{"componentChunkName":"component---src-gatsby-entities-reflection-tsx","path":"/perspectives/turning-points-for-ocean-glaciers-poles-ai-data-intelligence","result":{"data":{"platform":{"reflection":{"id":"6a4b79bb87a6338fb88b41f3","slug":"turning-points-for-ocean-glaciers-poles-ai-data-intelligence","path":"/perspectives/turning-points-for-ocean-glaciers-poles-ai-data-intelligence","name":"Turning Points for Ocean, Glaciers & Poles: AI & Data Intelligence","name_fr":null,"title":null,"title_fr":null,"pretitle":null,"pretitle_fr":null,"subtitle":null,"subtitle_fr":null,"published":"2026-07-06T09:47:39.98","edited":null,"content":{"plain":"Ideas from the speakers\nArtificial Intelligence is a complex and potentially emotionally fraught topic. Climate and AI are a double-edged sword. And it feels like AI is happening to us. Most of the ‘AI’ being used is generative AI, but applied AI, once called advanced analytics, is lagging, not for technological reasons, but political ones. These ideas framed the discussion on brainstorming a plan to leverage AI and data to benefit oceans, glaciers, and the poles.\nThe first speaker, an expert in climate and global development, currently working in technology, sketched the AI landscape. She detailed how, when AI fluency is developed within climate organizations, people on the ground can have agency and positively influence society. That this is how the climate community uses AI to build an ‘army of good.’ That we need not be frightened, and that we ought to embrace the benefits of new technology.\nThe second expert, experienced in biodiversity monitoring and conservation implementation, opened: What is going to occur when we don’t have adequate statistics to feed data models? How then do we acquire and amalgamate species data from all across the Earth? She noted that this is occurring, with the assistance of AI, but not aquatically. Compounding the problem is the nature of oceans; there are fewer occurrence points for oceanic species, raising further questions about how we quantify habitat in the ocean and impact on individual species.\nThe third speaker, working at the forefront of developing technology for monitoring biodiversity and planetary systems, asked: how can we collect data in larger spaces, over more time? And how can we have better tools at our disposal? The speaker acknowledged that sensors and our capabilities for analyzing data have progressed significantly recently, but that adoption of other sectors’ methods has been slow. A solution may be partnering with non-environmental companies on tools, including imaging, acoustics, and eDNA. The speaker advocated for involving groups outside the environmental space to increase scalability and heighten capabilities.\nInsights from the audience\nThe audience split up to discuss the use of AI and data intelligence through three lenses.\nThe group focused on literacy and agency asked: How can we build AI capabilities? To effectively use AI, what capacities should different stakeholders have? Because of AI’s ability to quickly complete data analysis tasks and heighten frontline capabilities, someone asked if there could be initiatives to grow AI capacities. Some eagerly asked about what AI could mean for local partners. How can they use AI? Are there unintended consequences? Important questions, all asked while the pace of AI development presents a unique challenge to attaining fluency for local partners, while some boards desire AI adoption regardless of fit, and fundraising teams utilize AI models to determine who gets funded. Against this background, proposed solutions suggested organizations look beyond the upfront costs of AI adoption, instead to the cost of failure to adapt. The group also acknowledged the degree to which AI is the colonization of knowledge and data. Final thoughts centered around the understanding of the inevitability of AI adoption, and that we need to embrace it and discern how to do it responsibly, ethically, and expand access, or actors in the environmental space will be left behind.\nThe second group focused on potential solutions architects, and who we might be overlooking. First however, the concern that we already lack sufficient data, but training AI requires more, arose. A marine biologist shared their attempted to use AI to identify organisms populating specific environments, but that existing models needed to be reconfigured. Another shared that AI was useful in filling knowledge gaps on their small team. Someone followed this by sharing that they felt that AI could bring in perspectives from other sectors, for instance, granting a scientist a business perspective through an AI. The group concluded that AI will need more data to be used effectively at scale, but in the meantime, AI could be used to help startups and smaller organizations build solutions.