>> From Cambridge, Massachusetts.
It's theCUBE.
Covering MIT Chief Data Officer
and Information Quality Symposium 2019.
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>> Welcome back to MIT everybody,
you're watching theCUBE.
The leader in live tech coverage.
My name is Dave Vellante.
I'm here with Paul Gillin, my co-host.
Tom Davenport, he's here.
He's the president's distinguished professor
at Babson College CUBE alum
Good to see you again, Tom.
Thanks for coming on.
>> Glad to be here.
>> So yeah, this is, let's see.
The 13th annual MIT CDOIQ.
>> It's unlucky
>> Yeah
(laughing)
>> Sure is this year.
>> Our seventh, I think, so.
>> Really.
>> So maybe we'll offset, you know.
So, you gave a talk earlier.
Should we be afraid of the machines,
or should we embrace them?
>> I think we should embrace them,
because so far they are not capable
of replacing us.
I mean, you know.
When we hit the singularity,
which I'm not sure will ever happen,
but it's certainly not going to happen anytime soon,
or we'll have a different answer.
But now, good at small narrow tasks,
not so good at doing a lot of the things that we do.
So I think we're fine,
although as I said in my talk,
I have some survey data suggesting that large
US corporations, their senior executives,
a substantial number of them, more than half,
would like to automate as many jobs as possible, they say.
So that's a little scary.
But, fortunately for us humans,
I think it's going to be a while before they succeed.
>> Now we had a case last year where Mcdonald's
employees were agitating for increase in the minimum wage.
And the management used the threat of roboticizing
the hamburger making process.
Which can be done.
>> Right.
>> To get them to back down.
Are you think we're going to see more of that,
where maybe AI is used as a threat?
>> Well, I haven't heard too many other examples.
I think for those highly structured,
you know, relatively low level tasks,
it's quite possible.
Particularly if we do end up raising
the minimum wage beyond a point where
it's economical to pay humans to do the work.
But, I would like to think that,
if we gave humans the opportunity they could
do more than they're doing now in many cases.
And one of the things I was saying,
is that I think companies are generally,
there are some exceptions,
but most companies are not starting
to retrain their workers.
Amazon recently announced they're going to spend
700 million to retrain their workers
to do things that AI and robots can't.
But that's pretty rare.
Certainly that level of commitment is very rare.
So I think it's time for companies to start
stepping up and saying, "How can we develop
"a better combination of humans and machines".
>> Well the work by, you know, Brynjolfsson and McAfee,
which is a little dated now.
But it definitely suggests that there is some
things to be concerned about.
Of course ultimately their prescription
was one of an optimist, and education
>> Yeah.
>> And so forth, but.
You know the key point there is machines
have always replaced humans.
But now, in terms of cognitive functions.
But you see it everywhere.
You drive to the airport,
now it's electronic billboards.
It's not some person putting it up.
The kiosks, et cetera.
But you know, you've used the term, pave the cow path.
We don't want to protect the past from the future.
All right, so to your point right.
Retraining, education.
I mean that's the opportunity here, isn't it.
>> Yeah.
>> And the potential is enormous.
>> Well and you know, let's face it.
We haven't had much in the way of productivity
improvements in the US,
or any other advanced economy lately.
So we need some.
>> Yeah. >> I guess, you know,
replacement of humans by machines.
But my argument has always been,
you know, you can handle innovation better.
You can avoid, sort of race to the bottom
that automation sometimes leads to.
If you think creatively about
humans and machines working as colleagues, in many cases.
>> Well you remember in the PC boom,
And I forget who the Fed Chairman was,
it might have been Greenspan.
You could see, you know, progress everywhere
except in the productivity numbers.
>> That was an MIT professor, Robert Solo.
>> Oh okay, right, and then. >> He won the Noble Prize.
>> But then shortly thereafter, >> Yeah.
>> There was a huge productivity boom.
So I mean is there a, maybe a pent up.
>> Well, God knows.
I mean, everybody's wondering.
We've been spending literally trillions on IT.
And you would think that it would have
led to productivity, but you know.
Certain things like social media,
I think reduce productivity in the workplace.
And you know, we're all chatting,
and talking, and slacking and so on all over the place.
Maybe that's just not conducive to getting work done.
>> It depends what you do with that social media, right.
(laughing)
>> If you're in our business,
it's actually a good thing.
>> It's phenomenal to see political coverage these days,
which is almost entirely consists
of reprinting politician's tweets.
>> Exactly I guess it's made life easier for them.
Although those poor people reporters sitting
in the White House waiting for a press conference.
They're not doing very well.
>> No, well, there aren't many
reporters left at the White House.
>> Yeah, yeah.
>> Where do you see, in your consulting work,
in your academic work.
Where do you see AI being used most effectively
in organizations right now.
And where do you think that's going to be
three years from now?
>> Well, I mean the general category of activity,
of used case is the sort of,
I sometimes call it boring AI.
It's data integration.
One thing that's being discussed a lot at this conference.
It's connecting your invoices
to your contracts to see,
did we actually get the stuff that we contracted for.
It's doing a little bit better job,
of identifying fraud and doing it faster.
So all of those things are quite feasible.
They're just not that exciting.
What we're not seeing, are curing cancer,
creating fully autonomous vehicles.
You know the really aggressive moon shots
that we've been trying for a while,
just haven't succeeded at.
>> What if we kind of expand AI
as sort of a rubric for all this new cool stuff
that's coming out.
So considering all these new techs weather AI,
Blockchain, new security approaches.
When do you think that machines
will be able to make better diagnoses than doctors?
>> Well I think in a very narrow sense,
in some cases it can do it now.
>> Mhm.
>> But the thing is, first of all, take a radiologist.
