Collaborating Authors

Consumer Health

Unsupervised machine learning application in ACL


De-Sheng Chen,* Tong-Fu Wang,* Jia-Wang Zhu, Bo Zhu, Zeng-Liang Wang, Jian-Gang Cao, Cai-Hong Feng, Jun-Wei Zhao Department of Sports Medicine and Arthroscopy, Tianjin Hospital of Tianjin University, Tianjin, People's Republic of China *These authors contributed equally to this work Correspondence: Jia-Wang Zhu Department of Sports Medicine and Arthroscopy, Tianjin Hospital of Tianjin University, Tianjin, People's Republic of China Email [email protected] Purpose: We aim to present an unsupervised machine learning application in anterior cruciate ligament (ACL) rupture and evaluate whether supervised machine learning-derived radiomics features enable prediction of ACL rupture accurately. Patients and Methods: Sixty-eight patients were reviewed. Their demographic features were recorded, radiomics features were extracted, and the input dataset was defined as a collection of demographic features and radiomics features. The input dataset was automatically classified by the unsupervised machine learning algorithm. Then, we used a supervised machine learning algorithm to construct a radiomics model.

How stressed out are factory-farmed animals? AI might have the answer.


Facial recognition technology is rapidly becoming ubiquitous, used in everything from security cameras to smartphones. But in the near future, humans may not be the only ones to be digitally captured. Researchers are training forms of artificial intelligence to recognize individual animals by their faces alone -- and even discern their emotional state just by reading their expressions. Much of the research into animal facial expressions has focused on species like dogs and horses. But some of the most cutting-edge work is aimed at an unlikely subject: the farmed hog.

Would a robot trust you? Developmental robotics model of trust and theory of mind


The technological revolution taking place in the fields of robotics and artificial intelligence seems to indicate a future shift in our human-centred social paradigm towards a greater inclusion of artificial cognitive agents in our everyday environments. This means that collaborative scenarios between humans and robots will become more frequent and will have a deeper impact on everyday life. In this setting, research regarding trust in human–robot interactions (HRI) assumes a major importance in order to ensure the highest quality of the interaction itself, as trust directly affects the willingness of people to accept information produced by a robot and to cooperate with it. Many studies have already explored trust that humans give to robots and how this can be enhanced by tuning both the design and the behaviour of the machine, but not so much research has focused on the opposite scenario, that is the trust that artificial agents can assign to people. Despite this, the latter is a critical factor in joint tasks where humans and robots depend on each other's effort to achieve a shared goal: whereas a robot can fail, so can a person. For an artificial agent to know when to trust or distrust somebody and adapt its plans to this prediction can make all the difference in the success or failure of the task. Our work is centred on the design and development of an artificial cognitive architecture for a humanoid autonomous robot that incorporates trust, theory of mind (ToM) and episodic memory, as we believe these are the three key factors for the purpose of estimating the trustworthiness of others. We have tested our architecture on an established developmental psychology experiment [1] and the results we obtained confirm that our approach successfully models trust mechanisms and dynamics in cognitive robots. Trust is a fundamental, unavoidable component of social interactions that can be defined as the willingness of a party (the trustor) to rely on the actions of another party (the trustee), with the former having no control over the latter [2].

Punjab Startup's Device Can Tell How Safe Your Milk is in 40 Secs With 99% Accuracy


How safe is the milk you drink? In a 2020 report published by the Consumer Guidance Society of India (CGSI), 79% of branded or loosely packed milk in Maharashtra was adulterated. In other words, barely 21% of the samples tested by CGSI complied with the standards specified by the Food Safety and Standard Authority of India (FSSAI). The problem of milk adulteration is not confined to only one state. In states like Punjab, for example, there are regular reports of raids being conducted by the state against illegal factories making spurious milk and milk products.

Machine Learning Core Repository on GitHub, 4 Application Examples to Try Machine Learning in Sensors in Minutes - ELE Times


ST published its machine learning core repository on GitHub, with examples and configuration files, to vastly improve the developers' experience. Artificial intelligence is notoriously difficult because it relies on data science. Additionally, creating the right algorithm, such as a decision tree, and setting it up, can also be tricky. Unfortunately, all these issues tend to limit the number of engineers that can easily start working on machine learning applications. Hence, we published a repository on GitHub to solve this problem.

