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Learning Causal Models of Autonomous Agents using Interventions

arXiv.org Artificial Intelligence

One of the several obstacles in the widespread use of AI systems is the lack of requirements of interpretability that can enable a layperson to ensure the safe and reliable behavior of such systems. We extend the analysis of an agent assessment module that lets an AI system execute high-level instruction sequences in simulators and answer the user queries about its execution of sequences of actions. We show that such a primitive query-response capability is sufficient to efficiently derive a user-interpretable causal model of the system in stationary, fully observable, and deterministic settings. We also introduce dynamic causal decision networks (DCDNs) that capture the causal structure of STRIPS-like domains. A comparative analysis of different classes of queries is also presented in terms of the computational requirements needed to answer them and the efforts required to evaluate their responses to learn the correct model.


A generalized forecasting solution to enable future insights of COVID-19 at sub-national level resolutions

arXiv.org Artificial Intelligence

COVID-19 continues to cause a significant impact on public health. To minimize this impact, policy makers undertake containment measures that however, when carried out disproportionately to the actual threat, as a result if errorneous threat assessment, cause undesirable long-term socio-economic complications. In addition, macro-level or national level decision making fails to consider the localized sensitivities in small regions. Hence, the need arises for region-wise threat assessments that provide insights on the behaviour of COVID-19 through time, enabled through accurate forecasts. In this study, a forecasting solution is proposed, to predict daily new cases of COVID-19 in regions small enough where containment measures could be locally implemented, by targeting three main shortcomings that exist in literature; the unreliability of existing data caused by inconsistent testing patterns in smaller regions, weak deploy-ability of forecasting models towards predicting cases in previously unseen regions, and model training biases caused by the imbalanced nature of data in COVID-19 epi-curves. Hence, the contributions of this study are three-fold; an optimized smoothing technique to smoothen less deterministic epi-curves based on epidemiological dynamics of that region, a Long-Short-Term-Memory (LSTM) based forecasting model trained using data from select regions to create a representative and diverse training set that maximizes deploy-ability in regions with lack of historical data, and an adaptive loss function whilst training to mitigate the data imbalances seen in epi-curves. The proposed smoothing technique, the generalized training strategy and the adaptive loss function largely increased the overall accuracy of the forecast, which enables efficient containment measures at a more localized micro-level.


Oklahoma mom of 11 rescues members of Afghan all-girls robotics team

FOX News

Photojournalist documents the reality women face in Afghanistan on'America Reports' An Oklahoma mother of 11 flew to Qatar earlier this month to help rescue 10 members of Afghanistan's all-girls robotics team, and is hoping to save more as the Taliban takes power in Kabul. Allyson Reneau, a 60-year old-Harvard graduate with a Masters degree in international relations and U.S. space policy, took it upon herself to try and save members of the Afghan Girls Robotic Team, according to NBC. She flew into Qatar on Aug. 9 after making a "Hail Mary" call to a former roommate at the U.S. Embassy there to help get the girls from the advancing Taliban, known for their oppressive treatment of women. Reneau had been in contact with the team -- made of girls ages 16 to 18 -- since 2019, when she worked on the board of directors for Explore Mars and met the girls when they attended the organization's annual Humans to Mars conference. The team was hailed in Western media as the future of the war-ravaged country, as well as a shining example of how women's rights had improved after the U.S. invaded following 9/11.


Apple's Photo-Scanning Plan Sparks Outcry From Policy Groups

WIRED

More than 90 policy groups from the US and around the world signed an open letter urging Apple to drop its plan to have Apple devices scan photos for child sexual abuse material (CSAM). This story originally appeared on Ars Technica, a trusted source for technology news, tech policy analysis, reviews, and more. Ars is owned by WIRED's parent company, Condé Nast. "The undersigned organizations committed to civil rights, human rights, and digital rights around the world are writing to urge Apple to abandon the plans it announced on 5 August 2021 to build surveillance capabilities into iPhones, iPads, and other Apple products," the letter to Apple CEO Tim Cook said. "Though these capabilities are intended to protect children and to reduce the spread of child sexual abuse material (CSAM), we are concerned that they will be used to censor protected speech, threaten the privacy and security of people around the world, and have disastrous consequences for many children." The Center for Democracy and Technology (CDT) announced the letter, with CDT Security and Surveillance Project codirector Sharon Bradford Franklin saying, "We can expect governments will take advantage of the surveillance capability Apple is building into iPhones, iPads, and computers.


