Fwd: [EBM Help] RESPONSES: Use of artificial intelligence (AI) in ocean conservation and management
Lesley H
Hope you are all having a spectacular new year!
Sharing this email from Open Communications from the Ocean (OCTO) with those who are interested in marine conservation. If you would like to join that listserve, please go here.
Best,
Lesley
From: sarah--- via EBMHelp <ebm...@list.octogroup.org>
Date: Wed, Jan 14, 2026 at 12:00 PM
Subject: [EBM Help] RESPONSES: Use of artificial intelligence (AI) in ocean conservation and management
To: Sarah Carr <sa...@octogroup.org>
Cc: <sa...@octogroup.org>, OCTO's EBM Help List <ebm...@list.octogroup.org>
Dear EBM Help community,
In November 2025, OCTO surveyed our communities (EBM Help, MPA Help, OceanPlastic List, OCTO Updates) about how ocean conservation and management practitioners are (or are not) currently using AI for their work. We got an amazing response with 173 submissions – thank you all so much for sharing your work and thoughts with us!
Below are the responses from community members that are currently using AI on how they are using it for their ocean conservation and management work (97 responses). As you scroll down, at the top is a categorization/overview of the variety of ways that practitioners are using AI. The raw responses from individual community members are below that. Given the number and variety of responses, we used AI (ChatGPT) to create the initial categorization of responses, and we then checked and improved upon that categorization.
If there are any responses from community members that particularly interest you, let us know, and we can make connections.
If you or colleagues have not responded to the survey but would like to add your experiences and thoughts on AI use in the field, we are still gathering responses here. We will share additional results from the survey soon, including ways that community members would like to be using AI and their concerns about the use of AI in the field.
Thank you again for sharing your experiences and expertise!
Sarah Carr
CATEGORIZATION OF AI USE BY OCEAN CONSERVATION AND MANAGEMENT PRACTITIONERS
1. COMPUTER VISION AND IMAGE / VIDEO ANALYSIS
1.1 Species Identification (Megafauna, Fish, Marine Mammals, etc.)
Uses involving recognition, identification, counting, and measuring organisms (and populations) from images, drones, remotely operated vehicles (ROVs), and satellite data.
Entries:
- Manta ray identification
- Turtle identification; turtle facial recognition
- Seal Codex for harbor seal identification
- Happywhale / Flukebook for humpback whales
- Counting whales from space (SPACEWHALE)
- White shark sizing/counting from drone footage
- Salmonid identification/enumeration for fisheries management
- AI species detection from baited remote underwater video systems (BRUVs) (training global classifiers)
- Fish identification from creel surveys (planned)
- Accelerating species identification in monitoring programs
- Species identification using unmanned aerial vehicles (UAVs) for megafauna
- Predictive AI to detect fish, sharks, seabirds from electronic monitoring footage
- Marine debris vs. driftwood/tundra test runs (contains species/detection overlap)
1.2 Habitat, Ecosystem, and Environmental Mapping
Extracting habitat and environmental condition information from imagery.
Entries:
- Seagrass mapping
- Benthos surveys and automated benthic composition (CoralNet, Coral Point Count with Excel extensions or CPCE)
- Coral identification (reef monitoring, ReefCloud)
- Coral cover classification (Great Reef Census deep learning)
- Ecosystem mapping via satellite spectral machine learning
- Reef health status analysis (satellite image comparisons)
- Seafloor mapping and habitat assessments (satellite/aerial)
- Marine habitat anomaly detection from ROV imagery
1.3 Marine Debris Detection and Classification and Pollution Monitoring
Identifying and classifying marine debris and pollution features.
Entries:
- EPS vs. durable buoy differentiation for aquaculture policy implementation
- Identifying the “10 most serious debris items” (Ten-2-One campaign)
- Plastic pollution item classification from drones
- Automated marine debris item classification (fieldwork)
- Oil spill detection from satellite imagery (SkyTruth Cerulean; INPE project)
- Debris detection tests with ChatGPT (marine debris among driftwood/tundra)
1.4 Monitoring, Control, Surveillance (MCS) and Enforcement
AI systems supporting illegal fishing detection, vessel pattern analysis, and maritime behavior modeling.
Entries:
- Behavioral modeling of human activity at sea with compliance expert systems
- IUU detection (Marine Monitor in Baja California Sur)
- MPA surveillance (Skylight platform)
- Automated scanning of Sentinel-1 radar for pollution events (SkyTruth)
2. MODELING, FORECASTING AND QUANTITATIVE ANALYSIS
2.1 Population/Ecosystem Modeling and Parameterization
AI used to improve quantitative ecological modeling.
