Even as artificial intelligence (AI) is forecast to exceed human capabilities across a range of industries it is also predicted to augment human labor. In finance, AI is already helping financial advisors augment financial planning while enhancing investment strategy. And in medicine, AI diagnostics systems have proven to be far more accurate than doctors in diagnosing heart disease and cancerous growths. In fact, McKinsey lists some 400 use cases representing $6 trillion in value across 19 industries in which AI will augment human work. What will the impact of AI be on the nature of government?

Are you prepared for the Age of Machine Intelligence? That's a time when machines anticipate consumers' choices before they are made. That age is nearer than many people realize, according to author/futurist Mike Walsh, who said business leaders need to understand how the new reality impacts the decisions they make. The National Automatic Merchandising Association show, held last week in Las Vegas, made an appropriate setting for Walsh's message, given the number of exhibits and education sessions featuring artificial intelligence. While these new technologies impact many industries, the convenience services industry has experienced a significant boost in recent years thanks to AI, micro markets, cashless readers, digital signage, telemetry-based remote machine monitoring, smart sensor shelving, facial detection and voice technology.

What makes artificial intelligence intelligent? Is it able to learn from errors or recognize, say, the letters of the alphabet in a set of random shapes like a human can? These are some of the questions developers of AI ask. What began as sluggish programs on hulking machines has taken the form of code that anyone in a particular field could test out and manipulate to suit their needs. Jae Ho Sohn, a radiologist at the University of California at San Francisco, is adapting and working with an AI algorithm to analyze thousands of positron emission tomography (PET) scans to search for early signs of Alzheimer's.

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.