-When you think about data-driven techniques, you think about language, and you think about logic and inference, how do we build AI systems that combine these things?

Because we love the incredible language abilities of large language models, but we need precise, reliable, accurate problem solving to solve tough enterprise problems, how do we do that?

And we have an answer to that.

So I think our vision, or EC, or Elemental Cognition's vision for how to architect AI systems, you know, ended up being right with a vengeance, as you see now what's happening in the AI space.

[ Clapboard snapping ] -David, just as a starter, just your name and who you are.

-I'm Dave Ferrucci, CEO and founder of Elemental Cognition, an AI company.

-So, let's just go back and start with the house where you grew up.

Describe the house.

-I grew up in the Bronx, at an area called Pelham Parkway.

And it was, you know, a small -- you know, a single-family home on a small plot of land in sort of a mostly Italian and Jewish neighborhood.

I think when a lot of people think of the Bronx, they think very urban, but this was a little bit more suburban, if you will.

-What were your parents like?

-My dad immigrated from Italy.

My mother was born here.

And they had kind of a rough life, especially because, you know, my mom had lost two young children.

And so struggling through that, you know, for married couples, that was really, really difficult.

Eventually, my parents got divorced, but they were still sort of very close and very supportive of each other and especially of me and my sister Diana.

And so that was kind of the family unit, you know, before the divorce.

But even after the divorce, it was kind of a very supportive family.

-What did your father do?

-So, my father worked in construction and he was very focused on you have to get educated.

You know, education is everything.

And it was like you don't want to do what we do.

You know, you want to become a doctor.

You want to become a lawyer.

And there was so much respect for those professions at the time.

Still is, but it was really the pinnacle, right, is to have someone working in -- you know, a parent working in construction and saying, like, "This isn't the life for you.

You want to go out there and become a doctor or a lawyer."

So, and in my case, the focus was very much on becoming a doctor.

-What was your mother's role in all this?

What was she like?

-So, my mother was -- -Is also incredibly willful.

Both my parents are incredibly independent-minded.

You know, nothing is taken for granted.

Everything's questioned.

There's always, like, this search for the truth, search for what's really going on.

And of course they had different perspectives.

And I played I think a big role, you know, in their lives, as well.

The role that I played was to get each to understand, to see the world through the other person's eyes... to get them each to understand each other.

And that was hard.

[ Chuckles ] Because I was always in battle.

They got breaks, but there was no break for me.

Because when I was with my mom, I was always representing my dad's perspective... and when I was with my dad representing my mom's perspective.

So... I was always empathizing.

-That's a big weight for a young guy... -Yeah.

-...to carry.

-Well, I never I never shied away from taking on challenges.

I felt compelled, really.

-As a kid in school, what kind of a kid were you?

Were you a studier?

-When I was a little kid, apparently, I was very rambunctious.

I mean, the story that my parents told me was that they bought me a Tonka truck... and because it couldn't be broken, 'cause the advertisement was "You can't break this."

And, again, I don't remember this, but my parents told me this story.

And my father came home that evening that, you know, they bought me a truck.

And I was standing in the driveway with the truck in two pieces and said, "I did it!

I broke it!"

[ Laughs ] I guess it was a challenge.

And so yeah, as a young kid, I was.

But I think as, you know, the burden of, you know, "You got to go to school, and you got to do well, and you got to become a doctor, and this is what --" You know?

And so a lot of it -- it was very much focused on schoolwork.

One summer -- this was actually in high school.

You know, you finish school.

So one of the things I like to do, I'd get out of school and it was summertime, is I'd, you know, blast Alice Cooper.

[ Laughs ] "School's Out for Summer.

School's out forever."

And, so, meanwhile, my dad's in his study, and he's going, "What are you doing?"

[ Laughs ] I said, "It's summer."

"So why aren't you doing schoolwork?"

I said, "Because we don't have school.

You don't have to do schoolwork."

"Yeah, but you should be studying and preparing for the next year."

Like, there was no pause, really, that was allowed.

And he sees this ad, local college.

Actually, it was Iona College in New Rochelle.

Was giving -- it was billed as an advanced math class.

It was actually a computer-programming class on a PDP-11, learning BASIC programming language.

And so he says, "You should do this.

So this way, this keeps your mind active.

You learn something new, and you go there."

Meanwhile, it's interesting, because he very much wanted me to become a medical doctor.

No one really knew what computer science was.

