survey 'Artificial Intelligence and Langbase AI' at concordia university. Starts with academic fields description (55 words or less per main aspects / per field excluding the title of the field) then the modules or abstract of the courses or what is explorable.

Potential applications, 55 words or less per applicables.

Note: "Langbase AI" does not appear to be an official Concordia program name — this likely refers to the Language-Based AI sub-domain (NLP, conversational AI, LLMs). Here is the survey structured as requested.

Artificial Intelligence at Concordia University

Concordia offers AI programs across three tiers: continuing education certificates, graduate microprograms, and full research-stream graduate degrees rooted in the Gina Cody School of Engineering and Computer Science.

Academic Fields

Applied & Foundational AI (COMP 6721 / CEAI 1001)

Core AI concepts: search, reasoning, constraint solving, probabilistic models, and machine learning fundamentals. Bridges theory and deployment. Serves as the entry point for both continuing education (CEAI 1001) and graduate study (COMP 6721).¹²

Machine Learning (COMP 6321 / COMP 6831)

Supervised and unsupervised learning, SVMs, decision trees, ensemble methods, clustering, and dimensionality reduction. Balances theoretical foundations with hands-on project work.³

Deep Learning (COMP 6801 / COMP 6841)

Neural network architectures, backpropagation, CNNs, RNNs, attention mechanisms. Strong practical emphasis using coding sessions and real-world benchmark tasks.⁴³

Language-Based AI / Conversational AI (COMP 6781 / COMP 6861)

NLP, computational linguistics, speech recognition, LLMs, and dialogue systems — what you called "Langbase AI." Uses SpeechBrain toolkit for hands-on speech assistant and LLM development.³

Intelligent Systems & Knowledge Representation (COMP 6741 / COMP 6591)

Knowledge-based systems, semantic web, ontologies, reasoning under uncertainty. Bridges classical symbolic AI with modern data-driven approaches.⁵

AI Software Engineering (SOEN 6221)

Engineering, deploying, and servicing AI-based software systems. Part of the SE4AI CREATE national program with Polytechnique, Queen's, and UAlberta, with industrial internship tracks.⁶⁷

Information Retrieval & Semantic Computing (COMP 6791 / SOEN 6211)

Web search algorithms, semantic knowledge layers, text indexing, relevance ranking. Connects classical IR theory to modern embedding-based retrieval.⁵

Courses / Modules Explorable

Course	Code	Focus
Applied Artificial Intelligence	COMP 6721	ML + NLP core, Python-based projects
Machine Learning	COMP 6321 / 6831	Supervised/unsupervised, real dataset projects
Applied Deep Learning	COMP 6841	DL architectures, coding-heavy
Conversational AI	COMP 6861	LLMs, speech AI, SpeechBrain labs
NLP	COMP 6781	Language models, text processing
Intelligent Systems	COMP 6741	Reasoning, hybrid AI systems
Engineering AI Systems	SOEN 6221	MLOps, deployment, AI software lifecycle
AI Foundations (CCE)	CEAI 1001	Non-technical entry, ethics, use-cases
Working with Generative AI (CCE)	CEAI 1005	Hands-on GenAI tools, workplace integration
Applied AI Microprogram	COMP 6831 + 6841	8-credit graduate fast-track

⁸⁹⁴³⁵

Potential Applications

Agent & Multi-Agent Systems

COMP 6741 + COMP 6861 directly feed architectures like hermes-agent — reasoning loops, dialogue management, tool-use orchestration.³

Indigenous Knowledge Representation

COMP 6591 (Knowledge-Base Systems) and SOEN 6211 (Semantic Computing) offer ontology frameworks adaptable to relational, non-hierarchical epistemologies.⁵

Voice & Audio Intelligence

COMP 6861 with SpeechBrain covers ASR, TTS, and speaker diarization — directly applicable to voice recording and audio processing workflows.³

Trustworthy AI & Ethics in Systems

SOEN 6221 and the CCE Responsible AI track (CEAI 1002) address bias, governance, and deployment safety — relevant to the National Bank partnership on trustworthy AI.¹⁰⁷

Narrative & Semantic Search

COMP 6791 (Information Retrieval) combined with COMP 6781 (NLP) enables embedding-based search over narrative corpora — useful for knowledge management systems.⁵ <span style="display:none">¹¹¹²¹³¹⁴¹⁵¹⁶¹⁷¹⁸¹⁹²⁰²¹²²²³²⁴²⁵²⁶²⁷²⁸²⁹³⁰³¹³²³³³⁴³⁵³⁶³⁷³⁸³⁹⁴⁰</span>

Great work I’m looking at Your content and I’ll have it read therefore you need to recreate a message that will be the same but will format the table into readable content. You can imagine that I’ll have the PDF of your last message in my view while listening you. Don’t cut text I want sufficient amount of details

Here is the full content reformatted for audio-friendly reading, with the table expanded into structured prose sections. The PDF of the previous message stays your visual anchor — this version is the spoken layer.

