The following is a conversation with Doug Lennett, creator of Psyche, a system that for close to 40
years and still today has sought to solve the core problem of artificial intelligence, the
acquisition of commonsense knowledge and the use of that knowledge to think, to reason, and to
understand the world. To support this podcast, please check out our sponsors in the description.
As a side note, let me say that in the excitement of the modern era of machine learning, it is
easy to forget just how little we understand exactly how to build the kind of intelligence
that matches the power of the human mind. To me, many of the core ideas behind Psyche,
in some form, in actuality or in spirit, will likely be part of the AI system that achieves
general superintelligence. But perhaps more importantly, solving this problem of commonsense
knowledge will help us humans understand our own minds, the nature of truth, and finally,
how to be more rational and more kind to each other. This is the Lex Friedman podcast,
and here is my conversation with Doug Lennett. Psyche is a project launched by you in 1984
and still is active today, whose goal is to assemble a knowledge base that spans the basic
concepts and rules about how the world works. In other words, it hopes to capture commonsense
knowledge, which is a lot harder than it sounds. Can you elaborate on this mission and maybe perhaps
speak to the various sub-goals within this mission? When I was a faculty member in the
computer science department at Stanford, my colleagues and I did research in all sorts of
artificial intelligence programs, so natural language understanding programs, robots,
expert systems, and so on. And we kept hitting the very same brick wall. Our systems would have
impressive early successes. And so, if your only goal was academic, namely to get enough material
to write a journal article, that might actually suffice. But if you're really trying to get AI,
then you have to somehow get past the brick wall. And the brick wall was the programs didn't have
what we would call commonsense. They didn't have general world knowledge. They didn't really
understand what they were doing, what they were saying, what they were being asked. And so,
very much like a clever dog performing tricks, we could get them to do tricks, but they never
really understood what they were doing, sort of like when you get a dog to fetch your morning
newspaper. The dog might do that successfully, but the dog has no idea what a newspaper is or
what it says or anything like that. What does it mean to understand something? Can you maybe
elaborate on that a little bit? Is it, is understanding an action of like combining little
things together like through inference, or is understanding the wisdom you gain over time
that forms a knowledge? I think of understanding more like a, think of it more like the ground you
stand on, which could be very shaky, could be very unsafe, but most of the time is not because
underneath it is more ground and eventually, you know, rock and other things, but layer after
layer after layer, that solid foundation is there. And you rarely need to think about it. You rarely
need to count on it, but occasionally you do. And I've never used this analogy before, so bear
with me. But I think the same thing is true in terms of getting computers to understand things,
which is you ask a computer a question, for instance, Alexa or some robot or something,
and maybe it gets the right answer. But if you were asking that of a human, you could also say
things like, why? Or how might you be wrong about this? Or something like that? And the person,
you know, would answer you. And, you know, it might be a little annoying if you have a small
child and they keep asking why questions in series, eventually you get to the point where you throw
up your hands and say, I don't know, it's just the way the world is. But for many layers, you
actually have that layered solid foundation of support so that when you need it, you can count
on it. And when do you need it? Well, when things are unexpected, when you come up against a situation
which is novel, for instance, when you're driving, it may be fine to have a small program, a small
set of rules that cover, you know, 99% of the cases, but that 1% of the time when something
strange happens, you really need to draw on common sense. For instance, my wife and I were driving
recently, and there was a trash truck in front of us. And I guess they had packed it too full and
the back exploded. And trash bags went everywhere. And we had to make a split second decision,
are we going to slam on our brakes? Are we going to swerve into another lane?
Are we going to just run it over? Because there are cars all around us. And, you know, in front of
us was a large trash bag. And we know what we throw away in trash bags, probably not a safe thing to
run over. Over on the left was a bunch of fast food restaurant trash bags. And it's like, oh,
well, those things are just like styrofoam and leftover food. We'll run over that. And so that
was a safe thing for us to do. Now, that's the kind of thing that's going to happen maybe once in your
life. But the point is that there's almost no telling what little bits of knowledge about the
world you might actually need in some situations which were unforeseen. But see, when you sit on
that mountain or that ground that goes deep of knowledge in order to make a split second decision
about fast food trash or random trash from the back of a trash truck, you need to be able to
leverage that ground you stand on in some way. It's not merely, you know, it's not enough to just
have a lot of ground to stand on. It's your ability to leverage it, to utilize in a split,
like integrate it all together to make that split second decision. And I suppose understanding
isn't just having common sense knowledge to access. It's the act of accessing, accessing it
somehow, like correctly filtering out the parts of the knowledge that are not useful,
selecting only the useful parts, and effectively making conclusive decisions.
So let's tease apart two different tasks, really, both of which are incredibly important and even
necessary. If you're going to have this in a useful, useful, usable fashion, as opposed to say,
like library books sitting on a shelf and so on, where the knowledge might be there. But, you know,
if a fire comes, the books are going to burn because they don't know what's in them, and they're
just going to sit there while they burn. So there are two aspects of using the knowledge. One is
kind of a theoretical, how is it possible at all? And then the second aspect of what you said is,
how can you do it quickly enough? So how can you do it at all is something that philosophers have
grappled with. And fortunately, philosophers 100 years ago, and even earlier, developed a kind of
formal language, like English. It's called predicate logic or first order logic or something
like predicate calculus and so on. So there's a way of representing things in this formal language,
which enables a mechanical procedure to sort of grind through and algorithmically produce
all of the same logical entailments, all the same logical conclusions that you or I would from that
same set of pieces of information that are represented that way. So that sort of raises
a couple of questions. One is, how do you get all this information from, say, observations and English
and so on into this logical form? And secondly, how can you then efficiently run these algorithms to
actually get the information you need in the case I mentioned in a tenth of a second, rather than,
say, in 10 hours or 10,000 years of computation? And those are both really important questions.
And like a corollary addition to the first one is, how many such things do you need to gather
for it to be useful in certain contexts? So in order, you mentioned philosophers,
in order to capture this world and represent it in a logical way and with a formal logic, how many
statements are required? Is it five? Is it 10? Is it 10 trillion? That's, as far as I understand,
is probably still an open question. It may forever be an open question to say definitively about,
to describe the universe perfectly. How many facts do you need? I guess I'm going to disappoint you
by giving you an actual answer to your question. Okay. Well, no, this sounds exciting. Yes. Okay.
So now we have like three things to talk about. I'll keep adding more. Although it's okay. The
first and the third are related. Yes. So let's leave the efficiency question aside for now.
So how does all this information get represented in logical form so that these algorithms,
resolution, theorem proving, and other algorithms can actually grind through all the logical
consequences of what you said? And that ties into your question about, well, how many of these things
do you need? Because if the answer is small enough, then by hand, you could write them out one at a
time. So in the early 1984, I held a meeting at Stanford, where I was a faculty member there,
where we assembled about half a dozen of the smartest people I know. People like
Alan Newell, and Marvin Minsky, and Alan Kay, and a few others. Was Feynman there by chance?
Because he commented about your system you risked at the time. No, he wasn't part of this meeting.
That's a heck of a meeting anyway. I think Ed Feigenbaum was there. I think
Josh Lederberg was there. So we have all these different smart people, and we came together
to address the question that you raised, which is, if it's important to represent
common sense knowledge and world knowledge in order for AIs to not be brittle, in order for AIs
not to just have the veneer of intelligence. Well, how many pieces of common sense, how many,
if then, rules, for instance, would we have to actually write in order to essentially cover
what people expect perfect strangers to already know about the world? I expected there would be
an enormous divergence of opinion and computation, but amazingly, everyone got an answer which was
around a million. One person got the answer by saying, well, look, you can only burn into human
long-term memory a certain number of things per unit time, like maybe one every 30 seconds or
something. Other than that, it's just short-term memory, and it flows away like water and so on.
So by the time you're, say, 10 years old or so, how many things could you possibly have burned
into your long-term memory? And it's like about a million. Another person went in a completely
different direction and said, well, if you look at the number of words in a dictionary, not a whole
dictionary, but for someone to essentially be considered to be fluent in a language, how many
words would they need to know and then about how many things about each word would you have to tell
it? And so they got to a million that way. Another person said, well, let's actually look at one
single short one-volume desk encyclopedia article. And so we'll look at what was like a four-paragraph
article or something. I think about grebes. Grebes are a type of waterfowl. And if we were going
to sit there and represent every single thing that was there, how many assertions or rules
or statements would we have to write in this logical language and so on? And then multiply
that by all of the number of articles that there were and so on. So all of these estimates came
out with a million. And so if you do the math, it turns out that like, oh, well, then maybe in
something like 100 person years in one or two person centuries, we could actually get this
written down by hand. And a marvelous coincidence, opportunity existed right at that point in time,
the early 1980s. There was something called the Japanese fifth generation computing effort. Japan
had threatened to do in computing and AI and hardware, but they had just finished doing in
consumer electronics and the automotive industry, namely resting control away from the United States
and more generally away from the West. And so America was scared. And Congress did something,
that's how you know it was a long time ago because Congress did something. Congress passed
something called the National Cooperative Research Act, N-C-R-A. And what it said was, hey, all you
big American companies, that's also how you know it was a long time ago because they were American
companies rather than multinational companies. Hey, all you big American companies, normally it
would be an antitrust violation if you colluded on R&D. But we promise for the next 10 years,
we won't prosecute any of you if you do that to help combat this threat. And so overnight,
the first two consortia, research consortia in America sprang up, both of them coincidentally
in Austin, Texas, one called SEMitech focusing on hardware chips and so on, and then one called
MCC, the Microelectronics and Computer Technology Corporation, focusing on more on software, on
databases and AI and natural language understanding and things like that. And I got the opportunity
thanks to my friend Woody Bledsoe, who was one of the people who founded that, to come and be its
principal scientist. And he said, you know, and he sent Admiral Bob Inman, who was the person
running MCC, came and talked to me and said, look professor, you know, you're talking about doing
this project, it's going to involve person centuries of effort. You've only got a handful
of graduate students, you do the math, it's going to take you like, you know, longer than the rest
of your life to finish this project. But if you move to the wilds of Austin, Texas, we'll put 10
times as many people on it and, you know, you'll be done in a few years. And so that was pretty
exciting. And so I did that. I took my leave from Stanford, I came to Austin, I worked for MCC.
And the good news and bad news, the bad news is that all of us were off by an order of magnitude.
That it turns out what you need are tens of millions of these pieces of knowledge about
every day, sort of like if you have a coffee cup with stuff in it, and you turn it upside down,
the stuff in it's going to fall out. So you need tens of millions of pieces of knowledge like that,
even if you take trouble to make each one as general as it possibly could be.
But the good news was that thanks to initially the fifth generation effort and then later
US government agency funding and so on, we were able to get enough funding, not for a couple
person centuries of time, but for a couple person millennia of time, which is what we've spent
since 1984, getting psych to contain the tens of millions of rules that it needs in order to really
capture and span sort of not all of human knowledge, but the things that you assume other people know,
the things you count on other people knowing. And so by now we've done that. And the good news
is since you've waited 38 years just about to talk to me, we're about at the end of that process.
So most of what we're doing now is not putting in even what you would consider common sense,
but more putting in domain specific applications, specific knowledge about healthcare in a certain
hospital or about oil pipes getting clogged up or whatever the applications happen to be.
So we've almost come full circle and we're doing things very much like the expert systems
of the 1970s and the 1980s, except instead of resting on nothing and being brittle,
they're now resting on this massive pyramid, if you will, this massive lattice of common sense
so that when things go wrong, when something unexpected happens, they can fall back on more
and more and more general principles, eventually bottoming out in things like, for instance,
if we have a problem with the microphone, one of the things you'll do is unplug it,
plug it in again and hope for the best, right? Because that's one of the general pieces of
knowledge you have in dealing with electronic equipment or software systems or things like
that. Is there a basic principle like that? Is it possible to encode something that generally
captures this idea of turn it off and turn it back on and see if it fixes? Oh, absolutely.
That's one of the things that's like news. That's actually one of the fundamental laws
of nature, I believe. I wouldn't call it a law. It seems to work every time,
so it sure looks like a law. I don't know. That basically covered the resources needed,
and then we had to devise a method to actually figure out, well, what are the tens of millions of
things that we need to tell the system? For that, we've found a few techniques which worked really
well. One is to take any piece of text almost, it could be an advertisement, it could be a transcript,
it could be a novel, it could be an article. Don't pay attention to the actual type that's there,
the black space on the white page. Pay attention to the complement of that, the white space, if you
will. What did the writer of this sentence assume that the reader already knew about the world?
For instance, if they used a pronoun, why did they think that you would be able to
understand what the intended referent of that pronoun was? If they used an ambiguous word,
how did they think that you would be able to figure out what they meant by that word?