How AI-powered tech landed man in jail with scant evidence

#artificialintelligence

Michael Williams' wife pleaded with him to remember their fishing trips with the grandchildren, how he used to braid her hair, anything to jar him back to his world outside the concrete walls of Cook County Jail. His three daily calls to her had become a lifeline, but when they dwindled to two, then one, then only a few a week, the 65-year-old Williams felt he couldn't go on. He made plans to take his life with a stash of pills he had stockpiled in his dormitory. Williams was jailed last August, accused of killing a young man from the neighborhood who asked him for a ride during a night of unrest over police brutality in May. But the key evidence against Williams didn't come from an eyewitness or an informant; it came from a clip of noiseless security video showing a car driving through an intersection, and a loud bang picked up by a network of surveillance microphones. Prosecutors said technology powered by a secret algorithm that analyzed noises detected by the sensors indicated Williams shot and killed the man. "I kept trying to figure out, how can they get away with using the technology like that against me?" said Williams, speaking publicly for the first time about his ordeal. Williams sat behind bars for nearly a year before a judge dismissed the case against him last month at the request of prosecutors, who said they had insufficient evidence.


DEMix Layers: Disentangling Domains for Modular Language Modeling

arXiv.org Artificial Intelligence

We introduce a new domain expert mixture (DEMix) layer that enables conditioning a language model (LM) on the domain of the input text. A DEMix layer is a collection of expert feedforward networks, each specialized to a domain, that makes the LM modular: experts can be mixed, added or removed after initial training. Extensive experiments with autoregressive transformer LMs (up to 1.3B parameters) show that DEMix layers reduce test-time perplexity, increase training efficiency, and enable rapid adaptation with little overhead. We show that mixing experts during inference, using a parameter-free weighted ensemble, allows the model to better generalize to heterogeneous or unseen domains. We also show that experts can be added to iteratively incorporate new domains without forgetting older ones, and that experts can be removed to restrict access to unwanted domains, without additional training. Overall, these results demonstrate benefits of explicitly conditioning on textual domains during language modeling.


Checkers just revealed a shop without tills, run on AI and machine vision

#artificialintelligence

Retail giant Shoprite on Wednesday revealed that it is testing an automated Checkers concept store with no cashiers, or till points." Checkers Rush is a "no queues, no checkout, no waiting" concept store, it said. "Using advanced AI camera technology to identify the products being taken off the shelves, Checkers Rush bills users' bank cards upon exit." The store is revealed in a promotional video for Shoprite X, the company's new digital innovation unit. The shop is available to staff at ShopriteX offices near the company's home office, above Checkers Hyper Brackenfell in Cape Town.


Malaria infection and severe disease risks in Africa

Science

Understanding how changes in community parasite prevalence alter the rate and age distribution of severe malaria is essential for optimizing control efforts. Paton et al. assessed the incidence of pediatric severe malaria admissions from 13 hospitals in East Africa from 2006 to 2020 (see the Perspective by Taylor and Slutsker). Each 25% increase in community parasite prevalence shifted hospital admissions toward younger children. Low rates of lifetime infections appeared to confer some immunity to severe malaria in very young children. Children under the age of 5 years thus need to remain a focus of disease prevention for malaria control. Science , abj0089, this issue p. [926][1]; see also abk3443, p. [855][2] The relationship between community prevalence of Plasmodium falciparum and the burden of severe, life-threatening disease remains poorly defined. To examine the three most common severe malaria phenotypes from catchment populations across East Africa, we assembled a dataset of 6506 hospital admissions for malaria in children aged 3 months to 9 years from 2006 to 2020. Admissions were paired with data from community parasite infection surveys. A Bayesian procedure was used to calibrate uncertainties in exposure (parasite prevalence) and outcomes (severe malaria phenotypes). Each 25% increase in prevalence conferred a doubling of severe malaria admission rates. Severe malaria remains a burden predominantly among young children (3 to 59 months) across a wide range of community prevalence typical of East Africa. This study offers a quantitative framework for linking malaria parasite prevalence and severe disease outcomes in children. [1]: /lookup/doi/10.1126/science.abj0089 [2]: /lookup/doi/10.1126/science.abk3443