5 insider tech travel hacks you'll use every single trip

USATODAY - Tech Top Stories

Every summer, I get the travel itch. Before you head out, make sure your home is locked down. The bad news is security cameras, from video doorbells to a full-fledged security system, aren't always hack-proof out of the box. Tap or click here for the steps to make sure only you can access your security and video doorbell camera's feed. Speaking of cameras, best check your rental for any hidden cameras.

Assessing human habitability and migration


Habitability loss is increasingly recognized as an important dimension of climate risk assessment and one with complex linkages to migration. Most habitability assessments, like climate risk assessments more generally, are based on “top-down” approaches that apply quantitative models using uniform methodologies and generalizable assumptions at global and regional scales, privileging physical sciences over social science–informed understandings of local vulnerability and adaptive capacity. Many assessments have focused on a single climate hazard threshold (such as permanent inundation or the 1-in-100-year flood), and a subset have implied that outmigration may be one of the few viable adaptation responses ([ 1 ][1]). There is a risk that such climate determinism minimizes the potential for human agency to find creative, locally appropriate solutions. Although top-down modeling can serve a useful purpose in identifying potential future “hot spots” for habitability decline and potential outmigration, only by integrating “bottom-up” insights related to place-based physical systems and social contexts, including potential adaptive responses, will we arrive at a more nuanced understanding. This integrated framework would encourage development of policies that identify the most feasible and actionable local adaptation options across diverse geographies and groups, rather than options that are deterministic and one-size-fits-all and encourage binary “migrate or not” decisions. We propose a set of recommendations centered around building the research and assessment knowledge base most needed to inform policy responses around habitability loss and migration. We define habitability as the environmental conditions in a particular setting that support healthy human life, productive livelihoods, and sustainable intergenerational development. Climate change may undermine one or more of the following associated, interacting, dimensions of habitability: basic human survival ([ 2 ][2]), livelihood security ([ 3 ][3]), and societies' capacity to manage environmental risks ([ 4 ][4]). Rapid rates of climate change and departures from historical variability ranges can increase risks, especially when coupled with nonclimate stressors. In such instances, threats to habitability may be evident in changing flows of human migration, whether forced or voluntary ([ 5 ][5]). Most habitability assessments have relied on outputs from top-down models. This approach is conducive to system-level prediction, producing quantitative outputs that are globally comparable, such as single physical hazard thresholds that are either assumed or empirically based. Much recent work reflects a blend of long-term, high-resolution historical climate data where available, combined with projections across a large suite of global climate models driven by multiple representative concentration pathways (RCPs) representing trajectories of greenhouse gas concentrations. Another critical element is inclusion of extreme events, often expressed as a frequency of occurrence or a magnitude associated with a given recurrence period. In turn, top-down demographic and economic models, which form the basis for the shared socioeconomic pathways (SSPs) projecting global socioeconomic trajectories, provide a picture of future population and development that can also inform projections of people and assets at risk. Climate projections can also drive sectoral impact assessments—for example, empirically by extending historical statistical relationships between climate variability and the affected sector. More commonly, projections from standardized climate simulations drive sectoral impact models that dynamically simulate key features, such as crop growth. Top-down migration models use relative changes in sectoral impacts across regions along with other information as a means of projecting future population flows. Thus, these models project responses to habitability changes in regions where varying conditions may lead to outmigration, inmigration, or both. The standardized nature of top-down methods facilitates comparisons—for example, of regions most at risk of crossing habitability thresholds associated with a climate hazard, and when. The top-down perspective can also reveal large-scale trends and interconnected features of global systems. However, there are several limitations. First, local and regional geophysical and sector-specific factors can drive hazards and risks at scales missed by global analyses. Second, less-modeled, place-specific characteristics of populations, such as health and socioeconomic status, shape both exposure and vulnerability. Third, adaptation choices and activities are embedded in historical context and culturally specific individual and community values and objectives that cannot easily be incorporated in models. Fourth, high-impact outcomes—associated, for example, with compound extreme events and abrupt changes in climate, ecological, and social systems—may be underestimated because of top-down model limitations such as the inability to credibly resolve evolving correlation structures across variables, space, and time, and key system sensitivities and feedbacks within and across systems ([ 6 ][6]). For example, climate phenomena teleconnected across great distances may lead to “breadbasket” failures in key food-producing regions and price shocks that can seriously reduce food security among vulnerable populations far away