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By Fabien Chraim, Co-founder and VP of R&D at Civil Maps Last week, we hosted some distinguished house guests. By now, our neighborhood is used to a steady stream of automotive execs and R&D types from all over the world, but this particular group was comprised of lawmakers and the mission was purely about learning and discussion. After Federal Secretary for Transport and Digital Infrastructure of Germany Alexander Dobrindt and his entourage landed at SFO, their first stop was our in Albany, CA. Arriving in a Mercedes sprinter van and a large black SUV, the sight of diplomats piling out of vehicles and journalists in our front yard drew a few residents out of their houses to check out what was happening. home-office Secretary Dobrindt’s representatives reached out a few weeks ago, expressing the desire to learn about Civil Maps in order to better understand the future of autonomous driving. Our company works on technology that is critical to this task; we give cognition to cars, enabling them to gain context about their driving environment and crowdsource dynamic 3D maps. While the group behaved mostly like good students, listening intently and asking many questions, they are actually policy with regards to autonomous vehicle legislation. leaders On January 25th, Germany became the first country to providing initial frameworks for autonomous and semi-autonomous vehicle research and driving. Mr. Dobrindt and his team, who drafted the legislation, wanted to reassure German automakers, who collectively , that federal policy would be supportive of their research initiatives. In the future, the Secretary says this will likely entail the use of a “black box” to record when driverless systems are active. pass legislation produced 8.6 percent of the world’s automobiles in 2016 “There are certain prerequisites that need to be met,” said Secretary Dobrindt told reporters who gathered at our house/office for the visit. “For example, the vehicle is equipped with a black box which can tell at all times whether the vehicle or the human was in control over the vehicle or if the computer was functioning well at all times.” After exchanging initial greetings on our driveway, the group piled into the house. Our conference room, formerly the master bedroom for past tenants at 1605 Sonoma Ave, was stuffed with journalists, policy makers, and a few members of our own team. It was my pleasure to lead the discussion covering these topics: What is — Civil Maps gives cars contextual awareness, enabling autonomous vehicles to make better decisions for safer driving and crowdsourcing of 3D map data. Cognition for Cars Making Sense of Sensor Data — From point cloud to localizing in six dimensions within five cm precision using Civil Maps’ Signature-based Localization s — Projecting 3D HD semantic map data (lane markings, signage, and etc.) into the field of view of the car’s sensors in real time. The goal: enabling rider trust, while facilitating the reliable navigation and safety of an autonomous vehicle. Augmented Reality Map Crowdsourcing of maps through connected vehicle swarms Potential Solutions to the High Cost of Autonomous Vehicle R&D — Low compute and cost-effective sensing equipment, (beta) Atlas DevKit How Simulation Engines Accelerate Development Working with automotive OEM Partners Globally After some Q&A, Mr. Dobrindt went outside to get a demo of our Augmented Reality Maps and localization technology. Using low processing power and highly advanced compression of sensor data in real time, the car localizes itself in six dimensions and is presented with a machine readable map. With this information, the car knows where it is and exactly what to focus on in the scene while driving. This clarity reduces the cognitive load on the vehicle. The demo shows what the car sees, which is only the information it needs for navigation and safety. Before the delegation left for its next destination, we gave them an ample supply of Civil Maps schwag and said our quick goodbyes. Their next stop that day would be the Stanford Center for Automotive Research. For their last day of the two-day trip, they were scheduled to visit Otto (Uber). Secretary Dobrindt and his group made a similar learning trip to the U.S. about three years ago. Back then, he visited Facebook and Google.

Autonomous Driving Regulators At Our Doorstep

In Vietnam, Porn Is Better Served and More Accessible Than Starbucks

I Tried Perplexity For a Week, And I Don't Think AI Search Engines Can Replace Google.. Yet

আমি এক সপ্তাহের জন্য বিভ্রান্তির চেষ্টা করেছি, এবং আমি মনে করি না এআই সার্চ ইঞ্জিনগুলি গুগলকে প্রতিস্থাপন করতে পারে.. তবুও

Ich habe Perplexity eine Woche lang ausprobiert und glaube nicht, dass KI-Suchmaschinen Google ersetzen können.

Probé Perplexity durante una semana y no creo que los motores de búsqueda con inteligencia artificial puedan reemplazar a Google... todavía

J'ai essayé Perplexity pendant une semaine et je ne pense pas que les moteurs de recherche IA puissent remplacer Google... pour l'instant

मैंने एक सप्ताह तक पेरप्लेक्सिटी की कोशिश की, और मुझे नहीं लगता कि एआई सर्च इंजन गूगल की जगह ले सकते हैं.. अभी तक

私は1週間Perplexityを試してみましたが、AI検索エンジンがGoogleに取って代わることができるとは思いません...まだ

일주일 동안 Perplexity를 사용해 보았는데, AI 검색 엔진이 아직은 Google을 대체할 수 있다고 생각하지 않습니다.

Eu tentei o Perplexity por uma semana e não acho que os mecanismos de busca de IA possam substituir o Google... ainda

Я пробовал Perplexity в течение недели, и я не думаю, что поисковые системы на основе искусственного интеллекта могут заменить Google... Пока

Perplexity'yi Bir Hafta Boyunca Denedim ve Yapay Zeka Arama Motorlarının Google'ın Yerini Alabileceğini Düşünmüyorum.. Henüz

Tôi đã thử Perplexity trong một tuần và tôi không nghĩ công cụ tìm kiếm AI có thể thay thế Google..

