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You are here: Home / Awards and Prizes and Challenges / AI for Climate and
Nature Grand Challenge


AI FOR CLIMATE AND NATURE GRAND CHALLENGE

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Deadline: 30-Jul-24

The Bezos Earth Fund is exploring new ideas for multiplying the impact of
climate and nature efforts using modern AI.

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The AI for Climate and Nature Grand Challenge is a global $100 million
initiative from the Bezos Earth Fund. The first round of awards will focus on
sustainable proteins, power grid optimization, and biodiversity conservation, in
addition to embracing visionary wildcard solutions for climate and nature.

FOCUS AREAS

 * Sustainable proteins
   * While the current generation of sustainable proteins may fall short of
     convincing consumers of their ability to deliver a product equal or better
     to animal-sourced meat, they have only touched the surface of the
     possibilities in terms of optimizing ingredients and processing. New
     approaches to discovering and unlocking the myriad of ingredients and
     processing methods that deliver a product that best mimics the complexity
     of meat will bring about a new generation of sustainable protein products
     that can meet expectations of consumers. Some of the opportunity areas for
     using AI-based approaches to enhance the quality and cost of sustainable
     proteins include:
     * End-product formulation: The complexity of food ingredients like “protein
       isolates,” which contain thousands of molecules, make it difficult to
       have predictive capacity to determine which formulation adjustments will
       deliver the desired effects. Instead of a scientist doing this manually,
       with so many combinations of variables, AI can help them try and fail
       many times, quickly.
       * For example, scientists have recently used AI to find an isolated
         protein in mung beans that has similar properties to scrambled eggs.
         Without AI, it would have taken years to identify this solution.
     * Protein engineering: The production of proteins themselves is central to
       the shift to sustainable proteins. Proteins have evolved over billions of
       years and are comprised of chains of amino acids that fold in complex
       three-dimensional shapes. AI can be used to design such patterns by
       modifying genetic sequences to optimize for certain properties. AI allows
       protein formulation to move from an iterative art to more of a predictive
       science.
     * Strain/cell line development: Regardless of the organism being used to
       develop a sustainable protein product (plant crops for plant-based meat,
       microbial strains for fermentation-derived proteins, or animal cell lines
       for cultivated meat), AI can improve the understanding of what
       modifications to these organisms would facilitate greater growth
       efficiency or bias them toward desirable traits.
     * Feedstock optimization and cost reduction: For fermentation and
       cultivated meat, feedstocks for cell growth are a major contributor to
       unit economics and are highly influential in the overall sustainability
       of the production process. Optimizing feedstocks and culture media
       formulations is a highly complex process that requires multi-variate
       analysis and nuanced trade-offs between performance, cost, availability,
       and many other factors. Next-generation growth media can use AI to test
       thousands of novel combinations of ingredients and rapidly adapt
       feedstock formulations to changes in feedstock cost/availability.
     * Process development and efficiency improvement: Digital twin technologies
       can facilitate rapid process development improvements for greater
       efficiency using manufacturing process simulations. Biosensors and
       process monitoring technologies coupled with AI can inform process tweaks
       for subsequent runs without the cost- and time-intensive need to test
       multiple parameter tweaks in an actual pilot- or demo-scale run.
 * Power grid optimization
   * AI emerges as a potential game-changer in addressing some of the challenges
     faced by electricity grids globally. By optimizing grid operations,
     anticipating and mitigating disruptions, and supporting the seamless
     integration of variable renewable energy (VRE) into grid operations, AI can
     accelerate the clean energy transition while enhancing grid resilience for
     communities. AI-driven solutions have the potential to optimize grid
     operations, saving an estimated $300 billion in efficiency gains within
     this decisive decade alone, according to one study.
   * Recent advancements in AI technologies, such as predictive modeling,
     real-time data analysis, and intelligent automation, offer unique
     opportunities to transform the grid system. These advancements can be
     leveraged to optimize transmission siting, enhance power flow, improve
     renewable energy forecasting, and efficiently coordinate distributed energy
     resources such as rooftop solar installations on homes. By harnessing the
     power of AI, they can create a more efficient, sustainable, and
     cost-effective grid that seamlessly integrates renewable energy sources, as
     many countries are striving to decrease the reliance of their grids on
     fossil fuels.
   * Opportunity areas include:
     * Grid optimization: How might AI optimize power flows, reduce congestion
       and enable seamless integration of renewable energy sources to create a
       more efficient, sustainable and cost-effective grid? By leveraging
       predictive models, AI can provide a solution to optimize transmission
       siting, enhance power flow and dynamically increase transmission
       capacity. These optimizations can lead to significant cost savings and
       facilitate the accelerated integration of variable renewable energy
       sources, such as solar and wind, into the grid. Specific use cases of AI
       applications in grid optimization include predictive maintenance,
