Technology industry veterans Tom Davenport and Tom Seibel have seen firsthand how data, analytics and artificial intelligence have changed business models over the past three decades. In a conversation with ThoughtSpot Chief Data Strategy Officer Cindi Howson at VentureBeat's Transform 2021 conference on Tuesday, Davenport and Siebel shared their insights into how technology has boosted innovation and how it can transform industries, as well as the dangers it presents. Davenport said the biggest change he's seen in his career has been the democratization of technology. "There's been a continual move toward the software being easier to use, and being able to do more things on its own -- automated analytics, automated data science, automated machine learning," Davenport said. "I think we're poised for even more democratization, which is great. I think overall there's some issues that it raises, but it really opens up this field to a lot more people who may not have been nerdy enough to study statistics and get into the details of how you create various models."

Netflix has hired veteran video game executive Mike Verdu, signaling the video streaming service is poised to expand into another fertile field of entertainment. Verdu's addition as Netflix's vice president of game development, confirmed Thursday, comes as the company seeks to sustain the momentum it gathered last year when people turned to the video streaming service to get through lockdowns imposed during the pandemic. Netflix wound up adding 37 million worldwide subscribers last year, by far the largest annual gain in its history. But the landscape has changed dramatically now that the easing pandemic has allowed people to return to a semblance of their normal lives. The number of Americans applying for unemployment benefits has reached its lowest level since the pandemic struck last year, further evidence that the U.S. economy and job market are quickly rebounding from the pandemic recession.

During the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic, polymerase chain reaction (PCR) tests were generally reported only as binary positive or negative outcomes. However, these test results contain a great deal more information than that. As viral load declines exponentially, the PCR cycle threshold (Ct) increases linearly. Hay et al. developed an approach for extracting epidemiological information out of the Ct values obtained from PCR tests used in surveillance for a variety of settings (see the Perspective by Lopman and McQuade). Although there are challenges to relying on single Ct values for individual-level decision-making, even a limited aggregation of data from a population can inform on the trajectory of the pandemic. Therefore, across a population, an increase in aggregated Ct values indicates that a decline in cases is occurring. Science , abh0635, this issue p. [eabh0635][1]; see also abj4185, p. [280][2] ### INTRODUCTION Current approaches to epidemic monitoring rely on case counts, test positivity rates, and reported deaths or hospitalizations. These metrics, however, provide a limited and often biased picture as a result of testing constraints, unrepresentative sampling, and reporting delays. Random cross-sectional virologic surveys can overcome some of these biases by providing snapshots of infection prevalence but currently offer little information on the epidemic trajectory without sampling across multiple time points. ### RATIONALE We develop a new method that uses information inherent in cycle threshold (Ct) values from reverse transcription quantitative polymerase chain reaction (RT-qPCR) tests to robustly estimate the epidemic trajectory from multiple or even a single cross section of positive samples. Ct values are related to viral loads, which depend on the time since infection; Ct values are generally lower when the time between infection and sample collection is short. Despite variation across individuals, samples, and testing platforms, Ct values provide a probabilistic measure of time since infection. We find that the distribution of Ct values across positive specimens at a single time point reflects the epidemic trajectory: A growing epidemic will necessarily have a high proportion of recently infected individuals with high viral loads, whereas a declining epidemic will have more individuals with older infections and thus lower viral loads. Because of these changing proportions, the epidemic trajectory or growth rate should be inferable from the distribution of Ct values collected in a single cross section, and multiple successive cross sections should enable identification of the longer-term incidence curve. Moreover, understanding the relationship between sample viral loads and epidemic dynamics provides additional insights into why viral loads from surveillance testing may appear higher for emerging viruses or variants and lower for outbreaks that are slowing, even absent changes in individual-level viral kinetics. ### RESULTS Using a mathematical model for population-level viral load distributions calibrated to known features of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) viral load kinetics, we show that the median and skewness of Ct values in a random sample change over the course of an epidemic. By formalizing this relationship, we demonstrate that Ct values from a single random cross section of virologic testing can estimate the time-varying