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    MIT delegation mainstreams biodiversity conservation at the UN Biodiversity Convention, COP16

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    For the first time, MIT sent an organized engagement to the global Conference of the Parties for the Convention on Biological Diversity, which this year was held Oct. 21 to Nov. 1 in Cali, Colombia.The 10 delegates to COP16 included faculty, researchers, and students from the MIT Environmental Solutions Initiative (ESI), the Department of Electrical Engineering and Computer Science (EECS), the Computer Science and Artificial Intelligence Laboratory (CSAIL), the Department of Urban Studies and Planning (DUSP), the Institute for Data, Systems, and Society (IDSS), and the Center for Sustainability Science and Strategy.In previous years, MIT faculty had participated sporadically in the discussions. This organized engagement, led by the ESI, is significant because it brought representatives from many of the groups working on biodiversity across the Institute; showcased the breadth of MIT’s research in more than 15 events including panels, roundtables, and keynote presentations across the Blue and Green Zones of the conference (with the Blue Zone representing the primary venue for the official negotiations and discussions and the Green Zone representing public events); and created an experiential learning opportunity for students who followed specific topics in the negotiations and throughout side events.The conference also gathered attendees from governments, nongovernmental organizations, businesses, other academic institutions, and practitioners focused on stopping global biodiversity loss and advancing the 23 goals of the Kunming-Montreal Global Biodiversity Framework (KMGBF), an international agreement adopted in 2022 to guide global efforts to protect and restore biodiversity through 2030.MIT’s involvement was particularly pronounced when addressing goals related to building coalitions of sub-national governments (targets 11, 12, 14); technology and AI for biodiversity conservation (targets 20 and 21); shaping equitable markets (targets 3, 11, and 19); and informing an action plan for Afro-descendant communities (targets 3, 10, and 22).Building coalitions of sub-national governmentsThe ESI’s Natural Climate Solutions (NCS) Program was able to support two separate coalitions of Latin American cities, namely the Coalition of Cities Against Illicit Economies in the Biogeographic Chocó Region and the Colombian Amazonian Cities coalition, who successfully signed declarations to advance specific targets of the KMGBF (the aforementioned targets 11, 12, 14).This was accomplished through roundtables and discussions where team members — including Marcela Angel, research program director at the MIT ESI; Angelica Mayolo, ESI Martin Luther King Fellow 2023-25; and Silvia Duque and Hannah Leung, MIT Master’s in City Planning students — presented a set of multi-scale actions including transnational strategies, recommendations to strengthen local and regional institutions, and community-based actions to promote the conservation of the Biogeographic Chocó as an ecological corridor.“There is an urgent need to deepen the relationship between academia and local governments of cities located in biodiversity hotspots,” said Angel. “Given the scale and unique conditions of Amazonian cities, pilot research projects present an opportunity to test and generate a proof of concept. These could generate catalytic information needed to scale up climate adaptation and conservation efforts in socially and ecologically sensitive contexts.”ESI’s research also provided key inputs for the creation of the Fund for the Biogeographic Chocó Region, a multi-donor fund launched within the framework of COP16 by a coalition composed of Colombia, Ecuador, Panamá, and Costa Rica. The fund aims to support biodiversity conservation, ecosystem restoration, climate change mitigation and adaptation, and sustainable development efforts across the region.Technology and AI for biodiversity conservationData, technology, and artificial intelligence are playing an increasing role in how we understand biodiversity and ecosystem change globally. Professor Sara Beery’s research group at MIT focuses on this intersection, developing AI methods that enable species and environmental monitoring at previously unprecedented spatial, temporal, and taxonomic scales.During the International Union of Biological Diversity Science-Policy Forum, the high-level COP16 segment focused on outlining recommendations from scientific and academic community, Beery spoke on a panel alongside María Cecilia Londoño, scientific information manager of the Humboldt Institute and co-chair of the Global Biodiversity Observations Network, and Josh Tewksbury, director of the Smithsonian Tropical Research Institute, among others, about how these technological advancements will help humanity achieve our biodiversity targets. The panel emphasized that AI innovation was needed, but with emphasis on direct human-AI partnership, AI capacity building, and the need for data and AI policy to ensure equity of access and benefit from these technologies.As a direct outcome of the session, for the first time, AI was emphasized in the statement on behalf of science and academia delivered by Hernando Garcia, director of the Humboldt Institute, and David Skorton, secretary general of the Smithsonian Institute, to the high-level segment of the COP16.That statement read, “To effectively address current and future challenges, urgent action is required in equity, governance, valuation, infrastructure, decolonization and policy frameworks around biodiversity data and artificial intelligence.”Beery also organized a panel at the GEOBON pavilion in the Blue Zone on Scaling Biodiversity Monitoring with AI, which brought together global leaders from AI research, infrastructure development, capacity and community building, and policy and regulation. The panel was initiated and experts selected from the participants at the recent Aspen Global Change Institute Workshop on Overcoming Barriers to Impact in AI for Biodiversity, co-organized by Beery.Shaping equitable marketsIn a side event co-hosted by the ESI with CAF-Development Bank of Latin America, researchers from ESI’s Natural Climate Solutions Program — including Marcela Angel; Angelica Mayolo; Jimena Muzio, ESI research associate; and Martin Perez Lara, ESI research affiliate and director for Forest Climate Solutions Impact and Monitoring at World Wide Fund for Nature of the U.S. — presented results of a study titled “Voluntary Carbon Markets for Social Impact: Comprehensive Assessment of the Role of Indigenous Peoples and Local Communities (IPLC) in Carbon Forestry Projects in Colombia.” The report highlighted the structural barriers that hinder effective participation of IPLC, and proposed a conceptual framework to assess IPLC engagement in voluntary carbon markets.Communicating these findings is important because the global carbon market has experienced a credibility crisis since 2023, influenced by critical assessments in academic literature, journalism questioning the quality of mitigation results, and persistent concerns about the engagement of private actors with IPLC. Nonetheless, carbon forestry projects have expanded rapidly in Indigenous, Afro-descendant, and local communities’ territories, and there is a need to assess the relationships between private actors and IPLC and to propose pathways for equitable participation. 

    Panelists pose at the equitable markets side event at the Latin American Pavilion in the Blue Zone.

