Come join us on June 11 for an ocean themed, casual vibe coding session! This meetup is a fantastic opportunity to break the ice for our ongoing AI Clinic and to meet our clinicians Devi and Emily. We will spend a couple of hours building small ocean focused projects together using various artificial intelligence tools.

Absolutely no experience is required. Just bring your curiosity and a willingness to experiment!

What to expect:
We are keeping the format very simple. We will start with brief introductions and share some project ideas. From there, you can form small groups, work solo, or we might even decide to build a single project all together. We will spend the majority of the time building, experimenting, and helping each other, followed by some optional demos at the end.

A few ideas already on the table:

• A web viewer for hosting ocean video datasets and annotations

• Tools that automate repetitive parts of ocean and environmental research workflows

• Ocean data exploration and visualization projects

• Whatever interesting ocean related idea you have been wanting to try!

When and Where:

• Date: June 11 (Thursday)

• Time: 2:00 PM to 3:30 PM ADT

• Location: Hybrid (Join us in person at the Steele Ocean Sciences Building Room 344, 6375 Edzell Castle Cir, Halifax, or virtually from anywhere)

Watch the event recording:

https://youtu.be/4NTUD-lH7Mk

Download the Slides: https://drive.google.com/file/d/1A0LOyG2e7ELWjJnV3T9Zf4i0oN-WU6yZ/view?usp=drive_link

Video Timeline:

• 0:00:00 | Slide #1-3 | Opening & Welcome

• 0:08:22 | Slide #4-42 |Keynote: The Pan-Canadian AI Strategy - From Research to Impact (Dr. Elissa Strome (CIFAR))

• 0:56:47 | Slide #44-59 | Environmental Data as a Geopolitical Battleground: AI & Open Data Sovereignty (Dr. Schallum Pierre (Université Laval))

• 1:15:36 | Slide #61-68 | From Project to Community: Building Bridges through a Community (Shayla Fitzsimmons (CIOOS Atlantic))

• 1:25:37 | Slide #70-83 | Interactive Session

• 1:40:55 | Slide #85-132 | Keynote: ML Models for Decarbonized & Efficient Maritime Supply Chains (Dr. Loubna Benabbou (UQAR & Mila))

• 2:18:58 | Slide #134-155 | Scaling up MPA Effectiveness Tracking for Global Targets (Beth Pike & Nikki Harasta (Marine Conservation Institute))

• 2:38:02 | Slide #157-176 | Successfully Delivering AI & Technology Products for Ocean Impact (Ted Schmitt (Allen Institute for AI - Ai2))

• 3:03:40 | Slide #178-188 | From Process to Impact: An AI Acceleration Methodology for Conservation (Diego Padilla Huamán (Equilibria) & Fanny M. Cornejo (Yunkawasi))

• 3:23:53 | Slide #189-190 | Wrap-up & Closing

Additional Resources shared by presenters:

Dr. Elissa Strome

• First Nations Principles of OCAP

• CIFAR Indigenous Perspectives in AI course

• Ontario Privacy Commissioner Principles for Responsible AI

• ISED Voluntary Code of Conduct for Generative AI

Dr. Schallum Pierre

• Article in French published in the Revue Défense Nationale: “Artificial Intelligence in ISR: The Ethical Stakes and Perspectives for Canada in the Face of Hybrid Threats” https://shs.cairn.info/journal-revue-defense-nationale-2026-1-page-93?lang=en&tab=resume

Dr. Loubna Benabbou

• ETA prediction: https://ieeexplore.ieee.org/abstract/document/10373707

• ETA monitoring: https://link.springer.com/article/10.1007/s13437-024-00342-9

• Speed prediction: https://www.mdpi.com/2077-1312/11/1/191?trk=organization_guest_main-feed-card-text

• Speed T water prediction: https://neurips.cc/virtual/2024/100566

• Currents Data downscalling: https://www.climatechange.ai/papers/neurips2025/49

• Emission prediction: https://www.climatechange.ai/papers/neurips2025/87

• Bio-Maribe Data Denoising: https://neurips.cc/virtual/2025/loc/san-diego/126987

Nikki Harasta and Beth Pike:

• MPAtlas: https://mpatlas.org/

• The MPA Guide: https://mpa-guide.protectedplanet.net/resources

• Gem Generator Prompt: https://github.com/Epersonf/gemini-gems/blob/main/Gem-Generator.md

• Elements of AI: https://course.elementsofai.com/

• Building Systems with ChatGPT API: https://learn.deeplearning.ai/courses/chatgpt-building-system/lesson/k0pk1/introduction

• Building Apps with Windsurf: https://learn.deeplearning.ai/courses/build-apps-with-windsurfs-ai-coding-agents/lesson/ym8if/introduction

• Windsurf: https://windsurf.com/pricing

 Questions that received written answers:

Shayla Fitzsimmons

Q: Could you tell us about some of the learning opportunities available in the Community?

A: Absolutely! We currently have two free and self-paced courses available, one that introduces AI from an oceans lens, and a second that introduces AI and ethics in an ocean context. And that's just the start--keep on eye on the community, there's more to come!

Dr. Loubna Benabbou

Q: Has there been any work done on how vessel optimization efforts (optimizing speed, fuel usage, etc.) contribute to or subtract from noise reduction efforts?

A: We are working on that in a research project with my colleagues at ISMER and we will soon publish a paper on the subject.

