Artificial intelligence innovation sprint

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Artificial intelligence innovation sprint

Unlock the potential of ai for social good

Il corso è disponibile in English
How can artificial intelligence support your future capacity development projects?

Our personal and professional lives are shaped by technology. But, the applications, stories, and debates around artificial intelligence are too optimistic about its promises and risks. Moving forward, there is a clear need to broaden the benefits of digital transformation by ensuring equal access for all. In response to these needs, the International Training Centre of the ILO’s Learning Innovation team leads and partners in the areas of the future of learning, the future of work, and technological change. As an international platform for dialogue and interactive learning, the Centre convenes thought leaders and learners in pursuit of these goals.

The AI Lab is not a traditional course. It’s not a MOOC, nor is it a standard online community of practice. It is a creative, human-centered experiment based on your objectives or on the vision that you would like to advance in your project, organization or institution.

Focus: Potential and positive impact

In times of disruption, leapfrogging with the right technologies can bring about transformational change. Since data will drive progress toward sustainable development in the future, we introduce participants to ideas like exponential change, neural networks, pattern recognition and machine learning.

Once participants learn the new vocabulary, they will start a learning project trajectory with three dimensions:

Vision and strategy development

  • What can AI do and how will it impact my organization?
  • What are the steps of developing an AI strategy?

Roadmap

  • What are the steps that we need to take to follow our overall vision?
  • What are the steps of filling in the AI Business Model Canva?

Portfolio of solutions

  • What are the steps of translating the vision into practice?
  • What are the shapes of an initial project idea or prototype?

This is not an AI solutions trajectory, but it does aim to raise awareness about a fast-changing landscape. The AI Lab is the beginning of a long term exploration of what kind of transformational journey you could start.

“Our intelligence is what makes us human, and AI is an extension of that quality.”

– Yann LeCun

Methodology: Design thinking sprints

Building on design thinking methods, the AI Lab simulates fast and iterative sprints to build a minimum viable product (MVP) with elements of AI, ML or automated decision capacity.

Four thematic webinars support knowledge sharing and engagement with global experts to design specific AI data, cloud, and intelligence topics of choice.

Participants will also be able to talk and learn from one another, through informal online coffee breaks among groups of interest.

Phase 1 (2 weeks) - Discovery and AI myths  

  • The five phases of design thinking (Discovery, Interpretation, Ideation, Experimentation, Evolution)
  • Human-centered design
  • Curiosity-based learning (market research and challenging assumptions about AI)
  • AI vocabulary and the elements of AI, AI from A to Z
  • Demystifying AI myths

Phase 2 (2 weeks) - Interpretation and issue identification

  • Unpack and synthesize findings in the discovery phase
  • Explore the unknowns in data and problem identification about your AI-dea project 
  • Four thematic webinars linked to specific AI topics of choice
  • Informal online knowledge exchange coffee breaks in peer groups

Phase 3 (2 weeks) - Ideation and AI-dea pitch

  • Focus on idea generation with creative solutions
  • Open and human-centered value concept development
  • Technical mentoring and coaching trajectory
  • Pitch gallery, expert and peer feedback

Phase 4 (2 weeks) - Experimentation and building prototype

  • Focus on creating rapid prototyping
  • Start making design choices with a paper prototype
  • Focus on capturing learnings and the experimentation process
  • Two peer-learning sessions and various coffee meet ups

Phase 5 (2 weeks) - Evolution and testing

  • Focus on co-testing and co-reflection on built prototypes 
  • Test with target groups at least 10 times
  • Revise and review design choices

Two peer-testing sessions and various coffee meet ups

Phase 6 (2 weeks) - Co-creation and learning

  • Focus on failures and moments
  • Document and capture learnings and the process
  • Two peer-learning sessions and various coffee meet ups
  • Two two-hour group and team reflection and reporting sessions 
Contact us

For more information on the AI Lab, email: 

Tom Wambeke, Chief of Learning Innovation Programme

t.wambeke@itcilo.org

learninginnovation@itcilo.org