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The Future of Watson, AI, and Machine Learning - Armen Pischdotchian
24:15 with Nick Pettit and Armen PischdotchianIBM Tech Mentor Armen Pischdotchian talks with Nick Pettit about the future of the IBM Watson platform, artificial intelligence, and machine learning.
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Hi everyone, I'm Nick Pettit and
I am here with Armen Pischdotchian,
0:00
and he is an Academic Tech Mentor
at IBM Watson.
0:05
How are you doing today?
0:08
>> Nick what a pleasure,
thank you, doing great.
0:10
>> Cool, well first I just want
to ask you what is Watson?
0:12
>> Great question to start off with.
0:17
Let me bring us back to 2011.
0:20
That's when we had the Jeopardy game,
and interesting story behind that.
0:24
Sony Corporation,
that happens to own the Jeopardy game,
0:30
the American quiz show, knew about Watson.
0:34
They approached us and they said so,
you say that you have a system that can
0:37
actually beat these two Jeopardy
champions, Brad Rutter, Ken Jennings?
0:41
We said yes, we do,
we have a high confidence.
0:46
It's never a yes or no, it's probably.
0:51
So we have a high confidence that
we can beat these two champions.
0:53
They said fine, and
we set up a stage at out conference room,
0:56
and the rest is history.
1:01
>> Well, so you did Jeopardy, and
then what happened from there?
1:03
>> And so Watson eventually was born,
it became practisized.
1:10
It became a product.
1:16
>> Right.
1:17
>> And I'm always reminded
of our CEO Ginny Rometty,
1:18
that she has termed the coin
augmented intelligence.
1:24
The idea in here is that
Watson is helping people and
1:31
machines work together to create knowledge
from data that enhances human expertise.
1:37
Watson isn't here to supplant,
and remove jobs.
1:45
It's here merely as a second
opinion to the doctor.
1:50
In fact, I'm reminded of an example.
1:53
A personal example that I witnessed.
1:57
On the way here from the airport we were,
the taxi that I was in,
1:59
was cut off by this giant
truck in front of us, and
2:03
we could see the reason was because
the truck was trying to avoid another
2:07
car that clearly you could tell
the driver was texting and driving.
2:12
>> Right.
2:16
>> And so
you had to swerve if front of our lane.
2:17
>> Right.
2:19
>> The taxi driver said to me Armen,
you know my brother is a truck driver, and
2:19
they're frankly, truck drivers
are some of the best drivers, period.
2:23
They have a preference to drive at night,
but they can't.
2:28
They're mandated to sleep.
2:33
They have to log that they have slept.
2:36
>> Right.
2:38
>> So this puts them in
traffice during the rush hours.
2:39
So I'm thinking to myself how great would
it be if this autonomous vessel did
2:42
the driving on its own, across country,
allowed the truck driver to sleep,
2:48
when he woke up,
as it got closer to the destination,
2:52
then he would open up the laptop and
do inventory.
2:55
Nobody's job has gone away,
but the mundane,
2:58
the arduous has been taken away, so
now we can focus on what is meaningful.
3:01
>> Right, so
you call this augmented intelligence.
3:07
How do you see this being
used in the real world?
3:10
>> I think one place that it's becoming
prevalent is in the field of data science.
3:13
Let me take a step back in here.
3:20
If you looked at data,
85% of it is unstructured.
3:23
These are tweets, emails, MRIs,
3:30
CAT scans, audio conversations, videos.
3:33
That is where I want to gain my insight,
my knowledge.
3:40
Not so much from structured data
that happens to happily reside in
3:46
a table that I can call it with
a SQL call from a table or a column.
3:51
Now, structured data, transactional
data also has its importance.
3:58
I think where these two come together
is in the field of data science.
4:03
>> So what it sounds like is that Watson
is able to elevate the level of questions
4:07
that you can ask of this data.
4:12
>> Yes, let me add to that.
4:13
It is accurate, it is accurate.
