December 31, 2018 • 21 mins
After a couple episodes of deep neuroscience, co-host Christy Maver takes us back to the business side of things in an interview with Numenta CEO Donna Dubinsky. They discuss the challenge of navigating a dual mission, why she believes it’s important even for non-neuroscientists to understand how the brain works, and where she believes the company is going.
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Episode Transcript
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Christy (00:00):
You are listening to Numenta On Intelligence, a
monthly podcast about howintelligence works in the brain
and how to implement it innonbiological systems.
I'm Christy Maver.
For the past couple episodes, myco-host Matt Taylor has
continued his Interview with aNeuroscientist series and gone
pretty deep into variousneuroscience topics.
(00:21):
For our final episode of 2018,we're taking it back to the
business side.
I interviewed our CEO DonnaDubinsky actually a few months
ago, but we saved this episodeto be the final one of the year.
During our chat, I asked herabout the business evolution of
Numenta, the challenges ofnavigating both a scientific and
a machine intelligence missionand why she believes it's so
(00:43):
important, even fornon-neuroscientists, to
understand how the brain works.
As always, you can keep up withour latest progress by signing
up for our newsletter andvisiting numenta.com.
All right, here's my interviewwith Numenta CEO Donna Dubinsky.
(01:07):
Hi, I'm Christy Maver and I'msitting here today with Donna
Dubinsky, CEO and Co-Founder ofNumenta.
Hi, Donna.
Donna (01:14):
Hello, Christy.
Christy (01:16):
So, thank you for being on Numenta On Intelligence.
Donna (01:19):
My pleasure.
Christy (01:19):
I'm specifically excited to talk to you because
as you know, we do some prettydetailed neuroscience research
as a company here.
And like me, you do not comefrom a scientific background, so
I'm really excited to talk toyou more about Numenta, our
work, what it means andparticularly why you are drawn
(01:40):
to it.
So the first question I want toask you is one that I get often
which is, Numenta is such aunique company.
How do you explain to peoplewhat it is that we do?
Donna (01:53):
Well, Numenta is a unique company.
It's not so much that it's aunique effort, but I think the
form of it is really unique tohave created a scientific entity
that also has a commercialmission is a unique structure.
There certainly are scientistsworking on hard problems
throughout the world andacademia and private research
(02:15):
institutes, but other than maybebig companies that have research
components you don't certainlysee it very often in a startup.
In fact, I think it'sinteresting because so many of
the venture capitalists aroundlook at it and say,"Oh, nobody's
doing big thinking in big workanymore", but we are.
But I don't think the venturecapitalists are really
(02:39):
interested in that kind of bigwork.
They're really interested inincremental work.
So we're weird because we'redoing science and big ideas in a
commercial form.
Christy (02:45):
Right, right.
And you mentioned this dualmission, right?
So there's the scientificmission which has to do with
reverse engineering the brain,and then there's a business
mission of applying that tomachine intelligence.
So, how difficult has it been tosteer the ship and navigate
between those two missions?
Donna (03:05):
That's pretty difficult, and I think it's not clear yet
what the ultimate resolution ofthat will be.
We don't really know what thebest way is to commercialize it,
but let me just address why isit we want to commercialize it.
It's fine to have a scientificmission.
Why do we need to commercializeit?
And the reason is notnecessarily a monetary reason;
(03:29):
the real reason we want tocommercialize it is for impact.
Both Jeff and I(Jeff Hawkins andI) have had extensive experience
with developer communities overthe years, where we've seen
clever, amazing, fascinatingwork done by people who build on
a platform we've created, suchas the Palm Pilot or the Treo,
and our idea when we createdthis company as a for-profit was
(03:54):
to enable a whole generation ofdevelopers to build value-added
products on top of thistechnology.
We felt that a commercialopportunity is what would draw
those developers much more thana nonprofit structure, but we're
still actually kind of early inthat process, so we're not sure
how it's all going to work out.
Christy (04:14):
Right, right.
And there's actually a HarvardBusiness Case about this.
Donna (04:18):
Yes, there is.
I recommend reading it.
It covers a lot of our differentbusiness models.
Christy (04:24):
Right, right.
So let me ask you, why do youthink it's important to
understand how the brain works?
It's such a big problem, butwhat does it mean to you?
Why is it important?
Donna (04:38):
Once you start thinking about this, you start looking
around your world and realizeeverything manmade that we see
is created by our brains, ourcollective brains.
I look at a giant skyscraper,and I have to stop and marvel.