\nThe third group discussed the ecosystem at large. Questions revolved around bridging the gap between scalable research and needing new data. Also, how to find ways to incorporate new data into models, and who should be involved? Discussion landed on incentives and adoption challenges. Seemingly, the main issue is that oceans data is going from ‘analogue’ to AI; the leap is tremendous. Furthermore, its development and implementation are only being perceived as an extra cost, and if that data doesn’t generate tangible benefits, the industry won’t reach a tipping point. So then, ventured an audience member, who are we missing at the table who really understands these incentives? And where and when does this data start creating value? Concluding thoughts were that there must be more effective data sharing; otherwise, weak observational data will get baked into AI models, leaving oceans data collection even further behind.\n","text":"# Ideas from the speakers\nArtificial Intelligence is a complex and potentially emotionally fraught topic. Climate and AI are a double-edged sword. And it feels like AI is happening to us. Most of the ‘AI’ being used is generative AI, but applied AI, once called advanced analytics, is lagging, not for technological reasons, but political ones. These ideas framed the discussion on brainstorming a plan to leverage AI and data to benefit oceans, glaciers, and the poles.\n\nThe first speaker, an expert in climate and global development, currently working in technology, sketched the AI landscape. She detailed how, when AI fluency is developed within climate organizations, people on the ground can have agency and positively influence society. That this is how the climate community uses AI to build an ‘army of good.’ That we need not be frightened, and that we ought to embrace the benefits of new technology.\n\nThe second expert, experienced in biodiversity monitoring and conservation implementation, opened: What is going to occur when we don’t have adequate statistics to feed data models? How then do we acquire and amalgamate species data from all across the Earth? She noted that this is occurring, with the assistance of AI, but not aquatically. Compounding the problem is the nature of oceans; there are fewer occurrence points for oceanic species, raising further questions about how we quantify habitat in the ocean and impact on individual species.\n\nThe third speaker, working at the forefront of developing technology for monitoring biodiversity and planetary systems, asked: how can we collect data in larger spaces, over more time? And how can we have better tools at our disposal? The speaker acknowledged that sensors and our capabilities for analyzing data have progressed significantly recently, but that adoption of other sectors’ methods has been slow. A solution may be partnering with non-environmental companies on tools, including imaging, acoustics, and eDNA. The speaker advocated for involving groups outside the environmental space to increase scalability and heighten capabilities.\n\n# Insights from the audience\nThe audience split up to discuss the use of AI and data intelligence through three lenses.\n\nThe group focused on literacy and agency asked: How can we build AI capabilities? To effectively use AI, what capacities should different stakeholders have? Because of AI’s ability to quickly complete data analysis tasks and heighten frontline capabilities, someone asked if there could be initiatives to grow AI capacities. Some eagerly asked about what AI could mean for local partners. How can they use AI? Are there unintended consequences? Important questions, all asked while the pace of AI development presents a unique challenge to attaining fluency for local partners, while some boards desire AI adoption regardless of fit, and fundraising teams utilize AI models to determine who gets funded. Against this background, proposed solutions suggested organizations look beyond the upfront costs of AI adoption, instead to the cost of failure to adapt. The group also acknowledged the degree to which AI is the colonization of knowledge and data. Final thoughts centered around the understanding of the inevitability of AI adoption, and that we need to embrace it and discern how to do it responsibly, ethically, and expand access, or actors in the environmental space will be left behind.\n\nThe second group focused on potential solutions architects, and who we might be overlooking. First however, the concern that we already lack sufficient data, but training AI requires more, arose. A marine biologist shared their attempted to use AI to identify organisms populating specific environments, but that existing models needed to be reconfigured. Another shared that AI was useful in filling knowledge gaps on their small team. Someone followed this by sharing that they felt that AI could bring in perspectives from other sectors, for instance, granting a scientist a business perspective through an AI. The group concluded that AI will need more data to be used effectively at scale, but in the meantime, AI could be used to help startups and smaller organizations build solutions.