Disinformation has become a central feature of the COVID-19 crisis. According to a recent poll, false or misleading information about the pandemic reaches close to half of all online news consumers in the U.K. As this type of malign information and high-tech "deepfake" imagery can spread so fast online, it poses a risk to democratic societies worldwide by increasing public mistrust in governments and public authorities -- a phenomenon referred to as "truth decay." New research, however, highlights new ways to detect and dispel disinformation online. There are several factors that may account for the rapid spread of disinformation during the COVID-19 pandemic. Given the global nature of the pandemic, more groups are using disinformation to further their agendas.

If you marched in recent Black Lives Matter protests in Atlanta, Los Angeles, Minneapolis or New York, there's a chance the mobile analytics company Mobilewalla gleaned demographic data from your cellphone use. Last week, Mobilewalla released a report detailing the race, age and gender breakdowns of individuals who participated in protests in those cities during the weekend of May 29th. What is especially disturbing is that protestors likely had no idea that the tech company was using location data harvested from their devices. As BuzzFeed News explains, Mobilewalla buys data from sources like advertisers, data brokers and ISPs. It uses AI to predict a person's demographics (race, age, gender, zip code, etc.) based on location data, device IDs and browser histories.

Unplanned hospital readmission is an indicator of patients' exposure to risk and an avoidable waste of medical resources. To address the unplanned readmission issue, in 2010, the Affordable Care Act (ACA) created the Hospital Readmissions Reduction Program to penalize the hospitals whose 30-day readmission rates are higher than expected [1]. According to data released by the Centers for Medicare & Medicaid Services (CMS), since the program began on Oct. 1, 2012, hospitals have experienced nearly $2.5 billion of penalties assessed on hospitals for readmissions, including an estimated $564 million in fiscal year 2018, $144 million more than in 2016 [2]. In addition to hospital readmission, intensive care unit (ICU) readmission brings further financial risk, along with morbidity and mortality risks [3,4]. Premature ICU discharge may potentially expose patients to the risks of unsuitable treatment, which further leads to an avoidable mortality [5].

The traditional safety measures in the space of cyber security rely on antivirus programming like firewalls. Different tools distinguish and forestalls web security dangers. In this case, the timely updates of the antivirus software in accordance with the latest threats and the mentality of an individual who is accountable for security would decide the degree of security site on the digital platform. AI relies on creative innovations like Machine Learning, Deep Learning, Natural Language Processing, and so forth to make it hard for programmers to access servers and other important data put away inside PCs. AI has crossed many milestones and now it's turn for cyber security. So let's learn how Artificial Intelligence in Cyber security is helpful.

A group of Democratic Senators and House representatives have introduced a bill that seeks to ban federal use of facial recognition technology. It follows an incident in which Detroit police wrongfully arrested a man after a facial recognition system incorrectly flagged him as a suspect. That's believed to be the first wrongful arrest of its kind in the US. Senators Ed Markey (Massachusetts) and Jeff Merkley (Oregon) authored the Facial Recognition and Biometric Technology Moratorium Act, which Reps. The aim of the bill is to "prohibit biometric surveillance by the federal government without explicit statutory authorization."

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Maybe it will take out missiles beyond the earth's atmosphere, incinerate targets well beyond the horizon with high-powered laser weapons and instantly stop a multi-faceted series of incoming attacks all at the same time? Perhaps it will use AI-empowered algorithms to launch a large fleet of networked surface, air and undersea drones, able to launch coordinated attacks at long ranges? All of these capabilities, advanced well beyond the current state-of-the-art into a new generation of maritime warfare weapons, are likely to figure prominently in the Navy's current conceptual work on a new generation of destroyers to emerge more than a decade from now – the Future Surface Combatant.