4:16
In fact, some of the services that we will
have in this course, the conversation and
4:18
discovery service, lend themselves
to that insight extraction.
4:23
I always like to think of the conversation
service, the chatbot that we would build,
4:27
about imagine all the things that
you know that you don't know.
4:33
Well, wonderful.
4:39
We can then use a conversation
service to find,
4:40
at least you know what it
is that you don't know.
4:43
Now think about all things that you
don't even know you don't know.
4:45
That's insight.
4:50
I think that is where
the discoveries surface,
4:52
especially if it's connected
to the conversation service.
4:55
We will explore that in
the course as we move forward.
4:58
That's where insights are extracted
where understanding and
5:02
knowledge is extracted from information
>> So
5:09
tell me about the future,
you're talking about the present and
5:13
what we can do right now,
but where is this all going?
5:17
>> Currently there are three
major calibers of AI
5:21
>> There is Artificial Narrow
5:26
Intelligence, and
that's where we are today.
5:28
The most fantastic technologies at work,
5:33
whether it is self-driving cars,
or the work done by Amazon,
5:37
Twitter, Facebook, IBM, Google,
we’re at Artificial Narrow Intelligence.
5:43
To have a better understanding of where
we are now let me take a moment and
5:49
talk about where we may
find ourselves tomorrow.
5:53
Artificial General Intelligence, AGI,
this is where the machine is equal,
5:56
and perhaps, millions of times
further ahead than a human.
6:03
Case in point, an example,
Carnegie Mellon University,
6:08
CMU, Robotics Department.
6:14
I had the pleasure of being
invited there for a lecture, and
6:17
when you walk into see a muse robotics
division, you're greeted by a robot.
6:22
This is not a humanoid robot.
6:26
>> Right.
6:28
>> Cameras, laptop, and
a pod, and track wheels.
6:30
I had registered, it knew me,
hello Armen, welcome.
6:34
What a refreshing greeting that is,
Dan, can I help you?
6:37
But since my bio was available,
so it had face recognition,
6:42
it clearly knew it was, and it takes
a look at the appointment that I may have,
6:47
and I was told later that if
I have arrived early enough,
6:52
It would suggest if I wanted to go
to the cafeteria for refreshments.
6:55
In this case I happen to be just in
time to get to my meeting, and so
7:01
I asked the robot,
can you take me to John's office?
7:06
The robot said, sure, follow me.
7:11
>> [LAUGH]
>> So I followed the robot.
7:12
It said on the screen in 20 feet,
it will turn left, and 30 feet,
7:14
it will turn right.
7:18
Fine, I did as it did.
7:20
As it went by the elevators,
one of those software engineer types,
7:22
neural network software engineer types,
kicked their chair in front of the robot.
7:26
It stopped.
7:31
It sees the chair, it stopped,
laser, everything,
7:32
nothing that's using technology,
that part.
7:37
I stop as well, looked at the software
engineer with somewhat of a puzzled face,
7:40
and it went around the chair, and
then I went around the chair, and
7:45
finally we got to John's office.
7:48
John asked the robot two questions.
7:51
Two very difficult questions for
any machine.
7:56
What happened at the elevator?
8:01
Why were you late?
8:05
>> [LAUGH]
>> The system is gaining an awareness
8:06
of its surrounding, of its environment.
8:11
It's gaining experience.
8:14
We're doing similar work at our
Almaden Research Facilities in California,
8:16
where we call it embodied cognition.
8:22
IBM Fellow Grady Booch, and his
colleagues are working on exactly that.
8:25
Systems that gain experience,
and this isn't memory.
8:30
That brings me to
Artificial Superintelligence.
8:34
I think one example would suffice to say,
consider this.
8:38
If I ask Watson today,
Watson tell me about neural networks.
8:43
Watson would say, I'm sorry Armen,
I don't know about neural networks.
8:48
Perhaps you can teach me.
