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Opinion/Middendorf: Artificial intelligence and the future of warfare

#artificialintelligence

J. William Middendorf, who lives in Little Compton, served as Secretary of the Navy during the Ford administration. His recent book is "The Great Nightfall: How We Win the New Cold War." Thirteen days passed in October 1962 while President John F. Kennedy and his advisers perched at the edge of the nuclear abyss, pondering their response to the discovery of Russian missiles in Cuba. Today, a president may not have 13 minutes. Indeed, a president may not be involved at all. "Artificial intelligence is the future, not only for Russia, but for all humankind. It comes with colossal opportunities but also threats that are difficult to predict. Whoever becomes the leader in this sphere will become the ruler of the world."


2021 Healthcare Cybersecurity Priorities: Experts Weigh In

#artificialintelligence

Healthcare cybersecurity is in triage mode. As systems are stretched to the limits by COVID-19 and technology becomes an essential part of everyday patient interactions, hospital and healthcare IT departments have been left to figure out how to make it all work together, safely and securely. Most notably, the connectivity of everything from thermometers to defibrillators is exponentially increasing the attack surface, presenting vulnerabilities IT professionals might not even know are on their networks. Get the whole story and DOWNLOAD the eBook now – on us!] The result has been a newfound attention from ransomware and other malicious actors circling and waiting for the right time to strike. Rather than feeling overwhelmed in the current cybersecurity environment, it's important for healthcare and hospital IT teams to look at security their networks as a constant work in progress, rather than a single project with a start and end point, according to experts Jeff Horne from Ordr and G. Anthony Reina who participated in Threatpost's November webinar on Heathcare Cybersecurity. "This is a proactive space," Reina said. "This is something where you can't just be reactive. You actually have to be going out there, searching for those sorts of things, and so even on the technologies that we have, you know, we're, we're proactive about saying that security is an evolving, you know, kind of technology, It's not something where we're going to be finished." Healthcare IT pros, and security professionals more generally, also need to get a firm handle on what lives their networks and its potential level of exposure. The fine-tuned expertise of healthcare connected machines, along with the enormous cost to upgrade hardware in many instances, leave holes on a network that simply cannot be patched. "Because, from an IT perspective, you cannot manage what you can't see, and from a security perspective, you can't control and protect what you don't know," Horne said. Threatpost's experts explained how healthcare organizations can get out of triage mode and ahead of the next attack. The webinar covers everything from bread and butter patching to a brand-new secure data model which applies federated learning to functions as critical as diagnosing a brain tumor. Alternatively, a lightly edited transcript of the event follows below. Thank you so much for joining. We have an excellent conversation planned on a critically important topic, Healthcare cybersecurity. My name is Becky Bracken, I'll be your host for today's discussion. Before we get started, I want to remind you there's a widget on the upper right-hand corner of your screen where you can submit questions to our panelists at any time. We encourage you to do that. You'll have to answer questions and we want to make sure we're covering topics most interesting to you, OK, sure. Let's just introduce our panelists today. First we have Jeff Horne. Jeff is currently the CSO at Ordr and his priors include SpaceX.


Gunmen Assassinate Iran's Top Nuclear Scientist in Ambush, Provoking New Crisis

NYT > Middle East

Iranian officials, who have always maintained that their nuclear ambitions are for peaceful purposes, not weapons, expressed fury and vowed revenge over the assassination, calling it an act of terrorism and warmongering that they quickly blamed on Israeli assassins and the United States. The White House, C.I.A. and Israeli officials declined to comment. But Mr. Fakhrizadeh's assassination -- only 10 months after the United States killed the powerful spymaster at the head of Iran's security machinery in a drone attack in Iraq -- could greatly complicate President-elect Joseph R. Biden Jr.'s plans to reactivate the 2015 nuclear agreement between Tehran and six other nations, which curtailed Iran's nuclear activities. Mr. Biden's transition team had no immediate comment on the assassination. President Trump withdrew the United States from the nuclear accord in 2018, unraveling the signature foreign policy achievement of his predecessor, Barack Obama, and isolating the United States from Western allies who tried to keep the agreement intact.


