We're about to enter the "exascale era" of computing, which could have widespread positive impacts for governments, businesses and society at large. The U.S. Department of Energy recently announced contracts for supercomputers that will each provide more than an exaflop of performance. That means they can perform 1 quintillion mathematical calculations (called floating-point operations, or flops) every second. The work done on these systems will impact all our lives. And the technologies developed for them will enhance computing systems at all scales, transforming the future of the enterprise.

Robotics applications and jobs recruiting are multiplying exponentially as artificial intelligence (AI) breakthroughs accelerate robot improvement. This was discussed during my recent appearance on WGN Radio. The global average for industrial robots per 10,000 manufacturing workers grew from 66 in 2015 to 85 in 2017. This is according to the International Federation of Robotics (IFR). Robotics improvements, such as advanced machine vision and sensors as well as artificial intelligence (AI) breakthoughs have rapidly increased robot breakthroughs.

What artificial intelligence will never be able to give us") is the title of the book that the sociologist Francesco Morace has dedicated to the prospects opened up with the arrival of extremely powerful new technology yet to be discovered. How can we get our bearings in this new world? Will machines end up replacing us? Or will they develop and bring out the true potential of humans? These are open questions, the answers to which are yet to be written.

The U.S. health care system uses commercial algorithms to guide health decisions. Obermeyer et al. find evidence of racial bias in one widely used algorithm, such that Black patients assigned the same level of risk by the algorithm are sicker than White patients (see the Perspective by Benjamin). The authors estimated that this racial bias reduces the number of Black patients identified for extra care by more than half. Bias occurs because the algorithm uses health costs as a proxy for health needs. Less money is spent on Black patients who have the same level of need, and the algorithm thus falsely concludes that Black patients are healthier than equally sick White patients. Reformulating the algorithm so that it no longer uses costs as a proxy for needs eliminates the racial bias in predicting who needs extra care. Science, this issue p. 447; see also p. 421 Health systems rely on commercial prediction algorithms to identify and help patients with complex health needs. We show that a widely used algorithm, typical of this industry-wide approach and affecting millions of patients, exhibits significant racial bias: At a given risk score, Black patients are considerably sicker than White patients, as evidenced by signs of uncontrolled illnesses. Remedying this disparity would increase the percentage of Black patients receiving additional help from 17.7 to 46.5%. The bias arises because the algorithm predicts health care costs rather than illness, but unequal access to care means that we spend less money caring for Black patients than for White patients.

Most of the computer applications in law enforcement exploit the ability of the computer to handle large sets of data and to bring the relevant items to the user's attention when needed. A relatively untapped capability lies with artificial intelligence, using the computer's capacity for analysis. Artificial intelligence is the common term, but a more accurate way of describing this capability is "machine learning." This involves first teaching the computer to recognize the object(s) you're interested in, then to get the machine to find that object amid many others. Humans learn to do this without thinking about the process, but getting the computer to do the same thing takes some understanding of how we learn and what it is we are recognizing.

There is no shortage of statistics on the increasing uptake of artificial intelligence (AI) in business. With adoption poised to grow over the next few years, this will undoubtedly lead to the discovery of new use cases and applications of the technology that were never before on the radar of business leaders. While this anticipated growth is exciting from a technological standpoint, it is also fraught with ethical landmines. As the associate director of the Charlotte Visualization Center at the University of North Carolina at Charlotte, I oversee internal and external research on the use of big data in industries such as science, business and medicine, so I'm familiar with the frequent intersection of ethics and AI. In today's digital age, organizations have access to unprecedented volumes of consumer data -- including personally identifiable information (PII), which can range from your name, credit card information and medical records to IP addresses and biometric data such as fingerprints.

Artificial intelligence is helping prisons and jails monitor inmate phone calls to learn of criminal activity and potential suicides. The technology uses speech recognition, semantic analytics and machine learning software to build databases of searchable words, ABC News reports. The technology companies notify law enforcement when the system picks up suspicious language. One company, LEO Technologies based in Los Angeles, sends investigators to prisons that use its services to add phrases and slang from the prison and surrounding area to its database. Inmates are warned that their calls are being recorded, but they make incriminating statements on the phone anyway.

Fox News Flash top headlines for Oct. 25 are here. Check out what's clicking on Foxnews.com NASA has unveiled its plan to send a new lunar rover, VIPER, to the surface of the Moon. "VIPER is going to rove on the South Pole of the moon and assess where the water ice is," said NASA Administrator Jim Bridenstine during a wide-ranging speech at the International Astronautical Congress in Washington D.C. on Friday. The government space agency notes that the Moon has vast reservoirs of water ice, an amount that could potentially reach millions of tons.

BROOKLYN, New York, Tuesday, October 22, 2019 – The ubiquity and sheer volume of data generated today give experts in virtually every domain ample information to track everything from financial trends, disaster evacuation routes, and street traffic, to animal migrations, weather patterns, and disease vectors. But using this data to build visualizations of complex predictive models using machine learning is a challenge to experts who lack the requisite computer science skills. A team at the NYU Tandon School of Engineering's Visualization and Data Analytics (VIDA) lab, led by Claudio Silva, professor in the department of computer science and engineering, developed a framework called VisFlow, by which those who may not be experts in machine learning can create highly flexible data visualizations from almost any data. Furthermore, the team made it easier and more intuitive to edit these models by developing an extension of VisFlow called FlowSense, which allows users to synthesize data exploration pipelines through a natural language interface. The research, "FlowSense: A Natural Language Interface for Visual Data Exploration with a Dataflow System" won the best-paper award at this year's IEEE Conference on Visual Analytics Science and Technology (VAST).

Founded in 1992, we are a fast-growing department undertaking internationally respected research in the areas of computer science, human-machine interaction, artificial intelligence and applied mathematics. Much of our research takes place at the interfaces of these disciplines. We maintain a large network of industry partners and provide education through one bachelor's programme and two master's programmes, all of which are nationally ranked #1 in their cohort according to the most recent education rankings. Situated in the heart of Europe and within 30 kilometers from the German and Belgian borders, Maastricht and its 120,000 inhabitants have a strong international character. It is a safe, vibrant city with a history spanning more than 2,000 years. The city's rich past is reflected everywhere in the streets: the ratio of monuments-to-inhabitants is roughly 1:73.