8:53
Fine, we'll ingest a PDF or
crawl the web, and he will learn.
8:55
As opposed to the day after tomorrow,
if I ask Watson,
9:00
Watson tell me about artificial networks,
and
9:04
Watson would say something more like this,
I'm sorry, Armand,
9:08
I know that I don't know about
artificial neural networks.
9:14
Perhaps you can teach me.
9:19
That's self-awareness.
9:21
Suppose you might ask when is
this going to come to bear?
9:25
Ray Kurzweil, at Singularity University,
is currently at Google,
9:29
a futurist,
predicts that AGI would be around 2040,
9:35
at our doorstep, and
9:39
he thinks ASI would be somewhere around
2060, for systems to have that awareness.
9:43
>> So data science is rapidly
emerging as one of the most
9:49
important career paths of the future.
9:52
What does a data scientist need to know?
9:55
>> Perfect question,
absolutely, absolutely.
10:00
I'm reminded that of
Professor Mortissa Bidar from
10:03
Ryderson College in Canada
via University of Toronto.
10:08
I think he summarized it perfectly,
he said that.
10:14
Curiosity is the beginning stepping
stone for a data scientist.
10:19
Is two bedrooms more valuable, or
is having more land more valuable?
10:26
Just that sense of curiosity to question
everything in their surroundings.
10:32
Knowledge of various machine
learning methods helps.
10:40
Although he also is quick to point out
that data science is a team sport.
10:47
One doesn't need to be master of all.
10:53
You can pass the baton from
specialty to specialty.
10:56
But it helps for
a data scientist to be versed in some
11:00
languages that data scientists use.
11:05
Python, they're especially
working with Jupiter Notebooks,
11:09
R, Skylab, depending on various
applications that they want to.
11:16
For example, R lends itself very
well to heavily statistical work,
11:22
regression and so forth.
11:28
Python is a bit more of a general
language and it's an easier to master.
11:30
And it also lends itself well
to machine learning models.
11:37
And data scientists would detect
the data that they have collected.
11:43
And they would run it through
various models to try and
11:48
see what sort of accuracy each model gets.
11:51
They will try things such as support
vector machines, they may try random
11:55
forest trees, Bayesian inferences,
I call these brute statistics.
12:00
The core logistical regression
is the engine that's running.
12:05
If their data happens to be multimodal,
a lot of variables,
12:09
they would perhaps try neural networks.
12:16
With images,
12:20
it lends itself that much more readily
to convolutional neural networks.
12:21
CNN's, of course,
have their own drawback but
12:26
the diminishing gradient problem.
12:29
Imagine when you take a copy of a paper,
and then you take a copy of that copy,
12:34
then a copy of that copy, eventually
it starts to become not so clear.
12:41
Perhaps by the tenth copy,
you can't even read whatever it is.
12:46
That somewhat is akin to
the diminishing gradient.
12:52
The first layers of
a convolutional neural network
12:56
train much slower than the latter layers.
12:59
The first layers might just look for
edges that would say that this,
13:03
my chin is an edge,
doesn't know it's a chin, that's an edge.
13:07
A layer later,
13:11
we would found two other edges that always
appear in the two sides of this one edge.
13:12
So those are the layers that launch lower,
and it perhaps lends to a little
13:16
bit more inaccuracies, but then
the other layers take that, inherit it.
13:22
So they might find that perhaps they want
to use another method called recurrent,
13:27
or recursive neural networks,
recurrent or recursive.
13:32
It actually treats each layer
as though that's an input node.
13:37
So it starts over freshly,
it has a feedback mechanism.
13:43
They might even want to try without their
algorithm's Long Short-Term Memory, LTSM.
13:47
We're working with that quite a bit at IBM
to help gain the system an experience.
13:53
And of course, a scientist then
they may pass the baton or
13:59
the professor hide their
stresses that the visualization,
14:05
the explanation of what it
is they have collected.
14:10
What it is that they have
built patterns out of.