reproductive number of the virus in a population, which we validate using data collected from comprehensive SARS-CoV-2 testing in long-term care facilities. Using a more flexible approach to modeling infection incidence, we also develop a method that can reliably estimate the epidemic trajectory in even more-complex populations, where interventions may be implemented and relaxed over time. This method performed well in estimating the epidemic trajectory in the state of Massachusetts using routine hospital admissions RT-qPCR testing data—accurately replicating estimates from other sources for the entire state. ### CONCLUSION This work provides a new method for estimating the epidemic growth rate and a framework for robust epidemic monitoring using RT-qPCR Ct values that are often simply discarded. By deploying single or repeated (but small) random surveillance samples and making the best use of the semiquantitative testing data, we can estimate epidemic trajectories in real time and avoid biases arising from nonrandom samples or changes in testing practices over time. Understanding the relationship between population-level viral loads and the state of an epidemic reveals important implications and opportunities for interpreting virologic surveillance data. It also highlights the need for such surveillance, as these results show how to use it most informatively. ![Figure][3] Ct values reflect the epidemic trajectory and can be used to estimate incidence. ( A and B ) Whether an epidemic has rising or falling incidence will be reflected in the distribution of times since infection (A), which in turn affects the distribution of Ct values in a surveillance sample (B). ( C ) These values can be used to assess whether the epidemic is rising or falling and estimate the incidence curve. Estimating an epidemic’s trajectory is crucial for developing public health responses to infectious diseases, but case data used for such estimation are confounded by variable testing practices. We show that the population distribution of viral loads observed under random or symptom-based surveillance—in the form of cycle threshold (Ct) values obtained from reverse transcription quantitative polymerase chain reaction testing—changes during an epidemic. Thus, Ct values from even limited numbers of random samples can provide improved estimates of an epidemic’s trajectory. Combining data from multiple such samples improves the precision and robustness of this estimation. We apply our methods to Ct values from surveillance conducted during the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic in a variety of settings and offer alternative approaches for real-time estimates of epidemic trajectories for outbreak management and response. [1]: /lookup/doi/10.1126/science.abh0635 [2]: /lookup/doi/10.1126/science.abj4185 [3]: pending:yes

Artificial intelligence and machine learning (AI/ML) algorithms are increasingly developed in health care for diagnosis and treatment of a variety of medical conditions ([ 1 ][1]). However, despite the technical prowess of such systems, their adoption has been challenging, and whether and how much they will actually improve health care remains to be seen. A central reason for this is that the effectiveness of AI/ML-based medical devices depends largely on the behavioral characteristics of its users, who, for example, are often vulnerable to well-documented biases or algorithmic aversion ([ 2 ][2]). Many stakeholders increasingly identify the so-called black-box nature of predictive algorithms as the core source of users' skepticism, lack of trust, and slow uptake ([ 3 ][3], [ 4 ][4]). As a result, lawmakers have been moving in the direction of requiring the availability of explanations for black-box algorithmic decisions ([ 5 ][5]). Indeed, a near-consensus is emerging in favor of explainable AI/ML among academics, governments, and civil society groups. Many are drawn to this approach to harness the accuracy benefits of noninterpretable AI/ML such as deep learning or neural nets while also supporting transparency, trust, and adoption. We argue that this consensus, at least as applied to health care, both overstates the benefits and undercounts the drawbacks of requiring black-box algorithms to be explainable. It is important to first distinguish explainable from interpretable AI/ML. These are two very different types of algorithms with different ways of dealing with the problem of opacity—that AI predictions generated from a black box undermine trust, accountability, and uptake of AI. A typical AI/ML task requires constructing an algorithm that can take a vector of inputs (for example, pixel values of a medical image) and generate an output pertaining to, say, disease occurrence (for example, cancer diagnosis). The algorithm is trained on past data with known labels, which means that the parameters of a mathematical function that relate the inputs to the output are estimated from that data. When we refer to an algorithm as a “black box,” we mean that the estimated function relating inputs to outputs is not understandable at an ordinary human level (owing to, for example, the function relying on a large number of parameters, complex combinations of parameters, or nonlinear transformations of parameters). Interpretable AI/ML (which is not the subject of our main criticism) does roughly the following: Instead of using a black-box function, it