NASA's Perseverance rover aims to find out whether ancient Mars was warm and wet or cold and dry. NASA's newest Mars rover, Perseverance, is going back in time to the bottom of a vanished lake. If all goes well, in February 2021 it will land in Jezero crater and pop the dust covers off its camera lenses. Towering in front of it, in all likelihood, will be a 60-meter cliff of mudstone: the edge of a fossilized river delta. These lithified martian sediments could hold answers to urgent questions about the earliest days of Earth's chilly, parched neighbor: How did this pintsize planet, so distant from a faint young Sun, support liquid water on its surface? How much water was there, and how long did it persist? And did Mars ever spawn life? The 45-kilometer-wide crater is an intriguing target. Billions of years ago, when life was just beginning on Earth, water broke through its western rim and spilled into its interior, carrying sediments that settled and piled up in thick, meandering braids that today can be seen from space, as plain as day. “It's kind of like the Mississippi delta, but smaller,” says Raymond Arvidson, a planetary geologist at Washington University in St. Louis. The water filled the crater like a bathtub until, 250 meters deep, it breached the eastern rim. And then, just as mysteriously as it arrived, the water disappeared. Scientists have traced the tracks of ancient water across Mars ever since the 1970s, when orbiters revealed branching valley networks that matched the dendritic shape of water-eroded valleys on Earth. In the 1990s, the Mars Global Surveyor zoomed in on deeply incised gullies that could only have been carved by powerful flows of water—and may even have glimpsed shorelines from an ancient ocean. Later orbiters found evidence of abundant clay-bearing minerals that need water to form. More recently, the Curiosity rover, Perseverance's predecessor, has charted the existence of a long-lived lake at the bottom of its adopted home, Gale crater. Some scientists believe the water shows ancient Mars was warm for millions of years, a favorable climate for life to emerge. Others say the climate was cold and dry, punctuated by sporadic bursts of water that only lasted for hundreds or thousands of years—a much more difficult environment for life to take root. Along with the question of past life, says Ken Farley, the mission's project scientist and a geologist at the California Institute of Technology (Caltech), Mars's ancient climate “is the biggest unanswered question.” Perseverance will tackle both questions, although the search for life will take longer. The rover, developed by NASA's Jet Propulsion Laboratory (JPL) and set for launch next month from Cape Canaveral Air Force Station in Florida, is also the start of an audacious campaign that will ferry to Earth about 30 samples of martian rock and grit. Perseverance will gather the samples, and NASA and the European Space Agency (ESA) are designing two follow-up missions to retrieve them, aiming for launches in 2026 ( Science , 22 November 2019, p. [932][1]). The complex mechanisms needed to drill and store these cores limited the room on board for tools to chemically analyze samples and look for organic molecules. Until the samples reach labs on Earth, the question of whether life once existed in Jezero will probably go unanswered. “We'll have to be patient,” says Tanja Bosak, a geobiologist at the Massachusetts Institute of Technology and member of the rover's science team. The story of the martian climate, on the other hand, will be etched across Jezero's surface, visible to an array of rover instruments. Scientists can only make a rough guess at the lake's age, but they think it formed 3.8 billion years ago, about the same time as the valley networks, over hundreds or thousands of years. Unlike Curiosity's target, Gale crater, which offers a snapshot of a moment some 3.5 billion years ago when Mars was likely drying out, Jezero and its surroundings will grant access to more than 500 million years of martian history, including some of the planet's oldest terrain, says Bethany Ehlmann, a Caltech planetary scientist and member of the science team. “We have the potential for a really rich history of climate.” BY DESIGN, PERSEVERANCE borrows much from Curiosity: a six-wheeled chassis the size of a small SUV, an imaging turret, a radioisotope power source. “From the outside it looks the same,” says Allen Chen, one of the rover's lead engineers at JPL. “But it's got it where it counts.” That includes advanced new imaging instruments, landing capabilities, and a complex drilling system—innovations that led its budget, originally pitched as a bargain at $1.5 billion, to balloon and end up matching Curiosity's $2.7 billion price tag, which includes operations. To analyze samples in its onboard lab, Curiosity's drill only needed to pulverize rock. Perseverance, in contrast, must drill intact cores, each about the size of a thick piece of school chalk, and store them within titanium tubes. The system also has to keep the cores safe and clean, to prevent Earth-borne microbes and molecules from being mistaken for martian ones when the cores finally arrive back on Earth. In the end, engineers dreamed up a system involving two robotic arms, nine drill bits, 43 sample tubes, and a rotating carousel. “When you look at it, you won't think of it being simple,” says Adam Steltzner, the rover's chief engineer at JPL, “but it was the simplest we could imagine.” Building and testing that system nearly delayed a mission straining to meet tight deadlines. In October 2019, engineers discovered the tubes seized up inside the drill bit when tested in martian conditions. “For me it was a moment of despair,” Farley says. “How were we ever going to fix this?” The problem, it turned out, was that the rover was too