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At the Intersection of Climate and AI, Machine Learning is Revolutionizing Climate Science

admin — Fri, 24 Jan 2025 15:47:51 +0000

At the Intersection of Climate and AI, Machine Learning is Revolutionizing Climate Science admin Fri, 01/24/2025 - 10:47

Exponential growth in big data and computing power is transforming climate science, where machine learning is playing a critical role in mapping the physics of our changing climate.

“What is happening within the field is revolutionary,” says School of Earth and Atmospheric Sciences Associate Chair and Professor Annalisa Bracco, adding that because many climate-related processes — from ocean currents to melting glaciers and weather patterns — can be described with physical equations, these advancements have the potential to help us understand and predict climate in critically important ways.

Bracco is the lead author of a new review paper providing a comprehensive look at the intersection of AI and climate physics.

The result of an international collaboration between Georgia Tech’s Bracco, Julien Brajard (Nansen Environmental and Remote Sensing Center), Henk A. Dijkstra (Utrecht University), Pedram Hassanzadeh (University of Chicago), Christian Lessig (European Centre for Medium-Range Weather Forecasts), and Claire Monteleoni (University of Colorado Boulder), the paper, ‘Machine learning for the physics of climate,’ was recently published in Nature Reviews Physics.

“One of our team’s goals was to help people think deeply on how climate science and AI intersect,” Bracco shares. “Machine learning is allowing us to study the physics of climate in a way that was previously impossible. Coupled with increasing amounts of data and observations, we can now investigate climate at scales and resolutions we’ve never been able to before.”

Connecting hidden dots

The team showed that ML is driving change in three key areas: accounting for missing observational data, creating more robust climate models, and enhancing predictions, especially in weather forecasting. However, the research also underscores the limits of AI — and how researchers can work to fill those gaps.

“Machine learning has been fantastic in allowing us to expand the time and the spatial scales for which we have measurements,” says Bracco, explaining that ML could help fill in missing data points — creating a more robust record for researchers to reference. However, like patching a hole in a shirt, this works best when the rest of the material is intact.

“Machine learning can extrapolate from past conditions when observations are abundant, but it can’t yet predict future trends or collect the data we need,” Bracco adds. “To keep advancing, we need scientists who can determine what data we need, collect that data, and solve problems.”

Modeling climate, predicting weather

Machine learning is often used when improving climate models that can simulate changing systems like our atmosphere, oceans, land, biochemistry, and ice. “These models are limited because of our computing power, and are run on a three-dimensional grid,” Bracco explains: below the grid resolution, researchers need to approximate complex physics with simpler equations that computers can solve quickly, a process called ‘parameterization’.

Machine learning is changing that, offering new ways to improve parameterizations, she says. “We can run a model at extremely high resolutions for a short time, so that we don’t need to parameterize as many physical processes — using machine learning to derive the equations that best approximate what is happening at small scales,” she explains. “Then we can use those equations in a coarser model that we can run for hundreds of years.”

While a full climate model based solely on machine learning may remain out of reach, the team found that ML is advancing our ability to accurately predict weather systems and some climate phenomena like El Niño.

Previously, weather prediction was based on knowing the starting conditions — like temperature, humidity, and barometric pressure — and running a model based on physics equations to predict what might happen next. Now, machine learning is giving researchers the opportunity to learn from the past. “We can use information on what has happened when there were similar starting conditions in previous situations to predict the future without solving the underlying governing equations,” Bracco says. “And all while using orders-of-magnitude less computing resources.”

The human connection

Bracco emphasizes that while AI and ML play a critical role in accelerating research, humans are at the core of progress. “I think the in-person collaboration that led to this paper is, in itself, a testament to the importance of human interaction,” she says, recalling that the research was the result of a workshop organized at the Kavli Institute for Theoretical Physics — one of the team’s first in-person discussions after the Covid-19 pandemic.

“Machine learning is a fantastic tool — but it's not the solution to everything,” she adds. “There is also a real need for human researchers collecting high-quality data, and for interdisciplinary collaboration across fields. I see this as a big challenge, but a great opportunity for computer scientists and physicists, mathematicians, biologists, and chemists to work together.”

Funding: National Science Foundation, European Research Council, Office of Naval Research, US Department of Energy, European Space Agency, Choose France Chair in AI.

DOI: https://doi.org/10.1038/s42254-024-00776-3

Summary sentence

A Georgia Tech-led review paper recently published in Nature Reviews Physics is exploring the ways machine learning is revolutionizing the field of climate physics — and the role human scientists might play.

Summary

A Georgia Tech-led review paper recently published in Nature Reviews Physics is exploring the ways machine learning is revolutionizing the field of climate physics — and the role human scientists might play.

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Wed, 01/22/2025 - 12:00

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Fri, 01/24/2025 - 10:47

Cellular Study Uncovers 'Whole-Body' Impacts of Endurance Exercise

admin — Thu, 02 May 2024 20:45:41 +0000

Cellular Study Uncovers 'Whole-Body' Impacts of Endurance Exercise admin Thu, 05/02/2024 - 16:45

In a group of papers released May 1 in the journal Nature, scientists are one step closer to a whole-body map of the body’s cellular responses to endurance exercise — identifying striking “all tissue effects” of training, even in tissues from organs not normally associated with movement.

The findings are the latest product of the Molecular Transducers of Physical Activity Consortium (MoTrPAC), a ten-year effort launched in 2016 by the National Institutes of Health (NIH) to uncover how exercise improves and maintains our health at the molecular level.

Georgia Institute of Technology bioanalytical chemist Facundo Fernández and Emory University biochemist Eric Ortlund lead one of the Consortium’s Chemical Analysis Sites, joining researchers across the country to collect and translate data from animals and more than 2,000 volunteers into comprehensive maps of the cellular changes throughout the body in response to exercise.

The $226 million MoTrPAC NIH Common Fund investment also hopes to help people with chronic illnesses identify specific physical activities to improve individual health, and to potentially unearth therapeutic targets — medicines that might mimic the positive effects of exercise.

MoTrPAC’s latest group of papers details data from studies in rats, uncovering how endurance exercise affects biological molecules and “all tissues of the body,” as well as tissues and gene expression, along with striking tissue differences between male and female organisms.

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Facundo M. Fernandez, is Regents’ Professor and Vasser Woolley Foundation Chair in Bioanalytical Chemistry at Georgia Tech. He also serves as associate editor of the Journal of the American Society for Mass Spectrometry (JASMS).

Eric Ortlund is a professor in the Department of Biochemistry at Emory University and a member of the Discovery and Developmental Therapeutics Research Program at Winship Cancer Institute.