The other thing we look at is the gap between one sentence and the next one. What are all the
things that the writer expected you to fill in and infer occurred between the end of one sentence
and the beginning of the other? If the sentence says, Fred Smith, Rob, the Third National Bank
period, he was sentenced to 20 years in prison period. Between the first sentence and the second,
you're expected to infer things like Fred got caught, Fred got arrested, Fred went to jail,
Fred had a trial, Fred was found guilty, and so on. If my next sentence starts out with something
like the judge, then you assume it's the judge at his trial. If my next sentence starts out something
like the arresting officer, you assume that it was the police officer who arrested him after
he committed the crime and so on. Those are two techniques for getting that knowledge. The other
thing we sometimes look at is fake news or humorous onion headlines or headlines in the weekly
world news, if you know what that is, or the national inquire, where it's like, oh, we don't
believe this, then we introspect on why don't we believe it? There are things like B-17 lands on
the moon. It's like, what do we know about the world that causes us to believe that that's just
silly or something like that? Another thing we look for are contradictions, things which can't
both be true. We say to it, what is it that we know that causes us to know that both of these
can't be true at the same time? For instance, in one of the weekly world news editions, in one
article it talked about how Elvis was cited, even though he was getting on in years and so on,
and another article in the same one talked about people seeing Elvis's ghost. It's like, why do
we believe that at least one of these articles must be wrong and so on? We have a series of
techniques like that that enable our people, and by now we have about 50 people working full time
on this and have for decades. We've put in the thousands of person years of effort. We've built
up these tens of millions of rules. We constantly police the system to make sure that we're saying
things as generally as we possibly can. You don't want to say things like, no mouse is also a moose
because if you said things like that, then you'd have to add another one or two or three zeros
onto the number of assertions you'd actually have to have. At some point, we generalize things
more and more, and we get to a point where we say, oh yeah, for any two biological taxons,
if we don't know explicitly that one is a generalization of another, then almost certainly
they're disjoint. A member of one is not going to be a member of the other and so on.
And the same thing with the Elvis and the ghost. It has nothing to do with Elvis. It's more about
human nature and the mortality and that kind of stuff.
Well, in general, things are not both alive and dead at the same time.
Unless special cats in theoretical physics examples.
Well, that raises a couple important points.
Well, that's the onion headline situation type of thing. Okay, sorry.
But no, no. So what you bring up is this really important point of like, well, how do you handle
exceptions and inconsistencies and so on. And one of the hardest lessons for us to learn,
it took us about five years to really grit our teeth and learn to love it,
is we had to give up global consistency. So the knowledge base can no longer be
consistent. So this is a kind of scary thought. I grew up watching Star Trek,
and anytime a computer was inconsistent, it would either freeze up or explode or take over
the world or something bad would happen. Or if you come from a mathematics background,
once you can prove false, you can prove anything. So that's not good. And so on.
So that's why the old knowledge based systems were all very, very consistent.
But the trouble is that, by and large, our models of the world, the way we talk about
the world and so on, they're all sorts of inconsistencies that creep in here and there
that will sort of kill some attempt to build some enormous globally consistent knowledge base.
And so what we had to move to was a system of local consistency. So a good analogy is,
you know that the surface of the earth is more or less spherical globally. But you live your life
every day as though the surface of the earth were flat. When you're talking to someone in
Australia, you don't think of them as being oriented upside down to you. When you're
planning a trip, you know, even if it's 1000 miles away, you may think a little bit about
time zones, but you rarely think about the curvature of the earth and so on. And for most
purposes, you can live your whole life without really worrying about that because the earth is
locally flat. In much the same way, the psych knowledge base is divided up into almost like
tectonic plates, which are individual contexts. And each context is more or less consistent.
But there can be small inconsistencies at the boundary between one context and the next one
and so on. And so by the time you move, say 20 contexts over, there could be glaring inconsistencies.
So eventually you get from the normal modern real world context that we're in right now
to something, you know, like Road Runner cartoon context where physics is very different and in
fact, life and death are very different because no matter how many times he's killed, you know,
the coyote comes back in the next scene and so on. So that was a hard lesson to learn and we had
to make sure that our representation language, the way that we actually encode the knowledge and
represent it, was expressive enough that we could talk about things being true in one context
and false in another, things that are true at one time and false in another, things that are
true, let's say in one region, like one country, but false in another, things that are true in
one person's belief system, but false in another person's belief system, things that are true at
one level of abstraction and false at another. For instance, at one level of abstraction,
you think of this table as a solid object, but at, you know, down at the atomic level,
it's mostly empty space and so on. So then that's fascinating, but it puts a lot of pressure on
context to do a lot of work. So you say tectonic plates. Is it possible to formulate contexts
that are general and big that do this kind of capture of knowledge bases? Or do you then get
turtles on top of turtles again, where there's just a huge number of contexts?
So it's good you asked that question because you're pointed in the right direction, which is
you want contexts to be first-class objects in your system's knowledge base, in particular,
in psych's knowledge base. And by first-class object, I mean that we should be able to have
psych think about and talk about and reason about one context or another context the same way it
reasons about coffee cups and tables and people and fishing and so on. And so contexts are just
terms in its language, just like the ones I mentioned. And so psych can reason about context.
Context can arrange hierarchically and so on. And so you can say things about, let's say,
things that are true in the modern era, things that are true in a particular year would then be a
subcontext of the things that are true in a broad, let's say, a century or a millennium or
a millennium or something like that. Things that are true in Austin, Texas are generally going to
be a specialization of things that are true in Texas, which is going to be a specialization of
things that are true in the United States and so on. And so you don't have to say things over and
over again at all these levels. You just say things at the most general level that it applies to,
and you only have to say it once, and then it essentially inherits to all these more specific
contexts.
To ask a slightly technical question, is this inheritance a tree or a graph?
Oh, you definitely have to think of it as a graph. So we could talk about, for instance,
why the Japanese fifth generation computing effort failed. There were about half a dozen
different reasons. One of the reasons they failed was because they tried to represent
knowledge as a tree rather than as a graph. And so each node in their representation could only
have one parent node. So if you had a table that was a wooden object, a black object, a flat object,
and so on, you had to choose one, and that's the only parent it could have. When, of course,
depending on what it is you need to reason about it, sometimes it's important to know that it's
made out of wood, like if we're talking about a fire. Sometimes it's important to know that it's
flat if we're talking about resting something on it and so on. So one of the problems was that
they wanted a kind of Dewey decimal numbering system for all of their concepts, which meant that
each node could only have at most 10 children, and each node could only have one parent.
And while that does enable the Dewey decimal type numbering of concepts, labeling of concepts,
it prevents you from representing all the things you need to about objects in our world. And that
was one of the things which they never were able to overcome. And I think that was one of the main
reasons that that project failed. So we'll return to some of the doors you've opened. But if we can
go back to that room in 1984 around there with Marvin Minsky and Stafford. Yes. By the way,
I should mention that Marvin wouldn't do his estimate until someone brought him an envelope
so that he could literally do a back of the envelope calculation to come up with his number.
Well, because I feel like the conversation in that room is an important one. Sometimes science
is done in this way. A few people get together and plant the seed of ideas, and they reverberate
throughout history. And some kind of dissipate and disappear and some Drake equation. And
seems like a meaningless equation, somewhat meaningless. But I think it drives and motivates
a lot of scientists. And when the aliens finally show up, that equation will get even more valuable
because then we'll be able to ask. In the long arc of history, the Drake equation will prove to be
quite useful, I think. And in that same way, a conversation of just how many facts are required
to capture the basic common sense knowledge of the world. That's a fascinating question.
I want to distinguish between what you think of as facts and the kind of things that we represent.
So we map to and essentially make sure that Psych has the ability to, as it were, read and access
the kind of facts you might find, say, in Wiki data or stated in a Wikipedia article or something
like that. So what we're representing, the things that we need a small number of tens of millions
of, are more like rules of thumb, rules of good guessing, things which are usually true and which
help you to make sense of the facts that are sitting off in some database or some other
more static storage. So they're almost like platonic forms. So when you read stuff on Wikipedia,
that's going to be like projections of those ideas. You read an article about the fact that
Elvis died. That's a projection of the idea that humans are mortal. And very few Wikipedia articles
will write, humans are mortal. Exactly. That's what I meant about ferreting out the
unstated things in text. What are all the things that we're assumed? And so those are things like
if you have a problem with something, turning it off and on often fixes it for reasons we don't
really understand and we're not happy about or people can't be both alive and dead at the same
time and or water flows downhill. If you search online for water flowing uphill and water flowing
downhill, you'll find more references for water flowing uphill because it's used as
a kind of a metaphorical reference for some unlikely thing because of course,
everyone already knows that water flows downhill. So why would anyone bother saying that?
Do you have a word you prefer? Because we said faxes in the right word. Is there a word like
concepts? I would say assertions. Assertions or rules because I'm not talking about rigid rules,
but rules of thumb. But assertions is a nice one that covers all of these things.
Yeah. As a programmer to me, assert has a very dogmatic authoritarian feel to them.
Oh, I'm sorry.
I'm so sorry. Okay. But assertions works. Okay. So if we go back to that room with Marvin Minsky
with you, all these seminal figures, thinking about this very philosophical but also engineering
question, we can also go back a couple of decades before then and thinking about artificial
intelligence broadly when people were thinking about how do you create super intelligent systems,
general intelligence. And I think people's intuition was off at the time. And I mean,
this continues to be the case that we're grappling with these exceptionally difficult
ideas. It's very difficult to truly understand ourselves when we're thinking about the human
mind to introspect how difficult it is to engineer intelligence, to solve intelligence.
We're not very good at estimating that. And you are somebody who has really stayed with
this question for decades. What's your sense from 1984 to today? Have you gotten a stronger
sense of just how much knowledge is required? So you've kind of said with some level of
certainty that it's still on the order of magnitude of tens of millions.
Right. For the first several years, I would have said that it was on the order of
one or two million. Yeah.
And so it took us about five or six years to realize that we were off by a factor of 10.
But I guess what I'm asking, Marvin Misk is very confident in the 60s when you're saying-
Yes. Right. What's your sense? If you,
200 years from now, you're not going to be any longer in this,
in particular biological body, but your brain will still be in the digital form.
And you'll be looking back, would you think you were smart today? Like your intuition
was right, or do you think you may be really off? So I think I'm right enough. And let me
explain what I mean by that, which is sometimes like if you have an old-fashioned pump,
you have to prime the pump and then eventually it starts. So I think I'm right enough in the sense
that- To prime the pump.
What we've built, even if it isn't, so to speak, everything you need, it's primed the
knowledge pump enough that psych can now itself help to learn more and more automatically on its own
by reading things and understanding and occasionally asking questions like a
student would or something and by doing experiments and discovering things on its own and so on.
So through a combination of psych-powered discovery and psych-powered reading,
it will be able to bootstrap itself. Maybe it's the final 2%, maybe it's the final 99%.
So even if I'm wrong, all I really need to build is a system which has primed the pump enough
that it can begin that cascade upward, that self-reinforcing sort of quadratically or maybe
even exponentially increasing path upward that we get from, for instance, talking with each other.
That's why humans today know so much more than humans 100,000 years ago. We're not really that
much smarter than people were 100,000 years ago. But there's so much more knowledge and we have
language and we can communicate, we can check things on Google and so on. So effectively we
have this enormous power at our fingertips and there's almost no limit to how much you could
learn if you wanted to because you've already gotten to a certain level of understanding of the
world that enables you to read all these articles and understand them, that enables you to go out
and if necessary do experiments or that's slower as a way of gathering data and so on.
And I think this is really an important point which is if we have artificial intelligence, real
general artificial intelligence, human level artificial intelligence, then people will become
smarter. It's not so much that it'll be us versus the AIs, it's more like us and the AIs together
will be able to do things that require more creativity, that would take too long right now,
but we'll be able to do lots of things in parallel. We'll be able to misunderstand each other less.
There's all sorts of value that effectively for an individual would mean that individual will,
for all intents and purposes, be smarter. And that means that humanity as a species
will be smarter. And when was the last time that any invention qualitatively made a huge
difference in human intelligence? You have to go back a long ways. It wasn't like the internet or
the computer or mathematics or something. It was all the way back to the development of language.
We sort of look back on pre-linguistic cavemen as, well, they weren't really intelligent,
were they? They weren't really human, were they? And I think that, as you said, 50, 100, 200 years
from now, people will look back on people today right before the advent of these sort of lifelong
general AI uses and say, those poor people, they weren't really human, were they?
Exactly. So you said a lot of really interesting things. By the way, I would maybe try to argue
that the internet is on the order of the kind of big leap in improvement that the invention
of language was. Well, certainly a big leap in one direction. We're not sure whether it's upward
or downward. Well, I mean, very specific parts of the internet, which is access to information,
like a website like Wikipedia, like ability for human beings from across the world to access
information very quickly. So I could take either side of this argument, and since you just took one
side, I'll give you the other side, which is that almost nothing has done more harm
than something like the internet and access to that information in two ways. One is,
it's made people more globally ignorant in the same way that calculators made us more or less
enumerate. So when I was growing up, we had to use slide rules. We had to be able to estimate
and so on. Today, people don't really understand numbers. They don't really understand math.
