字幕表 動画を再生する
>> Welcome everyone.
Thank you for coming to the Intel AI lounge
and joining us here for this economist world event.
My name is Jack.
I'm the chief architect of our
autonomist driving solutions at Intel
and I'm very happy to be here and to be joined
by an esteemed panel of colleagues who are joining to,
I hope, engage you all in a frayed dialogue and discussion.
There will be time for questions as well,
so keep your questions in mind.
Jot them down so you ask them to us later.
So first, let me introduce the panel.
Next to me we have Michelle,
who's the co-founder and CEO of Fine Mind.
She just did an interview here shortly.
Fine Mind is a company that provides a technology platform
for retailers and brands that uses artificial intelligence
as the heart of the experiences
that her company's technology provides.
Joe from Intel is the head of partnerships and acquisitions
for artificial intelligence and software technologies.
He participated in the recent acquisition of Movidius,
a computer vision company that Intel recently acquired
and is involved in a lot of smart city activities as well.
And then finally, Sarush, who is data scientist by training,
but now has JDA labs,
which is researching emerging technologies
and their application in the supply chain worldwide.
So at the end of the day, the internet things
that artificial intelligence really promises
to improve our lives in quite incredible ways
and change the way that we live and work.
Often times the first thing that we think about
when we think about AI is Skynet,
but we at Intel believe in AI for good
and that there's a lot of things that can happen
to improve the way people live, work, and enjoy life.
So as things in the Internet, as things become connected,
smart, and automated, artificial intelligence
is really going to be at the heart of those new experiences.
So as I said my role is the architect
for autonomous driving.
It's a common place when people think
about artificial intelligence,
because what we're trying to do is
replace a human brain with a machine brain,
which means we need to endow that machine
with intelligent thoughts, contexts, experiences.
All of these things that sort of make us human.
So computer vision is the space, obviously,
with cameras in your car that people often think about,
but it's actually more complicated than that.
How many of us have been in a situation on a two lane road,
maybe there's a car coming towards us,
there's a road off to the right, and you sort of sense,
"You know what?
That car might turn in front of me."
There's no signal.
There's no real physical cue, but just something about
what that driver's doing where they're looking tells us.
So what do we do? We take our foot off the accelerator.
We maybe hover it over the brake, just in case, right?
But that's intelligence that we take for granted
through years and years and years of driving experience
that tells us something interesting is happening there.
And so that's the challenge that we face in terms
of how to bring that level of human intelligence
into machines to make our lives better and richer.
So enough about automated vehicles though,
let's talk to our panelists about some of the areas
in which they have expertise.
So first for Michelle, I'll ask...
Many of us probably buy stuff online everyday, every week,
every hour, hourly delivery now.
So a lot has been written about
the death of traditional retail experiences.
How will artificial intelligence and the technology
that your company has rejuvenate that retail experience,
whether it be online or in the traditional
brick and mortar store?
>> Yeah, excuse me.
So one of the things that I think is a common misconception.
You hear about the death of the brick and mortar store,
the growth of e-commerce.
It's really that e-commerce is beating brick and mortar
in growth only and there's still
over 90% of the world's commerce is done
in physical brick and mortar store.
So e-commerce, while it has the growth,
has a really long way to go and I think one of the things
that's going to be really hard to replace
is the very human element of interaction and connection
that you get by going to a store.
So just because a robot named Pepper comes up to you
and asks you some questions,
they might get you the answer you need faster
and maybe more efficiently, but I think as humans
we crave interaction and shopping
for certain products especially,
is an experience better enjoyed in person with other people,
whether that's an associate in the store
or people you come with to the store
to enjoy that experience with you.
So I think artificial intelligence can help it be
a more frictionless experience,
whether you're in store or online to get you from point A
to buying the thing you need faster,
but I don't think that it's going to ever completely replace
the joy that we get by physically going out into the world
and interacting with other people to buy products.
>> You said something really profound.
You said that the real revolution
for artificial intelligence in retail will be invisible.
What did you mean by that?
>> Yeah, so right now I think that
most of the artificial intelligence that's being applied
in the retail space is actually not something
that shoppers like you and I see when we're on a website
or when we're in the store.
It's actually happening behind the scenes.
It's happening to dynamically change the webpage
to show you different stuff.
It's happening further up the supply chain, right?
With how the products are getting manufactured,
put together, packaged, shipped, delivered to you,
and that efficiency is just helping retailers be smarter
and more effective with their budgets.
And so, as they can save money in the supply chain,
as they can sell more product with less work,
they can reinvest in experience,
they can reinvest in the brand,
they can reinvest in the quality of the products,
so we might start noticing those things change,
but you won't actually know that that has anything to do
with artificial intelligence, because not always in a robot
that's rolling up to you in an aisle.
