Add Novant Health to the growing list of health systems that have opened institutes dedicated to artificial intelligence. The health system, based in Winston-Salem, North Carolina, launched the Novant Health Institute of Innovation & Artificial Intelligence (AI), which will use AI to enhance personalized patient care. The institute will focus on the advanced technologies required to provide highly personalized care and accelerated solutions with actionable data and insights for preventive prediction, diagnosis and treatment to Novant Health's patients, the health system said. Novant Health consists of 640 care locations, including 15 hospitals and hundreds of outpatient facilities and physician clinics servicing patients in Virginia, North Carolina, South Carolina and Georgia. To drive this work, Novant will partner with the health system's physicians as well as technology companies, research organizations, universities and other healthcare organizations to leverage the work already in place within Novant Health's digital products and services team.

However, reaching consensus is likely to prove difficult on some key issues, in particular those involving trade. Participating nations have clashing interests, most notably the U.S. and China. "First, I would like to stress the importance of tapping into data, which is the source of innovation," Hiroshige Seko, minister of economy, trade and industry, said at the beginning of the digital economy session. Seko said that "ensuring the free flow of data internationally is indispensable to the economic development of the world as a whole." He then introduced a concept called "Data Fee Flow with Trust," or DFFT, which he said would promote free data flows while securing trust related to privacy and security.

Jeff Bezos wants to colonize space in order to'save the Earth.' At Amazon's inaugural Re:MARS conference in Las Vegas, Bezos broke down how his rocket company, Blue Origin, could play a major role in the future of space exploration. Bezos recently unveiled Blue Origin's lunar lander, which is a key component of the company's plans to conduct space missions and explore the moon's surface. At Amazon's inaugural Re:MARS conference in Las Vegas, CEO Jeff Bezos broke down how his rocket company, Blue Origin, could play a major role in the future of space exploration The comments came during an interview with Jenny Freshwater, Amazon's director of forecasting. The interview was briefly disrupted by an animal rights protester, Priya Sawhney of Direct Action Everywhere, who grilled Bezos on the treatment of chickens at Amazon-affiliated farms, before being briskly whisked off stage.

According to a recent investigation, an estimated 33-50% of the world's coral reefs have undergone degradation, believed to be as a result of climate change. A strong driver of climate change and the subsequent environmental impact are greenhouse gases such as methane. However, the exact relation climate change has to the environmental condition cannot be easily established. Remote sensing methods are increasingly being used to quantify and draw connections between rapidly changing climatic conditions and environmental impact. A crucial part of this analysis is processing spectroscopy data using radiative transfer models (RTMs) which is a computationally expensive process and limits their use with high volume imaging spectrometers. This work presents an algorithm that can efficiently emulate RTMs using neural networks leading to a multifold speedup in processing time, and yielding multiple downstream benefits.

Though machine learning algorithms excel at minimizing the average loss over a population, this might lead to large discrepancies between the losses across groups within the population. To capture this inequality, we introduce and study a notion we call maximum weighted loss discrepancy (MWLD), the maximum (weighted) difference between the loss of a group and the loss of the population. We relate MWLD to group fairness notions and robustness to demographic shifts. We then show MWLD satisfies the following three properties: 1) It is statistically impossible to estimate MWLD when all groups have equal weights. 2) For a particular family of weighting functions, we can estimate MWLD efficiently. 3) MWLD is related to loss variance, a quantity that arises in generalization bounds. We estimate MWLD with different weighting functions on four common datasets from the fairness literature. We finally show that loss variance regularization can halve the loss variance of a classifier and hence reduce MWLD without suffering a significant drop in accuracy.

A key component of most neural network architectures is the use of normalization layers, such as Batch Normalization. Despite its common use and large utility in optimizing deep architectures that are otherwise intractable, it has been challenging both to generically improve upon Batch Normalization and to understand specific circumstances that lend themselves to other enhancements. In this paper, we identify four improvements to the generic form of Batch Normalization and the circumstances under which they work, yielding performance gains across all batch sizes while requiring no additional computation during training. These contributions include proposing a method for reasoning about the current example in inference normalization statistics which fixes a training vs. inference discrepancy; recognizing and validating the powerful regularization effect of Ghost Batch Normalization for small and medium batch sizes; examining the effect of weight decay regularization on the scaling and shifting parameters γ and β; and identifying a new normalization algorithm for very small batch sizes by combining the strengths of Batch and Group Normalization.

Explainable machine learning (ML) has been implemented in numerous open source and proprietary software packages and explainable ML is an important aspect of commercial predictive modeling. However, explainable ML can be misused, particularly as a faulty safeguard for harmful black-boxes, e.g. fairwashing, and for other malevolent purposes like model stealing. This text discusses definitions, examples, and guidelines that promote a holistic and human-centered approach to ML which includes interpretable (i.e. white-box ) models and explanatory, debugging, and disparate impact analysis techniques.

This paper provides a general framework to study the effect of sampling properties of training data on the generalization error of the learned machine learning (ML) models. Specifically, we propose a new spectral analysis of the generalization error, expressed in terms of the power spectra of the sampling pattern and the function involved. The framework is build in the Euclidean space using Fourier analysis and establishes a connection between some high dimensional geometric objects and optimal spectral form of different state-of-the-art sampling patterns. Subsequently, we estimate the expected error bounds and convergence rate of different state-of-the-art sampling patterns, as the number of samples and dimensions increase. We make several observations about generalization error which are valid irrespective of the approximation scheme (or learning architecture) and training (or optimization) algorithms. Our result also sheds light on ways to formulate design principles for constructing optimal sampling methods for particular problems.

In future autonomous systems, wireless multi-hop communication is key to enable collaboration among distributed agents at low cost and high flexibility. When many agents need to transmit information over the same wireless network, communication becomes a shared and contested resource. Event-triggered and self-triggered control account for this by transmitting data only when needed, enabling significant energy savings. However, a solution that brings those benefits to multi-hop networks and can reallocate freed up bandwidth to additional agents or data sources is still missing. To fill this gap, we propose control-guided communication, a novel co-design approach for distributed self-triggered control over wireless multi-hop networks. The control system informs the communication system of its transmission demands ahead of time, and the communication system allocates resources accordingly. Experiments on a cyber-physical testbed show that multiple cart-poles can be synchronized over wireless, while serving other traffic when resources are available, or saving energy. These experiments are the first to demonstrate and evaluate distributed self-triggered control over low-power multi-hop wireless networks at update rates of tens of milliseconds.

We investigate how an adversary can optimally use its query budget for targeted evasion attacks against deep neural networks in a black-box setting. We formalize the problem setting and systematically evaluate what benefits the adversary can gain by using substitute models. We show that there is an exploration-exploitation tradeoff in that query efficiency comes at the cost of effectiveness. We present two new attack strategies for using substitute models and show that they are as effective as previous query-only techniques but require significantly fewer queries, by up to three orders of magnitude. We also show that an agile adversary capable of switching through different attack techniques can achieve pareto-optimal efficiency. We demonstrate our attack against Google Cloud Vision showing that the difficulty of black-box attacks against real-world prediction APIs is significantly easier than previously thought (requiring approximately 500 queries instead of approximately 20,000 as in previous works).