NORTHAMPTON, MA / ACCESSWIRE / January 20, 2023 / This Mentoring Month we're celebrating IBM mentors who help mentees connect to the world of work. Kyle volunteers with organizations from mentoring students in building robots to tutoring high school programming classes to teaching girls to program mobile apps for social impact.

Learn more about IBM skills programs and mentors at https://skillsbuild.org/

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Remember when, not so long ago, tech companies couldn’t hire fast enough? Talent wars were fought and won all over Silicon Valley, as firms battled it out for the best in the business, each offering free-flowing perks more lavish than the next. As profits continued to climb, it seemed as if the party would never end. 

Then it did. And the layoff announcements began. First Amazon started letting go of what would be 18,000 employees, before companies such as Salesforce, IBM, Snap, and Coinbase followed suit. Twitter let go of almost half its workforce, and Meta laid off around 11,000 people. 

Last week, Google’s parent company, Alphabet, said it planned to lay off 12,000 of its people, Microsoft  said it would cut 10,000 employees, and Spotify said it would reduce its staff by 6 percent, about 600 people. In total, more than 216,000 tech employees have been laid off by more than 1,185 companies since the start of 2022, according to Layoffs.fyi, a site that tracks job cuts in the sector. 

Goodbye The Great Resignation, hello The Great Apprehension. It’s little wonder that 78 percentof American workers are scared about their job security, while 85 percent of workers rank job loss as a top concern, according to the latest Edelman Trust Barometer. Layoffs are among life’s most challenging experiences, after all.  

Know the signs 

So, how do you know when one might be coming? There are some signs to look out for: a memo from the CEO about cost savings or efficiency, and/or the departure of some top-level executives. There might be a shift in communication and transparency. Or maybe it just feels like everyone’s walking on eggshells.  

If any of this sounds familiar, now is not the time to idly sit and wait. Instead, arm yourself with options, and perhaps even see this as an opportunity to take your career to the next level.  

But first, know that being laid off isn’t a reflection of you and your hard work––it highlights your former company’s lack of planning, or a change in its strategy. Also, don’t worry about how it’s going to look on your resume. Getting laid off no longer carries the stigma it once did, particularly at this point in time.  

When you start to look for a new role, write down exactly what you want in your career, and what’s important to you in your next move. These values will help keep you aligned during your search. Do you want to work for a big company or a small company? Would you consider moving states or countries? Maybe it’s time for a different career?  

Enter, some good news (finally): There are lots of industries that are booming––the federal government, nonprofits, private companies, healthcare and higher education––and they’re just waiting to scoop up people with the right skills. What’s more, the labor market is still strong, with 10 million jobs currently open––up 60 percent pre-pandemic levels., meaning the overall unemployment rate in the U.S. is at a low 3.5 percent.  

And finally, not all companies have announced a round of job cuts in the last six months. Here are three firms that are hiring, plus, you can discover hundreds more open roles on The Hill Jobs. 

Senior Manager, International Tax, Deloitte 

As a Tax Senior Manager, you will work within an engagement team and draw on experience in accounting and taxation, assisting clients with their implementation of specific international tax structures and processes. Overseeing complex tax computation projects for clients in diverse industries and researching and preparing materials for consulting projects is also a key component of this role. To be suitable, you’ll need eight years’ of experience providing tax planning services or preparing and reviewing client work, preferably with a focus on international taxation. A Bachelor’s degree in accounting, finance or another business-related field is needed as is previous Big 4 experience, public accounting or consulting experience. Get the full job description here. 

Manager, Illiquid Credit Fund Management, The Carlyle Group 

The Carlyle Group is looking for a Manager, Illiquid Credit Fund Management to assist in all aspects of the fund management of certain Illiquid Credit Carry Funds, with involvement across the broader credit fund management group. Responsibilities will include accounting and reporting, oversight and review of quarterly fund closes, which includes review of related support for all balance sheet and income statement accounts. A Bachelor’s degree in accounting and/or finance is required for this position, with a CPA preferred. A minimum of five to seven years’ experience either in private credit or public accounting with supervisory experience, plus experience in alternatives, familiar with fund accounting/GP/LP structures and/or SMAs is necessary. Apply for this job here. 