>> So you mentioned the supply chain.
That's something that we hear about a lot,
but frankly for most of us,
I think it's very hard to understand
what exactly that means,
so could you educate us a bit on what exactly
is the supply chain and how is artificial intelligence
being implied to improve it?
>> Sure, sure.
So for a lot of us, supply chain is the term
that we picked up when we went to school
or we read about it every so often,
but we're not that far away from it.
It is in fact a key part of what Michelle calls
the invisible part of one's experience.
So when you go to a store and you're buying a pair of shoes
or you're picking up a box of cereal,
how often do we think about,
"How did it ever make it's way here?"
We're the constituent components.
They probably came from multiple countries
and so they had to be manufactured.
They had to be assembled in these plants.
They had to then be moved,
either through an ocean vessel or through trucks.
They probably have gone through multiple warehouses
and distribution centers and then finally into the store.
And what do we see?
We want to make sure that when I go
to pick up my favorite brand of cereal, it better be there.
And so, one of the things where AI is going to help
and we're doing a lot of active work in this,
is in the notion of the self learning supply chain.
And what that means is really bringing
in these various assets and actors of the supply chain.
First of all, through IOT and others, generating the data,
obviously connecting them,
and through AI driving the intelligence,
so that I can dynamically figure out the fact
that the ocean vessel that left China
on it's way to Long Beach has been delayed by 24 hours.
What does that mean when you go to a Foot Locker
to buy your new pair of shoes?
Can I come up with alternate sourcing decisions,
so it's not just predicting.
It's prescribing and recommending as well.
So behind the scenes, bringing in a lot of the,
generating a lot of the data,
connecting a lot of these actors
and then really deriving the smarts.
That's what the self learning supply chain is all about.
>> Are supply chains always international
or can they be local as well?
>> Definitely local as well.
I think what we've seen over the last decades,
it's kind of gotten more and more global,
but a lot of the supply chain
can really just be within the store as well.
You'd be surprised at how often retailers
do not know where their product is.
Even is it in the front of the store?
Is it in the back of the store?
Is it in the fitting room?
Even that local information is not really available.
So to have sensors to discover where things are
and to really provide that efficiency,
which right now doesn't exist,
is a key part of what we're doing.
>> So Joe, as you look at companies out there
to partner or potentially acquire,
do you tend to see technologies
that are very domain specific for retail or supply chain
or do you see technologies that could bridge
multiple different domains
in terms of the experiences we could enjoy?
>> Yeah, definitely. So both.
A lot of infant technologies
start out in very niched use cases,
but then there are technologies that are pervasive
across multiple geographies and multiple markets.
So, smart cities is a good way to look at that.
So let's level set really quick on smart cities
and how we think about that.
I have a little sheet here to help me.
Alright, so, if anybody here played Sim City before,
you have your little city
that's a real world that sits here, okay?
So this is reality and you have little buildings and cars
and they all travel around
and you have people walking around with cell phones.
And what's happening is as we develop smart cities,
we're putting sensors everywhere.
We're putting them around utilities, energies, water.
They're in our phones.
We have cameras and we have audio sensors in our phones.
We're placing these on light poles,
which is existing sustaining power points around the city.
So we have all these different sensors
and they're not just cameras and microphones,
but they're particulate sensors.
They're able to do environmental monitoring
and things like that.
And so, what we have is we have this physical world
with all these sensors here.
And then what we have is we've created
basically this virtual world that has a great memory
because it has all the data from all the sensors
and those sensors really act as ties,
if you think of it like a quilt, trying a quilt together.
You bring it down together and everywhere you have a stitch,
you're stitching that virtual world
on top of the physical world
and that just enables incredible amounts of innovation
and creation for developers, for entrepreneurs,
to do whatever they want to do
to create and solve specific problems.
So what really makes that possible
is communications, connectivity.
So that's where 5G comes in.
So with 5G it's not just a faster form of connectivity.
It's new infrastructure.
It's new communication.
It includes multiple types of
communication and connectivity.
And what it allows it to do is all those little sensors
can talk to each other again.
So the camera on the light pole can talk
to the vehicle driving by or the sensor on the light pole.
And so you start to connect everything
and that's really where artificial intelligence
can now come in and sense what's going on.
It can then reason, which is neat,
to have computer or some sort of algorithm
that actually reasons based on a situation
that's happening real time.
And it acts on that, but then you can iterate on that
or you can adapt that in the future.
So if we think of an actual use case, we'll think of
a camera on a light post that observes an accident.
Well it's programmed to automatically notify
emergency services that there's been an accident.
But it knows the difference between a fender bender
and an actual major crash where we need
to send an ambulance or maybe multiple firetrucks.