Entries:
- AI extraction of ecological model parameters from literature
- Machine learning for environmental/species modeling
- Estimating clam dispersal distance with “disperseNN”
- Modeling used in reef surveys (Great Reef Census deep learning)
- Forecasting, modeling using convolutional neural networks (CNNs) and machine learning
- Out-of-kind mitigation modeling exploration
- Optimization, algebraic manipulation, simulation development (R/Python/Stata)
2.2 Spatial Analysis, Remote Sensing, Image-Derived Modeling
Entries:
- Reef health comparisons from satellite spectral bands
- Oil slick detection and geospatial database (SkyTruth Cerulean)
- Habitat mapping (coral, seagrass, benthic) tied to spatial models
- Ecosystem mapping via spectral machine learning
3. TEXT-BASED, LARGE LANGUAGE MODEL (LLM), AND NATURAL LANGUAGE PROCESSING (NLP) APPLICATIONS
3.1 Writing and Editing Assistance
Drafting, improving, rephrasing, and organizing text.
Entries:
- Drafting emails, reports, proposals, briefs
- Improving clarity, grammar, flow (many entries)
- Editing social media captions
- Turning articles into improved versions for public consumption
- Writing assistance for English-as-second-language practitioners
- Creating bios, public-facing snippets, and outreach text
- Improving project documents, pitch decks, roadmaps
- Correcting and shortening writing
- Structured narratives (organizing user-provided drafts)
- Editing for non-scientific audiences
3.2 Document Summarization and Synthesis
Summaries of long documents, meeting notes, interview databases, literature, etc.
Entries:
- AI notetakers for meetings (multiple entries: Zoom AI Notes, Copilot)
- Summarizing long reports, documents, and literature
- Summarizing papers, extracting action items
- Summarizing meeting minutes
- Organizing qualitative “lessons learned”
- First-pass summaries for many documents at scale
- Transforming large documents into briefing notes
- Converting qualitative workshop notes into quantitative data
- Reviewing similarities/differences across papers
- Generating podcasts of papers for auditory learning
3.3 Literature Review and Research Assistance
AI as a research assistant.
Entries:
- Literature search performed by AI
- Building paper databases, insight repositories
- Deep research on topics (Gemini)
- Regulatory and legal research (MPAs, environmental law)
- Quick screenings of search results
- Researching conservation tools
- Reviewing key terms in papers
- Searching for policies and guidelines
- Cross-referenced literature review tools
3.4 Translation and Language Support
Entries:
- Translating training materials
- General translation of reports, articles, text
- English as a Second Language (ESL) support for more formal writing
- Translation applied to coral reef monitoring platforms' results
3.5 NLP for Qualitative Data and Policy/Regulatory Analysis
Entries:
- Qualitative → quantitative survey/focus group NLP (LLM-based)
- Categorizing qualitative workshop data
- NLP extraction of “knowledge flows” from policy documents (published example)
- Summaries and analysis of regulatory documents and proposals
- Preliminary risk profiles for projects via Copilot
- Scanning policies, regulations, and laws
- Assessing International Finance Corporation (IFC) performance standards alignment for coastal/marine projects
4. CODING, DATA ANALYSIS AND SOFTWARE DEVELOPMENT
4.1 Coding Assistance and Debugging
Entries:
- Code debugging (R, Python, Stata)
- Writing code for data analysis
- Generating workflows and functions
- Prototyping apps, R packages, Stata routines
- Code writing: image analysis, signal processing
4.2 Data Processing and Analysis
Entries:
- Big image data processing pipelines
- Data consolidation and cleaning
- Automating data ingest and video annotation
- Speeding up analysis workflows
- Analysis for reports, manuscripts
5. PROJECT MANAGEMENT AND ADMINISTRATIVE AUTOMATION
5.1 Administrative Tasks
Entries:
- Meeting note-taking (AI notetakers, Copilot, Zoom AI)
- Writing internal notes, agendas, invites
- Formatting reports into chapters
- Human resources (HR) and recruitment assistance
- Organizing to-do lists
- Generating project plans and work plans
5.2 Grant Writing and Proposal Development
Entries:
- Drafting components of proposals and grants
- Automating grant application boilerplate
- Grant support for policy/regulatory sections
- Writing pitch decks and funding materials
6. EDUCATION, TRAINING AND CAPACITY BUILDING
6.1 Learning Tools
Entries:
- Structured technical learning (statistics, ecology, fisheries modeling)
- Online AI courses (PR/admin support)
- Podcast-style conversions of papers
- Guided teaching on new topics
- Agents monitoring developments in industry/science/policy
6.2 Training Material Development
Entries:
- Translation of training materials
- Creating infographics and visuals
- Guidelines for using AI for coastal fisheries (coming 2026)
- Presentation improvement and time-limit tuning
7. COMMUNICATIONS AND OUTREACH
7.1 Public-facing Communication
Entries:
- Writing/formatting articles on environmental issues
- Generating social media content, captions
- Creating outreach-ready infographics
- PR image creation
- Improving clarity of messaging across projects
8. NOVEL AI APPLICATIONS AND STARTUP/PRODUCT DEVELOPMENT
Entries:
- “Okhtapus” — AI-assisted global replication and innovation platform for coastal resilience
- Automated ROV anomaly detection for port infrastructure
RAW RESPONSES OF AI USE BY OCEAN CONSERVATION AND MANAGEMENT PRACTITIONERS
Responses to survey question 2: “If you answered "Yes" above, please describe any ways you (and/or your organization) are using AI in your ocean conservation and management work?”