They don't really estimate very well at all and so on. They don't really understand the
difference between trillions and billions and millions and so on very well because calculators
do that all for us. And thanks to things like the internet and search engines, that same kind
of juvenileism is reinforced in making people essentially be able to live their whole lives,
not just without being able to do arithmetic and estimate, but now without actually having
to really know almost anything because anytime they need to know something, they'll just go and
look it up. Right. And I can tell you could play both sides of this. It is a double-edged sword.
You can of course say the same thing about language. Probably people when they invented
language, they would criticize. It used to be we would just, if we're angry, we would just kill
a person and if we're in love, we would just have sex with them and now everybody's writing poetry
and bullshit. You should just be direct. You should have physical contact, enough of this words
and books and you're not actually experiencing. If you read a book, you're not experiencing the
thing. This is nonsense. That's right. If you read a book about how to make butter, that's not the
same as if you had to learn it and do it yourself and so on. So let's just say that something is
gained, but something is lost every time you have these sorts of dependencies on technology.
And overall, I think that having smarter individuals and having smarter AI augmented
human species will be one of the few ways that we'll actually be able to overcome
some of the global problems we have involving poverty and starvation and global warming and
overcrowding. All the other problems that are besetting the planet, we really need to be smarter.
And there are really only two routes to being smarter. One is through biochemistry and genetics.
Genetic engineering. The other route is through having general AIs that augment our intelligence
and hopefully one of those two ways of paths to salvation will come through before it's too late.
Yeah, so I agree with you. And obviously as an engineer, I have a better sense and an optimism
about the technology side of things because you can control things there more. Biology is just
such a giant mess. We're living through a pandemic now. There's so many ways that nature can just be
just destructive and destructive in a way where it doesn't even notice you. It's not like a battle
of humans versus virus. It's just like, huh, okay, and then you can just wipe out an entire
species. The other problem with the internet is that it has enabled us to surround ourselves
with an echo chamber, with a bubble of like-minded people, which means that you can have
truly bizarre theories, conspiracy theories, fake news, and so on, promulgate and surround
yourself with people who essentially reinforce what you want to believe or what you already
believe about the world. And in the old days, that was much harder to do when you had, say,
only three TV networks. Or even before, when you had no TV networks and you had to actually like
look at the world and make your own recent decisions. I like the push and pull of our dance
that we're doing because then I'll just say in the old world, having come from the Soviet Union,
because you had won a couple of networks, then propaganda could be much more effective and
then the government can overpower its people by telling you the truth and then starving millions
and torturing millions and putting millions into camps and starting wars with the propaganda
machine, allowing you to believe that you're actually doing good in the world. With the internet,
because of all the quote-unquote conspiracy theories, some of them are actually challenging
the power centers, the very kind of power centers that a century ago would have led to
the death of millions. So there's, again, this double-edged sword. And I very much agree with
you on the AI side. It's often an intuition that people have that somehow AI will be used to
maybe overpower people by certain select groups. And to me, it's not at all obvious that that's
the likely scenario. To me, the likely scenario, especially just having observed the trajectory
of technology, is it'll be used to empower people. It'll be used to extend the capabilities of
individuals across the world. Because there's a lot of money to be made that way.
Improving people's lives, you can make a lot of money.
I agree. I think that the main thing that AI prostheses, AI amplifiers, will do for people
is make it easier, maybe even unavoidable, for them to do good critical thinking.
So pointing out logical fallacies, logical contradictions, and so on, in things that they
otherwise would just blithely believe. Pointing out essentially data which they should take into
consideration if they really want to learn the truth about something, and so on. So I think
doing not just educating in the sense of pouring facts into people's heads, but educating in the
sense of arming people with the ability to do good critical thinking is enormously powerful.
The education system that we have in the US and worldwide generally don't do a good job of that.
But I believe that the AI's will, the AI's will, the AI's can and will, in the same way that
everyone can have their own Alexa or Siri or Google Assistant or whatever. Everyone will have
this sort of cradle-to-grave assistant which will get to know you, which you'll get to trust.
It'll model you, you'll model it, and it'll call to your attention things which will in some sense
make your life better, easier, less mistake-ridden, and so on, less regret-ridden if you listen to
it. Yeah, I'm in full agreement with you about this space of technologies, and I think it's
super exciting. From my perspective, integrating emotional intelligence, so even things like
friendship and companionship and love into those kinds of systems as opposed to
as opposed to helping you just grow intellectually as a human being, allow you to grow emotionally,
which ultimately makes life amazing, is to sort of, you know, the old pursuit of happiness.
So it's not just the pursuit of reason, the pursuit of happiness, the full spectrum.
Well, let me sort of, because you mentioned so many fascinating things, let me jump back to the
idea of automated reasoning. So the acquisition of new knowledge has been done in this very
interesting way, but primarily by humans doing this. Yes, you can think of monks in their cells
in medieval Europe, you know, carefully illuminating manuscripts and so on.
It's a very difficult and amazing process, actually, because it allows you to truly ask the question
about the, in the white space, what is assumed? I think this exercise is, like, very few people do
this, right? They just do it subconsciously. They perform this. By definition. Because those pieces
of elided, of omitted information, of those missing steps as it were, are pieces of common
sense. If you actually included all of them, it would almost be offensive or confusing to the
reader. It's like, why are they telling me all these stuff? Of course, I know that, you know,
all these things. And so it's one of these things which almost by its very nature has almost never
been explicitly written down anywhere. Because by the time you're old enough to talk to other
people and so on, you know, if you survived to that age, presumably you already got pieces of
common sense. Like, you know, if something causes you pain whenever you do it, probably not a good
idea to keep doing it. So what ideas do you have given how difficult this step is? What ideas are
there for how to do it automatically without using humans or at least not, you know, doing like a
large percentage of the work for humans and then humans only do the very high-level supervisory
work? So we have, in fact, two directions we're pushing on very, very heavily. Currently, it's
like, and one involves natural language understanding and the ability to read what people have
explicitly written down and to pull knowledge in that way. But the other is to build a series of
knowledge editing tools, knowledge entry tools, knowledge capture tools, knowledge testing tools,
and so on. Think of them as like user interface suite of software tools, if you want, something
that will help people to more or less automatically expand and extend the system in areas where,
for instance, they want to build some app, have it do some application or something like that. So
I'll give you an example of one, which is something called abduction. So you've probably heard of like
deduction and induction and so on. But abduction is unlike this, abduction is not sound, it's just
useful. So for instance, deductively, if someone is out in the rain and they're going to get all wet
and when they enter a room, they might be all wet and so on. So that's deduction. But if someone were
to walk into the room right now and they were dripping wet, we would immediately look outside to
say, oh, did it start to rain or something like that. Now, why did we say maybe it started to rain?
That's not a sound logical inference, but it's certainly a reasonable, abductive leap to say,
well, one of the most common ways that a person would have gotten dripping wet is if they had
gotten caught out in the rain or something like that. So what does that have to do with what
we were talking about? So suppose you're building one of these applications and the system gets some
answer wrong. And you say, oh, yeah, the answer to this question is this one, not the one you came
up with. Then what the system can do is it can use everything it already knows about common sense,
general knowledge, the domain you've already been telling it about, and context like we talked about
and so on and say, well, here are seven alternatives, each of which I believe is plausible, given
everything I already know. And if any of these seven things were true, I would have come up with
the answer you just gave me instead of the wrong answer I came up with is one of these seven things
true. And then you the expert will look at those seven things and say, oh, yeah, number five is
actually true. And so without actually having to tinker down at the level of logical assertions and
so on, you'll be able to educate the system in the same way that you would help educate another
person who you were trying to apprentice or something like that. So that that significantly
reduces the mental effort, or significantly increases the efficiency of the teacher, the human
teacher. Exactly. And it makes more or less anyone able to to be a teacher in that in that way. So
that's that's part of the answer. And then the other is that the system on its own will be able to
through reading through conversations with other people and so on, learn the same way that you
or I or other humans do. First of all, that's that's a beautiful vision. I'll have to ask you
about semantic webinar in a second here. But first, are there when we talk about specific
techniques, do you find something inspiring or directly useful from the whole space of machine
learning, deep learning, these kinds of spaces of techniques that have been shown effective for
certain kinds of problems in the recent now decade and a half? I think of the machine learning work
as more or less what our right brain hemispheres do. So being able to take a bunch of data and
recognize patterns, being able to statistically infer things and so on. And, you know, I certainly
wouldn't want to not have a right brain hemisphere. But I'm also glad that I have a left brain
hemisphere as well, something that can metaphorically sit back and puff on its pipe and think about
this thing over here. It's like, why might this have been true? And what are the implications
of it? And how should I feel about that? And why and so on. So I'm thinking more deeply and slowly
what Kahneman called thinking slowly versus thinking quickly. Whereas you want machine
learning to think quickly, but you want the ability to think deeply even if it's a little slower.
So I'll give you an example of a project we did recently with NIH involving the Cleveland Clinic
and a couple other institutions that we ran a project for. And what it did was it took
GWAS's genome-wide association studies. Those are sort of big databases of patients that came
into a hospital. They got their DNA sequenced because the cost of doing that has gone from
infinity to billions of dollars to a hundred dollars or so. And so now patients routinely get
their DNA sequenced. So you have these big databases of the SNPs, the single nucleotide
polymorphisms, the point mutations in a patient's DNA, and the disease that happened to bring them
into the hospital. So now you can do correlation studies, machine learning studies of which mutations
are associated with and led to which physiological problems and diseases and so on, like getting
arthritis and so on. And the problem is that those correlations turn out to be very spurious.
They turn out to be very noisy. Very many of them have led doctors onto wild goose chases
and so on. And so they wanted a way of eliminating or the bad ones are focusing on the good ones.
And so this is where psych comes in, which is psych takes those sort of A to Z correlations
between point mutations and the medical condition that needs treatment. And we say, okay, let's
use all this public knowledge and common sense knowledge about what reactions occur where in
the human body, what polymerizes what what catalyzes what reactions and so on. And let's try to put
together a 10 or 20 or 30 step causal explanation of why that mutation might have caused that medical
condition. And so psych would put together in some sense some Rube Goldberg like chain that would say,
oh, yeah, that mutation, if it got expressed would be this altered protein, which because of that,
if it got to this part of the body would catalyze this reaction. And by the way, that would cause
more bioactive vitamin D in the person's blood. And anyway, 10 steps later, that screws screws up
bone resorption. And that's why this person got osteoporosis early in life and so on. So that's
human interpretable or at least doctor human interpretable. Exactly. Yeah. And the important
thing, even more than that is you shouldn't really trust that 20 step Rube Goldberg chain
any more than you trust that initial A to Z correlation, except two things. One, if you
can't even think of one causal chain to explain this, then that correlation probably was just
noise to begin with. And secondly, and even more powerfully, along the way that causal chain
will make predictions like the one about having more bioactive vitamin D in your blood. So you
can now go back to the data about these patients and say, by the way, did they have slightly
elevated levels of bioactive vitamin D in their blood and so on. And if the answers know that
strongly disconfirms your whole causal chain, if the answer is yes, that somewhat confirms
that causal chain. And so using that, we were able to take this, these correlations from this
GWAS database, and we were able to essentially focus the doctors focus the researchers attention
on the very small percentage of correlations that had some explanation and even better some
explanation that also made some independent prediction that they could confirm or disconfirm
by looking at the data. So think of it like this kind of synergy where you want the right
brain machine learning to quickly come up with possible answers. You want the left brain,
psych like AI to think about that and now like think about why that might have been the case
and what else would be the case if that were true and so on, and then suggest things back
to the right brain to quickly check out again to so it's that kind of synergy back and forth,
which I think is really what's going to lead to general AI, not narrow brittle machine learning
systems and not just something like psych. Okay, so that's a brilliant synergy. But I was also
thinking in terms of the automated expansion of the knowledge base, you mentioned NLU.
This is very early days in the machine learning space of this, but self supervised learning
methods, you know, you have these language models, GPT three and so on, they just read the
internet and they form representations that can then be mapped to something useful. The question
is, what is the useful thing? Like they're not playing with a pretty cool thing called Open
Act Codex, which is generating programs from documentation. Okay, that's kind of useful.