And then you can create iterations
and that learns to become more smart.
Let's say there was a vehicle that was in the accident
that had a little yellow placard on it that said hazard.
You're going to want to send different types
of emergency services out there.
So you can iterate on what it actually does
and that's a fantastic world to be in
and that's where I see AI really playing.
>> That's a great example of what it's all about
in terms of making things smart, connective, and autonomous.
So Michelle as somebody who has founded the company
and the space with technology that's trying
to bring some of these experiences to market,
there may be folks in the audience
who have aspirations to do the same.
So what have you learned over the course
of starting your company and developing the technology
that you're now deploying to market?
>> Yeah, I think because AI is such a buzz word.
You can get a dot AI domain now,
doesn't mean that you should use it for everything.
Maybe 7, 10, 15 years ago...
These trends have happened before.
In the late 90s, it was technology
and there was technology companies
and they sat over here and there was everybody else.
Well that not true anymore.
Every company uses technology.
Then fast forward a little bit,
there was social media was a thing.
Social media was these companies over here
and then there was everybody else and now every company
needs to use social media or actually maybe not.
Maybe it's a really bad idea for you
to spend a ton of money on social media
and you have to make that choice for yourself.
So the same thing is true with artificial intelligence
and what I tell...
I did a panel on AI for Adventure Capitalists last week,
trying to help them figure out when to invest
and how to evaluate and all that kind of stuff.
And what I would tell other aspiring entrepreneurs is
"AI is means to an end.
"It's not an end in itself."
So unless you're a PH.D in machine learning
and you want to start an AI as a service business,
you're probably not going to start an AI only company.
You're going to start a company for a specific purpose,
to solve a problem, and you're going to use AI
as a means to an end, maybe, if it makes sense to get there,
to make it more efficient and all that stuff.
But if you wouldn't get up everyday for ten years
to do this business that's going to solve whatever problem
you're solving or if you wouldn't invest in it
if AI didn't exist, then adding dot AI
at the end of a domain is not going to work.
So don't think that that will help you
make a better business.
>> That's great advice.
Thank you.
Surash, as you talked about the automation then
of the supply chain, what about people?
What about the workers whose jobs may be lost
or displaced because of the introduction of this automation?
What's your perspective on that?
>> Well, that's a great question.
It's one that I'm asked quite a bit.
So if you think about the supply chain
with a lot of the manufacturing plants,
with a lot of the distribution centers,
a lot of the transportation,
not only are we talking about driverless cars
as in cars that you and I own,
but we're talking about driverless delivery vehicles.
We're talking about drones and all of these on the surface
appears like it's going to displace human beings.
What humans used to do,
now machines will do and potentially do better.
So what are the implications around human beings.
So I'm asked that question quite a bit,
especially from our customers and my general perception
on this is that I'm actually cautiously optimistic
that human beings will continue
to do things that are strategic.
Human beings will continue to do things
that are creative and human being will probably continue
to do things that are truly catastrophic,
that machines simply have not been able to learn
because it doesn't happen very often.
One thing that comes to mind is when ATM machines
came about several years ago before my time,
that displaced a lot of teller jobs in the banking industry,
but the banking industry did not go belly up.
They found other things to do.
If anything, they offered more services.
They were more branches that were closed
and if I were to ask any of you now if you would go back
and not have 24/7 access to cash,
you would probably laugh at me.
So the thing is, this is AI for good.
I think these things might have temporary impact
in terms of what it will do to labor and to human beings
but I think we as human beings will find
bigger, better, different things to do
and that's just in the nature of the human journey.
>> Yeah, there's definitely a social acceptance angle
to this technology, right?
Many of us technologists in the room,
it's easier for us to understand what the technology is,
how it works, how it was created,
but for many of our friends and family, they don't.
So there's a social acceptance angle to this.
So Michelle as you see this technology deployed
in retail environments, which is a space
where almost every person in every country goes,
how do you think about making it feel comfortable
for people to interact with this kind of technology
and not be afraid of the robots
or the machines behind the curtain.
>> Yeah, that's a great question.
I think that user experience always has to come first,
so if you're using AI for AI's sake or for the cool factor,
the wow factor, you're already doing it wrong.
Again, it needs to solve a problem
and what I tend to tell people who are like,
"Oh my God. AI sounds so scary.
"We can't let this happen."
I'm like, "It's already happening
"and you're already liking it.
"You just don't know
"because it's invisible in a lot of ways."
So if you can point of those scenarios
where AI has already benefited you and it wasn't scary
because it was a friendly kind of interaction,
you might not even have realized it was there
versus something that looks so different and...
Like panic driving.