       renewable energy forecasting, and dynamic grid control.
     * Energy management: How might AI enable intelligent demand response,
       improve renewable energy forecasting, and efficiently coordinate
       distributed energy resources to balance supply and demand in real time?
       AI can analyze vast amounts of data from smart meters, weather forecasts,
       and consumer behavior to predict energy demand accurately. This enables
       the grid to intelligently shift loads to match renewable energy supply,
       optimize energy storage and coordinate distributed energy resources, such
       as electric vehicles and battery storage, to maintain a stable and
       efficient grid.
     * Resilience and inclusivity: How might AI enhance grid resilience, predict
       and mitigate outages, and ensure reliable, affordable, and clean
       electricity access for all, particularly in the face of increasing
       climate change-related disruptions? AI can analyze historical data,
       weather patterns, and grid performance to predict potential outages and
       better inform future preventive maintenance. By automating fault
       detection, isolation, and service restoration, AI can significantly
       reduce the duration and impact of outages. Moreover, AI can help identify
       and prioritize underserved communities, ensuring that the benefits of a
       modernized grid are distributed equitably and that no one is left behind
       in the transition to a clean energy future.
 * Biodiversity conservation
   * AI applications offer a range of tools and technologies that significantly
     contribute to monitoring, analyzing, protecting, and restoring diverse
     ecosystems. One key application is the use of AI in data analysis, where
     machine learning algorithms can process vast datasets, identify patterns,
     and predict changes in biodiversity in space and time that can support
     conservation efforts. This aids researchers in monitoring species
     populations and trends, tracking migration patterns, and assessing
     ecosystem health. It can additionally help the conservation community
     identify the highest conservation priority areas, informing the design of
     wildlife corridors and protected areas. Furthermore, machine learning
     algorithms can assist in identifying endangered species, combating illegal
     poaching, and managing invasive species. Integrating AI into conservation
     strategies enhances the precision and speed of decision-making, ultimately
     contributing to the preservation of biodiversity in the face of numerous
     environmental challenges. It’s also worth noting that the abovementioned
     applications need not apply only to wild species. They can also be used on
     domesticated species, most notably cattle. Computer vision and facial
     recognition platforms would avoid the need for physical tagging, and AI can
     also leverage satellite imaging and machine learning algorithms to trace
     cattle movement across large tracts of land.
   * Some of the potential tools and uses that could be used with AI for
     biodiversity conservation include:
     * Endangered species monitoring: Important species that are targets for
       conservation can often be elusive, hard to track, and unknown in terms of
       behavior and population numbers. Camera traps have opened a new frontier
       by capturing images of elusive species in different ecosystems, and today
       there are millions of images that can be used to identify and monitor
       biodiversity, but they don’t have the capacity to analyze them.
     * Discovery of hidden biodiversity: The vast majority of species that they
       conserve are neither seen nor heard and come in the form of microbes and
       fungal communities. Environmental DNA is a tool that is transforming the
       ability to do conservation science. One notable example is the use of AI
       in eDNA metabarcoding analysis. Machine learning algorithms can process
       large volumes of genetic data extracted from environmental samples,
       identifying and cataloging species present in ecosystems more rapidly and
       accurately than traditional methods. This accelerates biodiversity
       assessments, aiding in the monitoring of endangered or elusive species.
       Additionally, AI can contribute to the development of predictive models
       that analyze eDNA data alongside environmental variables, helping
       researchers understand how different factors impact biodiversity.
     * Bioacoustics tools: These tools allow them to build soundscapes,
       comprised of the diverse sounds produced by ecosystems, that offer
       valuable insights into the health and dynamics of natural environments.
       This type of tracing can aid conservation and rewilding efforts by
       furthering understanding of population numbers, preferred habitats, and
       human interactions. AI algorithms, particularly those designed for audio
       analysis, can efficiently process large volumes of acoustic data,
       distinguishing and identifying individual species’ vocalizations. This
       capability enables researchers to monitor biodiversity, assess population
       dynamics, and detect changes in ecosystems over time. AI-driven acoustic
       monitoring systems facilitate real-time data collection, aiding in the
       early identification of disturbances or threats to wildlife.
       Additionally, AI can contribute to the creation of responsive
       soundscapes, mimicking natural conditions to attract and support specific
       species. By harnessing AI in the analysis and management of soundscapes,
       conservationists can better protect and restore nature.
 * Wildcard solutions
   * Climate change and nature loss are complicated, urgent issues — and they
     want to understand how modern AI can help identify and accelerate
     solutions. Do you have an idea for driving impact within climate change
     mitigation or nature protection using AI? If you and your team have a
     transformative idea that doesn’t fit within other first-round focus areas,
     they encourage you to submit it in the wildcard category.