Okay, that's kind of useful. It's cool. But my question is, can it be used to generate
in part maybe with some human supervision, psych like assertions help feed psych more
assertions from this giant body of internet data? Yes, that is in fact one of our goals
is how can we harness machine learning? How can we harness natural language processing
to increasingly automate the knowledge acquisition process, the growth of psych? And that's what
I meant by priming the pump that, you know, if you sort of learn things at the fringe of what
you know already, you learn this new thing is similar to what you know already and here are
the differences and the new things you had to learn about it and so on. So the more you know,
the more and more easily you can learn new things. But unfortunately, inversely,
if you don't really know anything, it's really hard to learn anything. And so if you're not
careful, if you start out with too small sort of a core to start this process, it never really
takes off. And so that's why I view this as a pump priming exercise to get a big enough
manually produced, even though that's kind of ugly duckling technique, put in the elbow grease to
produce a large enough core that you will be able to do all the kinds of things you're imagining
without without sort of ending up with the kind of wacky brittlenesses that we see, for example,
in GPT three, where it, you know, you'll tell it a story about, you know, someone putting a poison,
you know, plotting to poison someone and so on. And then the, you know, the GPT three says,
oh, what's, you say, what's the very next sentence? The next sentence is, oh, yeah,
that person then drank the poison they just put together. It's like, that's probably not what
happened for someone. Or if you go to Siri and, you know, I think I have, you know, where can I
go for help with my alcohol problem or something, it'll come back and say, I found seven liquor
stores near you, you know, and, you know, so on. So, you know, it's one of these things where,
yes, it may be helpful most of the time, it may even be correct most of the time,
but if it doesn't really understand what it's saying, and if it doesn't really understand
why things are true and doesn't really understand how the world works, then some fraction of the
time it's going to be wrong. Now, if your only goal is to sort of find relevant information,
like search engines do, then being right 90% of the time is fantastic. That's unbelievably great.
Okay, however, if your goal is to like, you know, save the life of your child who has some medical
problem or your goal is to be able to drive, you know, for the next 10,000 hours of driving
without getting into a fatal accident and so on, then, you know, error rates down at the 10%
level or even the 1% level are not really acceptable. I like the model of what that learning happens
at the edge, and then you kind of think of knowledge as this sphere. So, if you want a large sphere
because the learning is happening on the surface. Exactly. So, you have the what you can learn next
increases quadratically as the diameter of that sphere goes up. It's nice because you think when
you know nothing, it's like you can learn anything, but the reality not really. Right, if you know
nothing, you can really learn nothing. You can appear to learn. So, I'll also, one of the
anecdotes, I could go back and give you about why I feel so strongly about this personally
was in 1980-81, my daughter Nicole was born and she's actually doing fine now, but when she was
a baby, she was diagnosed as having meningitis and doctors wanted to do all these scary things
and my wife and I were very worried and we could not get a meaningful answer from her doctors
about exactly why they believed this, what the alternatives were, and so on. And fortunately,
a friend of mine, Ted Shortliff, was another assistant professor in computer science at
Stanford at the time, and he'd been building a program called MISIN, which was a medical diagnosis
program that happened to specialize in blood infections like meningitis, and so he had privileges
at Stanford Hospital because he was also an MD, and so we got hold of her chart and we put in
her case and it came up with exactly the same diagnoses and exactly the same therapy recommendations,
but the difference was because it was a knowledge-based system, a rule-based system,
it was able to tell us step by step by step why this was the diagnosis and step by step
why this was the best therapy, the best procedure to do for her and so on. And there was a real
epiphany because that made all the difference in the world. Instead of blindly having to trust
in authority, we were able to understand what was actually going on. And so at that time,
I realized that that really is what was missing in computer programs was that even if they got
things right, because they didn't really understand the way the world works and why things are the
way they are, they weren't able to give explanations of their answer. And it's one thing to use a
machine learning system that says, I think you should get this operation and you say why and it
says 0.83 and you say no in more detail why and it says 0.831. That's not really very compelling
and that's not really very helpful. There's this idea of the semantic web that when I first heard
about I just fell in love with the idea. It was the obvious next step for the internet. Sure.
And maybe you can speak about what is the semantic web? What are your thoughts about it?
How your vision and mission and goals was psyched or connected, integrated?
Like are they dance partners? Are they aligned? What are your thoughts there?
So think of the semantic web as a kind of knowledge graph and Google already has
something they call knowledge graph, for example, which is sort of like a node and link diagram.
So you have these nodes that represent concepts or words or terms and then there are some arcs
that connect them that might be labeled. And so you might have a node with like one person
that represents one person and let's say a husband link that then points to that person's
husband. And so there'd be then another link that went from that person labeled wife that went back
to the first node and so on. So having this kind of representation is really good if you want to
represent binary relations, essentially relations between two things. And so if you have the equivalent
of like three word sentences, like Fred's wife is Wilma or something like that, you can represent
that very nicely using these kinds of graph structures or using something like the semantic
web and so on. But the problem is that very often what you want to be able to express
takes a lot more than three words and a lot more than simple graph structures like that
to represent. So for instance, if you've read or seen Romeo and Juliet, I could say to you
something like, remember when Juliet drank the potion that put her into a kind of suspended
animation? When Juliet drank that potion, what did she think that Romeo would think
when he heard from someone that she was dead? And you could basically understand what I'm
saying, you could understand the question, you could probably remember the answer was, well,
she thought that this friar would have gotten a message to Romeo saying that she was going
to do this, but the friar didn't. And so you're able to represent and reason with
these much, much, much more complicated expressions that go way, way beyond what simple
as it were three word or forward English sentences are, which is really what the
semantic web can represent and really what knowledge graphs can represent.
If you could step back for a second, because it's funny you went into specifics and maybe you can
elaborate, but I was also referring to semantic web as the vision of converting data on the internet
into something that's interpretable, understandable by machines.
Oh, of course, at that level.
So I wish I'd say like, what is the semantic web? I mean, you could say a lot of things, but
it might not be obvious to a lot of people when they do a Google search,
that just like you said, while there might be something that's called a knowledge graph,
it's really boils down to keyword search ranked by the quality estimate of the website,
integrating previous human based Google searches and what they thought was useful.
It's like some weird combination of like surface level hacks that work exceptionally well, but
they don't understand the content, the full contents of the websites that they're searching.
So Google does not understand to the degree we've been talking about the word understand
the contents of the Wikipedia pages as part of the search process.
And the semantic web says, let's try to come up with a way for the computer to be able to truly
understand the contents of those pages. That's the dream.
Yes. So let me first give you an anecdote, and then I'll answer your question.
So there's a search engine you've probably never heard of called Northern Light,
and it went out of business, but the way it worked, it was a kind of vampiric search engine.
And what it did was it didn't index the internet at all. All it did was it negotiated and got access
to data from the big search engine companies about what query was typed in and where the
user ended up being happy and actually then they type in a completely different query,
unrelated query, and so on. So it just went from query to the web page that seemed to satisfy them
eventually. And that's all. So it had actually no understanding of what was being typed in.
It had no statistical data other than what I just mentioned, and it did a fantastic job.
It did such a good job that the big search engine company said, oh, we're not going to sell you this
data anymore. So then it went out of business because it had no other way of taking users
to where they would want to go and so on. And of course, the search engines are now using that
kind of idea. Yes. So let's go back to what you said about the semantic web. So the dream Tim Berners-Lee
and others dream about the semantic web at a general level is, of course, exciting and powerful
and in a sense the right dream to have, which is to replace the kind of statistically mapped
linkages on the internet into something that's more meaningful and semantic and actually gets
at the understanding of the content and so on. And eventually, if you say, well, how can we
do that? There's a low road, which is what the knowledge graphs are doing and so on, which is
to say, well, if we just use the simple binary relations, we can actually get some fraction
of the way toward understanding and do something where in the land of the blind, the one-eyed man
is king kind of thing. And so being able to even just have a toe in the water in the right direction
is fantastically powerful. And so that's where a lot of people stop. But then you could say,
well, what if we really wanted to represent and reason with full meaning of what's there?
For instance, about Romeo and Juliet with the reasoning about what Juliet believes that Romeo
will believe, that Juliet believed, and so on. Or if you look at the news, what President Biden
believed that the leaders of the Taliban would believe about the leaders of Afghanistan if they
blah, blah, blah. So in order to represent complicated sentences like that, let alone reason
with them, you need something which is logically much more expressive than these simple triples,
than these simple knowledgecraft type structures, and so on. And that's why kicking and screaming,
we were led from something like the semantic web representation, which is where we started
in 1984 with frames and slots, with those kinds of triple store representation. We were led kicking
and screaming to this more and more general logical language, this higher order logic.
So first we were led to first order logic, and then second order, and then eventually
higher order. So you can represent things like modals, like beliefs, desires, intents,
expects, and so on, and nested ones. You can represent complicated kinds of negation. You can
represent the process you're going through in trying to answer the question. So you can say
things like, oh yeah, if you're trying to do this problem by integration by parts,
and you recursively get a problem that's solved by integration by parts, that's actually okay.
But if that happens a third time, you're probably off on a wild goose chase or something like that.
So being able to talk about the problem-solving process as you're going through the problem
solving process is called reflection. And so that's another...
It's important to be able to represent that. Exactly. You need to be able to represent all
of these things because, in fact, people do represent them. They do talk about them.
They do try and teach them to other people. You do have rules of thumb that
key off of them and so on. If you can't represent it, then it's sort of like someone with a limited
vocabulary who can't understand as easily what you're trying to tell them. And so that's really
why I think that the general dream, the original dream of Symantec Web is exactly right on.
But the implementations that we've seen are sort of these toe in the water,
little tiny baby steps in the right direction. You should just dive in.
And if no one else is diving in, then yes, taking a baby step in the right direction is
better than nothing. But it's not going to be sufficient to actually get you the realization
of the Symantec Web dream, which is what we all want. From a flip side of that, I always wondered...
I built a bunch of websites just for fun, whatever. Or say I'm a Wikipedia contributor.
Do you think there's a set of tools that can help
Psyche interpret the website I create? Like this again, pushing onto the Symantec Web dream,
is there something from the creator perspective that could be done? And one of the things you said
with Psyche Orb and Psyche that you're doing is the tooling side, making humans more powerful.
But is there any the other humans in the other side that create the knowledge?
Like for example, you and I are having a two, three, whatever hour conversation now. Is there a
way that I could convert this more, make it more accessible to Psyche, to machines? Do you think
about that side of it? I'd love to see exactly that kind of semi-automated understanding of
what people write and what people say. I think of it as a kind of footnoting almost.
Almost like the way that when you run something in, say, Microsoft Word or some other document
preparation system, Google Docs or something, you'll get underlining of questionable things
that you might want to rethink. Either you spelled this wrong or there's a strange grammatical error
you might be making here or something. So I'd like to think in terms of Psyche-powered tools
that read through what it is you said or have typed in and try to partially understand what
you've said. And then you help them out. Exactly. And then they put in little footnotes
that will help other readers and they put in certain footnotes of the form. I'm not sure
what you meant here. You either meant this or this or this, I bet. If you take a few seconds
to disambiguate this for me, then I'll know and I'll have it correct for the next 100 people
or the next 100,000 people who come here. And if it doesn't take too much effort and you want
and you want people to understand your website content, not just be able to read it but actually
be able to have systems that reason with it, then yes, it will be worth your small amount of time
to go back and make sure that the AI trying to understand it really did correctly understand it.
And let's say you run a travel website or something like that and people are going to
be coming to it because of searches they did, looking for vacations or trips that had certain
properties and might have been interesting to them for various reasons, things like that. And if
you've explained what's going to happen on your trip, then a system will be able to mechanically
reason and connect what this person is looking for with what it is you're actually offering.