I think that's why the driverless car thing
is a big deal because you're so used to seeing,
in America at least,
someone on the left side of the car in the front seat.
And not seeing that is like, woah, crazy.
So I think that it starts with the experience
and making it an acceptable kind of interface
or format that doesn't give you that,
"Oh my God. Something is wrong here," kind of feeling.
>> Yeah, that's a great answer.
In fact, it reminds me there was this really amazing study
by a Professor Nicholas Eppily
that was published in the journal of social psychology
and the name of this study was called A Mind In A Machine.
And what he did was he took subjects
and had a fully functional automated vehicle and then
a second identical fully functional automated vehicle,
but this one had a name and it had a voice
and it had sort of a personality.
So it had human anthropomorphics characteristics.
And he took people through these two different scenarios
and in both scenarios he's evil
and introduced a crash in the scenario
where it was unavoidable.
There was nothing going to happen.
You were going to get into an accident in these cars.
And then afterwards, he pulled the subjects and said,
"Well, what did you feel about that accident?
"First, what did you feel about the car?"
They were more comfortable in the one
that had anthropomorphic features.
They felt it was safer and they'd be more willing
to get into it, which is not terribly surprising,
but the kicker was the accident.
In the vehicle that had a voice and a name, they actually
didn't blame the self-driving car they were in.
They blamed the other car.
But in the car that didn't have anthropomorphic features,
they blamed the machine.
They said there's something wrong with that car.
So it's one of my favorite studies
because I think it does illustrate
that we have to remember the human element
to these experiences and as artificial intelligence
begins to replace humans, or some of us even,
we need to remember that we are still social beings
and how we interact with other things,
whether they be human or non-human, is important.
So, Joe, you talk about evaluating companies.
Michelle started a company.
She's gotten funding.
As you go out and look at new companies
that are starting up, there's just so much activity,
companies that just add dot AI to the name as Michelle said,
how do you cut through the noise
and try to get to the heart of is there any value
in a technology that a company's bringing or not?
>> Definitely. Well, each company
has it's unique, special sauce, right?
And so, just to reiterate what Michelle was talking about,
we look for companies that are really good
at doing what they do best, whatever that may be,
whatever that problem that they're solving
that a customer's willing to pay for,
we want to make sure that that company's doing that.
No one wants a company that just has AI in the name.
So we look for that number one and the other thing we do
is once we establish that we have a need
or we're looking at a company based on
either talent or intellectual property,
we'll go in and we'll have to do a vetting process
and it takes a whole.
It's a very long process and there's legal involved
but at the end of the day, the most important thing
for the start up to remember
is to continue doing what they do best and continue
to build upon their special sauce
and make sure that it's very valuable to their customer.
And if someone else wants to look at them
for acquisition so be it,
but you need to be meniacally focused on your own customer.
That's my two cents.
>> I'm thinking again about this concept
of embedding human intelligence,
but humans have biases right?
And sometimes those biases aren't always good.
So how do we as technologists in this industry
try to create AI for good and not unintentionally
put some of our own human biases into models
that we train about what's socially acceptable or not?
Anyone have any thoughts on that?
>> I actually think that the hype about AI taking over
and destroying humanity, it's possible and I don't want to
disagree with Steven Hawking as he's way smarter than I am.
But he kind of recognizes it could go both ways
and so right now, we're in a world
where we're still feeding the machine.
And so, there's a bunch of different issues
that came up with humans feeding the machine
with their foibles of racism and hatred and bias
and humans experience shame which causes them
to lash out and what to put somebody else down.
And so we saw that with Tay, the Microsoft chatbot.
We saw that with even Google's fake news.
They're like picking sources now to answer the question
in the top box that might be the wrong source.
Ads that Google serves often show men high paying jobs,
$200,000 a year jobs, and women don't get those same ones.
So if you trace that back, it's always coming back
to the inputs and the lens
that humans are coming at it from.
So I actually think that we could be in a way better place
after this singularity happens
and the machines are smarter than us
and they take over and they become our overlords.
Because when we think about the future,
it's a very common tendency for humans to fill in the blanks
of what you don't know in the future with what's true today.
And I was talking to you guys at lunch.
We were talking about this harbored psychology professor
who wrote a book and in the book
he was talking about how 1950s,
they were imagining the future
and all these scifi stories and they have flying cars
and hovercrafts and they're living in space,
but the woman still stays at home and everyone's white.
So they forgot to extrapolate the social things
to paint the picture in,
but I think when we're extrapolating into the future
where the computers are our overlords,
we're painting them with our current reality,
which is where humans are kind of terrible (laughs).
And maybe computers won't be
and they'll actually create this Utopia for us.
So it could be positive.
>> That's a very positive view.
>> Thanks. >> That's great.
So do we have this all figured out?