How could we– little, puny, weakhumans– build such an incredible
(04:59):
structure?
How could we create theseincredible medicines and
surgeries?
How do we do all of theseamazing things?
Well, it's an accumulation of abunch of individuals' brains
that have solved a millionindividual problems to make us
stronger and faster andhealthier and a lot of positive
(05:20):
attributes.
So as I think about that, Imarvel at the brain and its
capability to do this.
And the question is, can wefigure out how that works and
figure out a way to take some ofthat power to have machines help
us do those amazing activitiesand be even stronger and
healthier and more powerful?
Christy (05:40):
And there must be certain examples that come up
from time to time that reallykind of, you know, those"Aha"
moments of, oh wow, that's whatthe brain is doing.
There are so many things that wedon't really think about, and
one of the examples that alwaysresonated with me I think is
something that you had said,which is you observed that with
(06:02):
any other part of your body,like you could have a heart
transplant and be fine, right?
But the world only exists inyour brain, and we are our
brains, right?
Donna (06:12):
You are your brain.
If you have a heart transplant,you're still Christy.
If you had a brain transplantand put my brain in your body,
you'd certainly be different.
But I think that it's reallytrue, but it's more for me the
output of that brain.
It's the idea that so manypeople have solved so many
(06:33):
amazing problems through humanhistory, and it made our lives
better.
You know, I was a history majorin college.
I look back at a century, twocenturies, a millennium ago, and
you think about the number ofpeople who died of disease and
natural disasters and poorbuildings and all of the things
that created human heartache andtrauma in the world, and so many
(06:54):
of those have been addressedover many years through
incredible engineering andscience, and there's many more
yet to do.
So, can we be a part of thathuman progression and making the
world a better place?
That's the question.
Christy (07:09):
So let me ask you– There was a blog post that you
co-wrote with Jeff Hawkins acouple of years ago called,"What
is Machine Intelligence vs.
Machine Learning vs.
Deep Learning?", right?
And we often get questionsbecause those terms are thrown
around so often that they'vealmost become meaningless.
(07:29):
Everything now is defined as AI,Siri is AI, you know.
But that post from two yearsago, the two of you really took
a look at the landscape andbroke it down at a high level to
say,"Here's how we'redifferent." So can you touch on
that perspective of what it isthat makes us different for
(07:52):
people who might hear,"Oh,machine intelligence.
Got it, so you're buildingrobots."
Donna (07:56):
It certainly does all sound the same.
I think for outsiders and evenfor me, I read websites of
people working in this field andI think to myself,"It sounds
just like what we're doing.
How is it different?" But whenyou spend a couple of weeks here
or even a couple of days and youlisten to the meetings, it's
pretty clear pretty quickly howdifferent it is.
The team here is delving intoincredible detail on the actual
(08:22):
neuroanatomy, what is happeningwith one neuron and you know,
dendrites and synapses andtalking to each other, whereas
all the people I think ineverything else has referred to
as AI are working on much lessof a realistic neurological
model.
It sounds the same.
(08:43):
They say it's neural nets.
They say it's got differentweights for cells, and they call
it the same kind of terminology,but it is nowhere near as
in-depth of a true biologicalconstraint, if you will, as it
is that we do here so ourapproach is much more biological
(09:06):
in nature.
Christy (09:08):
And do you see that biological approach being
embraced, or do you see it asmore of a fringe approach?
Donna (09:17):
It's not yet, but I think there's a growing realization
that it is an interesting placeto look.
I think most of the AI world isrealizing that they're hitting a
wall is a bit strong of a term.
I mean, they've accomplished alot.
They have a lot left they coulddo, but what they're
accomplishing is really with oldkinds of algorithms and adding
(09:40):
more power and more data.
That's really what has createdthis resurgence of"AI", and the
smart people who work in thefield have looked at that and
said, you know, we're not reallydoing this in any fundamentally
smart way.
We're doing it by brute force,so how could we do some of this
in a smarter way?
(10:00):
Well, maybe we should look tothe one instantiation we know of
in the world that is actuallyintelligent, which is the brain.
So we see people coming aroundat that point of view, but they
don't really know how to goafter it.
If you think about it, it makessense because there's two wholly
separate groups of people.
There's neuroscientists whodelve down deeply into issues of
(10:22):
the brain relative to disease orlearning or other concerns, and
then there's computer scientistswho really know nothing about
neuroscience, and they don'treally intersect.
And this is, I think, one of thefew places in the world where
they intersect at a world classlevel.
