Category Archives: Centre for the Edge

Reconstructing jobs

Some coauthors and have a new report out: Reconstructing jobs: Creating good jobs in the age of artificial intelligence.  This essay builds on the previous two from our “future or work” series,  Cognitive collaboration and Reconstructing work, published on DU Press (now Deloitte Insights) as part of Deloitte Review #20 (DR20) and #21 (DR21) respectively.

Cognitive collaboration‘s main point was that there are synergies between humans and computers, and that solution crafted by a human and computer in collaboration is superior to, and different from, a solution made either human or computer in isolation. Reconstructing work built on this, pointing out the difference between human and machine was not in particular knowledge or skills exclusive to either; indeed, if we frame work in terms of prosecuting tasks than we must accept that there are no knowledge or skills required that are uniquely human. What separates us from the robots is our ability to work together to make sense of the world and create new knowledge, knowledge that can then be baked in machines to make it more precise and efficient. This insight provided the title of the second essay – Reconstructing work – as it argued that we need to think differently about how we construct work if we want the make the most of the opportunities provided by AI.

This third essay in the series, Reconstructing jobs, takes a step back and looks these jobs of the future might look like. The narrative is built around a series of concrete examples – from contact centres through wealth management to bus drivers – to show how we might create this next generation of jobs. These are jobs founded on an new division of labour: humans creating new knowledge, making sense of the world to identify and delineate problems; AI plans solutions to these problems; and good-old automation to delivers. To do this we must create good jobs, as it is good jobs that make the most of our human abilities as creative problem identifiers. These jobs are also good for firms as, when combined suitably with AI, they will provide superior productivity. They’re also good job for the community, as increased productivity can be used to provide more equitable services and to support *learning by doing* within the community, a rising tide that lives all boats.

The essay concludes by pointing out that there is no inevitability about the nature of work in the future. As we say in the essay:Clearly, the work will be different than it is today, though how it is different is an open question. Predictions of a jobless future, or a nirvana where we live a life of leisure, are most likely wrong. It’s true that the development of new technology has a significant effect on the shape society takes, though this is not a one-way street, as society’s preferences shape which technologies are pursued and which of their potential uses are socially acceptable.

The question is then, what do we want these jobs of the future to look like?

Redefining education @ TAFE NSW >Engage 2017

C4tE AU was invited to TAFE NSW’s annual >Engage event to present a 15 minute overview of our Redefining education report, which had caught the attention of the event’s organisers.

The report ask a simple question:

In a world where our relationship with knowledge has changed – why remember what we can google? – should our relationship with education change as well?

and then chases this idea down the rabbit hole to realise that what we mean by “education” and “to be educated” need to change in response.

The presentation is a 15 minute TED format thing. You can find it on Vimeo.

The report is on Deloitte’s web site.

Reconstructing work

Some coauthors and I have a new(wish) report out – Reconstructing work: Automation, artificial intelligence, and the essential role of humans – on DU Press as part of Deloitte Review #21 (DR21). (I should note that I’ve been a bit lax in posting on this blog, so this is quite late.)

The topic of DR21 was ‘the future of work’. Our essay builds on the “Cognitive collaboration” piece published in the previous Deloitte Review (DR20).

The main point in Cognitive collaboration was that there are synergies between humans and computers. A solution crafted by a human and computer in collaboration is superior to, and different from, a solution made either human or computer in isolation. The poster child for this is freestyle chess where chess is a team sport with teams containing both humans and computers. Recently, during the development of our report on ‘should everyone learn how to code’ (To code to not to code, is that the question? out the other week, but more on that later), we found emerging evidence that this is a unique and teachable skill that crosses multiple domains.

With this new essay we started by thinking about how one might apply this freestyle chess model to more pedestrian work environments. We found that coming up with a clean division of labour between – breaking the problem into seperate tasks for human and machine – was clumsy at best. However if you think of AI as realising *behaviours* to solve *problems*, rather than prosecuting *tasks* to create *products*, then integrating human and machine is much easier. This aligns better with the nature of artificial intelligence (AI) technologies.

As we say is a forthcoming report:

AI or ‘cognitive computing’ […] are better thought of as automating behaviours rather than tasks. Recognising a kitten in a photo from the internet, or avoiding a pedestrian that has stumbled onto the road, might be construed as a task, though it is more natural to think of it as a behaviour. Task implies a piece of work to be done or undertaken, an action (a technique) we choose to do. Behaviour, on the other hand, implies responding to the changing world around us, a reflex. We don’t choose to recognise a kitten or avoid the pedestrian, though we might choose (or not) to hammer in a nail when one is presented. A behaviour is something we reflexively do in response to appropriate stimulus (an image of a kitten, or even a kitten itself poised in-front of us, or the errant pedestrian).