Transpara breast AI by ScreenPoint Medical reaches major milestone in the lead up to …

#artificialintelligence

It is the first and remains the only DEEP LEARNING system to be FDA cleared for use on both 2D and 3D mammograms and now is first of its kind to …


The New Weapons Of War Are Coming As The US Military Develops Weaponized Laser Beams - Walid Shoebat

#artificialintelligence

In the original "Austin Powers" film, the character Dr. Evil asked for "sharks with frickin' laser beams attached to their heads" in order to kill Austin Powers. While this scene is ridiculous and funny, the concept of using "laser beams" as a weapon is not new. However, with the rise in technology of robotics and AI, The Drive reports that the US government has been working on the'weapons of the future', which seem to be energy-directed weapons such as laser beams. All the microelectronics and iPhones and so forth, were starting to be drilled and the touch screens were being scribed, and all this whole industrial space opened up, and the reason was that these fiber lasers were very efficient at converting electrical power to optical power. The beam quality that came out of the fiber-laser, meaning the ability for that beam to be focusable, to provide a high-intensity spot to do things, like melt metal and drill holes, the beam quality was very high.


Spent Rockets Are Dangerous Space Trash, but They Could Be the Future of Living and Working in Orbit

Mother Jones

This piece was originally published in Wired and appears here as part of our Climate Desk Partnership. In early October, a dead Soviet satellite and the abandoned upper stage of a Chinese rocket narrowly avoided a collision in low Earth orbit. If the objects had crashed, the impact would have blown them to bits and created thousands of new pieces of dangerous space debris. Only a few days prior, the European Space Agency had published its annual space environment report, which highlighted abandoned rocket bodies as one of the biggest threats to spacecraft. The best way to mitigate this risk is for launch providers to deorbit their rockets after they've delivered their payload. But if you ask Jeffrey Manber, that's a waste of a perfectly good giant metal tube.


Artificial Intelligence for the Indo-Pacific: A Blueprint for 2030

#artificialintelligence

As even the most inattentive observer of contemporary international politics will attest, technological competition – mostly, but not always, between the U.S. and its allies on one hand, and China and Russia on the other – has once again risen to the fore. Analysts, so far, have approached this issue from various angles: what it means in terms of military balances, the possibility of international cooperation, what a technological edge implies for domestic policies, and so on. The outgoing Trump administration has made technological contestation with China a cornerstone of its strategic policy, emphasizing the need for the United States to maintain its edge when it comes to artificial intelligence (AI), quantum information science, and aerospace and other critical technologies, among others. Other Indo-Pacific powers, such as Australia, India, and Japan, have also joined the fray in pushing both new and emerging tech at home as well as promoting collaboration around it between "like-minded countries." In June this year, a Global Partnership on Artificial Intelligence of 14 states along with the European Union was launched, to facilitate collective AI research as well as implementation.


Robots on the rise as Americans experience record job losses amid pandemic

The Guardian

They can check you in and deliver orange juice to your hotel room, answer your questions about a missing package, whip up sushi and pack up thousands of subscription boxes. And, perhaps most importantly, they are completely immune to Covid-19. While people have had a hard time in the coronavirus pandemic, robots are having a moment. The Covid-19 pandemic has left millions of Americans unemployed – disproportionately those in the service industries where women and people of color make up the largest share of the labor force. In October, 11 million people were unemployed in the US, compared with about 6 million people who were without a job during the same time last year.