FUNDING INFORMATION

 * The Grand Challenge will offer up to $100 million in total funding over three
   rounds.
 * Each round will include the following two phases: In Phase 1, up to 30 Seed
   Grantees will receive $50,000 each and an invitation to join the Phase 2
   Innovation Sprint. At the end of Phase 2, up to 15 Implementation Grantees
   will receive up to $2 million each.

HOW THE GRAND CHALLENGE WILL UNFOLD?

 * The Grand Challenge will unfold across three rounds and offer up to $100
   million in total funding. The first round will include the following two
   phases.
   * Phase 1: Proposals
     * The first phase will award up to 30 seed grants for promising ideas that
       multiply impact using modern AI. Phase 1 is open to all eligible
       applicants.
     * During the first phase:
       * Potential applicants will have access to a virtual information session,
         webinars introducing AI and discussing the focus areas, and a
         collection of curated resources as they develop their proposals.
       * A submission should articulate the proposed solution and the problem it
         addresses, the near-term and long-term impacts the solution will have,
         the potential for scaling the solution, the resources needed for
         development, and the approach to responsible development of the
         solution.
       * Phase 1 submissions are due by 5:59 p.m. Eastern Time (9:59 p.m. UTC)
         on Tuesday, July 30. Applicants must accept the Grand Challenge
         Agreement to submit a proposal.
       * An expert review panel and multidisciplinary judging panel will
         evaluate submissions according to Phase 1 selection criteria. Based on
         their evaluation, judges will recommend up to 30 awardees.
       * The Bezos Earth Fund will determine the final slate of Seed Grantees.
         After completing a Bezos Earth Fund grant agreement, each Seed Grantee
         will receive $50,000 and an exclusive invitation to participate in
         Phase 2.
   * Phase 2: Implementation plans
     * Seed Grantees will enter a virtual Innovation Sprint; these grantees will
       receive targeted support and resources from AI experts as they develop
       detailed implementation plans. Phase 2 is open exclusively to Seed
       Grantees.
     * The virtual Innovation Sprint will include:
       * Teaming support. Seed Grantees will build partnerships to enable
         development and implementation of their proposals.
       * Expert mentorship. Expert mentors will guide Seed Grantees as they
         refine their proposals and prepare for implementation.
       * Webinars. Seed Grantees will join exclusive webinars to hear from
         influential experts at the forefront of climate, nature, and AI.
       * Additional resources. Seed Grantees may receive access to additional
         resources, which may include computing infrastructure or access to
         relevant datasets.
     * Seed Grantees will submit their implementation plans and present them to
       the judging panel. Judges will evaluate submissions according to the
       Phase 2 selection criteria and recommend up to 15 awardees. The Bezos
       Earth Fund will select the final slate of Implementation Grantees. Phase
       2 will culminate with the announcement of up to 15 Implementation
       Grantees; each team will each receive up to $2 million to close the gap
       between concept and viability.
   * Post-award implementation
     * Following Phase 2, Implementation Grantees will bring their solutions to
       life over the course of two years; during this time, they will test,
       iterate, and report on the effectiveness of their approaches. Awardees
       will also convene in person at the AI for Climate and Nature Summit in
       2025.
     * The Bezos Earth Fund will announce more information on future rounds of
       the Grand Challenge at a later date. Sign up for the newsletter for
       announcements.