I mean, we have world classneuroscience and world class
computer science together,sitting, working these things
(10:44):
out, and in most places it justsimply isn't that.
Christy (10:49):
So this touches on something I've heard you talk
about which is related toNumenta, but also kind of
related to your history andcareer about having a front seat
at these evolutions ofcomputing.
Can you explain that forlisteners?
Donna (11:03):
Oh, I've had an incredible career and very lucky
and very happy where I ended upover the course of my career.
Not that it's over yet, but it'sdefinitely been a long time.
And that career has put me atthe front row seat for four
(11:23):
major generations of computing.
The first one was personalcomputing when I joined Apple
very early in its inception andwe had the notion of putting a
computer on every person's desk,and that seemed outrageous at
the time.
Of course, now everybody looksat it and says," Well, of
course!", But it wasn't soobvious then, and we helped make
that happen and Apple.
And then the second majorrevolution was at Palm where we
(11:46):
said, these devices need to comeoff the desktop and go into your
pocket.
Personal computing is not thecomputer sitting on your desk.
It's not very personal.
The one that's going to be theone you carry in your pocket or
your purse..
that's your personal computer ofchoice, and this was really Jeff
[Hawkins]'s vision that I boughtinto and we made that happen at
(12:06):
Palm in a significant way.
We really led the handheldcomputing revolution and then at
Handspring, we led therevolution in smartphones and
really saying that thesehandheld devices need to be
connected to each other andconnected to the world.
That was a front row seat atthat revolution, and now my
fourth revolution is a front rowseat at intelligent computing,
(12:27):
and so it's been anextraordinary privilege for my
career to be able to be a partof these amazing revolutions,
particularly since I'm not thetechnical person– I'm not the
engineer, I'm not the scientist.
I never could have done thesethings as a protagonist, if you
will, but I have been able to bea key part of the team that
(12:51):
builds the infrastructure aroundit, the marketing, the sales,
all of the things that theseideas need to come into the
world.
I've been able to be a part ofthat team.
Christy (13:03):
And what is your vision for Numenta?
I mean ultimately the way you'relooking back on those first few
evolutions, I assume, will bethe way that you and others look
back on Numenta.
But, where do you see Numentagoing?
Donna (13:23):
It's never obvious when you're in it, where it's going
and exactly what theapplications will be.
I like to say when we inventedhandheld computing, we never
imagined Uber.
It just, you know, again, thisis where that development
community is so importantbecause people will take these
core technologies into placesyou'd never could have imagined,
(13:43):
so I believe the same thing willhappen with this.
We are going to solve thefundamental problems here of the
brain and how it works.
There will be machines that willbe built on those principles.
There will be developers thatwill put those machines to all
sorts of incredible uses.
And I think if you look down theroad in say 10 years, let's make
it far enough that you can'thold me into account, but in 10
(14:06):
years I think we will see realimpact in the world as a result
of this work and in ways that wesimply cannot predict.
And I know that frustratespeople because people want to
know today, what's the killerapp, and it's just virtually
impossible to guess.
We could make up a bunch ofstuff, but it won't be right.
(14:28):
And so we'd rather focus ongetting the core right and
getting the tools out there forother people to see, how can
this apply to a problem that hasstymied them?
We've seen people come to us forall sorts of problems where
they're frustrated, whether it'sin text analytics or in farming
solutions or people came to usabout monitoring beer in kegs.
(14:52):
We've had every possibleapplication, and those are the
things that are going to be herein prominence in another 10
years.
Christy (15:01):
And do you worry about the, you know, there are so many
conversations and some loudvoices out there that are kind
of the scaremongers and the feartactics?
Do you worry about that eitherwith regards to Numenta or with
regards to just the space ingeneral?
Donna (15:20):
I think it's always fair to worry about these things, but
I think that any new technologyhas worries that need to be
managed.
I like drones as an example.
We came along with drones, andnow we don't want them being
flown near airports where it candisturb an airplane, so we need
rules for that.
We don't want them flying aroundand looking into all of our
(15:44):
windows.
The privacy implications of thatare pretty serious.
We better regulate that.
There's always going to bemalicious uses of new
technologies, and there's alwaysgoing to need to be regulation
to try to contain those uses asmuch as possible, so I don't see
this new technology in any waydifferent than that.
(16:06):
I think the fears aboutintelligent machines taking over
are just absurd.
These machines and theseprograms..
They will not have agency, theywill not have intent, they will
not have emotions unlesssomebody programs that into
these machines, and in whichcase you can un-program it or
(16:27):
unplug the programs.