uses a transparent (“white-box”) function that is in an easy-to-digest form, for example, a linear model whose parameters correspond to additive weights relating the input features and the output or a classification tree that creates an intuitive rule-based map of the decision space. Such algorithms have been described as intelligible ([ 6 ][6]) and decomposable ([ 7 ][7]). The interpretable algorithm may not be immediately understandable by everyone (even a regression requires a bit of background on linear relationships, for example, and can be misconstrued). However, the main selling point of interpretable AI/ML algorithms is that they are open, transparent, and capable of being understood with reasonable effort. Accordingly, some scholars argue that, under many conditions, only interpretable algorithms should be used, especially when they are used by governments for distributing burdens and benefits ([ 8 ][8]). However, requiring interpretability would create an important change to ML as it is being done today—essentially that we forgo deep learning altogether and whatever benefits it may entail. Explainable AI/ML is very different, even though both approaches are often grouped together. Explainable AI/ML, as the term is typically used, does roughly the following: Given a black-box model that is used to make predictions or diagnoses, a second explanatory algorithm finds an interpretable function that closely approximates the outputs of the black box. This second algorithm is trained by fitting the predictions of the black box and not the original data, and it is typically used to develop the post hoc explanations for the black-box outputs and not to make actual predictions because it is typically not as accurate as the black box. The explanation might, for instance, be given in terms of which attributes of the input data in the black-box algorithm matter most to a specific prediction, or it may offer an easy-to-understand linear model that gives similar outputs as the black-box algorithm for the same given inputs ([ 4 ][4]). Other models, such as so-called counterfactual explanations or heatmaps, are also possible ([ 9 ][9], [ 10 ][10]). In other words, explainable AI/ML ordinarily finds a white box that partially mimics the behavior of the black box, which is then used as an explanation of the black-box predictions. Three points are important to note: First, the opaque function of the black box remains the basis for the AI/ML decisions, because it is typically the most accurate one. Second, the white box approximation to the black box cannot be perfect, because if it were, there would be no difference between the two. It is also not focusing on accuracy but on fitting the black box, often only locally. Finally, the explanations provided are post hoc. This is unlike interpretable AI/ML, where the explanation is given using the exact same function that is responsible for generating the output and is known and fixed ex ante for all inputs. A substantial proportion of AI/ML-based medical devices that have so far been cleared or approved by the US Food and Drug Administration (FDA) use noninterpretable black-box models, such as deep learning ([ 1 ][1]). This may be because blackbox models are deemed to perform better in many health care applications, which are often of massively high dimensionality, such as image recognition or genetic prediction. Whatever the reason, to require an explanation of black-box AI/ML systems in health care at present entails using post hoc explainable AI/ML models, and this is what we caution against here. Explainable algorithms have been a relatively recent area of research, and much of the focus of tech companies and researchers has been on the development of the algorithms themselves—the engineering—and not on the human factors affecting the final outcomes. The prevailing argument for explainable AI/ML is that it facilitates user understanding, builds trust, and supports accountability ([ 3 ][3], [ 4 ][4]). Unfortunately, current explainable AI/ML algorithms are unlikely to achieve these goals—at least in health care—for several reasons. ### Ersatz understanding Explainable AI/ML (unlike interpretable AI/ML) offers post hoc algorithmically generated rationales of black-box predictions, which are not necessarily the actual reasons behind those predictions or related causally to them. Accordingly, the apparent advantage of explainability is a “fool's gold” because post hoc rationalizations of a black box are unlikely to contribute to our understanding of its inner workings. Instead, we are likely left with the false impression that we understand it better. We call the understanding that comes from post hoc rationalizations “ersatz understanding.” And unlike interpretable AI/ML where one can confirm the quality of explanations of the AI/ML outcomes ex ante, there is no such guarantee for explainable AI/ML. It is not possible to ensure ex ante that for any given input the explanations generated by explainable AI/ML algorithms will be understandable by the user of the associated output. By not providing understanding in the sense of opening up the black box, or revealing its inner workings, this approach does not guarantee to improve trust and allay any underlying moral, ethical, or legal concerns. There are some circumstances where the problem of ersatz understanding may not be an issue. For example, researchers may find