Editorial Director (Senior Writer & Editor), Commonwealth Foundation 

The Commonwealth Foundation is seeking a forward-thinking Senior Writer & Editor to develop a world-class editorial capability and to lead the editorial strategy for owned and placed content. 

You’ll serve as lead writer and editor within the organization, pitch editors at prominent state and national outlets, and oversee the content pipeline, working closely with members of the Commonwealth Foundation production team to provide exceptional writing and editing services that advance policy goals and business objectives. Over five years’ experience as a policy and opinion writer, able to craft strong arguments and secure op-ed placements in prominent outlets at the state and national level is required as is a track record of identifying opportunities and executing on them rapidly, either independently or by mobilizing a team. Find out more about this role here. 

For more career opportunities and to find a role that you love, visit The Hill Jobs.  

Powerful machine-learning models are being used to help people tackle tough problems such as identifying disease in medical images or detecting road obstacles for autonomous vehicles. But machine-learning models can make mistakes, so in high-stakes settings it’s critical that humans know when to trust a model’s predictions.

Uncertainty quantification is one tool that improves a model’s reliability; the model produces a score along with the prediction that expresses a confidence level that the prediction is correct. While uncertainty quantification can be useful, existing methods typically require retraining the entire model to deliver it that ability. Training involves showing a model millions of examples so it can learn a task. Retraining then requires millions of new data inputs, which can be expensive and difficult to obtain, and also uses huge amounts of computing resources.

Researchers at MIT and the MIT-IBM Watson AI Lab have now developed a technique that enables a model to perform more effective uncertainty quantification, while using far fewer computing resources than other methods, and no additional data. Their technique, which does not require a user to retrain or modify a model, is flexible enough for many applications.

The technique involves creating a simpler companion model that assists the original machine-learning model in estimating uncertainty. This smaller model is designed to identify different types of uncertainty, which can help researchers drill down on the root cause of inaccurate predictions.

“Uncertainty quantification is essential for both developers and users of machine-learning models. Developers can utilize uncertainty measurements to help develop more robust models, while for users, it can add another layer of trust and reliability when deploying models in the real world. Our work leads to a more flexible and practical solution for uncertainty quantification,” says Maohao Shen, an electrical engineering and computer science graduate student and lead author of a paper on this technique.

Shen wrote the paper with Yuheng Bu, a former postdoc in the Research Laboratory of Electronics (RLE) who is now an assistant professor at the University of Florida; Prasanna Sattigeri, Soumya Ghosh, and Subhro Das, research staff members at the MIT-IBM Watson AI Lab; and senior author Gregory Wornell, the Sumitomo Professor in Engineering who leads the Signals, Information, and Algorithms Laboratory RLE and is a member of the MIT-IBM Watson AI Lab. The research will be presented at the AAAI Conference on Artificial Intelligence.

Quantifying uncertainty

In uncertainty quantification, a machine-learning model generates a numerical score with each output to reflect its confidence in that prediction’s accuracy. Incorporating uncertainty quantification by building a new model from scratch or retraining an existing model typically requires a large amount of data and expensive computation, which is often impractical. What’s more, existing methods sometimes have the unintended consequence of degrading the quality of the model’s predictions.

The MIT and MIT-IBM Watson AI Lab researchers have thus zeroed in on the following problem: Given a pretrained model, how can they enable it to perform effective uncertainty quantification?

They solve this by creating a smaller and simpler model, known as a metamodel, that attaches to the larger, pretrained model and uses the features that larger model has already learned to help it make uncertainty quantification assessments.

“The metamodel can be applied to any pretrained model. It is better to have access to the internals of the model, because we can get much more information about the base model, but it will also work if you just have a final output. It can still predict a confidence score,” Sattigeri says.

