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This AI tool lets you visualize how climate change could affect your home

USATODAY - Tech Top Stories

A new tool with cutting-edge image recognition AI lets you visualize the future effects of climate change on any place in the world -- including your own home. The project, titled "This Climate Does Not Exist," lets you enter the address of your current home or your favorite travel destination and see what it could look like years later once climate change has taken its toll. You can see how Disneyland will look like covered in smog, the way extreme smog blanketed Beijing in 2014. You can see what your childhood home will look like after it is flooded by rising sea levels, the way floods devastated Indonesia in 2020 after widespread deforestation. Jakarata floods: Thousands caught in floods in Jakarta, Indonesia's sinking capital Extreme weather events due to climate change are already impacting corners of the globe.

Artificial intelligence in the real world


Charles is currently editorial director for Asia at Economist Impact. He covers a territory spanning from Australia to India. His team works with many Western multinationals from the Fortune 500 but increasingly with Asian multinationals, governments, SMEs and high-growth technology firms as well. A native Australian, Charles is currently based in Singapore and has most recently managed the regions technology research practice. He is a frequent speaker at technology events, recently giving keynote presentations at events in Singapore, Australia, Jakarta and Kuala Lumpur.

ASEAN deadlocked in selection of special envoy to Myanmar

The Japan Times

SINGAPORE – Five months since the military toppled a democratically elected government in Myanmar, ASEAN has not been able to appoint a special envoy to help defuse the member country's political crisis -- and a major obstacle seems to be disunity within the group. Disagreement among Association of Southeast Asian Nations countries over the envoy's selection appears to be helping Myanmar's military, which wants to buy time to solidify its rule, but that has also led to frustrations for some within ASEAN who want to engage in the issue more actively. Leaders of the 10-member group had agreed at a summit in the Indonesian capital Jakarta in April on a "five-point consensus" that included appointing a special ASEAN envoy to Myanmar. The envisaged envoy would try to mediate in the dialogue process between various parties in the country -- where pro-democracy forces were ousted from power in the Feb. 1 coup, with civilian leader Aung San Suu Kyi put under house arrest. ASEAN sources say there are three nominees: Virasakdi Futrakul, a former Thai deputy foreign minister and veteran diplomat; Hassan Wirajuda, a former Indonesian foreign minister, and Razali Ismail, a Malaysian who was a U.N. special envoy for Myanmar in the 2000s tasked with facilitating national reconciliation and democratization in the country.

Senior Data Scientist - Demand Generation


About the Role As our Senior Data Scientist, you'll be an integral player in the Demand and Incentives Data Team based in Jakarta. With the latest cutting-edge data science tech at your disposal, you'll focus your efforts on bringing our incentive systems to the next level, employing various quantitative techniques such as Machine Learning, Optimization, Simulation, Bayesian Techniques to drive asymmetric values for our businesses at Gojek. You'll be heavily involved in ideation, research, and building prototypes, and the folks in the Data Science Platform will bring your models to production. Your efforts will directly influence the stability and scalability of Gojek's demand & incentives stream, and thus to company's top and bottom line as a whole. What You Will Do Drive the long term vision of the Machine Learning-based incentive systems and own its implementation end-to-end Enhance the technical excellence of the team and bring the data science products in your stream to the next level Work with other Data Scientists, Machine Learning Engineers, and Business users to build, deploy, and scale data science solutions for incentive systems Utilize your experience in data science, machine learning, software engineering, distributed systems to develop these systems; work with the platform team to take the systems to production Work with Business teams to continuously refine and improve the systems to cater to Gojek's ever-evolving needs What You Will Need At least 5 years of experience as a Data Scientist/ Machine Learning Engineer, with solid understanding of Data Science and Machine Learning fundamentals and experience taking Data Science models into production Experience in Python, R, Golang/Java, Unix; along with knowledge of good software design principles and TDD Working knowledge of Cloud-based solutions (GCP/ AWS), Stream Data Processing Frameworks (Beam) and mature Deep Learning frameworks (e.g.

