So why don't you just introduce yourself.
My name is Ryszard Michalski.
I am PRC professor of computational sciences
at George Mason University in Fairfax, Virginia.
I came to the University, to George Mason University,
about eighteen years ago from the University of Illinois.
When I started my career, scientific career here
in America, originally I started my work in Poland.
Came from Poland, and got degree, master degree,
actually something equivalent to a bachelor's degree,
from the Warsaw University of Technology.
Then I was, in some sense, privileged
to go to Saint Petersburg Institute of Technology.
And what year was this?
It was 1960-61.
Did they have a computer there?
Yes, they were building first, very early, [? rational ?]
computers.
And I did my master's degree in automatic control.
I designed a specialized computer
that would measure the various parameters of the wind.
And then that system, which I actually designed and developed
hardware.
And then the system was supposed to be sent somewhere
to Siberia.
I didn't know where.
And was supposed to transmit signals
informing about the weather and the wind
in this particular area.
So that was my master degree.
And then I came back to Poland and started
to work at the Institute of Computers.
One can say it was mathematical machines as they called it.
And I started to design and I was involved
in designing a Polish computer.
And that computer was called, I remember, [? Zanpor. ?]
But soon after I moved to the East of automatic control,
where issues were somewhat different.
And I remember I got a task to decipher a system, which
was a relay based system, a very complicated automaton
and had no documentation.
They asked me to create documentation
of that very complex system.
So it's like looking at your brain
but of course it wasn't like brain
it was something much simpler.
But for me it's still very complicated.
I had to follow all the wires, how they're connected,
how the switches work electronically
and create a diagram of how it works.
So it was a very complicated, not very exciting job.
But I think I did it.
And then I got the task which was first, one may say,
machine learning task.
I was asked to develop a system that
would recognize handwritten, continuous characters,
alphanumeric characters.
It was very exciting task.
And I got some initial ideas from Mr. [? Karpinsky. ?]
He was, in already that time, one of the best constructors
of Polish computers, a pioneer.
And he came with this idea.
Let's build a reader of handwritten characters.
Can you do it?
So I worked very hard.
But I must say something earlier,
that before I started doing that project
I worked also on what is now called neural nets.
So I designed a system for optimizing a neural nets,
or threshold logic systems of that time.
And this was my supposedly first PhD phase of finish that work.
And I hope that now I have PhD so I can do something else.
But I was asked no, no, no, you cannot PhD now you have to do
something else because this is very important.
This [INAUDIBLE] character cognition
so I started to work on that.
I finished.
It was viewed as a great success.
We could--
How did it read it in?
It has a diode, an array of diodes,
which are light sensitive, and they were transmitting signals
to the computer, which was very primitive compared
to current computers.
But it worked.
I was able to really work, because it was learning system.
It used naive-based approach, but I
didn't know that it is naive based,
but it had more than naive-based because it simply learned also
automatically in parallel what features
of the letters I imported.
So I designed the initial set of possible features
that are interesting to discover in handwritten characters.
So left line on the left, right line on the left.
And maybe two lines, one long, one short, that kind of things.
I designed whole set of features, quite large.
And that system was picking up the features
which are relevant to a given character,
and then learned to characterize this collection of features
that would be representing a given letter,
regardless how it is written.
And so this went very well.
And everything would be very happy.
But well, then my boss got into some trouble.
So he left the Institute and I could not really
continue this work.
And the idea was well, maybe we should get second PhD
for that work.
But he wanted to do together with me,
which was fine with me.
But it turns out that you cannot get one work and two people who
will get PhD.
It doesn't work this way.
So my second chance PhD didn't pan out,
so I had to do something else.
And so I moved to designing or optimizing Boolean functions.
It was mathematical.
It didn't require any equipment.
So I was independent.
I did it in another institution.
I don't want to go into details why
I had to go through such a complicated process
but that's is how it works.