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    The research presentation and subsequent panel with representatives of the association for Carbon Project Developers in Colombia Asocarbono, Fondo Acción, and CAF further discussed recommendations for all actors in the value chain of carbon certificates — including those focused on promoting equitable benefit-sharing and safeguarding compliance, increased accountability, enhanced governance structures, strengthened institutionality, and regulatory frameworks  — necessary to create an inclusive and transparent market.Informing an action plan for Afro-descendant communitiesThe Afro-Interamerican Forum on Climate Change (AIFCC), an international network working to highlight the critical role of Afro-descendant peoples in global climate action, was also present at COP16.At the Afro Summit, Mayolo presented key recommendations prepared collectively by the members of AIFCC to the technical secretariat of the Convention on Biological Diversity (CBD). The recommendations emphasize:creating financial tools for conservation and supporting Afro-descendant land rights;including a credit guarantee fund for countries that recognize Afro-descendant collective land titling and research on their contributions to biodiversity conservation;calling for increased representation of Afro-descendant communities in international policy forums;capacity-building for local governments; andstrategies for inclusive growth in green business and energy transition.These actions aim to promote inclusive and sustainable development for Afro-descendant populations.“Attending COP16 with a large group from MIT contributing knowledge and informed perspectives at 15 separate events was a privilege and honor,” says MIT ESI Director John E. Fernández. “This demonstrates the value of the ESI as a powerful research and convening body at MIT. Science is telling us unequivocally that climate change and biodiversity loss are the two greatest challenges that we face as a species and a planet. MIT has the capacity, expertise, and passion to address not only the former, but also the latter, and the ESI is committed to facilitating the very best contributions across the institute for the critical years that are ahead of us.”A fuller overview of the conference is available via The MIT Environmental Solutions Initiative’s Primer of COP16. More

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      New AI tool generates realistic satellite images of future flooding

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      Visualizing the potential impacts of a hurricane on people’s homes before it hits can help residents prepare and decide whether to evacuate.MIT scientists have developed a method that generates satellite imagery from the future to depict how a region would look after a potential flooding event. The method combines a generative artificial intelligence model with a physics-based flood model to create realistic, birds-eye-view images of a region, showing where flooding is likely to occur given the strength of an oncoming storm.As a test case, the team applied the method to Houston and generated satellite images depicting what certain locations around the city would look like after a storm comparable to Hurricane Harvey, which hit the region in 2017. The team compared these generated images with actual satellite images taken of the same regions after Harvey hit. They also compared AI-generated images that did not include a physics-based flood model.The team’s physics-reinforced method generated satellite images of future flooding that were more realistic and accurate. The AI-only method, in contrast, generated images of flooding in places where flooding is not physically possible.The team’s method is a proof-of-concept, meant to demonstrate a case in which generative AI models can generate realistic, trustworthy content when paired with a physics-based model. In order to apply the method to other regions to depict flooding from future storms, it will need to be trained on many more satellite images to learn how flooding would look in other regions.“The idea is: One day, we could use this before a hurricane, where it provides an additional visualization layer for the public,” says Björn Lütjens, a postdoc in MIT’s Department of Earth, Atmospheric and Planetary Sciences, who led the research while he was a doctoral student in MIT’s Department of Aeronautics and Astronautics (AeroAstro). “One of the biggest challenges is encouraging people to evacuate when they are at risk. Maybe this could be another visualization to help increase that readiness.”To illustrate the potential of the new method, which they have dubbed the “Earth Intelligence Engine,” the team has made it available as an online resource for others to try.The researchers report their results today in the journal IEEE Transactions on Geoscience and Remote Sensing. The study’s MIT co-authors include Brandon Leshchinskiy; Aruna Sankaranarayanan; and Dava Newman, professor of AeroAstro and director of the MIT Media Lab; along with collaborators from multiple institutions.Generative adversarial imagesThe new study is an extension of the team’s efforts to apply generative AI tools to visualize future climate scenarios.“Providing a hyper-local perspective of climate seems to be the most effective way to communicate our scientific results,” says Newman, the study’s senior author. “People relate to their own zip code, their local environment where their family and friends live. Providing local climate simulations becomes intuitive, personal, and relatable.”For this study, the authors use a conditional generative adversarial network, or GAN, a type of machine learning method that can generate realistic images using two competing, or “adversarial,” neural networks. The first “generator” network is trained on pairs of real data, such as satellite images before and after a hurricane. The second “discriminator” network is then trained to distinguish between the real satellite imagery and the one synthesized by the first network.Each network automatically improves its performance based on feedback from the other network. The idea, then, is that such an adversarial push and pull should ultimately produce synthetic images that are indistinguishable from the real thing. Nevertheless, GANs can still produce “hallucinations,” or factually incorrect features in an otherwise realistic image that shouldn’t be there.“Hallucinations can mislead viewers,” says Lütjens, who began to wonder whether such hallucinations could be avoided, such that generative AI tools can be trusted to help inform people, particularly in risk-sensitive scenarios. “We were thinking: How can we use these generative AI models in a climate-impact setting, where having trusted data sources is so important?”Flood hallucinationsIn their new work, the researchers considered a risk-sensitive scenario in which generative AI is tasked with creating satellite images of future flooding that could be trustworthy enough to inform decisions of how to prepare and potentially evacuate people out of harm’s way.Typically, policymakers can get an idea of where flooding might occur based on visualizations in the form of color-coded maps. These maps are the final product of a pipeline of physical models that usually begins with a hurricane track model, which then feeds into a wind model that simulates the pattern and strength of winds over a local region. This is combined with a flood or storm surge model that forecasts how wind might push any nearby body of water onto land. A hydraulic model then maps out where flooding will occur based on the local flood infrastructure and generates a visual, color-coded map of flood elevations over a particular region.“The question is: Can visualizations of satellite imagery add another level to this, that is a bit more tangible and emotionally engaging than a color-coded map of reds, yellows, and blues, while still being trustworthy?” Lütjens says.The team first tested how generative AI alone would produce satellite images of future flooding. They trained a GAN on actual satellite images taken by satellites as they passed over Houston before and after Hurricane Harvey. When they tasked the generator to produce new flood images of the same regions, they found that the images resembled typical satellite imagery, but a closer look revealed hallucinations in some images, in the form of floods where flooding should not be possible (for instance, in locations at higher elevation).To reduce hallucinations and increase the trustworthiness of the AI-generated images, the team paired the GAN with a physics-based flood model that incorporates real, physical parameters and phenomena, such as an approaching hurricane’s trajectory, storm surge, and flood patterns. With this physics-reinforced method, the team generated satellite images around Houston that depict the same flood extent, pixel by pixel, as forecasted by the flood model.“We show a tangible way to combine machine learning with physics for a use case that’s risk-sensitive, which requires us to analyze the complexity of Earth’s systems and project future actions and possible scenarios to keep people out of harm’s way,” Newman says. “We can’t wait to get our generative AI tools into the hands of decision-makers at the local community level, which could make a significant difference and perhaps save lives.”The research was supported, in part, by the MIT Portugal Program, the DAF-MIT Artificial Intelligence Accelerator, NASA, and Google Cloud. 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      in Environment