Machine Learning Engineer - Merchant Platform


About the Role Fasten your helmet and climb on board if you're ready to be our Machine Learning Engineer. In this role, you'll be a crucial player within the Merchant Platform, using and building machine learning as a microservice, integrating it with the core service, and establishing data pipelines for structured & unstructured data. In close collaboration with the Data Science, Data Engineering, and Product Engineering teams, you'll get your hands dirty in complex ML, data pipeline, and service product tech stacks. By automating processes and integrating ML models into our products & services, your efforts will help ensure a robust and efficient Merchant Platform for Gojek. What You Will Do Collaborate with Data Science team to gather the requirement for model parameter Build the feature extraction script to automate the process for the ML model Collaborate with product engineer to integrate ML model into product/service Process data from streaming/raw data based on user needs Collaborate with other Data Engineers to develop data and model pipelines Design distributed systems to apply machine learning and data science techniques What You Will Need At least 2 years of experience as a Software Engineer or ML Engineer, with fluency and experience in Clojure, Elixir, Python, or Java Basic knowledge in data science, and familiarity with ML libraries such as Pandas, Scikit, or Tensorflow Proven track-record in building large-scale, high-throughput, low-latency production systems Experience building data stream processes Familiarity with SQL and NoSQL Database Ability to implement CI/CD and TDDAbout the Team Our Merchant Platform team is a big family of around 60 people based across Jakarta, Yogyakarta, and India.

Surge Pricing, Artificial Intelligence, and Responsibility


On my first work trip to Jakarta 14 January 2016 for Grab, multiple terrorist bombs exploded a couple of miles from the GrabBike office where I had just arrived. People were fleeing cafes and restaurants around the attack site. My new colleagues were shaken, glad to be safe, looking to help. There was news of crowds on the streets trying to get away, confirmed by a spike in booking requests from the blocks around the explosion. My colleagues remembered the 2002 Bali bombings, and knew we should get people to spread out.

