The year is 2030. That’s less than a billion seconds from now.

For most everyone in the workforce – and certainly everyone at the higher end of the workforce – one thing has become absolutely clear

They’re spending as much time learning the next thing they’ll be doing as they spend doing the whatever skills they’ve mastered.

In a broad sense, that’s what work looks like in 2030.  Professional practice has become inseparable from what we would today call ‘professional development’ – but the way they work in 2030 is really rather different.

There’s an enormous emphasis on mentoring.

There’s an enormous emphasis on machine-guided skills acquisition

And all of that is happening continuously.

The barrier between what constitutes education and what constitutes practice has well and truly collapsed.

It didn’t collapse for any one reason, or because of any one decision.

It collapsed because the whole world changed.

To see why, let’s back up a bit from 2030, and look back almost a billion seconds, to 2001.

*story of Wikipedia*

Wikipedia is a fixed star in our universe now.

It’s one of the few things online that people unambiguously trust.

But it wasn’t always that way.

As far as any can tell, I was the first person to discuss Wikipedia with educators, at a 2004 conference in Victoria.

It was a curiosity. Then it became a joke. Then it became a threat. Then it became a fear. And then it won.

The kids know they need to consult primary sources – they’ve had that drummed into them.

But of course now Wikipedia includes footnotes and links to all of those primary sources. So it serves both as factual reference and index to human knowledge – in a way Google couldn’t even dream of.

Wikipedia as a thing is good.

But Wikipedia as a model for knowledge creation – well that’s better.

After all, it’s not supposed to work. Theory said that expertise is the way to construct a compendium of knowledge.

Practice has proven that we’re all experts.

No one is expert in everything. But most of us are deeply knowledgeable about something.

Wikipedia is where we share that.

This is the clever bit about Wikipedia, which we never realised until we saw it in practice – that people have expertise, and that we feel compelled to share this expertise.

That was true long before Wikipedia. It’s part of what makes us human.

Wikipedia gave us a place to share that instantly made our expertise available globally.

Which is a whole other amazing thing. Because it is quite literally the sharing of the contents of hundreds of millions of minds.

That model makes sense, and is replicable around any area of knowledge that we might want to designate – provided people are interested enough to kick start the virtuous cycle of contribution and improvement.

Jimmy Wales, the founder of Wikipedia, told me it takes five dedicated individuals to kick-start Wikipedia in a new language (and they have a few hundred languages now). Five dedicated individuals – who probably start by translating articles from another version of Wikipedia.

They’re doing that because they’re passionate about the project. That passion drives them. Just as it drives every contribution to Wikipedia.


Turns out that each of us have a passionate desire to share – and a passionate desire to mentor.

That passion may be very selective – we want to mentor or share on a specific subject, or topic within a subject.

Conversely we may be passionately interested in and seeking mentoring on a specific subject or topic within a subject.

That’s a human quality. It’s been part of us from year dot.

Our educational system – which grew out of a need for a uniformly educated workforce to serve in the industrial economy – took a different approach.

Kids come in one end, and – half a billion seconds later – they pop out the other end fit for roles in an industrial economy.

An economy that in a broad sense no longer exists.

It’s not that we don’t make things, it’s that the way we make them is almost completely different to how we made them a century ago, when our educational techniques came to into practice.

And yet, along side all of that industrial education, the apprenticeship system – which is all about mentoring – remained in place.

If you were thinking of a highly skilled trade – whether building or plumbing or medicine or the law – you have years of mentoring before you’re considered fit to practice.

So we have these parallel systems – industrial and passionately human – running side by side.

But now, well, most of what the industrial method offers can be delivered just as effectively through other means.

The machines are getting better at this.

And how that happened is quite a story in itself – and it’s an important story because it tells us a lot of how the future looks like for STEM education.


Everyone is running around talking about artificial intelligence and how it’s going to change everything and put us all out of work and fire all the missiles and pretty soon we get from a demonstration of a self-driving car all the way to Skynet without ever really stopping to wonder if this is realistic.

I’ve lost count of the number of ‘reports’ telling us that thirty, forty or fifty percent of all jobs will be obsolete within 20 years because of artificial intelligence.

And it’s all so weird.

Because it’s all predicated on the assumption that we’re going to stay absolutely stationary for the next twenty years.

My mum retired about 20 years ago. She spent all of her career as a secretary – migrating to the role of ‘executive assistant’ as the jobs as secretaries simply vanished into thin air, replaced by word processors and email and scheduling software and all that.

Secretaries at one time comprised a huge segment of the ‘pink collar’ workforce. And they were put out of work by machines.