I mean, your average people didn't really know what computer science was, certainly not what artificial intelligence was.

And so he was like, "Go out there and just -- It's math, and you'll learn math, and you'll do whatever."

So I went to the class, and it changed the course of my whole life.

I was just blown away.

I was just blown -- Not because of the programming language or the class itself, but because what just struck me was that I can give this machine these instructions, and it would carry out those instructions tirelessly, forever and ever.

[ Chuckles ] And so now the issue for me became, "Well, what can I describe?

Can I describe how we think?

Because if I describe how we think, then I can get the machine to do thinking for me.

And wouldn't that be amazing?

Because, then, I would have this partner that would help me think and do work for me and allow me to be more creative and -- you know?

And, you know, and I thought to myself, because I spent so much time doing and worrying about school, knowledge was king.

Knowing stuff was so critical to doing anything.

And exploring ideas.

And if I can just capture that thinking... And I was obsessed, you know, with that.

And I didn't know anything about what it meant from a career perspective, or I didn't suddenly that day say, "Oh, I want to become a computer programmer."

I was just obsessed with that notion.

And I liked to program, 'cause it was like a series of puzzles and brain twisters, and how could I get the computer to do this, and how could I get the computer to do that, you know?

And so that kind of journey started even though I was off on this path to go to college, you know, as a major in biology.

The biology program had an Apple II computer.

They weren't doing much with it at the time.

And I ended up developing software for the biology department -- ecology software, statistical packages, graphing software, software for the physiology department -- physiology course, the ecology course.

I did analog-to-digital interfacing.

My friend had an Etch A Sketch, and I programmed an Etch A Sketch, and he was using it in drew -- [ Laughing ] It was like I just couldn't stop doing it, the desire to just understand how these machines work, how you can program them, and what the nature of describing algorithms was like.

You know, what were the limits of it?

What were the possibilities of it?

I still wasn't thinking about it from a career perspective.

I was on my path to be a medical doctor.

-How did you get from there to becoming part of IBM, joining IBM?

-It happened when I was in college.

I had worked on all this computer stuff for the school.

I loved it, but I was still on the path to become a doctor.

And I had to take the MCATs -- you know, the entrance exams for medical school.

And, you know, you take an MCAT preparation class and the instructor says, "Okay, please open your MCATs to page whatever and take the chemistry tests for the next 45 minutes," whatever.

So I opened it up, and I look at it, and I did fine in chemistry.

I had no problems with chemistry.

I didn't have any prejudice against chemistry.

But all of a sudden, it hit me, "I don't want to do this.

I don't want to be a medical doctor."

And all my premed friends were in the room, you know?

And I just closed the book, and I got up.

And they looked at me, and they go, "What are you doing?"

And I went to the instructor and said -- I didn't say, "Hey, I'm leaving the class," or whatever.

I said, "I don't want to be a doctor."

[ Laughing ] I said to the guy.

He looks at me, and I give him the booklet.

And he says, "Well, you know, you still have to pay the $500."

I said, "Yeah.

That's the least of my troubles right now."

Because I knew my trouble was I was gonna have to talk to my dad about that.

Ended up going to RPI for computer science.

It wasn't known for artificial Intelligence.

But at that point, I didn't have an undergraduate computer science degree.

I had, you know, great grades.

I did some of my own programming.

RPI gave me a shot, accepted me there.

And I started doing regular graduate studies in that.

And I was interested in artificial intelligence and doing things and projects related to that.

I wanted to do something that related to language and computation, so language and intelligence.

And there was nothing like that at the time.

But there was this class called -- I think it was Language Structures.

And it was actually taught in, like, the literature department.

It wasn't taught in the computer science department or in the engineering department.

And it was about, you know, the logical structure of language, the primitives that allow us to construct language from ideas, to organize ideas in the form of language, and what the fundamentals of that kind of were.

And I found it fascinating.

And I said, "I want to build a computer program."

Anytime I saw anything I liked, I thought, "Can I build a computer program that does this?"

And I was like, you know, I wanted to kind of build a computer program that does what was called semantic networks to represent knowledge in terms of semantic network connections between ideas and words, and can I get it to answer questions?

And this became my master's thesis.

It's called COSMOS.

And RPI had, like, an industry day or something.

And this group from IBM Research, from the T.J.

Watson Research Center, came up.

And they saw me giving a pitch on COSMOS.