it helpful to generate testable hypotheses through many different approximations to a black-box algorithm to advance research or improve an AI/ML system. But this is a very different situation from regulators requiring AI/ML-based medical devices to be explainable as a precondition of their marketing authorization. ### Lack of robustness For an explainable algorithm to be trusted, it needs to exhibit some robustness. By this, we mean that the explainability algorithm should ordinarily generate similar explanations for similar inputs. However, for a very small change in input (for example, in a few pixels of an image), an approximating explainable AI/ML algorithm might produce very different and possibly competing explanations, with such differences not being necessarily justifiable or understood even by experts. A doctor using such an AI/ML-based medical device would naturally question that algorithm. ### Tenuous connection to accountability It is often argued that explainable AI/ML supports algorithmic accountability. If the system makes a mistake, the thought goes, it will be easier to retrace our steps and delineate what led to the mistake and who is responsible. Although this is generally true of interpretable AI/ML systems, which are transparent by design, it is not true of explainable AI/ML systems because the explanations are post hoc rationales, which only imperfectly approximate the actual function that drove the decision. In this sense, explainable AI/ML systems can serve to obfuscate our investigation into a mistake rather than help us to understand its source. The relationship between explainability and accountability is further attenuated by the fact that modern AI/ML systems rely on multiple components, each of which may be a black box in and of itself, thereby requiring a fact finder or investigator to identify, and then combine, a sequence of partial post hoc explanations. Thus, linking explainability to accountability may prove to be a red herring. Explainable AI/ML systems not only are unlikely to produce the benefits usually touted of them but also come with additional costs (as compared with interpretable systems or with using black-box models alone without attempting to rationalize their outputs). ### Misleading in the hands of imperfect users Even when explanations seem credible, or nearly so, when combined with prior beliefs of imperfectly rational users, they may still drive the users further away from a real understanding of the model. For example, the average user is vulnerable to narrative fallacies, where users combine and reframe explanations in misleading ways. The long history of medical reversals—the discovery that a medical practice did not work all along, either failing to achieve its intended goal or carrying harms that outweighed the benefits—provides examples of the risks of narrative fallacy in health care. Relatedly, explanations in the form of deceptively simple post hoc rationales can engender a false sense of (over)confidence. This can be further exacerbated through users' inability to reason with probabilistic predictions, which AI/ML systems often provide ([ 11 ][11]), or the users' undue deference to automated processes ([ 2 ][2]). All of this is made more challenging because explanations have multiple audiences, and it would be difficult to generate explanations that are helpful for all of them. ### Underperforming in at least some tasks If regulators decide that the only algorithms that can be marketed are those whose predictions can be explained with reasonable fidelity, they thereby limit the system's developers to a certain subset of AI/ML algorithms. For example, highly nonlinear models that are harder to approximate in a sufficiently large region of the data space may thus be prohibited under such a regime. This will be fine in cases where complex models—like deep learning or ensemble methods—do not particularly outperform their simpler counterparts (characterized by fairly structured data and meaningful features, such as predictions based on relatively few patient medical records) ([ 8 ][8]). But in others, especially in cases with massively high dimensionality—such as image recognition or genetic sequence analysis—limiting oneself to algorithms that can be explained sufficiently well may unduly limit model complexity and undermine accuracy. If explainability should not be a strict requirement for AI/ML in health care, what then? Regulators like the FDA should focus on those aspects of the AI/ML system that directly bear on its safety and effectiveness—in particular, how does it perform in the hands of its intended users? To accomplish this, regulators should place more emphasis on well-designed clinical trials, at least for some higher-risk devices, and less on whether the AI/ML system can be explained ([ 12 ][12]). So far, most AI/ML-based medical devices have been cleared by the FDA through the 510(k) pathway, requiring only that substantial equivalence to a legally marketed (predicate) device be demonstrated, without usually requiring any clinical trials ([ 13 ][13]). Another approach is to provide individuals added flexibility when they interact with a model—for example, by allowing them to request AI/ML outputs for variations of inputs or with additional data. This encourages buy-in from the users and reinforces the model's robustness, which we think is more intimately tied to building