I just use it normally for writing or collecting information
We have used Chat GPT to do a practice run on the capabilities of AI to detect marine debris among driftwood and tundra.
We use it more indirectly, in that turtle facial recognition software and benthic composition analysis use AI to generate results.
Marine species monitoring and protection, habitat mapping and health assessment (satellite and aerial imagery analysis and seafloor mapping), combating Illegal, Unreported, and Unregulated (IUU) fishing.
We had been deployed Marine Monitors along the east coast of Baja California Sur, to detect, prevent and understand illegal fishing in No Take Zones, MPA´s, and fishing Refugees
On the field, we use AI for automated item classification (for rapid assessment) via images or videos of marine debris. In house, we use AI for proposal development, paper summarizing, and translations.
1. Object detection, identification and classification (for the species of interest) in still and video imagery.
2. Parametrization of population and ecosystem models - using AI to scan literature for parameter values
research, writing, communications, graphics, analysis
Compare data from different reports on reef health status / to analyze satellite images from specific bands
Speed up data analysis
we use a ROV to collect data from submerged infrastructure in ports. The AI algorithm is applied to images to identify anomalies.
help to understand technical documents, help in coding and analysis
CHATGPT, CLAUDE,
Gemini to streamline documents; Google Notebook LM to synthesize masses of documents and query them; Zoom AI notes
Using AI to help correct and shorten captions for conservation related social media posts.
I have used LLMs to assist in the categorization of qualitative data from participative workshops on various conservation and management subjects.
Process and analyse big image data
Mainly in managing our administrative workload.
To help me with narrative text -- I give it my rough draft and ask it to organize the narrative using my writing style (I gave it writing samples) which I then go in an edit.
I have also recently interrogated the logical flow of a theoretical framework with it, aiming to improve the structure.
computer vision to count and size white sharks from drone footage; computer vision to collect data on plastic pollution item types removed from rivers
Analysing citizen science collected imagery as part of the Great Reef Census. Details are published in: Broadscale reconnaissance of coral reefs from citizen science and deep learning CL Lawson, KM Chartrand, CM Roelfsema, A Kolluru, PJ Mumby
Environmental Monitoring and Assessment 197 (7), 1-21.
I use ChatGPT for research, writing and editing help. I use it to do deep dives or sense check topics, or to help formalize or write proposals
Two ways so far - we hired a company that uses AI to interpret trail cam videos which is much quicker than the old way of a person going frame by frame. I'm also just starting a month-long online course about AI for mostly PR and administrative tasks. Since time/money is a serious limitation for us as a non-profit and we always want to have more resources directed towards field and conservation-focused work, I think using AI for these types of things may be more efficient. I also now run many random questions through AI to get a basic understanding of a situation, so it's a useful tool for sparking thoughts and discussions.
Quite minimally to edit grammar and help reduce length of text etc.
Mostly just for writing, summarizing, or idea generation.
To help me in translation of training materials and brainstorm on several training ideas
1. We use an AI notes taker to collect meeting minutes 2. We use AI to structure and generate documents such as reports, planning or strategic briefs, emails etc. Note that all original ideas and content come from us, and any work produced by AI is thoroughly fact-checked and edited before completion
Saving time doing tasks such as creating bios, summarizing efforts for short snippets/soundbites for public outreach. We are also utilizing fish identification training modules to extract data from underwater video surveys.
