Last month, Anthony Fauci, the director of the National Institute of Allergy and Infectious Diseases, spoke at a biotech conference, where he emphasized how much is still unknown about the coronavirus. "I thought H.I.V. was a complicated disease," he said. "It's really simple compared to what's going on with COVID-19." To anyone who knows the history of AIDS research--Fauci has spent much of his career studying the disease--this was a dismaying thing to hear. In 1984, President Reagan's Health and Human Services Secretary, Margaret Heckler, said, "We hope to have a vaccine ready for testing in about two years."

> Science's COVID-19 reporting is supported by the Pulitzer Center and the Heising-Simons Foundation California was a COVID-19 success story—until suddenly it wasn't. Early in the pandemic, the state seemed to have the new coronavirus under control, but it has begun to ride a wave there, with records set in daily cases several times this month, and deaths on the rise. California officials whose COVID-19 responses were once hailed as enlightened are now receiving criticism—and some of the sharpest is coming from scientists seeking to help guide the state's fight against the virus. Since April, epidemiologists from Stanford University and several University of California (UC) campuses have sought detailed COVID-19 case and contact-tracing data from state and county health authorities for research they hope will point to more effective approaches to slowing the pandemic. “It's a basic mantra of epidemiology and public health: Follow the data” to learn where and how the disease spreads, says Rajiv Bhatia, a physician and epidemiologist who teaches at Stanford and is among those seeking the California data. But the agencies have refused requests filed from April through late June, Science has learned. They cited multiple reasons including workload constraints and privacy concerns—even though records can be deidentified, and federal health privacy rules have been relaxed for research during the pandemic. As a result, Bhatia says, “In 4 months of the epidemic, collecting millions of records, no one in California or at the CDC [U.S. Centers for Disease Control and Prevention] has done the basic epidemiology.” Other states also fail to share highly specific information for their COVID-19 cases, which some scientists warn is hampering efforts to identify targeted measures that could stem the spread of SARS-CoV-2 without full-scale lockdowns. Bhatia and epidemiologists across the country are especially aggrieved after recent news reports revealed states are feeding the same data they desire to a federal contractor, Palantir Technologies, that has drawn criticism for data work supporting Immigration and Customs Enforcement deportations. For a data platform dubbed HHS Protect, Palantir is aggregating information on the spread of the new coronavirus on behalf of the U.S. Department of Health and Human Services (HHS), drawing on more than 225 data sets, including demographic statistics, community-based tests, and a wide range of state-provided data. (This week, sparking concern among public health experts, epidemiologists, and others, HHS also instructed hospitals to provide data on COVID-19 cases and patient information directly to the Palantir system—largely via a second contractor—rather than to CDC as they have for decades.) Aggregated COVID-19 case and death data by county, and often by age and race, are publicly available in much of the country. But few locales link those cases and deaths to other information typically collected on the individuals, such as ZIP codes, occupations, living conditions, and known contacts with others ill with COVID-19. A survey of public data dashboards for all 50 states, Washington, D.C., and Puerto Rico by Prevent Epidemics, a group led by former CDC Director Tom Frieden, found that just 2% of data for 15 key COVID-19 indicators were fully reported. Only 40% of the data were partially reported—with glaring deficiencies for testing and contact tracing. Bhatia and colleagues say that detailed COVID-19 case data could be mined to find factors most responsible for the “biggest bundles of hospitalizations and deaths.” He hypothesizes the data would, for example, confirm that even as commerce opens up, hospitalizations and deaths mostly emerge from familiar flashpoints. He cites care facilities for the elderly and large households that include infected essential workers who are asymptomatic or have mild symptoms; they may then pass the disease to relatives who have risk factors making them more vulnerable to severe illness. “We think you can be more strategic on your interventions if you know where exposures actually occur,” says Jeffrey Klausner, a physician and epidemiologist at UC Los Angeles, who is also seeking his state's data. For example, case data might confirm patchy evidence that indoor dining is risky, but parks and beaches are generally safe. If so, reopening outdoor settings with reasonable precautions might boost the economy and allay fears that severe risk of infection is ubiquitous. As the pandemic evolves, regular reassessment of granular data on cases is vital, says Natalie Dean, a University of Florida (UF) biostatistician. “We have this whole new world now, where we are opening things back up. We have this shifting set of environments—indoor dining, bars, open