Niraj Swami, The Nature Conservancy - AI + Nature - H2O World 2019 NYC
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This session was recorded in NYC on October 22nd, 2019 and can be viewed here: https://youtu.be/_YUo5dGZqEM Conservation & AI Bio: Niraj Swami is an avid technologist & innovator with a passion for building solutions at the intersection of Artificial Intelligence, human-centered design and behavioral economics. Niraj has led AI and innovation strategy for enterprises, non-profits and startups in the human capital, healthcare and conservation industries. He enjoys exploring the AI-Human relationship with ventures that weigh on purpose, fairness and balance. Niraj is the founder of SCAD AI, a boutique purpose-tech venture firm, and a Senior Advisor for Applied AI & Innovation Ventures at The Nature Conservancy, a global non-profit solving the planet’s greatest conservation problems. Niraj holds an Honors MBA from the University of Chicago Booth School of Business and a Summa Cum Laude Software Engineering degree from Marquette University. When not tinkering with ideas, he enjoys writing music and traveling in search of orcas in the wild.
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Niraj Swami, The Nature Conservancy - AI + Nature - H2O World 2019 NYC
- 1. Technology & Conservation Niraj Swami Global Science Senior Advisor, Applied AI & Innovation Ventures
- 2. Niraj Swami | @nswami Global Science Senior Advisor, Applied AI & Innovation Ventures Technology & Conservation THE NATURE CONSERVANCY2
- 3. © Kyle Obermann AT THE NATURE CONSERVANCY we apply sound science to solve big problems.
- 6. What if we weaved in science, insights & intelligence into the daily fabric of scientists, conservationists, decision-makers and even citizens? Emerging technologies and tools like Artificial Intelligence, Human-centered Design & Advanced Data Science techniques can accelerate how our work is measured & democratized. What if we tech-enable our “how”? Photo by Casey Horner on Unsplash THE NATURE CONSERVANCY 6
- 7. HALFTHEWORLD’SCITIESAND 75% OF FARMS EXPERIENCE RECURRINGWATER SHORTAGES AND DATA ISN’T BEING USEDTO FIX
- 8. © Kenny Braun AI for Fire Management
- 9. Machine Learning to Measure Conservation Impact © Mark Godfrey
- 10. • Going beyond satellite imagery: Capturing new dimensions of ground truth with audio sensors! • Monitoring & evaluation using drone footage & artificial intelligence! • And many more in our pipeline... More Initiatives Photo by Erik Odiin on Unsplash THE NATURE CONSERVANCY 10
- 11. THE NATURE CONSERVANCY 11 1 IMPACT ENGINE2DATA EXCHANGE 2 TECH-ENABLED FOCUS AREAS to help us harness tech for conservation.
- 12. THE NATURE CONSERVANCY 12 By scientists, for everyone TOOL 1: DATA EXCHANGE • Thousands of datasets • Open access • Expert-oriented, but not impenetrable
- 13. THE NATURE CONSERVANCY 13 Turning data & context into actionable insights TOOL 2: IMPACT ENGINE
- 15. With the Techstars Partnership we’ve already begun to address technologygaps and practical applications.