Are there any big challenges that remain in our industries?
>> I want to add a little bit more to the learning
because I'm a data scientist by training and a lot of times,
I run into folks who think
that everything's been figured out.
Everything is done. This is so cool.
We're good to go and one of the things
that I share with them is something
that I'm sure everyone here can relate to.
So if a kindergartner goes to school
and starts to spell profanity,
that's not because the kid knows anything good or bad.
That is what the kid has learned at home.
Likewise, if we don't train machines well,
it's training will in fact be biased to your point.
So one of the things that we have to kep in mind
when we talk about this is we have to be careful as well
because we're the ones doing the training.
It doesn't automatically know what is good or bad
unless that set of data is also fed to it.
So I just wanted to kind of add to your...
>> Good. Thank you.
So why don't we open it up a little bit for questions.
Any questions in the audience for our panelists?
There's one there looks like (laughs).
Emily, we'll get to you soon.
>> I had a question for Sarush
based on what you just said about us training
or you all training these models and teaching them things.
So when you deploy these models to the public
with them being machine learning and AI based,
is it possible for us to retrain them
and how do you build in redundancies for the public
like throwing off your model and things like that?
What are some of the considerations that go into that?
>> Well, one thing for sure is training is continuous.
So no system should be trained once,
deployed, and then forgotten.
So that is something that we as AI professionals
need to absolutely, because...
Trends change as well.
What was optimal two years ago is no longer optimal.
So that part needs to continue to happen
and we're the where the whole IOT space is so important
is it will continue to generate relevant consumable data
that these machines can continuously learn.
>> So how do you decide what data though, is good or bad,
as you retrain and evolve that data over time?
As a data scientist, how do you do selection on data?
>> So, and I want to piggyback on what Michelle said
because she's spot on.
What is the problem that you're trying to solve?
It always starts from there
because we have folks who come in to CIOs,
"Oh look.
"When big data was hot, we started to collect
"a lot of the data, but nothing has happened."
But data by itself doesn't automatically do magic for you,
so we ask, "What kind of problem are you trying to solve?
"Are you trying to figure out
"what kinds of products to sell?
"Are you trying to figure out
"the optimal assortment mix for you?
"Are you trying to find the shortest path
"in order to get to your stores?"
And then the question is, "Do you now have the right data
"to solve that problem?"
A lot of times we put the science
and I'm a data scientist by training.
I would love to talk about the science,
but really, it's the problem first.
The data and the science, they come after.
>> Thanks, good advice.
Any other questions in the audience?
Yes, one right up here.
(laughing)
>> Test, test. Can you hear me?
>> Yep.
>> So with AI machinery becoming more commonplace
and becoming more accessible to developers and visionaries
and thinkers alike rather than being just a giant warehouse
of a ton of machines and you get one tiny machine learning,
do you foresee more governance coming into play
in terms of what AI is allowed to do
and the decisions of what training data is allowed
to be fed to Ais in terms of influence?
You talk about data determining
if AI will become good or bad,
but humans being the ones responsible
for the training in the first place, obviously,
they can use that data to influence as they,
just the governance and the influence.
>> Jack: Who wants to take that one?
>> I'll take a quick stab at it.
So, yes, it's going to be an open discussion.
It's going to have to take place,
because really, they're just machines.
It's machine learning.
We teach it.
We teach it what to do, how to act.
It's just an extension of us and in fact,
I think you had a really great conversation
or a statement at lunch where you talked about
your product being an extension of a designer because,
and we can get into that a little bit, but really,
it's just going to do what we tell it to do.
So there's definitely going to have to be discussions
about what type of data we feed.
It's all going to be centered around the use case
and what that solves the use case.
But I imagine that that will be a topic of discussion
for a long time about what we're going to decide to do.
>> Jack: Michelle do you want to comment on this thought
of taking a designer's brain
and putting it into a model somehow?
>> Well, actually, what I wanted to say was that
I think that the regulation and the governance
around it is going to be self imposed
by the the developer and data science community first,
because I feel like even experts who have been doing this
for a long time don't rally have their arms
fully around what we're dealing with here.
And so to expect our senators, our congressmen, women,
to actually make regulation around it is a lot,
because they're not technologists by training.
They have a lot of other stuff going on.
If the community that's already doing the work
doesn't quite know what we're dealing with,
then how can we expect them to get there?
So I feel like that's going to be a long way off,
but I think that the people who touch and feel
and deal with models and with data sets
and stuff everyday are the kind of people
who are going to get together and self-regulate for a while,
if they're good hearted people.
And we talk about AI for good.
Some people are bad.
Those people won't respect those convenance
that we come up with, but I think
that's the place we have to start.