Using AI created agents to help assess alignment against IFC PS and related for all kinds of marine and coastal projects in the energy, transportation, and mining sectors
As English is not my main language, I use to help translate and write reports in more formal manner
draft emails, record meeting notes, outline presentations, condense material for briefing notes, draft meeting invites / agendas, support science communication for public audiences
As a research assistant
Virtually assisting and improving the messaging and talking points of every project document, Pitchdeck, videos, roadmap plans and proposals
I use it to help me with coding in R, with improving flow of text when writing reports/manuscripts
Basic use: taking meeting notes, summarising documents, generating resource lists, redacting/rephrasing/reducing text in proposals/reports, translation.
Assisting with data analysis through code development, summarizing meetings, note taking, secondary research after a primary lit. review is completed, brainstorming.
SPC is currently developing guidelines on how to use AI to produce information and awareness products on pacific coastal fisheries and community-based fisheries management. Should be edited and published early 2026.
By counting whales from space using satellite imagery and AI, our service SPACEWHALE can study previously unexplored areas and accelerate the designation of Important Marine Mammal Areas (IMMAs) and Marine Protected Areas (MPAs) - key tools in helping tackle the global climate and biodiversity crises and supporting coastal communities.
I am using AI for many tasks related to my work on Ocean related issues. I have one specific project attempting to use LLMs Natural Language Processing to convert qualitative surveys and focus group information into quantitative data. I also use LLMs (Gemini at work as that's all we have access to) for help searching the literature, regulations and laws as well as summarizing content of these large documents. I use LLMs for brainstorming model structure and solving optimization problems and algebraic manipulation, developing data analysis code in Stata and Python, and developing simulations and numerical solutions in Python. Outside of my salaried job I use Claude and ChatGPT for these tasks as well as agentic capabilities to monitor developments in industry, science and policy etc.
I have been using ChatGPT to write first drafts of work plans for projects and contracts. This work is done when I work from home on my personal computer. Our State ITS agency has not approved the use of AI on State systems.
Translating foreign language texts; extracting and formatting text from lengthy documents; conducting online searches; cleaning up text
I use AI to help consolidate large chunks of data, documents, etc. I also sometimes use it to come up with creative solutions to various "problems".
Summarizing meetings and complex or large documents, first drafts, literature review and summary
With project Cerulean, SkyTruth uses AI to automatically scan incoming Sentinel-1 radar satellite imagery from the European Space Agency to detect likely oil slicks. We publish these slicks and associated metadata in a publicly accessible (via API) geospatial database, and on an interactive map. With other (non-AI) algorithms, we analyze the slicks along with AIS vessel tracking data and an offshore infrastructure dataset jointly produced with Global Fishing Watch to identify the likely sources of these pollution events.
We use AI to help research and speed up the compilation of information, meeting summaries, etc. AI-supported platforms, such as Skylight are also being explored to promote their use in remote MPA surveillance and enforcement.
For translation and help finding applicable laws for marine protected areas
We are using chatgpt to automatize daily admin tasks such as compilation of information into report chapters, compiling the meeting minutes, etc.
Perplexity AI subscription for research, analysis, cost estimating, referencing, and writing support. Massive time saver and support for being self-employed. Also very powerful for inter-disciplinary work, which so much of marine conservation and management is.
AI to quickly understand the scope and relevance of specific issues, as a starting place to launch analyses; to write first drafts of some letters; to evaluate the strength of a written document (e.g., as AI tool to evaluate strengths and weaknesses and recommendations for improvements). Different LLMs have different pros/cons. Notebook LM is good for developing internal libraries that can then be queried. With business account, the Notebook is internal, so you can upload confidential information to include in the database. Other LLMs like Claude and ChatGPT are good for broader queries and scanning the internet. All of this work needs to be verified and further developed with human input but they are good ways to help brainstorm and organize thoughts. Another interesting thing to do is to use the LLMs to make podcasts of published papers as a way to learn about topics through listening rather than reading. And get summaries and so on.
Have been using CoralNet to conduct CPCE benthic assessments of cover
We (my students largely) have been using AI tools to identify the species of certain marine megafauna while using UAV for monitoring and surveys
Experimenting with using Copilot (Enterprise/ private version) to organize and summarize qualitative written descriptions of lessons learned. Experimenting with (enterprise/ private) Copilot to provide a preliminary risk profile and review of project description and safeguards materials, for review by a safeguards professional and project manager.