But we seem to have survived. Some of the folks in those positions retired, other retrained and moved into other roles that opened up in those organisations.

That’s not a one-off. That’s the entire history of the industrial era, from the buggy-whip manufacturer to the farm hand to the world’s biggest manufacturer of film cameras.

Change happens —and we find ways to make it work for us.

We believe this change to be different. And it is, in a way, because we particularly privilege ‘thinking’ as a uniquely human task, and feel very uncomfortable when something else seems to be thinking for us.

And here’s where I take a machete to all of our pretensions about artificial intelligence. Because we imagine so much more than it is.

Let me tell you a story that happened a year ago…

*Ke Jie and AlphaGo*

Artificial intelligence is simply learning from your mistakes multiplied by the intensity and focus of a computer. Tens of millions of mistakes.

We’re lucky in that we learn millions of times faster than a computer.

And we’re lucky in that we can teach one another what we’ve learned.

So yes, we can write a program that can lose tens of thousands of games of Go until it can beat a mediocre human player. We can let it lose hundreds of thousands of games until it can beat a world-class human player.

Now at that point you have tool that a human can’t beat. Does that mean humans are suddenly obsolete — or does it mean that you now have the perfect tool for a human to train against?

Keep that thought in mind, we’ll come back to it.

I mentor a range of different startup companies, and a few of these are very heavily involved in using artificial intelligence to radically transform some industries.

One of them – Abyss Solutions – uses a combination of submersible robotics and machine learning to provide a solution that allows a municipal water utility – with hundreds of kilometers of drains, culverts, and piping – to conduct a detailed visual inspection without putting a diver into a situation where they have to perform a dangerous manual inspection situation.

There is a human operator, guiding the submersible in its scan, and that scanned video footage would have had to have been laboriously analysed by a human being for any potential flaws or defects in the site being examined.

It’s exacting work, and comes with a fair degree of boredom.

Abyss Solutions uses artificial intelligence to scan the video footage, highlighting for a human inspector the bits of the scan that require a closer look, removing the need for hours of pointless labour, and allowing the human inspector to do what they do best – focus on the areas and issues that might need repair.

This makes inspections cheaper, safer, and more reliable – and it’s why Abyss Systems now has a contract to inspect Hoover Dam in Nevada.

It points to the emerging model of artificial intelligence, that is a big component of another startup I’ve worked with, APS.

The folks at APS are hard-core chemical process engineers. That’s a field we don’t hear a lot of, but nearly every modern material manufactured at scale has chemical process engineering behind it.

The process itself is largely trial and error. An engineer will propose a pathway to get from a range of input compounds to a desired output compound, then either try it out, or pop all of it into a simulator to see how it all might work. They’ll likely fail at that, learn from their failure, and try again.

Hmm, that sounds familiar. Learn from failure & try again. That sounds ideal for artificial intelligence – and this is exactly what APS realised. So they built a tool uses artificial intelligence – in conjunction with the simulator – to generate novel chemical processes, try them out in the simulator, then refine their results.

Turns out that this works – and often generates completely new and unexpected synthesis pathways, simply because the artificial intelligence isn’t constrained by examples they’ve seen before or were taught in the classroom. It’s working from a blank slate.

As I mentored APS, I came to realise they’d opened up something much bigger than chemical process engineering. They’d worked out a general approach for artificial intelligence that closely couples it with simulators, to rapidly run through the mistakes it needs to make as it moves toward a solution.

Every soon, that is going to be a general STEM tool.

Someone designing a building or a fiber-optic cable or a new alloy will be using these kinds of AI aids the same way many engineers already use tools like Mathematic to help them solve complex engineering problems.

Does this mean we won’t need engineers? Not likely. It means that the engineers will be focusing on the most interesting and most human parts of the process.

And it means – as we can see both in the example of Abyss Systems and in APS – that our machine future is one of partnership and collaboration. It is emphatically not a future of unemployment and desolation. Our work is going to grow more interesting and more meaningful because of the machine partnership.

And there’s one last example to bring this home.

**story of AlphaGo Zero**

Ok, so Google could make the best better – big deal, right?

Well, Ke Jie – so thoroughly trounced by AlphaGo last year – has been playing AlphaGo Zero. He won’t win any games – AlphaGo Zero is too good for that – but he’s been getting better.

The best human player in the world has increased his own standing as Go’s grand-grandmaster precisely because he has a completely implacable opponent.

Artificial intelligence makes the best of us even better.