clean. The tubes had been baked at 350°C for 1 hour, which not only sterilized them, but also vaporized a hydrocarbon film. The team hadn't realized that the film, a patina that forms on nearly any metal exposed to Earth's atmosphere, was needed as a grease. After several stressful months, they developed a cleaning routine that limited the baking to 150°C and included a series of chemical washes. That left a small amount of the film on the outside of the tubes but no trace inside, where it might contaminate samples. “We leave nothing behind, like a good hiker,” Steltzner says. After the issue was resolved, another mote of organic material began to threaten the mission's launch. By February, the coronavirus pandemic had postponed the launch of ESA's Rosalind Franklin rover, which already had parachute problems, until 2022, the next Mars launch window. Determined to hit its window, NASA shuttled a skeleton crew to and from Florida for the rover's final inspections, while most JPL engineers did what they could from their California homes. “It's fascinating how much of it you can do from your living room,” says Jennifer Trosper, the rover's deputy project manager for surface operations. “We're used to remote operation. We just had to move it back a little earlier.” In May, with the rover already stacked on the spacecraft that would ferry it to Mars, a C-130 transport plane delivered the cleaned sample tubes, quarantined in nitrogen-filled cases, to Cape Canaveral. Engineers loaded the tubes just before a heat shield sealed the rover within its landing capsule. A last-minute arrival of the tubes was always the plan to limit contamination risks. Also, Trosper adds, “We just finished them.” On 20 July, a 3-week launch window opens up. Seven months after an Atlas V rocket puts it on a path to Mars, the rover will plunge through the barely there martian atmosphere. Just as for Curiosity, a “sky crane” hovering on retrorockets will unspool Perseverance on a tether and lower it to the ground. But there's an important improvement: A camera on the rover's belly will assess the landscape as it descends and compare it to a stored map of safe landing spots. The sky crane will fire its thrusters to divert to one of these zones, enabling the rover to land far closer to its target than Curiosity did in 2012, in a nearly circular, 8-kilometer-wide landing ellipse at the delta's edge. FROM THAT MOMENT it will be a player in what Nature Geoscience dubbed a “war” over Mars's ancient climate. What Curiosity saw at Gale crater convinced some geologists that ancient Mars remained warm for millions of years. Sediments probably built up more slowly on Mars than on Earth, so the thick sediments at Gale suggested “this lake almost certainly existed for tens of millions of years, maybe longer,” says John Grotzinger, the Caltech geologist who led Curiosity's science for its first few years. If so, the lake would have endured climate variations driven by chaotic wobbles in the planet's tilt, which varies from 10° to 60°. Something must have kept the planet warm while the lake shifted between tropical and arctic latitudes. “Did we land in one weirdo place on Mars? Probably not,” Grotzinger says. But what warmed the climate is a mystery, he admits. “Something is missing, and we don't know what that is yet.” To the opposing camp, that's grounds for skepticism about a warm early Mars. In 1991, James Kasting, a planetary scientist at Pennsylvania State University, University Park, reported that an atmosphere of carbon dioxide (CO2) and water vapor, both greenhouse gases, was not capable of keeping the ancient planet wet and warm for millions of years. The atmosphere would have been too thin, and the early Sun too weak. Mars “must have had a phenomenal greenhouse effect,” Arvidson says, double what exists now on Earth. To this day, even with more sophisticated models, “The climatologists haven't figured out how to do it,” he adds. That has led these scientists to argue that martian water flowed in bursts lasting just thousands of years—brief exclamations in an eternal deep freeze. That is a Mars that climate models can simulate, says Robin Wordsworth, a planetary scientist at Harvard University. Its ancient volcanoes could have belched a lot of hydrogen, a strong but short-lived greenhouse gas. Periodic bursts of water could have rusted iron-bearing minerals, releasing more hydrogen to the air. Or asteroid strikes, more common in that era, could have released hydrogen if they hit regions rich in ice or subsurface water. “For all of them you can make episodic warming work,” Wordsworth says. “But not warm and wet.” The rocks in Gale crater can also support this view, Ehlmann says. They lack certain minerals that should be present if they were exposed to water for 1 million years or more. Jim Bell, a planetary scientist at Arizona State University, Tempe, has concluded that ancient Mars was probably like Antarctica, icy and dry, with spurts of melt. “More Earth-like does not mean like most of the Earth.” PERSEVERANCE WILL NEED the head start provided by a precise landing to try to settle the issue. During its 2-year primary mission, it will take advantage of upgraded wheels and autonomous navigation capabilities to briskly traverse more than 15 kilometers—a distance Curiosity took more than 4 years to cover. The rover will collect its first 20 samples for an eventual return mission from the geologically diverse terrain it will cross. The first samples are likely to be rocks thought to come from an eruption that covered parts of the crater after the lake dried up. Volcanic rocks contain trace radioactive elements that decay at a certain rate, a clock that lab scientists on Earth can use to date the eruption, putting a lower limit on the age of the lake. Mission scientists also hope to find outcrops of older