Computer vision to ID / enumerate salmonids (data used for in-season management of nearshore subsistence / commercial fisheries)
mostly for project management support and administrative work, background research, and ideation
I use it to make initial general summaries of documents, in case I need to review many documents. I also use it to query databases that contain responses on interviews. Lastly, I use it to brainstorm ideas for focus group discussions.
benthos surveys, data analysis, report writing
I use AI to do bibliographic research, to improve English or rewriting of text and to improve graphically my presentations.
Data analysis and correction when writing reports
Mainly to shorten and clarify text, especially for non-scientific readers, and to help with R code. Sometimes we use it as a place to start with a general literature search.
getting a framework to start with writing and translating
machine learning tools for environmental and species modelling
I used machine learning models to map ecosystems based on spectral data and satellite imagery. Technically this is a subset of AI, but it's been around a while and it's not generative AI, so it hasn't attracted the same level of hype.
I am applying a recently published machine learning tool, 'disperseNN', to estimate average parent-offspring dispersal distance across my population genetic sampling domain for surf clams, Solidissima solidissima.
Currently we use AI in our coral identification (training stage previously), second we use AI to help us with our paper, proposal making and also gather information on conservation tools available around the world.
for writing assistance and PR image creation
CNN's for image recognition and classification; some forecasting and modelling with machine learning, and some steps towards using LLM's for generating content or analysing large text
We have developed machine learning systems to understand human behaviour at sea, and expert systems to determine the compliance of that behaviour to support human decision making.
Artificial Intelligence can be effectively utilized for curating ocean state report summaries and conducting data analysis. However, I do not apply it extensively in my current work, as oceanography is not my primary field. Nonetheless, I am keen to deepen my understanding of AI and explore its applications, having completed several certification courses in this area from esteemed institutions such as MathWorks, Mercator Ocean International (France), and Copernicus Marine Service (Europe).
Image analysis, code generation and signal processing
I use it like an administrative assistant and first-pass copy editor; e.g. I will ask it to organize tables of data, to lightly edit sentences to improve grammar, clarity and flow, or identify copy editing errors in long documents. It can synthesize similarities and differences between 2-3 different sources, extract action items from meeting notes, and organize to-do lists. It helps me shorten my ideas into succinct, professional sounding emails- especially if the point I want to make might come across as contentious. I also use it in massive literature reviews to quickly search for key terms in papers- helps me quickly eliminate off-topic papers returned in search results, and which papers are worth a longer scan or a full read. Sometimes I have it make non-technical, simple diagrams or charts. I also frequently use it as a "dummy check" to understand what audiences might think if they looked up terms that I used, or if they asked AI to summarize my paper for key takeaways.
I use AI to help my ocean conservation and management work in a number of ways. I use AI extensively to support my ocean conservation and management work. These tools assist with general daily tasks as well as highly targeted technical problems. Broadly, I rely on AI for: (1) writing, (2) coding, (3) research, (4) brainstorming, (5) structured learning, and (6) specialized analytical tasks.
AI has been most useful for writing everything from drafting emails and memos to developing grant proposals and technical reports. I also use it regularly for rapid code prototyping, condensing older scripts into cleaner workflows or functions, and debugging. For research-oriented tasks, I frequently use Gemini™s Deep Research to generate organized background reviews on topics such as deep-sea mining or giant clam ecology, complete with reference trails for deeper investigation.
Brainstorming with models like ChatGPT has been surprisingly productive. These sessions often feel like intellectually stimulating late-night discussions in a university library useful for project development, exploring novel analytical approaches (e.g., modeling dome-shaped selectivity in small-scale fisheries), or breaking open a complex problem from a fresh angle.
I also use AI for structured learning, spending time each week reviewing technical subjects such as coral physiology, Bayesian statistics, or virtual population analysis. Gemini™s guided learning tools have been particularly helpful.
Beyond large language models, we also use specialized AI tools for targeted applications. CoralNet helps streamline photoquadrat processing, and we are working with partners to develop AI-assisted identification of fish from creel surveys. In the near future, we plan to use AI to identify and measure fish from our BRUVS imagery.
Overall, AI has become an integral part of both our productivity and our analytical capacity, while still operating under strong human oversight to ensure accuracy, ethics, and ecological relevance.
As a software engineer, I use it for programming, but also for most information gathering task
summarising papers, chatgpt prompts, creation of onfographics
currently using AI at test scale to automate data ingest, video data annotation, and data organization.