Study co-authors from Georgia Tech also include David A. Gaul (School of Chemistry and Biochemistry, along with Samuel G. Moore (Petit Institute of Bioengineering and Biosciences). Emory University co-authors also include Tiantian Zhang and Zhenxin Hou (Department of Biochemistry).

Funding: The MoTrPAC Study is supported by multiple NIH grants and institutes, as well as the National Science Foundation (NSF), the Knut and Alice Wallenberg Foundation, and NORC at the University of Chicago.

NIH grants include: U24OD026629 (Bioinformatics Center), U24DK112349, U24DK112342, U24DK112340, U24DK112341, U24DK112326, U24DK112331, U24DK112348 (Chemical Analysis Sites), U01AR071133, U01AR071130, U01AR071124, U01AR071128, U01AR071150, U01AR071160, U01AR071158 (Clinical Centers), U24AR071113 (Consortium Coordinating Center), U01AG055133, U01AG055137 and U01AG055135 (PASS/Animal Sites); as well as NHGRI Institutional Training Grant in Genome Science 5T32HG000044; National Heart, Lung, and Blood Institute of the National Institute of Health F32 postdoctoral fellowship award F32HL154711; National Institute on Aging P30AG044271 and P30AG003319.

Subtitle

MoTrPAC scientists are creating a whole-body map of molecular responses to endurance training — finding striking “all tissue effects” in a new set of studies

Summary sentence

Exercise is good for you. To understand why, MoTrPAC scientists are creating a whole-body map of molecular responses to endurance training — finding striking “all tissue effects” in a new set of studies, featured on the May cover of the journal Nature.

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Thu, 05/02/2024 - 12:00

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Thu, 05/02/2024 - 16:44

Researchers Reveal Roadmap for AI Innovation in Brain and Language Learning

admin — Tue, 19 Mar 2024 16:13:41 +0000

Researchers Reveal Roadmap for AI Innovation in Brain and Language Learning admin Tue, 03/19/2024 - 12:13

One of the hallmarks of humanity is language, but now, powerful new artificial intelligence tools also compose poetry, write songs, and have extensive conversations with human users. Tools like ChatGPT and Gemini are widely available at the tap of a button — but just how smart are these AIs?

A new multidisciplinary research effort co-led by Anna (Anya) Ivanova, assistant professor in the School of Psychology at Georgia Tech, alongside Kyle Mahowald, an assistant professor in the Department of Linguistics at the University of Texas at Austin, is working to uncover just that.

Their results could lead to innovative AIs that are more similar to the human brain than ever before — and also help neuroscientists and psychologists who are unearthing the secrets of our own minds.

The study, “Dissociating Language and Thought in Large Language Models,” is published this week in the scientific journal Trends in Cognitive Sciences. The work is already making waves in the scientific community: an earlier preprint of the paper, released in January 2023, has already been cited more than 150 times by fellow researchers. The research team has continued to refine the research for this final journal publication.

“ChatGPT became available while we were finalizing the preprint,” Ivanova explains. “Over the past year, we've had an opportunity to update our arguments in light of this newer generation of models, now including ChatGPT.”

Form versus function

The study focuses on large language models (LLMs), which include AIs like ChatGPT. LLMs are text prediction models, and create writing by predicting which word comes next in a sentence — just like how a cell phone or email service like Gmail might suggest what next word you might want to write. However, while this type of language learning is extremely effective at creating coherent sentences, that doesn’t necessarily signify intelligence.

Ivanova’s team argues that formal competence — creating a well-structured, grammatically correct sentence — should be differentiated from functional competence — answering the right question, communicating the correct information, or appropriately communicating. They also found that while LLMs trained on text prediction are often very good at formal skills, they still struggle with functional skills.

“We humans have the tendency to conflate language and thought,” Ivanova says. “I think that’s an important thing to keep in mind as we're trying to figure out what these models are capable of, because using that ability to be good at language, to be good at formal competence, leads many people to assume that AIs are also good at thinking — even when that's not the case.

It's a heuristic that we developed when interacting with other humans over thousands of years of evolution, but now in some respects, that heuristic is broken,” Ivanova explains.

The distinction between formal and functional competence is also vital in rigorously testing an AI’s capabilities, Ivanova adds. Evaluations often don’t distinguish formal and functional competence, making it difficult to assess what factors are determining a model’s success or failure. The need to develop distinct tests is one of the team’s more widely accepted findings, and one that some researchers in the field have already begun to implement.

Creating a modular system

While the human tendency to conflate functional and formal competence may have hindered understanding of LLMs in the past, our human brains could also be the key to unlocking more powerful AIs.

Leveraging the tools of cognitive neuroscience while a postdoctoral associate at Massachusetts Institute of Technology (MIT), Ivanova and her team studied brain activity in neurotypical individuals via fMRI, and used behavioral assessments of individuals with brain damage to test the causal role of brain regions in language and cognition — both conducting new research and drawing on previous studies. The team’s results showed that human brains use different regions for functional and formal competence, further supporting this distinction in AIs.

“Our research shows that in the brain, there is a language processing module and separate modules for reasoning,” Ivanova says. This modularity could also serve as a blueprint for how to develop future AIs.

“Building on insights from human brains — where the language processing system is sharply distinct from the systems that support our ability to think — we argue that the language-thought distinction is conceptually important for thinking about, evaluating, and improving large language models, especially given recent efforts to imbue these models with human-like intelligence,” says Ivanova’s former advisor and study co-author Evelina Fedorenko, a professor of brain and cognitive sciences at MIT and a member of the McGovern Institute for Brain Research.

Developing AIs in the pattern of the human brain could help create more powerful systems — while also helping them dovetail more naturally with human users. “Generally, differences in a mechanism’s internal structure affect behavior,” Ivanova says. “Building a system that has a broad macroscopic organization similar to that of the human brain could help ensure that it might be more aligned with humans down the road.”

In the rapidly developing world of AI, these systems are ripe for experimentation. After the team’s preprint was published, OpenAI announced their intention to add plug-ins to their GPT models.

“That plug-in system is actually very similar to what we suggest,” Ivanova adds. “It takes a modularity approach where the language model can be an interface to another specialized module within a system.”

While the OpenAI plug-in system will include features like booking flights and ordering food, rather than cognitively inspired features, it demonstrates that “the approach has a lot of potential,” Ivanova says.

The future of AI — and what it can tell us about ourselves

While our own brains might be the key to unlocking better, more powerful AIs, these AIs might also help us better understand ourselves. “When researchers try to study the brain and cognition, it's often useful to have some smaller system where you can actually go in and poke around and see what's going on before you get to the immense complexity,” Ivanova explains.

However, since human language is unique, model or animal systems are more difficult to relate. That's where LLMs come in.

“There are lots of surprising similarities between how one would approach the study of the brain and the study of an artificial neural network” like a large language model, she adds. “They are both information processing systems that have biological or artificial neurons to perform computations.”