trust. This is a different approach to providing insight into a model's inner workings. Such interactive processes are not new in health care, and their design may depend on the specific application. One example of such a process is the use of computer decision aids for shared decision-making for antenatal counseling at the limits of gestational viability. A neonatologist and the prospective parents might use the decision aid together in such a way to show how various uncertainties will affect the “risk:benefit ratios of resuscitating an infant at the limits of viability” ([ 14 ][14]). This reflects a phenomenon for which there is growing evidence—that allowing individuals to interact with an algorithm reduces “algorithmic aversion” and makes them more willing to accept the algorithm's predictions ([ 2 ][2]). ### From health care to other settings Our argument is targeted particularly to the case of health care. This is partly because health care applications tend to rely on massively high-dimensional predictive algorithms where loss of accuracy is particularly likely if one insists on the ability of good black-box approximations with simple enough explanations, and expertise levels vary. Moreover, the costs of misclassifications and potential harm to patients are relatively higher in health care compared with many other sectors. Finally, health care traditionally has multiple ways of demonstrating the reliability of a product or process, even in the absence of explanations. This is true of many FDA-approved drugs. We might think of medical AI/ML as more like a credence good, where the epistemic warrant for its use is trust in someone else rather than an understanding of how it works. For example, many physicians may be quite ignorant of the underlying clinical trial design or results that led the FDA to believe that a certain prescription drug was safe and effective, but their knowledge that it has been FDA-approved and that other experts further scrutinize it and use it supplies the necessary epistemic warrant for trusting the drug. But insofar as other domains share some of these features, our argument may apply more broadly and hold some lessons for regulators outside health care as well. ### When interpretable AI/ML is necessary Health care is a vast domain. Many AI/ML predictions are made to support diagnosis or treatment. For example, Biofourmis's RhythmAnalytics is a deep neural network architecture trained on electrocardiograms to predict more than 15 types of cardiac arrhythmias ([ 15 ][15]). In cases like this, accuracy matters a lot, and understanding is less important when a black box achieves higher accuracy than a white box. Other medical applications, however, are different. For example, imagine an AI/ML system that uses predictions about the extent of a patient's kidney damage to determine who will be eligible for a limited number of dialysis machines. In cases like this, when there are overarching concerns of justice— that is, concerns about how we should fairly allocate resources—ex ante transparency about how the decisions are made can be particularly important or required by regulators. In such cases, the best standard would be to simply use interpretable AI/ML from the outset, with clear predetermined procedures and reasons for how decisions are taken. In such contexts, even if interpretable AI/ML is less accurate, we may prefer to trade off some accuracy, the price we pay for procedural fairness. We argue that the current enthusiasm for explainability in health care is likely overstated: Its benefits are not what they appear, and its drawbacks are worth highlighting. For health AI/ML-based medical devices at least, it may be preferable not to treat explainability as a hard and fast requirement but to focus on their safety and effectiveness. Health care professionals should be wary of explanations that are provided to them for black-box AI/ML models. Health care professionals should strive to better understand AI/ML systems to the extent possible and educate themselves about how AI/ML is transforming the health care landscape, but requiring explainable AI/ML seldom contributes to that end. 1. [↵][16]1. S. Benjamens, 2. P. Dhunnoo, 3. B. Meskó , NPJ Digit. Med. 3, 118 (2020). [OpenUrl][17][PubMed][18] 2. [↵][19]1. B. J. Dietvorst, 2. J. P. Simmons, 3. C. Massey , Manage. Sci. 64, 1155 (2018). [OpenUrl][20] 3. [↵][21]1. A. F. Markus, 2. J. A. Kors, 3. P. R. Rijnbeek , J. Biomed. Inform. 113, 103655 (2021). [OpenUrl][22][PubMed][18] 4. [↵][23]1. M. T. Ribeiro, 2. S. Singh, 3. C. 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Babic, 3. T. Evgeniou, 4. I. G. Cohen , NPJ Digit. Med. 3, 53 (2020). [OpenUrl][38] 13. [↵][39]1. U. J. Muehlematter, 2. P. Daniore, 3. K. N. Vokinger , Lancet Digit. Health 3, e195 (2021). [OpenUrl][40] 14. [↵][41]1. U. Guillen, 2. H. Kirpalani , Semin. Fetal Neonatal Med. 23, 25 (2018). [OpenUrl][42][PubMed][18] 15. [↵][43]Biofourmis, RhythmAnalytics (2020); [www.biofourmis.com/solutions/][44]. Acknowledgments: We thank S. Wachter for feedback on an earlier version of this manuscript. All authors contributed equally to the analysis and drafting of the paper. Funding: S.G. and I.G.C. were supported by a grant from the Collaborative Research Program for Biomedical Innovation Law, a scientifically independent collaborative research program supported by a Novo Nordisk Foundation grant (NNF17SA0027784). I.G.C. was also supported by Diagnosing in the Home: The Ethical, Legal, and Regulatory Challenges and Opportunities of Digital Home Health, a grant from the Gordon and Betty Moore Foundation (grant agreement number 9974). Competing interests: S.G. is a member of the Advisory Group–Academic of the American Board of Artificial Intelligence in Medicine. I.G.C. serves as a bioethics consultant for Otsuka on their Abilify MyCite product. I.G.C. is a member of the Illumina ethics advisory board. I.G.C. serves as an ethics consultant for Dawnlight. The authors declare no other competing interests. 