Evolving threat

Science

New variants have changed the face of the pandemic. What will the virus do next? ![Figure][1] CREDITS: (GRAPHIC) N. DESAI/ SCIENCE ; (DATA) NEXTSTRAIN; GISAID Edward Holmes does not like making predictions, but last year he hazarded a few. Again and again, people had asked Holmes, an expert on viral evolution at the University of Sydney, how he expected SARS-CoV-2 to change. In May 2020, 5 months into the pandemic, he started to include a slide with his best guesses in his talks. The virus would probably evolve to avoid at least some human immunity, he suggested. But it would likely make people less sick over time, he said, and there would be little change in its infectivity. In short, it sounded like evolution would not play a major role in the pandemic's near future. “A year on I've been proven pretty much wrong on all of it,” Holmes says. Well, not all: SARS-CoV-2 did evolve to better avoid human antibodies. But it has also become a bit more virulent and a lot more infectious, causing more people to fall ill. That has had an enormous influence on the course of the pandemic. The Delta strain circulating now—one of four “variants of concern” identified by the World Health Organization, along with four “variants of interest”—is so radically different from the virus that appeared in Wuhan, China, in late 2019 that many countries have been forced to change their pandemic planning. Governments are scrambling to accelerate vaccination programs while prolonging or even reintroducing mask wearing and other public health measures. As to the goal of reaching herd immunity—vaccinating so many people that the virus simply has nowhere to go—“With the emergence of Delta, I realized that it's just impossible to reach that,” says Müge Çevik, an infectious disease specialist at the University of St. Andrews. Yet the most tumultuous period in SARS-CoV-2's evolution may still be ahead of us, says Aris Katzourakis, an evolutionary biologist at the University of Oxford. There's now enough immunity in the human population to ratchet up an evolutionary competition, pressuring the virus to adapt further. At the same time, much of the world is still overwhelmed with infections, giving the virus plenty of chances to replicate and throw up new mutations. Predicting where those worrisome factors will lead is just as tricky as it was a year and a half ago, however. “We're much better at explaining the past than predicting the future,” says Andrew Read, an evolutionary biologist at Pennsylvania State University, University Park. Evolution, after all, is driven by random mutations, which are impossible to predict. “It's very, very tricky to know what's possible, until it happens,” Read says. “It's not physics. It doesn't happen on a billiard table.” Still, experience with other viruses gives evolutionary biologists some clues about where SARS-CoV-2 may be headed. The courses of past outbreaks show the coronavirus could well become even more infectious than Delta is now, Read says: “I think there's every expectation that this virus will continue to adapt to humans and will get better and better at us.” Far from making people less sick, it could also evolve to become even deadlier, as some previous viruses including the 1918 flu have. And although COVID-19 vaccines have held up well so far, history shows the virus could evolve further to elude their protective effect—although a recent study in another coronavirus suggests that could take many years, which would leave more time to adapt vaccines to the changing threat. Holmes himself uploaded one of the first SARS-CoV-2 genomes to the internet on 10 January 2020. Since then, more than 2 million genomes have been sequenced and published, painting an exquisitely detailed picture of a changing virus. “I don't think we've ever seen that level of precision in watching an evolutionary process,” Holmes says. Making sense of the endless stream of mutations is complicated. Each is just a tiny tweak in the instructions for how to make proteins. Which mutations end up spreading depends on how the viruses carrying those tweaked proteins fare in the real world. The vast majority of mutations give the virus no advantage at all, and identifying the ones that do is difficult. There are obvious candidates, such as mutations that change the part of the spike protein—which sits on the surface of the virus—that binds to human cells. But changes elsewhere in the genome may be just as crucial—yet are harder to interpret. Some genes' functions aren't even clear, let alone what a change in their sequence could mean. The impact of any one change on the virus' fitness also depends on other changes it has already accumulated. That means scientists need real-world data to see which variants appear to be taking off. Only then can they investigate, in cell cultures and animal experiments, what might explain that viral success. The most eye-popping change in SARS-CoV-2 so far has been its improved ability to spread between humans. At some point early in the pandemic, SARS-CoV-2 acquired a mutation called D614G that made it a