我试用了 Perplexity 一周，我认为人工智能搜索引擎暂时还不能取代谷歌

Table of Links Abstract and 1. Introduction 2. Methods 2.1. Modeling Panspermia and Terraformation 2.2. Identifying the Presence of Terraformed Planets and 2.3. Software and Availability 3. Results 3.1. Panspermia can increase the correlation between planets’ compositions and positions 3.2. Likely terraformed planets can be identified from clustering 4. Summary and Discussion 5. Acknowledgements and References APPENDIX A. Appendix 5. ACKNOWLEDGEMENTS The authors would like to thank Estelle Janin and Cole Mathis for encouraging and productive conversations, and for feedback on the manuscript. REFERENCES Affholder, A., Guyot, F., Sauterey, B., Ferri`ere, R., & Mazevet, S. 2021, Nature Astronomy, doi: 10.1038/s41550-021-01372-6 Balbi, A., & Grimaldi, C. 2020, Proceedings of the National Academy of Sciences, 117, 21031, doi: 10.1073/pnas.2007560117 Bartlett, S., Li, J., Gu, L., et al. 2022, Nature Astronomy, 6, 387, doi: 10.1038/s41550-021-01559-x Benner, S. A. 2010, Astrobiology, 10, 1021, doi: 10.1089/ast.2010.0524 Bich, L., & Green, S. 2018, Synthese, 195, 3919, doi: 10.1007/s11229-017-1397-9 Bixel, A., & Apai, D. 2021, The Astronomical Journal, 161, 228, doi: 10.3847/1538-3881/abe042 Carroll-Nellenback, J., Frank, A., Wright, J., & Scharf, C. 2019, The Astronomical Journal, 158, 117, doi: 10.3847/1538-3881/ab31a3 Catling, D. C., Krissansen-Totton, J., Kiang, N. Y., et al. 2018, Astrobiology, 18, 709, doi: 10.1089/ast.2017.1737 Checlair, J. H., Villanueva, G. L., Hayworth, B. P. C., et al. 2021, The Astronomical Journal, 161, 150, doi: 10.3847/1538-3881/abdb36 Cleaves, H. J., Hystad, G., Prabhu, A., et al. 2023, Proceedings of the National Academy of Sciences, 120, e2307149120, doi: 10.1073/pnas.2307149120 Cleland, C. E. 2012, Synthese, 185, 125, doi: 10.1007/s11229-011-9879-7 Cleland, C. E., & Chyba, C. F. 2002, Origins of Life and Evolution of the Biosphere, 32, 387, doi: 10.1023/A:1020503324273 Cockell, C. S. 2022, in New Frontiers in Astrobiology (Elsevier), 1–17, doi: 10.1016/B978-0-12-824162-2.00009-9 Gobat, R. 2021, The Astrophysical Journal, 16 Green, J., Hoehler, T., Neveu, M., et al. 2021, Nature, 598, 575, doi: 10.1038/s41586-021-03804-9 Grimaldi, C., Lingam, M., & Balbi, A. 2021, The Astronomical Journal, 162, 23, doi: 10.3847/1538-3881/abfe61 Hahsler, M., Piekenbrock, M., & Doran, D. 2019, Journal of Statistical Software, 91, 1, doi: 10.18637/jss.v091.i01 Harman, C. E., & Domagal-Goldman, S. 2018, in Handbook of Exoplanets, ed. H. J. Deeg & J. A. Belmonte (Cham: Springer International Publishing), 1–22, doi: 10.1007/978-3-319-30648-3 71-1 Janin, E. 2021, Astronomy & Geophysics, 62 Kim, H., Smith, H. B., Mathis, C., Raymond, J., & Walker, S. I. 2019, Science Advances, 5, eaau0149, doi: 10.1126/sciadv.aau0149 Kinney, D., & Kempes, C. 2022, Biology & Philosophy, 37, 22, doi: 10.1007/s10539-022-09859-w Kovacevic, A. B. 2022, arXiv:2202.07347 [astro-ph, q-bio]. http://arxiv.org/abs/2202.07347 Lenardic, A., Seales, J., & Covington, A. 2022, International Journal of Astrobiology, 1, doi: 10.1017/S1473550422000222 Lenardic, A., Seales, J., Moore, W. B., & Jellinek, A. M. 2023, Nature Astronomy, 1, doi: 10.1038/s41550-023-02031-8 Lin, H. W., & Loeb, A. 2015, The Astrophysical Journal, 810, L3, doi: 10.1088/2041-8205/810/1/L3 Lingam, M., Grimaldi, C., & Balbi, A. 2021, Monthly Notices of the Royal Astronomical Society, 509, 4365, doi: 10.1093/mnras/stab3108 Lynas, M., Houlton, B. Z., & Perry, S. 2021, Environmental Research Letters, 16, 114005, doi: 10.1088/1748-9326/ac2966 Mariscal, C., & Doolittle, W. F. 2020, Synthese, 197, 2975, doi: 10.1007/s11229-018-1852-2 Marshall, S. M., Mathis, C., Carrick, E., et al. 2021, Nature Communications, 12, 3033, doi: 10.1038/s41467-021-23258-x Mix, L. J. 2015, Astrobiology, 15, 15, doi: 10.1089/ast.2014.1191 Moore, W. B., Lenardic, A., Jellinek, A. M., et al. 2017, Nature Astronomy, 1, 1, doi: 10.1038/s41550-017-0043 Olejarz, J., Iwasa, Y., Knoll, A. H., & Nowak, M. A. 2021, Nature Communications, 12, 3985, doi: 10.1038/s41467-021-23286-7 Pacetti, E., Turrini, D., Schisano, E., et al. 2022, The Astrophysical Journal, 937, 36, doi: 10.3847/1538-4357/ac8b11 Rideout, J. R., Caporaso, G., Bolyen, E., et al. 2023, biocore/scikit-bio: scikit-bio 0.5.9: Maintenance release, Zenodo, doi: 10.5281/zenodo.8209901 Satopaa, V., Albrecht, J., Irwin, D., & Raghavan, B. 2011, in 2011 31st International Conference on Distributed Computing Systems Workshops (Minneapolis, MN, USA: IEEE), 166–171, doi: 10.1109/ICDCSW.2011.20 Schwieterman, E. W., Meadows, V. S., Domagal-Goldman, S. D., et al. 2016, The Astrophysical Journal, 819, L13, doi: 10.3847/2041-8205/819/1/L13 Schwieterman, E. W., Kiang, N. Y., Parenteau, M. N., et al. 2018, Astrobiology, 18, 663, doi: 10.1089/ast.2017.1729 Smith, H. B., & Mathis, C. 2023, BioEssays, 2300050, doi: 10.1002/bies.202300050 Smith, H. H., Hyde, A. S., Simkus, D. N., et al. 2021, Life, 11, 498, doi: 10.3390/life11060498 Sol´e, R. V., & Munteanu, A. 2004, Europhysics Letters (EPL), 68, 170, doi: 10.1209/epl/i2004-10241-3 Tasker, E., Tan, J., Heng, K., et al. 2017, Nature Astronomy, 1, 0042, doi: 10.1038/s41550-017-0042 Tasker, E., Unterborn, C., Laneuville, M., et al., eds. 2020, Planetary diversity: rocky planet processes and their observational signatures, AAS-IOP astronomy (Bristol, UK: IOP Publishing) The scikit-bio development team. 2020, scikit-bio: A bioinformatics library for data scientists, students, and developers. http://scikit-bio.org Totani, T. 2023, International Journal of Astrobiology, 22, 347, doi: 10.1017/S147355042300006X Vickers, P., Cowie, C., Dick, S. J., et al. 2023, Astrobiology, ast.2022.0084, doi: 10.1089/ast.2022.0084 Walker, S. I., Bains, W., Cronin, L., et al. 2018, Astrobiology, 18, 779, doi: 10.1089/ast.2017.1738 Wong, M. L., Prabhu, A., Williams, J., Morrison, S. M., & Hazen, R. M. 2023, Journal of Geophysical Research: Planets, 128, e2022JE007658, doi: 10.1029/2022JE007658 Wright, J. T., Mullan, B., Sigurdsson, S., & Povich, M. S. 2014, The Astrophysical Journal, 792, 26, doi: 10.1088/0004-637X/792/1/26 Authors:
(1) Harrison B. Smith, Earth-Life Science Institute, Tokyo Institute of Technology, Ookayama, Meguro-ku, Tokyo, Japan, and Blue Marble Space Institute of Science, Seattle, Washington, USA (hbs@elsi.jp);