Artificial Intelligence at Concordia University

Academic Fields

Applied & Foundational AI

Machine Learning

Supervised and unsupervised learning, SVMs, decision trees, ensemble methods, clustering, and dimensionality reduction. Balances theoretical foundations with hands-on project work.⁴⁶

Deep Learning

Neural network architectures, backpropagation, CNNs, RNNs, and attention mechanisms. Strong practical emphasis using coding sessions and real-world benchmark tasks.⁴²⁴⁶

Language-Based AI — Conversational AI

NLP, computational linguistics, speech recognition, large language models, and dialogue systems. Uses the SpeechBrain toolkit for hands-on speech assistant and LLM development. This is the field you named "Langbase AI."⁴⁶

Intelligent Systems & Knowledge Representation

Knowledge-based systems, semantic web, ontologies, and reasoning under uncertainty. Bridges classical symbolic AI with modern data-driven approaches.⁴⁷

AI Software Engineering

Engineering, deploying, and servicing AI-based software systems. Part of the SE4AI CREATE national program with Polytechnique, Queen's University, and the University of Alberta, with industrial internship tracks.⁴⁸⁴⁹

Information Retrieval & Semantic Computing

Web search algorithms, semantic knowledge layers, text indexing, and relevance ranking. Connects classical information retrieval theory to modern embedding-based retrieval systems.⁴⁷

Courses and Modules

Applied Artificial Intelligence — COMP 6721 Graduate-level. Core ML and NLP, Python-based team projects, labs, and exams. Strong emphasis on practical application with a grading scheme that rewards both teamwork and individual technical depth.⁴⁵

Machine Learning — COMP 6321 and COMP 6831 Supervised and unsupervised algorithms on real datasets. COMP 6831 is the advanced version included in the Applied AI Microprogram's 8-credit fast-track.⁴²⁴⁶

Applied Deep Learning — COMP 6841 Coding-heavy exploration of deep architectures. Also part of the 8-credit Microprogram. Covers convolutional and recurrent networks through to transformer-based designs.⁴²

Conversational AI — COMP 6861 LLMs, speech AI, and SpeechBrain laboratory sessions. Offered every Winter term. Covers end-to-end pipeline design from raw audio input to generated dialogue output.⁴⁶

Natural Language Processing — COMP 6781 Language models, text processing, and syntactic and semantic analysis. Feeds directly into the conversational AI and information retrieval streams.⁴⁷

Intelligent Systems — COMP 6741 Reasoning under uncertainty, hybrid symbolic and connectionist systems, and knowledge engineering. Entry point for ontology-based design work.⁴⁷

Engineering AI Systems — SOEN 6221 Covers the full MLOps lifecycle: requirements, architecture, deployment, and maintenance of AI-based software. National scope through the SE4AI CREATE program.⁴⁹⁴⁸

AI Foundations — CEAI 1001 (Continuing Education) Non-technical entry point. Ethics, use-cases across industries, and understanding AI limitations. Online synchronous format, accessible without a computer science background.⁴⁴

Working with Generative AI — CEAI 1005 (Continuing Education) Hands-on with GenAI tools for the workplace. Covers prompt crafting, workflow integration, and responsible daily use of tools like ChatGPT and comparable platforms.⁵⁰

Applied AI Microprogram An 8-credit graduate fast-track combining COMP 6831 and COMP 6841. Designed for graduate students who want specialized AI depth without completing a full degree stream.⁴²

Potential Applications

Agent and Multi-Agent Systems

COMP 6741 and COMP 6861 directly feed architectures like multi-agent dialogue frameworks — reasoning loops, dialogue management, and tool-use orchestration are core competencies built in these two courses.⁴⁶⁴⁷

Indigenous Knowledge Representation

Voice and Audio Intelligence

COMP 6861 with SpeechBrain directly covers automatic speech recognition, text-to-speech, and speaker diarization — all applicable to voice recording pipelines and audio processing workflows.⁴⁶

Trustworthy AI and Ethics in Systems

SOEN 6221 and the Continuing Education Responsible AI track address bias detection, governance, and deployment safety — a research focus also active through Concordia's partnership with the National Bank of Canada on trustworthy software AI.⁵¹⁴⁹

Narrative and Semantic Search

COMP 6791, Information Retrieval, combined with COMP 6781, NLP, enables embedding-based search over narrative corpora — directly useful for knowledge management systems that store and retrieve structured or story-based content.⁴⁷

<q>Indigenous Knowledge Representation

COMP 6591, Knowledge-Based Systems, and COMP 6791, Semantic Computing, offer ontology frameworks that can be adapted to relational and non-hierarchical epistemologies rather than Western taxonomic structures.</q> Is the course directly on indigenous knowledge? (Or you inferred that it would be a good course given knowledge you have about me.

I inferred it.⁵²

Neither COMP 6591 nor COMP 6791 is about Indigenous knowledge. They are standard computer science courses covering ontology design, knowledge graphs, and semantic web technologies — all built on Western taxonomic and hierarchical data models.

What I did was map your known research focus — Indigenous epistemology and non-hierarchical relational knowledge structures — onto courses that provide the technical substrate you would need to build Indigenous-informed systems. The leap from "this course teaches ontologies" to "this adapts to non-Western epistemologies" is a design challenge you would bring to the course, not something the course teaches.