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Last month, at the G7 Leaders' Summit in Cornwall, United Kingdom, the leading industrial nations addressed the sustainable and safe use of space, making space debris a priority and calling on other nations to follow suit. This is good news because space is becoming increasingly congested, and strong political will is needed for the international space community to start using space sustainably and preserve the orbital environment for the space activities of future generations. There are more than 28,000 routinely tracked objects orbiting Earth. The vast majority (85%) are space debris that no longer serve a purpose. These debris objects are dominated by fragments from the approximately 560 known breakups, explosions, and collisions of satellites or rocket bodies. These have left behind an estimated 900,000 objects larger than 1 cm and a staggering 130 million objects larger than 1 mm in commercially and scientifically valuable Earth orbits. Today's already active satellite infrastructure provides a multitude of critical services to modern society, including communication, weather, navigation, and Earth-monitoring missions. Its loss would severely damage modern society. Furthermore, a new era in space has just started, driven by commercial, low-latency broadband services that rely on large constellations of satellites in low Earth orbit. These will revolutionize connectivity on the ground and in the air. However, they will also increase space traffic. The satellites to be launched over the next 5 years will surpass the number launched globally over the entire history of spaceflight. Congestion in space is only going to get worse. It is apparent that debris mitigation strategies—defined two decades ago by experts in the world's leading space agencies—are ever more important. They aim to prevent explosive breakups by venting residual energy from space systems at the end of their missions, and to “dispose” of a space object through a final maneuver that causes it to reenter Earth's atmosphere. Although these strategies are widely recognized, dozens of large space objects are still stranded every year in critical orbital regions where they will remain for several hundred years. And an average of eight fragmentation events in orbit occur annually, adding more pollution and increasing the likelihood of more collisions. Operations in space are themselves facing the burden of increasing evasive maneuvers to prevent losing a mission. In the most densely populated orbital altitudes, space objects are receiving dozens of collision warnings per day, of which only the most critical can be avoided. The number of such alerts will grow as large constellations of satellites come online. Another important facet of the debris problem is the risk on Earth from reentering objects. Between 100 and 200 metric tons of human-made hardware reenters Earth's atmosphere every year in an uncontrolled fashion. Heat-resistant material, like titanium or stainless steel, can survive the harsh reentry conditions. Progress can be made by advancing technology to ensure spaceflight safety. For example, the European Space Agency's Space Safety Programme is developing solutions that make disposal and energy passivation actions more fail-safe. “Deorbiting kits” will provide redundant propulsion and communication to ensure disposal of a spacecraft even after it ceases to function. A new field of “design-to-demise” will aim to replace critical components with less heat-resistant material to limit their chance of reaching ground upon reentry. In addition, a more systematic deployment of ground-based laser tracking could increase the accuracy of space surveillance data and consequently limit the number of collision avoidance alerts. Laser power could even transfer a small amount of momentum to objects to prevent their collisions. On top of that, missions, such as Clearspace-1, will aim to remove targeted debris through robotic capture. An internationally binding regime for the management of debris and space traffic is pending. Thus far, space missions have been supervised on the national level only, and states have been encouraged to translate the nonbinding space debris guidelines into national regulations. Space, however, is a commonly used resource with a limited capacity. International harmonization of space traffic would be required for an efficient and interference-free use of space. The coordinated use of the available radio frequencies could serve as a template. Furthermore, the implementation of space debris mitigation requirements should be tracked, following internationally binding principles. New and affordable technical solutions might stimulate more ambitious steps in international regulation to preserve space for the spacefarers of tomorrow.