Mapping the global threat of land subsidence


Subsidence, the lowering of Earth's land surface, is a potentially destructive hazard that can be caused by a wide range of natural or anthropogenic triggers but mainly results from solid or fluid mobilization underground. Subsidence due to groundwater depletion ([ 1 ][1]) is a slow and gradual process that develops on large time scales (months to years), producing progressive loss of land elevation (centimeters to decimeters per year) typically over very large areas (tens to thousands of square kilometers) and variably affects urban and agricultural areas worldwide. Subsidence permanently reduces aquifer-system storage capacity, causes earth fissures, damages buildings and civil infrastructure, and increases flood susceptibility and risk. During the next decades, global population and economic growth will continue to increase groundwater demand and accompanying groundwater depletion ([ 2 ][2]) and, when exacerbated by droughts ([ 3 ][3]), will probably increase land subsidence occurrence and related damages or impacts. To raise awareness and inform decision-making, we evaluate potential global subsidence due to groundwater depletion, a key first step toward formulating effective land-subsidence policies that are lacking in most countries worldwide. A large-scale systematic literature review reveals that during the past century, land subsidence due to groundwater depletion occurred at 200 locations in 34 countries [see supplementary materials (SM)]. However, subsidence extent is only known for one-third of these records, information on the impacts is scarce, and mitigation measures were implemented only in a few locations. In China, widespread subsidence affects cities developed in the main sedimentary basins. In Indonesia, coastal subsidence in Jakarta is so severe that government authorities are planning to move the capital to the island of Borneo. In Japan, subsidence affected several cities during the 20th century, including more than 4 m of subsidence in Tokyo, before groundwater management practices mitigated further subsidence. Iran currently hosts some of the fastest-sinking cities in the world (25 cm year--1) because of unregulated groundwater pumping. In Europe, the greatest impact of subsidence occurs in the Netherlands, where subsidence is primarily responsible for placing 25% of the country below the mean sea level and increasing the flooding risk. Subsidence in the Po River Plain in Italy started during the second half of the 20th century and currently threatens 30% of the Italian population, contributing to recurrent coastal flooding during extreme high tides in Venice. In North America, intense groundwater depletion triggers subsidence from California's Central Valley, with as much as 9 m of subsidence in the past century, to the Atlantic and Gulf of Mexico coastal plains in the United States, where subsidence is increasing flooding risk. In México, subsidence rates are among the highest worldwide (as much as 30 cm year-1), affecting small structurally controlled intermontane basins where the main urban centers developed, causing an important but unaccounted economic impact. Spatial analysis of subsidence locations identified in our global database (see SM) reveals that subsidence has preferentially occurred in very flat areas where unconsolidated sediments accumulated in alluvial basins or coastal plains, and where urban or agricultural areas developed in temperate or arid climates characterized by prolonged dry periods. Land subsidence has generally occurred in water-stressed basins, where the combination of groundwater withdrawal and natural groundwater discharge outpaced groundwater recharge, resulting in groundwater storage losses, groundwater depletion, and compaction of susceptible aquifer systems. In the affected basins, land subsidence mainly occurred in highly populated areas, with half of documented occurrences in areas susceptible to flooding. In coastal zones, the combined effects of absolute sea-level rise and land subsidence contribute to relative sea-level rise ([ 4 ][4]). The contribution from land subsidence may exceed the contribution from absolute sea-level rise by a factor of 10 or more and could be especially critical for 21% of the geographic locations identified in our database, where land elevation is less than 1 m above the mean sea level. On the basis of the spatial analysis findings, a global model is proposed to combine the main variables influencing subsidence to identify environmental settings favoring land subsidence and the anthropogenic factors leading to groundwater depletion (see SM). Statistical analyses of lithology, land-surface slope, land cover, and Koppen-Geiger climate classes are used to predict global subsidence susceptibility at a spatial resolution of 1 km2. The probability of groundwater depletion is estimated by identifying urban and irrigated areas suffering water stress and where groundwater demand is high. The analyses do not consider subsidence magnitude and rate, owing to the lack of this information at a global scale. Hence, the combination of subsidence susceptibility and the probability of groundwater depletion is used to predict a “proxy” of subsidence hazard, which permits identification of exposed areas where the probability of land subsidence occurrence is high or very high. Even though these results do not necessarily translate to direct impacts or damages, they are useful for identifying potential subsidence areas where further local-scale analysis is necessary. T he comparison of our model predictions with an independent validation dataset reveals a 94% capability to distinguish between subsidence and nonsubsidence areas, according to the value of the area under the receiver operating characteristic curve (see SM). The global exposure to potential