There is technology available today that can make every election day going forward safe, efficient, and most importantly, secure. If we look to AI and innovation, we can see the future of election day. No long lines, no waiting on ballots to be dumped and counted. No wondering if your mailed or absentee vote was counted and counted correctly. Instantaneous, secure and 100% accurate results.

Metal-organic frameworks (MOFs) are a class of porous and crystalline materials that are synthesized from inorganic metal ions or clusters connected to organic ligands. Shown are two such materials, HKUST-1 and MIL-100(Fe). An artificial intelligence technique -- machine learning -- is helping accelerate the development of highly tunable materials known as metal-organic frameworks (MOFs) that have important applications in chemical separations, adsorption, catalysis, and sensing. Utilizing data about the properties of more than 200 existing MOFs, the machine learning platform was trained to help guide the development of new materials by predicting an often-essential property: water stability. Using guidance from the model, researchers can avoid the time-consuming task of synthesizing and then experimentally testing new candidate MOFs for their aqueous stability.

Point clouds can be captured by an ever-increasing number of means to understand the surrounding reality and detect critical developments. Diverse applications of 3D laser scanning or'Lidar', which is a technology on a sky-rocketing path to be used for mapping and surveying, are changing the way we collect and refine topographic data. Which technologies and processes are building the capability for high-density 3D data? This article outlines the latest industry developments. National topographic databases store data refined from field measurements, imagery and laser scanning data at certain specifications and purposes, but lack the ability to adapt to ever-changing needs and situational awareness. 'Data on demand' is a recognized megatrend in the geospatial industry.

Fox News contributor Joe Concha weighs in on the mainstream media's coverage of election celebrations vs. Trump rallies on'America's Newsroom.' Washington Post media columnist Margaret Sullivan suggested that her journalist peers "never fully learned how to cover" President Trump but "might have saved democracy" following his projected defeat against Joe Biden. "Over the past four or five years, I've been sharply critical of the media, including that subset I like to call the'reality-based press,'" Sullivan wrote on Sunday. "My continuing complaint has been that mainstream journalism never quite figured out how to cover President Trump, the master of distraction and insult who craved media attention and knew exactly how to get it, regardless of what it meant for the good of the nation." Sullivan indicated that the press was too obedient of the "deeply abnormal president," writing "When he said'jump,' journalists all too often said'how high?'" and that the media "constantly sought to normalize him, treating his deranged tweets like legitimate news and piously forecasting, every time he sounded the least bit calm, that he was becoming'presidential.'"

IBM Chief Executive Officer Arvind Krishna sent the following letter to President-elect Joe Biden congratulating him on his election and outlining policy initiatives where IBM seeks to work with the incoming administration. We also congratulate Vice President-elect Kamala Harris for her historic, groundbreaking election. In your speech on Saturday evening, you spoke about bringing the country together to tackle the monumental challenges our nation faces. IBM is committed to working with your Administration to do its part. We share your vision of using science to control the virus, widening economic opportunities, achieving racial justice and combatting the climate crisis.

Artificial Intelligence (AI) is a digital technology that has significantly impacted the development of humanity. The convergence of the availability of a vast amount of big data, speed and scalability of cloud computing platforms, and the advancement of sophisticated machine learning algorithms have given birth to an array of innovations in Artificial Intelligence (AI). The term artificial intelligence has been around since the 1950s although its full implication came almost four decades later. The twenty-first century is the time when AI saw emerging applications and technologies that helped people shape their lives to the modern era. Ultimately, in recent years, AI has seen unprecedented growth.