volcanic rocks that sit below the delta mudstones, marking an eruption that occurred before the water arrived. Those would provide an upper age limit, making it possible to roughly bracket the lake's existence. “When were these habitable environments in absolute time, and how quickly did they come and go?” Ehlmann asks. As the rover rolls along the lake bottom, a ground-penetrating radar mounted on its belly will fire, recording echoes that reveal the textures of sediment up to 10 meters below the surface. “We'll be creating a giant ribbon of data,” says David Paige, a planetary scientist at the University of California, Los Angeles, and the instrument's deputy principal investigator. The reflections could help determine whether the lake was open water or covered in ice. Fine mud would suggest open water; anomalously large stones would suggest ice, which could have carried them to the middle of the lake before dropping them. From there the rover will visit the fine-grained clay-bearing mudstones of the lower parts of the delta. Here the hunt for past life will take the lead. On Earth, such clays blanket living things and preserve them as fossils. In similar clays at Gale crater, Curiosity scientists detected traces of complex organic compounds that resembled kerogen, the feedstock of oil. But they could not determine whether the compounds, detected at levels of a few dozen parts per million, were produced by ancient life, or deposited on the martian surface by meteorites, which often contain complex organic molecules. Two instruments mounted at the end of Perseverance's main robotic arm may help tell the difference. One will fire an ultraviolet laser at the rocks; the other will bombard them with x-rays. The radiation re-emitted by atoms in the rocks could reveal organic chemistry. Mapping any organics in a rock could also say something about their origin. A uniform signal would favor meteoritic fallout, whereas a lumpier distribution, and the presence of minerals that hint at microbe-fueling reactions, could be a sign of life—and a green light to drill a sample. As the rover forges a path up the delta, the fine mudstone will give way to rough sandstone. The team will keep an eye out for exposures of opal-like rocks that have recently been spotted from space. Opal forms from a solution of silica and water, and on Earth the deposits are classic fossil-hunting spots. That's because the mineral creeps into organic layers and preserves fossil structures, Bosak says. “That's where we find the most beautifully preserved microbial mats.” The rover's cameras will search for such structures, but Bosak doubts they will be seen—even on Earth, they are not often apparent until polished in the lab. ![Figure][2] CREDITS: (GRAPHIC) C. BICKEL/ SCIENCE ; (MAP) NASA/JPL/MSSS/ESA/DLR/FU-BERLIN/J.COWART, CC-BY-SA 3.0 IGO; (DATA) FERGASON ET AL. /PLANETARY DATA SYSTEM EXPERIMENTAL DATA RECORD The sand grains, washed in by the long-lost river, could also say something about what caused Mars's early warmth—whether steady or intermittent—to dissipate after the delta formed. Some of the sand grains, eroded from volcanic rocks, will contain radioactive isotopes that make it possible to date them. Scientists on Earth will also examine certain minerals to look for the frozen imprint of a magnetic field. Mars is believed to have had a magnetic field early in its history, generated by a molten dynamo in the planet's interior. The field would have failed as the dynamo cooled and shut down, and some believe that explains Mars's radical change in climate. A weakening field could have allowed charged particles from the Sun to erode the planet's once-thick atmosphere. Water would have escaped to space, making the planet colder and drier. Magnetic signatures teased out of the sand grains could show whether the decline of the field preceded—and perhaps caused—the climate change. AFTER NEARLY 2 YEARS of frantic drilling, the rover will climb one of the delta's fingers to reach the shores of Jezero's paleolake, fast against the crater's edge. Orbiters have spotted a bathtub ring of carbonate rocks running around the crater rim in a narrow band, likely where the lake's warm shallows were. On Earth, such deposits are known to preserve fossilized stromatolites, bumpy cauliflowerlike mounds formed by the growth of bacteria. “They are an ideal place to look for past life,” says Briony Horgan, a planetary scientist at Purdue University. That is, she says, if the deposits were formed by the lake, and not by hot water created by the crater-forming impacts. If the lake was responsible for the carbonate deposits, they will offer a window on the ancient martian atmosphere, which supplied the CO2 that formed them. By comparing carbon isotopes from carbonates in the bathtub ring and in older rocks outside the crater, scientists could learn how levels of atmospheric CO2—and the greenhouse effect it drives—changed over this time. The outcrops around these shallows, near the entry point of the river, could also betray something about the climate at the time the delta was laid down, says Timothy Goudge, a planetary scientist at the University of Texas, Austin. Layering in the outcrops will reveal how much water was needed to form the delta, how long it flowed, and whether it came in the brief floods or steady flows. Cracks in river bottom rocks could be wedges opened up by continuous freeze-thaw cycles—a sign of persistent frigid conditions. The shallows will likely mark the end of the primary mission. But the rover ought to have many more years left on its odometer. Engineers want the rover, while still healthy, to drop some samples on flat, accessible terrain where a later mission can retrieve them. But it may drill some sites twice, and it will continue to collect—in case the rover itself is healthy enough to deliver