ELIGIBILITY CRITERIA

 * The first round of the Grand Challenge invites grant proposals from eligible
   organizations: U.S.-based 501(c)(3) entities and global academic
   institutions. In keeping with the Bezos Earth Fund’s commitment to equity and
   access, eligible applicants may collaborate with organizations all over the
   world to develop their proposals. Proposals from non-affiliated individuals
   are not eligible.
 * To establish and maintain eligibility for participating in the Challenge,
   each Lead Entity and Contributing Entity represents, warrants, agrees and
   covenants as follows:
   * It is a legal entity organized under the laws of a state, country or other
     governmental body that is not subject to U.S. sanctions as specified on the
     U.S. Department of Treasury website.
   * It satisfies all Challenge requirements specified on the website (the
     “Challenge Website”), the terms of which are incorporated herein by
     reference as though fully set forth, and this Agreement.
   * All applications, whether submitted by a single entity or group of
     entities, must designate a single entity as the “Lead Entity,” that, if the
     grant application is selected, will be the Earth Fund’s grantee. The Lead
     Entity is responsible for fulfilling the obligations designated to such
     Lead Entity under this Agreement and the Challenge Website, including
     submitting the grant application, submitting this Agreement signed by each
     Contributing Entity, signing the grant agreement, receiving the grant
     award, and additional obligations identified from time to time by the Earth
     Fund in support of the administration and operation of the Challenge. For
     applications submitted by a single entity, that entity shall be the Lead
     Entity for that application.  A Lead Entity, whether applying alone or
     jointly, must be either (1) a domestic U.S. organization that is exempt
     from taxation under Section 501(c)(3) of the U.S. Internal Revenue Code, or
     (2) an academic institution from anywhere in the world.
   * The Lead Entity must designate an individual point of contact who is
     affiliated with and authorized to act on behalf of the Lead Entity as the
     “Team Lead.” The Team Lead must be the older of: 18 years of age or the age
     of majority in the state or country where the individual resides. Team
     Leads will be responsible for all Challenge-related communications with the
     Administrators or Bezos Earth Fund. The Team Lead may not be a citizen of
     or reside in a country that is subject to U.S. sanctions as specified on
     the U.S. Department of Treasury website.  Any notices by the Earth Fund or
     either Administrator shall be deemed delivered once sent via email to the
     Team Lead using the email address provided in the grant application.
   * Each Contributing Entity agrees that the Lead Entity and Team Lead may
     undertake all tasks required to advance the grant application and in
     connection with this Agreement and all Challenge-related agreements and
     other obligations.  For the avoidance of doubt, any Contributing Entity
     acknowledges that the grants provided pursuant to this Challenge will be
     paid solely to entities that are academic institutions or U.S. entities
     that are exempt from taxation under Section 501(c)(3) of the U.S. Internal
     Revenue Code.
   * No Grant Applicant may be owned, managed, or controlled by employees,
     officers, directors or board members of the Earth Fund or either
     Administrator.
   * Phase 2 participation is restricted to Phase 1 awardees. To be eligible for
     Phase 2 grant awards, Phase 1 awardees must have complied with all
     requirements to participate in relevant activities for Phase 2 as described
     on the Challenge Website.
   * Any agreement signed, or obligation undertaken regarding a Lead Entity or
     Contributing Entity’s participation in the Challenge that conflicts with
     this Agreement may make the Grant Applicant ineligible.

For more information, visit Bezos Earth Fund.

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