(2) Lana Sinapayen, Sony Computer Science Laboratories, Kyoto, Japan and National Institute for Basic Biology, Okazaki, Japan (lana.sinapayen@gmail.com). This paper is available on arxiv under CC BY-NC-ND 4.0 Deed license. Table of Links Abstract and 1. Introduction Abstract and 1. Introduction 2. Methods 2.1. Modeling Panspermia and Terraformation 2.1. Modeling Panspermia and Terraformation 2.2. Identifying the Presence of Terraformed Planets and 2.3. Software and Availability 2.2. Identifying the Presence of Terraformed Planets and 2.3. Software and Availability 3. Results 3.1. Panspermia can increase the correlation between planets’ compositions and positions 3.1. Panspermia can increase the correlation between planets’ compositions and positions 3.2. Likely terraformed planets can be identified from clustering 3.2. Likely terraformed planets can be identified from clustering 4. Summary and Discussion 4. Summary and Discussion 5. Acknowledgements and References 5. Acknowledgements and References APPENDIX APPENDIX A. Appendix A. Appendix 5. ACKNOWLEDGEMENTS The authors would like to thank Estelle Janin and Cole Mathis for encouraging and productive conversations, and for feedback on the manuscript. REFERENCES Affholder, A., Guyot, F., Sauterey, B., Ferri`ere, R., & Mazevet, S. 2021, Nature Astronomy, doi: 10.1038/s41550-021-01372-6 Balbi, A., & Grimaldi, C. 2020, Proceedings of the National Academy of Sciences, 117, 21031, doi: 10.1073/pnas.2007560117 Bartlett, S., Li, J., Gu, L., et al. 2022, Nature Astronomy, 6, 387, doi: 10.1038/s41550-021-01559-x Benner, S. A. 2010, Astrobiology, 10, 1021, doi: 10.1089/ast.2010.0524 Bich, L., & Green, S. 2018, Synthese, 195, 3919, doi: 10.1007/s11229-017-1397-9 Bixel, A., & Apai, D. 2021, The Astronomical Journal, 161, 228, doi: 10.3847/1538-3881/abe042 Carroll-Nellenback, J., Frank, A., Wright, J., & Scharf, C. 2019, The Astronomical Journal, 158, 117, doi: 10.3847/1538-3881/ab31a3 Catling, D. C., Krissansen-Totton, J., Kiang, N. Y., et al. 2018, Astrobiology, 18, 709, doi: 10.1089/ast.2017.1737 Checlair, J. H., Villanueva, G. L., Hayworth, B. P. C., et al. 2021, The Astronomical Journal, 161, 150, doi: 10.3847/1538-3881/abdb36 Cleaves, H. J., Hystad, G., Prabhu, A., et al. 2023, Proceedings of the National Academy of Sciences, 120, e2307149120, doi: 10.1073/pnas.2307149120 Cleland, C. E. 2012, Synthese, 185, 125, doi: 10.1007/s11229-011-9879-7 Cleland, C. E., & Chyba, C. F. 2002, Origins of Life and Evolution of the Biosphere, 32, 387, doi: 10.1023/A:1020503324273 Cockell, C. S. 2022, in New Frontiers in Astrobiology (Elsevier), 1–17, doi: 10.1016/B978-0-12-824162-2.00009-9 Gobat, R. 2021, The Astrophysical Journal, 16 Green, J., Hoehler, T., Neveu, M., et al. 2021, Nature, 598, 575, doi: 10.1038/s41586-021-03804-9 Grimaldi, C., Lingam, M., & Balbi, A. 2021, The Astronomical Journal, 162, 23, doi: 10.3847/1538-3881/abfe61 Hahsler, M., Piekenbrock, M., & Doran, D. 2019, Journal of Statistical Software, 91, 1, doi: 10.18637/jss.v091.i01 Harman, C. E., & Domagal-Goldman, S. 2018, in Handbook of Exoplanets, ed. H. J. Deeg & J. A. Belmonte (Cham: Springer International Publishing), 1–22, doi: 10.1007/978-3-319-30648-3 71-1 Janin, E. 2021, Astronomy & Geophysics, 62 Kim, H., Smith, H. B., Mathis, C., Raymond, J., & Walker, S. I. 2019, Science Advances, 5, eaau0149, doi: 10.1126/sciadv.aau0149 Kinney, D., & Kempes, C. 2022, Biology & Philosophy, 37, 22, doi: 10.1007/s10539-022-09859-w Kovacevic, A. B. 2022, arXiv:2202.07347 [astro-ph, q-bio]. http://arxiv.org/abs/2202.07347 Lenardic, A., Seales, J., & Covington, A. 2022, International Journal of Astrobiology, 1, doi: 10.1017/S1473550422000222 Lenardic, A., Seales, J., Moore, W. B., & Jellinek, A. M. 2023, Nature Astronomy, 1, doi: 10.1038/s41550-023-02031-8 Lin, H. W., & Loeb, A. 2015, The Astrophysical Journal, 810, L3, doi: 10.1088/2041-8205/810/1/L3 Lingam, M., Grimaldi, C., & Balbi, A. 2021, Monthly Notices of the Royal Astronomical Society, 509, 4365, doi: 10.1093/mnras/stab3108 Lynas, M., Houlton, B. Z., & Perry, S. 2021, Environmental Research Letters, 16, 114005, doi: 10.1088/1748-9326/ac2966 Mariscal, C., & Doolittle, W. F. 2020, Synthese, 197, 2975, doi: 10.1007/s11229-018-1852-2 Marshall, S. M., Mathis, C., Carrick, E., et al. 2021, Nature Communications, 12, 3033, doi: 10.1038/s41467-021-23258-x Mix, L. J. 2015, Astrobiology, 15, 15, doi: 10.1089/ast.2014.1191 Moore, W. B., Lenardic, A., Jellinek, A. M., et al. 2017, Nature Astronomy, 1, 1, doi: 10.1038/s41550-017-0043 Olejarz, J., Iwasa, Y., Knoll, A. H., & Nowak, M. A. 2021, Nature Communications, 12, 3985, doi: 10.1038/s41467-021-23286-7 Pacetti, E., Turrini, D., Schisano, E., et al. 2022, The Astrophysical Journal, 937, 36, doi: 10.3847/1538-4357/ac8b11 Rideout, J. R., Caporaso, G., Bolyen, E., et al. 2023, biocore/scikit-bio: scikit-bio 0.5.9: Maintenance release, Zenodo, doi: 10.5281/zenodo.8209901 Satopaa, V., Albrecht, J., Irwin, D., & Raghavan, B. 2011, in 2011 31st International Conference on Distributed Computing Systems Workshops (Minneapolis, MN, USA: IEEE), 166–171, doi: 10.1109/ICDCSW.2011.20 Schwieterman, E. W., Meadows, V. S., Domagal-Goldman, S. D., et al. 2016, The Astrophysical Journal, 819, L13, doi: 10.3847/2041-8205/819/1/L13 Schwieterman, E. W., Kiang, N. Y., Parenteau, M. N., et al. 2018, Astrobiology, 18, 663, doi: 10.1089/ast.2017.1729 Smith, H. B., & Mathis, C. 2023, BioEssays, 2300050, doi: 10.1002/bies.202300050 Smith, H. H., Hyde, A. S., Simkus, D. N., et al. 2021, Life, 11, 498, doi: 10.3390/life11060498 Sol´e, R. V., & Munteanu, A. 2004, Europhysics Letters (EPL), 68, 170, doi: 10.1209/epl/i2004-10241-3 Tasker, E., Tan, J., Heng, K., et al. 2017, Nature Astronomy, 1, 0042, doi: 10.1038/s41550-017-0042 Tasker, E., Unterborn, C., Laneuville, M., et al., eds. 2020, Planetary diversity: rocky planet processes and their observational signatures, AAS-IOP astronomy (Bristol, UK: IOP Publishing) The scikit-bio development team. 2020, scikit-bio: A bioinformatics library for data scientists, students, and developers. http://scikit-bio.org Totani, T. 2023, International Journal of Astrobiology, 22, 347, doi: 10.1017/S147355042300006X Vickers, P., Cowie, C., Dick, S. J., et al. 2023, Astrobiology, ast.2022.0084, doi: 10.1089/ast.2022.0084 Walker, S. I., Bains, W., Cronin, L., et al. 2018, Astrobiology, 18, 779, doi: 10.1089/ast.2017.1738 Wong, M. L., Prabhu, A., Williams, J., Morrison, S. M., & Hazen, R. M. 2023, Journal of Geophysical Research: Planets, 128, e2022JE007658, doi: 10.1029/2022JE007658 Wright, J. T., Mullan, B., Sigurdsson, S., & Povich, M. S. 2014, The Astrophysical Journal, 792, 26, doi: 10.1088/0004-637X/792/1/26 Authors: (1) Harrison B. Smith, Earth-Life Science Institute, Tokyo Institute of Technology, Ookayama, Meguro-ku, Tokyo, Japan, and Blue Marble Space Institute of Science, Seattle, Washington, USA (hbs@elsi.jp); (2) Lana Sinapayen, Sony Computer Science Laboratories, Kyoto, Japan and National Institute for Basic Biology, Okazaki, Japan (lana.sinapayen@gmail.com). Authors: Authors: (1) Harrison B. Smith, Earth-Life Science Institute, Tokyo Institute of Technology, Ookayama, Meguro-ku, Tokyo, Japan, and Blue Marble Space Institute of Science, Seattle, Washington, USA ( hbs@elsi.jp ); hbs@elsi.jp (2) Lana Sinapayen, Sony Computer Science Laboratories, Kyoto, Japan and National Institute for Basic Biology, Okazaki, Japan ( lana.sinapayen@gmail.com ). lana.sinapayen@gmail.com This paper is available on arxiv under CC BY-NC-ND 4.0 Deed license. This paper is available on arxiv under CC BY-NC-ND 4.0 Deed license. available on arxiv