bit more infectious. That version spread around the world; almost all current viruses are descended from it. Then in late 2020, scientists identified a new variant, now called Alpha, in patients in Kent, U.K., that was about 50% more transmissible. Delta, first seen in India and now conquering the world, is another 40% to 60% more transmissible than Alpha. Read says the pattern is no surprise. “The only way you could not get infectiousness rising would be if the virus popped into humans as perfect at infecting humans as it could be, and the chance of that happening is incredibly small,” he says. But Holmes was startled. “This virus has gone up three notches in effectively a year and that, I think, was the biggest surprise to me,” Holmes says. “I didn't quite appreciate how much further the virus could get.” Bette Korber at Los Alamos National Laboratory and her colleagues first suggested that D614G, the early mutation, was taking over because it made the virus better at spreading. She says skepticism about the virus' ability to evolve was common in the early days of the pandemic, with some researchers saying D614G's apparent advantage might be sheer luck. “There was extraordinary resistance in the scientific community to the idea this virus could evolve as the pandemic grew in seriousness in spring of 2020,” Korber says. ![Figure][1] CREDITS: (GRAPHIC) N. DESAI/ SCIENCE ; (DATA) NEXTSTRAIN; GISAID Researchers had never watched a completely novel virus spread so widely and evolve in humans, after all. “We're used to dealing with pathogens that have been in humanity for centuries, and their evolutionary course is set in the context of having been a human pathogen for many, many years,” says Jeremy Farrar, head of the Wellcome Trust. Katzourakis agrees. “This may have affected our priors and conditioned many to think in a particular way,” he says. Another, more practical problem is that real-world advantages for the virus don't always show up in cell culture or animal models. “There is no way anyone would have noticed anything special about Alpha from laboratory data alone,” says Christian Drosten, a virologist at the Charité University Hospital in Berlin. He and others are still figuring out what, at the molecular level, gives Alpha and Delta an edge. Alpha seems to bind more strongly to the human ACE2 receptor, the virus' target on the cell surface, partly because of a mutation in the spike protein called N501Y. It may also be better at countering interferons, molecules that are part of the body's viral immune defenses. Together those changes may lower the amount of virus needed to infect someone—the infectious dose. In Delta, one of the most important changes may be near the furin cleavage site on spike, where a human enzyme cuts the protein, a key step enabling the virus to invade human cells. A mutation called P681R in that region makes cleavage more efficient, which may allow the virus to enter more cells faster and lead to greater numbers of virus particles in an infected person. In July, Chinese researchers posted a preprint showing Delta could lead to virus levels in patient samples 1000 times higher than for previous variants. Evidence is accumulating that infected people not only spread the virus more efficiently, but also faster, allowing the variant to spread even more rapidly. The new variants of SARS-CoV-2 may also cause more severe disease. For example, a study in Scotland found that an infection with Delta was about twice as likely to lead to hospital admission than with Alpha. It wouldn't be the first time a newly emerging disease quickly became more serious. The 1918–19 influenza pandemic also appears to have caused more serious illness as time went on, says Lone Simonsen, an epidemiologist at Roskilde University who studies past pandemics. “Our data from Denmark suggests it was six times deadlier in the second wave.” A popular notion holds that viruses tend to evolve over time to become less dangerous, allowing the host to live longer and spread the virus more widely. But that idea is too simplistic, Holmes says. “The evolution of virulence has proven to be quicksand for evolutionary biologists,” he says. “It's not a simple thing.” Two of the best studied examples of viral evolution are myxoma virus and rabbit hemorrhagic disease virus, which were released in Australia in 1960 and 1996, respectively, to decimate populations of European rabbits that were destroying croplands and wreaking ecological havoc. Myxoma virus initially killed more than 99% of infected rabbits, but then less pathogenic strains evolved, likely because the virus was killing many animals before they had a chance to pass it on. (Rabbits also evolved to be less susceptible.) Rabbit hemorrhagic disease virus, by contrast, got more deadly over time, probably because the virus is spread by blow flies feeding on rabbit carcasses, and quicker death accelerated its spread. Other factors loosen the constraints on deadliness. For example, a virus variant that can outgrow other variants within a host can end up dominating even if it makes the host sicker and reduces