Friday 12 October 2018

ULear 2018 - Mike Walsh - Preparing the next generation for the algorithmic age

Keynote - Preparing the next generation for the algorithmic age
Mike Walsh













Abstract:
We live in an age of wonder - cars that drive themselves, platforms that anticipate our needs, and robots capable of everything from advanced manufacturing to complex surgery. Automation, algorithms and AI will transform every facet of daily life, but will the next generation be prepared for the radical redesign of the workforce and the skills that will be needed to survive? While many fear that robots will take their jobs, the rise of machine intelligence begs a more important question: what is the true potential of human intelligence in the 21st century?


The role of education now is to build the society of the future - what do we want and need our students to become and learning now so that our future will thrive.  

We are focusing on what millennials are doing and wanting but we really need to pay closer attention to our 7 and 8 year olds.  They are the generation of digital natives who can intuitively use the current technology.

As we have grown up the Big Data wave has become bigger and bigger.  Our experiences are being continually personalised based on our trends and patterns to live a more individualised lifestyle.  What will this be like for our kids growing up?

An example of China - using We Chat or WeiShing as a platform for individualised living and society.  You cannot live there without it , even homeless people have a smartphone which enables them to use this app.  It is used for everything from booking services, buying food, communication, coordinating aspects of daily life, fines etc.  People earn a social credit score which can then fine the for wrong behaviour - jay walking, disruptive behaviour… the consequences vary but can lead to people not being able to leave the country if they have not earned enough points. - is this the start of the future for other countries? Is this too dystopian?  

When AI and automation started happening in the industrial revolution the jobs of people changed.  The machines did not take away their jobs but instead made them automated so the role of people changed to maintaining the machines and technologies.  This made many industries more efficient and therefore productivity increased and sales went up seeing an increase in industry.

People get initially worried that AI will take away their job but it is more about realising that AI will assist us to become more efficient and the role and job of humans will change to focus more on relationships - the aspect that AI cannot do! We still need human interaction to remain connected!

Using an educational lense, it may be beneficial to teach students in a structured way that allows them to unpack and understand problems and solve them. This is Computational Thinking.  We do not need to just teacher students to code but more the understanding behind this to break down a problem and find strategies to automise its solution - as this is efficient and more cost effective.

As a consequence we need to teach our students and learners how to be accepting of ambiguity and the uncertain.  Our learners and younger generations are not used to hearing the word ‘NO!’ - we need to get them understanding that no is going to be a increasingly common language because AI will not be able to understand and complete everything - humans will need to problem solve this more.  

We also need to learn to centre ourselves to be able to make sound ethical judgements.  Understanding the obvious rights and wrong but also the shades of grey that will occur when we outsource work to computers.  Many of the leaders of the future will find themselves in this position where ethical decisions have to be made is response to AI.  Eg. Mark Zuckerberg did not break any legal laws when he sold information and rights to Cambridge Analytica. When he compromised was the laws and ethics set by people.  How do we ensure that out learners develop a good moral compass? What does this mean for our teaching today?

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