A Biosignature Based on Modeling Panspermia and Terraformation: References

Authors:
(1) Harrison B. Smith, Earth-Life Science Institute, Tokyo Institute of Technology, Ookayama, Meguro-ku, Tokyo, Japan, and Blue Marble Space Institute of Science, Seattle, Washington, USA (hbs@elsi.jp);
(2) Lana Sinapayen, Sony Computer Science Laboratories, Kyoto, Japan and National Institute for Basic Biology, Okazaki, Japan (lana.sinapayen@gmail.com). Table of Links Abstract and 1. Introduction 2. Methods 2.1. Modeling Panspermia and Terraformation 2.2. Identifying the Presence of Terraformed Planets and 2.3. Software and Availability 3. Results 3.1. Panspermia can increase the correlation between planets’ compositions and positions 3.2. Likely terraformed planets can be identified from clustering 4. Summary and Discussion 5. Acknowledgements and References APPENDIX A. Appendix ABSTRACT A fundamental goal of astrobiology is to detect life outside of Earth. This proves to be an exceptional challenge outside of our solar system, where strong assumptions must be made about how life would manifest and interact with its planet. Such assumptions are required because of the lack of a consensus theory of living systems, or an understanding of the possible extent of planetary dynamics. Here we explore a model of life spreading between planetary systems via panspermia and terraformation. Our model shows that as life propagates across the galaxy, correlations emerge between planetary characteristics and location, and can function as a population-scale agnostic biosignature. This biosignature is agnostic because it is independent of strong assumptions about any particular instantiation of life or planetary characteristic—by focusing on a specific hypothesis of what life may do, rather than what life may be. By clustering planets based on their observed characteristics, and examining the spatial extent of these clusters, we demonstrate (and evaluate) a way to prioritize specific planets for further observation—based on their potential for containing life. We consider obstacles that must be overcome to practically implement our approach, including identifying specific ways in which better understanding astrophysical and planetary processes would improve our ability to detect life. Finally, we consider how this model leads us to think in novel ways about hierarchies of life and planetary scale replication. 1. INTRODUCTION It is difficult to attribute, with certainty, observable features of exoplanets to extraterrestrial life (Moore et al. 2017; Tasker et al. 2017; Green et al. 2021; Cockell 2022; Lenardic et al. 2022, 2023; Smith & Mathis 2023). This is exemplified by the insufficient (and often ill-defined) working definitions of life that are used to interpret observational data, and by the growing number of false positives for traditional biosignatures (Cleland & Chyba 2002; Benner 2010; Cleland 2012; Mix 2015; Schwieterman et al. 2016; Bich & Green 2018; Harman & Domagal-Goldman 2018; Schwieterman et al. 2018; Mariscal & Doolittle 2020; Janin 2021; Vickers et al. 2023). One way in which the community aims to overcome the problems with defining life is to develop “agnostic” biosignatures—detecting signs of life that are not particular to Earth-life or any other hypothetical instances of life (Marshall et al. 2021; Smith et al. 2021). Yet, agnostic biosignature proposals are sometimes built on restricted concepts of habitability (e.g., requiring rocky, watery planets), or simple anomaly detection (i.e., without using a working definition of life) (Kinney & Kempes 2022; Cleaves et al. 2023). A proposed remedy for the issues surrounding traditional biosignatures has been to use so-called “statistical” biosignatures—relying on integrating multiple lines of evidence to increase confidence in a discovery, or generating ensembles of data to better constrain the probabilities P(data|abiotic) and P(data|life) (in Bayesian speak). (Lin & Loeb 2015; Catling et al. 2018; Walker et al. 2018; Affholder et al. 2021; Bixel & Apai 2021). However, such statistical biosignatures often rely on assumptions about the prior probability of abiogenesis, or on the existence of unambiguous biosignatures for single planets. These approaches are often trying to solve more specific issues, like estimating the frequency of Earth-like life, the frequency of planets originating from panspermia compared to abiogenesis, or finding evidence of a specific metabolic process (Lin & Loeb 2015; Walker et al. 2018; Affholder et al. 2021; Checlair et al. 2021; Kovacevic 2022). Here we ask, can we detect the presence of life if we postulate that life is spreading between and terraforming planets?[1] This is an astrobiological “hinge proposition” of the kind described by Kinney & Kempes as necessary to make sure “that the door of astrobiology can turn properly” under conditions of deep uncertainty (Kinney & Kempes 2022). It is also somewhat the converse of a question posed in other panspermia related work: Can the prevalence of panspermia be constrained when assuming we have a way to detect life? (Lin & Loeb 2015; Balbi & Grimaldi 2020; Grimaldi et al. 2021; Lingam et al. 2021; Kovacevic 2022). In fact, our postulates (of terraformation and panspermia) are less peculiar than they might at first seem: while existing work doesn’t always explicitly refer to terraformation, it must be assumed—in the sense that the mere presence of life stable at geological timescales would create environmental feedback with a planet, and if that life is to be detectable it must modify a planet’s observables. Such planetary modification by life is a well documented phenomena—e.g., the rise of O2 during the great oxygenation event (Lynas et al. 2021), or the rise of CO2 from human industrial activity (Olejarz et al. 2021)). The feasibility of interstellar lithopanspermia (nonintelligent exchange of material via rocks) has been discussed at length in other work (see e.g. CarrollNellenback et al. (2019); Grimaldi et al. (2021); Gobat (2021); Totani (2023) and references therein). Crucially, it appears plausible, although likelihoods and rates vary considerably depending on assumptions on timing, amount of material ejected, organismal hardiness, and capture rates, among other features. Arguments have likewise been made for the feasibility of directed interstellar panspermia by intelligent life (Wright et al. 2014). Ultimately, our postulates of panspermia and terraformation are merely well understood hallmarks of life (proliferation via replication, and adaptation with bi-directional environmental feedback), escalated to the planetary scale, and executed on an interstellar scale. We use a simulated model to show that statistical correlations between the spatial distribution of planets around different host stars, and their observable characteristics would itself be evidence of life, without the need for a separate biosignature that could reliably detect life on any given planet in isolation. The agnosticism of this biosignature is inseparable from its emergence at the scale of a population of planets—singleton planetary anomalies might be explained away by unknown geochemical processes, or targeted simply because they are anomalous (without a clear hypothesis of why they should be explained by life). Hypothesizing that life spreads via panspermia and terraformation allows us to search for biosignatures while forgoing any strong assumptions about not only the peculiarities of life (e.g., its metabolism) and planetary habitability (e.g., requiring surface liquid water) (Gobat 2021), but even the potential breadth of structure and chemical complexity underpinning living systems (Sol´e & Munteanu 2004; Kim et al. 2019; Bartlett et al. 2022; Wong et al. 2023). We first describe our model (Sec. 2.1) and approach for statistically identifying the presence of terraformed planets (Sec. 2.2). We show that evolving our model in time can increase the correlation between planets’ compositions and positions (Sec. 3.1). To identify specific planets likely to have been terraformed, we cluster planets by observable characteristics, then select clusters which are spatially localized and cause a decrease in correlation when removed (Secs. 2.2.3, 2.2.4 ). We evaluate these clusters by calculating how well they correctly classify terraformed planets as being terraformed, and non-terraformed planets as being non-terraformed (Sec. 3.2). Finally, we discuss how our results might change due to theoretical and observational constraints, identify specific ways in which better understanding astrophysical and planetary processes could improve life detection, and speculate on the concept of life at the scale of populations of planets. This paper is available on arxiv under CC BY-NC-ND 4.0 Deed license. [1] Here, “life spreading” refers to interstellar panspermia, and “terraforming planets” refers to modifying observable characteristics of planets. Authors: (1) Harrison B. Smith, Earth-Life Science Institute, Tokyo Institute of Technology, Ookayama, Meguro-ku, Tokyo, Japan, and Blue Marble Space Institute of Science, Seattle, Washington, USA (hbs@elsi.jp); (2) Lana Sinapayen, Sony Computer Science Laboratories, Kyoto, Japan and National Institute for Basic Biology, Okazaki, Japan (lana.sinapayen@gmail.com). Authors: Authors: (1) Harrison B. Smith, Earth-Life