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

What does the future mean to the education industry? Futurists tend to get a bad wrap because they often make technological predictions. Mike Walsh argues that successfully predicting the future is more about paying attention to people, not the technology in their lives.

 

While in Japan, Walsh shared his thinking around Masayoshi Son’s ability to raise hundreds of millions of dollars by starting his thinking 15-20 yrs into the future, and simply working backwards to see what support and infrastructure would be needed to make that future a reality. He then goes to find those companies to invest in and if they don’t exist, he creates them! You see, what Masayoshi does differently is that he looks at the people needed to create a distant dream, not the technology. And so, Walsh surmises, we needn’t be looking at the current crop of Millennials to make predictions about education in the next 12-15 years, as by then, they will be “as old and as miserable as the rest of us!” The people we should be looking, Walsh describes as the most terrifying generation we’ve ever encountered, are eight year olds!

 

Why are eight year olds so different?

The way our current crop of primary aged students interact with technology is vastly different to the generation previous to them. Walsh points out that this digitally native group of users develops an almost intrinsic understanding of the algorithmic framework that drives interactions from an impossibly young age. It’s this genuine difference in the way they interact with technology that Walsh believes will lead to a very different way of thinking around the way we connect with and explore knowledge.

 

It’s not the screen that’s interesting, it’s the experiences and the way technology has interacted with it. YouTube has changed the way an entire generation watches TV. Every experience children have now has been customised and hyper-individualised by the data collected by social media. Children now are at the beginning of a true algorithmic society, a social credit score based society. Terrified yet? The currency and fabric of daily life is fast becoming driven by data, artificial intelligence, algorithms and machine learning. Computers themselves are constantly adapting, writing their own code and programming, no longer reliant on the dinosaurs of the MS-DOS prompt generation.


"The minute you  joined Facebook, your kids left!” 

 

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Adaptive Learning

The reason adaptive and machine learning has so much potential is because it allows us to truly take the world’s knowledge, understand an individual’s needs and to personalise and tailor it to algorithmic, logical perfection. Students of tomorrow have the opportunity to be taken along their own learning journey, at their own pace and scale, for vastly reduced sums of money. As Walsh points out, this is not to say human teachers are not important, just that we are entering an age whereby content and opportunity can be delivered in a scaled way, that has previously been inconceivable. And we’re going to need it! Walsh continues on to share that the skills, knowledge and understandings required to function successfully in an algorithmic age are not being taught in today’s schools. As we stand at a precipice, faced with the landscape of tomorrow’s society, how can teaching knowledge and skills of yesterday, prepare leaders and learners of tomorrow? We need to start by articulating what those skills might be…

 

Automation of Industry

When farm jobs started to decline during automation, the westernised education system began to evolve. Many smart and forward thinking people realised the need to invest in new forms of education in order to prepare people for the future. Technology doesn’t destroy jobs, it simply changes them. It’s not always a straightforward process and often the realisation takes a little time. Sometimes enabling technology, even though it can be hugely disruptive, can actually increase the number of people employed in an industry. Take ATMs for example. Some bank tellers lost their jobs, however because paying the number of people who worked as tellers reduced, it meant that more branches could be opened- thus increasing the number of people working for the banks!

 

Automation

 

It’s becoming a case of looking at the type of people that will thrive in an environment that focuses on both the world of people as well as having a strong understanding of how to leverage data and apply it. Computational thinking is not about teaching children to code, it’s about how to leverage technology to break a problem down and find a strategy to automate its solution. Thinking about the future, this gives students the ability to both understand the essence of a problem as well as a knowledge of the tools and processes to combat it.

 

Key Skills for the next generation

As we see the rise of this hybrid approach, the understanding of the problem and the data to solve it using computer science and technology, we need to teach the next generation to be more comfortable with ambiguity. We are in danger of preparing students for a world that has become obsolete by the time they leave education. The CEO of Netflix looks for employees that can exercise “good judgement in ambiguous situations.” This is harder than it sounds. As we leave a structured education system that has exams and allocated time, hierarchy and structure, and they walk into a world that has huge unknown, how can we make sure they cope. How do we teach students to process unpredictability and handle ambiguity?

 

Another element we need to help learners become aware of is the power of machine thinking and artificial intelligence. Here Walsh sites Deep Blue (an AI) beating world Chess Master Gary Kasparov. Reflecting on this event, it became clear that the computer was not trying to beat the world’s greatest human chess player by a substantial margin, it was simply trying to do the very minimum to win by just one point. What this means is that we need to understand how a computer ‘sees’ the world and problem solving. A computer will conserve resources, not try to focus on the end goal and winning big. A computer will work out the simplest way to win and this way is often not an approach that a human will see, let alone take.

 

Achievement

 

The final element we need to take into account is to teach students to centre themselves, find the right moral compass and make good ethical judgements. Here Walsh suggests that perhaps studying computing is not the best way forward, but the studying of philosophy in order to help build decision making capacity using a strong moral compass. This is not about following the laws of a land, it’s about following the laws of trust, set by humans. As the debates around privacy and our data continue to rage, we are entering a time where understanding the tech is important, but understanding underlying motivations and human behaviour is even more valuable.

 

“The algorithmic age is an opportunity to embrace new and exciting ways of thinking...”

 


Q&A with Mike

 

  • Are our experiences within the digital economy going to get wider and bigger?

It’s impossible to not participate in the future. It may become impossible to get a bank loan or go a

bout daily efforts as you’ll have no transparency and digital value. With kids, we have about 9-10yrs where people shelter them from tech. If we don’t teach them how to function appropriately and effectively, then how can we expect them to function?

 
  • How can we avoid programmer bias being transferred to AI?

This is important. We need to interrogate the code that is produced. How was the data collected? Are they discriminatory? There’s a need to have well educated teachers and others so they can be part of the discussion.

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  • Small data: The rights, the voice and the individual. How do we as teachers ensure that the rights of our children are at the forefront?

People assume it’s a binary thing. They think it’s either about human interest or corporation driven outcomes. I see it as a combination. As we scale up good education into remote communities or for larger class sizes, it should be a partnership. Everyone is at a different rate of learning and we can leverage small or big data to find what someone knows and unlock their potential.

  • Teachers in the future: They need to be informed, discerning, questioning and listening. So what might it actually look like?

Teachers need to be as good as the tech they use. I don’t believe classrooms will disappear. The power of humans together is incredibly. People working from home is beginning to end because their best ideas come from the old school analogue way of being face to face. In 10-20 yrs we won’t have virtual classes. If anything the tech will be less visible. It’s the data that sits behind it that will really shape the system.

 

  • Are humans learning to think less for themselves therefore teaching ourselves to becoming less intelligent?

In many ways we don’t have the same memories because we have google! We live in times when we don’t even need to remember phone numbers. Tech has become an extension of our memory and perception. Does it makes us stupid? I think it’s changed us. It should allow us to extend ourselves.

  • As someone who travels world as a global nomad- where do you think the patterns around where people live lie? Will travel decrease because of tech?

It feels like we’re going backwards. How did we lose Concord? Even with tech, our ability to see more digitally makes us want to see it more physically. I hope it will make people want to see more. Autonomous cars, flying cars and drones, all will change how we interact and how we design where we learn. We need to remember not to forget what it means to keep in touch and be human.

uLearn18 Keynote3a H

uLearn18 Keynote3b H

Blog post by James Hopkins
 
CORE Education

CORE Education

CORE is a world-class, professional learning and development organisation, providing a diverse range of innovative, inspiring, and creative solutions to support educators and learners.
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