14:14
After all, machine learning is a fancy
way of saying build me patterns.
14:17
And that visualization is what they then
need to pass on to the c-level executives.
14:22
Or to their end users to impact
the decision that they need to make.
14:28
>> Very cool stuff.
14:34
So you mentioned that we're going from,
Artificial
14:35
narrow intelligence to artificial general
intelligence and then super intelligence.
14:41
What are the steps along the way
between each one of those milestones?
14:47
>> A wonderful question.
14:53
Let's go a little bit further back.
14:54
In the beginning, and
I wanted to quote Pedro Domingos,
14:59
in his wonderful book,
The Master Algorithm.
15:04
He depicts stages.
15:10
Stage one is calculations.
15:13
That was perhaps in the beginning of the
20th century, let's just think about it.
15:16
Even prior to that, mathematics.
15:22
That lended to stage two,
methods and algorithms.
15:25
Think of C++, the object oriented program.
15:29
That lead itself to stage three,
analysis regression, tried and tested.
15:33
It led itself then to stage four,
supervised learning or optimization.
15:41
Very much a machine learning
methodology used nowadays.
15:47
Supervised learning has the benefit
of you not needing to have so
15:52
much data, but it is arduous because
you have to label that data.
15:57
Oftentimes, it leads to greater accuracies
than your other methods of learning.
16:04
A little bit more challenging
is unsupervised learning,
16:11
that's problem solving.
16:14
Think of the Amazon recommender app,
or Netflix,
16:16
the recommender engines
are unsupervised learning.
16:19
I may buy a certain book and
16:23
in the bottom it says others
also purchased books as such.
16:25
Now Amazon doesn't know my habits
of what books I like or so forth.
16:31
But it has a lot of data of folks
who purchased this particular book
16:35
have also purchased these other books.
16:39
Perhaps one of those might be
something that I'm looking for.
16:41
In unsupervised learning,
data is clustered, but it's not labeled.
16:45
One might go and label a few and you could
then call that semi-supervised learning,
16:51
and that itself is another approach.
16:57
The one that really gets me is stage six,
there are seven stages.
17:01
Stage six, We called that
17:06
unsupervised asking, rhetorical learning.
17:10
What do I mean by that?
17:16
Let me give you an example, consider this.
17:18
Let's say, I get on my phone and
17:20
I say, Watson, my muscles are hurt.
17:24
My joints are sore, my nose is clogged,
what's wrong with me?
17:30
So Watson goes in the back and
does it's data analysis.
17:38
And my phone is the browser is
geo-sensitive so it knows for
17:44
example that I happen to be in
Massachusetts, it does a spacial analysis.
17:49
It does a temporal analysis,
it finds out that it is October.
17:56
It looks at NOAA,
National Oceanic Atmospheric Association,
18:02
and finds out that it hasn't rained in a
while and the pollen count is quite high.
18:07
It comes back and asks me a question,
this isn't something you put in a chatbot,
18:14
this is a rhetorical question,
do you have fever?
18:19
I say no, I don't.
18:23
Negation is huge, it's a big algorithm.
18:26
No, I don't have a fever.
18:29
It goes back takes another look
at the data that is collected.
18:30
Comes back and says,
I'm 89% positive that you have allergies,
18:34
4% positive that you have the flu.
18:39
And you can imagine from there, well,
what should I take for my allergies?
18:44
You might recommend a certain
drug off the shelf.
18:50
I don't take any drugs at all,
of any kind.
18:53
Then it might say,
why don't you try the local honey?
18:55
[LAUGH]
>> [LAUGH]
18:58
>> That leads us to stage seven as you
19:00
were alluding to it, and
that's the notioin of unsupervised action,
19:02
doing, we're not there yet.
19:07
That would be artificial
super intelligence.
19:09
It's an unbounded action, unbounded
it's not stuck between a zero and
19:13
a one in the table that
you put in a graph.
19:17
Well, that's when robots make
their own decisions, frankly.