Science Institute, Tokyo Institute of Technology, Ookayama, Meguro-ku, Tokyo, Japan, and Blue Marble Space Institute of Science, Seattle, Washington, USA ( hbs@elsi.jp ); hbs@elsi.jp (2) Lana Sinapayen, Sony Computer Science Laboratories, Kyoto, Japan and National Institute for Basic Biology, Okazaki, Japan ( lana.sinapayen@gmail.com ). lana.sinapayen@gmail.com Table of Links Abstract and 1. Introduction Abstract and 1. Introduction 2. Methods 2.1. Modeling Panspermia and Terraformation 2.1. Modeling Panspermia and Terraformation 2.2. Identifying the Presence of Terraformed Planets and 2.3. Software and Availability 2.2. Identifying the Presence of Terraformed Planets and 2.3. Software and Availability 3. Results 3.1. Panspermia can increase the correlation between planets’ compositions and positions 3.1. Panspermia can increase the correlation between planets’ compositions and positions 3.2. Likely terraformed planets can be identified from clustering 3.2. Likely terraformed planets can be identified from clustering 4. Summary and Discussion 4. Summary and Discussion 5. Acknowledgements and References 5. Acknowledgements and References APPENDIX APPENDIX A. Appendix A. Appendix ABSTRACT A fundamental goal of astrobiology is to detect life outside of Earth. This proves to be an exceptional challenge outside of our solar system, where strong assumptions must be made about how life would manifest and interact with its planet. Such assumptions are required because of the lack of a consensus theory of living systems, or an understanding of the possible extent of planetary dynamics. Here we explore a model of life spreading between planetary systems via panspermia and terraformation. Our model shows that as life propagates across the galaxy, correlations emerge between planetary characteristics and location, and can function as a population-scale agnostic biosignature. This biosignature is agnostic because it is independent of strong assumptions about any particular instantiation of life or planetary characteristic—by focusing on a specific hypothesis of what life may do, rather than what life may be. By clustering planets based on their observed characteristics, and examining the spatial extent of these clusters, we demonstrate (and evaluate) a way to prioritize specific planets for further observation—based on their potential for containing life. We consider obstacles that must be overcome to practically implement our approach, including identifying specific ways in which better understanding astrophysical and planetary processes would improve our ability to detect life. Finally, we consider how this model leads us to think in novel ways about hierarchies of life and planetary scale replication. 1. INTRODUCTION It is difficult to attribute, with certainty, observable features of exoplanets to extraterrestrial life (Moore et al. 2017; Tasker et al. 2017; Green et al. 2021; Cockell 2022; Lenardic et al. 2022, 2023; Smith & Mathis 2023). This is exemplified by the insufficient (and often ill-defined) working definitions of life that are used to interpret observational data, and by the growing number of false positives for traditional biosignatures (Cleland & Chyba 2002; Benner 2010; Cleland 2012; Mix 2015; Schwieterman et al. 2016; Bich & Green 2018; Harman & Domagal-Goldman 2018; Schwieterman et al. 2018; Mariscal & Doolittle 2020; Janin 2021; Vickers et al. 2023). One way in which the community aims to overcome the problems with defining life is to develop “agnostic” biosignatures—detecting signs of life that are not particular to Earth-life or any other hypothetical instances of life (Marshall et al. 2021; Smith et al. 2021). Yet, agnostic biosignature proposals are sometimes built on restricted concepts of habitability (e.g., requiring rocky, watery planets), or simple anomaly detection (i.e., without using a working definition of life) (Kinney & Kempes 2022; Cleaves et al. 2023). A proposed remedy for the issues surrounding traditional biosignatures has been to use so-called “statistical” biosignatures—relying on integrating multiple lines of evidence to increase confidence in a discovery, or generating ensembles of data to better constrain the probabilities P(data|abiotic) and P(data|life) (in Bayesian speak). (Lin & Loeb 2015; Catling et al. 2018; Walker et al. 2018; Affholder et al. 2021; Bixel & Apai 2021). However, such statistical biosignatures often rely on assumptions about the prior probability of abiogenesis, or on the existence of unambiguous biosignatures for single planets. These approaches are often trying to solve more specific issues, like estimating the frequency of Earth-like life, the frequency of planets originating from panspermia compared to abiogenesis, or finding evidence of a specific metabolic process (Lin & Loeb 2015; Walker et al. 2018; Affholder et al. 2021; Checlair et al. 2021; Kovacevic 2022). Here we ask, can we detect the presence of life if we postulate that life is spreading between and terraforming planets?[1] This is an astrobiological “hinge proposition” of the kind described by Kinney & Kempes as necessary to make sure “that the door of astrobiology can turn properly” under conditions of deep uncertainty (Kinney & Kempes 2022). It is also somewhat the converse of a question posed in other panspermia related work: Can the prevalence of panspermia be constrained when assuming we have a way to detect life? (Lin & Loeb 2015; Balbi & Grimaldi 2020; Grimaldi et al. 2021; Lingam et al. 2021; Kovacevic 2022). In fact, our postulates (of terraformation and panspermia) are less peculiar than they might at first seem: while existing work doesn’t always explicitly refer to terraformation, it must be assumed—in the sense that the mere presence of life stable at geological timescales would create environmental feedback with a planet, and if that life is to be detectable it must modify a planet’s observables. Such planetary modification by life is a well documented phenomena—e.g., the rise of O2 during the great oxygenation event (Lynas et al. 2021), or the rise of CO2 from human industrial activity (Olejarz et al. 2021)). The feasibility of interstellar lithopanspermia (nonintelligent exchange of material via rocks) has been discussed at length in other work (see e.g. CarrollNellenback et al. (2019); Grimaldi et al. (2021); Gobat (2021); Totani (2023) and references therein). Crucially, it appears plausible, although likelihoods and rates vary considerably depending on assumptions on timing, amount of material ejected, organismal hardiness, and capture rates, among other features. Arguments have likewise been made for the feasibility of directed interstellar panspermia by intelligent life (Wright et al. 2014). Ultimately, our postulates of panspermia and terraformation are merely well understood hallmarks of life (proliferation via replication, and adaptation with bi-directional environmental feedback), escalated to the planetary scale, and executed on an interstellar scale. We use a simulated model to show that statistical correlations between the spatial distribution of planets around different host stars, and their observable characteristics would itself be evidence of life, without the need for a separate biosignature that could reliably detect life on any given planet in isolation. The agnosticism of this biosignature is inseparable from its emergence at the scale of a population of planets—singleton planetary anomalies might be explained away by unknown geochemical processes, or targeted simply because they are anomalous (without a clear hypothesis of why they should be explained by life). Hypothesizing that life spreads via panspermia and terraformation allows us to search for biosignatures while forgoing any strong assumptions about not only the peculiarities of life (e.g., its metabolism) and planetary habitability (e.g., requiring surface liquid water) (Gobat 2021), but even the potential breadth of structure and chemical complexity underpinning living systems (Sol´e & Munteanu 2004; Kim et al. 2019; Bartlett et al. 2022; Wong et al. 2023). We first describe our model (Sec. 2.1) and approach for statistically identifying the presence of terraformed planets (Sec. 2.2). We show that evolving our model in time can increase the correlation between planets’ compositions and positions (Sec. 3.1). To identify specific planets likely to have been terraformed, we cluster planets by observable characteristics, then select clusters which are spatially localized and cause a decrease in correlation when removed (Secs. 2.2.3, 2.2.4 ). We evaluate these clusters by calculating how well they correctly classify terraformed planets as being terraformed, and non-terraformed planets as being non-terraformed (Sec. 3.2). Finally, we discuss how our results might change due to theoretical and observational constraints, identify specific ways in which better understanding astrophysical and planetary processes could improve life detection, and speculate on the concept of life at the scale of populations of planets. This paper is available on arxiv under CC BY-NC-ND 4.0 Deed license. This paper is available on arxiv under CC BY-NC-ND 4.0 Deed license. available on arxiv [1] Here, “life spreading” refers to interstellar panspermia, and “terraforming planets” refers to modifying observable characteristics of planets.