They will not develop thesethings on their own.
So, I think that they can beused for malicious purposes.
I get that it will need to beregulated.
It will need to be controlled inthe best way.
It can be just as any technologyis, and I don't see it as
fundamentally different.
Christy (16:49):
So, part of navigating the dual mission over the years
has meant that at times we'vefocused on applications and even
one enterprise application inparticular, and then other times
it's more the research.
Do you see Numenta working onapplications or is it unknown?
Donna (17:08):
Anything's possible.
We might work on applicationsourselves.
We might partner with people whowant to work on applications.
Right now, we're taking apartnering attitude.
People who come to us and want athe license to the software and
the intellectual property, weare very willing to discuss a
license with them.
We have several licensees, andthey're working on all very
different problems.
And so we're very open topartnering, and we'll just see.
Christy (17:33):
And what do you suggest for people that aren't
neuroscientists or maybe aren'teven computer scientists, but
are interested in the work andwant to somehow be a part of
this or even wondering,"Whatshould I do?
Tell me what steps I need totake.
Do I need to act on this now?"If our story resonates with
(17:54):
someone, then the obvious nextquestion is,"Okay, what should I
do?" What advice do you have forpeople, whether it's individuals
or companies that buy into whatwe're doing here?
Donna (18:05):
Well, I kind of hear two questions in that.
So the first question is, ifyou're just intellectually
engaged and you want tounderstand,"How does the brain
work?
It's interesting.
You're solving that problem."There's a few things you can do.
First, a good place to start isto read Jeff's book, On
Intelligence.
It's old.
It's doesn't at all have any ofthe new theories in it, but it
(18:25):
lays out some of the problems ina very easy-to-understand way
that has captured a lot ofpeople.
The book has continued to dowell other than chapter six,
which I give you permission toskip.
Christy (18:40):
And it even says in the book at the beginning of the
chapter, skip this.
Donna (18:44):
It's just very biologically detailed, but I
always say that because chapterseven and eight are very
important.
I don't want people to put itdown.
But I think starting with thebook is one thing, and then I
think looking at our website andreading some of the more
accessible parts of the thingswe've written, the video that
we've put up– Why Brains Matteris a very good video.
There are a lot of snippets ofinformation that are there that
(19:08):
I think could help that person.
Now the second question is, ifyou're a company and you want to
get started on actually applyingthis work, what would you do?
The easiest place to get startedtoday is the problem of anomaly
detection on streaming data.
Something your brain does isworks with streaming data, as
opposed to static data.
(19:28):
We are making predictions andthen finding anomalies
constantly in everything that wedo.
We have implemented softwarethat does that.
It's available in our opensource community, and that would
be the lowest hanging fruit of away to experiment with the
technology.
We have a product called HTMStudio.
You can easily take your dataand flow it into HTM Studio and
(19:52):
see if we can find anomaliesthat you can't otherwise find.
We have found latency innetworks earlier than other
techniques.
It's much more effective thanthreshold systems, which is the
way most anomaly detectionworks.
And I think if I were in acompany today and I just wanted
to start experimenting, that'swhere I would start, with the
(20:14):
idea of getting more deeply inwould enable other applications
such as prediction orclassification.
Ultimately, I think the area wemight have the greatest impact
is sensorimotor integration androbotics, but that's not really
ready to be implemented yet.
Those theories are still gettingthe t's crossed and the i's
dotted, and we're not quiteready to say,"Hey, all you
(20:35):
robotics companies out there.
Come jump in today", butstarting with anomaly detection
is a very doable thing today.
Christy (20:42):
A good place to start.
So the sensorimotor integrationthat you mentioned is really,
that's really been the focus forthe past couple of years, right?
Donna (20:51):
Yes.
It's very, very exciting andpotentially has a lot of
implications.
You only need to look at a smallchild playing with a ball and
feeling it all over and lookingat it and bouncing it and biting
it.
And you know, when you see howthe human brain is learning
about the world through itssensorimotor interaction with
(21:13):
the world, that's when yourealize, hey, there's something
here that is fundamental abouthow we learn and how that child
builds a model of a ball and theattributes of a ball that it
applies to a novel ball.
The next time it sees a ball, heor she sees a ball, that's
profound implications for thiswork at the ability for us to
(21:36):
mimic some of that learningabout the world.
Christy (21:39):
Which ultimately, we hope will be the foundation for
true machine intelligence.
Donna (21:45):
Absolutely.
Christy (21:46):
That's the goal.
Donna (21:47):
Yes, it is.