from the regions experiencing the climate stress. Fortunately, top-down approaches are increasingly being paired with bottom-up approaches that offer a specificity that can help address these challenges. Bottom-up conceptual and/or computational modeling of complex adaptive systems can be designed to simulate the local experience of losing habitability over time. In the breadbasket case above, models of local responses can be paired with global models of international food trade that set boundary conditions. For example, agent-based models (ABMs) set up simulations with agents empirically calibrated to behaviorally respond to changing environmental conditions: the loss of assets and livelihood opportunities, threats to life, and changing structure of social networks. Modeling can be trained on local data to understand and predict important feedbacks at higher spatial and temporal resolution than is possible with global models. ABMs can be calibrated to examine a range of individual-actor preferences and test the effect of local decision-making to plausibly depict tradeoffs among adaptation options, including migration ([ 7 ][7]). As another bottom-up example, qualitative information can be coproduced with diverse stakeholders, including subject matter experts, to explore high-impact scenarios and local solutions that will be missed by top-down approaches. Of course, bottom-up approaches have their limitations as well. For example, their specificity makes it difficult to compare across geographies and groups, and individual methodological decisions can appear arbitrary. Furthermore, bottom-up computational models such as ABMs are still limited by a lack of empirical data with which to calibrate model parameters. Here, we walk through the habitability challenges of two climate hazard examples, demonstrating the strengths and limitations of top-down approaches and how bottom-up perspectives lead to different policy-relevant insights. ### Sea level rise and extreme sea level events Recent years have seen growing complexity and nuance in assessments. Global assessments have supplemented climate model outputs by considering a broad range of sea level change components and including, for example, expert elicitation as a means of estimating low-probability, high-consequence outcomes ([ 8 ][8]). High-spatial-resolution digital elevation models and consideration of changes in the frequency and intensity of societally relevant metrics such as recurrence intervals and extreme values of coastal high water have been integrated into global products. Using many of the above advances, Kulp and Strauss estimated that the number of people exposed annually to coastal flooding under constant population could increase from 250 million people today to, by 2100, 310 million to 420 million under an intermediate scenario to 380 million to 630 million under a high-end scenario ([ 1 ][1]). Other studies have included changes in storms, hyper-local positive correlations between population density and subsidence, population projections consistent with SSP-RCP combinations, and assets at risk. Additional refinements have focused on specific coastal locations, adding critical context at the expense of global information. For example, Storlazzi et al. framed their assessment of tipping-point risks to atolls around two metrics—annual overwash events that threaten infrastructure, and salinization of groundwater—that are specifically relevant for atolls given their small size, uniformly low elevation, and relative isolation and found that habitability is threatened in most atoll islands by the middle of the 21st century, far sooner than permanent-inundation–based studies would suggest ([ 9 ][9]). Some local studies have included dynamic interaction between coastal waters and adjacent landforms. Other local and regional studies have considered social dimensions of human vulnerability, as well as in situ adaptation, using empirically calibrated agent-based livelihood decision models that span multiple climate, RCP, and SSP scenarios ([ 7 ][7]). The three dimensions of habitability demonstrate why no single coastal flood metric threshold can be determined in a top-down way. For the direct survivability dimension, key factors include future flood control, feasibility of evacuation, and the stochasticity of individual storms. For livelihood, saline intrusion, for example, could benefit some sectors such as specialized aquaculture, even as it harms most sectors and people. And for the societal resilience dimension, large-scale factors such as levels of inequity, strength of governance and social networks, and quality of infrastructure will be critical. As sea levels rise and coastal flooding becomes more common, social, economic, and political factors in some locations will conspire to induce sudden loss of habitability far sooner than physical hazard–based thresholds such as permanent inundation would suggest, as risk perception and long-term economic viability shift. For example, increases in insurance premiums could negatively affect asset values and tax revenues, leading to deteriorating infrastructure and services. The timing of such threshold-crossing cannot be predicted on the basis of top-down models alone. In some instances, shocks can lead to rapid learning, adjustment, and in situ adaptation, at least temporarily. ![Figure][10] Frequent exceedance by 2100 of historically rare climate thresholds Under the high-emissions scenario RCP8.5, at most coastal locations extreme sea level events historically defined as 1-in-100-year events are projected to range in frequency from once per year to more than 10 times per year due to the effects of sea level rise alone. Only point locations where historical event data are available are shown. Projected number of days per year by 2100 exceeding a 33°C wet bulb globe temperature (WBGT) in a high-emissions scenario are also depicted. Under standard assumptions of wind and solar radiation, a WBGT of 33°C corresponds