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- Hi, I'm Prasad Setty.
I'm excited today to be chatting with James Manyika,
Senior Vice President of Technology and Society at Google.
Welcome to the chat, James.
- Good to see you again, Prasad,
and thank you for having me.
- How has the pandemic influenced
what you think about as the future of work?
- We suddenly got launched into
this massive global experiment
about remote work and hybrid work.
Should people work from home or hybrid in what form?
The fact that we actually have those choices to debate
relies on the fact that we have
these digital infrastructure now in place
that allows us to work from home,
to work in the office, go back and forth.
Maybe even move cities,
maybe even move the geography of work itself.
This is quite extraordinary.
- You've been looking at the impact
of technology on society.
James, what does the population at large
get right about artificial intelligence
and where do they have misconceptions
about what AI and ML is all about?
- There's a general conception, a concern,
I think that we're facing a jobless future
and these technologies are gonna lead to a jobless future.
I don't think that's quite right.
Much of the research that I've done
and many others have done now is the fact that
if you take AI and ML and automation
and try to apply it to the issue of work,
you have to look at a task level as to,
which tasks can be automated
and augmented by technology and how.
When you take that task level view,
you quickly realize that in fact
the proportion of jobs where all the constituent tasks
could be automated is actually not that high.
In our analysis, roughly 10%.
Whereas you actually find that there are actually,
many more jobs, 60%,
whereas something like a third
of their constituent activities could be automated.
The net of that is we're gonna see
more complimenting of jobs or augmentation of jobs
much more than substitution of jobs.
- How do you see AI and ML influencing
inclusion within organizations?
- An important issue on DEI is
where the issues of inclusion are more about
place as opposed to just people.
So we know that for example,
businesses and companies and certain parts of the country
will get affected more than others.
We know that probably because of the demographic profiles,
but also the nature of businesses
and their footprints in different parts of the country.
They're trying to pay attention
to how these technologies adapt
and understand how different people
participate in the labor markets.
And we are starting to pay attention to issues around
the bias that can come from algorithms
when it comes to hiring.
We know that in the labor markets,
this often comes in with regards to
when we use algorithms to do sourcing and recruiting
and review profiles and so forth.
And we already know that when
a lot of ML / AI research is actually,
highlighted the fact that
often when you're training algorithms on datasets,
just because of history,
often the word doctor is next to a he
and often the word nurse is very correlated
with, historically, with her.
So this is another area where we're gonna
need to be very, very purposeful
to make sure we are taking efforts
to make sure we debias algorithms.
They don't do these sort of things.
In fact, one of the fun things I was excited to see
Google announce at IO recently was the Monk Scale,
where imaging technology has historically
been based on what's called the Fitzpatrick Scale,
which has never quite fully recognized
the full richness of skin tones and color tones,
and for people like me.
And so, Google actually invested
working with researchers at Harvard
and created the Monk Scale,
which tries to expand the range of skin tones
that algorithms can actually recognize and understand.
And so, I think that's the kind of purposeful work
to try and address these issues of bias
that we should even continue to do more and more of.
- Thank you so much, James.
You shared so many great kernels of wisdom.
Appreciate the conversation and thank you so much
for sharing your thoughts with us.
- Thank you, and thanks for having me, Prasad.
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