They design the metamodel to produce the uncertainty quantification output using a technique that includes both types of uncertainty: data uncertainty and model uncertainty. Data uncertainty is caused by corrupted data or inaccurate labels and can only be reduced by fixing the dataset or gathering new data. In model uncertainty, the model is not sure how to explain the newly observed data and might make incorrect predictions, most likely because it hasn’t seen enough similar training examples. This issue is an especially challenging but common problem when models are deployed. In real-world settings, they often encounter data that are different from the training dataset.

“Has the reliability of your decisions changed when you use the model in a new setting? You want some way to have confidence in whether it is working in this new regime or whether you need to collect training data for this particular new setting,” Wornell says.

Validating the quantification

Once a model produces an uncertainty quantification score, the user still needs some assurance that the score itself is accurate. Researchers often validate accuracy by creating a smaller dataset, held out from the original training data, and then testing the model on the held-out data. However, this technique does not work well in measuring uncertainty quantification because the model can achieve good prediction accuracy while still being over-confident, Shen says.

They created a new validation technique by adding noise to the data in the validation set — this noisy data is more like out-of-distribution data that can cause model uncertainty. The researchers use this noisy dataset to evaluate uncertainty quantifications.

They tested their approach by seeing how well a meta-model could capture different types of uncertainty for various downstream tasks, including out-of-distribution detection and misclassification detection. Their method not only outperformed all the baselines in each downstream task but also required less training time to achieve those results.

This technique could help researchers enable more machine-learning models to effectively perform uncertainty quantification, ultimately aiding users in making better decisions about when to trust predictions.

Moving forward, the researchers want to adapt their technique for newer classes of models, such as large language models that have a different structure than a traditional neural network, Shen says.

The work was funded, in part, by the MIT-IBM Watson AI Lab and the U.S. National Science Foundation.

Foundation models are changing the way that artificial intelligence (AI) and machine learning (ML) are able to be used. All that power comes with a cost though, as building AI foundation models is a resource-intensive task.

IBM announced today that it has built out its own AI supercomputer to serve as the literal foundation for its foundation model–training research and development initiatives. Named Vela, it’s been designed as a cloud-native system that makes use of industry-standard hardware, including x86 silicon, Nvidia GPUs and ethernet-based networking.

The software stack that enables the foundation model training makes use of a series of open-source technologies including Kubernetes, PyTorch and Ray. While IBM is only now officially revealing the existence of the Vela system, it has actually been online in various capacities since May 2022.

“We really think this technology concept around foundation models has huge, tremendous disruptive potential,” Talia Gershon, director of hybrid cloud infrastructure research at IBM, told VentureBeat. “So, as a division and as a company, we’re investing heavily in this technology.”

The AI- and budget-friendly foundation inside Vela

IBM is no stranger to the world of high-performance computing (HPC) and supercomputers. One of the fastest supercomputers on the planet today is the Summit supercomputer built by IBM and currently deployed in the Oak Ridge National Laboratory.

The Vela system, however, isn’t like other supercomputer systems that IBM has built to date. For starters, the Vela system is optimized for AI and uses x86 commodity hardware, as opposed to the more exotic (and expensive) equipment typically found in HPC systems.

Unlike Summit, which uses the IBM Power processor, each Vela node has a pair of Intel Xeon Scalable processors. IBM is also loading up on Nvidia GPUs, with each node in the supercomputer packed with eight 80GB A100 GPUs. In terms of connectivity, each of the compute nodes is connected via multiple 100 gigabits-per-second ethernet network interfaces. 

Vela has also been purpose built for cloud native, meaning it runs Kubernetes and containers to enable application workloads. More specifically, Vela relies on Red Hat OpenShift, which is Red Hat’s Kubernetes platform. Vela has also been optimized to run PyTorch for ML training and uses Ray to help scale workloads.