And they had started an AI project at IBM.

And they said, "Would you like to come for an interview for a job?

And I went there, and the T.J.

Watson Research Center was just beautiful -- majestic, really -- beautiful, modern, yet kind of classic architecture mix.

As you enter, there was a desk, and it was all stone.

And I went there, and I did an interview, and I got offered a job.

And so even though I didn't finish my PhD, I was like, "This is just too good to be true."

And I started working there.

And it was an AI project.

It was in conjunction with the group at Carnegie Mellon University, so they were partnering with them.

I didn't have as big of a role as I would have liked.

I was only a master's student as opposed to a PhD, and they mostly just hired PhDs.

I wanted to get my PhD anyway, so I'm gonna go back and get my PhD.

Of course my father at this point was like, "Oh, my gosh.

You actually got a job in this stuff at this great company at IBM, and you're gonna quit?"

[ Laughs ] Anyway, I quit, and I went back to RPI, and I got my PhD.

While I was getting my PhD... I was looking for a really good project to exercise my thesis on and looking around for that inside of RPI and even outside of RPI.

And I actually get a call from somebody at IBM Research who I'd never met before, wasn't part of when I first went there... and said, "You know, you came up in a list of résumés and kind of what you're doing at RPI that we review.

Are you interested in this kind of project?"

And I said, "Sure," and I get a job at IBM Research.

-So you're back at IBM.

You're working on, my understanding, question-and-answering technology.

-Yeah.

-And there's this push to have a big kind of event.

-Yeah.

-Walk me through all of what -- -So I go back to IBM.

The IBM execs are thinking about what's the next grand challenge that IBM could take on.

And at around that time, Ken Jennings was winning on "Jeopardy!"

He had like 72-game winning streak.

And people started thinking, "Hey, wow.

Can we create a machine that is that good at taking these 'Jeopardy!'

questions," which were, you know, punny and tricky, you know, "and answer these questions as precisely and correctly?

And can it even know what it knows?"

'Cause, you know, do you buzz in?

You have to buzz in, you know, if you think you know.

You don't buzz if you don't think you know.

Otherwise you lose points.

So there are lots of interesting challenges.

And it was popular -- an opportunity to kind of be on television for IBM.

And so they shopped this around, you know, across... They shopped it around across IBM, especially IBM Research, for 2000, 2004, 2005.

And all the researchers were saying, "No.

This is impossible.

This is crazy.

You'll look ridiculous."

The vast input across the company was no.

When it got around to me -- For the first couple of years, it was shopped around, I was very busy doing something else.

I didn't really understand what the hope and expectation was.

And, then, when I had a little bit more time to focus on it, it came around again -- This was the end of 2006, And I said, like, "This is a perfect fit for me and what I'm trained for and what I built this team that does open-domain question answering for.

This is really a perfect fit.

It looks impossible, but let me think about it."

And so I think about it.

I did some personal experiments.

And I came back, and I convinced everybody that while very hard, there's a shot.

I think it is possible.

My managers at the time and my mentor, Art Sokolow, was very supportive of this.

We went to Paul Horn and pitched it, and Paul Horn was like, "Okay.

Let's do it.

Like, this is the kind of thing IBM does," which was fabulous.

You know, and I get my team committed to this.

I have some really challenging conversations with the team, because even some of some of the people on my technical team were like, "This is too hard.

We're going to embarrass ourselves."

And, you know, I had a call to action.

I was like, "You know, we're working in this space, natural-language question answering.

You know, we're chugging away at it.

We've done a number of government projects.

We knew were all the other universities stood on this on a much easier version of the problem.

They're only doing, like, on average like 35%, or consistently in that range accuracy.

To win at "Jeopardy!

", you had to do 75% and better, and you had to know what you knew.

You had to, you know, have an accurate confidence.

In other words, if I think I know this answer, in fact you do.

If you don't, you don't.

Right?

And the state-of-the-art systems were nowhere near good at this.

So there were some very serious challenges.

And, you know, I said to the researchers -- And I said, "Guys... let's say we don't take this project and you keep chugging along.

And, you know, let's say you publish, I don't know, three, four, or five papers a year on advancing this whole thing based on your years of experience doing this.

And if I come back to you four years from now, five years from now, will you know whether or not you can do the 'Jeopardy!'

problem after all those papers and accolades?"

And they looked at me, and they said, "We still wouldn't know."

I said, "Come on.