samples to the Earth return mission. After the primary mission, many on the team will be eager to escape the delta for the ancient, mysterious terrain to the west. Deposits of clay and carbonate seen there also needed water to form. If bands of water-weathered rock capped by water-deposited sediments are visible on its mesas, a temperate climate may have prevailed. Alternatively, as Ehlmann and others believe, this landscape could be what's left of an icy subsurface that was heated by nearby giant impacts 4 billion years ago and turned into an underground hydrothermal system capable of fostering life. “That would point to an ancient Mars that was habitable, but not so warm,” she says. Whatever answers the rover finds, it will mark the end of an era on Mars. For decades, NASA has dominated exploration of the planet's surface, culminating in the increasingly ambitious rovers of the past 2 decades. “We've had the privilege and responsibility to do a systematic investigation of a planet,” says Jim Watzin, director of NASA's Mars Exploration Program. Other nations will soon add their rovers, starting with China this year (see sidebar, p. [1420][3]). But Perseverance also kicks off a new era. The sample return effort it anchors “will be the first round trip to another planet by humanity,” Watzin says. Bringing Mars to Earth will enable scientists to probe the secrets of the Red Planet more deeply. If humans follow in the rover's tracks in the coming decades, as the United States and China have vowed, the terrain they encounter may seem strange. But it will be familiar ground. [1]: http://www.sciencemag.org/content/366/6468/932 [2]: pending:yes [3]: http://www.sciencemag.org/content/368/6498/1420

There is a growing demand to be able to “explain” machine learning (ML) systems' decisions and actions to human users, particularly when used in contexts where decisions have substantial implications for those affected and where there is a requirement for political accountability or legal compliance ([ 1 ][1]). Explainability is often discussed as a technical challenge in designing ML systems and decision procedures, to improve understanding of what is typically a “black box” phenomenon. But some of the most difficult challenges are nontechnical and raise questions about the broader accountability of organizations using ML in their decision-making. One reason for this is that many decisions by ML systems may exhibit bias, as systemic biases in society lead to biases in data used by the systems ([ 2 ][2]). But there is another reason, less widely appreciated. Because the quantities that ML systems seek to optimize have to be specified by their users, explainable ML will force policy-makers to be more explicit about their objectives, and thus about their values and political choices, exposing policy trade-offs that may have previously only been implicit and obscured. As the use of ML in policy spreads, there may have to be public debate that makes explicit the value judgments or weights to be used. Merely technical approaches to “explaining” ML will often only be effective if the systems are deployed by trustworthy and accountable organizations. The promise of ML is that it could lead to better decisions, yet concerns have been raised about its use in policy contexts such as criminal justice and policing. A fundamental element of the demand for explainability is for explanation of what the system is “trying to achieve.” Most policy decision-making makes extensive use of constructive ambiguity to pursue shared objectives with sufficient political consensus. There is thus a tension between political or policy decisions, which trade off multiple (often incommensurable) aims and interests, and ML, typically a utilitarian maximizer of what is ultimately a single quantity and which typically entails explicit weighting of decision criteria. We focus on public policy decision-making using ML algorithms that learn the relationships between data inputs and decision outputs. As a first step, policy-makers need to decide among a number of possible meanings of explainability. These range from causal accounts and post hoc interpretations of decisions ([ 3 ][3]) to assurance that outcomes are reliable or fair in terms of the specified objectives for the system ([ 4 ][4]). For example, the explainability requirements for ML systems used by local authorities to determine benefit payments will differ greatly from those required for the enforcement of competition policy with respect to pricing by online merchants. Each of the specific meanings of explainability has different technical requirements, which will imply choices about where efficiency and cost might need to be sacrificed to deliver both explainability and the desired outcomes. Choosing which meaning is relevant is far from a technical question (though what can be provided depends on what is technically feasible). Thus, those seeking explainability will need to specify, in terms translatable to how ML systems operate, what exactly they mean, and what kind of evidence would satisfy their demand ([ 5 ][5]). It must also be possible to monitor whatever explanations are provided, and there must be practical methods to enforce compliance. Furthermore, policy institutions starting to deploy algorithmic or ML-based decision systems, such as the police, courts, and government agencies, are operating in the context of declining trust in some aspects of public life. This context is important for understanding demands for explainability, as these may in part reflect broader legitimacy demands of the policy-making process. If an organization is not trusted, its automated decision procedures will likely also be distrusted. This implies a broader need for trustworthy processes and institutions, for “intelligent accountability” as the result of informed and