retail buildings, offices, gyms. When we think of what are pressure points, there's a lot we just don't know yet. … We have to have ‘a learning architecture’ in place where there's always some level of reflection.” In the absence of clear, localized data from public authorities, some clinics in California have done their own research. After conducting thousands of COVID-19 tests in Oakland, “We have been able to pinpoint where some of the outbreaks are, both geographically and in terms of setting,” leading to highly targeted health education and testing outreach, says Noha Aboelata, a physician who heads the city's Roots Community Health Center, which primarily serves people of color in underserved communities. Without neighborhood-level intelligence for public health outreach, you get “a one-size-fits-all solution that might exacerbate the problem,” she says. “Withholding the information is going to lead to deaths.” In response to Science 's questions, the California Department of Public Health wrote that even deidentified data “can be used alone or in combination with publicly available information to identify an individual.” Caitlin Rivers, an epidemiologist at Johns Hopkins University's Center for Health Security, calls reidentification a valid concern, but argues it would happen so rarely that the risk shouldn't justify blanket denials of data requests during the pandemic. “There's a lot of space in the middle that we haven't really explored,” she adds. For example, to obviate some privacy concerns, Bhatia's group requested case reports giving 10-year age ranges rather than specific ages, the week of COVID-19 onset rather than a specific date, and an occupational group rather than specific occupation. To show the value of richer data, Bhatia turned to Florida, which offers fairly detailed information on each of the more than 316,000 COVID-19 cases recorded there so far. The data set enabled him to graph, week by week, infections by age and whether the source of transmission was known. He found that early in the pandemic, the source was known for 80% of children, and 50% to 60% of adults. As Florida relaxed restrictions on businesses and other aspects of life, known sources of transmission remained at similar levels, even though casual contact with strangers was apparently increasing. Because some of the unknown sources of transmission were certainly asymptomatic or mildly symptomatic family or friends, such a finding suggests crowded beaches are playing a smaller role in Florida's surge in infections than, say, increased numbers of large family gatherings at home or repopulated offices. “If people know that 50% or 60% of infections are resulting from people they know, including family, friends, and co-workers, they may better interpret risk,” Bhatia says. Even Florida's data exclude key details that some researchers view as essential to map and respond to the pandemic most effectively—including ZIP codes; more complete racial designations; and specifics on cases in long-term care facilities, jails, and prisons. That hampers targeted responses, says Thomas Hladish, an infectious disease researcher at UF who consulted extensively with state officials about COVID-19 data from March until this month. “A lot of the inconsistencies that you see are reasonably explained by well-intentioned people who are scrambling to reinvent [data fields and formats] on the fly without the appropriate technical background.” The Miami Herald also recently reported that municipal officials have not been able to get the state to provide case details they need to attack local outbreaks. The Florida Department of Health did not respond to Science 's requests for comment. Epidemiologists praise more forthcoming agencies. The New York City Department of Health and Mental Hygiene posts unusually complete, continually updated data sets on COVID-19—showing detailed information on tests, cases, and deaths for 177 discrete neighborhoods—and uses them to map hot spots. It offers probable and confirmed deaths by age, race or ethnicity, underlying conditions, and other factors. One clear finding: Lower income areas, with a higher concentration of large households, suffered from COVID-19 at many times the rate of most wealthy areas. The city's health commissioner, Oxiris Barbot, says the system was crucial in decreasing cases by about 94% and deaths by about 98% since they peaked in April. “The transparency in data helped to paint a picture of how acute a situation we were in and the degree to which we needed New Yorkers to comply with what we were asking them to do,” she says. “It helped highlight as early as possible the ways in which the virus was ravaging Black and brown communities.” And the granular data allowed a calibrated response—including offers of hotel rooms to help people living in crowded conditions isolate when diagnosed with COVID-19. “Had it not been for that data analysis we would have been much slower in the response, and … many more lives would have been lost,” Barbot says. “These are the right type of efforts using the right type of data,” Bhatia says. Figuring out how to stop the pandemic is “the biggest and most impactful policy decision we've seen in our lifetimes,” he adds. But in California and elsewhere, “We're trying to predict the future without analyzing the data that's in front of us. That's a failure.”

