Educating for AI – one of the most critical problems in AI



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


  • 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



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

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Comment by Klais Surik on April 29, 2021 at 5:18am

The learning process is really not that difficult, if you already have a reliable assistant, which is just the same will help to write various written works, for example. I already know them, you can see here that this service does my assignment and at the same time does not charge expensive. So I think it is worth the effort to apply. I, for example, and so did. Therefore I recommend the rest.

Comment by ajit jaokar on April 10, 2019 at 11:37am

thanks Vishal! 

Comment by Vishal Kapur on April 7, 2019 at 6:10am

Yes, each country should have a AI Education Roadmap and technical universities should for AI courses.. Like in India we have NPTEL, that gives training on AI and ML..

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