      A vision for U.S. science success

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      White House science advisor Arati Prabhakar expressed confidence in U.S. science and technology capacities during a talk on Wednesday about major issues the country must tackle.“Let me start with the purpose of science and technology and innovation, which is to open possibilities so that we can achieve our great aspirations,” said Prabhakar, who is the director of the Office of Science and Technology Policy (OSTP) and a co-chair of the President’s Council of Advisors on Science and Technology (PCAST). “The aspirations that we have as a country today are as great as they have ever been,” she added.Much of Prabhakar’s talk focused on three major issues in science and technology development: cancer prevention, climate change, and AI. In the process, she also emphasized the necessity for the U.S. to sustain its global leadership in research across domains of science and technology, which she called “one of America’s long-time strengths.”“Ever since the end of the Second World War, we said we’re going in on basic research, we’re going to build our universities’ capacity to do it, we have an unparalleled basic research capacity, and we should always have that,” said Prabhakar.“We have gotten better, I think, in recent years at commercializing technology from our basic research,” Prabhakar added, noting, “Capital moves when you can see profit and growth.” The Biden administration, she said, has invested in a variety of new ways for the public and private sector to work together to massively accelerate the movement of technology into the market.Wednesday’s talk drew a capacity audience of nearly 300 people in MIT’s Wong Auditorium and was hosted by the Manufacturing@MIT Working Group. The event included introductory remarks by Suzanne Berger, an Institute Professor and a longtime expert on the innovation economy, and Nergis Mavalvala, dean of the School of Science and an astrophysicist and leader in gravitational-wave detection.Introducing Mavalvala, Berger said the 2015 announcement of the discovery of gravitational waves “was the day I felt proudest and most elated to be a member of the MIT community,” and noted that U.S. government support helped make the research possible. Mavalvala, in turn, said MIT was “especially honored” to hear Prabhakar discuss leading-edge research and acknowledge the role of universities in strengthening the country’s science and technology sectors.Prabhakar has extensive experience in both government and the private sector. She has been OSTP director and co-chair of PCAST since October of 2022. She served as director of the Defense Advanced Research Projects Agency (DARPA) from 2012 to 2017 and director of the National Institute of Standards and Technology (NIST) from 1993 to 1997.She has also held executive positions at Raychem and Interval Research, and spent a decade at the investment firm U.S. Venture Partners. An engineer by training, Prabhakar earned a BS in electrical engineering from Texas Tech University in 1979, an MA in electrical engineering from Caltech in 1980, and a PhD in applied physics from Caltech in 1984.Among other remarks about medicine, Prabhakar touted the Biden administration’s “Cancer Moonshot” program, which aims to cut the cancer death rate in half over the next 25 years through multiple approaches, from better health care provision and cancer detection to limiting public exposure to carcinogens. We should be striving, Prabhakar said, for “a future in which people take good health for granted and can get on with their lives.”On AI, she heralded both the promise and concerns about technology, saying, “I think it’s time for active steps to get on a path to where it actually allows people to do more and earn more.”When it comes to climate change, Prabhakar said, “We all understand that the climate is going to change. But it’s in our hands how severe those changes get. And it’s possible that we can build a better future.” She noted the bipartisan infrastructure bill signed into law in 2021 and the Biden administration’s Inflation Reduction Act as important steps forward in this fight.“Together those are making the single biggest investment anyone anywhere on the planet has ever made in the clean energy transition,” she said. “I used to feel hopeless about our ability to do that, and it gives me tremendous hope.”After her talk, Prabhakar was joined onstage for a group discussion with the three co-presidents of the MIT Energy and Climate Club: Laurentiu Anton, a doctoral candidate in electrical engineering and computer science; Rosie Keller, an MBA candidate at the MIT Sloan School of Management; and Thomas Lee, a doctoral candidate in MIT’s Institute for Data, Systems, and Society.Asked about the seemingly sagging public confidence in science today, Prabhakar offered a few thoughts.“The first thing I would say is, don’t take it personally,” Prabhakar said, noting that any dip in public regard for science is less severe than the diminished public confidence in other institutions.Adding some levity, she observed that in polling about which occupations are regarded as being desirable for a marriage partner to have, “scientist” still ranks highly.“Scientists still do really well on that front, we’ve got that going for us,” she quipped.More seriously, Prabhakar observed, rather than “preaching” at the public, scientists should recognize that “part of the job for us is to continue to be clear about what we know are the facts, and to present them clearly but humbly, and to be clear that we’re going to continue working to learn more.” At the same time, she continued, scientists can always reinforce that “oh, by the way, facts are helpful things that can actually help you make better choices about how the future turns out. I think that would be better in my view.”Prabhakar said that her White House work had been guided, in part, by one of the overarching themes that President Biden has often reinforced.“He thinks about America as a nation that can be described in a single word, and that word is ‘possibilities,’” she said. “And that idea, that is such a big idea, it lights me up. I think of what we do in the world of science and technology and innovation as really part and parcel of creating those possibilities.”Ultimately, Prabhakar said, at all times and all points in American history, scientists and technologists must continue “to prove once more that when people come together and do this work … we do it in a way that builds opportunity and expands opportunity for everyone in our country. I think this is the great privilege we all have in the work we do, and it’s also our responsibility.” More

