mass extinction is underway. Animal species are disappearing at 1,000 to 10,000 times the natural rate. We recently reported on scientists using artificial intelligence to analyze photos to help track at-risk species such as giraffes and whale sharks. Now AI is being used to analyze sound to help protect forest elephants in central Africa. Mainly due to poachers and habitat destruction, the number of forest elephants went from an estimated 100,000 in 2011 to fewer than 40,000 today.

IBM (NYSE: IBM) today announced the first recipients and list of global women leaders and pioneers in AI for business. The list recognizes and celebrates women across a variety of industries and geographies for pioneering the use of AI to advance their companies in areas such as innovation, growth, and transformation. IBM will celebrate the honorees during an inaugural recognition event on June 12, 2019 at the IBM Watson Experience Center in New York, New York where the women will share their experiences leading AI initiatives in their organizations. Students from IBM's P-Tech program will attend to hear from these leaders who have applied AI technology in diverse and meaningful ways to help drive business innovation. "Artificial Intelligence is poised to drive dramatic advances in every industry," said Michelle Peluso, SVP, Digital Sales & CMO, IBM, who also serves as Leader of IBM's Women's Initiative.

The Genie is out of the bottle, and nothing can be done now. It is going to transform not just your homes but your whole workplace and much more. Well, don't get so excited it is not the Genie from the Aladdin's Magic Lamp. I am talking about the Genie of the technical world. And it is the big word in the town, Artificial Intelligence aka.

Developing countries suffer from traffic congestion, poorly planned road/rail networks, and lack of access to public transportation facilities. This context results in an increase in fuel consumption, pollution level, monetary losses, massive delays, and less productivity. On the other hand, it has a negative impact on the commuters feelings and moods. Availability of real-time transit information - by providing public transportation vehicles locations using GPS devices - helps in estimating a passenger's waiting time and addressing the above issues. However, such solution is expensive for developing countries. This paper aims at designing and implementing a crowd-sourced mobile phones-based solution to estimate the expected waiting time of a passenger in public transit systems, the prediction of the remaining time to get on/off a vehicle, and to construct a real time public transit schedule. Trans-Sense has been evaluated using real data collected for over 800 hours, on a daily basis, by different Android phones, and using different light rail transit lines at different time spans. The results show that Trans-Sense can achieve an average recall and precision of 95.35% and 90.1%, respectively, in discriminating lightrail stations. Moreover, the empirical distributions governing the different time delays affecting a passenger's total trip time enable predicting the right time of arrival of a passenger to her destination with an accuracy of 91.81%.In addition, the system estimates the stations dimensions with an accuracy of 95.71%.