subsidence is evaluated by calculating the number of inhabitants living in potential subsidence areas, i.e., subsidence hazard proxy, and the equivalent gross domestic product (GDP). T his “proxy” of exposed assets is calculated assuming that GDP per capita is homogeneous within each country. Finally, the evolution of potential global subsidence and the related exposure is predicted for 2040 for a global change scenario based on steady population growth and increasing greenhouse gas emissions (Shared Socioeconomic Pathways 2, Representative Concentration Pathway 8.5), which accounts for the greatest sea-level rise projections. ![Figure][5] Potential global subsidence The color scale indicates the probability intervals classified from very low (VL) to very high (VH), for every 30-arcsec resolution pixel (1 km by 1 km at the Equator). The white hatched polygons indicate countries where groundwater data is unavailable, and the potential subsidence only includes information on the susceptibility. See maps of other regions in supplementary materials. GRAPHIC: N. DESAI/ SCIENCE Our results suggest that potential subsidence threatens 12 million km2 (8%) of the global land surface with a probability greater than 50% (MH to VH in the figure). Potential subsidence areas are concentrated in and near densely urban and irrigated areas with high water stress and high groundwater demand, overlying some of the largest and most depleted aquifer systems ([ 5 ][6]) in Asia (e.g., North China Plain) and North America (e.g., Gulf of Mexico coastal plain); coastal and river delta areas worldwide (e.g., Vietnam, Egypt, or the Netherlands); and inland sedimentary basins of México, Iran, and the Mediterranean countries. Potential subsidence is lower in Africa, Australia, and South America, owing to the lower groundwater depletion ([ 6 ][7]). In central Africa, potential subsidence only includes information on the susceptibility, as groundwater depletion is unknown. In this region, subsidence susceptibility (see fig. S6) could be useful to prevent subsidence impacts on developing cities that during the next decades could rely more on the available groundwater resources. To evaluate the exposure to potential subsidence, we focus on areas where the potential subsidence probability is high or very high (see the figure). The cumulative potential subsidence area amounts to 2.2 million km2, or 1.6% of the land; includes 1.2 billion inhabitants, or 19% of the global population; and has an exposed GDP of US$ 8.19 trillion, or 12% of the global GDP. Hi gh-income countries account for 62% of the global exposed GDP but only 11% of the global exposed population, whereas low-income countries account for 54% of the global exposed population and 12% of the global exposed GDP. It is expected that the capability of low-income countries to implement the political, regulatory, and socioeconomic measures necessary to prevent and mitigate subsidence impact will be less than that for high-income countries. Potential subsidence threatens 484 million inhabitants living in flood-prone areas, 75% of whom live in fluvial areas and 25% of whom live near the coast. This number of threatened inhabitants corresponds to 50% of the global population exposed to flooding hazards according to previous estimates ([ 7 ][8]), demonstrating the importance of considering potential subsidence in global flooding risk analyses. Most of the global population exposed to potential subsidence live in Asia (86%), which is about 10 times the combined exposed population of North America and Europe (9%). The results indicate that 97% of the exposed global population is concentrated in 30 countries (see SM). India and China share the top two rankings of potential subsidence in terms of spatial extent and exposed population. Egypt and the Netherlands have the largest populations living in potential subsidence areas that are below the mean sea level. The greatest population densities in potential subsidence areas occur in Egypt and Indonesia, whereas the relative exposure per country, measured as the exposed population normalized by the total population, is greater than 30% for Egypt, Bangladesh, Netherlands, and Italy. The United States ranks first in terms of GDP exposed to potential subsidence, owing to its high GDP per capita. Combination of the aforementioned metrics permits derivation of a potential subsidence index ranking (see SM). Seven of the first ten ranked countries have the greatest subsidence impact, accounting for the greatest amount of reported damages (Netherlands, China, USA, Japan, Indonesia, México and Italy). During this century, climate change will cause serious impacts on the world's water resources through sea-level rise, more frequent and severe floods and droughts, changes in the mean value and mode of precipitation (rain versus snow), and increased evapotranspiration. Prolonged droughts will decrease groundwater recharge and increase groundwater depletion, intensifying subsidence. The global potential subsidence is predicted for 2040 using the same subsidence metrics and available global projections of water stress, water demand variations, climate, and population (see SM). Although predicted potential subsidence areas increase only by 7% globally, the threatened population is predicted to rise by 30%, affecting 1.6 billion inhabitants, 635 million of whom will be living in flood-prone areas. These changes will not be homogeneous. Between 2010 and 2040, the predicted population exposed to potential subsidence increases more than 80% in the Philippines, Iraq, Indonesia, México, Israel, Netherlands, Algeria, and Bangladesh. The increase will be moderate, less than 30%, for China, the United States, Italy, and Iran. Potential subsidence is forecasted to decrease in Japan and Germany, owing to effective groundwater management