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      in Environment

      Advancing urban tree monitoring with AI-powered digital twins

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      The Irish philosopher George Berkely, best known for his theory of immaterialism, once famously mused, “If a tree falls in a forest and no one is around to hear it, does it make a sound?”What about AI-generated trees? They probably wouldn’t make a sound, but they will be critical nonetheless for applications such as adaptation of urban flora to climate change. To that end, the novel “Tree-D Fusion” system developed by researchers at the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL), Google, and Purdue University merges AI and tree-growth models with Google’s Auto Arborist data to create accurate 3D models of existing urban trees. The project has produced the first-ever large-scale database of 600,000 environmentally aware, simulation-ready tree models across North America.“We’re bridging decades of forestry science with modern AI capabilities,” says Sara Beery, MIT electrical engineering and computer science (EECS) assistant professor, MIT CSAIL principal investigator, and a co-author on a new paper about Tree-D Fusion. “This allows us to not just identify trees in cities, but to predict how they’ll grow and impact their surroundings over time. We’re not ignoring the past 30 years of work in understanding how to build these 3D synthetic models; instead, we’re using AI to make this existing knowledge more useful across a broader set of individual trees in cities around North America, and eventually the globe.”Tree-D Fusion builds on previous urban forest monitoring efforts that used Google Street View data, but branches it forward by generating complete 3D models from single images. While earlier attempts at tree modeling were limited to specific neighborhoods, or struggled with accuracy at scale, Tree-D Fusion can create detailed models that include typically hidden features, such as the back side of trees that aren’t visible in street-view photos.The technology’s practical applications extend far beyond mere observation. City planners could use Tree-D Fusion to one day peer into the future, anticipating where growing branches might tangle with power lines, or identifying neighborhoods where strategic tree placement could maximize cooling effects and air quality improvements. These predictive capabilities, the team says, could change urban forest management from reactive maintenance to proactive planning.A tree grows in Brooklyn (and many other places)The researchers took a hybrid approach to their method, using deep learning to create a 3D envelope of each tree’s shape, then using traditional procedural models to simulate realistic branch and leaf patterns based on the tree’s genus. This combo helped the model predict how trees would grow under different environmental conditions and climate scenarios, such as different possible local temperatures and varying access to groundwater.Now, as cities worldwide grapple with rising temperatures, this research offers a new window into the future of urban forests. In a collaboration with MIT’s Senseable City Lab, the Purdue University and Google team is embarking on a global study that re-imagines trees as living climate shields. Their digital modeling system captures the intricate dance of shade patterns throughout the seasons, revealing how strategic urban forestry could hopefully change sweltering city blocks into more naturally cooled neighborhoods.“Every time a street mapping vehicle passes through a city now, we’re not just taking snapshots — we’re watching these urban forests evolve in real-time,” says Beery. “This continuous monitoring creates a living digital forest that mirrors its physical counterpart, offering cities a powerful lens to observe how environmental stresses shape tree health and growth patterns across their urban landscape.”AI-based tree modeling has emerged as an ally in the quest for environmental justice: By mapping urban tree canopy in unprecedented detail, a sister project from the Google AI for Nature team has helped uncover disparities in green space access across different socioeconomic areas. “We’re not just studying urban forests — we’re trying to cultivate more equity,” says Beery. The team is now working closely with ecologists and tree health experts to refine these models, ensuring that as cities expand their green canopies, the benefits branch out to all residents equally.It’s a breezeWhile Tree-D fusion marks some major “growth” in the field, trees can be uniquely challenging for computer vision systems. Unlike the rigid structures of buildings or vehicles that current 3D modeling techniques handle well, trees are nature’s shape-shifters — swaying in the wind, interweaving branches with neighbors, and constantly changing their form as they grow. The Tree-D fusion models are “simulation-ready” in that they can estimate the shape of the trees in the future, depending on the environmental conditions.“What makes this work exciting is how it pushes us to rethink fundamental assumptions in computer vision,” says Beery. “While 3D scene understanding techniques like photogrammetry or NeRF [neural radiance fields] excel at capturing static objects, trees demand new approaches that can account for their dynamic nature, where even a gentle breeze can dramatically alter their structure from moment to moment.”The team’s approach of creating rough structural envelopes that approximate each tree’s form has proven remarkably effective, but certain issues remain unsolved. Perhaps the most vexing is the “entangled tree problem;” when neighboring trees grow into each other, their intertwined branches create a puzzle that no current AI system can fully unravel.The scientists see their dataset as a springboard for future innovations in computer vision, and they’re already exploring applications beyond street view imagery, looking to extend their approach to platforms like iNaturalist and wildlife camera traps.“This marks just the beginning for Tree-D Fusion,” says Jae Joong Lee, a Purdue University PhD student who developed, implemented and deployed the Tree-D-Fusion algorithm. “Together with my collaborators, I envision expanding the platform’s capabilities to a planetary scale. Our goal is to use AI-driven insights in service of natural ecosystems — supporting biodiversity, promoting global sustainability, and ultimately, benefiting the health of our entire planet.”Beery and Lee’s co-authors are Jonathan Huang, Scaled Foundations head of AI (formerly of Google); and four others from Purdue University: PhD students Jae Joong Lee and Bosheng Li, Professor and Dean’s Chair of Remote Sensing Songlin Fei, Assistant Professor Raymond Yeh, and Professor and Associate Head of Computer Science Bedrich Benes. Their work is based on efforts supported by the United States Department of Agriculture’s (USDA) Natural Resources Conservation Service and is directly supported by the USDA’s National Institute of Food and Agriculture. The researchers presented their findings at the European Conference on Computer Vision this month.  More