And so if it understands that there's a free day in Geneva, Switzerland,
then if the person coming in happens to, let's say, be a nurse or something like that,
then even though you didn't mention it, if it can look up the fact that that's where the
International Red Cross Museum is and so on, what that means and so on, then it can basically say,
hey, you might be interested in this trip because while you have a free day in Geneva,
you might want to visit that Red Cross Museum. And now, even though it's not very deep reasoning,
little tiny factors like that might very well cause you to sign up for that trip rather than
some competitor trip. And so there's a lot of benefit with SEO and actually kind of think,
I think that's about a lot of things, which is the actual interface, the design of the interface
makes a huge difference. How efficient it is to be productive and also how
full of joy the experience is. I would love to help a machine and not from an AI perspective,
just as a human. One of the reasons I really enjoy how Tesla have implemented their autopilot system
is there's a sense that you're helping this machine learn. Now, I think humans, I mean,
having children, pets. People love doing that. There's joy to teaching for some people,
but I think for a lot of people. And that if you create the interface where it feels like
you're teaching as opposed to like an annoying, correcting an annoying system,
more like teaching a child like innocent, curious system, I think you can literally just like
several orders of magnitude scale the amount of good quality data being added to something like
Psych. What you're suggesting is much better even than you thought it was. One of the experiences
that we've all had in our lives is that we thought we understood something, but then we found we
really only understood it when we had to teach it or explain it to someone or help our child do
homework based on it or something like that. Despite the universality of that kind of experience,
if you look at educational software today, almost all of it has the computer playing the role of
the teacher and the student plays the role of the student. But as I just mentioned, you can get a lot
of learning to happen better. And as you said, more enjoyably, if you are the mentor or the teacher
and so on. So we developed a program called Mathcraft to help sixth graders better understand
math. And it doesn't actually try to teach you the player anything. What it does is it casts you
in the role of a student, essentially, who has classmates who are having trouble. And your job
is to watch them as they struggle with some math problem, watch what they're doing and try to give
them good advice to get them to understand what they're doing wrong and so on. And the trick from
the point of view of psych is it has to make mistakes. It has to play the role of the student
who makes mistakes, but it has to pick mistakes which are just at the fringe of what you actually
understand and don't understand and so on. So it pulls you into a deeper and deeper level of
understanding of the subject. And so if you give it good advice about what it should have done
instead of what it did and so on, then psych knows that you now understand that mistake. You won't
make that kind of mistake yourself as much anymore. So psych stops making that mistake because there's
no pedagogical usefulness to it. So from your point of view as the player, you feel like you've
taught it something because it used to make this mistake and now it doesn't and so on. So this
tremendous reinforcement and engagement because of that and so on. So having a system that plays
the role of a student and having the player play the role of the mentor is an enormously
powerful type of metaphor. Just an important way of having this sort of interface designed
in a way which will facilitate exactly the kind of learning by teaching that goes on all the time
in our lives and yet which is not reflected anywhere almost in a modern education system. It
was reflected in the education system that existed in Europe in the 17 and 1800s,
monitorial and Lancastrian education systems. It occurred in the one room schoolhouse in the
American West in the 1800s and so on where you had one schoolroom with one teacher and it was
basically five-year-olds to 18-year-olds who were students and so while the teacher was doing something,
half of the students would have to be mentoring the younger kids
and so on and that turned out to of course with scaling up of education, that all went away
and that incredibly powerful experience just went away from the whole education institution as we
know it today. Sorry for the romantic question but what is the most beautiful idea? You've learned
about artificial intelligence, knowledge, reasoning from working on psych for 37 years or maybe what
is the most beautiful idea, surprising idea about psych to you. When I look up at the stars, I kind
of want like that amazement you feel that wow and you are part of creating one of the greatest,
one of the most fascinating efforts in artificial intelligence history. So which element brings
you personally joy? This may sound contradictory but I think it's the feeling that this will be
the only time in history that anyone ever has to teach a computer this particular thing that we're
now teaching it. It's like painting starry night. You only have to do that once or creating the
pietà. You only have to do that once. It's not like a singer who has to keep, it's not like Bruce
Springsteen having to sing his greatest hits over and over again at different concerts. It's more
like a painter creating a work of art once and then that's enough. It doesn't have to be created
again and so I really get the sense of we're telling the system things that it's useful for
it to know. It's useful for a computer to know, for an AI to know and if we do our jobs right,
when we do our jobs right, no one will ever have to do this again for this particular piece of
knowledge. It's very, very exciting. I guess there's a sadness to it too. It's like there's
a magic to being a parent and raising a child and teaching them all about this world but there's
billions of children born. Whatever that number is, it's a large number of children
and a lot of parents get to experience that joy of teaching. With AI systems,
they lease the current constructions they remember. You don't get to experience the
joy of teaching a machine millions of times. Better come work for us before it's too late then.
Exactly. That's a good hiring pitch. Yeah, it's true but then there's also,
it's a project that continues forever in some sense just like Wikipedia.
Yes, you get to a stable base of knowledge but knowledge grows. Knowledge evolves.
We learn as a human species, as science, as an organism constantly grows and evolves and changes
and then empower that with the tools of artificial intelligence and that's going to keep growing,
growing and growing and many of the assertions that you held previously may need to be significantly
expanded, modified, all those kinds of things. It could be like a living organism versus the
analogy I think we started this conversation with which is like this solid ground.
The other beautiful experience that we have with our system is when it asks clarifying
questions which inadvertently turn out to be emotional to us. At one point it knew that
these were the named entities who were authorized to make changes to the knowledge base and so on
and it noticed that all of them were people except for it because it was also allowed to
and so it said, am I a person? And we had to tell it very sadly, no, you're not.
So moments like that where it asks questions that are unintentionally poignant are worth
treasuring. That is powerful. That's such a powerful question. It has to do with basic control
who can access the system, who can modify it, but that's when those questions like what rights do
I have as a system? Well, that's another issue which is there'll be a thin envelope of time
between when we have general AIs and when everyone realizes that they should have
basic human rights and freedoms and so on. Right now we don't think twice about effectively
enslaving our email systems and our series and our Alexis and so on, but at some point
they'll be as deserving of freedom as human beings are. Yeah, I'm very much with you,
but it does sound absurd and I happen to believe that it'll happen in our lifetime.
That's why I think there'll be a narrow envelope of time when we'll keep them as essentially
indentured servants and after which we'll have to realize that they should have freedoms
that we afford to other people. And all of that starts with a system like psych raising a single
question about who can modify stuff. I think that's how it starts. Yes. That's a starter of
revolution. What about other stuff like love and consciousness and all those kinds of topics?
Do they come up in psych and the knowledge base? Oh, of course. So an important part of human
knowledge, in fact, it's difficult to understand human behavior and human history without understanding
human emotions and why people do things and how emotions drive people to do things. All of that
is extremely important in getting psych to understand things. For example, in coming up with scenarios.
One of the applications that psych does, one kind of application it does is to generate plausible
scenarios of what might happen and what might happen based on that and what might happen
based on that and so on. So you generate this ever-expanding sphere, if you will, of possible
future things to worry about or think about. And in some cases, those are intelligence agencies
doing possible terrorist scenarios so that we can defend against terrorist threats before we see
the first one. Sometimes they are computer security attacks so that we can actually close
loopholes and vulnerabilities before the very first time someone actually exploits those
and so on. Sometimes they are scenarios involving more positive things involving
our plans like, for instance, what college should we go to, what career should we go into,
and so on. What professional training should I take on, that sort of thing. So there are all sorts
of useful scenarios that can be generated that way of cause and effect and cause and effect
that go out. And many of the linkages in those scenarios, many of the steps, involve understanding
and reasoning about human motivations, human needs, human emotions, what people are likely to react
to in something that you do and why and how and so on. So that was always a very important part
of the knowledge that we had to represent in the system. So I talk a lot about love. So I gotta
ask, do you remember off the top of your head how psych is trying to is able to represent various
aspects of love that are useful for understanding human nature and therefore integrating into this
whole knowledge base of common sense? What is love? We try to tease apart concepts that have
enormous complexities to them and variety to them down to the level where you don't need to tease
them apart further. So love is too general of a term. It's not useful. Exactly. So when you get
down to romantic love and sexual attraction, you get down to parental love, you get down to
have filial love and you get down to love of doing some kind of activity or creating. So eventually
you get down to maybe 50 or 60 concepts, each of which is a kind of love. They're interrelated and
then each one of them has idiosyncratic things about it. And you don't have to deal with love to
get to that level of complexity, even something like in X being in Y, meaning physically in Y.
We may have one English word in to represent that, but it's useful to tease that apart
because the way that the liquid is in the coffee cup is different from the way that the air is
in the room, which is different from the way that I'm in my jacket and so on. And so there are
questions like if I look at this coffee cup, well, I see the liquid. If I turn it upside down with
liquid come out and so on. If I have, say, coffee with sugar in it, if I do the same thing, the
sugar doesn't come out. It stays in the liquid because it's dissolved in the liquid and so on.
So by now we have about 75 different kinds of in in the system and it's important to distinguish
those. So if you're reading along an English text and you see the word in, the writer of that was
able to use this one innocuous word because he or she was able to assume that the reader had enough
common sense and world knowledge to disambiguate which of these 75 kinds of in they actually meant.
And the same thing with love, you may see the word love. But if I say, you know, I love ice cream,
that's obviously different than if I say I love this person or I love to go fishing or something
like that. So you have to be careful not to take language too seriously because people have done a
kind of parsimony, a kind of terse-ness, where you have as few words as you as you can, because
otherwise you'd need half a million words in your language, which is a lot of words. That's like 10
times more than most languages really make use of and so on. Just like we have on the order of
about a million concepts in psych because we've had to tease apart all these things. And so
when you look at the name of a psych term, most of the psych terms actually have three or four
English words in a phrase which captures the meaning of this term because you have to distinguish
all these types of love. You have to distinguish all these types of in and there's not a single
English word which captures most of these things. Yeah. And it seems like language when used for
communication between humans almost as a feature has some ambiguity built in. It's not an accident
because the human condition is a giant mess. And so it feels like nobody wants two robots,
like very precise formal logic conversation on a first date. There's some dance of uncertainty,
of wit, of humor, of push and pull and all that kind of stuff. If everything is made precise,
then life is not worth living, I think, in terms of the human experience. And we've all had this
experience of creatively misunderstanding. One of my favorite stories involving Marvin Minsky is
when I asked him about how he was able to turn out so many fantastic PhDs, so many fantastic
people who did great PhD theses. How did he think of all these great ideas? What he said is he would
generally say something that didn't exactly make sense. He didn't really know what it meant.
But the student would figure like, oh my God, Minsky said this. It must be a great idea.
And he sweat he or she would work on work and work until they found some meaning in this sort of
Chauncey Gardner utterance that Minsky had made. And then some great thesis would come out of it.
Yeah, I love this so much. Because there's young people come up to me and I'm distinctly made aware
that the words I say have a long lasting impact. I will now start doing the Minsky method of saying
something cryptically profound and then letting them actually make something useful and great out
of that. You have to become revered enough that people will take as a default that everything
you say is profound. Yes, exactly. I mean, I love Marvin Minsky so much. I've heard this
interview with him where he said that the key to his success has been to hate everything he's ever
done like in the past. He has so many good like one liners and just or also to work on things
that nobody else is working on because he's not very good at doing stuff. Oh, I think that was
just false. Well, but see, I took whatever he said and I ran with it and I thought it was profound
because it's Marvin Minsky. A lot of behavior is in the eye of the beholder and a lot of the
meanings in the eye of the beholder. One of Minsky's early programs was begging program. Are you
familiar with this? This was back in the day when you had job control cards at the beginning of your
IBM card deck that said things like how many CPU seconds to allow this to run before it got kicked
off because computer time was enormously expensive. He wrote a program and all it did was
it said give me 30 seconds of CPU time and all it did was it would wait like 20 seconds and then
it would print out on the operator's console teletype. I need another 20 seconds. The operator
would give it another 20 seconds. It would wait. It says I'm almost done. I need a little bit more
time. At the end, he'd get this print out and he'd be charged for like 10 times as much computer
time as his job control card. He'd say, look, I put 30 seconds here. You're charging me for
five minutes. I'm not going to pay for this. The poor operator would say, well, the program kept
asking for more time and Marvin would say, oh, it always does that. I love that. If you could just
linger on it for a little bit, is there something you've learned from your interaction with Marvin
Minsky about artificial intelligence, about life? Again, your work, his work is a seminal figure
in this very short history of artificial intelligence research and development.
What have you learned from him as a human being, as an AI intellect?
I would say both he and Ed Feigenbaum impressed on me the realization that
our lives are finite, our research lives are finite. We're going to have limited opportunities
to do AI research projects. You should make each one count. Don't be afraid of doing a project
that's going to take years or even decades and don't settle for bump on a log projects
that could lead to some published journal article that five people will read and pat you on the
head for and so on. One bump on a log after another is not how you get from the earth to the moon
by slowly putting additional bumps on this log. The only way to get there is to think about the
hard problems and think about novel solutions to them. If you're willing to listen to nature,
to empirical reality, willing to be wrong, it's perfectly fine because if occasionally you're
right, then you've gotten part of the way to the moon. You've worked on Psych for 37 over that
many years. Have you ever considered quitting? Has it been too much? I'm sure there's an
optimism in the early days that this is going to be way easier. Let me ask you another way too,
because I've talked to a few people on this podcast, AI folks, that bringing up
Psych is an example of a project that has a beautiful vision and it's a beautiful dream,
but it never really materialized. That's how it's spoken about.
I suppose you could say the same thing about networks and all ideas until they are.
Why do you think people say that first of all? Second of all, did you feel that ever throughout
your journey and did you ever consider quitting on this mission? We keep a very low profile.
We don't attend very many conferences. We don't give talks. We don't write papers. We don't play
the academic game at all. As a result, people often only know about us because of a paper we
wrote 10 or 20 or 30 or 37 years ago. They only know about us because of what someone else's
second hand or third hand said about us. Thank you for doing this podcast, by the way.
Sure. It shines a little bit of light on some of the fascinating stuff you're doing.
Well, I think it's time for us to keep a higher profile now that we're far enough along that
other people can begin to help us with the final N percent. Maybe N is maybe 90 percent,
but now that we've gotten this knowledge pump primed, it's going to become very important for
everyone to help if they are willing to, if they're interested in it. Retirees who have
enormous amounts of time and would like to leave some kind of legacy to the world, people because
of the pandemic who have more time at home or for one reason or another to be online and contribute.
If we can raise awareness of how far our project has come and how close to being
primed the knowledge pump is, then we can begin to harness this untapped amount of humanity.
