Credit: Data Science Central
Background
One of the hardest problem in AI is not technical
It is social
Specifically, it is the problem of “educating people for living and working in a world dominated by AI”
This blog is based on my talk and notes in panels at the world government summit where the future society at the Harvard Kennedy school runs a workshop on AI on behalf of HIS EXCELLENCY OMAR BIN SULTAN AL OLAMA – Minister of State for Artificial Intelligence – United Arab Emirates
I also spoke of these ideas in the Kings College Legal tech and emerging technologies conference in London
The problem is acknowledged by many. Most countries now have an AI policy expressed in considerable detail (AI policy details) and there is a lot at stake. However, its not only about threats – there are also many potential advantages
Educating for AI – Dimensions
Educating for AI chould be considered in terms of problems and opportunities:
- The Finland model finland’s grand AI experiment 1 percent
- Educating for disrupted job losses
- Technology hubs – Technion, cifar etc
- Retraining teachers
- Educating young people
- New systems of meritocracy – ex Kaggle
- Considering AI for augmentation – ex Cobots
- AI for competitive advantage
To elaborate these ideas below
Countries
- The finland experience (as above)
- China’s push to AI as expressed in Kai Fu Lis book AI superpowers
- Learning from the success of CIFAR in Canada (as an organization in nurturing AI researchers)
- Educating in emerging markets
- Using education to solve local problems in emerging markets
- Learning from the Russian school of education ie strong maths focus – Moscow state school 57
- The UAE world government summit and emphasis on AI
Reskilling – Content and techniques
- Methods of learning online – Online Education / Moocs
- Basic literacy in AI (finland)
- Awareness of potential of new intellectual property in AI
- Reskilling after job losses
- Awareness of the role of maths and science
- Awareness of algorithms
- Simplifying the tools of AI
- Educating about the potential to share and reuse code (ex github) which makes it easier to adopt new technologies
- Creating an awareness of robotics
- Educating about data (opportunities and risks)
- Educating for AI with an optimistic view of the future but making aware of the risks
- Adopting the Kaggle model (meritocracy based problem solving)
- Educating about how to rethink the learning process (ex by using forums such as stack overflow to find solutions)
People and community
- Teaching other professions about the value of AI (e doctors)
- Reskilling teachers
- Leveraging the skills of teachers post retirement age
- Encouraging more women to take up AI and also under-represented groups
- Academia as a hub for innovation – for example Technion
- Pushing the boundaries in AI for education ex trying radical methods of learning such as AI complementing the teacher
- How to inspire the next generation for AI – Young Data Scientist
Conclusion
To conclude, Educating for AI is multi-faceted – with both risks and opportunities.
The futures of whole countries are at stake. I expect much activity in this domain
Image source; shutterstock
Credit: Data Science Central By: ajit jaokar