to a wet bulb temperature of roughly 31.5°C. [Sea level data are from figure 4.12 in ([ 8 ][8]); WBGT data are from fig ure 3 in ([ 12 ][11]).] GRAPHIC: N. DESAI/ SCIENCE BASED ON HORTON ETAL. ### Extreme heat Most assessments of future heat hazards have considered temperature only, although recent efforts are increasingly adopting a compound events framework—for example, considering how co-occurring extremes of high temperature and high humidity can modulate threats to habitability. Humid heat is particularly harmful to human health and the ability to engage in outdoor activities. Sherwood and Huber described a wet bulb temperature of 35°C as a threshold above which humans could not survive beyond approximately 6 hours owing to physiological and thermodynamic limits on the ability to cool through perspiration ([ 2 ][2]). Model-based studies have projected that this threshold could be crossed in the Persian Gulf and South Asia during the second half of the 21st Century under a high-emissions scenario ([ 10 ][12]). However, a finer-scale study found that this threshold has already been briefly crossed multiple times in populous cities. Although an absolute habitability threshold exists for the survivability dimension of extreme humid heat, some people will lose their ability to thermoregulate at much lower wet bulb temperatures. Mortality rates of the elderly, those with chronic health conditions, and those involved in strenuous activity rise dramatically well below the 35°C wet bulb threshold. In terms of the livelihood dimension, at ∼3.5°C of global warming above preindustrial levels, de Lima et al. project that in Sub-Saharan Africa and Southeast Asia increases in humid heat may decrease agricultural labor productivity by 30 to 50%, leading to larger agricultural sector impacts than are associated with direct temperature and CO2 effects on crops ([ 11 ][13]). However, air conditioning and other adaptations will enable—indeed, have enabled—some people to continue to live in places that exceed the 35°C threshold. Such an outcome increases inequity because those with no option but to work outdoors, or no access to affordable air conditioning, would be forced to migrate. And even for those with air conditioning, the third dimension of habitability—society's capacity to manage environmental risks—will be tested in unforeseen ways because it will be critical that air conditioning not fail. Sea level rise and extreme humid heat are far from the only climate hazards that have been assessed in the literature for potential habitability thresholds. For example, changes in surface moisture fluxes as mean precipitation and temperature shift are projected to have large impacts on dryland agriculture, fire regimes in forests, and water availability downstream from snow and glacier reservoirs. These and other hazards and impacts may overlap and interact across scales to affect habitability in complex ways, such as by potentially increasing the risk of conflict. Areas where current-day rare extreme sea level and humid heat events will occur with high frequency by the end of the century under a high emissions scenario of sea level rise and warming are identified in the figure ([ 8 ][8], [ 12 ][11]). The two metrics, corresponding to the current 1-in-100-year extreme sea level event and a wet bulb globe temperature of 33°C, respectively, are emblematic of top-down approaches. They thus represent an important point of entry for engagement with the bottom-up insights described above, as a step toward more nuanced habitability and migration assessments. Migration may result from threats to survival, upended livelihoods, or the breakdown in the collective capacity to adapt ([ 5 ][5]). However, research on climate change and migration makes clear that an even broader set of factors undergird migration decision-making. A decision to move is ultimately a personal or household judgment on factors that include local habitability. Involuntary migration occurs when people lack agency about the key dimensions of mobility, including the timing, destination, or duration of mobility or whether to migrate at all. Where agency is extremely low, involuntary migration may take different forms, including temporary or permanent displacement and distress migration. Distress migration—mass migration or displacement related to rapid deterioration in local circumstances—is a humanitarian concern because of the need for emergency interventions to avoid poor outcomes. Distress migration has been a common phenomenon throughout history but has risen and fallen on the global policy agenda largely as a function of whether or not wealthy industrialized countries are destinations. Also of humanitarian concern is the phenomenon of involuntary immobility, in which people are unable to move without help—the population most likely to require assistance relocating under managed retreat programs. Avoiding distress migration and involuntary immobility in favor of safe and orderly migration, as advanced by the Global Compact on Migration, is now a global policy priority, and the Compact calls on governments to “strengthen joint analysis and sharing of information to better map, understand, predict, and address migration movements” as a result of climate change impacts—all of which are essential aspects of habitability assessment. Many assessments posit some form of forced migration as an inevitable outcome of declining habitability. Yet, environmental stress rarely directly results in migration but works through a complex array of economic, demographic, social, and political proximate determinants that both initiate and sustain or modify flows. In any given population exposed to climate risks, different segments of the population respond to hazards differently and at different points in time, and as such, migration evolves with habitability through time. Whereas some may be able to migrate from deteriorating conditions without assistance, others may become immobile owing to