We're researchers.

Even if we fail, at least we'll learn why we failed, because we'll have enough investment to take a really honest crack at this thing."

And that convinced them.

And so, you know, they were on board.

We kicked this project off.

And, you know, the Watson thing kind of gets started.

There was still a lot of internal debate that went on.

You know lots of people at IBM were like, "This is crazy."

Some people would laugh.

I walked down the hallway.

They'd laugh at me.

They would say, "You're hilarious.

Why would you ever do this?

All you're gonna do is, like, risk your career."

You know?

For me, because I had been working in question answering for a long time, it was very clear what we were gonna do.

You know, the system was gonna get questions.

We were gonna decide whether or not to answer it and then answer it.

We wanted it to be about question answering.

We didn't want it to be about robotics or anthropomorphism.

So, some of the stipulations I had is I really don't want a human face.

I don't want a human hand involved.

You know, I didn't even want voice recognition.

I said voice recognition wasn't really up to snuff at the time, and it would just introduce error.

But, then, they wondered, you know, in actual competition, is it gonna be competitive?

Is it gonna be, like, unbelievably competitive where it was like no contest, or was it gonna be terrible?

And so there was this whole concern of whether it would be an interesting game one way or the other.

It was nerve-wracking, quite honestly.

I mean, so much was at stake.

It was a very special day.

They closed down the entire site.

All the IBM executives were invited.

My team was invited.

Senior people at the "Jeopardy!"

and Sony Pictures was invited.

You know, my biggest fear was that we had worked so hard, and we knew we built a system that can win against champions.

It wouldn't win every game, but it could win against champions.

And we proved that out in all these practice games and all these stats that we did.

But the game that we're gonna aired, there was there was a chance -- you know, 25% to 30% chance we would lose it.

And if we lost it, people would think, no, we didn't achieve the goal.

But we basically did achieve the goal, right?

So, we didn't achieve the goal to win every single game, but we can beat the best.

So it was kind of interesting, you know, how this whole thing was gonna be perceived.

And, then, we went on to play the game.

We didn't know we would win, because "Jeopardy!"

numbers can be very high, but it could flip on a moment because of a Double Jeopardy!

or a Final Jeopardy!

and the whole game can flip.

So it wasn't actually until, like, the last, I don't know, 10 questions on the final board where there was a Daily Double, and Watson and Ken Jennings were competing for it, and Watson got it and then got the answer right.

And, then, that clinched the game, and then we knew that we would win.

-Now we come to Watson.

We're looking for Bram Stoker.

And we find... [ Cheers and applause ] And the wager?

Hello.

$17,973.

♪♪ 0-And, then, you know, it started to be hyped.

Watson was hyped a lot.

It was being talked about as something more than it was.

It was the most advanced open-domain question-answering system at the time.

It could have helped a lot in improving search and in knowledge management and things like that -- a lot of what LLMs, for example, can do today, But that was the beginning of that.

It wasn't an LLM per se, but it was the type of technology that can get more value out of unstructured information, out of language.

There were lots of opportunities for that.

-So David, you accomplished the amazing thing you built.

Watson wins on "Jeopardy!"

And then you become an IBM Fellow.

-So, I become an IBM Fellow during the centennial.

It was an amazing time, incredible.

You know, I was full of pride.

We got so much publicity.

There was the PBS special, "The Smartest Machine in the World --" "Smartest Machine on Earth."

Steve Baker wrote a book, "Final Jeopardy."

So there were so many amazing things.

I was traveling all over the place around the world, giving pitches about Watson.

During the IBM centennial, we learned about what an amazing company IBM was, what an amazing company it is.

The amount of influence that it had on the industry was remarkable, and to be part of the whole IBM centennial.

So it couldn't have been more beautiful and rewarding and fulfilling for me, and I felt such a connection, you know, to the company.

And, you know, I remember the night I got home from the win, because my wife, Elizabeth, my kids weren't allowed to go, right?

They were in the group.

And this kind of will start to answer your question.

So, I got home that night, and, you know, my wife Elizabeth is like, "So it's over.

I'm waiting for the next thing now."

[ Laughs ] "What's next?"

So, I don't revel a lot in my successes.

I love to reflect on my journey, but I don't revel.

I'm ready for the next challenge very quickly.

I just wanted to pursue this independent idea I had about how AI should... It's funny because what happened with large language models, but it's like AI, we should create intelligences that separate language, logic, and data.