\n\nThe third group discussed the ecosystem at large. Questions revolved around bridging the gap between scalable research and needing new data. Also, how to find ways to incorporate new data into models, and who should be involved? Discussion landed on incentives and adoption challenges. Seemingly, the main issue is that oceans data is going from ‘analogue’ to AI; the leap is tremendous. Furthermore, its development and implementation are only being perceived as an extra cost, and if that data doesn’t generate tangible benefits, the industry won’t reach a tipping point. So then, ventured an audience member, who are we missing at the table who really understands these incentives? And where and when does this data start creating value? Concluding thoughts were that there must be more effective data sharing; otherwise, weak observational data will get baked into AI models, leaving oceans data collection even further behind."},"content_fr":{"plain":"","text":""},"openGraph":{"title":null,"description":{"plain":"Ideas from the speakers\nArtificial Intelligence is a complex and potentially emotionally fraught topic. Climate and AI are a double-edged sword. And it feels like AI is happening to us. Most of the ‘AI’ being used is generative AI, but applied AI, once called advanced analytics, is lagging, not for technological reasons, but political ones. These ideas framed the discussion on brainstorming a plan to leverage AI and data to benefit oceans, glaciers, and the poles.\nThe first speaker, an expert in climate and global development, currently working in technology, sketched the AI landscape. She detailed how, when AI fluency is developed within climate organizations, people on the ground can have agency and positively influence society. That this is how the climate community uses AI to build an ‘army of good.’ That we need not be frightened, and that we ought to embrace the benefits of new technology.\nThe second expert, experienced in biodiversity monitoring and conservation implementation, opened: What is going to occur when we don’t have adequate statistics to feed data models? How then do we acquire and amalgamate species data from all across the Earth? She noted that this is occurring, with the assistance of AI, but not aquatically. Compounding the problem is the nature of oceans; there are fewer occurrence points for oceanic species, raising further questions about how we quantify habitat in the ocean and impact on individual species.\nThe third speaker, working at the forefront of developing technology for monitoring biodiversity and planetary systems, asked: how can we collect data in larger spaces, over more time? And how can we have better tools at our disposal? The speaker acknowledged that sensors and our capabilities for analyzing data have progressed significantly recently, but that adoption of other sectors’ methods has been slow. A solution may be partnering with non-environmental companies on tools, including imaging, acoustics, and eDNA. The speaker advocated for involving groups outside the environmental space to increase scalability and heighten capabilities.\nInsights from the audience\nThe audience split up to discuss the use of AI and data intelligence through three lenses.\nThe group focused on literacy and agency asked: How can we build AI capabilities? To effectively use AI, what capacities should different stakeholders have? Because of AI’s ability to quickly complete data analysis tasks and heighten frontline capabilities, someone asked if there could be initiatives to grow AI capacities. Some eagerly asked about what AI could mean for local partners. How can they use AI? Are there unintended consequences? Important questions, all asked while the pace of AI development presents a unique challenge to attaining fluency for local partners, while some boards desire AI adoption regardless of fit, and fundraising teams utilize AI models to determine who gets funded. Against this background, proposed solutions suggested organizations look beyond the upfront costs of AI adoption, instead to the cost of failure to adapt. The group also acknowledged the degree to which AI is the colonization of knowledge and data. Final thoughts centered around the understanding of the inevitability of AI adoption, and that we need to embrace it and discern how to do it responsibly, ethically, and expand access, or actors in the environmental space will be left behind.\nThe second group focused on potential solutions architects, and who we might be overlooking. First however, the concern that we already lack sufficient data, but training AI requires more, arose. A marine biologist shared their attempted to use AI to identify organisms populating specific environments, but that existing models needed to be reconfigured. Another shared that AI was useful in filling knowledge gaps on their small team. Someone followed this by sharing that they felt that AI could bring in perspectives from other sectors, for instance, granting a scientist a business perspective through an AI. The group concluded that AI will need more data to be used effectively at scale, but in the meantime, AI could be used to help startups and smaller organizations build solutions.