In many ways, the human brain is still a black box, but openly available AIs offer a unique opportunity to see the synthetic system's inner workings and modify variables, and explore these corresponding systems like never before.

“It's a really wonderful model that we have a lot of control over,” Ivanova says. “Neural networks — they are amazing.”

Along with Anna (Anya) Ivanova, Kyle Mahowald, and Evelina Fedorenko, the research team also includes Idan Blank (University of California, Los Angeles), as well as Nancy Kanwisher and Joshua Tenenbaum (Massachusetts Institute of Technology).

DOI: https://doi.org/10.1016/j.tics.2024.01.011

Researcher Acknowledgements

For helpful conversations, we thank Jacob Andreas, Alex Warstadt, Dan Roberts, Kanishka Misra, students in the 2023 UT Austin Linguistics 393 seminar, the attendees of the Harvard LangCog journal club, the attendees of the UT Austin Department of Linguistics SynSem seminar, Gary Lupyan, John Krakauer, members of the Intel Deep Learning group, Yejin Choi and her group members, Allyson Ettinger, Nathan Schneider and his group members, the UT NLL Group, attendees of the KUIS AI Talk Series at Koç University in Istanbul, Tom McCoy, attendees of the NYU Philosophy of Deep Learning conference and his group members, Sydney Levine, organizers and attendees of the ILFC seminar, and others who have engaged with our ideas. We also thank Aalok Sathe for help with document formatting and references.

Funding sources

Anna (Anya) Ivanova was supported by funds from the Quest Initiative for Intelligence. Kyle Mahowald acknowledges funding from NSF Grant 2104995. Evelina Fedorenko was supported by NIH awards R01-DC016607, R01-DC016950, and U01-NS121471 and by research funds from the Brain and Cognitive Sciences Department, McGovern Institute for Brain Research, and the Simons Foundation through the Simons Center for the Social Brain.

Subtitle

A new study highlights how human neuroscience is paving the way for AI innovation — and what AI can teach us about ourselves.

Summary sentence

A new study co-led by Anna (Anya) Ivanova highlights how human neuroscience is paving the way for AI innovation — and what AI can teach us about ourselves.

Summary

A new study co-led by School of Psychology's Anna (Anya) Ivanova uncovers the relationship between language and thought in artificial intelligence models like ChatGPT, leveraging cognitive neuroscience research on the human brain. The results are a roadmap to developing new AIs — and to better understanding how we think and communicate.

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Tue, 03/19/2024 - 12:00

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Tue, 03/19/2024 - 12:11

A Rare Genetic Spotlight on Health Disparities for IBD

admin — Wed, 15 Nov 2023 16:31:25 +0000

A Rare Genetic Spotlight on Health Disparities for IBD admin Wed, 11/15/2023 - 11:31

The advent of whole genome sequencing technology has prompted an explosion in research into how genetics are associated with disease risk. But the vast majority of genetics research has been done on people of European ancestry, and genetics researchers have realized that in order to address health disparities, more needs to be done.

In a new study, Georgia Tech researchers investigated whether 25 rare gene variants known to be associated with inflammatory bowel disease (IBD) play a role in risk for African Americans. While the rare variant associations were recently discovered in individuals of European ancestry, contributing to about 15% of cases, it was unknown if and how those same rare gene variants might affect risk for African Americans.

Led by Greg Gibson, Regents’ Professor and Tom and Marie Patton Chair in the School of Biological Sciences, the study highlights the importance of considering genetic diversity and the mixing of ancestry in genetics research. The findings were published in the journal Genome Medicine.

“Because of major advancements in the last decade, we now know that most diseases are far more complex than we originally thought, in terms of genetics,” said Gibson, who is also director of the Center for Integrative Genomics at Georgia Tech. “Understanding whether genetic differences contribute to health disparities is a major point of focus for current genetics research, and we had an opportunity to test one idea with this study.”

Today, African Americans have a similar prevalence of various types of IBD as European Americans. But progression is often much worse: African Americans are more likely to progress to severe disease requiring colectomies and other major interventions.

Courtney Astore, a Ph.D. student in Gibson’s lab and first author on the paper, wanted to assess whether those same rare variants would have a similar effect on IBD risk in African Americans. In a collaboration with Subra Kugathasan from Emory University and the NIH’s IBD Genetics Consortium, Gibson’s lab had analyzed the complete genome sequences of over 3,000 genomes of African Americans, half with IBD. Astore used that database to conduct her analysis.

She started by plotting the difference in frequency of the rare variants, and quickly realized that there was a significant reduction in prevalence of the variants in African Americans. Through further computations, she estimated that European ancestry variants actually only made a very small contribution to IBD in African Americans (around 44 additional cases per 100,000 people), fourfold less than Americans of European ancestry.

“Prior to our analysis, we suspected that admixture may play a role in the presence of IBD-associated rare variants in African Americans,” Astore said. “When I saw the differences, that was when I realized that there was something important there that we needed to discover.”

Astore then used a method known as chromosome painting, which is a tool for visualizing where each segment of the genome comes from. She showed that the rare variants found in African Americans were almost always located on segments of European ancestry genomes.

In simple terms, the location of the variants indicated that the genes resulted from admixture — a scientific term for mixing of genetic backgrounds throughout ancestry — which enabled Astore to show that the mutations had arisen outside of Africa, and only began to appear in people of African ancestry over the last dozen generations.

To conclude the study, Gibson and Astore assessed the presence of other rare variants associated with a dozen other diseases, which similarly confirmed that the presence of the variants contributes to African Americans generally through admixture.

The findings are important for several reasons. First, they highlight the value of considering genetic diversity and admixture in all genetics research, and especially when investigating rare variants and their associations with complex disease. While they showed that the European variants were rare in African Americans, there are almost certainly rare variants that contribute to IBD in African Americans that have yet to be discovered and may point to biological mechanisms.

“Doing more genetic studies on diverse populations, and especially those that have admixture, is going to be pivotal for therapeutic discovery,” Astore said.

Precision medicine will eventually be tailored to a person’s genome, which means that in some cases knowing the identity of rare variants will help guide therapy. If that is the case, knowing the context of ancestry will be beneficial. It also means that if more research on diverse ancestry groups isn’t done, then new treatments might not be effective for all people. The team also emphasizes that genetics is not the only factor contributing to risk for complex diseases like IBD, and their study simply highlights that it cannot be assumed that genetic discoveries are risk factors for all people.

“Our study emphasizes that in order to move in the direction of greater health equity, it is absolutely crucial to do large-scale genetic sequencing for African Americans and all ancestry groups,” Gibson said. “We hope our work will encourage more research on both social determinants of health and the genetics of IBD across ancestries.”