We kind of have to understand the difference between language, logic, and knowledge.

These things get conflated in LLMs.

But when we architect AI systems -- And, you know, this is before large language models, but still, I've been working in natural-language processing for a very long time.

Let's learn how to communicate with language systems.

Let's learn how to formalize knowledge for performing exacting, precise inferencing that could solve problems with general-purpose problem solvers, which we know what to do.

Let's be able to trace knowledge back to its original sources, the facts and the underlying data.

'Cause when we think about human cognition and when we do things well, not just when we do things with language and we introduce our biases, but when we do things well, we follow a much more structured approach.

We look at information.

We look at what people say.

We look at where the information's coming from.

We assess it.

We weigh it.

We extract the knowledge we do.

We connect it to the data.

We do scientific analysis, and then we do mathematically oriented reasoning on that to make sure that our conclusions are right.

This is what we should be doing.

This is the sort of intelligence that complements human cognition, which could be full of biases.

This is why we follow methods like this, because we know that our language and our cognitive processes can be biased and distorted.

And, you know, we talk about hallucinations.

We really need to have a more structured approach.

We need to use all those things.

And so the idea was to kind of bring these things together.

And so we started Elemental Cognition working on that premise and developed an AI architecture capable of using language for what it's good for, but using mathematical, logical reasoning and inference for what it's good for -- the precision, the accuracy, the traceability -- and then using, again, large language models to smooth the communication with humans and to help extract the knowledge from all this content.

And so it's a more holistic architecture and it's now hot.

I think we will see AI continue to advance.

I think we will not suffer a winter.

We will see composite architectures that use LLMs and lots of other techniques to solve problems we definitely could not solve before.

So I think, you know, this is really significant and more than evolutionary, I think this is revolutionary, what you're seeing now.

This is the age of artificial intelligence is really beginning now.

There are fears I have.

We as a society have to focus on what we believe is right for us as people.

How do we formulate, commit to, adhere to the values that we care about -- excellence and integrity, honesty, commitment, compassion, right?

And AI is gonna be a tool, so there will be things that will be, you know, easier to do as a result of AI.

You know, that's fine.

Learn how to use it, but use it in ways that help humanity, don't hurt humanity.

And that's basically, you know, the way I position AI.

When people say, "Well, what if it does, you know, something that I already know how to do?"

I said, "There's always places to go.

There's always better ideas.

There's always thinking about what matters to humanity.

Learn how to use it to help humanity.

It's really that simple."

-Did you have heroes that you really looked up to -- people that really were a big influence on you?

-Picture this, right?

This small house in the Bronx, a little patch of land.

I think it was 100' x 80' or something like that.

And I'm in my basement, no windows, and I have my Apple II computer, and I'm just programming all the time.

And I'm imagining my future, and I would flip through the magazines we had in the house.

And the Time magazine -- this is now 1980 or 1981.

And Time magazine is an IBM ad that talked about IBM Fellows, and it was about freedom.

It starts with, you know, the headline... "...and we don't worry about where they put their feet.

They are IBM fellows.

They earned the title by having ideas that made a difference.

[ Voice breaking ] Their job is to have more ideas like that, but under a very special condition.

It's called freedom.

For a time of at least five years, an IBM Fellow is free to pursue any advanced project of value to IBM, even if chances for success may seem remote.

As a result, some of the great innovations of our time have come from IBM Fellows.

We may not always understand what they're doing, much less how they do it, but we know this -- the best way to inspire an IBM Fellow is to get out of the way.

Freedom to create.

The time to pursue... a difficult idea.

And, you know, if we're gonna struggle or take our time to do anything... at least the way I perceived it is why do the ordinary?

Why do the perfunctory?

If you're going to take time to get up in the morning... do the extraordinary.

-Why does that have such a powerful impact on you?

You're being, you know, emotional about talking about it.

What is it about that freedom that's just so powerful to you?

-The persistence to work against the odds... in my life... produced extraordinary value.

You know, "Why are you bothering trying to get your parents to understand each other?

You'll never get there."

"The Watson stuff -- Why are you doing this?

It's impossible.

You're gonna ruin your career --" you know, all these things about it's gonna take too long.

It's gonna be too hard.

It's not worth it.

There's too much risk.

So I think I valued independence.

I valued freedom, I valued the internal strength to persist.

[ Mid-tempo music plays ] ♪♪ ♪♪