A Biosignature Based on Modeling Panspermia and Terraformation: Abstract and Introduction

**HONG KONG, HongKong, July 4th, 2024/Chainwire/--**ASIC MARKETPLACE is celebrating 3 remarkable years of leading the industry as the most reliable Asic sourcing platform, serving robust ASIC Miners across the world, marking phenomenal achievement and growth in the mining landscape. ASIC marketplace has been in the industry since 2021, where it has performed as a pioneer in reselling the 12800+ ASIC Mining Hardware successfully to date across 80+ countries marketplace calls itself a successful and happy family of 4300+ customers globally due to the contributions it has made to deliver groundbreaking ASIC Miners to its customers at their doorstep. The company is known well for its innovative and economical range of ASIC Miners curated for every kind of consumer, its commitment to delivering the best, and its vision for elevating the mining experience for everyone who is or wants to be an integral part of the ASIC Mining industry. ASIC marketplace has reliably set a pertinent benchmark in the industry by collaborating with renowned players of the industry manufacturers to deliver high-quality products to help the miners reach their goals. Asic Marketplace constantly dedicates its success and extends warm thanks to its team members and customers for the unwavering support that they provided them over these three excellent years. The mining industry evolves each day, and new advancements are being introduced every time, which demand new innovative mining machines. Asic Marketplace stands firm as a pillar here that makes it possible for novices as well as professionals to cater for their mining needs, which speaks volumes about the work they have done over the years. The three-year milestone is a testament to the success of their collaboration with hi-tech industry leaders and manufacturers, which solidifies their position as a winning and highly recognized eCommerce platform. For more information, users can contact Asic Marketplace using the details mentioned below. About ASIC Marketplace ASIC Marketplace is a leading platform dedicated to providing a wide range of ASIC mining hardware. Founded in 2021, the company has quickly become a trusted name in the industry, known for its commitment to quality and customer satisfaction. With a presence in over 80 countries and a customer base exceeding 4,300 clients, ASIC Marketplace offers innovative and economical mining solutions to meet the diverse needs of the global mining community. Website: https://asicmarketplace.com/ Contact Manager Rain Lee Asic Marketplace rain@asicmarketplace.com This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here. **HONG KONG, HongKong, July 4th, 2024/Chainwire/--**ASIC MARKETPLACE is celebrating 3 remarkable years of leading the industry as the most reliable Asic sourcing platform, serving robust ASIC Miners across the world, marking phenomenal achievement and growth in the mining landscape. ASIC marketplace has been in the industry since 2021, where it has performed as a pioneer in reselling the 12800+ ASIC Mining Hardware successfully to date across 80+ countries marketplace calls itself a successful and happy family of 4300+ customers globally due to the contributions it has made to deliver groundbreaking ASIC Miners to its customers at their doorstep. The company is known well for its innovative and economical range of ASIC Miners curated for every kind of consumer, its commitment to delivering the best, and its vision for elevating the mining experience for everyone who is or wants to be an integral part of the ASIC Mining industry. ASIC Mining ASIC Mining ASIC marketplace has reliably set a pertinent benchmark in the industry by collaborating with renowned players of the industry manufacturers to deliver high-quality products to help the miners reach their goals. Asic Marketplace constantly dedicates its success and extends warm thanks to its team members and customers for the unwavering support that they provided them over these three excellent years. The mining industry evolves each day, and new advancements are being introduced every time, which demand new innovative mining machines. Asic Marketplace stands firm as a pillar here that makes it possible for novices as well as professionals to cater for their mining needs, which speaks volumes about the work they have done over the years. The three-year milestone is a testament to the success of their collaboration with hi-tech industry leaders and manufacturers, which solidifies their position as a winning and highly recognized eCommerce platform. For more information, users can contact Asic Marketplace using the details mentioned below. About ASIC Marketplace ASIC Marketplace is a leading platform dedicated to providing a wide range of ASIC mining hardware. Founded in 2021, the company has quickly become a trusted name in the industry, known for its commitment to quality and customer satisfaction. ASIC Marketplace ASIC Marketplace With a presence in over 80 countries and a customer base exceeding 4,300 clients, ASIC Marketplace offers innovative and economical mining solutions to meet the diverse needs of the global mining community. Website: https://asicmarketplace.com/ https://asicmarketplace.com/ https://asicmarketplace.com/ Contact Manager Rain Lee Asic Marketplace rain@asicmarketplace.com This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here. This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here . here here

