Ernst & Young LLP (EY) announced that the Department of Health and Human Services (HHS) has awarded a $49 million IDIQ (indefinite delivery indefinite quantity) contract vehicle to the EY US Government & Public Sector practice for intelligent automation and artificial intelligence (IAAI) products and services. The Health and Human Services Program Support Center is managing a government-wide contract vehicle for IAAI services. The intent of the vehicle is to promote innovation in this space through funding support for rapid prototyping and proof of concepts leveraging robotic process automation, natural language processing, machine learning, artificial intelligence and blockchain. "We're honored we've been selected and are presented with the opportunity to share our IAAI capabilities to help HHS and other agencies transform their operations," said Mike Herrinton, Partner and US Government & Public Sector Leader at Ernst & Young LLP. "EY implements digital solutions with modern technologies that can help agencies unlock the potential of their data and assets, and change the way customers interact with the government."

Gartner recently stated that all industries and levels of government agree the top three game-changing technologies today are AI/machine learning, data analytics/predictive analytics and cloud technologies. However, there are some primary sticking points when it comes to innovation in these areas. Government organizations continue to encounter challenges when trying to pursue these initiatives due to complex security and compliance requirements, poor scalability of legacy IT infrastructure, and perceived risks associated with cloud and IT modernization efforts. How can these challenges be addressed? One solution to these concerns is being addressed by Databricks, a leader in Unified Analytics and founded by the original creators of Apache Spark, and Booz Allen Hamilton, a leading provider of machine learning services to the U.S. Federal Government, by announcing the two companies are teaming up to provide a Unified Analytics solution to meet the U.S. government's mission requirements on any data, anywhere.

LOS ALAMOS, N.M., July 18, 2019--Three teams who applied novel machine learning methods to successfully predict the timing of earthquakes from historic seismic data are splitting $50,000 in prize money from an open, online Kaggle competition hosted by Los Alamos National Laboratory and its partners. "Crowdsourcing for new approaches in earthquake forecasting helps us leverage a wide range of expertise in addressing one of the most important problems in Earth science, because of the devastating consequences of large quakes," said Bertrand Rouet-Leduc, a Los Alamos researcher who prepared the data for the competition. "The winning teams' results could have the potential to improve earthquake hazard assessments that could save lives and billions of dollars in infrastructure." Current scientific studies related to earthquake forecasting focus on three key points: when the event will occur, where it will occur, and how large it will be. The Kaggle competition provided a challenging dataset that was based on previously published laboratory analysis, to give the competitors a taxing project to explore.

Elon Musk announced late Tuesday night that the final goal of Neuralink, his brain-machine interface startup, is to allow humans to "achieve a symbiosis with artificial intelligence," and that by "merging with AI," humans will be able to keep up with AI. Musk plans to begin human trials on an early version of Neuralink intended to treat brain injuries next year. "Ultimately we can do a full brain machine interface," Musk said in an announcement that was widely livestreamed. "This is going to sound pretty weird. Ultimately we can achieve a symbiosis with artificial intelligence. This is not a mandatory thing, this is something you can choose to have if you want. This is going to be really important at a civilization-level scale. Even in a benign AI scenario, we will be left behind. With a high-bandwidth brain machine interface we can go along for the ride and have the option of merging with AI." Musk has become famous for his moonshot projects, his lofty promises, his quick temper on Twitter, and his various plans for society that don't include input from the rest of us.

DGIST announced on Tuesday, July 16 that Senior Researcher Dae-gun Oh's team in the Collaborative Robots Research Center developed a radar system that can detect subminiature drones that are 3km away. This research is expected to make huge contributions to strengthening domestic industries and defense capabilities by securing a world-class radar sensing technology. As a result of discovering a North Korean drone in Paju in March 2014, South Korea's Ministry of National Defense has adopted a drone detection radar based on an overseas technology. Since last year, the ministry has devoted itself into building a combat system using drones and training specialized personnel by forming a drone unit to strengthen its defense capability. The necessity of enemy surveillance reconnaissance and the early detection of offensive drones has increased in Korea.