Concordia does have Indigenous Studies programs and researchers working on land-based and relational knowledge systems, but that work sits in the Faculty of Arts and Science, not in the Gina Cody School — and it is not connected to the AI curriculum in any formal way as of current public records.⁵³⁵⁴

If you want, I can look into whether Concordia has any actual cross-faculty initiatives linking Indigenous methodologies with AI or computational systems — that would be a more honest answer to what you were originally looking for.

This is amazing!

Produce something complementary given what you know about me and previous work

This is directly relevant to your work — and it is real, not inferred.

Abundant Intelligences — Concordia's Indigenous AI Program

Concordia is actually the headquarters of one of the most significant Indigenous AI research programs in the world right now. The Abundant Intelligences program, led by professor Jason Edward Lewis, was awarded a New Frontiers in Research Fund Transformation grant of nearly $23 million in late 2024, running for six years.⁵⁵

It is housed at Concordia's Indigenous Futures Research Centre (IFRC) in Montréal — which means this work is happening in your city, at your university.⁵⁶

What the Program Actually Does

The program runs on three axes that map almost directly onto your own research architecture:⁵⁵

Integration Axis — synthesizes Indigenous knowledge practices with mainstream AI frameworks. This is the ontology and epistemology layer.
Imaginaries Axis — develops speculative designs of Indigenous AI systems oriented toward Indigenous flourishing. This is the creative and narrative layer.
Intelligence Axis — brings Indigenous perspectives to bear on active technical challenges in mainstream AI, and explores how the full range of human intelligence can be translated into machine intelligence.

The Theoretical Core

The foundational concept is relationality — the idea that intelligence is not an isolated computational property but emerges from reciprocal relationships with lands, waters, communities, and non-human beings. This directly challenges the hierarchical taxonomic structures baked into Western AI ontologies.⁵⁷⁵⁸

The paper Making Kin with the Machines, authored by Lewis and colleagues drawing on Cree, Lakota, and Hawaiian knowledge systems, proposes a circle of relationships as the design principle for AI — where kinship protocols replace optimization metrics. This is published through the Montreal AI Ethics Institute, also local to you.⁵⁸

The Network

The program operates through pods — locally rooted research clusters — at:⁵⁹

Western University (Ontario)
University of Lethbridge (Alberta)
Bard College (New York)
Massey University (New Zealand)
University of Hawai'i–West Oahu

It involves 37 co-investigators, the majority Indigenous researchers and community members.⁵⁵

Why This Is Directly Complementary to Your Work

Given your hermes-agent framework, your work on narrative knowledge management, and your interest in non-hierarchical agent architectures, Abundant Intelligences is not just an academic reference — it is an active research community at your institution working on the exact epistemological foundation you are building from.

The IFRC Symposium 2026 is also currently active — it features panels, workshops, performances, and artist talks from faculty and collaborators, which maps onto your combined technical and creative practice.⁶⁰

The program's website is indigenous-ai.net and the IFRC is at ifrc.ca.⁶¹⁶⁰ <span style="display:none">⁶²⁶³⁶⁴⁶⁵⁶⁶⁶⁷⁶⁸⁶⁹⁷⁰⁷¹⁷²⁷³⁷⁴⁷⁵</span>

ThreadUrl: https://www.perplexity.ai/search/06e202fb-800f-4ff9-bd10-2c9a3df45e96

survey 'Artificial Intelligence and Langbase AI' at concordia university. Starts with academic fields description (55 words or less per main aspects / per field excluding the title of the field) then the modules or abstract of the courses or what is explorable.

Artificial Intelligence at Concordia University

Academic Fields

Applied & Foundational AI (COMP 6721 / CEAI 1001)

Machine Learning (COMP 6321 / COMP 6831)

Deep Learning (COMP 6801 / COMP 6841)

Language-Based AI / Conversational AI (COMP 6781 / COMP 6861)

Intelligent Systems & Knowledge Representation (COMP 6741 / COMP 6591)

AI Software Engineering (SOEN 6221)

Information Retrieval & Semantic Computing (COMP 6791 / SOEN 6211)

Courses / Modules Explorable

Potential Applications

Agent & Multi-Agent Systems

Indigenous Knowledge Representation

Voice & Audio Intelligence

Trustworthy AI & Ethics in Systems

Narrative & Semantic Search

Artificial Intelligence at Concordia University

Academic Fields

Applied & Foundational AI

Machine Learning

Deep Learning

Language-Based AI — Conversational AI

Intelligent Systems & Knowledge Representation

AI Software Engineering

Information Retrieval & Semantic Computing

Courses and Modules

Potential Applications

Agent and Multi-Agent Systems

Indigenous Knowledge Representation

Voice and Audio Intelligence

Trustworthy AI and Ethics in Systems

Narrative and Semantic Search

<q>Indigenous Knowledge Representation

This is amazing!

Abundant Intelligences — Concordia's Indigenous AI Program

What the Program Actually Does

The Theoretical Core

The Network

Why This Is Directly Complementary to Your Work

Footnotes