Classification of polarimetric synthetic aperture radar (PolSAR) images is an active research area with a major role in environmental applications. The traditional Machine Learning (ML) methods proposed in this domain generally focus on utilizing highly discriminative features to improve the classification performance, but this task is complicated by the well-known "curse of dimensionality" phenomena. Other approaches based on deep Convolutional Neural Networks (CNNs) have certain limitations and drawbacks, such as high computational complexity, an unfeasibly large training set with ground-truth labels, and special hardware requirements. In this work, to address the limitations of traditional ML and deep CNN based methods, a novel and systematic classification framework is proposed for the classification of PolSAR images, based on a compact and adaptive implementation of CNNs using a sliding-window classification approach. The proposed approach has three advantages. First, there is no requirement for an extensive feature extraction process. Second, it is computationally efficient due to utilized compact configurations. In particular, the proposed compact and adaptive CNN model is designed to achieve the maximum classification accuracy with minimum training and computational complexity. This is of considerable importance considering the high costs involved in labelling in PolSAR classification. Finally, the proposed approach can perform classification using smaller window sizes than deep CNNs. Experimental evaluations have been performed over the most commonly-used four benchmark PolSAR images: AIRSAR L-Band and RADARSAT-2 C-Band data of San Francisco Bay and Flevoland areas. Accordingly, the best obtained overall accuracies range between 92.33 - 99.39% for these benchmark study sites.

With robotics rapidly advancing, more effective human-robot interaction is increasingly needed to realize the full potential of robots for society. While spoken language must be part of the solution, our ability to provide spoken language interaction capabilities is still very limited. The National Science Foundation accordingly convened a workshop, bringing together speech, language, and robotics researchers to discuss what needs to be done. The result is this report, in which we identify key scientific and engineering advances needed. Our recommendations broadly relate to eight general themes. First, meeting human needs requires addressing new challenges in speech technology and user experience design. Second, this requires better models of the social and interactive aspects of language use. Third, for robustness, robots need higher-bandwidth communication with users and better handling of uncertainty, including simultaneous consideration of multiple hypotheses and goals. Fourth, more powerful adaptation methods are needed, to enable robots to communicate in new environments, for new tasks, and with diverse user populations, without extensive re-engineering or the collection of massive training data. Fifth, since robots are embodied, speech should function together with other communication modalities, such as gaze, gesture, posture, and motion. Sixth, since robots operate in complex environments, speech components need access to rich yet efficient representations of what the robot knows about objects, locations, noise sources, the user, and other humans. Seventh, since robots operate in real time, their speech and language processing components must also. Eighth, in addition to more research, we need more work on infrastructure and resources, including shareable software modules and internal interfaces, inexpensive hardware, baseline systems, and diverse corpora.

Typically, fair machine learning research focuses on a single decisionmaker and assumes that the underlying population is stationary. However, many of the critical domains motivating this work are characterized by competitive marketplaces with many decisionmakers. Realistically, we might expect only a subset of them to adopt any non-compulsory fairness-conscious policy, a situation that political philosophers call partial compliance. This possibility raises important questions: how does the strategic behavior of decision subjects in partial compliance settings affect the allocation outcomes? If k% of employers were to voluntarily adopt a fairness-promoting intervention, should we expect k% progress (in aggregate) towards the benefits of universal adoption, or will the dynamics of partial compliance wash out the hoped-for benefits? How might adopting a global (versus local) perspective impact the conclusions of an auditor? In this paper, we propose a simple model of an employment market, leveraging simulation as a tool to explore the impact of both interaction effects and incentive effects on outcomes and auditing metrics. Our key findings are that at equilibrium: (1) partial compliance (k% of employers) can result in far less than proportional (k%) progress towards the full compliance outcomes; (2) the gap is more severe when fair employers match global (vs local) statistics; (3) choices of local vs global statistics can paint dramatically different pictures of the performance vis-a-vis fairness desiderata of compliant versus non-compliant employers; and (4) partial compliance to local parity measures can induce extreme segregation.

Neural networks are vulnerable to input perturbations such as additive noise and adversarial attacks. In contrast, human perception is much more robust to such perturbations. The Bayesian brain hypothesis states that human brains use an internal generative model to update the posterior beliefs of the sensory input. This mechanism can be interpreted as a form of self-consistency between the maximum a posteriori (MAP) estimation of an internal generative model and the external environment. Inspired by such hypothesis, we enforce self-consistency in neural networks by incorporating generative recurrent feedback. We instantiate this design on convolutional neural networks (CNNs). The proposed framework, termed Convolutional Neural Networks with Feedback (CNN-F), introduces a generative feedback with latent variables to existing CNN architectures, where consistent predictions are made through alternating MAP inference under a Bayesian framework. In the experiments, CNN-F shows considerably improved adversarial robustness over conventional feedforward CNNs on standard benchmarks.