Protecting consumers from collusive prices due to AI

Science

The efficacy of a market system is rooted in competition. In striving to attract customers, firms are led to charge lower prices and deliver better products and services. Nothing more fundamentally undermines this process than collusion, when firms agree not to compete with one another and consequently consumers are harmed by higher prices. Collusion is generally condemned by economists and policy-makers and is unlawful in almost all countries. But the increasing delegation of price-setting to algorithms ([ 1 ][1]) has the potential for opening a back door through which firms could collude lawfully ([ 2 ][2]). Such algorithmic collusion can occur when artificial intelligence (AI) algorithms learn to adopt collusive pricing rules without human intervention, oversight, or even knowledge. This possibility poses a challenge for policy. To meet this challenge, we propose a direction for policy change and call for computer scientists, economists, and legal scholars to act in concert to operationalize the proposed change. Collusion among humans typically involves three stages (see the table). First, firms' employees with price-setting authority communicate with the intent of agreeing on a collusive rule of conduct. This rule encompasses a higher price and an arrangement to incentivize firms to comply with that higher price rather than undercut it in order to pick up more market share. For example, in 1995 the CEOs of Christie's and Sotheby's hatched their plans in a limo at Kennedy International Airport, and in 1994 the U.S. Federal Bureau of Investigation secretly taped the lysine cartel as they conspired in a Maui hotel room. At those meetings, they spoke about charging higher prices and how to enforce them. Second, successful communication results in the mutual adoption of a collusive rule of conduct, which commonly takes the form of a collusive pricing rule. A crucial component of this pricing rule is retaliatory pricing: Each firm raises its price and maintains that higher price under the threat of a “punishment,” such as a temporary price war, should it cheat and deviate from the higher price ([ 3 ][3]). It is this threat that sustains higher prices than would arise under competition. Third, firms set the higher prices that are the consequence of having adopted those collusive pricing rules. ![Figure][4] The process that produces higher prices To determine whether firms are colluding, one could look for evidence at any of the three stages. However, evidence related to the last two stages—pricing rules and higher prices—is generally regarded as insufficient to achieve the requisite level of confidence in the judicial realm. Economists know how to calculate competitive prices given demand, costs, and other relevant market conditions. But many of these factors are difficult to observe and, when observable, are challenging to measure with precision. Consequently, courts do not use the competitive price level as a benchmark to identify collusion. Likewise, it is difficult to assess whether the firms' rules of conduct are collusive because such rules are latent, residing in employees' heads. In practice, we may never observe the retaliatory lower prices from a firm that cheated, even though that response is there in the minds of the employees and it is the anticipation of such a response that sustains higher prices. In other words, we might lack the events that produce the data that could identify the collusive pricing rules. Furthermore, even if one could observe what looks like a price war, it would be difficult to rule out innocent explanations (such as a decrease in the firms' costs or a fall in demand). Given the latency of collusive pricing rules and the difficulty of determining whether prices are collusive or competitive, antitrust law and its enforcement have focused on the first stage: communications. Firms are found to be in violation of the law when communications (perhaps supplemented by other evidence) are sufficient to establish that firms have a “meeting of minds,” a “concurrence of wills,” or a “conscious commitment” that they will not compete ([ 4 ][5]). In the United States, more specifically, there must be evidence that one firm invited a competitor to collude and that the competitor accepted that invitation. The risk of false positives (i.e., wrongly finding firms guilty of collusion) has led courts to avoid basing their judgments on evidence of collusive pricing rules or collusive prices and instead to rely on evidence of communications. Although the use of pricing algorithms has a long history—airline