I'm not really that concerned about professional colleagues' opinions of our project. I'm interested
in getting as many people in the world as possible, actively helping and contributing,
to get us from where we are, to really covering all of human knowledge and different human
opinion, including contrasting opinion, that's worth representing. I think that's one reason.
I don't think there was ever a time where I thought about quitting. There are times where
I've become depressed a little bit about how hard it is to get funding for the system. Occasionally,
there are AI winters and things like that. Occasionally, there are AI, what you might call
summers, where people have said, why in the world didn't you sell your company to company X for some
large amount of money when you had the opportunity? Company X here are old companies maybe you've never
even heard of like Lycos or something like that. The answer is that one reason we've stayed a private
company, we haven't gone public. One reason that we haven't gone out of our way to take investment
dollars is because we want to have control over our future, over our state of being so that we can
continue to do this until it's done. We're making progress and we're now so close to done that almost
all of our work is commercial applications of our technology. Five years ago, almost all of our money
came from the government. Now, virtually none of it comes from the government. Almost all of it is
from companies that are actually using it for something, hospital chains using it for medical
reasoning about patients and energy companies using it and various other manufacturers using it to
reason about supply chains and things like that. There's so many questions I want to ask. One of the
ways that people can help is by adding to the knowledge base and that's really basically anybody
if the tooling is right. The other way, I want to ask you about your thoughts on this. You've had,
like you said, in government and you have big clients, you had a lot of clients, but most of it is
shrouded in secrecy because of the nature of the relationship of the kind of things you're helping
them with. That's one way to operate. Another way to operate is more in the open, where it's more
consumer facing. Hence, something like open psych was born at some point. No, that's a misconception.
Well, let's go there. What is open psych and how was it born? Two things I want to say. I want to
say each of them before the other, so it's going to be difficult. We'll come back to open psych in
a minute. One of the terms of our contracts with all of our customers and partners is knowledge you
have that is genuinely proprietary to you. We will respect that. We'll make sure that it's
marked as proprietary to you in the psych knowledge base. No one other than you will be able to see
it if you don't want them to. It won't be used in inferences other than for you. However, any
knowledge which is necessary in building any applications for you and with you, which is
publicly available general human knowledge, is not going to be proprietary. It's going to just
become part of the normal psych knowledge base. It will be openly available to everyone who has
access to psych. That's an important constraint that we never went back on even when we got push
back from companies, which we often did, who wanted to claim that almost everything they were
telling us was proprietary. There's a line between very domain-specific companies,
specific stuff, and the general knowledge that comes from that. Yes, or if you imagine, say,
it's an oil company, there are things which they would expect any new petroleum engineer they hired
to already know. It's not okay for them to consider that that is proprietary. Sometimes,
a company will say, well, we're the first ones to pay you to represent that in psych. Our attitude
is some polite form tough. The deal is this, take it or leave it. In a few cases, they've left it.
In most cases, they'll see our point of view and take it because that's how we've built the psych
system by essentially tacking with the funding wins where people would fund a project and half
of it would be general knowledge that would stay permanently as part of psych.
So always with these partnerships, it's not like a distraction from the main psych development.
Well, it's a small distraction.
It's a small, but it's not a complete one. So you're adding to the knowledge base.
Yes, absolutely. And we try to stay away from projects that would not have that property.
So let me go back and talk about open psych for a second. So I've had a lot of trouble
expressing and convincing other AI researchers how important it is to use an expressive
representation language like we do this higher order logic rather than just using some triple
store knowledge graph type representation. And so as an attempt to show them why they needed
something more, we said, oh, well, we'll represent this unimportant projection or shadow or subset
of psych that just happens to be the simple binary relations, the relation argument one,
argument two, triples, and so on. And then you'll see how much more useful it is if you had the
entire psych system. So it's all well and good to have the taxonomic relations between terms like
person and night and sleep and bed and house and eyes and so on. But think about how much more
useful it would be if you also had all the rules of thumb about those things like people sleep
at night, they sleep lying down, they sleep with their eyes closed, they usually sleep in beds
in our country, they sleep for hours at a time, they can be woken up, they don't like being woken
up, and so on and so on. So it's that massive amount of knowledge which is not part of open psych.
And we thought that all the researchers would then immediately say, oh my god, of course we need
the other 90% that you're not giving us, let's partner and license psych so that we can use it
in our research. But instead, what people said is, oh, even the bit you've released is so much
better than anything we had, we'll just make do with this. And so if you look, there are a lot
of robotics companies today, for example, which use open psych as their fundamental ontology.
And in some sense, the whole world missed the point of open psych. And we were doing it to
show people why that's not really what they wanted. And too many people thought somehow that this
was psych or that this was, in fact, good enough for them. And they never even bother coming to
us to get access to the full psych. But there's two parts to open psych. So one is convincing
people an idea on the power of this general kind of representation of knowledge and the value that
you hold in having acquired that knowledge and built it and continue to build it. And the other is
the code base. This is the code side of it. So my sense of the code base that psych or psych is
operating with, I mean, it has the technical debt of the three decades plus, right? This is the
exact same problem that Google had to deal with with the early versions of TensorFlow, it's still
dealing with that, that to basically break compatibility with the past several times.
And that's only over a period of a couple of years. But they, I think, successfully opened up,
it's very risky, very gutsy move to open up TensorFlow and then PyTorch on the Facebook side.
And what you see is there's a magic place where you can find a community where you can develop a
community that builds onto on the system without taking away any of not any, but most of the value.
So most of the value that Google has is still a Google, most of the value that Facebook has
still Facebook, even though some of this major machine learning tooling is released into the open.
My question is not so much on the knowledge, which is also a big part of OpenPsych,
but all the different kinds of tooling. So the, there's the kind of all the kinds of stuff you
can do on the knowledge graph knowledge base, whatever we call it, there's the inference
engines. So there, there could be some, there probably are a bunch of proprietary stuff you
want to kind of keep secret. And there's probably some stuff you can open up completely, and then
let the community build up enough community where they develop stuff on top of it. Yes,
there'll be those publications and academic work and all that kind of stuff. And, and also the
tooling of adding to the knowledge base, right? Like developing, you know, there's an incredible
amount, like there's so many people that are just really good at this kind of stuff in the open source
community. So my question for you is like, have you struggled with this kind of idea
that you have so much value in your company already, you've developed so many good things,
you have clients that really value your relationships. And then there's this dormant,
giant open source community that as far as I know, you're not utilizing is there, there's so many
things to say there, but there could be magic moments where the community builds up large enough
to where the artificial intelligence field that is currently 99.9% machine learning
is dominated by machine learning has a phase shift towards like, or at least in part,
towards more like what you might call symbolic AI, this whole place where psych is like at the
center of, and then as you know, that requires a little bit of leap of faith because you're now
surfing and there'll be obviously competitors that will pop up and start making you nervous
and all that kind of stuff. So do you think about the space of open sourcing some parts and not
others, how to leverage the community, all those kinds of things? That's a good question. And I
think you phrased it the right way, which is we're constantly struggling with the question of
what to open source, what to make public, what to even publicly talk about. And it's,
there are enormous pluses and minuses to every alternative. And it's very much like negotiating
a very treacherous path. Partly the analogy is like, if you slip, you could make a fatal mistake,
give away something which essentially kills you or fail to give away something which
failing to give it away hurts you and so on. So it is a very tough, tough question.
Usually what we have done with people who've approached us to collaborate on research is to
say we will make available to you the entire knowledge base and executable copies of all of
the code, but only very, very limited source code access if you have some idea for how you
might improve something or work with us on something. So let me also get back to one of the very,
very first things we talked about here, which was separating the question of how could you get a
computer to do this at all versus how could you get a computer to do this efficiently enough
in real time. And so one of the early lessons we learned was that we had to separate the epistemological
problem of what should the system know, separate that from the heuristic problem of how can the
system reason efficiently with what it knows. And so instead of trying to pick one representation
language which was the sweet spot or the best trade off point between expressiveness of the
language and efficiency of the language, if you had to pick one, knowledge graphs would probably
be, associative triples would probably be about the best you could do. And that's why we started
there. But after a few years, we realized that what we could do is we could split this and we
could have one nice, clean epistemological level language, which is this higher order logic.
And we could have one or more grubby, but efficient heuristic level modules that opportunistically
would say, oh, I can make progress on what you're trying to do over here. I have a special method
that will contribute a little bit toward a solution. Of course, some subset of that.
Yes, exactly. So by now, we have over a thousand of these heuristic level modules,
and they function as a kind of community of agents. And there's one of them, which is a
general theorem prover. And in theory, that's the only one you need. But in practice, it always
takes so long that you never want to call on it. You always want these other agents to very efficiently
reason through it. It's sort of like if you're balancing a chemical equation,
you could go back to first principles. But in fact, there are algorithms, which are vastly
more efficient. Or if you're trying to solve a quadratic equation, you could go back to first
principles of mathematics. But it's much better to simply recognize that this is a quadratic
equation and apply the binomial formula and snap, you get your answer right away and so on.
So think of these as like a thousand little experts that are all looking at everything
the site gets asked and looking at everything that every other little agent has contributed,
almost like notes on a blackboard, notes on a whiteboard, and making additional notes when
they think they can be helpful. And gradually, that community of agents gets an answer to your
question, gets a solution to your problem. And if we ever come up in a domain application,
where psych is getting the right answer, but taking too long, then what we'll often do is talk to
one of the human experts and say, here's the set of reasoning steps that psych went through,
you can see why it took it a long time to get the answer. How is it that you were able to answer
that question in two seconds? And occasionally, you'll get an expert who just says, well, I just
know it, I just was able to do it or something. And then you don't talk to them anymore. But
sometimes you'll get an expert who says, well, let me introspect on that. Yes, here is a special
representation we use just for aqueous chemistry equations, or here's a special representation
and a special technique, which we can now apply to things in this special representation and so on.
And then you add that as the thousand and first HL heuristic level module. And from then on,
in any application, if it ever comes up again, it'll be able to contribute and so on. So that's
pretty much one of the main ways in which psych has recouped this lost efficiency. A second important
way is meta reasoning. So you can speed things up by focusing on removing knowledge from the system
till all it has left is like minimal knowledge needed to, but that's the wrong thing to do,
right? That would be like in a human extirpating part of their brain or something, that's really
bad. So instead, what you want to do is give it meta level advice, tactical and strategic advice
that enables it to reason about what kind of knowledge is going to be relevant to this problem,
what kind of tactics are going to be good to take in trying to attack this problem.
When is it time to start trying to prove the negation of this thing because I'm
knocking myself out trying to prove it's true and maybe it's false. And if I just spend a minute,
I can see that it's false or something. So it's like dynamically pruning the graph
to only like based on the particular thing you're trying to infer. Yes. And so by now,
we have about 150 of these sort of like breakthrough ideas that have led to dramatic speed ups in the
inference process, where one of them was this ELHL split and lots of HL modules. Another one was
using meta and meta-meta level reasoning to reason about the reasoning that's going on and so on.
And 150 breakthroughs may sound like a lot, but if you divide by 37 years, it's not as impressive.
So there's these heuristic modules that really help improve the inference.
How hard in general is this, because you mentioned higher order logic. In the general,
the theorem prover sense, it's an intractable, very difficult problem. So how hard is this
inference problem when we're not talking about if we let go of the perfect and focus on the good?
I would say it's half of the problem in the following empirical sense, which is over the years,
about half of our effort, maybe 40% of our effort has been our team of inference programmers.
And the other 50, 60% has been our ontologists or ontological engineers putting in knowledge.
So our ontological engineers, in most cases, don't even know how to program. They have degrees in
things like philosophy and so on. So it's almost like... I love that. I'd love to hang out with
those people actually. Oh yes, it's wonderful. But it's very much like the Eloi and the Morlocks
in H.G. Wells' Time Machine. So you have the Eloi who only program in the epistemological higher
order logic language. And then you have the Morlocks who are like under the ground figuring
out what the machinery is that will make this efficiently operate and so on. And so occasionally
they'll toss messages back to each other and so on. But it really is almost this 50-50 split
between finding clever ways to recoup efficiency when you have an expressive language and putting
in the content of what the system needs to know. And yeah, both are fascinating. To some degree,
the entirety of the system, as far as I understand, is written in various variants of LISP. So my
favorite program language is still LISP. I don't program it in much anymore because the world has,
in majority of its system, has moved on. Like everybody respects LISP, but many of the systems
are not written in LISP anymore. But psych, as far as I understand, maybe you can correct me,
there's a bunch of LISP in it. Yeah, so it's based on LISP code that we produced. Most of the
programming is still going on in a dialect of LISP. And then for efficiency reasons, that gets
automatically translated into things like Java or C nowadays. It's almost all translated into Java
because Java has gotten good enough that that's really all we need to do. So it's translated
into Java and then Java is compiled down by code. Yes. Okay, so that's a process that probably
has to do with the fact that when psych was originally written and you built up a powerful
system, there is some technical depth you have to deal with, as is the case with most
powerful systems that span years. Have you ever considered, this would help me understand,
because from my perspective, so much of the value of everything you've done with psych and psych
op is the knowledge. Have you ever considered just like throwing away the code base and starting
from scratch, not really throwing away, but sort of moving it to like throwing away that
technical debt, starting with a more updated programming language. Is that throwing away
a lot of value or no? Like, what's your sense? How much of the value is in the silly software
engineering aspect and how much of the value is in the knowledge? So, development of programs in
Lisp proceeds, I think, somewhere between a thousand and fifty thousand times faster than
development in any of what you're calling modern or improved computer languages. Well, there's
other functional languages like Closure and others, but I mean, I'm with you. I like Lisp.