Asic Marketplace Celebrates 3 Remarkable Years Of Excellence In The Mining Industry

The Anxiety Paradox

Human-Driven Plays Co-Written with Dramatron

Drone deliveries could be the next big thing if logistics companies find a way to overcome the current hurdles. How long will it take them to figure things out? What Drone Delivery Could Look Like So far, the commercial sector has shown interest in various short-range drone applications. Last-mile delivery is one of the most likely uses for drones in logistics since the technology is better suited for brief trips. While conventional vans can travel incredibly long distances, the cost of deploying them in the last leg of shipping is absurdly high. Same-day delivery is another likely application because drones are small, fast, and lightweight. More importantly, they can typically only hold one package at a time, making them perfect for urgent order fulfillment. The food and grocery delivery surge highlights another potential application for cost-effective drones. While app-based services are popular, companies lose money paying their drivers. For example, Uber only — it lost millions every year prior. started making a profit in 2023 Pros of Using Drones for Package Deliveries There are numerous benefits to using drones for package deliveries. Fast Delivery Autonomous drones could cut delivery times in half. They can deliver lightweight packages because they’re agile and small. A large fleet could easily make thousands of deliveries daily. faster than conventional vehicles Inexpensive Operation Semi-trucks and delivery vans use diesel or gas, which is expensive and fluctuates in price often. On the other hand, drones use electricity, which is relatively inexpensive. In other words, their cost per trip is typically much lower. Higher Efficiency Drones are fast and use little fuel because roadways, traffic lights, congestion, or car accidents don’t hinder them. Instead, they can fly in a semi-direct path toward their target address. They're more efficient since they can achieve a higher delivery quota than trucks. Increased Accessibility Addresses in remote or congested areas can be challenging to get to by truck. Drones can travel to these spots with little trouble, increasing delivery accessibility — and potentially increasing order frequency. Automatic Delivery Autonomous drones pose less of a risk to public safety than self-driving vehicles. At the same time, they offer all of the benefits of automation — they can deliver packages around the clock, never have to take breaks, and won’t suffer from human-error-related mistakes. Cons of Using Drones for Package Deliveries As great as the benefits of drone deliveries are, downsides do exist. Unreliable Unfortunately, modern drones aren’t the most robust technology — a strong gust of wind or unpleasant weather can easily knock them off course. If conditions aren’t ideal, they won’t be able to leave the fulfillment center to make deliveries. Complex Currently, there are few out-of-the-box solutions for commercial drone delivery. Autonomous flight , requiring technology like cloud computing, artificial intelligence, computer vision, and GPS-independent navigation to work. Properly developing and integrating everything can be a highly complex undertaking. is very resource-intensive Involved Logistics companies can only deploy a fleet if they have storage space, chargers, navigation software, insurance, and maintenance. Developing the infrastructure to make drone deliveries is time-consuming and expensive. Expensive Commercial drones are expensive — on average, in 2024. Unfortunately, the price will only drop to $500 by 2028. Logistics companies will have to invest a significant portion of their budget into the upfront expenses alone. a single one costs $520 Error-Prone While conventional vehicles can experience a flat tire or a dead battery, the driver can fix it or call for assistance. If a drone suffers from a technical issue mid-air, it could land in a tree, on a roof or in the middle of a road, irreversibly damaging it and its package. Finding and retrieving it might be impossible if its geolocation features malfunction. Restricted Drones can only carry relatively lightweight packages. They’d have to be much larger to lift bulk or heavy objects — and who wants all that weight flying precariously above their head? Preventable Challenges to Widespread Adoption One of the most preventable challenges to the widespread adoption is drop-off uncertainty. Media often depicts drones gracefully swooping down and gingerly placing a package right where it needs to be. In reality, they need a pre-determined location like a parcel locker. Unoperated drones aren’t capable of the precision that placing a package at the right doorstep requires, so pick-up locations are ideal. However, they have to get relatively low to the ground to make these deliveries, meaning there’s a high likelihood of interference. While logistics companies can hope people remain civil, only some are fond of this novel delivery method. Residents of one town after the tech giant rolled them out. Although they’d face repercussions from destroying or damaging anything, the ensuing legal battles and replacement costs could be high. discussed shooting down Amazon’s drones At the very least, general public hesitation is another challenge halting widespread adoption. According to one survey, feel worried about potential drone accidents. Their concerns aren’t unfounded, either — drones are far more likely to crash than aircraft. nearly three in 10 people The issue of red tape is technically preventable but would be challenging in reality. The Federal Aviation Administration (FAA)’s Part 107 rule between the pilot and their drone at all times. Australia and the United Kingdom have similar regulations. This technicality all but prevents autonomous deliveries. requires a line of sight Moreover, operators must get a remote pilot certificate, register, certify, and pass tests before ever being able to fly — or watch over — a drone. Even if they have all these credentials, they can’t fly above 400 feet or over congested areas. The regulations are strict. While these challenges are all preventable with enough effort, money, and time, they still are massive obstacles to standardizing drone delivery. Unless a pioneering logistics company works to change things, they will remain in the way. Unavoidable Challenges to Widespread Adoption Logistics companies can’t remove every hurdle to adoption. Weather is one of the biggest obstacles — drones can’t fly in hail, fog, rain, sleet, or windy conditions. If it’s too hot out, their lifting capacity drops drastically, and their batteries drain much faster in the cold. Essentially, they are relatively useless unless it’s mild out. Their range is another longstanding challenge. Currently, autonomous drones can only get so far — a few miles, to be precise. For logistics companies to make an adequate return on investment, they’d need to build a micro-fulfillment center next to every major city. Obstacles are another frequently overlooked — and unavoidable — challenge. While autonomous delivery drones are often equipped with computer vision, artificial intelligence, and sensors, they won’t be able to recognize objects as well as humans. In other words, they have a much higher chance of running into power lines, low-flying aircraft, trees, birds, and people. What the Future of Drone Delivery Will Look Like While drone technology has potential, it’s facing some significant hurdles. Even if the FAA’s regulations suddenly disappeared, their success isn’t guaranteed. In fact, companies have yet to make much progress even though the FAA has already pulled back. Its 2021 rule change , enabling them to fly at night and over moving vehicles without a waiver. loosened commercial drone restrictions Still, the winds of change are definitely blowing. In 2021, registered their drones. More and more logistics enterprises are recognizing the potential of this technology. roughly 1.3 million commercial pilots Crucially, the general public’s awareness and acceptance of this technology is shifting. According to one study, to use package delivery drones if the option existed. While logistical adoption of the drone market, it likely won’t be an option at checkout for years. seven in 10 people would prefer is the fastest-growing portion Drones Could Be the Future of Logistics While delivery drones might not become a thing for years — possibly even decades — it’s clear that logistics brands are hard at work trying to make this technology happen. It could soon become the standard.