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      in Energy

      Ensuring a durable transition

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      To fend off the worst impacts of climate change, “we have to decarbonize, and do it even faster,” said William H. Green, director of the MIT Energy Initiative (MITEI) and Hoyt C. Hottel Professor, MIT Department of Chemical Engineering, at MITEI’s Annual Research Conference.“But how the heck do we actually achieve this goal when the United States is in the middle of a divisive election campaign, and globally, we’re facing all kinds of geopolitical conflicts, trade protectionism, weather disasters, increasing demand from developing countries building a middle class, and data centers in countries like the U.S.?”Researchers, government officials, and business leaders convened in Cambridge, Massachusetts, Sept. 25-26 to wrestle with this vexing question at the conference that was themed, “A durable energy transition: How to stay on track in the face of increasing demand and unpredictable obstacles.”“In this room we have a lot of power,” said Green, “if we work together, convey to all of society what we see as real pathways and policies to solve problems, and take collective action.”The critical role of consensus-building in driving the energy transition arose repeatedly in conference sessions, whether the topic involved developing and adopting new technologies, constructing and siting infrastructure, drafting and passing vital energy policies, or attracting and retaining a skilled workforce.Resolving conflictsThere is “blowback and a social cost” in transitioning away from fossil fuels, said Stephen Ansolabehere, the Frank G. Thompson Professor of Government at Harvard University, in a panel on the social barriers to decarbonization. “Companies need to engage differently and recognize the rights of communities,” he said.Nora DeDontney, director of development at Vineyard Offshore, described her company’s two years of outreach and negotiations to bring large cables from ocean-based wind turbines onshore.“Our motto is, ‘community first,’” she said. Her company works to mitigate any impacts towns might feel because of offshore wind infrastructure construction with projects, such as sewer upgrades; provides workforce training to Tribal Nations; and lays out wind turbines in a manner that provides safe and reliable areas for local fisheries.Elsa A. Olivetti, professor in the Department of Materials Science and Engineering at MIT and the lead of the Decarbonization Mission of MIT’s new Climate Project, discussed the urgent need for rapid scale-up of mineral extraction. “Estimates indicate that to electrify the vehicle fleet by 2050, about six new large copper mines need to come on line each year,” she said. To meet the demand for metals in the United States means pushing into Indigenous lands and environmentally sensitive habitats. “The timeline of permitting is not aligned with the temporal acceleration needed,” she said.Larry Susskind, the Ford Professor of Urban and Environmental Planning in the MIT Department of Urban Studies and Planning, is trying to resolve such tensions with universities playing the role of mediators. He is creating renewable energy clinics where students train to participate in emerging disputes over siting. “Talk to people before decisions are made, conduct joint fact finding, so that facilities reduce harms and share the benefits,” he said.Clean energy boom and pressureA relatively recent and unforeseen increase in demand for energy comes from data centers, which are being built by large technology companies for new offerings, such as artificial intelligence.“General energy demand was flat for 20 years — and now, boom,” said Sean James, Microsoft’s senior director of data center research. “It caught utilities flatfooted.” With the expansion of AI, the rush to provision data centers with upwards of 35 gigawatts of new (and mainly renewable) power in the near future, intensifies pressure on big companies to balance the concerns of stakeholders across multiple domains. Google is pursuing 24/7 carbon-free energy by 2030, said Devon Swezey, the company’s senior manager for global energy and climate.“We’re pursuing this by purchasing more and different types of clean energy locally, and accelerating technological innovation such as next-generation geothermal projects,” he said. Pedro Gómez Lopez, strategy and development director, Ferrovial Digital, which designs and constructs data centers, incorporates renewable energy into their projects, which contributes to decarbonization goals and benefits to locales where they are sited. “We can create a new supply of power, taking the heat generated by a data center to residences or industries in neighborhoods through District Heating initiatives,” he said.The Inflation Reduction Act and other legislation has ramped up employment opportunities in clean energy nationwide, touching every region, including those most tied to fossil fuels. “At the start of 2024 there were about 3.5 million clean energy jobs, with ‘red’ states showing the fastest growth in clean energy jobs,” said David S. Miller, managing partner at Clean Energy Ventures. “The majority (58 percent) of new jobs in energy are now in clean energy — that transition has happened. And one-in-16 new jobs nationwide were in clean energy, with clean energy jobs growing more than three times faster than job growth economy-wide”In this rapid expansion, the U.S. Department of Energy (DoE) is prioritizing economically marginalized places, according to Zoe Lipman, lead for good jobs and labor standards in the Office of Energy Jobs at the DoE. “The community benefit process is integrated into our funding,” she said. “We are creating the foundation of a virtuous circle,” encouraging benefits to flow to disadvantaged and energy communities, spurring workforce training partnerships, and promoting well-paid union jobs. “These policies incentivize proactive community and labor engagement, and deliver community benefits, both of which are key to building support for technological change.”Hydrogen opportunity and challengeWhile engagement with stakeholders helps clear the path for implementation of technology and the spread of infrastructure, there remain enormous policy, scientific, and engineering challenges to solve, said multiple conference participants. In a “fireside chat,” Prasanna V. Joshi, vice president of low-carbon-solutions technology at ExxonMobil, and Ernest J. Moniz, professor of physics and special advisor to the president at MIT, discussed efforts to replace natural gas and coal with zero-carbon hydrogen in order to reduce greenhouse gas emissions in such major industries as steel and fertilizer manufacturing.“We have gone into an era of industrial policy,” said Moniz, citing a new DoE program offering incentives to generate demand for hydrogen — more costly than conventional fossil fuels — in end-use applications. “We are going to have to transition from our current approach, which I would call carrots-and-twigs, to ultimately, carrots-and-sticks,” Moniz warned, in order to create “a self-sustaining, major, scalable, affordable hydrogen economy.”To achieve net zero emissions by 2050, ExxonMobil intends to use carbon capture and sequestration in natural gas-based hydrogen and ammonia production. Ammonia can also serve as a zero-carbon fuel. Industry is exploring burning ammonia directly in coal-fired power plants to extend the hydrogen value chain. But there are challenges. “How do you burn 100 percent ammonia?”, asked Joshi. “That’s one of the key technology breakthroughs that’s needed.” Joshi believes that collaboration with MIT’s “ecosystem of breakthrough innovation” will be essential to breaking logjams around the hydrogen and ammonia-based industries.MIT ingenuity essentialThe energy transition is placing very different demands on different regions around the world. Take India, where today per capita power consumption is one of the lowest. But Indians “are an aspirational people … and with increasing urbanization and industrial activity, the growth in power demand is expected to triple by 2050,” said Praveer Sinha, CEO and managing director of the Tata Power Co. Ltd., in his keynote speech. For that nation, which currently relies on coal, the move to clean energy means bringing another 300 gigawatts of zero-carbon capacity online in the next five years. Sinha sees this power coming from wind, solar, and hydro, supplemented by nuclear energy.“India plans to triple nuclear power generation capacity by 2032, and is focusing on advancing small modular reactors,” said Sinha. “The country also needs the rapid deployment of storage solutions to firm up the intermittent power.” The goal is to provide reliable electricity 24/7 to a population living both in large cities and in geographically remote villages, with the help of long-range transmission lines and local microgrids. “India’s energy transition will require innovative and affordable technology solutions, and there is no better place to go than MIT, where you have the best brains, startups, and technology,” he said.These assets were on full display at the conference. Among them a cluster of young businesses, including:the MIT spinout Form Energy, which has developed a 100-hour iron battery as a backstop to renewable energy sources in case of multi-day interruptions;startup Noya that aims for direct air capture of atmospheric CO2 using carbon-based materials;the firm Active Surfaces, with a lightweight material for putting solar photovoltaics in previously inaccessible places;Copernic Catalysts, with new chemistry for making ammonia and sustainable aviation fuel far more inexpensively than current processes; andSesame Sustainability, a software platform spun out of MITEI that gives industries a full financial analysis of the costs and benefits of decarbonization.The pipeline of research talent extended into the undergraduate ranks, with a conference “slam” competition showcasing students’ summer research projects in areas from carbon capture using enzymes to 3D design for the coils used in fusion energy confinement.“MIT students like me are looking to be the next generation of energy leaders, looking for careers where we can apply our engineering skills to tackle exciting climate problems and make a tangible impact,” said Trent Lee, a junior in mechanical engineering researching improvements in lithium-ion energy storage. “We are stoked by the energy transition, because it’s not just the future, but our chance to build it.” More