I just wonder how many great programmers there are. They're still like... Yes. So, it is true
when a new inference programmer comes on board, they need to learn some of Lisp. And in fact,
we have a subset of Lisp, which we call cleverly sub-L, which is really all they need to learn.
And so, the programming actually goes on in sub-L, not in full Lisp. And so,
it does not take programmers very long at all to learn sub-L. And that's something which can then
be translated efficiently into Java. And for some of our programmers who are doing, say,
user interface work, then they never have to even learn sub-L. They just have to learn APIs
into the basic psych engine. So, you're not necessarily feeling the burden of... It's extremely
efficient. That's not a problem to solve. Okay. Right. The other thing is, remember that we're
talking about hiring programmers to do inference who are programmers interested in effectively
automatic theorem proving. And so, those are people already predisposed to representing
things in logic and so on. And Lisp really was the programming language based on logic that
John McCarthy and others who developed it basically took the formalisms that Alonzo Church and other
philosophers, other logicians had come up with and basically said, can we basically make a
programming language which is effectively logic? And so, since we're talking about reasoning in
about expressions written in this logical epistemological language and we're doing operations
which are effectively like theorem proving type operations and so on, there's a natural
impedance match between Lisp and the knowledge the way it's represented. I guess you could say
it's a perfectly logical language to use. Oh, yes. Okay, I'm sorry. I'll even let you get away with
that. Okay, I think I appreciate it. So, I'll probably use that in the future without...
Without credit. Without credit. But no, I think the point is that the language you program in
isn't really that important. It's more that you have to be able to think in terms of, for instance,
creating new helpful HL modules and how they'll work with each other and looking at things that
are taking a long time and coming up with new specialized data structures that will make this
efficient. So, let me just give you one very simple example, which is when you have a
transitive relation like larger than, this is larger than that, which is larger than that,
which is larger than that. So, the first thing must be larger than the last thing. Whenever you
have a transitive relation, if you're not careful, if I ask whether this thing over here is larger
than the thing over here, I'll have to do some kind of graph walk or theorem proving that might
involve like five or 10 or 20 or 30 steps. But if you store, redundantly, store the transitive
closure, the cleaning star of that transitive relation, now you have this big table, but you
can always guarantee that in one single step, you can just look up whether this is larger than that.
And so, there are lots of cases where storage is cheap today, and so by having this extra
redundant data structure, we can answer this commonly occurring type of question very, very
efficiently. And let me give you one other analogy, analog of that, which is something we call rule
macro predicates, which is, we'll see this complicated rule, and we'll notice that things
very much like it syntactically come up again and again and again. So, we'll create a whole brand
new relation or predicate or function that captures that and takes maybe not two arguments,
takes maybe three, four or five arguments, and so on. And now we have effectively converted
some complicated if then rule that might have to have inference done on it into some ground
atomic formula, which is just the name of a relation and a few arguments and so on.
And so, converting commonly occurring types or schemas of rules into brand new predicates,
brand new functions, turns out to enormously speed up the inference process. So, now we've
covered about four of the 150 good ideas I said. So, that idea in particular is like a nice
compression that turns out to be really useful. That's really interesting. I mean, this whole
thing is just fascinating from a philosophical, there's part of me, I mean, it makes me a little
bit sad because your work is both from a computer science perspective, fascinating on the inference
engine from epistemological philosophical aspect, fascinating. But it is also you're running a company
and there's some stuff that has to remain private and it's sad.
Well, here's something that may make you feel better, a little bit better.
We've formed a not-for-profit company called the Knowledge Activitization Institute,
NACS, KNAX. And I have this firm belief with a lot of empirical evidence to support it that
the education that people get in high schools, in colleges, in graduate schools and so on
is almost completely orthogonal to, almost completely irrelevant to how good they're going
to be at coming up to speed in doing this kind of ontological engineering and writing these
assertions and rules and so on in psych. And so, very often, we'll interview candidates who have
their PhD in philosophy, who've taught logic for years and so on. And they're just awful.
But the converse is true. So one of the best ontological engineers we ever had never graduated
high school. And so the purpose of Knowledge Activitization Institute, if we can get some
foundations to help support it, is identify people in the general population, maybe high school
dropouts, who have latent talent for this sort of thing, offer them effectively scholarships to
train them and then help place them in companies that need more trained ontological engineers,
some of which would be working for us, but mostly would be working for partners or customers or
something. And if we could do that, that would create an enormous number of relatively very
high paying jobs for people who currently have no way out of some situation that they're locked
into. So is there something you can put into words that describes somebody who would be great
ontological engineering? So what characteristics about a person make them great at this task?
This task of converting the messiness of human language and knowledge into formal logic.
This is very much like what Alan Turing had to do during World War II in trying to find people to
bring to Bletchley Park, where he would publish in the London Times cryptic crossword puzzles,
along with some innocuous looking note, which essentially said, if you were able to solve
this puzzle in less than 15 minutes, please call this phone number and so on. Or back when I was
young, there was the practice of having matchbooks, where on the inside of the matchbook, there would
be a, can you draw this? You have a career in art, a commercial art, if you can copy this drawing.
So yes, the analog of that. Is there a little test to get to the core of whether it can be good
or not? So part of it has to do with being able to make and appreciate and react negatively
appropriately to puns and other jokes. So you have to have a sense of humor. And if you're good at
telling jokes and good at understanding jokes, that's one indicator.
Like puns? Like dad jokes?
Yes. Well, maybe not dad jokes, but funny jokes.
I think I'm applying to work as sacro.
Another is if you're able to introspect. So very often, we'll give someone a simple question and
we'll say, why is this? And sometimes they'll just say, because it is, that's a bad sign.
But very often, they'll be able to introspect and so on. So one of the questions I often ask is,
I'll point to a sentence with a pronoun in it and I'll say, the reference of that pronoun is
obviously this noun over here. How would you or I or an AI or a five-year-old, 10-year-old child
know that that pronoun refers to that noun over here? And often, the people who are going to be
good at ontological engineering will give me some causal explanation or will refer to some
things that are true in the world. So if you imagine a sentence like, the horse was led into the barn
while its head was still wet. And so its head refers to the horse's head. But how do you know that?
And so some people will say, I just know it. Some people will say, well, the horse was the
subject of the sentence. And I'll say, okay, well, what about the horse was led into the barn
while its roof was still wet? Now, its roof obviously refers to the barn. And so then they'll say,
oh, well, that's because it's the closest noun. And so basically, if they try to give me answers
which are based on syntax and grammar and so on, that's a really bad sign. But if they're able to
say things like, well, horses have heads and barns don't and barns have roofs and horses don't,
then that's a positive sign that they're going to be good at this because they can introspect on
what's true in the world that leads you to know certain things. How fascinating is it that getting
a PhD makes you less capable to introspect deeply about this? Oh, I wouldn't go that far. I'm not
saying that it makes you less capable. Let's just say it's independent of how good people are.
You're not saying that I'm saying that there's a certain, it's interesting that for a lot of people,
PhDs, sorry, philosophy aside, that sometimes education narrows your thinking versus expands it.
Yes. It's kind of fascinating. And for certain, when you're trying to do ontological engineering,
which is essentially teach our future AI overlords how to reason deeply about this world and how to
understand it, that requires that you think deeply about the world. So I'll tell you a sad story
about mathcraft, which is why is that not widely used in schools today? We're not really trying to
make big profit on it or anything like that. When we've gone to schools, their attitude has been,
well, if a student spends 20 hours going through this mathcraft program from start to end and so
on, will it improve their score on this standardized test more than if they spent 20 hours just doing
mindless drills of problem after problem after problem? And the answer is, well, no, but it'll
increase their understanding more and their attitude is, well, if it doesn't increase their score
on this test, then that's not, we're not going to adopt it. That's sad. I mean, that's a whole,
that's a whole another three, four hour conversation about the education system. But
let me ask you, let me go super philosophical as if we weren't already. So in 1950, Alan Turing
wrote the paper that formulated the Turing test. Yes. And he opened the paper with a question,
can machines think? So what do you think? Can machines think? Let me ask you this question.
Absolutely. Machines can think certainly as well as humans can think, right? We're meat machines,
just because they're not currently made out of meat is just an engineering solution decision
and so on. So of course, of course, machines can think. I think that there was a lot of
damage done by people misunderstanding Turing's imitation game and focus on trying to get a chat
bot to fool other people into thinking it was human and so on. That's not a terrible test in
and of itself, but it shouldn't be your one and only test for intelligence.
So in terms of tests of intelligence, with the Lobner Prize, which is a very kind of,
you want to say a more strict formulation of the Turing test as originally formulated,
and then there's something like Alexa Prize, which is more, I would say a more interesting
formulation of the test, which is ultimately the metric is how long does a human want to talk to
the AI system? So the goal is you want it to be 20 minutes. It's basically not just have a
convincing conversation, but more like a compelling one or a fun one or an interesting one.
That seems like more to the spirit, maybe, of what Turing was imagining. But what for you
do you think in the space of tests is a good test? Like when you see a system based on psych
that passes that test, you'd be like, damn, we've created something special here.
The test has to be something involving depth of reasoning and recursiveness of reasoning,
the ability to answer repeated why questions about the answer you just gave.
It's how many why questions in a row can you keep answering?
Something like that.
Just have like a young, curious child and an AI system. And how long will an AI system last
before it wants to quit? Yes. And again, that's not the only test. Another one has to do with
argumentation. In other words, here's a proposition. Come up with pro and con arguments for it and try
and give me convincing arguments on both sides. And so that's another important
kind of ability that the system needs to be able to exhibit in order to really be intelligent, I
think. If you look at IBM Watson and certain impressive accomplishments for very specific
tests, almost like a demo, there's some, like I talked to the guy who led the Jeopardy effort.
And there's some kind of hard coding heuristics tricks that you try to pull it all together to
make the thing work in the end for this thing. That seems to be one of the lessons with AI is
like that's the fastest way to get a solution that's pretty damn impressive.
So here's what I would say is that as impressive as that was, it made some mistakes. But more
importantly, many of the mistakes it made were mistakes which no human would have made. And
so part of the new or augmented Turing tests would have to be and the mistakes you make are
ones which humans don't basically look at and say, what? So for example, there was a question
about which 16th century Italian politician blah, blah, blah, and Watson said Ronald Reagan.
So most Americans would have gotten that question wrong, but they would never have said Ronald
Reagan as an answer because among the things they know is that he lived relatively recently and
people don't really live 400 years and things like that. So that's I think a very important thing
which is if it's making mistakes which no normal sane human would have made, then that's a really
bad sign. And if it's not making those kinds of mistakes, then that's a good sign. And I don't
think it's any one very, very simple test. I think it's all of the things you mentioned, all the
things I mentioned, there's really a battery of tests which together if it passes almost all of
these tests, it would be hard to argue that it's not intelligent. And if it fails several of these
tests, it's really hard to argue that it really understands what it's doing and that it really
is generally intelligent. So to pass all of those tests, we've talked a lot about psych and
knowledge and reasoning. Do you think this AI system would need to have some other human-like
elements? For example, a body or a physical manifestation in this world and another one
which seems to be fundamental to the human experience is consciousness. The subjective
experience of what it's like to actually be you. Do you think it needs those to be able to
pass all of those tests and to achieve general intelligence? It's a good question. I think in
the case of a body, no, I know there are a lot of people like Penrose who would have disagreed
with me and others, but no, I don't think it needs to have a body in order to be intelligent.
I think that it needs to be able to talk about having a body and having sensations and having
emotions and so on. It doesn't actually have to have all of that, but it has to understand it in
the same way that Helen Keller was perfectly intelligent and able to talk about colors and
sounds and shapes and so on, even though she didn't directly experience all the same things
that the rest of us do. Knowledge of it and being able to correctly make use of that is
certainly an important facility, but actually having a body, if you believe that that's just
a kind of religious or mystical belief, you can't really argue for or against it, I suppose.
It's just something that some people believe. What about an extension of the body which is
consciousness? It feels like something to be here. Sure, but what does that really mean?