Note: The IBD Genetics Consortium, of which Gibson is a part, organized the cohort of African Americans with IBD, and their samples were gathered at institutes across the country, including Emory University, Johns Hopkins University, Rutgers University, Cedars Sinai Los Angeles, and Mt. Sinai New York.

Funding: National Institutes of Health

DOI: https://doi.org/10.1186/s13073-023-01244-w

Summary sentence

In a new study, Georgia Tech researchers investigated whether 25 rare gene variants known to be associated with inflammatory bowel disease (IBD) play a role in risk for African Americans.

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Wed, 11/15/2023 - 12:00

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Wed, 11/15/2023 - 11:30

Physicists Solve Mysteries of Microtubule Movers

admin — Fri, 22 Sep 2023 19:46:41 +0000

Physicists Solve Mysteries of Microtubule Movers admin Fri, 09/22/2023 - 15:46

Active matter is any collection of materials or systems composed of individual units that can move on their own, thanks to self-propulsion or autonomous motion. They can be of any size — think clouds of bacteria in a petri dish, or schools of fish.

Roman Grigoriev is mostly interested in the emergent behaviors in active matter systems made up of units on a molecular scale — tiny systems that convert stored energy into directed motion, consuming energy as they move and exert mechanical force.

“Active matter systems have garnered significant attention in physics, biology, and materials science due to their unique properties and potential applications,” Grigoriev, a professor in the School of Physics at Georgia Tech, explains.

“Researchers are exploring how active matter can be harnessed for tasks like designing new materials with tailored properties, understanding the behavior of biological organisms, and even developing new approaches to robotics and autonomous systems,” he says.

But that’s only possible if scientists learn how the microscopic units making up active matter interact, and whether they can affect these interactions and thereby the collective properties of active matter on the macroscopic scale.

Grigoriev and his research colleagues have found a potential first step by developing a new model of active matter that generated new insight into the physics of the problem. They detail their methods and results in a new study published in Science Advances, “Physically informed data-driven modeling of active nematics.”

School of Physics graduate researcher Matthew Golden is the study's lead author. Co-authors are graduate researcher Jyothishraj Nambisan and Alberto Fernandez-Nieves, professor in the Department of Condensed Matter Physics at the University of Barcelona and a former associate professor of Physics at Georgia Tech.

A two-dimensional 'solution?'

The research team focused on one of the most common examples of active matter, a suspension of self-propelled particles, such as bacteria or synthetic microswimmers, in a liquid medium. These particles cluster, swarm, and otherwise form dynamic patterns due to their ability to move and interact with each other.

“In our paper, we use data from an experimental system involving suspensions of microtubules, which provide structural support, shape, and organization to eukaryotic cells (any cell with a clearly defined nucleus),” Grigoriev explains.

Microtubules, as well as actin filaments and some bacteria, are examples of nematics, rod-like objects whose "heads" are indistinguishable from their "tails.”

The motion of microtubules is driven by molecular motors powered by a protein, kinesin, which consumes adenosine triphosphate (ATP) dissolved in the liquid to slide a pair of neighboring microtubules past one another. The researcher’s system used microtubules suspended between layers of oil and water, which restricted their movement to two dimensions.

“That makes it easier to visualize the microtubules and track their motion. By changing the kinesin or ATP concentrations, we could control the motion of the microtubules, making this experimental setup by far one of the most popular in the study of active nematics and even more generally, active matter,” Grigoriev said.

‘This is where the story gets interesting’

Getting a clearer picture of microtubular movements was just one discovery in the study.

Another was learning more about the relationships between the characteristic patterns describing the orientation and motion of nematic molecules on a macroscopic scale. Those patterns, or topological defects, determine how the nematics orient themselves at the oil-water interface, that is in two spatial dimensions.

“Understanding the relationship between the flow — the global property of the system, or the fluid — and the topological defects, which describe the local orientation of microtubules, is one of the key intellectual questions facing researchers in the field,” Grigoriev said. “One needs to correctly identify the dominant physical effects which control the interaction between the microtubules and the surrounding fluid.”

“And this is where the story gets interesting,” Grigoriev adds. “For over a decade, it was believed that the key physics were well understood, with a large number of theoretical and computational studies relying on a generally accepted first principles model” — that is, one based on established science — “that was originally derived for active nematics in three spatial dimensions.”

In the Georgia Tech model, though, the dynamics of active nematics — more specifically, the length and time scales of the emerging patterns — are controlled by a pair of physical constants describing those assumed dominant physical effects: the stiffness of the microtubules (their flexibility), and the activity describing the stress, or force, generated by the kinesin motors.

“Using a data-driven approach, we inferred the correct form of the model demonstrating that, for two-dimensional active nematics, the dominant physical effects are different from what was previously assumed,” Grigoriev says. “In particular, the time scale is set by the rate at which bundles of microtubules are stretched by kinesin.” It is this rate, rather than the stress, that is constant.

The danger of confirmation bias

Grigoriev said the results of the study have important implications for understanding of active nematics and their emergent behaviors, explaining that they help rationalize a number of recent experimental results that were previously unexplained, such as how the density of topological defects scales with the concentration of kinesin and the viscosity of the fluid layers.

“More importantly, our results demonstrate the danger associated with traditional assumptions that established research communities often land on and have difficulty overcoming,” Grigoriev said. “While data-driven methods may have their own sources of bias, they offer a perspective which is different enough from more traditional approaches to become a valuable research tool in their own right.”

About Georgia Institute of Technology

The Georgia Institute of Technology, or Georgia Tech, is one of the top public research universities in the U.S., developing leaders who advance technology and improve the human condition. The Institute offers business, computing, design, engineering, liberal arts, and sciences degrees. Its more than 45,000 undergraduate and graduate students, representing 50 states and more than 148 countries, study at the main campus in Atlanta, at campuses in France and China, and through distance and online learning. As a leading technological university, Georgia Tech is an engine of economic development for Georgia, the Southeast, and the nation, conducting more than $1 billion in research annually for government, industry, and society.

Funding: This study was funded by the National Science Foundation, grant no. CMMI-2028454. “Physically informed data-driven modeling of active nematics,” DOI: 10.1126/sciadv.abq6120

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Physicists have developed a new model and clearer picture of molecular movements within active matter — bringing science a step closer to designing specific functions into new materials, and understanding emergent behaviors.

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Fri, 09/01/2023 - 12:00

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Fri, 09/22/2023 - 15:44

Thinning Ice Sheets May Drive Sharp Rise in Subglacial Waters

admin — Mon, 21 Aug 2023 14:26:31 +0000

Thinning Ice Sheets May Drive Sharp Rise in Subglacial Waters admin Mon, 08/21/2023 - 10:26

Two Georgia Tech researchers, Alex Robel and Shi Joyce Sim, have collaborated on a new model for how water moves under glaciers. The new theory shows that up to twice the amount of subglacial water that was originally predicted might be draining into the ocean – potentially increasing glacial melt, sea level rise, and biological disturbances.