Elon Musk said startup Neuralink, which aims to build a scalable implant to connect human brains with computers, has already implanted chips in rats and plans to test its brain-machine interface in humans within two years, with a long-term goal of people "merging with AI." Brain-machine interfaces have been around for awhile. Some of the earliest success with the technology include Brown University's BrainGate, which first enabled a paralyzed person to control a computer cursor in 2006. Since then a variety of research groups and companies, including the University of Pittsburgh Medical Center and DARPA-backed Synchron, have been working on similar devices. There are two basic approaches: You can do it invasively, creating an interface with an implant that directly touches the brain, or you can do it non-invasively, usually by electrodes placed near the skin. Neuralink, says Musk, is going to go the invasive route.

Robust machine learning relies on access to data that can be used with standardized frameworks in important tasks and the ability to develop models whose performance can be reasonably reproduced. In machine learning for healthcare, the community faces reproducibility challenges due to a lack of publicly accessible data and a lack of standardized data processing frameworks. We present MIMIC-Extract, an open-source pipeline for transforming raw electronic health record (EHR) data for critical care patients contained in the publicly-available MIMIC-III database into dataframes that are directly usable in common machine learning pipelines. MIMIC-Extract addresses three primary challenges in making complex health records data accessible to the broader machine learning community. First, it provides standardized data processing functions, including unit conversion, outlier detection, and aggregating semantically equivalent features, thus accounting for duplication and reducing missingness. Second, it preserves the time series nature of clinical data and can be easily integrated into clinically actionable prediction tasks in machine learning for health. Finally, it is highly extensible so that other researchers with related questions can easily use the same pipeline. We demonstrate the utility of this pipeline by showcasing several benchmark tasks and baseline results. These authors has an equal contribution, and should be considered co-first authors.

This paper contains a feasibility study of deep neural networks for the classification of Euro banknotes with respect to requirements of central banks on the ATM and high speed sorting industry. Instead of concentrating on the accuracy for a large number of classes as in the famous ImageNet Challenge we focus thus on conditions with few classes and the requirement of rejection of images belonging clearly to neither of the trained classes (i.e. classification in a so-called 0-class). These special requirements are part of frameworks defined by central banks as the European Central Bank and are met by current ATMs and high speed sorting machines. We also consider training and classification time on state of the art GPU hardware. The study concentrates on the banknote recognition whereas banknote class dependent authenticity and fitness checks are a topic of its own which is not considered in this work.

We introduce natural adversarial examples -- real-world, unmodified, and naturally occurring examples that cause classifier accuracy to significantly degrade. We curate 7,500 natural adversarial examples and release them in an ImageNet classifier test set that we call ImageNet-A. This dataset serves as a new way to measure classifier robustness. Like l_p adversarial examples, ImageNet-A examples successfully transfer to unseen or black-box classifiers. For example, on ImageNet-A a DenseNet-121 obtains around 2% accuracy, an accuracy drop of approximately 90%. Recovering this accuracy is not simple because ImageNet-A examples exploit deep flaws in current classifiers including their over-reliance on color, texture, and background cues. We observe that popular training techniques for improving robustness have little effect, but we show that some architectural changes can enhance robustness to natural adversarial examples. Future research is required to enable robust generalization to this hard ImageNet test set.

We develop tools for utilizing correspondence experiments to detect illegal discrimination by individual employers. Employers violate US employment law if their propensity to contact applicants depends on protected characteristics such as race or sex. We establish identification of higher moments of the causal effects of protected characteristics on callback rates as a function of the number of fictitious applications sent to each job ad. These moments are used to bound the fraction of jobs that illegally discriminate. Applying our results to three experimental datasets, we find evidence of significant employer heterogeneity in discriminatory behavior, with the standard deviation of gaps in job-specific callback probabilities across protected groups averaging roughly twice the mean gap. In a recent experiment manipulating racially distinctive names, we estimate that at least 85% of jobs that contact both of two white applications and neither of two black applications are engaged in illegal discrimination. To assess the tradeoff between type I and II errors presented by these patterns, we consider the performance of a series of decision rules for investigating suspicious callback behavior under a simple two-type model that rationalizes the experimental data. Though, in our preferred specification, only 17% of employers are estimated to discriminate on the basis of race, we find that an experiment sending 10 applications to each job would enable accurate detection of 7-10% of discriminators while falsely accusing fewer than 0.2% of non-discriminators. A minimax decision rule acknowledging partial identification of the joint distribution of callback rates yields higher error rates but more investigations than our baseline two-type model. Our results suggest illegal labor market discrimination can be reliably monitored with relatively small modifications to existing audit designs.