Christy (21:48):
Well, Donna, thanks for joining me today and chatting
about Numenta.
Donna (21:52):
Thank you.
Christy (21:53):
And thanks for listening.
You are listening to Numenta On Intelligence, a
monthly podcast about howintelligence works in the brain
and how to implement it innonbiological systems.
I'm Christy Maver.
For the past couple episodes, myco-host Matt Taylor has
continued his Interview with aNeuroscientist series and gone
pretty deep into variousneuroscience topics.
(00:21):
For our final episode of 2018,we're taking it back to the
business side.
I interviewed our CEO DonnaDubinsky actually a few months
ago, but we saved this episodeto be the final one of the year.
During our chat, I asked herabout the business evolution of
Numenta, the challenges ofnavigating both a scientific and
a machine intelligence missionand why she believes it's so
(00:43):
important, even fornon-neuroscientists, to
understand how the brain works.
As always, you can keep up withour latest progress by signing
up for our newsletter andvisiting numenta.com.
All right, here's my interviewwith Numenta CEO Donna Dubinsky.
(01:07):
Hi, I'm Christy Maver and I'msitting here today with Donna
Dubinsky, CEO and Co-Founder ofNumenta.
Hi, Donna.
Donna (01:14):
Hello, Christy.
Christy (01:16):
So, thank you for being on Numenta On Intelligence.
Donna (01:19):
My pleasure.
Christy (01:19):
I'm specifically excited to talk to you because
as you know, we do some prettydetailed neuroscience research
as a company here.
And like me, you do not comefrom a scientific background, so
I'm really excited to talk toyou more about Numenta, our
work, what it means andparticularly why you are drawn
(01:40):
to it.
So the first question I want toask you is one that I get often
which is, Numenta is such aunique company.
How do you explain to peoplewhat it is that we do?
Donna (01:53):
Well, Numenta is a unique company.
It's not so much that it's aunique effort, but I think the
form of it is really unique tohave created a scientific entity
that also has a commercialmission is a unique structure.
There certainly are scientistsworking on hard problems
throughout the world andacademia and private research
(02:15):
institutes, but other than maybebig companies that have research
components you don't certainlysee it very often in a startup.
In fact, I think it'sinteresting because so many of
the venture capitalists aroundlook at it and say,"Oh, nobody's
doing big thinking in big workanymore", but we are.
But I don't think the venturecapitalists are really
(02:39):
interested in that kind of bigwork.
They're really interested inincremental work.
So we're weird because we'redoing science and big ideas in a
commercial form.
Christy (02:45):
Right, right.
And you mentioned this dualmission, right?
So there's the scientificmission which has to do with
reverse engineering the brain,and then there's a business
mission of applying that tomachine intelligence.
So, how difficult has it been tosteer the ship and navigate
between those two missions?
Donna (03:05):
That's pretty difficult, and I think it's not clear yet
what the ultimate resolution ofthat will be.
We don't really know what thebest way is to commercialize it,
but let me just address why isit we want to commercialize it.
It's fine to have a scientificmission.
Why do we need to commercializeit?
And the reason is notnecessarily a monetary reason;
(03:29):
the real reason we want tocommercialize it is for impact.
Both Jeff and I(Jeff Hawkins andI) have had extensive experience
with developer communities overthe years, where we've seen
clever, amazing, fascinatingwork done by people who build on
a platform we've created, suchas the Palm Pilot or the Treo,
and our idea when we createdthis company as a for-profit was
(03:54):
to enable a whole generation ofdevelopers to build value-added
products on top of thistechnology.
We felt that a commercialopportunity is what would draw
those developers much more thana nonprofit structure, but we're
still actually kind of early inthat process, so we're not sure
how it's all going to work out.
Christy (04:14):
Right, right.
And there's actually a HarvardBusiness Case about this.
Donna (04:18):
Yes, there is.
I recommend reading it.
It covers a lot of our differentbusiness models.
Christy (04:24):
Right, right.
So let me ask you, why do youthink it's important to
understand how the brain works?
It's such a big problem, butwhat does it mean to you?
Why is it important?
Donna (04:38):
Once you start thinking about this, you start looking
around your world and realizeeverything manmade that we see
is created by our brains, ourcollective brains.
I look at a giant skyscraper,and I have to stop and marvel.
How could we– little, puny, weakhumans– build such an incredible
(04:59):
structure?
How could we create theseincredible medicines and
surgeries?
How do we do all of theseamazing things?