companies, for instance, have been using revenue management software for decades—concerns regarding algorithmic collusion have only recently arisen for two reasons. First, pricing algorithms had once been based on pricing rules set by programmers but now often rely on AI systems that learn autonomously through active experimentation. After the programmer has set a goal, such as profit maximization, algorithms are capable of autonomously learning rules of conduct that achieve the goal, possibly with no human intervention. The enhanced sophistication of learning algorithms makes it more likely that AI systems will discover profit-enhancing collusive pricing rules, just as they have succeeded in discovering winning strategies in complex board games such as chess and Go ([ 5 ][6]). Second, a feature of online markets is that competitors' prices are available to a firm in real time. Such information is essential to the operation of collusive pricing rules. In order for firms to settle on some common higher price, firms' prices must be observed frequently enough because sustaining those higher prices requires the prospect of punishing a firm that deviates from the collusive agreement. The more quickly the punishment is meted out, the less temptation to cheat. Thus, the emergence and persistence of higher prices through collusion is facilitated by rapid detection of competitors' prices, which is now often possible in online markets. For example, the prices of products listed on Amazon may change several times per day but can be monitored with practically no delay. In light of these developments, concerns regarding the possibility of algorithmic collusion have been raised by government authorities, including the U.S. Federal Trade Commission (FTC) ([ 6 ][7]) and the European Commission ([ 7 ][8]). These concerns are justified, as enough evidence has accumulated that autonomous algorithmic collusion is a real risk. The evidence is both experimental and empirical. On the experimental side, recent research has found the spontaneous emergence of collusion in computer-simulated markets. In these studies, commonly used reinforcement-learning algorithms learned to initiate and sustain collusion in the context of well-accepted economic models of an industry ([ 8 ][9], [ 9 ][10]) (see the figure). Collusion arose with no human intervention other than instructing the AI-enabled learning algorithm to maximize profit (i.e., algorithms were not programmed to collude). Although the extent to which prices were higher in such virtual markets varied, prices were almost always substantially above the competitive level. On the empirical side, a recent study ([ 10 ][11]) has provided possible evidence of algorithmic collusion in Germany's retail gasoline markets. The delegation of pricing to algorithms was found to be associated with a substantial 20 to 30% increase in the markup of stations' prices over cost. Although the evidence is indirect—because the authors of the study could not directly observe the timing of adoption of the pricing algorithms and thus had to infer it from other data—their findings are consistent with the results of computer-simulated market experiments. Algorithmic collusion is as bad as human collusion. Consumers are harmed by the higher prices, irrespective of how firms arrive at charging these prices. However, should algorithmic collusion emerge in a market and be discovered, society lacks an effective defense to stop it. This is because algorithmic collusion does not involve the communications that have been the route to proving unlawful collusion (as distinguished from instances in which firms' employees might communicate and then collude with the assistance of algorithms, as in a recent case involving poster sellers on Amazon Marketplace). And even if alternative evidentiary approaches were to arise, there is no liability unless courts are prepared to conclude that AI has a “mind” or a “will” or is “conscious,” for otherwise there can be no “meeting of minds” with algorithmic collusion. As a result, if algorithmic collusion occurs and is discovered by the authorities, currently it cannot be considered a violation of antitrust or competition law. Society would then have no recourse and consumers would be forced to continue to suffer the harm from algorithmic collusion's higher prices. ![Figure][4] Collusive pricing rules uncovered After the two algorithms have found their way to collusive prices (“learning phase,” left side), an attempt to cheat so as to gain market share is simulated by exogenously forcing Firm 1's algorithm to cut its price (“punishment phase,” right side). From the “shock” period onward, the algorithm regains control of the pricing. Firm 1's deviation is punished by the other algorithm, so firms enter into a price war that lasts for several periods and then gradually ends as the algorithms return to pricing at a collusive level. For better graphical representation, the time scales on the right and left sides of the figure are different. GRAPHIC: N. CARY/ SCIENCE FROM CALVANO ET AL. ([ 8 ][9]) There is an alternative path, which is to target the collusive pricing rules learned by the algorithms that result in higher prices ([ 11 ][12]). These latent rules of conduct may be uncovered when they have been adopted by algorithms. Whereas a court cannot get inside the head of an employee to determine why prices are what they are, firms' pricing algorithms can be audited and tested in controlled environments. One can then simulate all sorts of possible deviations from existing prices and observe the algorithms' reaction in the absence of any confounding factor. In principle, the latent pricing rules can thus be identified precisely. This approach was successfully used by researchers in ([ 8 ][9]) to verify that the pricing algorithms have indeed learned the collusive property of reward (keeping prices high unless a price cut occurs) and punishment (through retaliatory price wars should a price cut occur). To show this, the researchers momentarily overrode the pricing algorithm of one firm, forcing it to set a lower price. As soon as the algorithms regained control of the pricing, they engaged in a temporary price war, where lower prices were charged but then gradually returned to the collusive level. Having learned that undercutting the other firm's price brings forth a price war (with the associated lower profits), the algorithms evolved to maintain high prices (see the figure). It may seem paradoxical that collusion can be identified by the low retaliatory prices, which could be close to the competitive level, rather than by the high prices that are the ultimate concern for policy. But there are two important differences between retaliatory price wars and healthy competition. First, in the absence of the low-price perturbation, the price war remains hypothetical in that it is a threat that is not executed. Second, the price war shown in the figure is only temporary: Instead of permanently reverting to the competitive price level, the algorithms gradually return to the pre-shock prices. This is evidence that the price war is there to support high prices, not to produce low prices. Focusing on the collusive pricing rules is the key to identifying, preventing, and prosecuting algorithmic collusion (see the table). Policy cannot target the higher prices directly, nor can it target communications as they may not be present (unlike with human collusion). But the retaliatory pricing rules may now be observable, as firms' pricing algorithms can be audited and tested. We therefore propose that antitrust policy shift its focus from communications (with humans) to rules of conduct (with algorithms). Making the proposed change operational involves a broad research program that requires the combined efforts of economists, computer scientists, and legal scholars. One strand of this program is a three-step experimental procedure. The first step creates collusion in the lab for descriptively realistic models of markets. As the competitive price would be known by the experimenter, collusion is identified by high prices. Having identified an episode of collusion, the second step is to perform a post hoc auditing exercise to uncover the properties of the collusive pricing rules that produced those high prices. Some progress has been made on the identification of collusive rules of conduct adopted by algorithms, but much more work needs to be done. Economics provides several properties to watch out for. Of course, there is the retaliatory price war discussed above, which is what existing research has focused on (8, 9). Another property is price matching, whereby firms' prices move in sync: one firm changing its price and the other firm subsequently matching that change. Price matching has been documented for human collusion in various markets, but we do not yet know whether algorithms are capable of learning it. A third property is the asymmetry of price responses. When firms collude, they typically respond to a competitor's price cut more strongly—as part of a punishment—than to a price increase. No such asymmetry is to be expected when firms compete. The aforementioned properties are based on economic theory and studies of human collusion. Learning algorithms may devise rules