the likelihood of transmission. And an assumption about human respiratory diseases may not always hold: that a milder virus—one that doesn't make you crawl into bed, say—might allow an infected person to spread the virus further. In SARS-CoV-2, most transmission happens early on, when the virus is replicating in the upper airways, whereas serious disease, if it develops, comes later, when the virus infects the lower airways. As a result, a variant that makes the host sicker might spread just as fast as before. From the start of the pandemic, researchers have worried about a third type of viral change, perhaps the most unsettling of all: that SARS-CoV-2 might evolve to evade immunity triggered by natural infections or vaccines. Already, several variants have emerged sporting changes in the surface of the spike protein that make it less easily recognized by antibodies. But although news of these variants has caused widespread fear, their impact has so far been limited. Derek Smith, an evolutionary biologist at the University of Cambridge, has worked for decades on visualizing immune evasion in the influenza virus in so-called antigenic maps. The farther apart two variants are on Smith's maps, the less well antibodies against one virus protect against the other. In a recently published preprint, Smith's group, together with David Montefiori's group at Duke University, has applied the approach to mapping the most important variants of SARS-CoV-2 (see graphic, below). The new maps place the Alpha variant very close to the original Wuhan virus, which means antibodies against one still neutralize the other. The Delta variant, however, has drifted farther away, even though it doesn't completely evade immunity. “It's not an immune escape in the way people think of an escape in slightly cartoonish terms,” Katzourakis says. But Delta is slightly more likely to infect fully vaccinated people than previous variants. “It shows the possible beginning of a trajectory and that's what worries me,” Katzourakis says. ![Figure][1] CREDITS: (GRAPHIC) N. DESAI/ SCIENCE ; (DATA) DEREK SMITH/UNIVERSITY OF CAMBRIDGE; DAVID MONTEFIORI/DUKE UNIVERSITY Other variants have evolved more antigenic distance from the original virus than Delta. Beta, which first appeared in South Africa, has traveled the farthest on the map, although natural or vaccine-induced immunity still largely protects against it. And Beta's attempts to get away may come at a price, as Delta has outstripped it worldwide. “It's probably the case that when a virus changes to escape immunity, it loses other aspects of its fitness,” Smith says. The map shows that for now, the virus is not moving in any particular direction. If the original Wuhan virus is like a town on Smith's map, the virus has been taking local trains to explore the surrounding area, but it has not traveled to the next city—not yet. Although it's impossible to predict exactly how infectiousness, virulence, and immune evasion will develop in the coming months, some of the factors that will influence the virus' trajectory are clear. One is the immunity that is now rapidly building in the human population. On one hand, immunity reduces the likelihood of people getting infected, and may hamper viral replication even when they are. “That means there will be fewer mutations emerging if we vaccinate more people,” Çevik says. On the other hand, any immune escape variant now has a huge advantage over other variants. In fact, the world is probably at a tipping point, Holmes says: With more than 2 billion people having received at least one vaccine dose and hundreds of millions more having recovered from COVID-19, variants that evade immunity may now have a bigger leg up than those that are more infectious. Something similar appears to have happened when a new H1N1 influenza strain emerged in 2009 and caused a pandemic, says Katia Kölle, an evolutionary biologist at Emory University. A 2015 paper found that changes in the virus in the first 2 years appeared to make the virus more adept at human-to-human transmission, whereas changes after 2011 were mostly to avoid human immunity. It may already be getting harder for SARS-CoV-2 to make big gains in infectiousness. “There are some fundamental limits to exactly how good a virus can get at transmitting and at some point SARS-CoV-2 will hit that plateau,” says Jesse Bloom, an evolutionary biologist at the Fred Hutchinson Cancer Research Center. “I think it's very hard to say if this is already where we are, or is it still going to happen.” Evolutionary virologist Kristian Andersen of Scripps Research guesses the virus still has space to evolve greater transmissibility. “The known limit in the viral universe is measles, which is about three times more transmissible than what we have now with Delta,” he says. ![Figure][1] CREDITS: (GRAPHIC) N. DESAI/ SCIENCE ; (DATA) E. WALL ET AL., THE LANCET , 397:10292, 2331 (2021) The limits of immune escape are equally uncertain. Smith's antigenic maps show the space the virus has explored so far. But can it go much farther? If the variants on the map are like towns, then