The paper, published in Science Advances, “Contemporary Ice Sheet Thinning Drives Subglacial Groundwater Exfiltration with Potential Feedbacks on Glacier Flow,” is co-authored by Colin Meyer (Dartmouth), Matthew Siegfried (Colorado School of Mines), and Chloe Gustafson (USGS).

While there are pre-existing methods to understand subglacial flow, these techniques involve time-consuming computations. In contrast, Robel and Sim developed a simple equation, which can predict how fast exfiltration, the discharge of groundwater from aquifers under ice sheets, using satellite measurements of Antarctica from the last two decades.

“In mathematical parlance, you would say we have a closed form solution,” explains Robel, an assistant professor in the School of Earth and Atmospheric Sciences. “Previously, people would run a hydromechanical model, which would have to be applied at every point under Antarctica, and then run forward over a long time period.” Since the researchers’ new theory is a mathematically simple equation, rather than a model, “the entirety of our prediction can be done in a fraction of a second on a laptop,” Robel says.

Robel adds that while there is precedence for developing these kinds of theories for similar kinds of models, this theory is specific in that it is for the particular boundary conditions and other conditions that exist underneath ice sheets. “This is, to our knowledge, the first mathematically simple theory which describes the exfiltration and infiltration underneath ice sheets.”

“It's really nice whenever you can get a very simple model to describe a process — and then be able to predict what might happen, especially using the rich data that we have today. It’s incredible” adds Sim, a research scientist in the School of Earth and Atmospheric Sciences. “Seeing the results was pretty surprising.”

One of the main arguments in the paper underscores the potentially large source of subglacial water — possibly up to double the amount previously thought — that could be affecting how quickly glacial ice flows and how quickly the ice melts at its base. Robel and Sim hope that the predictions made possible by this theory can be incorporated into ice sheet models that scientists use to predict future ice sheet change and sea level rise.

A dangerous feedback cycle

Aquifers are underground areas of porous rock or sediment rich in groundwater. “If you take weight off aquifers like there are under large parts of Antarctica, water will start flowing out of the sediment,” Robel explains, referencing a diagram Sim created. While this process, known as exfiltration, has been studied previously, focus has been on the long time scales of interglacial cycles, which cover tens of thousands of years.

There has been less work on modern ice sheets, especially on how quickly exfiltration might be occurring under the thinning parts of the current-day Antarctic ice sheet. However, using recent satellite data and their new theory, the team has been able to predict what exfiltration might look like under those modern ice sheets.

“There's a wide range of possible predictions,” Robel explains. “But within that range of predictions there is the very real possibility that groundwater may be flowing out of the aquifer at a speed that would make it a majority, or close to a majority of the water that is underneath the ice sheet.”

If those parameters are correct, that would mean there's twice as much water coming into the subglacial interface than previous estimates assumed.

Ice sheets act like a blanket, sitting over the warm earth and trapping heat on the bottom, away from Antarctica’s cold atmosphere — and this means that the warmest place in the Antarctic ice sheet is at the bottom of a sheet, not on the surface. As an ice sheet thins, the warmer underground water can exfiltrate more readily, and this heat gradient can accelerate the melting that an ice sheet experiences.

“When the atmosphere warms up, it takes tens of thousands of years for that signal to diffuse through an ice sheet of the size, of the thickness, of the Antarctic ice sheet,” Robel explains. “But this process of exfiltration is a response to the already-ongoing thinning of the ice sheet, and it's an immediate response right now.”

Broad implications

Beyond sea level rise, this additional exfiltration and melt has other implications. Some of the places of richest marine productivity in the world occur off the coast of Antarctica, and being able to better predict exfiltration and melt could help marine biologists better understand where marine productivity is occurring, and how it might change in the future.

Robel also hopes this work will open the doorway to more collaborations with groundwater hydrologists who may be able to apply their expertise to ice sheet dynamics, while Sim underscores the need for more fieldwork.

“Getting the experimentalists and observationalists interested in trying to help us better constrain some of the properties of these water-laden sediments — that would be very helpful,” Sim says. “That's our largest unknown at this point, and it heavily influences the results.”

“It's really interesting how there's a potential to draw heat from deeper in the system,” she adds. “There's quite a lot of water that could be drawing more heat out, and I think that there's a heat budget there that could be interesting to look at.”

Moving forward, collaboration will continue to be key. “I really enjoyed talking to Joyce (Sim) about these problems,” Rober says, “because Joyce is an expert on heat flow and porous flow in the Earth's interior, and those are problems that I had not worked on before. That was kind of a nice aspect of this collaboration. We were able to bridge these two areas that she works on and that I work on.”

DOI: doi.org/10.1126/sciadv.adh3693

Funding: This work was supported by startup funds from the Georgia Tech Research Corporation (A.A.R. and S.J.S.) and NASA grant 80NSSC21K0912 (M.R.S.). Alex Robel (A.A.R.) is also the recipient of a National Science Foundation CAREER grant.

Summary sentence

Up to twice the amount of subglacial water that was originally predicted might be draining into the ocean – potentially increasing glacial melt, sea level rise, and biological disturbances.

Summary

Alex Robel and Shi Joyce Sim have a new model for how water moves under glaciers. Their theory shows that up to twice the amount of subglacial water that was originally predicted might be draining into the ocean – potentially increasing glacial melt, sea level rise, and biological disturbances.

Dateline

Mon, 08/21/2023 - 12:00

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About the photos: Images of Change
Glaciers are shrinking along western Antarctica, and NASA is documenting the melt. Explore and toggle satellite images with the NASA Earth Observatory.

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Mon, 08/21/2023 - 10:21

Scientists Unearth 20 Million Years of ‘Hot Spot’ Magmatism Under Cocos Plate

admin — Tue, 20 Jun 2023 13:59:40 +0000

Scientists Unearth 20 Million Years of ‘Hot Spot’ Magmatism Under Cocos Plate admin Tue, 06/20/2023 - 09:59

Ten years ago, Samer Naif made an unexpected discovery in Earth’s mantle: a narrow pocket, proposed to be filled with magma, hidden some 60 kilometers beneath the seafloor of the Cocos Plate.

Mantle melts are buoyant and typically float toward the surface — think underwater volcanoes that erupt to form strings of islands. But Naif’s imaging instead showed a clear slice of semi-molten rock: low-degree partial melts, still sandwiched at the base of the plate some 37 miles beneath the ocean floor.