- 16. © Rebecca Stumpf
- 17. Applications Open Jan. 7th Apr. 7th Applications Close Jul. 29th Program Launch Oct. 30th Demo Day Denver, Colorado
- 18. People solving conservation problems faster, with better data and easier access. © Mauro Mendula OUR VISION
Notas do Editor
- One of the reasons I came to TNC is my deep respect for the organization’s science TNC solves problems really well—for both nature and people We’re good at determining where biodiversity needs to be protected and finding ways to conserve it Whether that’s a marine protected area or a big national park And we’re good at applying solutions that help people, like water funds to provide cities with cleaner water or coastal conservation that makes communities more resilient to rising seas
- I think the best way to explain these tools is through example Here’s a global problem that TNC is committed to helping solve: recurring water shortages Half of our cities and three quarters of our farms experience shortages This affects agriculture, it affects urban water supplies, and of course it affects nature According to WWF’s Living Planet Index, freshwater species declined by a staggering 75% over the last 40 years
- We are already using technology like artificial intelligence and machine learning to make our solutions even more effective Essentially, AI means we rely on computers to use data to replicate human thinking—but reaching conclusions much more quickly Right now, for example, we’re working with a bunch of partners to thin and use controlled burning to reduce the risk of catastrophic wildfires in California’s Sierra Nevadas We’re testing an AI program designed to quickly assess whether our plan for thinning will leave enough open space to help prevent massive fires So for this project, the AI program will use daily, high-resolution satellite imagery of pre- and post-thinning work in near real time—the AI not only collects the images, it reviews them and makes decisions about where to focus efforts This saves fire crews from having to rely on expensive airplane overflights or less-accurate assessments from the ground So AI is helping us do this critical work both faster AND more accurately
- Another example is how we are using machine learning to measure the impacts of conservation projects We already have a great low-tech solution. Decades ago, TNC created state-based natural heritage programs to measure the condition of animals and plant communities. This data is used to justify tax credits for conservation easements, for example. Every state has such a program, and to ensure consistency every one uses the same A-D grades for ecological condition. The problem is that the data is collected by people going out into the field and counting things. It’s way too costly to do thing this way. So we are now partnering with a start-up, to train a deep learning model (AKA MACHINE LEARNING) that will predict these data using satellite images. We just got initial results last week for the first step using data from one plant community of high biodiversity importance, longleaf pine forests. Our model can classify these pine forests with 78% accuracy. This is a big deal in and of itself because currently we cannot distinguish these forests from commercial pine forests that are not important for biodiversity. The next step is to get additional data and train the model further to classify the ecological condition of different plant communities. Imagine this info in the hands of conservation organizations and decision makers. For the first time ever, we will be able to very quickly and very accurately evaluate project outcomes and inform future actions!
- My job at TNC is to get us to more of this, and move at the speed of tech I envision three BIG tools that will help us do that: A data exchange, an analytics-driven impact engine and a product shop
- So what do we do? And when I say “we,” I don’t just mean TNC I mean the world Think of that Waze example Waze offers you a better route to wherever you are going by analyzing data and making predictions We want to put the planet on a better route The first step is a data exchange Thousands upon thousands of datasets, all open access and available to experts, all kept fresh and up-to-date Publish data, publish data, publish data! To solve water scarcity, we will capture remotely-sensed data from watersheds all over the world, and collect it in the exchange Again, we is TNC, NGO partners, businesses, universities—the exchange will be place to hold any data that can contribute to a water solution
- The second tool is an impact engine that translates the data into information - By using machine learning to do the analytics The impact engine removes the expert requirement and lets lots of people access it fast Again, we are changing the route – not for your trip to Starbucks, but for the planet’s water use And telling TNC, for example, exactly where to put one of our own best tools: a Water Fund that is all about source water protection What a water fund does is pays for upstream land conservation to improve water supply for downstream communities But this is way bigger than TNC – with the right data and the right machine intelligence, the engine will be able to predict droughts better, so farmers can be proactive instead of reactive It will give the international finance community better information to make investment decisions through simulations and long term forecasting
- So how do we do that? We started with Techstars—a partnership we’ve recently launched to leverage that entrepreneurial spirit that has disrupted so many industries And put it to work for conservation Techstars is really about partnering with start-ups and learning from them as well We are not a technology company, obviously, but we absolutely see the value of this kind of partnership and we know technology is key to solving the world’s biggest problems
- So that’s what we feel like we need to do to bring conservation into the present And give the world’s problem solvers the data and tools they need to protect nature, tackle climate change, provide food and water sustainably and build healthy cities That’s our vision: people using technology to solve conservation problems faster and more effectively