In the opening pages of Burn-In , an FBI agent conducts close-quarters surveillance of a suspected terrorist bomber in Washington, D.C. Simultaneously, in New Jersey, an elderly gentleman listens attentively to the enthusiastic technological prognostications of a world-famous computer scientist and mathematician from the back of a hallowed lecture hall at Princeton University. Moments later, he bludgeons the speaker to death with his cane. In this, their second novel, coauthors Peter Warren Singer and August Cole—both renowned technology and policy experts—come close to perfecting the genre of educational and informative techno-thriller. Like their first such collaboration ([ 1 ][1]), this latest entry portrays a world in which conventional aspects of domestic security and law enforcement—combating terrorism, managing protests and social upheavals, tracking a serial killer, providing a secure environment on college campuses—all occur within a transformative technological context that both enables and simultaneously disrupts these myriad objectives. As the narrative unfolds, a complex tapestry of emergent, disruptive technologies is revealed. Far from the fanciful inventions that typically populate science fiction, the systems described herein are currently available or under development for imminent deployment. The D.C. traffic congestion with which agent Lara Keegan and her partner have to contend, for example, is mostly composed of driverless vehicles, their complex operational algorithms engaged in competitive maneuvering for even the slightest comparative advantage. If the agents invoke the emergency override protocol granted to law enforcement personnel and cause the other vehicles to move aside, the surveillance drones buzzing overhead will immediately transmit this activity to the news outlets that operate them, alerting the terrorist to their presence. Keegan's field of vision, meanwhile, is networked into an operations command center via virtual reality glasses, which display real-time data on the suspect's location. These “viz glasses” continuously exchange data with other law enforcement personnel, while simultaneously performing facial scans of the surrounding crowds, subjecting each passerby to massive digital analysis. Once apprehended, despite his uncooperative silence, the suspect's identity is unmasked by a Tactical Autonomous Mobility System (TAMS), a military robot whose combat utility proved minimal and is now being tested for possible use in domestic law enforcement scenarios. Keegan, we learn, has been selected to field-test this robotic deep-learning technology system because of her prior experience managing the deployment and “force mix” of unmanned systems for the Marine Corps in Afghanistan. In technology circles, what she has been asked to undertake is known as a burn-in, a lengthy trial run of any new technological breakthrough, designed to push it to its limits of reliable functionality. The novel also contains ample instances of what the Defense Advanced Research Projects Agency (DARPA) and the National Science Foundation dub the ethical, legal, and social implications (ELSI) of technological development and diffusion. Just before his death, for example, the Princeton computer scientist boasts to his elderly guest how his use of Linux open-source software to develop complex machine-learning algorithms has made artificial intelligence (AI) universally available and affordable for every conceivable purpose. As his killer peels off an AI-designed silicon facial mask (manufactured on a 3D printer to confuse the university's AI-assisted security and surveillance system), he reveals himself to be a former DARPA engineer whose wife and son were tragically killed in a Metro crash caused by dangerous emergent behaviors in one of the scientist's AI-governed public transportation systems. This narrative thread, and many others throughout the book, illustrate what coauthor Peter Warren Singer identified in his widely acclaimed book Wired for War (published in 2009) as a key constituent of technological innovation and advance: “Anything that can go wrong, will—at the worst possible moment.” The aim of this work of fiction is not merely to engage and entertain but also to educate and inform readers about the vast array of automated and increasingly intelligent autonomous systems that are proliferating in availability and use. The authors provide detailed documentation of the actual features and current use of these systems, together with a companion educational guide to help instructors use the novel to teach about the profound depths of the robotic and AI revolution that is taking place all around us. 1. [↵][2]1. P. W. Singer, 2. A. Cole , Ghost Fleet: A Novel of the Next World War (Houghton Mifflin Harcourt, 2015). [1]: #ref-1 [2]: #xref-ref-1-1 "View reference 1 in text"

This is an ethics guide for United States Intelligence Community personnel on how to procure, design, build, use, protect, consume, and manage AI and related data. Answering these questions, in conjunction with your agency-specific procedures and practices, promotes ethical design of AI consistent with the Principles of AI Ethics for the Intelligence Community. This guide is not a checklist and some of the concepts discussed herein may not apply in all instances. Instead, this guide is a living document intended to provide stakeholders with a reasoned approach to judgment and to assist with the documentation of considerations associated with the AI lifecycle. In doing so, this guide will enable mission through an enhanced understanding of goals between AI practitioners and managers while promoting the ethical use of AI.