limited options and insufficient resources, suffering progressive impoverishment and vulnerability unless social protection or planned relocation efforts are implemented ([ 5 ][5]). In situ adaptation, facilitated migration, and improving reception of migrants in (largely urban) destination areas are often more appropriate policies in these regions. Managed retreat has been proposed as a strategy for regions with declining habitability, but as a largely technical package of responses that includes buyouts, incentives, and planned relocation, among others, it does not currently translate well to most developing-world circumstances. The relationship between habitability and migration may be counterintuitive, as illustrated by the lack of evidence for migration away from low-lying delta areas despite acute risks ([ 7 ][7]). Migration itself affects habitability for those who are unable or unwilling to leave increasingly vulnerable circumstances, either positively, such as through incoming remittances, or negatively, such as through outmigration of the working-age demographic stratum and subsequent changes in economic dynamism and livelihood options. Flows may begin owing to entrenched poverty and environmental risks and then be sustained as migrant social networks lower barriers for those who initially remained behind. Although migration offers possibilities for advancing human well-being, as multiple dimensions of habitability are compromised, resulting forced migration will negatively affect human well-being. Migrants risk new constraints in urban informal settlements, and displaced persons may become permanently disconnected from their original communities and livelihoods in resettlement communities or refugee camps ([ 13 ][14]). Although top-down assessments oversimplify likely migratory responses to habitability declines, this does not necessarily imply that migration flows are overestimated. Multiple factors are driving migration in developing regions to varying degrees, including poor governance, perceived lack of opportunities, conflict, individual extreme events, and in some cases, climate-catastrophic discourses that add to a sense of hopelessness ([ 14 ][15]). Deeper and more contextualized understandings of migration dynamics aid in policy design, but the threats that result from declining habitability in combination with other drivers are real and may lead to substantial displacement of populations across a range of spatial scales. Top-down, threshold-based habitability assessments can serve a critical role in helping to identify priority regions and groups for integrated bottom-up work while revealing interactions in global systems that cannot be gleaned from the bottom-up work alone. Integration not only leads to better predictions of when and where habitability may diminish but also can be used to inform adaptation responses that themselves help preserve or restore habitability. Bottom-up assessments by definition provide finer, local resolution, and their richness of detail means that they require diverse participation and methods. To date, most locales have not been subject to such integrated habitability assessment. We thus encourage transdisciplinary, long-term coupled top-down and bottom-up habitability assessment [for example, ([ 15 ][16])] to complement and augment efforts such as the Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP), which has contributed so much to our understanding of potential future climate impacts on sectors such as agriculture, water, ecosystems, and health. Initial model intercomparison could focus on what regions and groups face diminishing habitability under different model configurations. Particularly where models agree on potential habitability hot spots, bottom-up modeling experiments could be conducted and compared on specified challenges to human survival, livelihoods, and capacity to manage risk, although standardization would be needed. The Intergovernmental Panel on Climate Change (IPCC) and national efforts can also help to develop this still inchoate middle space between top-down and bottom-up approaches to habitability and migration. Migration is emerging as a cross-cutting theme throughout the current IPCC assessment, and a special report on habitability and migration would both advance the knowledge base and showcase emerging methodologies. As one example, a climate change detection and attribution dimension would help inform dialogues about loss and damage under the Paris Agreement. Likewise, a discussion on migration across the Reasons for Concern commonly used in IPCC assessments ([ 5 ][5]) would allow us to distinguish how climate-induced migration, distress or otherwise, is distinct from other forms of migration. The complexity of the assessment challenge calls for a holistic, people-centric approach in which models, data aggregation, and ethnographic work are all advanced. Sectors such as engineering, hydrology, and reinsurance, that have historically been overreliant on physical models and hazard thresholds, operate at a scale that is ripe for habitability-relevant innovations at the interface between top down and bottom up. In this middle space, models could be used to examine policy scenarios instead of learning occurring exclusively from costly, time-consuming, real-world policy interventions that may put vulnerable people at risk. Greater communication among modelers will be key, and models must be validated with on-the-ground local research. To support migration and habitability modeling specifically, this would include data on when, where, and why people have moved or considered moving, how they define habitability, and the policy conditions that determine mobility outcomes ([ 14 ][15]). Furthermore, bottom-up research must account for the place-specific characteristics of populations—such as assets, livelihood opportunities, and social networks—that shape both exposure and adaptation. Investments in place-based social science thus help address