So that I developed this system and algorithm which
I called AQ, for solving very complex [? cognitive ?]
problems.
And that was my third topic for PhD,
but this time it was done in a different institution.
Nobody bothered me and somewhat in conspiracy.
So when I finished, people were surprised
that I had done this work and they wanted
to move it back to Warsaw.
But luckily, everything went OK because my professor
in the Silesian University of Technology was very supportive.
And I got very good reviews of my PhD.
And so I got my PhD and now I am free.
And everything would be very happy.
When you were there, how do you think
the atmosphere in Russia and Poland
was different from the working atmosphere here
in artificial intelligence and machine learning at the time?
I was very avid reader of the journals.
IEEE journal on computers.
And I also read with great fascination the book
by [? Minsk ?] and [? Papperd ?] on [? perceptrons. ?] But there
was a book that criticized that development and I remember that
I fully agree.
So when I was designing this handwritten character
recognition system, I deliberately
chose to work on it in a different way.
Not as a [? perceptron ?] type or neural net approach,
but the approach that characterizes the features,
or selects features of characters
and then learns how they combine together to characters
given class of letters.
Like letters I a, or B, capital, small, Greek letters,
digits, and so on.
So I already was ready for something
than just neural nets.
And I was lucky because I got selected,
as I was saying, this is [INAUDIBLE] sciences,
to go to the United States after my PhD.
And it was a very great opportunity.
And I was actually, I suppose, I was
selected to go for a Fulbright fellowship.
But I didn't believe that I would get it
because number of candidates was very large, like 80.
And I was only in 13th places.
What I did, I wrote a letter to one American scientist,
I think Raymond Miller, who heard about my work,
listened to my talk once and said,
Oh well if you want to go to America write me a letter.
And I wrote to him and he had recommended
to write to the University of Illinois and also to Purdue.
And I remember I got a letter from Purdue refusing,
saying that there's no position.
So when I got a letter from University of Illinois, well
I really wanted to go.
Because it was the time where they were building
ILLIAC 3, very large scale pattern recognition computer,
or computer for analyzing images.
I was so excited to be in such a place
that I didn't believe that they would give me an invitation.
So I didn't even open the letter.
I just didn't want to get upset that there
would be another denial.
And only when I was in a much better mood
sometime later I opened the letter.
I was very surprised that I am actually invited.
So that was such a great joy, except that--
And when was that?
It was 1971.
The only problem was that it was like this.
I wanted to go to Illinois and some time later,
before I decided about Illinois, because I knew that Illinois
is a very unlikely that I would be able to, that I
would be permitted to go.
Because it was relatively high salary
I was invited as the resident assistant professor.
So I knew that may be a big problem for me.
And then soon after I got actually
accepted to get a Fulbright fellowship.
To half year at MIT half year at Stanford.
Another great opportunity, but I've already made up my mind.
I want to go to Illinois.
And then it was the question, can I
get permission to go to Illinois, and not to go.
Because of the Communist government.
And I went to the secretary of the Polish Academy of Sciences
and presented this idea to him.
And he agreed that it is much better for me to go to Illinois
because somebody else can go, and somebody else
can get Fulbright fellowship.
So I go to Illinois because it was invitation only for me.
So that's what I chose, but then it
turned out that it was, how to say,
bad choice in the sense that the government didn't
want to permit me to go.
Oh no.
So suddenly I was nowhere.
I already lost my Fulbright fellowship
because I denied that.
And here I cannot go to Illinois.
So it's a huge struggle which I will not talk about because it
is not the main theme.
So after maybe half a year, eventually after a very, very
long, complicated process which I
can describe in some other time, I got permission.
So I came to Illinois.
And suddenly I found myself in that beautiful world
where there were scientists interested in big issues.
And [? Bluth ?] McCormick my new boss,
introduced me to the world of systems
that are supposed to recognize images and pictures.
And everything worked so well I just
fell in love in America, in science, university,
it was great.