The radical conclusion from this is that there is no knowledge or skill unique to a human. That’s because knowledge and skill – in this context – are defined relative to a task. We’re at a point that if we can define a task then we can automate it (given cost-benefit) so consequently there are no knowledge or skills unique to humans.

What separates us from the robots is our ability to work together to make sense of the world and create new knowledge, knowledge that can then be baked in machines to make it more precise and efficient. If we want to move forward, and deliver on the promise of AI and cognitive computing, then we need to shift the foundation of work. Hence the title: we need to “reconstruct work”.

The full essay is on the DP site, so head over and check it out.

To code or not to code: Mapping digital competence

We’re kicking off the next phase of our “Should everyone learn how to code?” project. This time around it’s a series of public workshops over late January and early February in Melbourne, Geelong, Sydney, Western Sydney, Hobart, Brisbane, and Adelaide. The purpose of the workshops is to try and create a mud-map describing what a digitally competent workforce might look like.

As the pitch goes…

Australia’s prosperity depends on equipping the next generation with the skills needed to thrive in a digital environment. But does this mean that everyone needs to learn how to code?

In the national series of round tables Deloitte Centre for the Edge and Geelong Grammar School hosted in 2016, the answer was “Yes, enough that they know what coding is.”

The greater concern, though, was ensuring that everyone is comfortable integrating digital tools into their work whatever that work might be, something that we termed ‘digital competence’. This concept was unpacked in an essay published earlier this year.

Now we’re turning our attention to the question: What does digital competence look like in practice, and how do we integrate it into the curriculum?

We are holding an invitation only workshop for industry and education to explore the following ideas:

  • What are the attributes of a digitally competent professional?
  • How might their digital competence change over their career?
  • What are the common attributes of digital competence in the workplace?
  • How might we teach these attributes?

If you’re interested in attending, or if you know someone who might be interested in attending, then contact me and we’ll add you to the list. Note that there’s only 24-32 places in each workshop and we want to ensure a diverse mix of people in each workshop, so we might not be able to fit everyone who’s interested, but we’ll do our best.

Cognitive collaboration

I have a new report out on DU PressCognitive Collaboration: Why humans and computers think better together – where a couple of coauthors and I wade into the “will AI destroy the future or create utopia” debate.

Our big point is that AI doesn’t replicate human intelligence, it replicates specific human behaviours, and the mechanisms behind these behaviours are different to those behind their human equivalents. It’s in these differences that opportunity lies, as there’s evidence that machine and human intelligence are complimentary, rather than in competition. As we say in the report “humans and machines are [both] better together”. The poster child for this is freestyle chess.

Eight years later [after Deep Blue defeated Kasparov in 1997], it became clear that the story is considerably more interesting than “machine vanquishes man.” A competition called “freestyle chess” was held, allowing any combination of human and computer chess players to compete. The competition resulted in an upset victory that Kasparov later reflected upon:

The surprise came at the conclusion of the event. The winner was revealed to be not a grandmaster with a state-of-the-art PC but a pair of amateur American chess players using three computers at the same time. Their skill at manipulating and “coaching” their computers to look very deeply into positions effectively counteracted the superior chess understanding of their grandmaster opponents and the greater computational power of other participants. Weak human + machine + better process was superior to a strong computer alone and, more remarkably, superior to a strong human + machine + inferior process. . . . Human strategic guidance combined with the tactical acuity of a computer was overwhelming.1)Garry Kasparov, “The chess master and the computer,” New York Review of Books, February 11, 2010, www.nybooks.com/articles/2010/02/11/the-chess-master-and-the-computer/. View in article

So rather than thinking of AI as our enemy, we should think of it as supporting us in our failings.

We’re pretty happy with the report – so happy that we’re already working on a follow on – so wander over to DU Press and check it out.

References   [ + ]

1. Garry Kasparov, “The chess master and the computer,” New York Review of Books, February 11, 2010, www.nybooks.com/articles/2010/02/11/the-chess-master-and-the-computer/. View in article

The future of retail: The need for a new trust architecture

Deloitte ran a series of breakfasts recently for the retail community, and they kindly asked C4tE to participate. My contribution, which you can find at Scribd or embedded below, sprang out of our recent report The Future of Exchanging Value: Cryptocurrencies and the trust economy(FoEV) when, during a chance conversation, Robbie (the left-brained person who leads the Spatial team) pointed out that that we were arguing for a new trust architecture in retail.