data gaps, providing ground-truthing that will strengthen simulations of the outcomes of interventions. Investments in early-warning systems could help to anticipate where distress migration may happen, a key step in informing policy. The shortcomings of adaptation planning and policy at current risk levels in wealthy countries hint at the global challenges ahead in a changing climate. In the United States, for example, federal and local risk assessments—let alone policies—are not presently centrally coordinated or comparable. There is woefully insufficient funding available for bottom-up adaptation efforts from the better-financed federal level. Policies toward population mobility—whether planned, internal responses or immigration from other countries—vary from inconsistent over time to incoherent and sometimes inhumane. Coproduction of knowledge across diverse groups will be a precondition for any breakthroughs. In some instances, a starting point may be to bring preexisting top-down habitability and migration assessments to communities, provided that community feedback is collected and integrated iteratively and before key policy decisions are made. In other instances, stakeholder engagement may begin with fewer top-down, nonprobabilistic approaches that can be developed with communities, such as storylines and scenarios. Storylines and scenarios lend themselves to exploration of the uncertainties that most influence habitability locally (for example, the potential for changing correlation structures in models) and which adaptation strategies should be explored for which groups. Deeper stakeholder engagement, coupled with the other recommendations above, thus provides a foundation for colearning, iteration, and developing flexible approaches to the challenge of diminishing habitability. To the extent that top-down, threshold-based approaches are used to define habitability universally, there is a risk of assuming a high likelihood of uniform outmigration or concluding with blanket policy recommendations around managed retreat. Basing assessments on nuanced definitions of habitability and integrating top-down with bottom-up approaches could encourage a broader range of policies tailored to specific locations and groups, including regions that have been put forth as likely receiving areas. A focus on the dimensions of habitability presented here, and bottom-up approaches, will invariably alter top-down projections of migration. Under wetbulb temperatures exceeding 35°C, high levels of outmigration from the Persian Gulf may be avoided if air conditioning is widely available and alternative livelihood options develop for those who would otherwise work outdoors. However, there will be regions where social tipping points and a sense of prevailing pessimism about the future—for example, owing to evolving risk perception or disinvestment by the private or public sectors—could contribute to outmigration far sooner and more suddenly than top-down habitability threshold–based methods would suggest. Global, regional, and national migration policies themselves will also play an important role in facilitating or impeding migration. What is already clear is that climate change will result in shifting population distributions and that this process will overall be harmful to the most vulnerable, including those who may be “trapped” in deteriorating circumstances. For the reasons described here, and as a matter of climate justice, many semi-arid regions, much of the tropics, and some low-lying deltas and islands should be high priorities for integrated transdisciplinary work on habitability risks and major investments in adaptation. But only by taking into account the complexities described here will we avoid climate determinism and instead implement proactive policies on adaptation and migration that in particular will address the needs of the most vulnerable. 1. [↵][17]1. S. A. Kulp, 2. B. H. Strauss , Nat. Commun. 10, 4844 (2019). [OpenUrl][18] 2. [↵][19]1. S. C. Sherwood, 2. M. Huber , Proc. Natl. Acad. Sci. U.S.A. 107, 9552 (2010). [OpenUrl][20][Abstract/FREE Full Text][21] 3. [↵][22]1. T. Tanner et al ., Nat. Clim. Chang. 5, 23 (2015). [OpenUrl][23] 4. [↵][24]1. J. Barnett, 2. W. N. Adger , Annu. Rev. Environ. Resour. 43, 245 (2018). [OpenUrl][25] 5. [↵][26]1. R. McLeman et al ., Clim. Change 165, 24 (2021). [OpenUrl][27] 6. [↵][28]1. N. Simpson et al ., One Earth 4, 489 (2021). [OpenUrl][29] 7. [↵][30]1. A. R. Bell et al ., Environ. Res. Lett. 16, 024045 (2021). [OpenUrl][31] 8. [↵][32]1. H.-O. Pörtner et al. 1. M. Oppenheimer et al ., in IPCC Special Report on the Ocean and Cryosphere in a Changing Climate, H.-O. Pörtner et al., Eds. (IPCC, 2019). 9. [↵][33]1. C. D. Storlazzi et al ., Sci. Adv. 4, eaap9741 (2018). [OpenUrl][34][FREE Full Text][35] 10. [↵][36]1. C. Raymond, 2. T. Matthews, 3. R. M. Horton , Sci. Adv. 6, eaaw1838 (2020). [OpenUrl][37][FREE Full Text][38] 11. [↵][39]1. C. Z. de Lima et al ., Environ. Res. Lett. 16, 044020 (2021). [OpenUrl][40] 12. [↵][41]1. D. Li, 2. J. Yuan, 3. R. E. E. Kopp , Environ. Res. Lett. 15, 064003 (2020). [OpenUrl][42] 13. [↵][43]1. A. Heslin et al ., in Loss and Damage from Climate Change (Springer, 2019), pp. 237–258. 14. [↵][44]1. H. Adams, 2. S. Kay , Environ. Sci. Policy 93, 129 (2019). [OpenUrl][45] 15. [↵][46]1. K. Grace, 2. S. Siddiqui, 3. B. F. Zaitchik , Nat. Food 2, 1 (2021). [OpenUrl][47] Acknowledgments: The authors thank four anonymous reviewers and C. Lesk for comments and K. MacManus for assistance with the map figure. R.M.H. and A.d.S. were supported by the Columbia Climate School and its Earth Institute, and A.d.S. received funding from NSF award 1934978. 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Artificial Intelligence Improves The Lives Of Patients, Doctors, And Hospital Administrators By Performing Tasks That Humans Could Normally Perform In A Fraction Of Time. Here Is How Artificial Intelligence Revolutionize The Digital Health Monitoring System