policies and population declines. Finally, potential subsidence is predicted to emerge in high-latitude northern countries like Canada and to increase in extent in Russia or Hungary, where climate change will favor longer dry seasons. Further advancements in the global evaluation of subsidence can be made when a global historical database on subsidence rate, magnitude, and extent has been compiled, which could be largely sourced from continental monitoring of surface displacements using satellite radar imagery ([ 8 ][9]). Widespread continuous monitoring of subsidence will permit better evaluation of the potential impact of land subsidence, especially in countries like Indonesia, México, and Iran, where local studies revealed the highest subsidence rates worldwide, but the national dimension of subsidence is still unknown. Further research also is necessary to evaluate the cost of damage caused by current and historical subsidence worldwide. The combination of damage information with hazard estimates will permit improved assessments of potential loss and design of cost-effective countermeasures. Presently, annual subsidence costs are only published for China (US$ 1.5 billion) and the Netherlands (US$ 4.8 billion) ([ 9 ][10]). The greater subsidence costs in the Netherlands owe to the exposed population below the mean sea level and the large investments made to prevent flooding. Our model, which does not yet consider mitigation measures, likely overestimates potential subsidence exposure in the Netherlands and Japan, where groundwater management has effectively controlled subsidence over the past decades ([ 10 ][11]). Our results identify 1596 major cities, or about 22% of the world's 7343 major cities that are in potential subsidence areas, with 57% of these cities also located in flood-prone areas. Moreover, subsidence threatens 15 of the 20 major coastal cities ranked with the highest flood risk worldwide ([ 11 ][12]), where potential subsidence can help delimit areas in which flooding risk could be increased and mitigation measures are necessary. Overall, potential global subsidence results can be useful to better define the spatial extent of poorly documented subsidence occurrences, discover unknown subsiding areas, prevent potential subsidence impacts wherever groundwater depletion occurs, and better identify areas where subsidence could increase the flooding risk. In any of these scenarios, an effective land-subsidence policy should include systematic monitoring and modeling of exposed areas, evaluation of potential damages, and cost-benefit analyses permitting implementation of adequate mitigation or adaptation measures. These measures should consider groundwater regulation and strategic long-term measures, such as the development of alternative water supplies and the protection and (or) enhancement of natural or artificial recharge of aquifers. Considering that the potential subsidence may affect 635 million inhabitants living in flood-prone areas in 2040, it is of prime importance that potential subsidence is quantified and systematically included in flood risk analyses and related mitigation strategies. [science.sciencemag.org/content/371/6524/34/suppl/DC1][13] 1. [↵][14]1. D. L. Galloway, 2. T. J. Burbey , Hydrogeol. J. 19, 1459 (2011). [OpenUrl][15] 2. [↵][16]1. J. S. Famiglietti , Nat. Clim. Chang. 4, 945 (2014). [OpenUrl][17] 3. [↵][18]1. K. E. Trenberth , Clim. Res. 47, 123 (2011). [OpenUrl][19][CrossRef][20][Web of Science][21] 4. [↵][22]1. J. P. M. Syvitski et al ., Nat. Geosci. 2, 681 (2009). [OpenUrl][23][CrossRef][24][Web of Science][25] 5. [↵][26]1. P. Döll, 2. H. Müller Schmied, 3. C. Schuh, 4. F. T. Portmann, 5. A. Eicker , Water Resour. Res. 50, 5698 (2014). [OpenUrl][27][CrossRef][28][PubMed][29] 6. [↵][30]1. R. G. Taylor et al ., Nat. Clim. Chang. 3, 322 (2013). [OpenUrl][31] 7. [↵][32]1. B. Jongman, 2. P. J. Ward, 3. J. C. J. H. Aerts , Glob. Environ. Change 22, 823 (2012). [OpenUrl][33] 8. [↵][34]1. R. Lanari et al ., Remote Sens. 12, 2961 (2020). [OpenUrl][35] 9. [↵][36]1. T. H. M. Bucx, 2. C. J. M. Van Ruiten, 3. G. Erkens, 4. G. De Lange , in Proceedings of the International Association of Hydrological Sciences 372, 485 (2015). [OpenUrl][37] 10. [↵][38]1. K. A. B. Jago-on et al ., Sci. Total Environ. 407, 3089 (2009). [OpenUrl][39][CrossRef][40][PubMed][41] 11. [↵][42]1. S. Hallegatte, 2. C. Green, 3. R. J. Nicholls, 4. J. Corfee-Morlot , Nat. Clim. Chang. 3, 802 (2013). [OpenUrl][43] 12. [↵][44]1. G. Herrera, 2. P. Ezquerro , Global Subsidence Maps, figshare (2020); 10.6084/m9.figshare.13312070. Acknowledgments: Four anonymous peer reviewers and S. E. Ingebritsen (U.S. Geological Survey) helped to improve the manuscript. Funding for this study was provided partly by the Spanish Research Agency (AQUARISK, PRX19/00065, TEC2017-85244-C2-1-P projects) and PRIMA RESERVOIR project, and by all the institutions represented in the Land Subsidence International Initiative from UNESCO. G.H.-G., P.E., R.T., M.B.-P, and J.L.-V. designed the study, performed the analysis, and wrote the initial manuscript with input from all other authors. R.M.M., E.C.-C., and M.R. advised on the susceptibility analysis. R.M.M., J.L., P.T., and G.E. advised on hazard analysis. D.C.-F., J.L., P.T., E.C.C., G.E., D.G., W.C.H., N.K., M.S., L.T., H.W., and S.Y. advised on global exposure analysis. R.T., M.B.P., R.M.M., J.L., P.T., W.-C.H., N.K., L.T., H.W., and S.Y. contributed essential data for the analysis. All the authors edited and revised the manuscript through the different reviews. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. government. The authors declare no competing interests. All data included in this study are available at figshare ([ 12 ][45]). 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Halodoc Harnesses AI To Push Telehealth Forward


