A research collaboration between LBNL, PNNL, Brown University, and NVIDIA has achieved exaflop (half-precision) performance on the Summit supercomputer with a deep learning application used to model subsurface flow in the study of nuclear waste remediation. Their achievement, which will be presented during the "Deep Learning on Supercomputers" workshop at SC19, demonstrates the promise of physics-informed generative adversarial networks (GANs) for analyzing complex, large-scale science problems. In science we know the laws of physics and observation principles – mass, momentum, energy, etc.," said George Karniadakis, professor of applied mathematics at Brown and co-author on the SC19 workshop paper. "The concept of physics-informed GANs is to encode prior information from the physics into the neural network. This allows you to go well beyond the training domain, which is very important in applications where the conditions can change." GANs have been applied to model human face ...

We have exciting opportunities for you to innovate, influence, transform, inspire and grow within our organization and we encourage you to apply to learn more! We are looking for senior software engineers that will be part of the Management Reporting team and contribute towards our vision to accelerate Microsoft growth by radically transforming financial experiences to be the most scalable, agile, intelligent and compliant. Our team builds line of business applications and services that allows Microsoft to manage statutory financial compliance responsibilities and provides insights/solutions that enable greater efficiency and profitability for the company. Our services are built using wide variety of technologies such as Spark, Scala, HDInsight, AngularJS, C#, microservices, SQL, NoSQL, Databricks and are deployed in Azure. We expect all our systems to run with a DevOps model and value investments in automation and telemetry to deliver the best possible services.

The fact that AI can pass on bias and prejudice is now widely recognized, probably because recent incidents of apparently racist or sexist algorithms involved big companies like Google and Amazon. A better understanding of how bad data gets encoded might make it easier to prevent. The large-scale machine learning AI that undergirds most recent advances relies on immense quantities of data. As the system feeds on the data provided, thousands of small adjustments are made to internal parameters to tweak how the data will be categorized. So, if the original training data is biased, the training is biased and the results will be biased.

Artificial Intelligence (AI) is opening up a new frontier by combining human creativity with technology to drive progress in our society and bring governments closer to their constituents. According to the 2018 United Nations (UN) e-Government Survey all 193 Member States have e-government systems in place, at different maturity levels, to deliver digital services and experiences to citizens. The three most commonly used e-government services are paying utilities (140 countries), submitting income taxes (139 countries), and registering a new business (126 countries). Denmark is heading the top 10 e-government development ranking, followed by Australia, the Republic of Korea, United Kingdom, Sweden, Finland, Singapore, New Zealand, France and Japan. The next phase of e-government will use AI to go beyond digitized and automated services and deliver better experiences to citizens.

Human rights or Civil Liberties issues are not considered in their true perspective world over. Traditionally Governments across the world have been investing heavily in knowing more and more about their citizens and residents. This hunger to know everything could have been catastrophic if civil liberties activists were not so active. Nevertheless, we are slowly moving towards a totalitarian and Orwellian world thanks to the super pervasive and intruding technologies. We anticipated this trend way back in 2009 when we started discussing about Human Rights Protection In Cyberspace.

We've been hearing a lot lately about how artificial intelligence (A.I.) and machine learning might make it even harder for older technologists to land a new job. But what if these emerging technologies could be used to eliminate (or at least reduce) the impact of ageism? If that sounds a little bit Pollyannaish, you're probably right. Even so, it's worth considering how automated tools for screening job applications could end up creating a more diverse workforce. Before we go into that, though, let's examine the "dark side": How automation could boost hiring bias and wreak absolute havoc on many folks' ability to find employment.

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In this paper, we investigate the effect of machine learning based anonymization on anomalous subgroup preservation. In particular, we train a binary classifier to discover the most anomalous subgroup in a dataset by maximizing the bias between the group's predicted odds ratio from the model and observed odds ratio from the data. We then perform anonymization using a variational autoencoder (VAE) to synthesize an entirely new dataset that would ideally be drawn from the distribution of the original data. We repeat the anomalous subgroup discovery task on the new data and compare it to what was identified pre-anonymization. We evaluated our approach using publicly available datasets from the financial industry. Our evaluation confirmed that the approach was able to produce synthetic datasets that preserved a high level of subgroup differentiation as identified initially in the original dataset. Such a distinction was maintained while having distinctly different records between the synthetic and original dataset. Finally, we packed the above end to end process into what we call Utility Guaranteed Deep Privacy (UGDP) system. UGDP can be easily extended to onboard alternative generative approaches such as GANs to synthesize tabular data.

An Israeli startup invested in heavily by American companies, including Microsoft, produces facial recognition software used to conduct biometric surveillance on Palestinians, investigations by NBC and Haaretz revealed. In June, Microsoft -- which has touted its framework for ethical use of facial recognition -- joined a group investment of $78 million to AnyVision, an international tech company based in Israel. One of AnyVision's flagship products is Better Tomorrow, a program that allows the tracking of objects and people on live video feeds, even tracking between independent camera feeds. AnyVision's facial recognition software is at the heart of a military mass surveillance project in the West Bank, according to the NBC and Haaretz reporting. An Israeli Defense Forces statement in February acknowledged the addition of facial recognition verification technology to at least 27 checkpoints between Israel and the West Bank to "upgrade the crossings" and, in an effort to "deter terror attacks," rapidly installed a network of over 1,700 cameras across the occupied territories.

There's still a long way to go before AI tools are fully integrated into the healthcare system, but there's enough AI already in the system that providers need to be increasingly well-versed in both the regulatory trends and the legal consequences thereof. So argues a team of attorneys from Polsinelli PC in the first of a three-part series posted in recent months at Bloomberg Law. The first thing for providers to recognize, say Iliana Peters, Liz Harding and Lindsay Dailey, is that while they may already recognize the growing role of AI in pharma circles or surgery robots, they "may not realize that the clinical decision support, claims review, and voice-to-text transcriptions tools that they use also include AI. Healthcare system IT staff also rely heavily on AI tools to detect and combat cyber threats to the information that healthcare providers need to provide quality care." And what that means is that "important state, federal, and international legal requirements" may already be in play in a way that is designed to control and monitor how personal information is used in healthcare decisions.