Registre des initiatives en IA
Toutes les initiatives inscrites au registre sont consultables ici, sans compte. Chaque fiche montre son avancement, ses jalons et l'historique de ses décisions. Les demandes encore en cours de triage n'y figurent pas : elles rejoignent le registre une fois examinées.
Filtrer
197 initiative(s) courante(s)
-
184
Recherches dans les courriels en réponse à des demandesLeverage M365 Copilot functionalities to leverage AI to perform email searches in response to certain requests, including those related to ATI/P
Idéation Secteur des programmes Automatisation et aide à la décision Automatisation des processus assistée par IA
-
112
ECCC exchanges on use of AI for ocean modellingExchanges with ECCC on implementation of machine learning methods for operational ocean forecasting, including potential uses for DFO models.
Pilote Sciences des écosystèmes et des océans Infrastructure et habilitation Gouvernance et éthique de l'IA
-
111
Digital Twins of the Ocean Task TeamNational exchanges on topics relating to ocean modelling for development of Digital Twins of the Ocean, including use of AI for modelling and products.
Pilote Sciences des écosystèmes et des océans Infrastructure et habilitation Gouvernance et éthique de l'IA
-
229
DFO PB LLM ServiceProvide an approved DFO internal LLM as a service in a PBMM environment. After our team performed a year-long pilot project providing an unclassified internal GenAI models for 14 teams to develop their custom chatbots, we have collected feedback and identified gaps in the availability of GenAI at DFO. One of the biggest gaps was the need for a secure model to be used with private or classified data. Our cloud platform is certified up to PBMM, so we are working to identify, deploy and approve an internal large language model (LLM), as well as make it available to DFO users through API. IT security team is involved in helping us secure and certify the model for use at DFO.
Pilote Dirigeant principal du numérique IA cognitive et générative MLOps et gestion des modèles
-
250
Agent Conversationnel du centre de données du MPO• Faster access to Data Centre information and processes • Reduced dependency on SMEs and email-based support • Improved staff onboarding and operational efficiency • Supports GoC digital-first and AI-enablement direction
Développement Politiques stratégiques Interaction et engagement IA conversationnelle et assistants virtuels
-
94
DFO Bot, deep learning application to analyse otolith images available in DFO DotsDevelopment of a neural network trained using otolith images, expert-derived age estimates, otolith annotations and annuli to obtain age estimates from otoliths.
Pilote Sciences des écosystèmes et des océans Perception et compréhension Vision par ordinateur
-
252
Centre d'IA du MPOThe DFO AI Hub is a centralized digital platform that demonstrates the application of artificial intelligence technologies to fisheries and oceans research, management, and operations. The platform serves as an educational tool that demonstrates how AI can help addresses business needs for faster, more accurate data analysis, improved decision support, and increased operational efficiency across research, monitoring, and management functions. It enables exploratory use of machine learning, computer vision, and natural language processing to process diverse data sources (e.g., imagery, sensor data, scientific reports), automate routine analytical tasks, and generate actionable insights for policy, compliance, and conservation programs.
Développement Politiques stratégiques Interaction et engagement Gouvernance et éthique de l'IA
-
226
DFO AI - PipelineDFO Pipeline Analyzer is a private Azure DevOps extension designed to assist development teams in diagnosing and resolving pipeline failures using AI. When a pipeline run fails, this task automatically triggers, extracts the provided error details, and queries a secure AI API to generate a human-readable explanation of the failure along with actionable troubleshooting steps. It will assist developers to make appropriate steps in right direction as they are getting suggestion with-in pipeline logs. Suggestion also provide corrected version of pipeline Key features: Runs only on pipeline failure (condition: failed()) Fully agent-agnostic: Works on self-hosted and Azure-hosted agents, across Windows, Linux, and macOS Provides clear error analysis and resolution guidance directly in pipeline logs Keeps your data secure by using your own OpenAI or other AI API key Fully private and scoped to your Azure DevOps organization We have working prototype of DFO-AI pipeline Analyzer by communicating with OpenAI securely. In next steps we are also exploring to integrate with our self-hosted AI Models (open-source via Ollama), currently in discussion
Pilote Dirigeant principal du numérique Infrastructure et habilitation MLOps et gestion des modèles
-
168
Development of bespoke data-limited tools using AI coding.The majority of Canadian fish stocks are data limited thus preventing full analytical stock assessments. Data limited tools are therefore the only approach. We are engaged in quick Ai development of custom data limited assessment approaches for exploited fish stocks
Pilote Sciences des écosystèmes et des océans IA cognitive et générative IA générative (texte, images, code)
-
220
Development of an underwater videography and AI system for studying fish passage effectivenessDesigned to train existing DFO tools with information on fish passage effectiveness and develop an SOP for training videography-based AI models in support of fish passage projects.
Pilote Sciences des écosystèmes et des océans Perception et compréhension Vision par ordinateur
-
124
Development of an underwater videography and AI system for studying fish passage effectivenessDesigned to train existing DFO tools with information on fish passage effectiveness and develop an SOP for training videography-based AI models in support of fish passage projects.
Pilote Sciences des écosystèmes et des océans Perception et compréhension Vision par ordinateur
-
88
Development of an AI matching model for bowhead whale photo-identificationUsing DFO’s existing database of bowhead whale photographs, the specific objectives are: 1) Create a machine learning model “detector” for bowhead whales. 2) Create a machine learning individual re-ID model for bowhead whales to re-identify bowheads. 3) To make the detector and matching models accessible through an easy to use, freely available, web-based application, Flukebook.org.