So he had passed very quickly and I
discovered that my algorithm, AQ for solving
very complex [? cognitive ?] problems,
actually can be used with a special language knowledge
representation, actually, for learning to recognize images.
So I actually designed this language
which I called variable valued logic,
which was sort of an extension of many valued logic.
And combining this many valued logic
with variable valued logic was AQ suddenly
gave a system that could learn rules,
characterizing all kinds of classes
general purpose learning system.
So this was very nice.
Of course, McCormick and others like my work
and they suggested that I stay another year.
So again, this was great except that it
was very hard to get permission from the government
that I would stay another year.
But somehow I got this permission,
so I stayed another year.
And now I was doing something which was basically learning.
But I didn't know that at that time
many scientists were already doing AI, believe that learning
is not worth to do.
So in some sense what brought me to AI
was ignorance, because I didn't know that learning it
is so difficult not worth to do it.
So once I did it because I just liked the idea.
And I so I was very early in the field of machine learning.
So then eventually I applied to various problems,
and it got interesting results.
And one of the insights that brought me some recognition
was to diagnose soybean diseases, which
were very important of course in Illinois.
And this was the time when people were
building [? EXPAL ?] systems.
But they were building expert systems
primarily by putting those rules into the system.
And I was able to actually learn the soybean
disease diagnostic rules from the cases of diseases
which we got from the experts.
But then, in order to make it into an interesting research
project, I also handcrafted the rules
which expert would be telling me that he is using
to diagnose those diseases.
And then what happened was that actually rules
which were learned by a computer using our AQ learning program
outperformed the rules which were programmed after.
So now this is really very exciting
and I got recognition, excited, also publicity for that work.
First of all, I got a nice grant to continue that work
from the National Science Foundation,
and I think also probably from other agencies.
So in any case, I was suddenly able to do more research
and have support for it.
And so on.
So that's how I came to machine learning, through ignorance.
But I already started to sort of be involved
in broader issues of AI when I attended,
in 1973, the conference on artificial intelligence
which has held in Stanford.
So at that time I already was of now
aware of the whole field which was developing
fast, exciting field.
And so then I was able to connect my research on rule
learning to the field of AI.
So that was quite exciting situation.
And I returned to Poland, by the way,
because I felt obliged morally that I have to come back.
But then after a while, I decided
to come back because I had offered
to become a professor at University of Illinois.
It was tenure so it was a great distinction
and I was very excited about such possibility.
And I got other offer so it was just overwhelming,
this opportunities it was.
Sounds like an exciting time.
And then some time later, after when
I was doing this soybean disease diagnosis and general purpose
rule learner, I met other colleagues
who are doing machine learning, or something
like machine learning.
First, staff at Stanford they were doing metadata.
Then at MIT, Winston did earlier work on his Arch program.
Also doing some forms of learning.
So when I had met with was Tom Mitchell in Tokyo in 1971,
we discussed the idea well, maybe
we could have a workshop on machine learning.
I don't mean to interrupt, but I think
we're running out of time.
I don't know if you--
You didn't tell me how much time I have.
Yeah it's about 15, 20 minutes.
It's already been 15 minutes?
It's close to 20.
I don't--
So maybe you can cut the beginning,
because what I was telling right now was actually what happened.
That we decided to organize this workshop with machine learning.
And then we reorganize in 1980, and that machine learning
workshop was the very first workshop in machine learning
study in the whole field.
So now after 23 years the whole field
went back to Carnegie Mellon, a few weeks ago,
and had 23rd workshop.
Actually, yeah 23rd International Conference
on Machine Learning, as it is called now.
So that first workshop, and then subsequent books
that Tom Mitchell and Jaime Carbonell and I produced,
Volume I, Volume II, Machine Learning Apprentice Approach
was the beginning of the whole field, which grew
into this very large field now.
Well thank you so much.
What an interesting journey.