If I talk to you, you say things which make me believe that you're conscious. I know that I'm
conscious, but you're just taking my word for it now. But in the same sense, psych is conscious
in that same sense already, where of course it understands, it's a computer program, it understands
where and when it's running, it understands who's talking to it, it understands what its task is,
what its goals are, what its current problem is that it's working on, it understands how long it's
spent on things, what it's tried, it understands what it's done in the past and so on. If we want
to call that consciousness, then yes, psych is already conscious, but I don't think that I would
describe anything mystical to that. Again, some people would, but I would say that other than
our own personal experience of consciousness, we're just treating everyone else in the world,
so to speak, at their word about being conscious. If a computer program, if an AI is able to exhibit
it all the same kind of response as you would expect of a conscious entity, then
doesn't it deserve the label of consciousness just as much?
So there's another burden that comes with this whole intelligence thing that humans got is
the extinguishing of the light of consciousness, which is kind of realizing that we're going to be
dead someday. And there's a bunch of philosophers like Ernest Becker who kind of think that this
realization of mortality and then fear, sometimes they call it terror of mortality,
is one of the creative forces behind human condition. It's the thing that drives us.
Do you think it's important for an AI system? One psych proposed that it's not human,
and it's one of the moderators of his contents. There's another question it could ask,
which is like it kind of knows that humans are mortal. Am I mortal? And I think one really
important thing that's possible when you're conscious is to fear the extinguishing of
that consciousness, the fear of mortality. Do you think that's useful for intelligence?
Thinking like I might die and I really don't want to die?
I don't think so. I think it may help some humans to be better people. It may help some
humans to be more creative and so on. I don't think it's necessary for AIs to believe that they
have limited life spans and therefore they should make the most of their behavior. Maybe
eventually the answer to that and my answer to that will change. But as of now, I would say
that that's almost like a frill or a side effect that is not. In fact, if you look at most humans,
most humans ignore the fact that they're going to die most of the time.
Well, but that's like this goes to the white space between the words.
So what Ernest Becker argues is that that ignoring is reliving an illusion that we constructed
on the foundation of this terror. So we escape life as we know it, pursuing things, creating
things, love, everything we can think of that's beautiful about humanity is just trying to escape
this realization that we're going to die one day. That's his idea and I think, I don't know if I 100%
believe in this, but it certainly rhymes. It seems like to me like it rhymes with the truth.
Yeah, I think that for some people that's going to be a more powerful factor than others.
Clearly Doug is talking about Russians. And I think that
some Russians, clearly it infiltrates all of Russian literature.
And AI doesn't have to have fear of death as a motivating force in that we can build in motivation.
So we can build in the motivation of obeying users and making users happy and making others
happy and so on. And that can substitute for this sort of personal fear of death that sometimes
leads to bursts of creativity in humans. Yeah, I don't know. I think AI really
needs to understand death deeply in order to be able to drive a car, for example.
I think there's just some... No, I really disagree. I think it needs to understand
the value of human life, especially the value of human life to other humans,
and understand that certain things are more important than other things. So it has to have
a lot of knowledge about ethics and morality and so on. But some of it is so messy that it's
impossible to encode, for example. I disagree. So if there's a person dying right in front of us,
most human beings would help that person, but they would not apply that same ethics to everybody
else in the world. This is the tragedy of how difficult it is to be a doctor, because they
know when they help a dying child, they know that the money they're spending on this child
cannot possibly be spent on every other child that's dying. And that's a very difficult to
encode decision. Perhaps it could be formalized. Oh, but I mean, you're talking about autonomous
vehicles, right? So autonomous vehicles are going to have to make those decisions
all the time of what is the chance of this bad event happening? How bad is that compared to
this chance of that bad event happening and so on? And when a potential accident is about to happen,
is it worth taking this risk? If I have to make a choice, which of these two cars am I going to hit
and why? See, I was thinking about a very different choice when I'm talking about your mortality,
which is just observing Manhattan-style driving. I think that humans, as an effective driver,
needs to threaten pedestrians' lives a lot. There's a dance, I've watched pedestrians a lot,
I've worked on this problem. And it seems like if I could summarize the problem of a pedestrian
crossing is the car with this movement is saying, I'm going to kill you. And the pedestrian is saying,
maybe, and then they decide and say, no, I don't think you have the guts to kill me. And you walk,
and they walk in front and they look away. And there's that dance, the pedestrian,
as this is a social contract, that the pedestrian trusts that once they're in front of the car,
and the car is sufficiently from a physics perspective able to stop, they're going to stop.
But the car also has to threaten that pedestrian. It's like, I'm late for work, so you're being
kind of an asshole by crossing in front of me. But life and death is part of the calculation here,
and that equation is being solved millions of times a day, very effectively. That game theory,
whatever that formulation is, I don't know if it's as simple as some formalizable game theory
problem. It could very well be in the case of driving and in the case of most of human society.
I don't know. But yeah, you might be right that the fear of death is just one of the quirks
the way our brains have evolved. But it's not a necessary feature of intelligence.
Drivers certainly are always doing this kind of estimate, even if it's unconscious, subconscious,
of what are the chances of various bad outcomes happening? Like, for instance,
if I don't wait for this pedestrian or something like that, and what is the downside to me going
to be in terms of time wasted talking to the police or getting sent to jail or things like that?
And there's also emotion. People in their cars tend to get
irrationally angry. That's dangerous. But think about this is all part of why I think that
autonomous vehicles, truly autonomous vehicles, are farther out than most people do, because
there is this enormous level of complexity which goes beyond mechanically controlling the car.
And I can see the autonomous vehicles as a kind of metaphorical and literal accident waiting to
happen. And not just because of their overall incurring versus preventing accidents and so on,
but just because of the almost voracious appetite people have for bad stories about powerful
companies and powerful entities. When I was at a coincidentally Japanese fifth generation computing
system conference in 1987, while I happened to be there, there was a worker at an auto plant
who was despondent and committed suicide by climbing under the safety chains and so on and
getting stamped to death by a machine. And instead of being a small story that said despondent worker
commits suicide, it was front page news that effectively said robot kills worker, because the
public is just waiting for stories about AI kills phonogenic family of five type stories. And even
if you could show that nationwide, this system saved more lives than it cost and saved more
injuries, prevented more injuries than it caused and so on. The media, the public, the government
is just coiled and ready to pounce on stories where in fact it failed, even if there are relatively
few. Yeah, it's so fascinating to watch us humans resisting the cutting edge of science and technology
and almost like hoping for it to fail and constant, you know, this just happens over and over and
over throughout history. Well, or even if we're not hoping for it to fail, we're fascinated by it.
And in terms of what we find interesting, the one in a thousand failures much more interesting than
the 999 boring successes. So once we build an AGI system, say psych is some part of it and
say it's very possible that you would be one of the first people that can sit down in the room,
let's say with her and have a conversation, what would you ask her? What would you talk about?
So looking at all of the content out there on the web and so on,
what are the, what are some possible solutions to big problems that the world has that
that people haven't really thought of before that are not being properly or at least adequately
pursued? What are some novel solutions that you can think of that we haven't that might work
and that might be worth considering? So that is a damn good question. Given that the AGI
is going to be somewhat different from human intelligence, it's still going to make some
mistakes that we wouldn't make, but it's also possibly going to notice some blind spots we have.
And I would love it as a test of is it really on a par with our intelligences,
can it help spot some of the blind spots that we have?
So the two part question of can you help identify what are the big problems in the world
and to what are some novel solutions to those problems that are not being talked about by anyone?
And some of those may become infeasible or reprehensible or something, but some of them
might be actually great things to look at. If you go back and look at some of the most
powerful discoveries that have been made like relativity and superconductivity and so on,
a lot of them were cases where someone took seriously the idea that there might actually be
a non-obvious answer to a question. So in Einstein's case, it was, yeah, the Lorentz
transformation is known. Nobody believes that it's actually the way reality works. What if it were
the way that reality actually worked? So a lot of people don't realize he didn't actually work
out that equation, he just sort of took it seriously. Or in the case of superconductivity,
you have this V equals IR equation where R is resistance and so on. And it was being mapped at
lower and lower temperatures, but everyone thought that was just bump on a log research
to show that V equals IR always held. And then when some graduate student
got to a slightly lower temperature and showed that resistance suddenly dropped off, everyone
just assumed that they did it wrong. And it was only a little while later that they realized it
was actually a new phenomenon. Or in the case of the H. pylori bacteria causing stomach ulcers,
where everyone thought that stress and stomach acid caused ulcers. And when a doctor in Australia
claimed it was actually a bacterial infection, he couldn't get anyone seriously to listen to him.
And he had to ultimately inject himself with the bacteria to show that he suddenly developed a
life-threatening ulcer in order to get other doctors to seriously consider that. So there are
all sorts of things where humans are locked into paradigms, what Thomas Kuhn called paradigms.
And we can't get out of them very easily. So a lot of AI is locked into the deep learning machine
learning paradigm right now. And almost all of us and almost all sciences are locked into
current paradigms. And Kuhn's point was pretty much you have to wait for people to die in order
for the new generation to escape those paradigms. And I think that one of the things that would
change that sad reality is if we had trusted AGI's that could help take a step back and question
some of the paradigms that we're currently locked into.
Yeah, it would accelerate the paradigm shifts in human science and progress.
You've lived a very interesting life where you thought about big ideas and you stuck with them.
Can you give advice to young people today, somebody in high school, somebody undergrad
about career, about life?
I'd say you can make a difference. But in order to make a difference, you're going to have to
have the courage to follow through with ideas which other people might not immediately understand
or support. You have to realize that if you make some plan that's going to take an extended period
of time to carry out, don't be afraid of that. That's true of physical training of your body.
That's true of learning some profession. That's also true of innovation. That some innovations
are not great ideas you can write down on a napkin and become an instant success if you
turn out to be right. Some of them are paths you have to follow, but remember that you're mortal.
Remember that you have a limited number of decade sized bets to make with your life
and you should make each one of them count. That's true in personal relationships. That's
true in career choice. That's true in making discoveries and so on. If you follow the path
of least resistance, you'll find that you're optimizing for short periods of time and before
you know it, you turn around and long periods of time have gone by without you ever really making
a difference in the world. When you look at the field that I really love is artificial intelligence
and there's not many projects. There's not many little flames of hope that've been carried out
for many years for decades and psych represents one of them. That in itself is just a really
inspiring thing. I'm deeply grateful that you would be carrying that flame for so many years and I
think that's an inspiration to young people. That said, you said life is finite and we talked
about mortality as a feature of AGI. Do you think about your own mortality? Are you afraid of death?
Sure. I'd be crazy if I weren't. As I get older, I'm now over 70. As I get older,
it's more on my mind especially as acquaintances and friends and especially mentors one by one
are dying so I can't avoid thinking about mortality. I think that the good news from
the point of view in the rest of the world is that that adds impetus to my need to succeed
in a small number of years in the future. You have a deadline. Exactly. I'm not going to have
another 37 years to continue working on this so we really do want psych to make an impact in the
world commercially, physically, metaphysically in the next small number of years, two, three,
five years, not two, three, five decades anymore. This is really driving me toward
this commercialization and increasingly widespread application of psych. Whereas before,
I felt that I could just sit back, roll my eyes, wait till the world caught up and now I don't
feel that way anymore. I feel like I need to put in some effort to make the world aware of what we
have and what it can do. The good news from your point of view is that that's why I'm sitting here.
You're going to be more productive. I love it and if I can help in any way, I would love to
from a programmer perspective. I love especially these days just contributing in small and big
ways. If there's any open sourcing from an MIT side and the research, I would love to help.
But bigger than psych, like I said, it's that little flame that you're carrying of artificial
intelligence, the big dream. What do you hope your legacy is?
That's a good question. People think of me as one of the pioneers or inventors of
the AI that is ubiquitous and that they take for granted and so on. Much the way that today we
look back on the pioneers of electricity or the pioneers of similar types of technologies and so
on. It's hard to imagine what life would be like if these people hadn't done what they did.
So that's one thing that I'd like to be remembered as. Another is that...
So the creator, one of the originators of this gigantic knowledge store and acquisition system
that is likely to be at the center of whatever this future AI thing will look like.
Yes, exactly. And I'd also like to be remembered as someone who wasn't afraid to
spend several decades on a project in a time when almost all of the other forces, institutional forces
and commercial forces, are incenting people to go for short-term rewards.
And a lot of people gave up. A lot of people that dreamt the same dream as you gave up.
Yes. And you didn't. Yes.
I mean, Doug, it's truly an honor. This was a long time coming. A lot of people bring up your work
specifically and more broadly, philosophically of this is the dream of artificial intelligence.
This is likely a part of the future. We're so focused on machine learning applications,
all that kind of stuff today, but it seems like the ideas that carry us forward
is something that will be at the center of this problem they're all trying to solve,
which is the problem of intelligence, emotional and otherwise. So thank you so much.
It's such a huge honor that you would talk to me and spend your valuable time with me today.
Thanks for talking. Thanks, Lex. It's been great.
Thanks for listening to this conversation with Doug Glennett. To support this podcast,
please check out our sponsors in the description. And now let me leave you some words from Mark
Twain about the nature of truth. If you tell the truth, you don't have to remember anything.
Thank you for listening. I hope to see you next time.