Pilote Sciences des écosystèmes et des océans Perception et compréhension Vision par ordinateur
-
164
Development of an abundance index for an under surveyed capelin stockRecherche qui vise à utiliser des méthodes d’apprentissage automatique (machine learning) dans le but de classifier les signaux acoustiques du capelan dans le Golfe du St-Laurent.
Idéation Sciences des écosystèmes et des océans Automatisation et aide à la décision Détection d'événements sonores
-
100
Development of AI technology for otolith and scale ageingAgeing of scales and otoliths is time-consuming, relies on skilled personnel, and often faces delays. AI tools like DFOdots offer a solution by enabling automated ageing using annotated reference collections, helping reduce backlogs and support training. Developing these AI models can enhance resilience, efficiency, and training capacity in ageing programs.
Pilote Sciences des écosystèmes et des océans Perception et compréhension Vision par ordinateur
-
122
Development and testing of artificial intelligence and machine learning to analyze underwater photo and video data to assess marine impacts and resourcesThis completed project (2020/21) aimed to utilize machine learning and imaging technology in fisheries science. The proponent planned to use Video and Image Analytics for Marine Environments (VIAME), an open-source system developed by NOAA, for two specific purposes: 1. Measure the movement of Atlantic cod across boundaries at the Gilbert Bay Marine Protected Area in Newfoundland and Labrador. 2. Assess the impact of seismic surveying on Atlantic cod as part of a project funded by the Environmental Studies Research Fund (ESRF). The automatic detection and count of fish near MPA boundaries was expected to reduce use of personnel time and effort.
Pilote Sciences des écosystèmes et des océans Perception et compréhension Vision par ordinateur
-
121
Develop a deep-learning model to age capelin otoliths: application for stock assessmentInvestigate the utility of applying a deep-learning model developed by the DFO Gulf Region for aging fish otoliths; specifically, to determine the age of capelin in the 2J3KL stock in Newfoundland for use in stock assessments. Otolith aging is currently done visually using a microscope, this proposed method would automate that process to save time and resources, while reducing human error and bias
Pilote Sciences des écosystèmes et des océans Automatisation et aide à la décision Vision par ordinateur
-
62
Deep sea habitat, organism and object detection and identification.Proposing a capstone project to Computer Science students from Camosun College to develop a pipeline for identifying objects and loading the results into Biigle for human review. TBD.
Idéation Sciences des écosystèmes et des océans Perception et compréhension Vision par ordinateur
-
90
Deep Learning Enhances Beluga Whale Research in the ArcticEsri Canada built a deep learning model that automatically detects beluga whales in imagery. The model was developed using satellite, drone and aerial imagery and reported accuracy is high, but the model has not been tested by DFO Science yet.
Pilote Sciences des écosystèmes et des océans Perception et compréhension Vision par ordinateur
-
4
Database Scripting Automationi would like to be able to review the scripts the DBAs run to ensure that we can automate and reduce the administrative workload. if possible we would then be able to provide more opporutnities for the group to do more architecturing then Administrative updates. haven'T delved this through, but i think it would be high value.
Idéation Dirigeant principal du numérique Automatisation et aide à la décision Automatisation des processus assistée par IA
-
70
Data Validation Tool (See B6, B22)Use large language models to help detect errors in data transcribed using optical character recognition. Apply validation rules to propose corrections and normalize data into a standardized format. (Digitizing non-machine-readable (handwritten) documents and extracts characters into a digital format) Llama 3.0 Large Language Model
Pilote Sciences des écosystèmes et des océans Automatisation et aide à la décision Automatisation des processus assistée par IA
-
152
Data Validation ToolUse large language models to help detect errors in data transcribed using optical character recognition. Apply validation rules to propose corrections and normalize data into a standardized format.
Pilote Sciences des écosystèmes et des océans Automatisation et aide à la décision Automatisation des processus assistée par IA
-
225
Innovation en matière de données – Initiative relative à la stratégie sur le saumon du PacifiqueDiscover innovative data and artificial intelligence (A.I) projects addressing complex salmon data challenges made possible through strategic investments from the Pacific Salmon Strategy Initiative (PSSI). PSSI initiatives harness the power of advanced data and A.I. technologies like machine learning, computer vision, and natural language processing to revolutionize how to support salmon conservation and rebuilding efforts in the modern data and digital era. By integrating advanced data and A.I. technologies with program activities such as habitat restoration, ecosystem planning, sustainable fishery management practices, and collaborative efforts in Science, the PSSI leverages modern tools to enhance planning and data-driven decision-making. Together, these data and A.I. efforts create high quality and more accessible data and enable better insights and actions to protect and restore Pacific salmon and its ecosystems.
Idéation Infrastructure et habilitation
-
169
Dark Vessel Detection PlatformUtilizes AI and machine learning to detect vessels in satellite imagery, predict movements, and flag potential IUU fishing globally.Automatically cross-references detected vessels with AIS and VMS data to identify unregistered or "dark" vessels. Supports international IUU fishing monitoring; new AI-driven features enable vessel identification from electro-optical imagery. Governance: Managed by DFO’s International Fisheries Enforcement team; technical oversight by MDA Space; coordinated with CSA and other OGDs. Guardrails: All data use complies with commercial licensing terms and GC privacy requirements; sharing restricted
Pilote Secteur des programmes Perception et compréhension Vision par ordinateur
-
83
Daily UseMapping, The AI Assist function in FME has been used to support code development for data processing while integrating and manipulating data in workbenches.
Pilote Secteur des programmes Automatisation et aide à la décision IA générative (texte, images, code)
-
84
Daily UseDashboard Development, Used the standalone M365 version of Copilot to assist with coding.
Pilote Secteur des programmes IA cognitive et générative IA générative (texte, images, code)