IBM has also built out a new workload-scheduling system for its new cloud-native supercomputer. For many of its HPC systems, IBM has long used its own Spectrum LSF (load-sharing facility) for scheduling, but that system is not what the new Vela supercomputer is using. IBM has developed a new scheduler called MCAD (multicluster app dispatcher) to handle cloud-native job scheduling for foundation model AI training.

IBM’s growing foundation model portfolio

All that hardware and software that IBM put together for Vela is already being used to support IBM’s foundation model efforts.

“All of our foundation models’ research and development are all running cloud native on that stack on the Vela system and IBM Cloud,” Gershon said.

Just last week, IBM announced a partnership with NASA to help build out foundation models for climate science. IBM is also working on a foundation model called MoLFormer-XL for life sciences that can help create new molecules in the future.

The foundation model work also extends to enterprise IT with the Project Wisdom effort that was announced in October 2022. Project Wisdom is being developed in support of the Red Hat Ansible IT configuration technology. Typically, IT system configuration can be a complicated exercise that requires domain knowledge to do properly. Project Wisdom aims to bring a natural language interface to Ansible, whereby users will simply type in what they want and the foundation model will understand and then help execute the desired task.

Gershon also hinted at a new IBM foundation model for cybersecurity that has not yet been publicly detailed and is being developed using the Vela supercomputer.

“We haven’t said much about it externally, I think on purpose,” Gershon said about the foundation model for cybersecurity. “We do believe this technology is going to be transformational in terms of detecting threats.”

While IBM is building out a portfolio of foundation models, it is not intending to directly compete against some of the well-known general foundation models, such as OpenAI’s GPT-3.

“We are not focused on necessarily building general AI, whereas maybe some other players kind of state that more as the goal,” Gershon said. “We’re interested in foundation models because we think that it has tremendous business value for enterprise use cases.”

New IBM Foundation Model Technology Leverages NASA Earth Science Data for Geospatial Intelligence

YORKTOWN HEIGHTS, N.Y., Feb. 1, 2023 /CNW/ -- IBM (NYSE: IBM) and NASA's Marshall Space Flight Center today announce a collaboration to use IBM's artificial intelligence (AI) technology to discover new insights in NASA's massive trove of Earth and geospatial science data. The joint work will apply AI foundation model technology to NASA's Earth-observing satellite data for the first time.

Foundation models are types of AI models that are trained on a broad set of unlabeled data, can be used for different tasks, and can apply information about one situation to another. These models have rapidly advanced the field of natural language processing (NLP) technology over the last five years, and IBM is pioneering applications of foundation models beyond language.

Earth observations that allow scientists to study and monitor our planet are being gathered at unprecedented rates and volume. New and innovative approaches are required to extract knowledge from these vast data resources. The goal of this work is to provide an easier way for researchers to analyze and draw insights from these large datasets. IBM's foundation model technology has the potential to speed up the discovery and analysis of these data in order to quickly advance the scientific understanding of Earth and response to climate-related issues.

IBM and NASA plan to develop several new technologies to extract insights from Earth observations. One project will train an IBM geospatial intelligence foundation model on NASA's Harmonized Landsat Sentinel-2 (HLS) dataset, a record of land cover and land use changes captured by Earth-orbiting satellites. By analyzing petabytes of satellite data to identify changes in the geographic footprint of phenomena such as natural disasters, cyclical crop yields, and wildlife habitats, this foundation model technology will help researchers provide critical analysis of our planet's environmental systems.

Story continues

Another output from this collaboration is expected to be an easily searchable corpus of Earth science literature. IBM has developed an NLP model trained on nearly 300,000 Earth science journal articles to organize the literature and make it easier to discover new knowledge. Containing one of the largest AI workloads trained on Red Hat's OpenShift software to date, the fully trained model uses PrimeQA, IBM's open-source multilingual question-answering system. Beyond providing a resource to researchers, the new language model for Earth science could be infused into NASA's scientific data management and stewardship processes.