How Close Are We to Widespread Drone Deliveries?

আমরা ড্রোন ডেলিভারি ব্যাপকভাবে কতটা কাছাকাছি?

Wie nah sind wir an einer weit verbreiteten Verbreitung von Drohnenlieferungen?

¿Qué tan cerca estamos de las entregas generalizadas de drones?

À quel point sommes-nous proches des livraisons généralisées par drones ?

हम व्यापक ड्रोन डिलीवरी के कितने करीब हैं?

ドローン配送の普及はどのくらい近づいているのでしょうか?

드론 배송이 널리 확산되는 데 얼마나 가까이 다가왔나요?

Quão perto estamos das entregas generalizadas de drones?

Насколько мы близки к распространению доставки дронами?

Yaygın Drone Teslimatlarına Ne Kadar Yakınız?

Chúng ta đã tiến gần đến việc giao hàng bằng máy bay không người lái rộng rãi đến mức nào?

我们距离广泛的无人机送货还有多远？

A term everyone has probably heard about is apparently widely misunderstood. Take Uber, for instance. Many believed it to be a perfect example of disruption when, in reality, Uber’s concept doesn’t really align with the core principles of the theory. As confusing as it may sound, not every business that somehow transforms the market can qualify as a disruption. . Disruptive innovation Before we get to the main idea of how disruptive innovation transformed the art market in particular, let’s start with the basics by figuring out the true meaning of the term in question. History of Disruptive Innovation is a term coined by Clayton Christensen, an American academic and business theorist, to describe a process by which a new product or service takes root initially in a low-end or new market and then gradually moves upmarket, eventually displacing established competitors. Disruptive innovation The concept of disruptive innovation has been widely applied to various industries, including technology, art, healthcare, education, manufacturing, etc. It has become an important framework for understanding how new technologies and business models can disrupt established industries and create new market opportunities. Disruptive Innovation in Tech Several businesses have been mislabeled as disruptive innovations, often due to their rapid growth or innovative products. One of such businesses is Uber. The definition we added above contains one of the reasons why Uber is not a disruptive innovation. As we mentioned, in order to be considered disruptive, new products or services should originate from a low-end or new market. Uber doesn’t originate from either. Uber was founded in 2009 in San Francisco. At that time, people were already used to calling cabs. Therefore, the market wasn’t new. On the other hand, it wasn’t a low-end opportunity since the cab service has not yet been as advanced. Another reason is Uber’s targeting a mainstream audience from the very beginning. One of the core principles of the theory is that disruptive innovations initially appeal to niche markets and only gain widespread adoption once they reach mainstream quality standards. So, was Uber a major breakthrough? Definitely. But is it really a disruptive innovation? Not really. Here are the core principles of the disruptive innovation theory: ocus on underserved segments; F implicity and affordability; S ncremental improvement; I oherent value network. C All the information above naturally raises a question: What can actually be considered disruptive innovation, and why does compliance with the theory actually matter? Well, some examples of the theory have affected our lives profoundly, like the Internet. This one must be obvious. Yet this disruption has led to creating even more disruptions. One of the classic examples is Amazon. You may be surprised, but the industry that’s been disrupted by the creation of the platform is bookselling. The reason for that is simple: Amazon doesn’t require physical stores to showcase and sell its products. Born from a humble garage business catering to online book enthusiasts, Amazon has turned into a digital titan, immeasurably impacting the physical retail landscape and securing an unrivaled market share. Its meteoric rise has cast a shadow over traditional bookstores, leading many to shut down. Today, Amazon's online shopping platform is well-known for a wide variety of products, from groceries to gadgets, all effortlessly accessible from one's doorstep. Another example of disruptive innovation is Netflix. This company hasn’t always been the way we know it. Netflix originally started as an online DVD and VHS rental with mail delivery. The company captured an opportunity to utilize the Internet and thus disrupted traditional DVD rentals, such as Blockbuster. As Netflix was expanding its service, having added streaming capabilities in 2007, the company gradually solidified its disruptive position. When the new service improved, and its content library grew, the company attracted more mainstream customers, leaving companies like Blockbuster behind. Disruptive Innovation in Art Perhaps the most revolutionizing disruptive innovations from the art world are and (Real World Assets). Let’s take a closer look at them and find out their working principles. art tokenization RWAs (RWAs) are a broad category of assets that exist outside of the blockchain but are represented by digital tokens on a blockchain. These assets can be anything from physical goods like real estate or commodities to intangible assets like intellectual property or financial instruments. Real-world assets The process of RWA tokenization involves creating a digital representation of the asset on a blockchain and issuing tokens that represent ownership of the asset. The tokens can then be traded on decentralized exchanges (DEXs) or used to access financial services. By the way, every image in this article was generated by AI – another major disruptive innovation that has already greatly affected modern art. Art can also be tokenized, and is actually a top-tier project in this sphere. Just as Netflix disrupted the traditional media landscape by making content accessible for many, 10101.art is democratizing art ownership by giving an opportunity for anyone to own a part of a physical masterpiece. 10101.art is the process of representing artwork as digital tokens on a blockchain. For instance, on 10101.art, we tokenize famous paintings by internationally renowned artists and ensure transparency of ownership and accessibility of masterpieces. The process of art tokenization can be broken down into the following steps: Art tokenization electing an artwork. This could be a physical artwork such as a painting or sculpture or a digital one such as a photograph or piece of graphic design. Tokenization of physical artworks can cause certain problems with authentication and insurance. Authentication requires having experienced dealers and curators on the team. Insurance, in turn, can be problematic because of the numerous anonymous owners. S reating a smart contract. A smart contract is a self-executing contract that is stored on a blockchain. It will define the terms of ownership of the artwork, such as the number of tokens that will be issued and the initial price. C ploading the artwork. The artwork will be uploaded to a decentralized file storage system. This ensures that the artwork is stored securely and can be easily accessed by anyone who owns a token. U inting the tokens. The tokens will be minted on the blockchain using the smart contract. This minting process will create a unique identifier for each token and associate it with the artwork. M isting the tokens for sale. The tokens will be listed for sale on an NFT marketplace – an online platform where people can buy and sell NFTs. L ransferring ownership. When a buyer purchases a token, they will be transferred ownership of the artwork. This ownership is recorded on the blockchain and is tamper-proof. T We have already tokenized and Warhol’s is next in line, and we are also preparing our and artworks for this process. And if you want to see the artwork live, we'll be waiting for you at our partner gallery opening at “The Ritz-Carlton” (DIFC) on . Follow our to learn more about the upcoming event. Banksy’s “Turf War” “Campbell's Soup Cans“ Dali Picasso April 15 social media Ready to begin your art journey powered by blockchain technologies? Explore platform, starting with the 'Turf War' collection by Banksy, and discover how you can be a part of art history in a new, tech-forward way! 10101.art

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