Well, it's an accumulation of abunch of individuals' brains
that have solved a millionindividual problems to make us
stronger and faster andhealthier and a lot of positive
(05:20):
attributes.
So as I think about that, Imarvel at the brain and its
capability to do this.
And the question is, can wefigure out how that works and
figure out a way to take some ofthat power to have machines help
us do those amazing activitiesand be even stronger and
healthier and more powerful?
Christy (05:40):
And there must be certain examples that come up
from time to time that reallykind of, you know, those"Aha"
moments of, oh wow, that's whatthe brain is doing.
There are so many things that wedon't really think about, and
one of the examples that alwaysresonated with me I think is
something that you had said,which is you observed that with
(06:02):
any other part of your body,like you could have a heart
transplant and be fine, right?
But the world only exists inyour brain, and we are our
brains, right?
Donna (06:12):
You are your brain.
If you have a heart transplant,you're still Christy.
If you had a brain transplantand put my brain in your body,
you'd certainly be different.
But I think that it's reallytrue, but it's more for me the
output of that brain.
It's the idea that so manypeople have solved so many
(06:33):
amazing problems through humanhistory, and it made our lives
better.
You know, I was a history majorin college.
I look back at a century, twocenturies, a millennium ago, and
you think about the number ofpeople who died of disease and
natural disasters and poorbuildings and all of the things
that created human heartache andtrauma in the world, and so many
(06:54):
of those have been addressedover many years through
incredible engineering andscience, and there's many more
yet to do.
So, can we be a part of thathuman progression and making the
world a better place?
That's the question.
Christy (07:09):
So let me ask you– There was a blog post that you
co-wrote with Jeff Hawkins acouple of years ago called,"What
is Machine Intelligence vs.
Machine Learning vs.
Deep Learning?", right?
And we often get questionsbecause those terms are thrown
around so often that they'vealmost become meaningless.
(07:29):
Everything now is defined as AI,Siri is AI, you know.
But that post from two yearsago, the two of you really took
a look at the landscape andbroke it down at a high level to
say,"Here's how we'redifferent." So can you touch on
that perspective of what it isthat makes us different for
(07:52):
people who might hear,"Oh,machine intelligence.
Got it, so you're buildingrobots."
Donna (07:56):
It certainly does all sound the same.
I think for outsiders and evenfor me, I read websites of
people working in this field andI think to myself,"It sounds
just like what we're doing.
How is it different?" But whenyou spend a couple of weeks here
or even a couple of days and youlisten to the meetings, it's
pretty clear pretty quickly howdifferent it is.
The team here is delving intoincredible detail on the actual
(08:22):
neuroanatomy, what is happeningwith one neuron and you know,
dendrites and synapses andtalking to each other, whereas
all the people I think ineverything else has referred to
as AI are working on much lessof a realistic neurological
model.
It sounds the same.
(08:43):
They say it's neural nets.
They say it's got differentweights for cells, and they call
it the same kind of terminology,but it is nowhere near as
in-depth of a true biologicalconstraint, if you will, as it
is that we do here so ourapproach is much more biological
(09:06):
in nature.
Christy (09:08):
And do you see that biological approach being
embraced, or do you see it asmore of a fringe approach?
Donna (09:17):
It's not yet, but I think there's a growing realization
that it is an interesting placeto look.
I think most of the AI world isrealizing that they're hitting a
wall is a bit strong of a term.
I mean, they've accomplished alot.
They have a lot left they coulddo, but what they're
accomplishing is really with oldkinds of algorithms and adding
(09:40):
more power and more data.
That's really what has createdthis resurgence of"AI", and the
smart people who work in thefield have looked at that and
said, you know, we're not reallydoing this in any fundamentally
smart way.
We're doing it by brute force,so how could we do some of this
in a smarter way?
(10:00):
Well, maybe we should look tothe one instantiation we know of
in the world that is actuallyintelligent, which is the brain.
So we see people coming aroundat that point of view, but they
don't really know how to goafter it.
If you think about it, it makessense because there's two wholly
separate groups of people.
There's neuroscientists whodelve down deeply into issues of
(10:22):
the brain relative to disease orlearning or other concerns, and
then there's computer scientistswho really know nothing about
neuroscience, and they don'treally intersect.
And this is, I think, one of thefew places in the world where
they intersect at a world classlevel.
I mean, we have world classneuroscience and world class
computer science together,sitting, working these things
(10:44):
out, and in most places it justsimply isn't that.
Christy (10:49):
So this touches on something I've heard you talk
about which is related toNumenta, but also kind of
related to your history andcareer about having a front seat
at these evolutions ofcomputing.