>> So really you're saying, I think,
for data scientists and those of us working in this space,
we have a social, ethical, or moral obligation
to humanity to ensure that our work is used for good.
>> Michelle: No pressure.
(laughing)
>> None taken.
Any other questions?
Anything else? >> I just wanted to
talk about the second part of what she said.
We've been working with a company that builds robots
for the store, a store associate if you will.
And one of their very interesting findings
was that the greatest acceptance of it right now
has been at car dealerships
because when someone goes to the car dealer
and we all have had terrible experiences doing that.
That's why we try to buy it online,
but just this perception that a robot would be unbiased,
that it will give you the information
without trying to push me
one way or the other. >> The hard sell.
>> So there's that perception side of it too that,
it isn't that the governance part of your question,
but more the biased perception side of what you said.
I think it's fascinating how we're already trained
to think that this is going to have an unbiased opinion,
whether or not that true.
>> That's fascinating.
Very cool.
Thank you Sarush. Any other questions in the audience?
No, okay.
Michelle, could I ask, you've got a station over there
that talks a little bit more about your company,
but for those that haven't seen it yet,
could you tell us a little bit about
what is the experience like
or how is the shopping experience different
for someone that's using your company's technology
than what it was before?
>> Oh, free advertising.
I would love to.
No, but actually, I started this company
because as a consumer I found myself
going back to the user experience piece,
just constantly frustrated with the user experience
of buying products one at a time and then getting zero help.
And then here I am having to google
how to wear a white blazer to not look like an idiot
in the morning when I get dressed with my white blazer
that I just bought and I was excited about.
And it's a really simple thing,
which is how do I use the product that I'm buying
and that really simple thing has been just
abysmally handled in the retail industry,
because the only tool that the retailers
have right now are manual.
So in fashion, some of our fashion customers
like John Varvatos is an example we have over there,
it's like a designer for high-end men's clothing,
and John Varvatos is a person,
it's not just the name of the company.
He's an actual person and he has a vision
for what he wants his products to look like
and the aesthetic and the style and there's a rockstar vibe
and to get that information into the organization,
he would share it verbally with PDFs, thing like that.
And then his team of merchandisers
would literally go manually and make outfits on one page
and then go make an outfit on another page
with the same exact items and then products
would go out of stock and they'd go around in circles
and that's a terrible, terrible job.
So to the conversation earlier about people losing jobs
because of artificial intelligence.
I hope people do lose jobs
and I hope they're the terrible jobs
that no one wanted to do in the first place,
because the merchandisers that we help,
like the one form John Varvatos,
literally said she was weeks away from quitting
and she got a new boss and said,
"If you don't ix this part of my job, I'm out of here."
And he had heard about us.
He knew about us and so he brought us in
to solve that problem.
So I don't think it's always a bad thing,
because if we can take that route, boring,
repetitive task off of human's plates,
what more amazing things can we do with our brain
that is only human and very unique to us
and how much more can we advance ourselves
and our society by giving the boring work
to a robot or a machine.
>> Well, that's fantastic.
So Joe, when you talk about Smart Cities,
it seems like people have been talking
about Smart Cities for decades
and often people cite funding issues,
regulatory environment or a host of other reasons
why these things haven't happened.
Do you think we're on the cusp of breaking through there
or what challenges still remain for fulfilling
that vision of a smart city?
>> I do, I do think we're on the cusp.
I think a lot of it has to do, largely actually,
with 5G and connectivity, the ability to process
and send all this data that needs to be shared
across the system.
I also think that we're getting closer
and more conscientious about security,
which is a major issue with IOT,
making sure that our in devices or our edge devices,
those things out there sensing, are secure.
And I think interocular ability
is something that we need to champion as well
and make sure that we basically work together
to enable these systems.
So very, very difficult to create
little, tiny walled gardens of solutions in a smart city.
You may corner a certain part of the market,
but you're definitely not going to have that ubiquitous benefit
to society if you establish those little walled gardens,
so those are the areas I think we need to focus on
and I think we are making serious progress in all of them.
>> Very good.
Michelle, you mentioned earlier
that artificial intelligence was all around us
in lots of places and things that we do on a daily basis,
but we probably don't realize it.
Could you share a couple examples?
>> Yeah, so I think everything you do online
for the most part, literally anything you might do,
whether that's googling something or you go to some article,
the ads might be dynamically picked
for you using machine learning models
that have decided what is appropriate based on you
and your treasure trove of data that you have out there
that you're giving up all the time
and not really understanding
you're giving up >> The shoes that follow you
around the internet right? >> Yeah, exactly.
So that's basically anything online.
I'm trying to give in the real-world.
I think that, to your point earlier about he supply chain,
just picking a box of cereal off the shelf
and taking it home, there's not artificial intelligence
in that at all, but the supply chain behind it.