We have enough problems these days. The last thing we need to worry about is our health or the high costs of care. That's why many people are turning to digital health solutions. As noted by The Wall Street Journal, many of these solutions can reportedly help manage diabetes, improve sleep, monitor heart health, encourage weight loss, track whether patients are sticking to physical therapy regimens, and more.(3) Due to the recent health scare, governments and healthcare systems worldwide may now realize how essential digital health has become.

Artificial Intelligence and Food Safety: Hype vs. Reality


To understand the promise and peril of artificial intelligence for food safety, consider the story of Larry Brilliant. Brilliant is a self-described "spiritual seeker," "social change addict," and "rock doc." During his medical internship in 1969, he responded to a San Francisco Chronicle columnist's call for medical help to Native Americans then occupying Alcatraz. Then came Warner Bros.' call to have him join the cast of Medicine Ball Caravan, a sort-of sequel to Woodstock Nation. That caravan ultimately led to a detour to India, where Brilliant spent 2 years studying at the foot of the Himalayas in a monastery under guru Neem Karoli Baba. Toward the end of the stay, Karoli Baba informed Brilliant of his calling: join the World Health Organization (WHO) and eradicate smallpox. He joined the WHO as a medical health officer, as a part of a team making over 1 billion house calls collectively. In 1977, he observed the last human with smallpox, leading WHO to declare the disease eradicated. After a decade battling smallpox, Brilliant went on to establish and lead foundations and start-up companies, and serve as a professor of international health at the University of Michigan. As one corporate brand manager wrote, "There are stories that are so incredible that not even the creative minds that fuel Hollywood could write them with a straight face."[1]

Building the engine that drives digital transformation

MIT Technology Review

This is the consensus view of an MIT Technology Review Insights survey of 210 members of technology executives, conducted in March 2021. These respondents report that they need--and still often lack-- the ability to develop new digital channels and services quickly, and to optimize them in real time. Underpinning these waves of digital transformation are two fundamental drivers: the ability to serve and understand customers better, and the need to increase employees' ability to work more effectively toward those goals. Two-thirds of respondents indicated that more efficient customer experience delivery was the most critical objective. This was followed closely by the use of analytics and insight to improve products and services (60%).