where are the country's natural boundaries—where does the ocean start? A crucial clue will be where the next few variants appear on the map, Smith says. Beta evolved in one direction away from the original virus and Delta in another. “It's too soon to say this now, but we might be heading for a world where there are two serotypes of this virus that would also both have to be considered in any vaccines,” Drosten says. Immune escape is so worrying because it could force humanity to update its vaccines continually, as happens for flu. Yet the vaccines against many other diseases—measles, polio, and yellow fever, for example—have remained effective for decades without updates, even in the rare cases where immune-evading variants appeared. “There was big alarm around 2000 that maybe we'd need to replace the hepatitis B vaccines,” because an escape variant had popped up, Read says. But the variant has not spread around the world: It is able to infect close contacts of an infected person, but then peters out. The virus apparently faces a trade-off between transmissibility and immune escape. Such trade-offs likely exist for SARS-CoV-2 as well. Some clues about SARS-CoV-2's future path may come from coronaviruses with a much longer history in humans: those that cause common colds. Some are known to reinfect people, but until recently it was unclear whether that's because immunity in recovered people wanes, or because the virus changes its surface to evade immunity. In a study published in April in PLOS Pathogens , Bloom and other researchers compared the ability of human sera taken at different times in the past decades to block virus isolated at the same time or later. They showed that the samples could neutralize strains of a coronavirus named 229E isolated around the same time, but weren't always effective against virus from 10 years or more later. The virus had evidently evolved to evade human immunity, but it had taken 10 years or more. “Immune escape conjures this catastrophic failure of immunity when it is really immune erosion,” Bloom says. “Right now it seems like SARS-CoV-2, at least in terms of antibody escape, is actually behaving a lot like coronavirus 229E.” Others are probing SARS-CoV-2 itself. In a preprint published this month, researchers tinkered with the virus to learn how much it has to change to evade the antibodies generated in vaccine recipients and recovered patients. They found that it took 20 changes to the spike protein to escape current antibody responses almost completely. That means the bar for complete escape is high, says one of the authors, virologist Paul Bieniasz of Rockefeller University. “But it's very difficult to look into a crystal ball and say whether that is going to be easy for the virus to acquire or not,” he says. “It seems plausible that true immune escape is hard,” concludes William Hanage of the Harvard T.H. Chan School of Public Health. “However, the counterargument is that natural selection is a hell of a problem solver and the virus is only beginning to experience real pressure to evade immunity.” And the virus has tricks up its sleeve. Coronaviruses are good at recombining, for instance, which could allow new variants to emerge suddenly by combining the genomes—and the properties—of two different variants. In pigs, recombination of a coronavirus named porcine epidemic diarrhea virus with attenuated vaccine strains of another coronavirus has led to more virulent variants of PEDV. “Given the biology of these viruses, recombination may well factor into the continuing evolution of SARS-CoV-2,” Korber says. Given all that uncertainty, it's worrisome that humanity hasn't done a great job of limiting the spread of SARS-CoV-2, says Eugene Koonin, a researcher at the U.S. National Center for Biotechnology Information. Some dangerous variants may only be possible if the virus hits on a very rare, winning combination of mutations, he says. It might have to replicate an astronomical number of times to get there. “But with all these millions of infected people, it may very well find that combination.” Indeed, Katzourakis adds, the past 20 months are a warning to never underestimate viral evolution. “Many still see Alpha and Delta as being as bad as things are ever going to get,” he says. “It would be wise to consider them as steps on a possible trajectory that may challenge our public health response further.” [1]: pending:yes


Growth and Evolution of Aquafarming in the AI Era

#artificialintelligence

Significant to economic stability across the world, the current scenario in the aqua-farming industry is far from what it was a decade ago. With fewer changes in people and processes, the growth and evolution of the aqua-farming sector have been steady in the past decade. Although the technological advancements have been limited, yet the onset of IoT has triggered the introduction of AI-based process adoptions and automation. The sector is fairly large and deals with the production, and supply of aquatic animals. Fish, shrimp, oysters, and algae farming are closely associated with the global food industry.