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,
而我想讨论Fuzzie and Techie这本书中所提到的
and one of the concepts that I like to discuss in the book The Fuzzie and the Techie
is this concept of intelligence augmentations
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’s 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 sort of from the
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
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
和人工智能结合起来 真正实现“字母的翻转” 并且
with artificial intelligence, really “flippingthe letters” and trying to
use intelligence augmentation as an AI realist
to sort of better the service that a travel agent can provide.
埃里克·科尔森 Stitch Fix 的首席算法官
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 34 3,500 stylist who have
their own propensity for delivering fashion they have their own biases as to the
the geography or the age or the
style preferences of somebody they might be servingclothing 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 to that particular stylist.
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 the sense of what Daniel Kahneman
the 2006 Nobel Prize winner in behavioral economics talks about is inside bias
and I think to the extent that we can think about inside outside bias
and trying to bring say 20% 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% of the people may make perfect sense to have them have complete backgrounds in
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% time but thinking about it for
20% people time 20% difference of methodology or difference of perspective.