The nutshell explanation of the idea is:

  • The current retail model is a constructed environment and shopping a learnt experience. This model is a response to the creation of mass market products and supply chains.
  • The model is build on there pillars: customers identifying a need, searching for a solution to the need, and then transacting with a merchant that they may not know or trust. Money – cash – facilitates this, as it enables us to transact with someone we don’t know and may never meet again.
  • However, a number of trends we saw in FoEV suggest that this model might be breaking down. The mid-market dies, consumers seized control of the customer-merchant relationship, peers replaced brands, value is now defined by the consumer rather than the producer, payments are moving away from the till, and shopping is becoming increasingly impulse driven.
  • What will retail look like in a world where need is never fully formed, search is irrelevant, and transactions are seen as distasteful? What is the new trust architecture?

See what you think of the presentation and feel free ping us if you have any thoughts.

The two reports mentioned in the presentation are:

Future of Retail – a New Trust Architecture by Peter Evans-Greenwood

Bitcoin, Blockchain, and Distributed Ledgers: What questions should we be asking?

Bitcoin, Blockchain & distributed ledgers: Caught between promise and reality

The latest report from the Centre for the Edge is out, Bitcoin, Blockchain & distributed ledgers: Caught between promise and reality. This report follows on from the one published in February, The Future of Exchanging Value: Cryptocurrencies and the trust economy (FoEV).

In the FoEV we looked at cryptocurrencies and Bitcoin, however we had to set aside a discussion on the technologies that underpin cryptocurrencies and their broader affect on society as the report was already quite long.

With Bitcoin, Blockchain & distributed ledger we pick up where we left off, and take close look at the opportunities and problems created by these technologies, and their regulatory implications.

We didn’t want this report to be yet-another explainer, since there’s already a lot of those out there, with the ensuing arguments over technology that invariably seem to follow them.

So rather than focus on the technology – the solution space – we focus on the potential applications – the problem space, to try and understand not just what is possible, but what is practical. This is resulted in a fail compact and pragmatic report focused on a few key areas, as we point out in the report’s introduction.

In From Bitcoin to Distributed Ledgers, we compare the Bitcoin’s ledger with the more familiar physical ledgers that preceded it, and develop the concept of a distributed ledger7 de ned in terms of the problems solved rather than the technologies used.

In A map of the distributed ledger landscape, we identify questions that should be asked when considering a new distributed ledger, creating a map of the solution landscape.

In Regulation, we explore the potential regulatory implications of these solutions, though we only focus on what is different with distributed ledgers. How does one regulate something no single person or organisation is accountable for?

In Applications, we review the strengths and weaknesses identi ed in the previous two sections to develop an understanding of what a distributed ledger can be and what it can’t be.

Finally, in Conclusions, we look at the technology’s potential and what the future might hold.

You can find the report on the Deloitte Australia web site.

To code or not to code, is that the question?

Centre for the Edge is dipping our toe into the education waters again after last years report, , Redefining EducationWe’re collaborating with Geelong Grammar‘s School of Creative Education to look into “Does everyone need to learn how to code?”

Computers are at the heart of the economy, and coding is at the heart of computers. Australia’s prosperity depends on equipping the next generation with the skills they need to thrive in this environment, but does this mean that we need to teach everyone how to code? Coding has a proud role in digital technology’s past, but is it an essential skill in the future? Our relationship with technology is evolving and coding, while still important, is just one of the many new skills that will be required.

Prime Minister Malcolm Turnbull has called for the country’s schools to introduce IT skills to students much earlier than they do now, suggesting that children as young as five or six should be introduced to coding. President Obama affirmed the need for coding education in his final state of the nation address. Some educators, however, are already pointing out that that teaching coding on its own might not be enough.

We will be holding a series of round table discussions across Geelong, Melbourne,  Sydney, Adelaide and Perth in May 2016 to explore the following questions:

  • What is the intention behind “we need to teach everyone to code”?
  • What educational and social outcomes we should be striving for?
  • Are there key skills from “learning to code” not covered in the current curriculum?
  • Is there a better definition for digital literacy?
  • How does digital literacy relate to coding and the rest of computer science?
  • How do we demystify digital technology and bring the community along?

Please contact me if you are interested in participating.

To code or not to code, is that the question?

Image: Ruiwen Chua.

The Future of Exchanging Value: Cryptocurrencies and the trust economy

FoEV2_coverOur latest piece at Centre for the Edge is out: The Future of Exchanging Value.