Q: Given AI for weather forecast research has gotten lots of attention recently, with some models like Aurora outperforming traditional models in storm prediction. Do you think there is a lot of potential for AI in oceanography, for example prediction of currents data?

A: Yes, I believe that hybrid physical models will accelerate the research and discovery in oceanography. We are working with our colleagues at ISMER to use ML models in current prediction.

Q: How confident are you that the maritime industry—particularly shipowners—will adopt artificial intelligence tools and agree to share their data?

A: It's very difficult to put a level of confidence, but in our collaboration, we are offering a free tool so they were very interested. We are working only with accessible data.

Q: I am curious about the precision of ML vs deep learning algorithms for vessel speed, ETA or emission prediction. Has there been important developments in deep learning approaches in recent years, how do they compare to traditional ML?

A: Effectively, with Trasformer and XLSTM we had a more accurate prediction.

Q: First off, thank you for your interesting and thorough presentation, my question is the following : If we do manage to reduce emissions by improving trajectories and technologies, have you taken into account how the rebound effect (reduction in expected gains from new technologies that increase the efficiency of resource use, because of behavioral or other systemic responses) could influence emissions in the long run?

A: Excellent question, we are working on that and also on how we can have a multi-objective optimization to take into account the reduction of emissions and noise, and also to ensure operational performance.

Beth Pike

Q: Great presentation! May have missed this, but what are the final outputs of the workflow you described, and how do they finally help you assess the MPAs?

A: Currently we are using the tool to check assessments, both before and after human conducted assessments to monitor for good outcomes and refining the tool. We hope to someday make the tool accessible to users who want to input data from their own MPAs to get an assessment for their MPA against the MPA Guide framework.

Ted Schmitt

Q: What standards or methodologies do you use on the back-end to integrate datasets from multiple sources?

A: We have so far integrated open source data, such as Sentinel 1, Sentinel 2, and Landsat. We are just beginning to pull in other data sources, and questions of standards and methodologies are still being developed. There is a whole question of how to make data AI ready. We are participating in efforts like the NSF funded FAIR grant: https://www.nsf.gov/awardsearch/show-award?AWD_ID=2531922

Q: What types of data do Skylight and Shippy use in their models?

A: Sentinel 1, Sentinel 2, VIIRS, and AIS are the primary satellite data sources. We do use high-res imagery from Maxar (now Vantor) on a very limited basis. But we squeeze as much as we can out of free, open source data to keep costs down. For Shippy, we have started pulling in data from sources like SkyTruth's Cerulian, Global Fishing Watch, and Protected Seas Navigator as just a few initial examples.

Q: Ted, thank you for a fantastic presentation! There are some interesting models that I'm keen to explore more. A number of your examples were understandably based on land data. Can the same principles be applied in the OlmoEarth Studio environment to develop models based on ocean data?

A: Thank you! We would very much like to do that, but have not yet begun. I'd be happy to have a conversation with anyone interested in doing that. My email is teds@allenai.org.

Fanny M. Cornejo

Q: Great presentation Fanny! You've described all kinds of different gains and benefits of implementing AI in your processes; was there anything that you think has been -lost- in making this switch? For example in some of the more human components of your work, such as storytelling.

A: Thank you for the question! Comms it’s a good example. We still “talk” to the AI tools we use to give context and ensure the stories still have a heart. However, in terms of giving structure, grammar, language, etc., it has definitely made things more efficient!

I am wondering if there is any open source model specialized in sensitive data detection to integrate in our application.

Thanks in advance,

Yagmur

As part of this main activity, we're also hosting a meetup event which is a fantastic opportunity to break the ice for our ongoing AI Clinic and to meet our clinicians Devi and Emily. Come join us on June 11 for an ocean themed, casual vibe coding session! We will spend a couple of hours building small ocean focused projects together using various artificial intelligence tools. See details and register here: https://buildingbridges.cioos.ca/launch-event/~event/ocean-vibe-coding-meetup-bCuBGEsP7RTMy5X

This is an introduction to AI ethics for people working in ocean sectors, particularly in not-for-profit contexts, and particularly in Canada. Part 1 covers the introduction to the course and AI ethics, its intersections with oceans work, and identifying stakeholders and rights-holders in ocean scenarios; data governance and stewardship; and bias, fairness and representation; while in Part 2 we'll explore transparency, reliability, and trust; human agency, the environmental costs of AI, and conflicting values; and accountability, governance, and legal considerations.

This course builds upon the basic mechanics of AI tools and systems introduced in the first Building Bridges course, with ethics as an important part of developing AI literacy. Learners will explore ocean AI scenarios and the ethical issues that arise from them, via lectures, external resources, exercises, and practical guidance. What discoveries can be made, if we can just connect the dots - safely, responsibly, and ethically?

"No country alone can afford the cost of sustaining the global ocean observing system necessary to perform long-term monitoring of the physical and biochemical state of the ocean. Thus, observing and monitoring the ocean requires a global collective approach that requires combining different observing systems often created for specific purposes."

World Ocean Database 2023: A Foundational Data Resource for and by the Global Ocean and Coastal Communities | Scientific Data

This literature review was developed through a collaboration of Building Bridges partners and written by Tiara Mulder at Dalhousie University.

Instead, CIOOS Atlantic has developed a data interpolation model to gap fill this kind of high intermittent sampling. Use the GitHub code to explore multiple ML algorithms including linear regression, polynomial regression, support vector regression, random forests, multilayer perceptron, recurrent neural networks, and autoencoders to assess these different approaches.