An Indonesian company is helping to push telemedicine forward by using artificial intelligence (AI) to give doctors feedback on how to improve patient care, according to Google's The Keyword. Jakarta-based Halodoc says that its telehealth platform uses AI to provide doctors with the feedback and mentoring that they would receive from fellow doctors in an in-person setting like a hospital. Halodoc has been developing the product with machine learning experts from Google's Late-Stage Accelerator using natural language processing in Bahasa Indonesia. The machine learning models are trained using information from thousands of doctor consultations, according to the Google report. Halodoc's app allows doctors to receive feedback on how well and quickly they perform services, along with advice on how to improve their patient consultations and an option to receive additional coaching from fellow physicians, according to the company.

Google Maps Keep Getting Better, Thanks To DeepMind's Machine Learning


Google users contribute more than 20 million pieces of information on Maps every day – that's more than 200 contributions every second. The uncertainty of traffic can crash the algorithms predicting the best ETA. There is also a chance of new roads and buildings being built all the time. Though Google Maps gets its ETA right most of the time, there is still room for improvement. Researchers at Alphabet-owned DeepMind have partnered with the Google Maps team to improve the accuracy of the real-time ETAs by up to 50% in places like Berlin, Jakarta, São Paulo, Sydney, Tokyo, and Washington D.C.

Google Maps and DeepMind enhance AI capabilities to improve route calculations


It has been nearly 13 years since Google Maps started providing traffic data to help people navigate their way around, alongside providing detail about whether the traffic along the route is heavy or light, the estimated travel time, and the estimated time of arrival (ETAs). In a bid to further enhance those traffic prediction capabilities, Google and Alphabet's AI research lab DeepMind have improved real-time ETAs by up to 50% in places such as Sydney, Tokyo, Berlin, Jakarta, Sao Paulo, and Washington DC by using a machine learning technique known as graph neural networks. Google Maps product manager Johann Lau said Google Maps uses aggregate location data and historical traffic patterns to understand traffic conditions to determine current traffic estimates, but it previously did not account for what traffic may look like if a traffic jam were to occur while on the journey. "Our ETA predictions already have a very high accuracy bar -- in fact, we see that our predictions have been consistently accurate for over 97% of trips … this technique is what enables Google Maps to better predict whether or not you'll be affected by a slowdown that may not have even started yet," he said in a blog post. The researchers at DeepMind said by using graph neural networks, this allowed Google Maps to incorporate "relational learning biases to model the connectivity structure of real-world road networks."