\nThe third group discussed the ecosystem at large. Questions revolved around bridging the gap between scalable research and needing new data. Also, how to find ways to incorporate new data into models, and who should be involved? Discussion landed on incentives and adoption challenges. Seemingly, the main issue is that oceans data is going from ‘analogue’ to AI; the leap is tremendous. Furthermore, its development and implementation are only being perceived as an extra cost, and if that data doesn’t generate tangible benefits, the industry won’t reach a tipping point. So then, ventured an audience member, who are we missing at the table who really understands these incentives? And where and when does this data start creating value? Concluding thoughts were that there must be more effective data sharing; otherwise, weak observational data will get baked into AI models, leaving oceans data collection even further behind.\n"},"image":{"url2x":null,"thumbnails":{"card":{"url":"https://res.cloudinary.com/shapeable/image/upload/c_limit,w_480/v1783331211/villars-institute/banner/x-11_image__55151898198_f9d7035824_o_xbfbpp.jpg","url2x":"https://res.cloudinary.com/shapeable/image/upload/c_limit,w_960/v1783331211/villars-institute/banner/x-11_image__55151898198_f9d7035824_o_xbfbpp.jpg"},"mainBanner":{"url":"https://res.cloudinary.com/shapeable/image/upload/c_limit,w_1440/v1783331211/villars-institute/banner/x-11_image__55151898198_f9d7035824_o_xbfbpp.jpg","url2x":"https://res.cloudinary.com/shapeable/image/upload/c_limit,w_2880/v1783331211/villars-institute/banner/x-11_image__55151898198_f9d7035824_o_xbfbpp.jpg"}}}},"challenge":{"icon":{"id":"684a2db82dd10058219bebce","name":"Emerging Technologies","component":"EmergingTechnologiesIcon"},"color":{"id":"65d5479fd5bcca2bcbc9db51","name":"Dark Red","value":"#8C1815"},"id":"65d54797d5bcca2bcbc9d99b","name":"Emerging Technologies","slug":"emerging-technologies","typeLabel":"Theme","badge":null,"path":"/themes/emerging-technologies","updated":"2025-06-12T04:07:03.29","__typename":"Platform_Challenge","_schema":{"label":"Theme","pluralLabel":"Themes"},"openGraph":{"id":"openGraph_challenge/emerging-technologies","title":"Emerging Technologies","image":{"id":"image_villars-institute/banner/theme-emerging-technologies_image__theme-emerging-technologies","url":"https://res.cloudinary.com/shapeable/image/upload/v1668989832/villars-institute/banner/theme-emerging-technologies_image__theme-emerging-technologies.jpg","url2x":null,"thumbnails":{"id":"thumbnails-file_villars-institute/banner/theme-emerging-technologies_image__theme-emerging-technologies","bubbleMedium":{"id":"thumbnails-bubble-medium-file_villars-institute/banner/theme-emerging-technologies_image__theme-emerging-technologies","url":"https://res.cloudinary.com/shapeable/image/upload/c_limit,w_96/v1668989832/villars-institute/banner/theme-emerging-technologies_image__theme-emerging-technologies.jpg","url2x":"https://res.cloudinary.com/shapeable/image/upload/c_limit,w_192/v1668989832/villars-institute/banner/theme-emerging-technologies_image__theme-emerging-technologies.jpg"}}}},"backgroundImage":{"id":"684a3ec6681a755d2aebd48e","image":{"id":"image_villars-institute/image-asset/emerging-technologies-background_image__emerging_technologies_tsvgsj","url":"https://res.cloudinary.com/shapeable/image/upload/v1749696186/villars-institute/image-asset/emerging-technologies-background_image__emerging_technologies_tsvgsj.webp","url2x":null}}},"color":{"id":"65d5479fd5bcca2bcbc9db51","name":"Dark Red","value":"#8C1815"},"typeLabel":"New View","intro":{"plain":"","text":""},"intro_fr":{"plain":"","text":""},"outro":{"text":""},"outro_fr":{"text":""},"videos":[],"imageAssets":[],"organisations":[],"challenges":[{"id":"65d54797d5bcca2bcbc9d99b","name":"Emerging Technologies","slug":"emerging-technologies","typeLabel":"Theme","badge":null,"path":"/themes/emerging-technologies","updated":"2025-06-12T04:07:03.29","__typename":"Platform_Challenge","_schema":{"label":"Theme","pluralLabel":"Themes"},"icon":{"id":"684a2db82dd10058219bebce","name":"Emerging Technologies","component":"EmergingTechnologiesIcon"},"color":{"id":"65d5479fd5bcca2bcbc9db51","name":"Dark Red","value":"#8C1815"},"advertisements":[]}],"authors":[{"id":"661692bbe8b4e558b90f6383","name":"Everett Johnson","slug":"everett-johnson","role":{"id":"rol_m02v21Sk7a2hKGj7","name":"Fellow"},"isMember":true,"bio":{"id":"661692bbe8b4e558b90f6383_bio","text":"Everett is a first year at Williams College and prospective Environmental Studies Major and Coastal and Ocean Studies Concentrator. 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