NASA has announced that it will attempt a'risky' manoeuvre to fix its 31-year-old Hubble space telescope later today. Hubble accidentally went offline due to a mysterious glitch on June 13 that took down one of its main computers. But NASA says it's located the source of the problem – a faulty power regulator in the computer's Power Control Unit (PCU). It will attempt a switch to a backup PCU staring Thursday (July 15), which, if successful, will bring Hubble back to normal science operations in'several days'. Hubble, a joint project of NASA, the European Space Agency (ESA) and the Canadian Space Agency (CSA), has been observing the universe for over three decades.

NAHA – The U.S. Marine Corps have held a drill in Japan with orders given in Japanese for the first time, according to the troops, in a move aimed at enhancing their partnership with the Self-Defense Forces. Although it remains unclear whether the Marines will interact in Japanese during actual operations, use of the language in Marine training suggests Washington is attempting to engage Japan's Ground-Self Defense Force in new operations involving remote islands, according to an SDF source. In a Marine exercise on April 29 at an airfield on Ie Island in Okinawa Prefecture, a Marine is confirmed to have directed other members in Japanese to move a rocket and fire it while pointing at a spot on the map. The exercise was part of the Marines' new Expeditionary Advanced Base Operations, or EABO, in which troops practice securing a base for an attack on an island. "We would very much like to increase our partnership and interoperability," said Capt.

The use of AI in space exploration is increasing at an unprecedented pace, with the market being valued at a staggering $2 billion and still growing. Richard Branson, founder of Virgin Galactic successfully soared to space on Sunday. Ex-Amazon boss, Jeff Bezos of Blue Origin is the next billionaire to travel to space this month. While these are potential space tourism initiatives, the space race began ever since humans found that there exists a place beyond earth. Starting with the launch of Sputnik in 1957, and the first man landing on the moon a decade later, the race to space was always on with nations trying their hands on exploring this exciting frontier.

Philosophers often argue that knowledge is neutral, but the use we make of it can bring good or bad consequences. Seeing technology as knowledge, Thomas Ferretti considers ethical and political issues raised by the ongoing revolution in artificial intelligence (AI) and machine learning (ML). We often think that acquiring more knowledge is always good. If I acquire the knowledge of calculus, I can now do some things that I could not do before. Yet, what philosophers call a "consequentialist" perspective might propose instead that knowledge itself is neutral: we must know how I am going to use my knowledge in practice before judging whether it has good or bad consequences.

"All men by nature desire to know." - Aristotle "He who does not know what the world is does not know where he is." - Marcus Aurelius "If I have seen further, it is by standing on the shoulders of giants." "The universe is a giant causal machine. The world is "at the bottom" governed by causal algorithms. Our bodies are causal machines. Our brains and minds are causal AI computers". The 3 biggest unknown unknowns are described and analyzed in terms of human intelligence and machine intelligence. A deep understanding of reality and its causality is to revolutionize the world, its science and technology, AI machines including. The content is the intro of Real AI Project Confidential Report: How to Engineer Man-Machine Superintelligence 2025: AI for Everything and Everyone (AI4EE). It is all a power set of {known, unknown; known unknown}, known knowns, known unknowns, unknown knowns, and unknown unknowns, like as the material universe's material parts: about 4.6% of baryonic matter, about 26.8% of dark matter, and about 68.3% of dark energy. There are a big number of sciences, all sorts and kinds, hard sciences and soft sciences. But what we are still missing is the science of all sciences, the Science of the World as a Whole, thus making it the biggest unknown unknowns. It is what man/AI does not know what it does not know, neither understand, nor aware of its scope and scale, sense and extent. "the universe consists of objects having various qualities and standing in various relationships" (Whitehead, Russell), "the world is the totality of states of affairs" (D. "World of physical objects and events, including, in particular, biological beings; World of mental objects and events; World of objective contents of thought" (K. How the world is still an unknown unknown one could see from the most popular lexical ontology, WordNet,see supplement. The construct of the world is typically missing its essential meaning, "the world as a whole", the world of reality, the ultimate totality of all worlds, universes, and realities, beings, things, and entities, the unified totalities. The world or reality or being or existence is "all that is, has been and will be". Of which the physical universe and cosmos is a key part, as "the totality of space and times and matter and energy, with all causative fundamental interactions".