AI system in Seal Codex for ID'ing harbor seals; Flukebook and Happywhale for ID'ing humpback whales
Reducing admin and HR receuitment burden
Analyses and visualisation of data
Frameworks for reporting and planning
Accelerating and automating the identification of species during monitoring
Guided teaching on topics either new or currently known
I used AI (specifically ChatGPT) to identify and analyze knowledge flows in policy documents. This refers to descriptions of knowledge exchange between different people or organizations. DOI of published article about this : https://doi.org/10.1007/s00267-025-02277-0
My organization (INPE/BR) has some official initiatives evolving ocean conservation but I'm not sure about the use of AI on it, what I know is that, I'm, together with 3 external colleagues, developing an approach in detecting oil spill in the ocean via satellite images and conventional AI. Although it does not use the state-of-the-art deep learning like CNN its output aims to be a potential helper in detecting oil spill by aggregating it to more advanced techniques.
Presentation of data - graphics, slides, data visualization
We use AI tools for grant funding applications. In the UK the funds open to our commercial business are mainly Government schemes which have a great deal of policy and regulatory sections that require detailed information. LLM tools are able to produce this very quickly and enable us to score highly on assessment. This enables us to focus on the actual project delivery focused sections.
Report writing can be helped by LLM tools but only really for boilerplate text as technical sections require real depth of knowledge. It is our experience that claims that LLMs offer graduate or PhD level researched answers are wholly over blown.
I sometimes use AI tools to support quick review and documents that I draft. In other times I put my thoughts down for a presentation but use AI tools to support me in refining the presentation, ensuring that I keep within the time limit and reach my communication goal. Also AI tools such as reefcloud and DataMermaid are used to support coral reef monitoring data analysis.
For ReefCloud - coral analysis
Assistance with coding in R for data analysis
Literature review; suggestions for potential matrix/cross tabulation analyses; coding for model building; sentiment analyses of open-ended responses to survey questions; possible models for out-of-kind mitigation
data analysis, proposal writing, report writing, translations
Building a papers database > lit review generator > insights repository. Coding assist. Building/upgrading BRUV species detection automation > global classifiers repository.
for writing, translating in other languages
Our startup, Okhtapus, is an AI-enabled global replication platform and marketplace network focused on ocean, coastal, climate and urban resilience from reef to roof. We connect solution Innovators, project Enablers and Funder/Investors - what we call the three hearts of the Okhtapus - to speed scaling and aggregating of what works.
1. We developed a technology using AI to evaluate the success or implementing level of governmental policy to replace Expanded Polystyrene Buoys for aquaculture with durable buoys. The AI technology distinguishes EPS buoys and durable buoys and produces replacement ratio over surveyed area.
2. We use AI technology to identify the 10 most serious and impactful debris items selected from long-term national shoreline monitoring results and our research results. We've operated our Ten-2-One Campaign to reduce 90% of 10 target items in number over 100m since 2022.
A colleague did a literature search on a particular topic for me.
I used AI to analyze, synthesize, and simplify the document. I also used it for coding static software like RStudio and for learning about different software.
We write informative articles on many environmental issues in the Cook Islands. We now put these articles through AI to improve the wat it reads, and also ask AI questions to get more information on the topic of the article
For writing/structuring code, and for suggesting edits in the manuscript writing process.
Predictive AI: To detect marine species (fish, sharks, seabirds) from electronic monitoring footage from fishing vessels.
Generative AI: To record and summarise key points and actions from meetings. To provide code for app development. To assist drafting of proposals for funding.
To present and summarize long reports and data.
AI improves our capabilities for ocean monitoring/forecasting and scenario development (DTO) for sustainable ocean management and is therefore useful for better managing and protecting the ocean.
We use Perplexity.ai to research questions. Perplexity.ai lists sources, making it easy to check if the AI is summarizing correctly.
___________________________
Sarah D. Carr, Ph.D.
Chief Knowledge Broker, OCTO
E-mail: sa...@octogroup.org
Website: www.octogroup.org
Contact us about custom solutions for your resource management needs!
Join OCTO’s discussion lists to connect with the global ocean community:
• MPA Help – Knowledge sharing for marine protected area (MPA) planning and management
• EBM Help – Knowledge sharing for managing marine ecosystems
• OceanPlastic – Discussion list about all things ocean plastic
• OCTO Updates – OCTO webinar and blog announcements
Unsubscribe from lists at the links above (bottom of the page, passwords not needed)
EBMHelp List
EBM...@list.octogroup.org
Change your digest option or unsubscribe at https://list.octogroup.org/mailman/listinfo/ebmhelp