Then, the observation provided an explanation for how tectonic plates can gradually slide, lubricated by partial melting. The study also “raised several questions about why magma is stored in a thin channel — and where the magma originated from,” says Naif, an assistant professor in the School of Earth and Atmospheric Sciences at Georgia Institute of Technology.

Fellow researchers went on to share competing interpretations for the cause of the channel — including studies that argued against magma being needed to explain the observation.

So Naif went straight to the source.

“I basically went on a multiyear hunt, akin to a Sherlock Holmes detective story, looking for clues of mantle magmas that we first observed in the 2013 Nature study,” he says. “This involved piecing together evidence from several independent sources, including geophysical, geochemical, and geological (direct seafloor sampling) data.”

Now, the results of that search are detailed in a new Science Advances article, “Episodic intraplate magmatism fed by a long-lived melt channel of distal plume origin”, authored by Naif and researchers from the U.S. Geological Survey at Woods Hole Coastal and Marine Science Center, Northern Arizona University, Lamont-Doherty Earth Observatory of Columbia University, the Department of Geology and Geophysics at Woods Hole Oceanographic Institution, and GNS Science of Lower Hutt, New Zealand.

Zeroing in

A relatively young oceanic plate — some 23 million years old — the Cocos Plate traces down the western coast of Central America, veering west to the Pacific Plate, then north to meet the North American Plate off the Pacific coast of Mexico.

Sliding between these two plates caused the devastating 1985 Mexico City earthquake and the 2017 Chiapas earthquake, while similar subduction between the Cocos and Caribbean plates resulted in the 1992 Nicaragua tsunami and earthquake, and the 2001 El Salvador earthquakes.

Scientists study the edges of these oceanic plates to understand the history and formation of volcanic chains — and to help researchers and agencies better prepare for future earthquakes and volcanic activity.

It’s in this active area that Naif and fellow researchers recently set out to document a series of magmatic intrusions just beneath the seafloor, in the same area that the team first detected the channel of magma back in 2013.

Plumbing the depths

For the new study, the team combined geophysical, geochemical, and seafloor drilling results with seismic reflection data, a technique used to image layers of sediments and rocks below the surface. “It helps us to see the geology where we cannot see it with our own eyes,” Naif explains.

First, the researchers observed an abundance of widespread intraplate magmatism. “Volcanism where it is not expected,” Naif says, “basically away from plate boundaries: subduction zones and mid-ocean ridges.”

Think Hawaii, where “a mantle plume of hot, rising material melts during its ascent, and then forms the Hawaii volcanic chain in the middle of the Pacific Ocean,” just as with the Cocos Plate, where the team imaged the volcanism fed by magma at the lithosphere-asthenosphere boundary — the base of the sliding tectonic plates.

“Below it is the convecting mantle,” Naif adds. “The tectonic plates are moving around on Earth's surface because they are sliding on the asthenosphere below them.”

The researchers also found that this channel below the lithosphere is regionally extensive — over 100,000 square kilometers — and is a “long-lived feature that originated from the Galápagos Plume,” a mantle plume that formed the volcanic Galápagos islands, supplying melt for a series of volcanic events across the past 20 million years, and persisting today.

Importantly, the new study also suggests that these plume-fed melt channels may be widespread and long-lived sources for intraplate magmatism itself — as well as for mantle metasomatism, which happens when Earth’s mantle reacts with fluids to form a suite of minerals from the original rocks.

Connecting the (hot spot) dots

“This confirms that magma was there in the past — and some of it leaked through the mantle and erupted near the seafloor,” Naif says, “in the form of sill intrusions and seamounts: basically volcanoes located on the seafloor.”

The work also provides compelling supporting evidence that magma could still be stored in the channel. “More surprising is that the erupted magma has a chemical fingerprint that links its source to the Galápagos mantle plume.”

“We learned that the magma channel has been around for at least 20 million years, and on occasion some of that magma leaks to the seafloor where it erupts volcanically,” Naif adds.

The team’s identified source of the magma, the Galápagos Plume, “is more than 1,000 kilometers away from where we detected this volcanism. It is not clear how magma can stay around in the mantle for such a long time, only to leak out episodically.”

Plume hunters wanted

The evidence that the team compiled is “really quite subtle and requires a detailed and careful study of a suite of seafloor observations to connect the dots,” Naif says. “Basically, the signs of such volcanism, while they are quite clear here, also require high resolution data and several different types of data to be able to detect such subtle seafloor features.”

So, “if we can see such subtle clues of volcanism here,” Naif explains, “it means a similar, careful analysis of high resolution data in other parts of the seafloor may lead to similar discoveries of volcanism elsewhere, caused by other mantle plumes.”

“There are numerous mantle plumes dotted across the planet. There are also numerous seamounts — at least 100,000 of them! — covering the seafloor, and it is anyone’s guess how many of them formed in the middle of the tectonic plates because of magma sourced from distant mantle plumes that leaked to the surface.”

Naif looks forward to continuing that search, from seafloor to asthenosphere.

###

Funding: National Science Foundation: OCE-0625178, U.S. Science Support Program

Citation: DOI: 10.1126/sciadv.add3761

About Georgia Tech

The Georgia Institute of Technology, or Georgia Tech, is one of the top public research universities in the U.S., developing leaders who advance technology and improve the human condition.

The Institute offers business, computing, design, engineering, liberal arts, and sciences degrees. Its more than 45,000 undergraduate and graduate students, representing 50 states and more than 148 countries, study at the main campus in Atlanta, at campuses in France and China, and through distance and online learning.

As a leading technological university, Georgia Tech is an engine of economic development for Georgia, the Southeast, and the nation, conducting more than $1 billion in research annually for government, industry, and society.

Subtitle

Situated 60 kilometers beneath the Pacific Ocean floor, the magma channel covers more than 100,000 square kilometers, and originated from the Galápagos Plume more than 20 million years ago.

Summary sentence

A team of scientists led by Georgia Tech have observed past episodic intraplate magmatism and corroborated the existence of a partial melt channel at the base of the Cocos Plate.

Summary

A team of scientists led by Georgia Tech have observed past episodic intraplate magmatism and corroborated the existence of a partial melt channel at the base of the Cocos Plate. Situated 60 kilometers beneath the Pacific Ocean floor, the magma channel covers more than 100,000 square kilometers, and originated from the Galápagos Plume more than 20 million years ago, supplying melt for multiple magmatic events — and persisting today.