"The beauty of foundation models is they can potentially be used for many downstream applications," said Rahul Ramachandran, senior research scientist at NASA's Marshall Space Flight Center in Huntsville, Alabama. "Building these foundation models cannot be tackled by small teams," he added. "You need teams across different organizations to bring their different perspectives, resources, and skill sets."

"Foundation models have proven successful in natural language processing, and it's time to expand that to new domains and modalities important for business and society," said Raghu Ganti, principal researcher at IBM. "Applying foundation models to geospatial, event-sequence, time-series, and other non-language factors within Earth science data could make enormously valuable insights and information suddenly available to a much wider group of researchers, businesses, and citizens. Ultimately, it could facilitate a larger number of people working on some of our most pressing climate issues."

Other potential IBM-NASA joint projects in this agreement include constructing a foundation model for weather and climate prediction using MERRA-2, a dataset of atmospheric observations. This collaboration is part of NASA's Open-Source Science Initiative, a commitment to building an inclusive, transparent, and collaborative open science community over the next decade.

For more information about this collaboration, visit the IBM Research Blog.

Statements regarding IBM's future direction and intent are subject to change or withdrawal without notice, and represent goals and objectives only.

Press contact
Bethany Hill McCarthy
IBM Research
Bethany@ibm.com

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SOURCE IBM

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U.S. space agency NASA isn’t just concerned about exploring outer space, it’s also concerned about helping humanity to learn more about the planet Earth and the impacts of climate change.

Today, NASA and IBM announced a partnership that will see the development of new artificial intelligence (AI) foundation models to help analyze geospatial satellite data, in a bid to help better understand and take action on climate change. To date, NASA has largely relied on the development of its own set of bespoke AI models to serve specific use cases. The promise of the foundation model approach is a large language model (LLM) that has been trained on lots of data that can serve as a more general purpose system that can be customized as needed.

Among the initial goals of the partnership is to train a foundation model on NASA’s Harmonized Landsat Sentinel-2 (HLS) dataset, which has petabytes of data collected from space about land use changes on Earth.

Beyond just helping to Boost the state of climate analysis on Earth, IBM is hopeful that the new foundation model that it develops jointly with NASA will have broader applicability and a positive impact for enterprise use cases of AI as well.

“What we’re doing with NASA is going to help us push innovation all the way from infrastructure and hardware up through distributed systems platforms, middleware and the applications themselves,” Priya Nagpurkar, VP, hybrid cloud platform and developer productivity at IBM Research, said during a press briefing announcing the partnership. “And it will include driving advances in AI architectures, and even data management techniques.”

Houston, we have a (big data) problem

To put it mildly, NASA has a lot of data.

Rahul Ramachandran, senior research scientist at NASA’s Marshall Space Flight Center in Huntsville, Alabama, explained during the press briefing that NASA actually has the largest collection of Earth observation data. That data has been collected to support NASA’s science mission to understand planet Earth as a complex system. The data comes from various instruments, and currently includes an archive with 70 petabytes of data. The archive is projected to grow within a few years to 250 petabytes.

“Clearly, given the scale of the data that we have, we have a big data problem,” Ramachandran said. “Our goal is to make our data discoverable, accessible and usable for broad scientific use in applications worldwide.”

Ramachandran added that NASA is always looking for new approaches and technologies that will help streamline the research process, as well as lower the barrier to entry for end users to utilize the complex science data held by the space agency. That’s where the development of foundation models comes into play to make it easier to benefit from the data that NASA has collected. 

The potential for the foundation model that NASA is building with IBM could literally be life changing for humanity.

For example, Ramachandran said that building a foundation model that has satellite image data could make it easier for someone in a disaster area to identify the extent of flooding, where the model automatically maps where the flooding is occurring. Another example could be identifying damage in a hurricane zone.

PyTorch and open-source AI will also benefit

On the technology side, IBM will be making extensive use of a series of technologies, including Red Hat OpenShift, for running the AI-training workloads and open-source machine learning frameworks, notably PyTorch.