of conduct that neither economists nor managers have imagined ( just as learning algorithms have done, for instance, in chess). To investigate this possibility, computer scientists might develop algorithms that explain their own behavior, thereby making the collusive properties more apparent. One way of doing so is to add a second module to the reinforcement-learning module that maximizes profits; this second module maps the state representation of the first one onto a verbal explanation of its strategy ([ 12 ][13]). Having uncovered collusive pricing rules, the third step is to experiment with constraining the learning algorithm to prevent it from evolving to collusion. Computer scientists are particularly valuable here, given that they are involved in similar tasks such as trying to constrain algorithms so that, for instance, they do not exhibit racial and gender bias ([ 13 ][14]). Once the capacities to audit pricing algorithms for collusive properties and to constrain learning algorithms so that they do not adopt collusive pricing rules have been developed, legal scholars are called upon to use that knowledge for purposes of prosecution and prevention. One route is to make certain pricing algorithms unlawful, perhaps under Section 5 of the FTC Act, which prohibits unfair methods of competition. In the area of securities law, the 2017 case U.S. v. Michael Coscia made illegal the use of certain programmed trading rules and thus provides a legal precedent for prohibiting algorithms. Another path is to make firms legally responsible for the pricing rules that their learning algorithms adopt ([ 14 ][15]). Firms may then be incentivized to prevent collusion by routinely monitoring the output of their learning algorithms. These are some of the avenues that can be pursued for preventing and shutting down algorithmic collusion. There are several obstacles down the road, including the difficulty of making a collusive property test operational, the lack of transparency and interpretability of algorithms, and courts' willingness and ability to incorporate technical material of this nature. In addition, there is the challenge of addressing algorithmic collusion without giving up the efficiency gains from pricing algorithms such as the quicker response to changing market conditions. As authorities prepare to take action ([ 15 ][16]), it is vital that computer scientists, economists, and legal scholars work together to protect consumers from the potential harm of higher prices. 1. [↵][17]1. A. Ezrachi, 2. M. Stucke , Virtual Competition: The Promise and Perils of the Algorithm-Driven Economy (Harvard Univ. Press, 2016). 2. [↵][18]1. S. Mehra , Minn. Law Rev. 100, 1323 (2016). [OpenUrl][19] 3. [↵][20]1. J. Harrington , The Theory of Collusion and Competition Policy (MIT Press, 2017). 4. [↵][21]1. L. Kaplow , Competition Policy and Price Fixing (Princeton Univ. Press, 2013). 5. [↵][22]1. D. Silver et al ., Science 362, 1140 (2018). [OpenUrl][23][Abstract/FREE Full Text][24] 6. [↵][25]“The Competition and Consumer Protection Issues of Algorithms, Artificial Intelligence, and Predictive Analytics,” Hearing on Competition and Consumer Protection in the 21st Century, U.S. Federal Trade Commission, 13–14 November 2018; [www.ftc.gov/news-events/events-calendar/ftc-hearing-7-competition-consumer-protection-21st-century][26]. 7. [↵][27]“Algorithms and Collusion—Note from the European Union,” OECD Roundtable, June 2017; [www.oecd.org/competition/algorithms-and-collusion.htm][28]. 8. [↵][29]1. E. Calvano, 2. G. Calzolari, 3. V. Denicolo, 4. S. Pastorello , Am. Econ. Rev. 110, 3267 (2020). [OpenUrl][30] 9. [↵][31]1. T. Klein , “Autonomous Algorithmic Collusion: Q-Learning Under Sequential Pricing,” Amsterdam Law School Research Paper 2018-15 (2019). 10. [↵][32]1. S. Assad, 2. R. Clark, 3. D. Ershov, 4. L. Xu , “Algorithmic Pricing and Competition: Empirical Evidence from the German Retail Gasoline Market,” CESifo Working Paper No. 8521 (2020). 11. [↵][33]1. J. Harrington , J. Compet. Law Econ. 14, 331 (2018). [OpenUrl][34] 12. [↵][35]1. Z. C. Lipton , ACM Queue 16, 30 (2018). [OpenUrl][36] 13. [↵][37]1. P. S. Thomas et al ., Science 366, 999 (2019). [OpenUrl][38][Abstract/FREE Full Text][39] 14. [↵][40]1. S. Chopra, 2. L. White , A Legal Theory for Autonomous Artificial Agents (Univ. of Michigan Press, 2011). 15. [↵][41]European Commission, document Ares(2020)2877634. Acknowledgments: The paper benefited from detailed and insightful comments by three anonymous reviewers. All authors contributed equally. The authors declare no competing interests. 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Science

