Lawmakers on the Hill sees a gap in the government's artificial intelligence strategy, so they're filling in. Will Hurd, R-Texas, and Robin Kelly, D-Ill., published a workforce AI white paper that calls for the rethinking of American education and workforce development in order for the U.S. to keep pace in the global race for AI dominance. The lawmakers worked with the Bipartisan Policy Center to release the paper, which is the first in a series of four. Congressional staff told FedScoop the lawmakers' work is not necessarily in reaction to the White House's but meant to be "complimentary." The white paper was a year in the making after Hurd and Kelly announced their bipartisan collaboration on AI policy to make up for the Trump administration's "woefully underprepared" approach to support American AI development, as Kelly put it.

It took a global pandemic and the death of George Floyd to put deep-seated social inequities, especially systemic racism, front and center for intense public debate. We may or may not be on the cusp of a redressing social injustice by reordering our legacy political and economic systems. Either way, a singular piece of technology – artificial intelligence (AI) -- is destined to profoundly influence which way we go from here. This is not just my casual observation. Those in power fully recognize how AI can be leveraged to preserve status-quo political and economic systems, with all of its built-in flaws, more or less intact.

Zebra Medical Vision, an Israeli medical imaging analytics company, said on Monday it received clearance from the U.S. Food and Drug Administration for its mammography technology. The company's latest cleared product uses artificial intelligence to prioritize and identify suspicious mammograms. The mammograms are automatically sent to Zebra's platform, where they are processed and analysed for suspected breast lesions. The HealthMammo product then returns its result to the radiologist. It is the company's first oncology tool to receive FDA clearance.

In the mid-nineteen-seventies, the West German Army, the Bundeswehr, built a vast underground bunker near the town of Traben-Trarbach. It was five stories deep, had nearly sixty thousand square feet of floor space, and was designed to withstand a nuclear attack. Eighty days' worth of survival provisions were stored inside, including an emergency power supply and more than a million litres of drinking water. You entered the facility through an air lock; the interior temperature was set to seventy degrees. The walls were concrete, thirty-one inches thick, and some were lined with copper.

This article originally appeared in Hacker Noon. In the spring of 1993, a Harvard statistics professor named Donald Rubin sat down to write a paper. Rubin's paper would go on to change the way that artificial intelligence is researched and practiced, but its stated goal was more modest: analyze data from the 1990 U.S. census, while preserving the anonymity of its respondents. It wasn't feasible to simply anonymize the data, because individuals could still be identified by their home address, phone number, or social security number, all of which was crucial to the analyses that Rubin's colleagues wanted to perform. To solve the problem, Rubin generated a set of anonymized census responses whose population statistics mirrored those of the original data set.

An online app called Amica is now using artificial intelligence to help separating couples make parenting arrangements and divide their assets. For many people, the coronavirus pandemic has put even the strongest of relationships to the test. A May survey conducted by Relationships Australia found 42 percent of 739 respondents experienced a negative change in their relationship with their partner under lockdown restrictions. There has also been a surge in the number of couples seeking separation advice. The Australian government has backed the use of Amica for those in such circumstances.

By harnessing the power of advanced Big Data analytics, Artificial Intelligence (AI), and Machine Learning (ML), one can reduce the risk of a breach …

Waterborne diseases affect more than 2 billion people worldwide, causing substantial economic burden. For example, the treatment of waterborne diseases costs more than $2 billion annually in the United States alone, with 90 million cases recorded per year. Among waterborne pathogen-related problems, one of the most common public health concerns is the presence of total coliform bacteria and Escherichia coli (E. Traditional culture-based bacteria detection methods often take 24-48 hours, followed by visual inspection and colony counting by an expert, according to the United States Environmental Protection Agency (EPA) guidelines. Alternatively, molecular detection methods based on, for example, the amplification of nucleic acids, can reduce the detection time to a few hours, but they generally lack the sensitivity for detecting bacteria at very low concentrations, and are not capable of differentiating between live and dead microorganisms.

In this paper we describe our submission to the Zerospeech 2020 challenge, where the participants are required to discover latent representations from unannotated speech, and to use those representations to perform speech synthesis, with synthesis quality used as a proxy metric for the unit quality. In our system, we use the Bayesian Subspace Hidden Markov Model (SHMM) for unit discovery. The SHMM models each unit as an HMM whose parameters are constrained to lie in a low dimensional subspace of the total parameter space which is trained to model phonetic variability. Our system compares favorably with the baseline on the human-evaluated character error rate while maintaining significantly lower unit bitrate.

Multi-human multi-robot (MH-MR) systems have the ability to combine the potential advantages of robotic systems with those of having humans in the loop. Robotic systems contribute precision performance and long operation on repetitive tasks without tiring, while humans in the loop improve situational awareness and enhance decision-making abilities. A system's ability to adapt allocated workload to changing conditions and the performance of each individual (human and robot) during the mission is vital to maintaining overall system performance. Previous works from literature including market-based and optimization approaches have attempted to address the task/workload allocation problem with focus on maximizing the system output without regarding individual agent conditions, lacking in real-time processing and have mostly focused exclusively on multi-robot systems. Given the variety of possible combination of teams (autonomous robots and human-operated robots: any number of human operators operating any number of robots at a time) and the operational scale of MH-MR systems, development of a generalized framework of workload allocation has been a particularly challenging task. In this paper, we present such a framework for independent homogeneous missions, capable of adaptively allocating the system workload in relation to health conditions and work performances of human-operated and autonomous robots in real-time. The framework consists of removable modular function blocks ensuring its applicability to different MH-MR scenarios. A new workload transition function block ensures smooth transition without the workload change having adverse effects on individual agents. The effectiveness and scalability of the system's workload adaptability is validated by experiments applying the proposed framework in a MH-MR patrolling scenario with changing human and robot condition, and failing robots.