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      in Energy

      Nanoscale transistors could enable more efficient electronics

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      Silicon transistors, which are used to amplify and switch signals, are a critical component in most electronic devices, from smartphones to automobiles. But silicon semiconductor technology is held back by a fundamental physical limit that prevents transistors from operating below a certain voltage.This limit, known as “Boltzmann tyranny,” hinders the energy efficiency of computers and other electronics, especially with the rapid development of artificial intelligence technologies that demand faster computation.In an effort to overcome this fundamental limit of silicon, MIT researchers fabricated a different type of three-dimensional transistor using a unique set of ultrathin semiconductor materials.Their devices, featuring vertical nanowires only a few nanometers wide, can deliver performance comparable to state-of-the-art silicon transistors while operating efficiently at much lower voltages than conventional devices.“This is a technology with the potential to replace silicon, so you could use it with all the functions that silicon currently has, but with much better energy efficiency,” says Yanjie Shao, an MIT postdoc and lead author of a paper on the new transistors.The transistors leverage quantum mechanical properties to simultaneously achieve low-voltage operation and high performance within an area of just a few square nanometers. Their extremely small size would enable more of these 3D transistors to be packed onto a computer chip, resulting in fast, powerful electronics that are also more energy-efficient.“With conventional physics, there is only so far you can go. The work of Yanjie shows that we can do better than that, but we have to use different physics. There are many challenges yet to be overcome for this approach to be commercial in the future, but conceptually, it really is a breakthrough,” says senior author Jesús del Alamo, the Donner Professor of Engineering in the MIT Department of Electrical Engineering and Computer Science (EECS).They are joined on the paper by Ju Li, the Tokyo Electric Power Company Professor in Nuclear Engineering and professor of materials science and engineering at MIT; EECS graduate student Hao Tang; MIT postdoc Baoming Wang; and professors Marco Pala and David Esseni of the University of Udine in Italy. The research appears today in Nature Electronics.Surpassing siliconIn electronic devices, silicon transistors often operate as switches. Applying a voltage to the transistor causes electrons to move over an energy barrier from one side to the other, switching the transistor from “off” to “on.” By switching, transistors represent binary digits to perform computation.A transistor’s switching slope reflects the sharpness of the “off” to “on” transition. The steeper the slope, the less voltage is needed to turn on the transistor and the greater its energy efficiency.But because of how electrons move across an energy barrier, Boltzmann tyranny requires a certain minimum voltage to switch the transistor at room temperature.To overcome the physical limit of silicon, the MIT researchers used a different set of semiconductor materials — gallium antimonide and indium arsenide — and designed their devices to leverage a unique phenomenon in quantum mechanics called quantum tunneling.Quantum tunneling is the ability of electrons to penetrate barriers. The researchers fabricated tunneling transistors, which leverage this property to encourage electrons to push through the energy barrier rather than going over it.“Now, you can turn the device on and off very easily,” Shao says.But while tunneling transistors can enable sharp switching slopes, they typically operate with low current, which hampers the performance of an electronic device. Higher current is necessary to create powerful transistor switches for demanding applications.Fine-grained fabricationUsing tools at MIT.nano, MIT’s state-of-the-art facility for nanoscale research, the engineers were able to carefully control the 3D geometry of their transistors, creating vertical nanowire heterostructures with a diameter of only 6 nanometers. They believe these are the smallest 3D transistors reported to date.Such precise engineering enabled them to achieve a sharp switching slope and high current simultaneously. This is possible because of a phenomenon called quantum confinement.Quantum confinement occurs when an electron is confined to a space that is so small that it can’t move around. When this happens, the effective mass of the electron and the properties of the material change, enabling stronger tunneling of the electron through a barrier.Because the transistors are so small, the researchers can engineer a very strong quantum confinement effect while also fabricating an extremely thin barrier.“We have a lot of flexibility to design these material heterostructures so we can achieve a very thin tunneling barrier, which enables us to get very high current,” Shao says.Precisely fabricating devices that were small enough to accomplish this was a major challenge.“We are really into single-nanometer dimensions with this work. Very few groups in the world can make good transistors in that range. Yanjie is extraordinarily capable to craft such well-functioning transistors that are so extremely small,” says del Alamo.When the researchers tested their devices, the sharpness of the switching slope was below the fundamental limit that can be achieved with conventional silicon transistors. Their devices also performed about 20 times better than similar tunneling transistors.“This is the first time we have been able to achieve such sharp switching steepness with this design,” Shao adds.The researchers are now striving to enhance their fabrication methods to make transistors more uniform across an entire chip. With such small devices, even a 1-nanometer variance can change the behavior of the electrons and affect device operation. They are also exploring vertical fin-shaped structures, in addition to vertical nanowire transistors, which could potentially improve the uniformity of devices on a chip.“This work definitively steps in the right direction, significantly improving the broken-gap tunnel field effect transistor (TFET) performance. It demonstrates steep-slope together with a record drive-current. It highlights the importance of small dimensions, extreme confinement, and low-defectivity materials and interfaces in the fabricated broken-gap TFET. These features have been realized through a well-mastered and nanometer-size-controlled process,” says Aryan Afzalian, a principal member of the technical staff at the nanoelectronics research organization imec, who was not involved with this work.This research is funded, in part, by Intel Corporation. More

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      in Energy

      Proton-conducting materials could enable new green energy technologies

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      As the name suggests, most electronic devices today work through the movement of electrons. But materials that can efficiently conduct protons — the nucleus of the hydrogen atom — could be key to a number of important technologies for combating global climate change.Most proton-conducting inorganic materials available now require undesirably high temperatures to achieve sufficiently high conductivity. However, lower-temperature alternatives could enable a variety of technologies, such as more efficient and durable fuel cells to produce clean electricity from hydrogen, electrolyzers to make clean fuels such as hydrogen for transportation, solid-state proton batteries, and even new kinds of computing devices based on iono-electronic effects.In order to advance the development of proton conductors, MIT engineers have identified certain traits of materials that give rise to fast proton conduction. Using those traits quantitatively, the team identified a half-dozen new candidates that show promise as fast proton conductors. Simulations suggest these candidates will perform far better than existing materials, although they still need to be conformed experimentally. In addition to uncovering potential new materials, the research also provides a deeper understanding at the atomic level of how such materials work.The new findings are described in the journal Energy and Environmental Sciences, in a paper by MIT professors Bilge Yildiz and Ju Li, postdocs Pjotrs Zguns and Konstantin Klyukin, and their collaborator Sossina Haile and her students from Northwestern University. Yildiz is the Breene M. Kerr Professor in the departments of Nuclear Science and Engineering, and Materials Science and Engineering.“Proton conductors are needed in clean energy conversion applications such as fuel cells, where we use hydrogen to produce carbon dioxide-free electricity,” Yildiz explains. “We want to do this process efficiently, and therefore we need materials that can transport protons very fast through such devices.”Present methods of producing hydrogen, for example steam methane reforming, emit a great deal of carbon dioxide. “One way to eliminate that is to electrochemically produce hydrogen from water vapor, and that needs very good proton conductors,” Yildiz says. Production of other important industrial chemicals and potential fuels, such as ammonia, can also be carried out through efficient electrochemical systems that require good proton conductors.But most inorganic materials that conduct protons can only operate at temperatures of 200 to 600 degrees Celsius (roughly 450 to 1,100 Fahrenheit), or even higher. Such temperatures require energy to maintain and can cause degradation of materials. “Going to higher temperatures is not desirable because that makes the whole system more challenging, and the material durability becomes an issue,” Yildiz says. “There is no good inorganic proton conductor at room temperature.” Today, the only known room-temperature proton conductor is a polymeric material that is not practical for applications in computing devices because it can’t easily be scaled down to the nanometer regime, she says.To tackle the problem, the team first needed to develop a basic and quantitative understanding of exactly how proton conduction works, taking a class of inorganic proton conductors, called solid acids. “One has to first understand what governs proton conduction in these inorganic compounds,” she says. While looking at the materials’ atomic configurations, the researchers identified a pair of characteristics that directly relates to the materials’ proton-carrying potential.As Yildiz explains, proton conduction first involves a proton “hopping from a donor oxygen atom to an acceptor oxygen. And then the environment has to reorganize and take the accepted proton away, so that it can hop to another neighboring acceptor, enabling long-range proton diffusion.” This process happens in many inorganic solids, she says. Figuring out how that last part works — how the atomic lattice gets reorganized to take the accepted proton away from the original donor atom — was a key part of this research, she says.The researchers used computer simulations to study a class of materials called solid acids that become good proton conductors above 200 degrees Celsius. This class of materials has a substructure called the polyanion group sublattice, and these groups have to rotate and take the proton away from its original site so it can then transfer to other sites. The researchers were able to identify the phonons that contribute to the flexibility of this sublattice, which is essential for proton conduction. Then they used this information to comb through vast databases of theoretically and experimentally possible compounds, in search of better proton conducting materials.As a result, they found solid acid compounds that are promising proton conductors and that have been developed and produced for a variety of different applications but never before studied as proton conductors; these compounds turned out to have just the right characteristics of lattice flexibility. The team then carried out computer simulations of how the specific materials they identified in their initial screening would perform under relevant temperatures, to confirm their suitability as proton conductors for fuel cells or other uses. Sure enough, they found six promising materials, with predicted proton conduction speeds faster than the best existing solid acid proton conductors.“There are uncertainties in these simulations,” Yildiz cautions. “I don’t want to say exactly how much higher the conductivity will be, but these look very promising. Hopefully this motivates the experimental field to try to synthesize them in different forms and make use of these compounds as proton conductors.”Translating these theoretical findings into practical devices could take some years, she says. The likely first applications would be for electrochemical cells to produce fuels and chemical feedstocks such as hydrogen and ammonia, she says.The work was supported by the U.S. Department of Energy, the Wallenberg Foundation, and the U.S. National Science Foundation. More

