New teams join AI2!

Four new teams bring exciting new opportunities for AI for the Common Good.

AI2
AI2
Sep 9 · 5 min read
The first official lunch with our new teammates at AI2 headquarters

AI2 is committed to AI for the betterment of humanity. Since 2014, our world-class research and engineering efforts have been geared toward advancing the field of AI in domains including natural language processing, computer vision, machine reading, scientific document understanding, and machine reasoning. We perform novel research, provide open and free resources for the research community, and endeavor to make AI technologies understandable and accessible.

This month, we greatly expanded our potential impact with the addition of four new teams who are focused on an array of exciting applied environmental and conservation issues. These teams, formerly a part of Vulcan Inc., tackle projects aimed at supporting wildlife conservation efforts with real-time software solutions, stopping illegal fishing with effective maritime observation, building better more efficient climate modeling techniques, and bringing the promise of machine learning to bear on new challenges in biodiversity conservation and ocean health. Learn more about each of them below.

EarthRanger

Elephants often stray from wildlife reserves and enter human settlements. These situations often result in the loss of crops, damage to homes, and even human and wildlife deaths. Preventing this is just one scenario for the application of EarthRanger: a free and innovative technology that is helping conservationists keep both wildlife and people safe. But the real-time software solution conceived by Paul Allen and developed to address the elephant crisis is now being used for so much more than preventing human-wildlife conflicts in Africa. From fighting a locust outbreak, tracking Atlantic white sharks, informing research, and stopping deforestation in Latin America, EarthRanger’s versatility highlights how impactful it has become.

A Save the Elephants researcher tracks elephants with an app powered by EarthRanger. Photo courtesy Frank af Petersens with Save the Elephants

EarthRanger has been a game-changer for rangers, ecologists, wildlife biologists because it’s helping them unlock the power of data for conservation. By streamlining the vast troves of information they are collecting — locations of elephants, rangers and vehicles, past poaching incidents, weather, and many more — into one system, EarthRanger is giving those on the frontlines the insights they need to protect wildlife, natural resources, and local communities across the globe. And the team is just getting started.

“We’re extremely excited to continue our mission to serve the conservation community from our new home within AI2,” says Jes Lefcourt, Senior Conservation Technology Director. “In addition to the existing, talented EarthRanger team, we’re thrilled to have access to some of the best and brightest minds in AI and look forward to developing entirely new ways of enabling conservationists in their efforts to safeguard the world’s wildlife.”

Skylight

Illegal, unreported, and unregulated (IUU) fishing isn’t only pushing our fish stocks to the brink but also putting marine ecosystems, coastal economies, and regional security in peril. If that wasn’t enough, IUU fishing is heavily linked to slavery, human rights violations, and transnational organized crime.

With so much at stake, governments, NGOs, and partners are leveraging Skylight’s technology to provide transparency and actionable intelligence to detect and deter IUU fishing. Skylight’s advanced machine learning analytics sift through millions of incoming data points across vast areas to generate real-time alerts for its users. Whether it's identifying commercial fishing vessels entering a country’s Marine Protected Area or tracking networks of illegal activity, Skylight is improving maritime transparency to protect our ocean, and the move to AI2 will amplify the team’s efforts in important new ways.

“The move to AI2 is an opportunity for Skylight to supercharge the use of AI to address IUU fishing,” says Ted Schmitt, Director, Conservation. “We now have a group of world-class researchers who will accelerate the contributions Skylight has already made.”

A watchstander at the Multinational Maritime Coordination Centre of Zone in the Gulf of Guinea. He uses Skylight (seen in the background) to coordinate operations between Benin, Nigeria, Niger, and Togo as well as other countries.

Climate Modeling

The goal of the Climate Modeling team is to improve the world’s understanding of climate change, its effects, and what actions can be taken today to help. Better data and technologies will inform how we mitigate and adapt to global impacts, such as sea-level rise, community destruction, and biodiversity loss.

The new AI2 Climate Modeling team

The technology behind climate models was first created 50 years ago. Much has changed in technology since then, and we now have the opportunity to make use of the latest advances in supercomputing, modern programming languages, and machine learning to improve climate models. The AI2 Climate Modeling team is building modern machine learning into current climate models to improve their performance in key areas and ultimately to refine climate change predictions. The team hopes to accelerate climate science by building models that leverage the world’s fastest supercomputers and use modern programming languages, helping scientists work more efficiently by allowing fine-grid weather and climate models to run up to tenfold faster and longer.

AllenMLI

Members of the AllenMLI team

The AllenMLI (Machine Learning Impact) Team’s objective is to amplify the promise of machine learning in areas of biodiversity conservation and ocean health. The team ideates and develop partnerships with domain expert scientists, helping them optimize data analysis and drive impact. Current areas of focus for AllenMLI include aerial and underwater computer vision and bio-acoustics—existing projects include marine mammal health metrics automation from aerial imagery as well as deep-learning-based bioacoustic signal analysis to identify dolphins or differentiate biotic versus abiotic sources of noise in the Salish Sea.

Original image courtesy of SR3/SEA NMFS Permit #19091

The AllenMLI team also advocates for machine learning as a key differentiator in AI2’s applied technology products such as Skylight (mentioned above), and they actively seek out and participate in educational science and technology outreach opportunities.

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