So the supply chain behind pretty much
everything we do even in television,
like how media gets to us and get consumed.
At some point in the supply chain,
there's artificial intelligence playing in there as well.
>> So to start us in the supply chain where we can get
the same day even within the hour delivery.
How do you get better than that?
What's coming that's innovative in the supply chain
that will be new in the future?
>> Well, so that is one example of it,
but you'd be surprised at how inefficient
the supply chain is, even with all the advances
that have already gone in,
whether it's physical advances around
building modern warehouses and modern manufacturing plants,
whether it's through software and others
that really help schedule things and optimize things.
What has happened in the supply chain
just given how they've evolved is they're very siloed,
so a lot of times the manufacturing plant
does things that the distribution folks do not know.
The distribution folks do things
that the transportation folks don't know
and then the store folks know nothing
other than when the trucks pulls up,
that's the first time they find out about things.
So where the great opportunity in my mind is,
in the space that I'm in, is really the generation of data,
the connection of data, and finally,
deriving the smarts that really help us improve efficiency.
There's huge opportunity there.
And again, we don't know it
because it's all invisible to us.
>> Good. Let me pause and see
if there's any questions in the audience.
There, we got one there.
>> Thank you. Hi guys, you alright?
I just had a question about ethics
and the teaching of ethics.
As you were saying, we feed the artificial intelligence,
whereas in a scenario which is probably a little bit
more attuned to automated driving,
in a car crash scenario between
do we crash these two people or three people?
I would be choosing two, whereas the scenario
may be it's actually better
to just crash the car and kill myself.
That thought would never go through my mind,
because I'm human.
My rule number one is self preservation.
So how do we teach the computer this sort of side of it?
Is there actually the AI ethic going to be
better than our own ethics?
How do we start?
>> Yeah, that's a great question.
I think the opportunity is there as Michelle
was talking earlier about maybe when you cross that chasm
and you get this new singularity,
maybe the AI ethics will be better than human ethics
because the machine will be able to think about
greater concerns perhaps other than ourselves.
But I think just from my point of view,
working in the space of automated vehicles,
I think it is going to have to be something that the industry,
and societies are different,
different geographies, and different countries.
We have different ways of looking at the world.
Cultures value different things and so I think technologists
in those spaces are going to have to get together
and agree amongst the community
from a social contract theory standpoint perhaps in a way
that's going to be acceptable to everyone
who lives in that environment.
I don't think we can come up with a uniform model
that would apply to all spaces,
but it's got to be something though that we all,
as members of a community, can accept.
And so yeah, that would be the right thing to do
in that situation and that's not going to be
an easy task by any means, which is, I think,
one of the reasons why you'll continue to see humans
have an important role to play in automated vehicles
so that the human could take over
in exactly that kind of scenario,
because the machines perhaps
aren't quite smart enough to do it
or maybe it's not the smarts or the processing capability.
It's maybe that we haven't as technologists and ethicists
gotten together long enough to figure out
what are those moral and ethical frameworks
that we could use to apply to those situations.
Any other thoughts?
>> Yeah, I wanted to jump in there real quick.
Absolutely questions that need to be answered,
but let's come together and make a solution
that needs to have those questions answered.
So let's come together first and fix the problems
that need to be fixed now so that
we can build out those types of scenarios.
We can now put our brainpower to work
to decide what to do next.
There was a quote I believe by Andrew Ningh Bidou
and he was saying in concerning deep questions
about what's going to happen in the future with AI.
Are we going to have AI overlords or anything like that?
And it's kind of like worrying
about overpopulation at the point of Mars.
Because maybe we're going to get there someday
and maybe we're going to send people there
and maybe we're going to establish a human population on Mars
and then maybe it will get too big
and then maybe we'll have problems on Mars,
but right now we haven't landed on the planet
and I thought that really does a good job of putting
in perspective that that overall concern
about AI taking over.
>> So when you think about AI being applied for good
and Michelle you talked about
don't do AI just for AI's sake, have a problem to solve,
I'll open it up to any of the three of you,
what's a problem in your life
or in your work experience that you'd love somebody
out here would go solve with AI?
>> I have one.
Sorry, I wanted to do this real quick.
There's roads blocked off and it's raining
and I have to walk a mile to find a taxi
in the rain right now after this to go home.
I would love for us to have some sort of ability
to manage parking spaces and determine when
and who can come in to which parts of the city
and when there's a spot downtown,
I want my autonomous vehicle to know which one's available
and go directly to that spot and I want it to be cued
in a certain manner to where I'm next in line and I know.
And so I would love for someone to go solve that problem.
There's been some development on the infrastructure side
for that kind of solution.