independent scrutiny, communicated clearly to the public ([ 6 ][6]). Satisfying the demand for explainability implies testing the trustworthiness of the organizations using ML systems to make decisions affecting individuals. Evaluation requires comparing outcomes against a benchmark, which can be the baseline situation, or a specified desired outcome. Taking the demand for explainability as a demand for accountability, the promise of ML is that it could lead to more legitimate and better decisions than humans can make, on some measure. Potential benefits are clearly demonstrable in some forms of medical diagnosis ([ 7 ][7]) or monitoring attempted financial fraud ([ 8 ][8]). In these domains, there is general agreement on a straightforward quantity to optimize, and the incentives of principals (citizens or customers) and agents (public or corporate decision-makers) are aligned. Public concern about the use of ML focuses on other domains, such as marketing or policing, where there may be less agreement about (or trust in) the aim of either the ML system or the organization using it. These concerns highlight a key challenge posed by the use of ML in policy decisions, which is that ML processes are almost always set up to optimize an objective function; this optimization goal can be described in anthropomorphic terms as the “intention” of the system. Yet there is often little or no explicit discussion by policy-makers when considering using ML systems about what conflicting goals, benefits, and risks may trade off against each other as a result. One reason for this is that it is inherently challenging to specify a concrete objective function in sociopolitical domains ([ 9 ][9]). For example, like current ML systems, economists' decisions are informed by estimates of statistical relationships between directly observable and unobservable variables, derived from data generated by a complex environment. Yet economic policies such as tax changes often fail to take into account all relevant factors in the decision environment, or likely behavior changes, in specifying the objective function ([ 10 ][10]). The use of ML systems in other policy contexts will expand the scope of such unintended consequences. Given that the dominant paradigm of machine learning is based on optimization, the use of ML in policy decisions thus speaks to a fundamental debate about social welfare. From the perspective of ethical theories, ML is largely consequentialist: A machine system is configured on the basis of its ability to achieve a desired outcome. Conventional policy analysis is similarly typically based on consequentialist economic social welfare criteria. The well-known impossibility theorems in social choice theory ([ 11 ][11]) establish that when the goal is to aggregate individual choices under a set of reasonable social decision rules, it is impossible to satisfy a set of desirable criteria simultaneously, and thus impossible to achieve a set of desired outcomes by optimizing a single quantity. Critics of consequentialist economic policy analysis argue that people have multidimensional, probably incommensurable, and possibly contradictory objectives, so that imposing utilitarian decision-making procedures will conflict both with reality and with ethical intuitions ([ 12 ][12]). Nevertheless, policy choices are made, so there has always been an unavoidable, albeit often implicit, trade-off or weighting of different objectives ([ 12 ][12]). For example, cost-benefit analysis can incorporate environmental and cultural, as well as financial, considerations, but converts all of these into monetary values. Any choice made when there are multiple interests or trade-offs will imply weights on the different components. As these trade-offs are codified into ML objective functions, the weights given to competing objectives comprise a first-line characterization of how conflicts will be resolved. Using ML systems in political contexts is extending the use of optimization; progress in making these ML systems more understandable to policy-makers will make the de facto choices between competing objectives more explicit than they have been previously ([ 13 ][13]). Greater explainability is therefore likely to have to lead to a more explicit political, not wholly technical, debate. Distilling concrete, unambiguous objectives in this way may turn out to be extremely challenging, for ambiguity about objectives is often useful in policy-making precisely because it blurs uncomfortable conflicts of interest. In many domains, policies generally emerge as a pragmatic compromise between fundamentally conflicting aims. For example, people who disagree about whether the justice system should be retributive or rehabilitative may well be able to agree on specific sentencing policies. Such incompletely theorized agreements “Play an important function in any well-functioning democracy consisting of a heterogeneous population” ([ 14 ][14], p. 1738). The omission of discussion of ultimate aims can make it easier to achieve consensus on difficult issues. As there is some (limited) scope to interpret means to achieve the objective with flexibility, the “weighting” of different fundamental aims remains implicit, and diverse political communities can make progress. An optimistic conclusion would be that being forced by the use of ML systems to be more explicit about policy objectives could promote useful debate leading in the long run to more considered outcomes. ML systems can be used to explore choices and outcomes on different counterfactual high-level objectives, such as retribution or rehabilitation in justice, enabling considered human judgments. However, it may in practice be impossible to specify what we collectively truly want in rigid code. For example, many local governments do not seem to be engaging in