I think one of the big counterintuitive points that I draw out in the book 'The Fuzzy and
the Techie' is this notion that just because you have technology skills doesn’t necessarily
make you relevant in tomorrow’s tech-led, data-infused economy.
And if you look at the underliers of jobs, there are tasks that make up every different
job that we have, and each of our jobs have some subset of tasks that are rote, that are
scripted, that are highly routine.
These are things that, over time, we can either program robots to do them if they’re manual
or we can program machines to do them through machine learning.
But there’s always a subset that’s more complicated or more complex that requires
collaboration, that requires communication or doing things that are on the frontier of
what we haven’t seen before; things that require improvisation.
And those are the very skills that require adaptive or flexible learning.
And so I think the counterintuitive point is that just because you have rote technical
ability you may actually be more susceptible to job automation than someone who has flexible
thinking skills.
So it’s this false dichotomy between having technical skills being relevant, having soft
skills being irrelevant.
In fact it’s more the opposite.
So when we think about automation it’s necessary that we engage with data, it’s necessary
that we learn the fundamentals of technology, that we learn to code, we learn to speak this
new language, but I think we can’t forget as well the soft skills that will enable us
to weather the changing economy as the machines continue to take more and more rote and scripted
tasks away from humans.
So the progress we’ve seen recently with things like deep learning with the DeepMind
and AlphaGo and sort of playing the world champion in Go—I mean, these are fundamentally
groundbreaking endeavors, I think the really interesting thing is that the artificial intelligence
we talk about today is still context dependent; it’s still dependent on being on a board,
being within the confines of a set of rules.
And so if you think about simple tasks, those are rules-based tasks that may be very easy
to automate, we’ve sort of migrated up the funnel where we’re talking about immensely
complicated tasks.
But I wouldn’t necessarily say that we’ve transgressed into complexity where there’s
really something that’s off the board, off the chess board entirely, off the Go board
entirely.
And so I think when we can move to a realm where we’re not context dependent, to me
that’s one of the definitional qualities of when it’s truly perhaps artificial intelligence
and not sort of A.I. that’s largely human led or human guided.
So, one of the new technologies that is in the daily media today that we focus on a lot
is this prospect of self-driving cars or automated vehicles on the roads.
And if you look under the hood of this new technology we realize that some of the biggest
challenges we’re facing are those that have to do with mixed-use environments.
So for example, when people migrate towards this new form of vehicle there’s not going
to be sort of a zero to one change, there’s going to be this mixed environment for a number
of years, potentially decades, where we have self-driving cars, perhaps partially automated
vehicles included with human-led cars.
And so one of the biggest challenges is more of an anthropological challenge of figuring
out: how do we get machines to interface with humans in an effective way?
So if you actually look at a company like Nissan some of the most important roles that
they’re hiring for are PhD anthropologists who are doing sort of ethnographic studies,
looking at human communication by gesture or by raising a nod or giving an eyebrow wave.
And these are small things that we sort of take for granted as implicit ways that we
communicate as humans, but if you’re trying to transcribe that into code and teach an
engineering team how to write the computer code for that, that becomes a very difficult
challenge between sort of anthropological research and engineering.
And I think that this is a perfect example of how we have to bring together both the
fuzzy and the techie to really meet and some of the biggest challenges today with self-driving
cars.
So in 2014 we were at the height of fear where there had been a study that came out with
the Oxford Martin School that looked at how 47 percent of U.S. jobs we’re at high risk
of machine automation.
This was also the year where Martin Ford’s 'Rise of the Robots' came out and the pendulum
swung all the way to fear.
And I think, taking a step back, earlier this year the McKinsey Global Institute came out
with a study where it looked at 800 occupations and drilled down into those occupations and
looked at the constituent tasks that made up each of those jobs, and it found that around
five percent of jobs had 100 percent of tasks that were things that could potentially be
automated by machines.
But the more interesting conclusion was that for roughly 60 percent of jobs only 30 percent
of the tasks within those jobs were things that machines could do over time.
And so as machines take over the rote, scripted, and routine aspects of jobs—the things that
we’ve done many times before, the things for which we have best practices or good practices,
things where you may have a note on the wall that says 'do it explicitly this order of
operations these ten steps,'—those are the things that we can program machines to do
in a fairly easy way over time.
There are obviously questions about the use of internal teams and dedicating resources
to optimize certain processes versus others.
These things aren’t all going to happen overnight.
I think there are a lot of management questions that have to do with resource allocation around
some of these issues.
So technical feasibility is only the first step of automation.
I think then there are many more steps that we need to consider.
But I think the interesting conclusion from the McKinsey Global Institute is that for
these 30 percent of tasks that may go to machines over time, what this does is it up-skills
or levels-up the human, where we now have to focus on the sort of non-routine aspects
of our job, the complex aspects.
And in this world of complexity where you may be good at one thing and I’m good at
something else we actually start task trading more, we actually start working on one thing
for one person and one thing for somebody else.
And in this environment of increasing task trading there is a guy named David Deming
who is an economist at the Harvard Graduate School of Education and Professor Deming talks
about how it's actually the soft skills that reduce the friction, reduce the transaction
costs when we trade tasks.
So in fact when machines are taking over the rote or routine aspects of our jobs and humans
are left with this 70 percent of complex or non-routine tasks it’s actually the soft
skills that start becoming more and more important.