Three elements are key to understanding the world of the middle 21st century:

  • Growing human capacity through connecting, sharing and learning;
  • Growing machine capacity as we teach machines how to learn;
  • Growing capacities of both humans and machines as we work together to bring out the best in one another.

All of that spells collaboration – with one another – and with our new tools.

But let’s put that in context of where we are here and what we’re looking to do – which is to keep kids deeply interested in the disciplines that will serve both them and the nation across the next billion seconds.

Is that such a big ask?

The drive to connect and share that’s innate and now operating at global scale is closely aligned to a similar drive that turns toddlers into perfect scientists, conducting all sorts of physical experiments in the world.

More than a hundred years ago Jean Piaget worked out the tenets of what we call Constructivism, identifying it as the educational framework that most naturally suits our innate drives.

It’s in the years since then that we’ve come to recognise that there’s a closely aligned need – to mentor and to be mentored. This wasn’t something Piaget studied specifically, and although its importance has been understood in human culture since – well since there were humans – its role has always been highly local, because mentoring has always been one-to-one.

It’s precisely that which has now changed. Sharing and learning are mentoring and being mentored, but at a vastly different scale – so they feel quite different. But the same essential principles apply.

And it’s here that the future of STEM education will find itself, in a mixture of student-driven learning and explorating, mentoring, being mentored, sharing and learning.

These are the essential educational tasks going forward, and part of our job is to understand each of them so well we can lean into them.

Constructivism – well that seems easy enough, though I had an illuminating conversation last week with a woman who sells simple robot kits into schools, and spent 2 hours with the whole teaching staff at a school doing a bit of professional development – having them build this robot.

More than a few of the educators were skeptical – they didn’t teach STEM, so why should they bother to learn how to build a robot.

Two hours later – when they’d successfully completed the task, they were singing a different tune, because they’d found in the task the joy of self-directed discovery, learning, and understanding. They’d gone from pessimistic to excited.

If this is going to work at all, we have to bring everyone along. Even the folks who think this doesn’t matter them. Especially those folks.

That’s going to be precondition for the biggest change we’ll see over the next billion seconds in education – a complete revisioning of assessment.

I’d almost say abandoning, but – well, let me explain what I mean here.

STEM education is not happening in some vacuum cut off from the rest of the world. Out there things are changing very quickly.

One of the things that will be happening – that I can already see happening within my immediate circle – is that by around 2030 we’ll spend about half our time learning how to do the next thing, and half our time doing the thing we’ve already mastered.

Except in rare and specific instances, it won’t mean leaving the workforce to spend months or years gaining another professional degree. The key difference is in how seamlessly this is going to be threaded into our basic work practice.

And that’ll be because it will be driven by our interest – harnessing the Constructivist impulse within each of us – supported by mentoring. We’ll be mentored continuously while working – it will be seen as part of the job.

Conversely we will mentor continuously while while working. That, too will be seen as part of our job.

All of this will be seamless. It will just be what we do, and it means our skills and capacities will always be evolving – and always in demand.

Now if that’s going to work for us folks out in the workforce, because it’s drawn from some first principles about how humans learn, why would artificially run our classrooms to a different set of rules?

We already have some peer mentoring in our classrooms. We need to amplify its role so that it becomes absolutely the norm for all students in all classrooms – both to help students build the necessary techniques in mentoring – listening, reflecting, emotional understanding – and to provide a necessary mechanism to spread the workload inside the classroom.

Teachers are profoundly time-constrained because so much mentoring in so many ways falls directly on them. A classroom of peer mentors – and, even better, an entire school of peer mentors – shifts the burden of mentoring. Teachers are certainly still mentoring, but they’re free to focus on the most interesting (or most difficult bits).

What does assessment mean in that world? Is it a test you take? Or is it when your peer mentor recognises an achievement of competence?

The world is not a series of jumping through hoops. It’s a process of continual learning and upskilling. Our educational philosophy and practice need to align with that reality if we really expect STEM to align with the future of work.

That future is collaborative. It’s people mentoring people.

That future uses incredible tools and provides the human support for the people learning how to use these tools to excel in their work.

The future is us learning at scale how to share in a way that helps us all meet one another’s needs.

That’s the future we need to put into practice today. We know it works, and we know how to do it. All we need to do is to engage our excitement and desire to change – which, let’s face it, is why we’ve put this day together.

We’re going the best we can in a time of tremendous change – and we need to lift our game.

We know how to do that: connect, share, and learning from one another.

Let’s get to it.

RDA Hunter STEM Conference 2018, 10 May 2018, Newcastle, New South Wales.

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