This report started life as a followup to a report we published in 2012. As we say in the current report:

Our findings in that report centred on the realisation that we were reaching the end of the initial build-out
of a digital payments infrastructure. The task of provisioning the infrastructure merchants require to accept real-time digital payments, or for two individuals to settle a debt, was largely complete. Consequently, our focus had shifted to streamlining the buying journey – from the pieces and parts to the whole.

Our key point then was that the future of exchanging value would be shaped by social forces – how payments fit into the end-to-end consumer experience – rather than the technological challenge of deploying yet-another generation of payments solutions.

This new report, which was intended to be a short update, when in an entirely different and much more interesting direction.

Our key insight this time is that we’re all thinking about money the wrong way.

It’s common to assume that we use money (cash, currency…) to build trust relationships. This assumes that our adoption of money stems from the coincidence of wants. I need shoes. You have shoes. You want a fish. I have a chicken. We use money to bridge the gap.

The problem is that this assumption is incorrect. As David Graeber points out in Debt: The First 5,000 Years, debt came before barter and the coincidence of wants. Most folk in antiquity didn’t need money. They knew everyone they interacted with, and could rely on the community to enforce the collection of a debt if need be. Money’s first use was as a measure of value, typically to help calculate damages in a criminal or civil manner. Communities had carefully drawn up lists to capture exactly what you owed, in a convenient currency, someone if you destroyed their house, stole their food. In Somalia, for example, they use camels (commodity money). The other uses of money – as a medium of exchange and store of value – came later.

This is a fascinating fact, is it points out that we have the consumer-merchant relationship backward. We’re focusing on the transaction when we should be focusing on the relationship. The future of payments is not micropayments and tap-and-go. Indeed, the future of payments might be to use a loyalty scheme (a complimentary currency) to anchor the relationship and then move the transactions from the centre of the relationship to the edge. This ties is cultural preferences that we have, and which equate money and transactions as “dirty”. The future of payments might be not to have payments at all.

Bitcoin and the whole cryptocurrency thing is influenced by this too. There’s a huge amount of noise in this area at the moment, and everyone one is waiting for the killer app that will drive Bitcoin (or another cryptocurrency) into mass adoption. If, however, you view Bitcoin adoption as a cultural problem, rather than the search for a killer app, then you end up at the conclusion that no cryptocurrency will become much more than a large niche. The best equivalent in the current environment that we’re all familiar with would be a large frequent flyer scheme. It’s hard to scale trust, even with technology support, and these frequent flyer schemes seem to up near a nature limit.

There is one use case for currencies growing larger, though: when a sovereign nation mandates that you pay taxes in a specific currency. This trick is behind all the major currencies, and was used by the colonial powers to pull conquered land into their monetary system. Acquire currency to pay tax, or we send the bruisers around.

We conclude in the report that the best analogy for cryptocurrencies is rum and cigarettes. Rum was used in Australia’s early days when there wasn’t enough government issued currency to go around. Cigarettes were used by prisoners or war as they had few other options.

We can expect cryptocurrencies to see some adoption in countries where the population doesn’t trust – or can’t access – the national currency. Argentina springs to mind. Cryptocurrencies are mush less useful in other countries with mature and stable economies.

A similar argument can be made against cryptocurrencies as internal reserve currencies. (And that argument is in the report.)

There’s a lot more in the report, and I’ve been told that it’s a bit of a ripping yard. Go grab a copy and read it.

The problem with platforms in the sharing economy

Platform_compressed-750x300

I have a new post up on the Deloitte Strategy blog.It’s the result of a chat I was having the other day with an economist colleague who opined that “platforms are an essential part of the sharing economy”.

As I point out in the post:

These platforms might be sufficient to kick-start the sharing economy, but they’re not necessary for its long term survival. There are alternative approaches to creating sharing economy solutions that do not rely on a centralised platform.

Platforms solve what we might call the discovery problem. When we’re creating a market it needs a mechanism for buyers and sellers to discover each other.

Rendezvous – where buyers and sellers meet at a common location – is probably the most common solution to discover. It’s also the one that firms prefer as it’s the easiest to monetise.

As I point out later in the post:

The recent emergence of blockchain – a distributed ledger solution – from the shadow of Bitcoin might be a sign that something has changed in the environment, something that is tipping the advantage away from centralised solutions and toward distributed ones.

This could be a big deal, as it blows a rather large hole in the business models of the sharing economy firms.

Check out the post and see the whole story.