public consultation when they adopt predictive ML systems, such as to flag “troubled” families that are likely to need interventions. Although steps such as explicitly adding uncertainty to the ML objective might address this challenge of imperfectly specified objectives in future, ML systems are unable at present to offer wisely moderated solutions to ambiguous objectives ([ 15 ][15]). Human decision-makers can make use of common sense or tacit knowledge, and often override decisions indicated by an economic model or other formal policy analysis, and they will be able to do the same when assisted by ML. Yet, demanding that ML systems be explainable is likely to make the trade-offs between different objectives far more explicit than has been the norm previously. Ultimately, the use of explainable ML systems in the public sector will make a broader debate about social objectives and social justice newly salient. Providing explanations requires being transparent about the systems' objectives — forcing clarity about choices and trade-offs previously often made implicitly — and how their predictions or decisions draw on patterns revealed by a fundamentally biased social and institutional system. Moreover, whereas democratic political systems often look to resolve conflicts through constructive ambiguity—or in other words, the failure to explain—ML systems may require ambiguous objectives to be resolved unequivocally. So, although the need for explainability certainly poses technical challenges, it poses political challenges too, which have not to date been widely acknowledged. 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As a way to see inside the body, revealing a tumor or a fetus, ultrasound is tried and true. But neuroscientists have a newer ambition for the technology: tinkering with the brain. At frequencies lower than those of a sonogram but still beyond the range of human hearing, ultrasound can penetrate the skull and boost or suppress brain activity. If researchers can prove that ultrasound safely and predictably changes human brain function, it could become a powerful, noninvasive research tool and a new means of treating brain disorders. How ultrasound works on the brain remains mysterious. But recent experiments have offered reassurance about safety, and small studies hint at meaningful effects in humans—dampening pain, for example, or subtly enhancing perception. “I've seen a lot of tantalizing data,” says Mark Cohen, a neuroscientist at the University of California, Los Angeles (UCLA). “While the challenges are very large, the potential of this thing is so much larger that we really have to pursue it.” Scientists can already modulate the brain noninvasively by delivering electric current or magnetic pulses across the skull. The U.S. Food and Drug Administration (FDA) has approved transcranial magnetic stimulation (TMS) to treat depression, migraine pain, and obsessive-compulsive disorder (OCD). But unlike magnetic or electric fields, sound waves can be focused—like light through a magnifying glass—on a point deep in the brain without affecting shallower tissue. For now, that combination of depth and focus is possible only with a surgically implanted wire. But ultrasound could temporarily disrupt a deep human brain region—the almond-shaped amygdala, a driver of emotional responses, for example, or the thalamus, a relay station for pain and regulator of alertness—to test its function or treat disease. Results in animals are encouraging. Experiments in the 1950s first showed ultrasound waves could suppress neural activity in a visual region of the cat brain. In rodents, aiming ultrasound at motor regions has triggered movements such as a twitch of a paw or whisker. And focusing it on a frontal region of monkey brains can change how the animals perform at eye movement tasks. But it's technically tricky to aim ultrasound through thick, dense skull bone and to show its energy has landed at the intended point. And ultrasound's effects on the brain can be hard to predict. How much it boosts or suppresses neural activity depends on many parameters, including the timing and intensity of ultrasound pulses, and even characteristics of the targeted neurons themselves. “I have tremendous excitement about the potential,” says Sarah Hollingsworth Lisanby, a psychiatrist at the National Institute of Mental Health who studies noninvasive neuromodulation. “We also need to acknowledge that there's a lot we have to learn,” she says. For one thing, researchers are largely in the dark about how sound waves and brain cells interact. “That's the million-dollar question in this field,” says Mikhail Shapiro, a biochemical engineer at the California Institute of Technology. At high intensities, ultrasound can heat up and kill brain cells—a feature neurosurgeons have exploited to burn away sections of brain responsible for tremors. Even at intensities that don't significantly increase temperature, ultrasound exerts a mechanical force on cells. Some studies suggest this force alters ion channels on neurons, changing the cells' likelihood of firing a signal to neighbors. If ultrasound works primarily via ion channels, “That's great news,” Shapiro says, “because that means we can look at where those channels are expressed and make some predictions about what cell types will be excited.” In a preprint on bioRxiv last month, Shapiro's team reported that exposing mouse neurons in a dish to ultrasound opens a particular set of calcium ion channels to render certain cells more excitable. But these channels alone won't explain ultrasound's effects, says Seung-Schik Yoo, a neuroscientist at Harvard University. He notes that ultrasound also appears to affect receptors on nonneuronal brain cells called glia. “It's very hard to [develop] any unifying theory about the exact mechanism” of