Today's interview is with Michael Jacobs, Sustainability & Social Innovation Leader, IBM.
What is IBM's Sustainability Accelerator?
Can You Give A Broad Overview Of The IBM Sustainability Accelerator?
Michael Jacobs - The IBM Sustainability Accelerator is a social innovation program that supports communities facing environmental or economic stress around the world, through technologies like AI and an ecosystem of experts. Each year, the program selects five new projects to develop and scale technology and AI solutions addressing topics like sustainable agriculture, clean energy, water management, or resilient cities. We also just launched a new request for proposals (RFP) for nonprofit and government organizations to form the next cohort of IBM Sustainability Accelerator projects. The new cohort topic will focus on AI solutions for more sustainable consumption and production, advancing progress against United Nations SDGs 9 or 12.
Unique Positioning
What Makes IBM Uniquely Positioned To Address Climate Adaptation Challenges At Scale?
Michael Jacobs - As a technology company, IBM is uniquely positioned to co-design software and hardware optimized for AI workloads through collaboration of our Research, Software, and Infrastructure organizations and Red Hat, and with the broader open source community. In particular, Gen AI has the power to combine operational and environmental data to predict and mitigate disruptions to business operations and beyond.
Artificial Intelligence
How Is IBM’s AI Reshaping The Way We Understand And Respond To Climate Risks?
Michael Jacobs - Gen AI has the power to combine operational and environmental data to predict and mitigate disruptions to business operations and beyond. For example, we can use data from energy grids, weather patterns, and usage trends to predict and adjust energy distribution in real time. This helps organizations become more efficient while also boosting the bottom line with cost savings. This is applicable for any organization — big or small companies, nonprofit organizations, and of course, governments. For example, IBM and the United Nations Development Programme just launched an interactive model that uses AI to forecast energy access through 2030. The model is available for 102 countries across Africa, Latin America, Asia Pacific and the Middle East, and it will help governments to make data-driven decisions and enable users to analyze complex energy issues.
Collaboration & Innovation
IBM Has Been A Driving Force In The Open-Source Community, Helping To Foster Partnerships Across Industries. Can You Elaborate On How IBM Acts As A Catalyst For Collaboration And Innovation In The Open-Source Space?
Michael Jacobs - One year ago, IBM, Meta and more than 50 leading organizations in AI announced the creation of the AI Alliance, a global collaboration committed to building, enabling, and advocating for open innovation in AI across the technology and societal landscape to ensure that AI technology is accessible, trusted, and beneficial to everyone.
In one AI Alliance affiliated project, IBM Research and Sustainable Energy for All (SEforALL) collaborated through the IBM Sustainability Accelerator to conceptualize and design an AI solution to predict where cities will grow, called the Modeling Urban Growth (MUG) AI model. The MUG model is trained on, and validates, historical data from satellite images; geographic data, such as slope and elevation; demographic data; and structural data, such as road layout, combining the data into a time series. MUG helps users to map future urbanization and associated infrastructure needs, enabling decision makers to prioritize communities and developing regions that need support for issues like electrification and energy services.
MUG is publicly available and open-sourced on GitHub. The code supports model training on data available for different countries.
The model is designed to be re-trained by users for any country in the world, using publicly accessible data. On GitHub, MUG includes an explanatory guidebook on running the code and making predictions using the same or different datasets, which further expands access to developers and decision-makers.
More broadly, with our commitment to open source, we also foster efficiency in providing a variety of foundation models in our watsonx platform on top of which others can build and fine-tune for their specific applications.
Success Stories
IBM Is Empowering Farmers Worldwide With Its Cloud And AI Expertise, Helping Them Adapt To The Challenges Of A Changing Climate. Could You Share Specific Examples Or Success Stories Highlighting How These Technologies Transform Agriculture?
Michael Jacobs – The Accelerator’s sustainable agriculture cohort is focused on making agriculture more sustainable ultimately increasing productivity and income, consumer awareness and the development of more sustainable markets.
For example, in December 2024, IBM and Texas A&M AgriLife launched an interactive Gen AI virtual assistant, called Soil & Water Assessment Tool (“SWAT”) VEXA, that provides soil and water information in a more usable format to support technical agriculture and natural resource decisions. The team used IBM Research’s Deep Search AI technology to process, curate and structure data, including water condition simulations, predictions of environmental impacts on land, and large volumes of geological and hydrological data. All this data comes from an existing model developed by Texas A&M AgriLife Research and the US Department of Agriculture (USDA). After this, IBM used watsonx.ai and IBM Granite (ibm/granite-13b-chat-v2) to develop a virtual assistant that provides the user with highly informed, accurate, and personalized responses to questions about agriculture, land and water management.
Interacting with the assistant supports policymakers and officials in making data-driven decisions about the land and water resources they manage, or to help them provide recommendations and advice to farmers and communities. Typically, this is time-consuming research that now is automated to provide specific and relevant information. Instead, VEXA enables leaders, experts and the general public to retrieve precise agricultural and natural resource insights from huge volumes of data much more efficiently and faster.
Over 65,000 people have directly benefitted from the Accelerator’s four completed sustainable agriculture projects.
Working With NASA
IBM Has Also Been Involved In Numerous Groundbreaking Initiatives Outside The Sustainability Accelerator. For Instance, Your Recent Partnership With NASA On The AI Model Prithvi WxC Is Fascinating. What Led To This Collaboration, And What Potential Impact Do You See This Project Having?
Michael Jacobs – AI can be a powerful tool to help us solve some of the social innovation challenges we face today. An exciting example of that is our collaboration with NASA to use IBM’s AI geospatial foundation model to discover new insights in NASA’s massive trove of Earth and geospatial science data, including satellite imaging data. This effort can help identify changes in the geographic footprint of phenomena such as natural disasters, cyclical crop yields and wildlife habitats, helping us better understand our planet’s environmental systems and potentially improving weather and climate predictions.
In the Accelerator, where applicable, IBM Research will advance innovative solutions with strategic expertise that could include data science, modelling, and visualization skills that contribute to the broader research community through white papers, patents, and conferences.
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Into The Future
What Message Would You Give Our Readers About The Most Important Things To Focus On In The Months Leading Up To 2030? What Are The Things That Should Be A Significant Focus For People And Decision-Makers In This Space?
Michael Jacobs - As we approach 2030, the transformative power of artificial intelligence demands a strategic and proactive approach from individuals and organizations alike. Social innovation will increasingly be impact by the capacity to leverage AI as a collaborative tool. Organizations and individuals must prioritize AI literacy, view technological adaptation as a continuous journey, and create ecosystems that democratize AI knowledge. The most successful will be those who can skillfully integrate AI technologies, develop adaptive workforce capabilities, and use these tools to solve complex challenges while maintaining the irreplaceable value of human insight and innovation.
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