Can you explain that forlisteners?
Donna (11:03):
Oh, I've had an incredible career and very lucky
and very happy where I ended upover the course of my career.
Not that it's over yet, but it'sdefinitely been a long time.
And that career has put me atthe front row seat for four
(11:23):
major generations of computing.
The first one was personalcomputing when I joined Apple
very early in its inception andwe had the notion of putting a
computer on every person's desk,and that seemed outrageous at
the time.
Of course, now everybody looksat it and says," Well, of
course!", But it wasn't soobvious then, and we helped make
that happen and Apple.
And then the second majorrevolution was at Palm where we
(11:46):
said, these devices need to comeoff the desktop and go into your
pocket.
Personal computing is not thecomputer sitting on your desk.
It's not very personal.
The one that's going to be theone you carry in your pocket or
your purse..
that's your personal computer ofchoice, and this was really Jeff
[Hawkins]'s vision that I boughtinto and we made that happen at
(12:06):
Palm in a significant way.
We really led the handheldcomputing revolution and then at
Handspring, we led therevolution in smartphones and
really saying that thesehandheld devices need to be
connected to each other andconnected to the world.
That was a front row seat atthat revolution, and now my
fourth revolution is a front rowseat at intelligent computing,
(12:27):
and so it's been anextraordinary privilege for my
career to be able to be a partof these amazing revolutions,
particularly since I'm not thetechnical person– I'm not the
engineer, I'm not the scientist.
I never could have done thesethings as a protagonist, if you
will, but I have been able to bea key part of the team that
(12:51):
builds the infrastructure aroundit, the marketing, the sales,
all of the things that theseideas need to come into the
world.
I've been able to be a part ofthat team.
Christy (13:03):
And what is your vision for Numenta?
I mean ultimately the way you'relooking back on those first few
evolutions, I assume, will bethe way that you and others look
back on Numenta.
But, where do you see Numentagoing?
Donna (13:23):
It's never obvious when you're in it, where it's going
and exactly what theapplications will be.
I like to say when we inventedhandheld computing, we never
imagined Uber.
It just, you know, again, thisis where that development
community is so importantbecause people will take these
core technologies into placesyou'd never could have imagined,
(13:43):
so I believe the same thing willhappen with this.
We are going to solve thefundamental problems here of the
brain and how it works.
There will be machines that willbe built on those principles.
There will be developers thatwill put those machines to all
sorts of incredible uses.
And I think if you look down theroad in say 10 years, let's make
it far enough that you can'thold me into account, but in 10
(14:06):
years I think we will see realimpact in the world as a result
of this work and in ways that wesimply cannot predict.
And I know that frustratespeople because people want to
know today, what's the killerapp, and it's just virtually
impossible to guess.
We could make up a bunch ofstuff, but it won't be right.
(14:28):
And so we'd rather focus ongetting the core right and
getting the tools out there forother people to see, how can
this apply to a problem that hasstymied them?
We've seen people come to us forall sorts of problems where
they're frustrated, whether it'sin text analytics or in farming
solutions or people came to usabout monitoring beer in kegs.
(14:52):
We've had every possibleapplication, and those are the
things that are going to be herein prominence in another 10
years.
Christy (15:01):
And do you worry about the, you know, there are so many
conversations and some loudvoices out there that are kind
of the scaremongers and the feartactics?
Do you worry about that eitherwith regards to Numenta or with
regards to just the space ingeneral?
Donna (15:20):
I think it's always fair to worry about these things, but
I think that any new technologyhas worries that need to be
managed.
I like drones as an example.
We came along with drones, andnow we don't want them being
flown near airports where it candisturb an airplane, so we need
rules for that.
We don't want them flying aroundand looking into all of our
(15:44):
windows.
The privacy implications of thatare pretty serious.
We better regulate that.
There's always going to bemalicious uses of new
technologies, and there's alwaysgoing to need to be regulation
to try to contain those uses asmuch as possible, so I don't see
this new technology in any waydifferent than that.
(16:06):
I think the fears aboutintelligent machines taking over
are just absurd.
These machines and theseprograms..
They will not have agency, theywill not have intent, they will
not have emotions unlesssomebody programs that into
these machines, and in whichcase you can un-program it or
(16:27):
unplug the programs.
They will not develop thesethings on their own.
So, I think that they can beused for malicious purposes.
I get that it will need to beregulated.
It will need to be controlled inthe best way.