ultrasound, he says. Regardless of mechanism, ultrasound is starting to show clear, if subtle, effects in humans. In 2014, a team at Virginia Polytechnic Institute and State University showed focused ultrasound could increase electrical activity in a sensory processing region of the human brain and improve participants' ability to discern the number of points being touched on their fingers. Neurologist Christopher Butler at the University of Oxford and colleagues have tested ultrasound during a more complex sensory task: judging the motion of drifting, jiggling dots on a screen. Last month at the Cognitive Neuroscience Society's annual meeting online, he reported that stimulating a motion-processing visual region called MT improved subjects' ability to judge which way the majority of the dots drifted. Ultrasound's effects have so far been subtler than those of TMS, says Mark George, a psychiatrist at the Medical University of South Carolina, who helped develop and refine that technology. With TMS, “you put it on your head and turn it on and your thumb moves,” he says. But the ultrasound experiments that prompted paw twitches in mice used intensities “so, so, so much higher than what we're being allowed to use in humans.” Regulators have limited human studies in part because ultrasound has the potential to cook the brain or cause damage through cavitation—the creation of tiny bubbles in tissue. In 2015, Yoo and colleagues found microbleeds, a sign of blood vessel damage, in sheep brains repeatedly exposed to ultrasound. “This was a huge speed bump,” says Kim Butts Pauly, a biophysicist at Stanford University. But in February in Brain Stimulation , her group reported microbleeds in control animals as well, suggesting this damage might result from dissection of the brains. Butts Pauly and Yoo now say they're confident the technology can be used safely. Cohen and collaborators recently tested safety in people by aiming ultrasound at regions slated for surgical removal to treat epilepsy. With FDA's OK, they used intensities up to eight times as high as the limit for diagnostic ultrasound. As they reported in a preprint on medRxiv in April, they found no significant damage to brain tissue or blood vessels. However, to find the limit of safety, researchers will likely need to go all the way to levels that damage tissue, Cohen says. Several teams are cautiously moving into tests of ultrasound as treatment. In 2016, UCLA neuroscientist Martin Monti and colleagues reported that a man in a minimally conscious state regained consciousness following ultrasound stimulation of his thalamus. Monti is preparing a publication on a follow-up study of three people with chronically impaired states of consciousness. After ultrasound, they showed increased responsiveness over a period of days—much faster than expected, Monti says, although the study included no control group. That research and the tests in epilepsy patients used an ultrasound device developed by BrainSonix Corporation. Its founder, UCLA neuropsychiatrist Alexander Bystritsky, hopes ultrasound can disrupt neural circuits that drive symptoms of OCD. A team at Massachusetts General Hospital and Baylor College of Medicine is planning a study in humans using the BrainSonix device, he says. Columbia University biomedical engineer Elisa Konofagou hopes to use ultrasound to treat Alzheimer's disease. Before COVID-19 interrupted participant recruitment, she and colleagues were preparing a pilot study to inject tiny gas-filled bubbles into the bloodstream of six people with Alzheimer's and use pulses of ultrasound to oscillate the microbubbles in blood vessels lining the brain. The mechanical force of those vibrations can temporarily pull apart the cells lining these vessels. The researchers hope opening this blood-brain barrier will help the brain clear toxic proteins. (Konofagou's team and others are also exploring this ultrasound-microbubble combination to deliver drugs to the brain.) In his first test of ultrasound after years of studying TMS, George looked to reduce pain. His team applied increasing heat to the arms of 19 participants, who tended to become more sensitive over repeated tests, reporting pain at lower temperatures by the last test. But if, between the first and last test, they had pulses of ultrasound aimed at the thalamus, their pain threshold dipped half as much. “This is definitely a double green light” to keep pursuing the technology, George says. George regularly treats depressed patients with TMS and has seen the technology save lives. “But everybody wonders if we could go deep with a different technology—that would be a game changer,” he says. “Ultrasound holds that promise, but the question is can it really deliver?”

When Soldiers enter a new environment, their mission demands they stay one step ahead of the enemy; however, they may find it challenging to maintain a high level of alertness if they're driving a combat vehicle across unfamiliar or dangerous terrain.The U.S. Army Combat Capabilities Development Command's Army Research Laboratory designated several research programs as essential for future Soldier capabilities. Of these major flagship programs, the Artificial Intelligence for Maneuver and Mobility, or AIMM, Essential Research Program, endeavors to reduce Soldier distractions on the battlefield through the integration of autonomous systems in Army vehicles.Dr. John Fossaceca, AIMM program manager, said he seeks to develop the foundational capabilities that will enable autonomy in the next generation of combat vehicles. This include the construction of a robotic combat vehicle that operates independently of the main combat vehicle."The "We don't want Soldiers to be operating these remote-controlled vehicles with their heads down, constantly paying attention to the vehicle in order to control it.