On June 18, the Food and Drug Administration authorized the use of pooled testing for identifying COVID-19 infections. The method allows up to four swabs to be tested at once – a strategy that is expected to greatly expand frequent testing to larger sections of the population. The idea is that if a bundled sample comes back positive, then all the individuals in that sample will need to be tested separately. If a bundled sample comes back clean, however, that's four people who don't need to be tested further, saving public health officials time and money. The FDA said it expects pooling will allow virus identification with fewer tests, which means more tests could be run at once, fewer testing supplies would be consumed and patients could likely receive results more quickly.

The Principles of Artificial Intelligence Ethics for the Intelligence Community are intended to guide personnel on whether and how to develop and use AI, to include machine learning, in furtherance of the IC's mission. To assist with the implementation of these Principles, the IC has also created an AI Ethics Framework to guide personnel who are determining whether and how to procure, design, build, use, protect, consume, and manage AI and other advanced analytics. We will employ AI in a manner that respects human dignity, rights, and freedoms. Our use of AI will fully comply with applicable legal authorities and with policies and procedures that protect privacy, civil rights, and civil liberties. We will provide appropriate transparency to the public and our customers regarding our AI methods, applications, and uses within the bounds of security, technology, and releasability by law and policy, and consistent with the Principles of Intelligence Transparency for the IC.

Both races are setting the stage for the next dominant world power. While research into AI and quantum technologies is being developed on a worldwide scale, with advances coming from different countries, China and the United States (US) are at the forefront of both races, with these technologies forming important stepping stones for geopolitical power accumulation. Indeed, China is currently playing the game for supremacy on both quantum technologies and AI, trying to surpass the US and become the leading world power (Smith-Goodson, 2019). If China wins the race for quantum supremacy then it will be in a leading geostrategic position, since it will become the major dominant power in the next technological infrastructure, if, along with quantum supremacy, China achieves AI supremacy (both classical and quantum), then it may topple the US, Russia, Europe and Asian geopolitical competition vectors. On the other hand, this race is not restricted to countries, it is a global geostrategic and geoeconomic race that includes cooperative networks involving the academia and the private sectors as well, indeed, the US geostrategic position depends strongly upon the private sector's US-based large technology companies' investment in quantum technologies. Regarding the issue of quantum supremacy, it is relevant to consider Kirkland (2020)'s reflection, quoting: "(…) One thing remains unchanged (…) and that is the glaring reality that those who manage to successfully harness the power of quantum mechanics will have supremacy over the rest of the world. How do you think they will use it?"

LITTLE ROCK, Ark., July 23, 2020 (GLOBE NEWSWIRE) -- INUVO, INC. (NYSE AMERICAN: INUV) ("Inuvo" or the "Company"), a leading provider of marketing technology, powered by IntentKey artificial intelligence that serves brands and agencies, today announced the pricing of an underwritten public offering of 20,000,000 shares of its common stock at a price to the public of $0.50 per share. The gross proceeds to Inuvo, Inc. from this offering are expected to be approximately $10,000,000, before deducting underwriting discounts and commissions and other estimated offering expenses. Inuvo has granted the underwriters a 45-day option to purchase up to an additional 1,500,000 shares of common stock to cover over-allotments, if any. The offering is expected to close on or about July 27, 2020, subject to customary closing conditions. A.G.P./Alliance Group Partners is acting as sole book-running manager for the offering.

Bureaucrats -- particularly those from the European Union (EU) -- rarely get the praise they deserve. By their nature, they are reserved, so they do not draw attention to themselves when things go well. When things go poorly, though, they make for a convenient target. So when the EU does something bold, we should give it its due. The EU's boldness in addressing a host of environmental problems head-on is unmatched.

The National Oceanic and Atmospheric Administration has released a new strategy to help expand the application of artificial intelligence in its missions. The NOAA AI strategy sets five goals to improve coordination, understanding, application and awareness of AI across the agency and one of the goals is establishing efficient organizational processes and structure to advance AI. There are five supporting objectives for the first goal, including the establishment of an AI center or a similar entity to facilitate coordination of AI research, data acquisition and algorithm development, among others. The second goal calls for the advancement of AI research and innovation in support of the agency's mission. One of the supporting objectives is establishing an annual research and development competition for AI applications in environmental science.