19:21
They have a community and
they elect a leader.
19:25
[LAUGH]
>> [LAUGH] So
19:28
what will life be like when we
just have this general artificial
19:30
intelligence everywhere,
even a super intelligence?
19:36
>> Great question.
19:41
Perhaps I'll take a smaller bite and
perhaps a more
19:44
immediate impact of this,
I think it would be as such.
19:49
We've all heard of APIs These APIs
19:54
are really a RESTful method
application programming interface for
20:01
various services to
interact with one another.
20:06
The notion behind that is a URL,
username and password, or an API key.
20:10
That's how you get from this room, where
we do conversation, to the next room,
20:15
where discovery is taking place.
20:18
I'm thinking that the next
would be not APIs but
20:22
something called natural
language interface.
20:26
Bots will be communicating
with one another.
20:30
Frankly, they would be
talking to one another.
20:33
A service, not an API, but a brokerage
bot that happens to be the restaurant
20:36
would be talking with the weather,
would be talking with the golf course.
20:41
And advising me that I should leave
the golf course soon because the weather's
20:46
about to change and the restaurant's
going to get booked really fast.
20:50
I think another approach might be
move over apps, here comes bots.
20:54
The notion of us downloading apps on our
telephone, I think the days are numbered.
21:00
Pretty soon, I would just look at
my phone and I would talk to it.
21:06
I'll say get me a car, I need to get to
21:09
Charlie's Restaurant and
tell me what's the special.
21:15
By the way, is that the rain
moving in tonight or later?
21:19
I don't need to download anything for
this.
21:23
I want the systems in the background
to do the brokerage on their own and
21:26
give me the information that I want.
21:31
Blockchain technology,
it's here already very much so.
21:34
I'll give you an example
of a Blockchain technology
21:38
that I think might work well
handful of years from now.
21:41
In your fridge, the milk has gotten
down to its lower threshold level,
21:46
so KPI, Key Performance Indicator,
has triggered a smart contract.
21:53
That smart contract is well
encrypted by Blockchain technology.
22:00
Apple Pay or Amazon Pay
automatically does the transaction.
22:05
The drone drops off
the milk on the front lawn,
22:12
the robot in the house goes,
picks up the milk, and brings it, and
22:15
put it in exact location at the fridge
to restock the refrigerator.
22:19
This entire thing really
is a possibility of
22:24
Blockchain technologies and
what it can do.
22:28
Nano bots, I think may be coming
our way sooner than we think.
22:33
It's quite possible that, I think,
22:37
in my lifetime I will have
nano bots in my bloodstreams.
22:39
Checking the salt, the sugar,
notifying me on my wearable device,
22:42
might be clothing if not a watch, that
our men stay off the salt and the sugar.
22:48
>> [LAUGH]
>> Perhaps if a certain outlier is
22:54
detected, it might
automatically notify my doctor.
22:58
I could get a call from my
doctor that says, Armen,
23:02
I've noticed that your
ankles are swelling.
23:05
That has something to do
with cardiovascular issues.
23:08
I might say,
I thought my shoes were kind of tight.
23:10
Why don't you come in tomorrow, all right?
23:14
Of course,
the world of embodied cognition that
23:16
we're working with at Amadin
research laboratories.
23:20
But the cognition is where systems now
have an awareness of their surrounding.
23:23
They gain experience,
some of the algorithms we use LSTM,
23:31
Long Short Term Memory,
23:35
go towards helping them in bringing
that feedback loop into the system.
23:37
Allowing you to understand that stairs
hurt if I go and get fall off passed it.
23:43
And humans do not like it when I do this
the experience is not the same as memory.
23:49
And the sense of embodied cognition
will give that to AI assistance.
23:57
>> That's a world I'm looking
forward to live again.
24:04
[LAUGH] Well, Armen, thank you so much for
being here today, we really appreciate it.
24:06
>> My pleasure Nick, thank you so much.
24:12
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