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      in Energy

      AI method radically speeds predictions of materials’ thermal properties

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      It is estimated that about 70 percent of the energy generated worldwide ends up as waste heat.If scientists could better predict how heat moves through semiconductors and insulators, they could design more efficient power generation systems. However, the thermal properties of materials can be exceedingly difficult to model.The trouble comes from phonons, which are subatomic particles that carry heat. Some of a material’s thermal properties depend on a measurement called the phonon dispersion relation, which can be incredibly hard to obtain, let alone utilize in the design of a system.A team of researchers from MIT and elsewhere tackled this challenge by rethinking the problem from the ground up. The result of their work is a new machine-learning framework that can predict phonon dispersion relations up to 1,000 times faster than other AI-based techniques, with comparable or even better accuracy. Compared to more traditional, non-AI-based approaches, it could be 1 million times faster.This method could help engineers design energy generation systems that produce more power, more efficiently. It could also be used to develop more efficient microelectronics, since managing heat remains a major bottleneck to speeding up electronics.“Phonons are the culprit for the thermal loss, yet obtaining their properties is notoriously challenging, either computationally or experimentally,” says Mingda Li, associate professor of nuclear science and engineering and senior author of a paper on this technique.Li is joined on the paper by co-lead authors Ryotaro Okabe, a chemistry graduate student; and Abhijatmedhi Chotrattanapituk, an electrical engineering and computer science graduate student; Tommi Jaakkola, the Thomas Siebel Professor of Electrical Engineering and Computer Science at MIT; as well as others at MIT, Argonne National Laboratory, Harvard University, the University of South Carolina, Emory University, the University of California at Santa Barbara, and Oak Ridge National Laboratory. The research appears in Nature Computational Science.Predicting phononsHeat-carrying phonons are tricky to predict because they have an extremely wide frequency range, and the particles interact and travel at different speeds.A material’s phonon dispersion relation is the relationship between energy and momentum of phonons in its crystal structure. For years, researchers have tried to predict phonon dispersion relations using machine learning, but there are so many high-precision calculations involved that models get bogged down.“If you have 100 CPUs and a few weeks, you could probably calculate the phonon dispersion relation for one material. The whole community really wants a more efficient way to do this,” says Okabe.The machine-learning models scientists often use for these calculations are known as graph neural networks (GNN). A GNN converts a material’s atomic structure into a crystal graph comprising multiple nodes, which represent atoms, connected by edges, which represent the interatomic bonding between atoms.While GNNs work well for calculating many quantities, like magnetization or electrical polarization, they are not flexible enough to efficiently predict an extremely high-dimensional quantity like the phonon dispersion relation. Because phonons can travel around atoms on X, Y, and Z axes, their momentum space is hard to model with a fixed graph structure.To gain the flexibility they needed, Li and his collaborators devised virtual nodes.They create what they call a virtual node graph neural network (VGNN) by adding a series of flexible virtual nodes to the fixed crystal structure to represent phonons. The virtual nodes enable the output of the neural network to vary in size, so it is not restricted by the fixed crystal structure.Virtual nodes are connected to the graph in such a way that they can only receive messages from real nodes. While virtual nodes will be updated as the model updates real nodes during computation, they do not affect the accuracy of the model.“The way we do this is very efficient in coding. You just generate a few more nodes in your GNN. The physical location doesn’t matter, and the real nodes don’t even know the virtual nodes are there,” says Chotrattanapituk.Cutting out complexitySince it has virtual nodes to represent phonons, the VGNN can skip many complex calculations when estimating phonon dispersion relations, which makes the method more efficient than a standard GNN. The researchers proposed three different versions of VGNNs with increasing complexity. Each can be used to predict phonons directly from a material’s atomic coordinates.Because their approach has the flexibility to rapidly model high-dimensional properties, they can use it to estimate phonon dispersion relations in alloy systems. These complex combinations of metals and nonmetals are especially challenging for traditional approaches to model.The researchers also found that VGNNs offered slightly greater accuracy when predicting a material’s heat capacity. In some instances, prediction errors were two orders of magnitude lower with their technique.A VGNN could be used to calculate phonon dispersion relations for a few thousand materials in just a few seconds with a personal computer, Li says.This efficiency could enable scientists to search a larger space when seeking materials with certain thermal properties, such as superior thermal storage, energy conversion, or superconductivity.Moreover, the virtual node technique is not exclusive to phonons, and could also be used to predict challenging optical and magnetic properties.In the future, the researchers want to refine the technique so virtual nodes have greater sensitivity to capture small changes that can affect phonon structure.“Researchers got too comfortable using graph nodes to represent atoms, but we can rethink that. Graph nodes can be anything. And virtual nodes are a very generic approach you could use to predict a lot of high-dimensional quantities,” Li says.“The authors’ innovative approach significantly augments the graph neural network description of solids by incorporating key physics-informed elements through virtual nodes, for instance, informing wave-vector dependent band-structures and dynamical matrices,” says Olivier Delaire, associate professor in the Thomas Lord Department of Mechanical Engineering and Materials Science at Duke University, who was not involved with this work. “I find that the level of acceleration in predicting complex phonon properties is amazing, several orders of magnitude faster than a state-of-the-art universal machine-learning interatomic potential. Impressively, the advanced neural net captures fine features and obeys physical rules. There is great potential to expand the model to describe other important material properties: Electronic, optical, and magnetic spectra and band structures come to mind.”This work is supported by the U.S. Department of Energy, National Science Foundation, a Mathworks Fellowship, a Sow-Hsin Chen Fellowship, the Harvard Quantum Initiative, and the Oak Ridge National Laboratory. More