The open-source PyTorch machine learning framework was started at Facebook (now known as Meta) and spun off as its own PyTorch Foundation in September 2022. IBM Research has been an active contributor to PyTorch, integrating capabilities into the PyTorch 1.13 release framework to help run large workloads on commodity hardware.

Raghu Ganti, principal researcher at IBM Research, said PyTorch is a core element of IBM’s AI strategy.

“We solely rely on PyTorch for training all our foundation models,” Ganti said. 

Ganti added that IBM will continue to contribute back to the PyTorch community as it continues to innovate on the technology to build increasingly powerful foundation models. In Ganti’s view, the joint effort with NASA to build foundation models will have multiple applications and broad impact.

“I think it will augment and accelerate the scientific process in terms of building and solving specific science problems,” he said. “Instead of people having to build their own individual machine learning pipeline, starting from collecting the large volumes of training data.”

New IBM Foundation Model Technology Leverages NASA Earth Science Data for Geospatial Intelligence

YORKTOWN HEIGHTS, N.Y., Feb. 1, 2023 /PRNewswire/ -- IBM (NYSE: IBM) and NASA's Marshall Space Flight Center today announce a collaboration to use IBM's artificial intelligence (AI) technology to discover new insights in NASA's massive trove of Earth and geospatial science data. The joint work will apply AI foundation model technology to NASA's Earth-observing satellite data for the first time.

Foundation models are types of AI models that are trained on a broad set of unlabeled data, can be used for different tasks, and can apply information about one situation to another. These models have rapidly advanced the field of natural language processing (NLP) technology over the last five years, and IBM is pioneering applications of foundation models beyond language.

Earth observations that allow scientists to study and monitor our planet are being gathered at unprecedented rates and volume. New and innovative approaches are required to extract knowledge from these vast data resources. The goal of this work is to provide an easier way for researchers to analyze and draw insights from these large datasets. IBM's foundation model technology has the potential to speed up the discovery and analysis of these data in order to quickly advance the scientific understanding of Earth and response to climate-related issues. 

IBM and NASA plan to develop several new technologies to extract insights from Earth observations. One project will train an IBM geospatial intelligence foundation model on NASA's Harmonized Landsat Sentinel-2 (HLS) dataset, a record of land cover and land use changes captured by Earth-orbiting satellites. By analyzing petabytes of satellite data to identify changes in the geographic footprint of phenomena such as natural disasters, cyclical crop yields, and wildlife habitats, this foundation model technology will help researchers provide critical analysis of our planet's environmental systems.

Another output from this collaboration is expected to be an easily searchable corpus of Earth science literature. IBM has developed an NLP model trained on nearly 300,000 Earth science journal articles to organize the literature and make it easier to discover new knowledge. Containing one of the largest AI workloads trained on Red Hat's OpenShift software to date, the fully trained model uses PrimeQA, IBM's open-source multilingual question-answering system. Beyond providing a resource to researchers, the new language model for Earth science could be infused into NASA's scientific data management and stewardship processes.

"The beauty of foundation models is they can potentially be used for many downstream applications," said Rahul Ramachandran, senior research scientist at NASA's Marshall Space Flight Center in Huntsville, Alabama. "Building these foundation models cannot be tackled by small teams," he added. "You need teams across different organizations to bring their different perspectives, resources, and skill sets." 

"Foundation models have proven successful in natural language processing, and it's time to expand that to new domains and modalities important for business and society," said Raghu Ganti, principal researcher at IBM. "Applying foundation models to geospatial, event-sequence, time-series, and other non-language factors within Earth science data could make enormously valuable insights and information suddenly available to a much wider group of researchers, businesses, and citizens. Ultimately, it could facilitate a larger number of people working on some of our most pressing climate issues."

Other potential IBM-NASA joint projects in this agreement include constructing a foundation model for weather and climate prediction using MERRA-2, a dataset of atmospheric observations. This collaboration is part of NASA's Open-Source Science Initiative, a commitment to building an inclusive, transparent, and collaborative open science community over the next decade.