SCI COMMUN### Infectious diseases The 11th Ebola outbreak in the Democratic Republic of the Congo (DRC) is officially over, giving the country respite from the disease for the first time in more than 2 years. On 18 November, the World Health Organization (WHO) announced that no new cases had been identified for 42 days, twice the incubation period for the deadly virus. The outbreak, in the western Équateur province, started in late May, just as a bigger one in the eastern DRC was coming to an end. (That outbreak had killed 2200 people.) The Équateur outbreak sickened 130 and killed 55; a campaign that vaccinated more than 40,000 people is credited with helping end it. Special portable coolers that keep the vaccine at −80°C for up to 1 week allowed health workers to administer the shots in communities deep in the rainforest, accessible only by boat or helicopter. The same technology will be useful in efforts to distribute COVID-19 vaccines in Africa, says Matshidiso Moeti, WHO's regional director. The coronavirus pandemic complicated the fight against Ebola, WHO says, but the expertise gained by local health workers in earlier outbreaks in the region was a major advantage. They will remain on the lookout for potential flare-ups. $1,000,000 —Gift from entertainer Dolly Parton in April to support development of Moderna's coronavirus vaccine, which the company last week said showed an efficacy of 94.5%. “I felt so proud to have been part of that little seed money,” Parton told BBC. ### Marine ecology The Allen Coral Atlas, a project to map the world's shallow coral reefs with high-resolution satellites, last week launched a monitoring system to detect coral bleaching events as they occur. When corals face extreme heat, they expel their algal symbionts, leaving them bone white and vulnerable to stress; repeated bleaching episodes, growing more common with global warming, can cause massive die-offs. The system detects the whitening using imagery from the privately owned Planet satellite constellation, processed with machine learning. A pilot has begun in Hawaii to use the data as an early warning system for researchers, to help them identify and study species both vulnerable and resistant to warming extremes. The monitoring of bleaching is expected to expand next year to shallow reefs globally. ### Diagnostics The U.S. Food and Drug Administration (FDA) issued its first emergency use authorization last week for an at-home diagnostic test that can detect the pandemic coronavirus in just minutes. However, the test might not be widely available until spring 2021. Produced by Lucira Health, a biotech company, it is expected to cost less than $50 and require a doctor's prescription. The company says it will soon distribute tests in parts of California and Florida; it says it needs time to scale up manufacturing for national distribution. Lucira's test amplifies viral genetic material, making it nearly as accurate as laboratory tests that use the polymerase chain reaction, the current gold standard. FDA previously approved at-home tests that must be mailed to a laboratory for analysis. Several other companies are working on rapid antigen tests, which detect viral particles, for home use. But concerns remain about antigen tests' reliability. Still, some public health specialists consider widely available, low-cost, at-home testing vital for controlling the pandemic. ### Funding A new U.S. National Institutes of Health (NIH) award will allow early-career investigators who want to shift research directions when applying for their first independent award to submit a proposal without first generating preliminary data to support their idea. Reviewers will instead assess the soundness of the project's approach. The Katz award is named for Stephen Katz, a longtime champion of young researchers who was director of the National Institute of Arthritis and Musculoskeletal and Skin Diseases when he died in 2018. The grant will build on an NIH policy that prioritizes proposals from early-stage investigators—those no more than 10 years from completing their training who are applying for their first research grant. The policy has been credited with raising their numbers from fewer than 600 supported in 2013 to more than 1300 last year. Applications for the first Katz awards are due on 26 January 2021. ### Leadership Democrats in Congress say a political appointee given a senior post at the U.S. National Institute of Standards and Technology (NIST) is unfit for the job because he lacks technical skills and holds pseudoscientific views about racial differences on IQ tests. On 9 November, Jason Richwine, an independent public policy analyst, took up the new position of deputy undersecretary of commerce for standards and technology, and Commerce Secretary Wilbur Ross subsequently issued an order that would put Richwine in charge of the $1 billion research agency if NIST Director Walter Copan leaves or is fired. On 17 November, Representative Eddie Bernice Johnson (D–TX), who leads the science committee in the U.S. House of Representatives, asked Ross to justify the moves. Richwine has advocated for more restrictive immigration policies, and his 2009 doctoral thesis argued that lower IQ scores by Mexican and Hispanic immigrants suggest a genetic component to intelligence that is “likely to persist over several generations.” ### Diversity The editors of Nature Communications say they are reviewing a paper that drew scalding criticism after it suggested that encouraging female junior scientists to work with female mentors could “hinder the careers of women.” The 17 November study, led by data scientist Bedoor AlShebli of New York University, Abu Dhabi, examined 3 million mentor-protégé pairs and how gender influenced the impact of papers later published by the protégés. Female protégés, it concluded, did better if they worked with male mentors. Critics pounced, noting the authors ignored reviewer complaints about the study's methods and arguing the journal was promoting a harmful and unfounded message. The article's authors said they welcome the review. ### Animal diseases European authorities reported on 19 November they have detected highly pathogenic avian influenza in 302 birds in eight countries. Only 18 cases were in poultry; most of the rest were in wild birds, the European Food Safety Authority and its partners said. The number of infected birds is expected to rise with winter migrations. Several flu strains were identified, but no people were reported to be infected, and the risk of that occurring is considered low; researchers studying the viruses found no genetic markers indicating they had adapted to infect mammals. But the threat to poultry is high, and the report's authors recommended bird producers increase precautions against infections. VACCINE APPLICATION Days after making public the final analysis of their 40,000-person COVID-19 vaccine trial, which found 95% efficacy, Pfizer and its German partner BioNTech filed for emergency authorization of the messenger RNA vaccine from the U.S. Food and Drug Administration—the first such request for a vaccine during the pandemic. They plan to seek additional approvals in other countries soon. Pfizer hopes to supply up to 50 million doses this year. REMDESIVIR PANNED A World Health Organization panel recommended against using the antiviral drug remdesivir to treat most hospitalized COVID-19 patients. Its review of four studies of 7000 people found that the drug, which the U.S. Food and Drug Administration approved last month for hospitalized patients, did not reduce mortality or speed recovery. But the panel encouraged further study of it. AMMO BAN Denmark has become the first nation to ban all lead-based hunting ammunition, including bullets and shotgun pellets, to protect wildlife. Hunters annually release about 2 tons of lead into Denmark's environment; waterbirds and other species eat the toxic material and die. European regulators are considering a ban like Denmark's.