We have a partnership Intel does with GE
and we're putting sensors that have,
it's an IOT sensor basically.
It's called City IQ.
It has environmental monitoring, audio, visual sensors
and it allows this type of use case to take place.
So I would love to see iterations on that.
I would love to see, sorry there's another one
that I'm particular about.
Growing up I lived in Southern California
right against the hills, a housing development,
because the hills and there was not a factory,
but a bunch of oil derricks back there.
I would love to have sensor that senses the particulate
in the air to see if there was too many fumes coming
from that oil field into my yard growing up as a little kid.
I would love for us to solve problems like that,
so that's the type of thing that we'll be able to solve.
Those are the types of innovations that will be able
to take place once we have these sensors in place,
so I'm going to sit down on that one
and let someone else take over.
>> I'm really glad you said the second one
because I was thinking,
"What I'm about to say is totally going to
"trivialize Joe's pain and I don't want to do that."
But cancer is my answer, because there's so much data
in health and all these patterns
are there waiting to be recognized.
There's so many things you don't know about cancer
and so many indicators that we could capture
if we just were able to unmask the data and take a look,
but I knew a brilliant company
that was using artificial intelligence specifically
around image processing to look at CAT scans
and figure out what the leading indicators
might be in a cancerous scenario.
And they pivoted to some way more trivial problem
which is still a problem
and not to trivialize parking an whatnot,
but it's not cancer.
And they pivoted away from this amazing opportunity
because of the privacy and the issues
with HIPPA around health data.
And I understand there's a ton of concern with it getting
into the wrong hands and hacking and all of this stuff.
I get that, but the opportunity in my mind
far outweighs the risk and the fact that they had to change
their business model and change their company essentially
broke my heart because they were really onto something.
>> Yeah that's a shame and it's funny you mention that.
Intel has an effort that we're calling the cancer cloud
and what we're trying to do is provide some infrastructure
to help with that problem and the way cancer treatments
work today is if you go to a university hospital
let's say here in Texas, how you interpret that scan
and how you respond and apply treatment,
that knowledge is basically just kept
within that hospital and within that staff.
And so on the other side of the country,
somebody could go in and get a scan
and maybe that scan brand new to that facility
and so they don't know how to treat it,
but if you had an opportunity with machine learning
to be able to compare scans from people,
not only just in this country,
but around the world and understand globally,
all of the hundreds of different treatment pads
that were applied to that particular kind of cancer,
think how many lives could be saved,
because then you're sharing knowledge
with what courses of treatment worked.
But it's one of those things like you say,
sometimes it's the regulatory environment
or it's other factors that hold us back
from applying this technology to do some really good things,
so it's a great example.
Okay, any other questions in the audience?
>> I have one. >> Good Emily.
>> So this goes off of the HIPPA question, which is,
and you were talking about
just dynamically displaying ads earlier.
What does privacy look like in a fully autonomous world?
Anybody can answer that one.
Are we still private citizens?
What does it look like? >> How about from a
supply chain standpoint?
You can learn a lot about somebody in terms
of the products that they buy and I think to all of us,
we sort of know maybe somebody's tracking
what we're buying but it's still creepy
when we think about how people
could potentially use that against us.
So, how do you from a supply chain
standpoint approach that problem?
>> Yeah and it's something that comes up in my life
almost every day because one of the thing's
we'd like to do is to understand consumer behavior.
How often am I buying?
What kinds of products am I buying?
What am I returning?
And so for that you need transactional data.
You really get to understand the individual.
That then starts to get into this area of privacy.
Do you know too much about me?
And so a lot of times what we do is data
is clearly anonymized so all we know is customer A
has this tendency, customer B has this tendency.
And that then helps the retailers
offer the right products to these customers,
but to your point, there are those privacy concerns
and I think issues around governance, issues around ethics,
issues around privacy, these will continue to be ironed out.
I don't think there's a solid answer
for any of these just yet.
>> And it's largely a reflection of society.
How comfortable are we with how much privacy?
Right now I believe we put the individual in control
of as much information as possible
that they are able to release or not.
And so a lot of what you said,
everyone's anonymizing everything at the moment,
but that may change as society's values change slightly
and we'll be able to adapt to what's necessary.
>> Why don't we try to stump the panel.
Anyone have any ideas on things in your life
you'd like to be solved with AI for good?
Any suggestions out there that we could then hear
from our data scientist and technologist and folks here?
Any ideas?
No?
Alright good.
Alright, well, thank you everyone.
Really appreciate your time.
Thank you for joining Intel
here at the AI lounge at Autonomous World.
We hope you've enjoyed the panel and we wish you
a great rest of your event here at South by Southwest.
(audience clapping)
(bright music)