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Tue, 06/20/2023 - 12:00

jess@cos.gatech.edu

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College of Sciences at Georgia Tech

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Tue, 06/20/2023 - 09:59

AI Hub at Georgia Tech to Unite Campus in Artificial Intelligence R&D and Commercialization Efforts

admin — Wed, 07 Jun 2023 16:11:31 +0000

AI Hub at Georgia Tech to Unite Campus in Artificial Intelligence R&D and Commercialization Efforts admin Wed, 06/07/2023 - 12:11

Artificial intelligence (AI) is a disruptive technology transforming industries and governments across the world. At Georgia Tech, developments in AI span many disciplines with dozens of campus centers and institutes. The newly announced AI Hub at Georgia Tech will unite AI entities across campus, enabling the Institute to align on goals to become an international thought leader in AI. It will also drive AI education and research and development toward real-world, responsible applications.

As an AI-powered university, Georgia Tech is embracing AI throughout the Institute, incorporating it into academic programs and research to assist and amplify human intelligence in all areas of work. The vision of AI Hub at Georgia Tech is to advance AI through discovery, interdisciplinary research, responsible deployment, and next-generation education to build a sustainable future.

“Georgia Tech’s integrated capabilities in the area of AI, machine learning, engineering, and interdisciplinary research are highly valuable to industry, government, and education,” said Chaouki Abdallah, executive vice president for research at Georgia Tech. “By bringing together researchers from across campus, we can harness our collective expertise in AI to work towards a common goal to become the leading university for AI research and application.”

Co-led by faculty members Irfan Essa and Larry Heck, AI Hub at Georgia Tech will lead in developing new paths in educating and training the next generation of the AI workforce. Additionally, it will serve as a dedicated space for decision makers and other stakeholders to access best-in-class resources to guide them through the complexities of commercializing and deploying AI.

“Georgia Tech is well positioned to pursue meaningful opportunities in AI by focusing our collective capabilities across campus not only in AI research but also in the integration and application of AI solutions,” said Larry Heck, interim co-director of AI Hub at Georgia Tech, GRA Eminent Scholar, Rhesa S. Farmer, Jr., Advanced Computing Concepts Chair, co-executive director of ML@GT, and professor with a joint appointment in the Schools of Electrical and Computer Engineering and Interactive Computing.

Georgia Tech has been actively engaged in AI research and education for decades, with more than 350 faculty working in fundamental and applied AI-related research across all six colleges, Georgia Tech Research Institute, and the majority of interdisciplinary research institutes and centers. The Institute has a strong foundation and advantage in AI, as the leading engineering university with an applied, solutions-focused approach. It was also the first public university to launch a computer science school.

“The discipline of AI has a deep history at Georgia Tech, and we continue to serve as leaders in many areas of AI research and education,” said Irfan Essa, interim co-director of AI Hub at Georgia Tech, distinguished professor, senior associate dean in the College of Computing, and co-executive director of ML@GT. “At present, we are seeing unprecedented growth in AI and responsible deployment is top of mind for many. AI Hub at Georgia Tech will bring all areas of AI under one umbrella to provide structure and governance as the Institute continues to lead and innovate in the discipline of AI, with the related disciplines of machine learning, robotics, and data science."

To become involved in AI Hub at Georgia Tech, contact irfan@gatech.edu or larryheck@gatech.edu.

Summary sentence

Newly announced AI Hub at Georgia Tech will unite AI entities across campus, enabling the Institute to align on goals to become an international thought leader in AI.

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Tue, 06/06/2023 - 12:00

georgia.parmelee@gatech.edu

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Machine Learning Predicts Biodiversity and Resilience in the Coral Triangle

admin — Thu, 09 Feb 2023 16:52:52 +0000

Machine Learning Predicts Biodiversity and Resilience in the Coral Triangle admin Thu, 02/09/2023 - 11:52

Coral reef conservation is a steppingstone to protect marine biodiversity and life in the ocean as we know it. The health of coral also has huge societal implications: reef ecosystems provide sustenance and livelihoods for millions of people around the world. Conserving biodiversity in reef areas is both a social issue and a marine biodiversity priority.

In the face of climate change, Annalisa Bracco, professor in the School of Earth and Atmospheric Sciences at Georgia Institute of Technology, and Lyuba Novi, a postdoctoral researcher, offer a new methodology that could revolutionize how conservationists monitor coral. The researchers applied machine learning tools to study how climate impacts connectivity and biodiversity in the Pacific Ocean’s Coral Triangle — the most diverse and biologically complex marine ecosystem on the planet. Their research, recently published in Nature Communications Biology, overcomes time and resource barriers to contextualize the biodiversity of the Coral Triangle, while offering hope for better monitoring and protection in the future.

“We saw that the biodiversity of the Coral Triangle is incredibly dynamic,” Bracco said. “For a long time, it has been postulated that this is due to sea level change and distribution of land masses, but we are now starting to understand that there is more to the story.”

Connectivity refers to the conditions that allow different ecosystems to exchange genetic material such as eggs, larvae, or the young. Ocean currents spread genetic material and also create the dynamics that allow a body of water — and thus ecosystems — to maintain consistent chemical, biological, and physical properties. If coral larvae are spread to an ecoregion where the conditions are very similar to the original location, the larvae can start a new coral.

Bracco wanted to see how climate, and specifically the El Niño Southern Oscillation (ENSO) in its phases — El Niño, La Niña, and neutral conditions — impacts connectivity in the Coral Triangle. Climate events that move large masses of warm water in the Pacific Ocean bring enormous changes and have been known to exacerbate coral bleaching, in which corals turn white due to environmental stressors and become vulnerable to disease.

“Biologists collect data in situ, which is extremely important,” Bracco said. “But it’s not possible to monitor enormous regions in situ for many years — that would require a constant presence of scuba divers. So, figuring out how different ocean regions and large marine ecosystems are connected over time, especially in terms of foundational species, becomes important.”

Machine Learning for Discovering Connectivity

Years ago, Bracco and collaborators developed a tool, Delta Maps, that uses machine learning to identify “domains,” or regions within any kind of system that share the same dynamic. Bracco initially used it to analyze domains of climate variability in models but also suspected it could be used to study ecoregions in the ocean.

For this study, they used the tool to map out domains of connectivity in the Coral Triangle using 30 years of sea surface temperature data. Sea surface temperatures change in response to ocean currents over scales of weeks and months and across distances of tens of kilometers. These changes are relevant to coral connectivity, so the researchers built their machine learning tool based on this observation, using changes in surface ocean temperature to identify regions connected by currents. They also separated the time periods that they were considering into three categories: El Niño events, La Niña events, and neutral or “normal” times, painting a picture of how connectivity was impacted during major climate events in particular ecoregions.

Novi then applied a ranking system to the different ecoregions they identified. She used rank page centrality, a machine learning tool that was invented to rank webpages on the internet, on top of Delta Maps to identify which coral ecoregions were most strongly connected and able to receive the most coral larvae from other regions. Those regions would be the ones most likely sustain and survive through a bleaching event.