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      in Environment

      “They can see themselves shaping the world they live in”

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      During the journey from the suburbs to the city, the tree canopy often dwindles down as skyscrapers rise up. A group of New England Innovation Academy students wondered why that is.“Our friend Victoria noticed that where we live in Marlborough there are lots of trees in our own backyards. But if you drive just 30 minutes to Boston, there are almost no trees,” said high school junior Ileana Fournier. “We were struck by that duality.”This inspired Fournier and her classmates Victoria Leeth and Jessie Magenyi to prototype a mobile app that illustrates Massachusetts deforestation trends for Day of AI, a free, hands-on curriculum developed by the MIT Responsible AI for Social Empowerment and Education (RAISE) initiative, headquartered in the MIT Media Lab and in collaboration with the MIT Schwarzman College of Computing and MIT Open Learning. They were among a group of 20 students from New England Innovation Academy who shared their projects during the 2024 Day of AI global celebration hosted with the Museum of Science.The Day of AI curriculum introduces K-12 students to artificial intelligence. Now in its third year, Day of AI enables students to improve their communities and collaborate on larger global challenges using AI. Fournier, Leeth, and Magenyi’s TreeSavers app falls under the Telling Climate Stories with Data module, one of four new climate-change-focused lessons.“We want you to be able to express yourselves creatively to use AI to solve problems with critical-thinking skills,” Cynthia Breazeal, director of MIT RAISE, dean for digital learning at MIT Open Learning, and professor of media arts and sciences, said during this year’s Day of AI global celebration at the Museum of Science. “We want you to have an ethical and responsible way to think about this really powerful, cool, and exciting technology.”Moving from understanding to actionDay of AI invites students to examine the intersection of AI and various disciplines, such as history, civics, computer science, math, and climate change. With the curriculum available year-round, more than 10,000 educators across 114 countries have brought Day of AI activities to their classrooms and homes.The curriculum gives students the agency to evaluate local issues and invent meaningful solutions. “We’re thinking about how to create tools that will allow kids to have direct access to data and have a personal connection that intersects with their lived experiences,” Robert Parks, curriculum developer at MIT RAISE, said at the Day of AI global celebration.Before this year, first-year Jeremie Kwapong said he knew very little about AI. “I was very intrigued,” he said. “I started to experiment with ChatGPT to see how it reacts. How close can I get this to human emotion? What is AI’s knowledge compared to a human’s knowledge?”In addition to helping students spark an interest in AI literacy, teachers around the world have told MIT RAISE that they want to use data science lessons to engage students in conversations about climate change. Therefore, Day of AI’s new hands-on projects use weather and climate change to show students why it’s important to develop a critical understanding of dataset design and collection when observing the world around them.“There is a lag between cause and effect in everyday lives,” said Parks. “Our goal is to demystify that, and allow kids to access data so they can see a long view of things.”Tools like MIT App Inventor — which allows anyone to create a mobile application — help students make sense of what they can learn from data. Fournier, Leeth, and Magenyi programmed TreeSavers in App Inventor to chart regional deforestation rates across Massachusetts, identify ongoing trends through statistical models, and predict environmental impact. The students put that “long view” of climate change into practice when developing TreeSavers’ interactive maps. Users can toggle between Massachusetts’s current tree cover, historical data, and future high-risk areas.Although AI provides fast answers, it doesn’t necessarily offer equitable solutions, said David Sittenfeld, director of the Center for the Environment at the Museum of Science. The Day of AI curriculum asks students to make decisions on sourcing data, ensuring unbiased data, and thinking responsibly about how findings could be used.“There’s an ethical concern about tracking people’s data,” said Ethan Jorda, a New England Innovation Academy student. His group used open-source data to program an app that helps users track and reduce their carbon footprint.Christine Cunningham, senior vice president of STEM Learning at the Museum of Science, believes students are prepared to use AI responsibly to make the world a better place. “They can see themselves shaping the world they live in,” said Cunningham. “Moving through from understanding to action, kids will never look at a bridge or a piece of plastic lying on the ground in the same way again.”Deepening collaboration on earth and beyondThe 2024 Day of AI speakers emphasized collaborative problem solving at the local, national, and global levels.“Through different ideas and different perspectives, we’re going to get better solutions,” said Cunningham. “How do we start young enough that every child has a chance to both understand the world around them but also to move toward shaping the future?”Presenters from MIT, the Museum of Science, and NASA approached this question with a common goal — expanding STEM education to learners of all ages and backgrounds.“We have been delighted to collaborate with the MIT RAISE team to bring this year’s Day of AI celebration to the Museum of Science,” says Meg Rosenburg, manager of operations at the Museum of Science Centers for Public Science Learning. “This opportunity to highlight the new climate modules for the curriculum not only perfectly aligns with the museum’s goals to focus on climate and active hope throughout our Year of the Earthshot initiative, but it has also allowed us to bring our teams together and grow a relationship that we are very excited to build upon in the future.”Rachel Connolly, systems integration and analysis lead for NASA’s Science Activation Program, showed the power of collaboration with the example of how human comprehension of Saturn’s appearance has evolved. From Galileo’s early telescope to the Cassini space probe, modern imaging of Saturn represents 400 years of science, technology, and math working together to further knowledge.“Technologies, and the engineers who built them, advance the questions we’re able to ask and therefore what we’re able to understand,” said Connolly, research scientist at MIT Media Lab.New England Innovation Academy students saw an opportunity for collaboration a little closer to home. Emmett Buck-Thompson, Jeff Cheng, and Max Hunt envisioned a social media app to connect volunteers with local charities. Their project was inspired by Buck-Thompson’s father’s difficulties finding volunteering opportunities, Hunt’s role as the president of the school’s Community Impact Club, and Cheng’s aspiration to reduce screen time for social media users. Using MIT App Inventor, ​their combined ideas led to a prototype with the potential to make a real-world impact in their community.The Day of AI curriculum teaches the mechanics of AI, ethical considerations and responsible uses, and interdisciplinary applications for different fields. It also empowers students to become creative problem solvers and engaged citizens in their communities and online. From supporting volunteer efforts to encouraging action for the state’s forests to tackling the global challenge of climate change, today’s students are becoming tomorrow’s leaders with Day of AI.“We want to empower you to know that this is a tool you can use to make your community better, to help people around you with this technology,” said Breazeal.Other Day of AI speakers included Tim Ritchie, president of the Museum of Science; Michael Lawrence Evans, program director of the Boston Mayor’s Office of New Urban Mechanics; Dava Newman, director of the MIT Media Lab; and Natalie Lao, executive director of the App Inventor Foundation. More