Category Archives: Reconstructing work

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?

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.

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