In this world where we focus so much on what we’re building, how we’re building it,
I think we need to take a step back and reconsider why we’re building, and really humanize
our technology, really bring together diverse teams of methodologies and people and mindsets
so that we can take our technology and actually apply it to the most fundamental human problems.
Today the conversation is largely about artificial intelligence, and one of the concepts that
I like to discuss in the book The Fuzzie and the Techie is this concept of intelligence
augmentation—so: thinking about using AI but using it in a way that’s augmenting
the ability of humans.
So Paul English, who was the creator of Kayak.com, he is a techie through and through, but he
also calls himself an AI realist; he’s somebody who believes in the promise of artificial
intelligence, but also realizes that this is not something that tomorrow or next year
or maybe perhaps in the next decade is going to completely take away from the characteristics
and the qualities of what a human can provide.
And so he’s now creating a company called Lola that’s based in Boston, and Lola is
sort of Kayak 2.0, where rather than trying to take the travel industry and put it online
he’s actually taking travel and putting it back into the hands of travel agents, real
people that are working on the phones dealing with people that are calling in to book travel.
And what he’s doing is he’s supplementing those travel agents with technology, with
artificial intelligence, really “flipping the letters” and trying to use intelligence
augmentation as an AI realist to sort of better the service that a travel agent can provide.
Eric Colson, who is the Chief Algorithms Officer at Stitch Fix, he uses machine learning—he
uses artificial intelligence, but to augment the human stylist.
So they have 60 or 70 data scientists working on creating machine learning algorithms, but
those are used to supplement the 3400, 3500 stylists who have their own propensities for
delivering fashion, they have their own biases as to the geography or the age or the style
preferences of somebody they might be serving clothing to.
And so the machine learning actually learns the bias of the human over time and tries
to mitigate that bias by offsetting the selection of clothing that they provide at that particular
stylist.
And I think that’s a really interesting example of artificial intelligence not necessarily
taking away from that stylist but actually augmenting, improving, helping them perform
better.
And I think that flipping the letters from AI to IA is really something that we should
be thinking more about today in the context of the AI debate.
I think it starts with job requisition and writing sort of the job descriptions that
we want to hire for.
And I think we are bombarded by applicants, we’re bombarded by new resumes and “data
driven processes”, and so the quick answer is to use natural language processing and
screen for keywords, to run things through a filter and draw out the resumes that really
hit the five key words that relate to your team.
And I think what this does is it creates sort of an “inside bias,” where you’re creating
and you’re bringing together people that all have sort of the same perspective, the
same backgrounds, and it can really sort of create in the sense of what Daniel Kahneman,
the 2006 Nobel Prize winner and behavioral economics talks about as “inside bias”.
And I think to the extent that we can think about inside/outside bias and trying to bring
say 20 percent of the team from a different perspective, from a different vector, from
a different methodology or background, that can really bring diversity to a team where,
if you have a data science team, 80 percent of the people may make perfect sense to have
them have complete backgrounds in data science—but what’s to say that 20 percent of the team
shouldn’t be philosophers or psychologists or anthropologists?
And I think that sort of mentality of almost “Google 20 percent time,” thinking about
it for 20 percent people time, 20 percent difference of methodology or difference of
perspective.