Which is one of the doctors I think most at risk from this.
Because they don't typically meet with patients
and they spend a lot of time looking at images.
It turns out that the lab experiments that say,
you know these are better than human radiologists,
the AI tend to be very narrow and what one
lab does is different from another lab.
So it just is going to take a very long time
to make it into production deployment
in the physician's office.
We'll probably have to have some
regulatory approval of it.
So that you know, the lab research is great.
It's just getting it into day to day reality is the problem.
>> Okay so, staying in this context
of digital, as a sort of umbrella topic.
Do you think large retail stores
will largely disappear?
>> Some sectors more than others.
For things that you don't need to touch
and feel and so before you order them,
certainly even that, obviously,
is happening more and more in online commerce.
What people are saying will disappear next,
is the human at the point of sale.
And we've been talking about that for a while, in grocery.
Not achieved so much yet in the US.
Amazon Go is a really interesting experiment,
where every time I go in there,
I try to shoplift, but it seems to be
able to prevent me. (laughing)
It took a while, now they have 12 stores.
It's not huge yet.
But, I think if you're in one of those jobs
that a substantial chunk of it is automate-able,
then you really want to start looking around,
thinking what else can I do to add value
to these machines.
>> Do you think traditional banks will
lose control of the payment system?
>> No, I don't.
Because the FinTax that you see thus far
keep getting bought by traditional banks,
so my guess is that
people will want that certainty,
and you know the funny thing about Blockchain,
we say in principle it's more secure
because it's you know, spread across
a lot of different ledgers,
but people keep hacking into Bitcoin,
so it makes you wonder.
I think Blockchain is going to take longer
than we thought as well.
So, you know, in my latest book.
Which is called "The AI Advantage",
I start out talking about Amara's Law.
This guy, Roy Amara, who was a futurist,
not nearly as well known as Moore's Law,
but it said you know, for every new technology
we tend to over estimate it's impact in the short run,
and under estimate
it's impact in the long run. >> In the long run, yeah.
>> And so I think AI will wind up doing great things,
we may of sort of,
tuned it out by the time it actually happens.
They oh yeah, we finally have autonomous vehicles.
We've been talking about it for 50 years, so.
>> All right last one.
So one of the democratic candidates,
of one of the 75 democratic candidates last night,
mentioned the chief manufacturing officer,
>> I saw that.
>> Well do you see that automation
will actually swing the pendulum and bring
back manufacturing to the US.
>> I think it could,
if we were really aggressive about
using digital technologies in manufacturing.
Doing 3D manufacturing,
doing digital twins of every device, and so on.
But we are not being as aggressive
as we ought to be,
and manufacturing companies have been kind of slow
and I think somewhat delinquent
in embracing these things.
So they're going to, I think, lose the ability to compete.
We'd have to really go at it in a big way
to bring it all back.
>> Just, we've got an election coming up.
There are a lot of concern following the last election,
about the potential of AI.
Chat bots, Twitter chat bots, deep fakes,
the technologies that obscure or alter reality.
Are you worried about what's coming in the next year,
in that capacity?
>> That could never happen, Paul.
We could never see anything like that.
(laughing)
Deep fakes I'm quite worried about, we don't seem--
I know there's some organizations working
on how we would certify an image as being real.
But we're not there yet.
My guess is, certainly by the time the election happens,
we're going to have all sorts of political candidates
saying things that they never really said,
through deep fakes and image manipulation, scary.
>> What do you think about the call
to break up big tech.
What's your position on that?
>> I think that it's a self-inflicted wound.
We just saw for example, that the automobile manufacturers
decided to get together, even though the federal government
isn't asking for better mileage, they said, "We'll do it,
"we'll work with you in California
"and the states that are more advanced".
If big tech had said, "we're going to work together
"to develop standards of ethical behavior
and privacy, and data and so on".
They could have prevented some of this.
Unless they change their attitude really quickly.
I've seen some of it,
sales force people are talking about,
you know, the need for data standards.
Data protection standards.
Unless they change quickly,
I think they're going to get legislation imposed,
and maybe get broken up.
It's going to-- it would take a while.
Depends on the next administration,
but they're not being smart about it.
>> Hm.
>> You look at all-- I'm sure you see a lot of demos
of advanced AI-type technology.
Over the last year, what has really impressed you?
>> You know, I think the biggest
advances have clearly been in image recognition.
I was looking the other day at some--
The big problem with that is,
you need a lot of labeled data.
That's one of the reasons why Google
was able to identify cat photos on the internet,
is we had a lot of labeled cat images
in the image net open source database.
But the ability to start generating images
to do synthetic labeled data,
I think could really make a big difference
in how rapidly image recognition works.
>> What do you mean synthetic?
I'm sorry, what do you mean synthetic?
>> Where we would actually create.
We wouldn't have to have somebody
going around taking pictures of cats.
We'd create a bunch of different cat photos,
label them as cat photos.
Have variations in them.
You know, unless we have a lot of variation in images.
That's one of the reasons why we can't
use autonomous vehicles yet,
because images differ in the rain,
and in the snow and so on.
We're going to have to have synthetic snow,
synthetic rain, to identify those images.
So the GPU chip still realizes
that's a pedestrian walking across there,
even though it's kind of fuzzed up.
Right now just a little bit of variation
in the image can throw off the recognition altogether.
>> All right, Tom, hey.
Thanks so much for coming to theCUBE.
It was great to see you, we got to go.
>> My pleasure.
>> And it's always good to catch you,
you're welcome.
All right, keep it right there everybody,
we'll be back from MIT CDOIQ
in Cambridge, Massachusetts.
Dave Vellante and Paul Gillin.
You're watching the Cube.
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