For more information about this collaboration, visit the IBM Research Blog.

Statements regarding IBM's future direction and intent are subject to change or withdrawal without notice, and represent goals and objectives only.

Press contact

Bethany Hill McCarthy

IBM Research

Bethany@ibm.com

View original content to download multimedia:https://www.prnewswire.com/news-releases/ibm-and-nasa-collaborate-to-research-impact-of-climate-change-with-ai-301735386.html

SOURCE IBM

IBM and NASA’s Marshall Space Flight Centre have announced a collaboration to use IBM’s artificial intelligence (AI) technology to discover new insights in NASA’s massive trove of Earth and geospatial science data. Their combined work will apply AI ‘foundation model’ technology to NASA’s Earth-observing satellite data for the first time, an IBM spokesperson informed businessline.

Foundation model at work

AI ‘foundation model technology’ is quickly gaining traction through models like ChatGPT and it has been applied to NASA’s Earth-observing satellite data for the first time, the spokesperson explained. Its goal is to advance the scientific understanding of and response to Earth and climate-related issues like natural disasters and warming temperatures.

‘Foundation models’ are types of AI models that are trained on a broad set of unlabelled data, can be used for different tasks, and can apply information about one situation to another. These models have rapidly advanced the field of natural language processing (NLP) technology over the last five years, and IBM is pioneering applications of foundation models beyond language.

Large volumes of data

Earth observations that allow scientists to study and monitor our planet are being gathered at unprecedented rates and volume. New and innovative approaches are required to extract knowledge from these vast data resources. The goal of this work is to provide an easier way for researchers to analyse and draw insights from these large datasets, the spokesperson said. IBM’s foundation model technology has the potential to speed up the discovery and analysis of these data in order to quickly advance the scientific understanding of Earth and response to climate-related issues. 

SWIR false colour composite of the snow-capped Himalayas on November 28, 2022. | Photo Credit: NASA IMPACT

IBM and NASA are planning to develop several new technologies to extract insights from Earth observations. One project will train an IBM geospatial intelligence foundation model on NASA’s Harmonised Landsat Sentinel-2 (HLS) dataset, a record of land cover and land use changes captured by Earth-orbiting satellites. By analysing petabytes of satellite data to identify changes in the geographic footprint of phenomena such as natural disasters, cyclical crop yields, and wildlife habitats, this foundation model technology will help researchers provide critical analysis of our planet’s environmental systems.

Develops new NLP model

Another output from this collaboration is expected to be an easily searchable corpus of Earth science literature. IBM has developed an NLP model trained on nearly three lakh Earth science journal articles to organise the literature and make it easier to discover new knowledge. Containing one of the largest AI workloads trained on Red Hat’s OpenShift software to date, the fully trained model uses PrimeQA, IBM’s open-source multilingual question-answering system. Beyond providing a resource to researchers, the new language model for Earth science could be infused into NASA’s scientific data management and stewardship processes.

Rahul Ramachandran, senior research scientist at NASA’s Marshall Space Flight Center in Huntsville, Alabama, said the beauty of foundation models is that the models can potentially be used for many downstream applications. “Building these foundation models cannot be tackled by small teams. You need teams across different organisations to bring their different perspectives, resources, and skill sets.” 

Valuable insights

Raghu Ganti, principal researcher at IBM, said foundation models have proven successful in NLP, and it’s time to expand that to new domains and modalities important for business and society.

“Applying foundation models to geospatial, event-sequence, time-series, and other non-language factors within Earth science data could make enormously valuable insights and information suddenly available to a much wider group of researchers, businesses, and citizens. Ultimately, it could facilitate a larger number of people working on some of our most pressing climate issues.”

Other potential IBM-NASA joint projects in this agreement include constructing a foundation model for weather and climate prediction using MERRA-2, a dataset of atmospheric observations, the spokesperson for IBM said. This collaboration is part of NASA’s Open-Source Science Initiative, a commitment to building an inclusive, transparent, and collaborative open science community over the next decade.