Climate Dynamics and Biodiversity

Bracco and Novi found that climate dynamics have contributed to biodiversity because of the way climate introduces variability to the currents in the equatorial Pacific Ocean. The researchers realized that alternation of El Niño and La Niña events has allowed for enormous genetic exchanges between the Indian and Pacific Oceans and enabled the ecosystems to survive through a variety of different climate situations.

“There is never an identical connection between ecoregions in all ENSO phases,” Bracco said. “In other parts of the world ocean, coral reefs are connected through a fixed, often small, number of ecoregions, and if you eliminate this fixed number of connections by bleaching all connected reefs, you will not be able to rebuild the corals in any of them. But in the Pacific the connections are changing all the time and are so dynamic that soon enough the bleached reef will receive larvae from completely different ecoregions in a different ENSO phase.”

They also concluded that, because of the Coral Triangle’s dynamic climate component, there is more possibility for rebuilding biodiversity there than anywhere else on the planet. And that the evolution of biodiversity in the Coral Triangle is not only linked to landmasses or sea levels but also to the evolution of ENSO through geological times. The researchers found that though ENSO causes coral bleaching, it has helped the Coral Triangle become so rich in biodiversity.

Better Monitoring Opportunities

Because coral reef survival has been designated a priority by the United Nations Sustainable Development Goals, Bracco and Novi’s research is poised to have broad applications. The researchers’ method identified which ecoregions conservationists should try hardest to protect and also the regions that conservationists could expect to have the most luck with protection measures. Their methodology can also help to identify which regions should be monitored more and the ones that could be considered lower priority for now due to the ways they are currently thriving.

“This research opens a lot of possibilities for better monitoring strategies, and especially how to monitor given a limited amount of resources and money,” Bracco said. “As of now, coral monitoring often happens when groups have a limited amount of funding to apply to a very specific localized region. We hope our method can be used to create a better monitoring over larger scales of time and space.”

CITATION: Novi, L., Bracco, A. “Machine learning prediction of connectivity, biodiversity and resilience in the Coral Triangle.” Commun Biol 5, 1359 (2022).

DOI: https://doi.org/10.1038/s42003-022-04330-8

Summary sentence

The team's new methodology offers hope for better coral connectivity monitoring and protection in the future.

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Dateline

Thu, 02/09/2023 - 12:00

catherine.barzler@gatech.edu

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Thu, 02/09/2023 - 11:38

AI-ALOE Brings AI-based Ecological Research Power To Local Technical College

admin — Tue, 25 Oct 2022 19:05:12 +0000

AI-ALOE Brings AI-based Ecological Research Power To Local Technical College admin Tue, 10/25/2022 - 15:05

During the summer, Duncan Hughes, an Environmental Technology instructor at North Georgia Technical College (NGTC) introduced his students to the web application Virtual Ecological Research Assistant, better known as VERA. It allowed students to construct conceptual models and ecological systems, as well as run interactive model simulations on the brook trout, a species of freshwater fish.

Hughes and his students sought to answer questions about reproduction and food supply, as they worked to add new complexities to the VERA application from different species of trout, circumstances, to changes. According to the Encyclopedia of Life (EOL), an international effort, led by the Smithsonian Institution's National Museum of Natural History, brook trout are found in three types of aquatic environments: rivers, lakes, and marine areas and their living requirements in these environments.

“Originally when we populated the brook trout, we noticed the brown trout shared the same life history and ecological information, but we were able to find enough information from the Encyclopedia of Life to differentiate those species,” said Hughes. “I had my students run through the process of building these components through an instructional-based format by having them manipulate some of the parameters and probabilities.”

VERA was developed by the Design & Intelligence Lab at Georgia Tech in collaboration with EOL. The technology is being used by students as an assisting tool and is publicly accessible. The data being collected from their usage is part of the research conducted at the NSF AI Institute for Adult Learning and Online Education (AI-ALOE).

“Users can jump into our program and conduct ‘what if’ experiments by adjusting simulation parameters. This is our way of providing an accessible and informal learning tool,” said Ashok Goel, director and co-principal Investigator of AI-ALOE and computer science professor at Georgia Tech. “Using VERA as an assessment tool is excellent. These students are using VERA in a way we are not.”

Goel was recently joined by Georgia Tech graduate researcher Andrew Hornback, research scientist Sandeep Kakar, and staff member Daniela Estrada at NGTC to learn more about the work in VERA and challenges Hughes and his students faced while using the application.

“The main struggle is limitation with the EOL and database,” said Hughes. “There are some species that we just can’t find, and sometimes it is glitchy and doesn’t work right away, but it is not insurmountable.”

Another challenge Hughes’ students found was not being able to find what they wanted to complete certain tasks, such as stream and environmental patterns of comparative fish ecosystems.

With that being known, AI-ALOE is working to address these issues and more to build and cater to specific student and teacher needs. At this time, the Design & Intelligence Laboratory is in the process of expanding VERA in the capability of its on-demand agent-based simulation generator, which would enable users to divide components into separate habitats.

“It was very interesting to see the results because antidotally through much research we were able to set up all these relationships and let them run the model, and the results were exactly what we would have hypothesized what they would be given those perimeters,” said Hughes.

The technical college has plans to introduce VERA to another classroom this semester held by Natural Resource Management instructor, Kevin Peyton.

About VERA

Interested in trying out VERA? Create an account at https://vera.cc.gatech.edu/. You can also find VERA’s user guide as well as a step-by-step tutorial at http://epi.vera.cc.gatech.edu/docs/exercise.

About AI-ALOE

The NSF AI Institute for Adult Learning and Online Education (AI-ALOE) is developing an AI-based transformative model for online adult learning through research and data collection.

About NGTC

North Georgia Technical College is a residential, public, multi-campus institution of higher education serving the workforce development needs of Northeast Georgia and part of the Technical College System of Georgia.

Summary sentence

The AI-ALOE Institute offers the Georgia Tech led web application VERA to local technical college.

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Tue, 10/25/2022 - 12:00

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Tue, 10/25/2022 - 12:35

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At the Intersection of Climate and AI, Machine Learning is Revolutionizing Climate Science

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Modeling climate, predicting weather

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Researchers Reveal Roadmap for AI Innovation in Brain and Language Learning

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Thinning Ice Sheets May Drive Sharp Rise in Subglacial Waters

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Scientists Unearth 20 Million Years of ‘Hot Spot’ Magmatism Under Cocos Plate

Zeroing in

Plumbing the depths

Connecting the (hot spot) dots

Plume hunters wanted

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AI Hub at Georgia Tech to Unite Campus in Artificial Intelligence R&D and Commercialization Efforts

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Machine Learning Predicts Biodiversity and Resilience in the Coral Triangle

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AI-ALOE Brings AI-based Ecological Research Power To Local Technical College

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