About
A practical home for Portlanders building and understanding AI together
Portland AI/ML is a community for builders, researchers, designers, artists, founders, operators, students, educators, and civic leaders working with AI and ML in practical, responsible, and locally useful ways.
Mission
Portland AI/ML helps people in Portland learn, build, evaluate, and responsibly deploy artificial intelligence and machine learning for real-world use.
Vision
Portland should not be only a consumer of AI tools created elsewhere. It should be a city where people understand the technology, shape its use, build local capacity, ask hard questions, and create systems that are useful, accountable, accessible, and humane.
Community principles
Useful beats impressive
Prioritize tools and methods that help people do real work, learn clearly, or make better decisions.
Human judgment stays central
AI systems should support people, not replace accountability, care, or domain expertise.
Evaluation is culture
Test outputs, measure performance, check assumptions, inspect data, and compare models.
Local context matters
Begin with Portland's organizations, artists, schools, nonprofits, civic systems, and communities.
Open learning, practical safeguards
Share knowledge while taking privacy, security, copyright, consent, bias, accessibility, and misuse seriously.
Beginners belong, experts go deep
Curiosity is enough to start, and there's room for serious technical work and creative practice.
Who it's for
Builders
Serious technical peers, projects, demos, tooling, and evaluation practice.
Beginners & students
Clear entry points, low-pressure learning, and friendly explanations.
Creative practitioners
A respected place for experimentation, critique, and rights questions.
Civic & nonprofit teams
Realistic AI guidance, risk awareness, and practical adoption help.
Small businesses
Workflow automation ideas without hype or unsafe data practices.
Educators & founders
A grounded local network to learn from and build capacity with.
Community norms
- Name uncertainty clearly.
- Do not present demos as proven systems.
- Disclose data sources when possible.
- Keep private and sensitive data out of public sessions.
- Respect creative ownership and attribution.
- Critique systems, not people.
You do not need to be an expert to participate. Curiosity is enough to start.
Join the community