It can be just as any technologyis, and I don't see it as
fundamentally different.
Christy (16:49):
So, part of navigating the dual mission over the years
has meant that at times we'vefocused on applications and even
one enterprise application inparticular, and then other times
it's more the research.
Do you see Numenta working onapplications or is it unknown?
Donna (17:08):
Anything's possible.
We might work on applicationsourselves.
We might partner with people whowant to work on applications.
Right now, we're taking apartnering attitude.
People who come to us and want athe license to the software and
the intellectual property, weare very willing to discuss a
license with them.
We have several licensees, andthey're working on all very
different problems.
And so we're very open topartnering, and we'll just see.
Christy (17:33):
And what do you suggest for people that aren't
neuroscientists or maybe aren'teven computer scientists, but
are interested in the work andwant to somehow be a part of
this or even wondering,"Whatshould I do?
Tell me what steps I need totake.
Do I need to act on this now?"If our story resonates with
(17:54):
someone, then the obvious nextquestion is,"Okay, what should I
do?" What advice do you have forpeople, whether it's individuals
or companies that buy into whatwe're doing here?
Donna (18:05):
Well, I kind of hear two questions in that.
So the first question is, ifyou're just intellectually
engaged and you want tounderstand,"How does the brain
work?
It's interesting.
You're solving that problem."There's a few things you can do.
First, a good place to start isto read Jeff's book, On
Intelligence.
It's old.
It's doesn't at all have any ofthe new theories in it, but it
(18:25):
lays out some of the problems ina very easy-to-understand way
that has captured a lot ofpeople.
The book has continued to dowell other than chapter six,
which I give you permission toskip.
Christy (18:40):
And it even says in the book at the beginning of the
chapter, skip this.
Donna (18:44):
It's just very biologically detailed, but I
always say that because chapterseven and eight are very
important.
I don't want people to put itdown.
But I think starting with thebook is one thing, and then I
think looking at our website andreading some of the more
accessible parts of the thingswe've written, the video that
we've put up– Why Brains Matteris a very good video.
There are a lot of snippets ofinformation that are there that
(19:08):
I think could help that person.
Now the second question is, ifyou're a company and you want to
get started on actually applyingthis work, what would you do?
The easiest place to get startedtoday is the problem of anomaly
detection on streaming data.
Something your brain does isworks with streaming data, as
opposed to static data.
(19:28):
We are making predictions andthen finding anomalies
constantly in everything that wedo.
We have implemented softwarethat does that.
It's available in our opensource community, and that would
be the lowest hanging fruit of away to experiment with the
technology.
We have a product called HTMStudio.
You can easily take your dataand flow it into HTM Studio and
(19:52):
see if we can find anomaliesthat you can't otherwise find.
We have found latency innetworks earlier than other
techniques.
It's much more effective thanthreshold systems, which is the
way most anomaly detectionworks.
And I think if I were in acompany today and I just wanted
to start experimenting, that'swhere I would start, with the
(20:14):
idea of getting more deeply inwould enable other applications
such as prediction orclassification.
Ultimately, I think the area wemight have the greatest impact
is sensorimotor integration androbotics, but that's not really
ready to be implemented yet.
Those theories are still gettingthe t's crossed and the i's
dotted, and we're not quiteready to say,"Hey, all you
(20:35):
robotics companies out there.
Come jump in today", butstarting with anomaly detection
is a very doable thing today.
Christy (20:42):
A good place to start.
So the sensorimotor integrationthat you mentioned is really,
that's really been the focus forthe past couple of years, right?
Donna (20:51):
Yes.
It's very, very exciting andpotentially has a lot of
implications.
You only need to look at a smallchild playing with a ball and
feeling it all over and lookingat it and bouncing it and biting
it.
And you know, when you see howthe human brain is learning
about the world through itssensorimotor interaction with
(21:13):
the world, that's when yourealize, hey, there's something
here that is fundamental abouthow we learn and how that child
builds a model of a ball and theattributes of a ball that it
applies to a novel ball.
The next time it sees a ball, heor she sees a ball, that's
profound implications for thiswork at the ability for us to
(21:36):
mimic some of that learningabout the world.
Christy (21:39):
Which ultimately, we hope will be the foundation for
true machine intelligence.
Donna (21:45):
Absolutely.
Christy (21:46):
That's the goal.
Donna (21:47):
Yes, it is.
Christy (21:48):
Well, Donna, thanks for joining me today and chatting
about Numenta.
Donna (21:52):
Thank you.
Christy (21:53):
And thanks for listening.
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