The following is a conversation with Doug Lenit, creator of Psych, a system that for close to 40

years, and still today, has sought to solve the core problem of artificial intelligence,

the acquisition of common sense 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 Psych,

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 common

sense 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 Lenit. Psych 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 common sense

knowledge, which is a lot harder than it sounds. Can you elaborate on this mission and maybe

perhaps speak to the various subgoals 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 common sense. 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 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 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 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 would answer you. And 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 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 were cars all around us. And 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 a common sense knowledge to access. It's the act of 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, usable fashion as opposed to say like library books sitting on a shelf and so on, where

the knowledge might be there, but 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 a 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 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 10th 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 like what, in order, you mentioned philosophers, in order to capture this

world and represent it in a logical way and with a form of logic, like how many statements are

required? Is it five? Is it 10? Is it 10 trillion? Is it like that? That's as far as I understand is

probably still an open question. It may forever be an open question just to say like 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.

We'll keep adding more.

Although it's okay. The first and the third are related. 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 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,

Eurisco, 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?

And I expected there would be an enormous divergence of opinion and computation. But

amazingly, everyone got an answer which was around a million. And 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. And 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, an 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 what 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, NCRA. 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 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 sent Admiral Bob Inman, who was the person running MCC, came and talked to me and said,

look, professor, you're talking about doing this project, it's going to involve

centuries of effort. You've only got a handful of graduate students, you do the math, it's going to

take you 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'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 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 application specific knowledge about health care 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 knowledge 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 Psych knows.

That's actually one of the fundamental laws of nature, I believe.

I wouldn't call it a law. It's more like a...

It seems to work every time. So it sure looks like a law. I don't know.

So 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?

And for that, we 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. And 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. So 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?

So if the sentence says, Fred Smith robbed the Third National Bank, period, he was sentenced to

20 years in prison, period. Well, 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, dot, dot, dot, then you assume it's the judge at his trial. If my next sentence starts out

something like, the arresting officer, dot, dot, dot, you assume that it was the police officer

who arrested him after he committed the crime and so on. So 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 Enquirer, where it's

like, oh, we don't believe this, then we introspect on why don't we believe it. So there are things

like, B17 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? Or another thing we look for are contradictions,

contradictions, things which can't both be true. And we say, 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. So it's like, why do we believe that at least one of these articles must be wrong and so

on? So 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. So 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. So 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. So

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. In general, things are

not both alive and dead at the same time. Yeah. 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, there are 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, even if it's a thousand 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 like roadrunner 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, 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 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 context 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 broader, let's say, a century 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 have 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 Stanford.

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. You know,

this is how 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, you know, Drake equation, and, you know,

they, you know, 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 get, be able to, 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 Wikidata 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 sort of sitting off in some database or

some other more static storage. So they're almost like platonic forms. So like 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 were 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. 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 facts isn't 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, Ed Fagamon, 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,

you know, 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 not,

when we're grappling with these exceptionally difficult ideas, we're not always, 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 the 1984 to today? Have you gotten a stronger sense of just how much

knowledge is required? 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. 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, you know, Marvin Misk is very

confident in the 60s. Yes. Right. What's your sense if you, you know, 200 years from now,

you're still, you know, you're not going to be any longer in this 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 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 although 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. We'll 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 prelinguistic 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,

you know, those poor people, they weren't really human, were they?

Mm hmm. 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, it's 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 innumerate.

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.

You're right. And I could tell you could play both sides of this and 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 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.

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 look at the world and make your

own reasoned 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 one or 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 a 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...

The AIs will. The AIs will, the AIs 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. And from my perspective, integrating emotional intelligence, so even things like

friendship and companionship and love into those kinds of systems, as opposed to helping you just

grow intellectually as a human being, allow you to grow emotionally, which is ultimately what makes

life amazing, is to sort of, you know, the old pursuit of happiness. So it's not just the pursuit

of reason, it's the pursuit of happiness too. 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

the acquisition of new knowledge has been done in this very interesting way, but primarily by humans

doing this. Just 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, right? 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? Of

course I 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, if you survived to that age, presumably you already got pieces of

common sense. Like 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 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 at PsychCorp. 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 those, 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 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 be a teacher in that way. So 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

a beautiful vision. I'll have to ask you about Semantic Web 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 has been able to do.

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 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? How should I feel about that

and why and so on? So 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 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 $100 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

a 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 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 docs are human interpretable.

Exactly. 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 answer is no, that

strongly disconfirms your whole causal chain. And if the answer is yes, that somewhat confirms

that causal chain. And so using that, we were able to take these correlations from this GWAS

database and we were able to essentially 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 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. 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, GPT3

and so on, that 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 now playing with a

pretty cool thing called OpenAI Codex, which is generating programs from documentation. 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 sort of ending up with the kind

of wacky brittlenesses that we see, for example, in GPT3, where you'll tell it a story about

someone plotting to poison someone and so on. And then GPT3 says, you say, what's the very next

sentence? And 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. Or if you go to Siri and I think I have, 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 and so on. So 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. However, if your goal is to save the life of your child who

has some medical problem or your goal is to be able to drive for the next 10,000 hours of driving

without getting into a fatal accident and so on, then error rates down at the 10% level or even

the 1% level are not really acceptable. I like the model of where that learning happens at the edge

and then you kind of think of knowledge as this sphere. So you want a large sphere because the

learning is happening on the surface. Exactly. So 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. One of the anecdotes I could go back and give you about why I feel so

strongly about this personally was in 1980, 1981, 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 Shortliffe, was another assistant professor

in computer science at Stanford at the time. And he'd been building a program called MISON,

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

and 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 an 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 with Psych are connected, integrated? 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. So, if you have 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 the message to Romeo saying that she

was going to do this, but the friar didn't. So, you're able to represent and reason with these

much, much, much more complicated expressions that go way, way beyond what simple three,

as it were, three word or four word 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 it'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 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 surface level hacks that work exceptionally well,

but they don't understand 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 webpage that seemed to

satisfy them eventually, and that's all. So, it had actual 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 sort of 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 the 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 knowledge graph 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 triples,

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 believes, desires, intends, 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 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 Semantic 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 Semantic Web dream, which is what we all want.

From a flip side of that, I always wondered… I've 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 I can help

Psych interpret the website I create? Like this, again, pushing onto the Semantic Web dream,

is there something from the creator perspective that could be done? And one of the things you

said with Psych Orb and Psych that you're doing is the tooling side, making humans more powerful.

But is there the other humans on 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 Psych, 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 Psych 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 hundred people or the next hundred thousand people who come here.

And if it doesn't take too much effort 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 may 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 I actually kind of think, I think

it'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 mean, 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, like, annoying, like correcting an annoying system, more like teaching a childlike,

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 enormously powerful type

of metaphor, just 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 school room 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 Pieta. 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.

Yeah, 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,

right? Like born or 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, at least 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 and 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 the 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. So 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,

you know, am I a person? And we had to like tell it very sadly, no, you're not. So the moments

like that where it asks questions that are unintentionally poignant are worth treasuring.

Wow, that is powerful. That's such a powerful question. It has to do with basic controller

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 Alexes 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 give, 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 the start of a 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. And all of that is extremely important

in getting Psych to understand things. For example, in coming up with scenarios. So 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 terrorists 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 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 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, will the 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,

right? 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, 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 terceness where you have as few words 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

like 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'd

swear he or she would work on work and work until they found some meaning in this sort of Chauncey

Gardner like 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. Exactly. 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 one liners 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. But a lot of behavior is in the eye of the beholder and a lot of the

meaning is in the eye of the beholder. One of Minsky's early programs was begging program.

Are you familiar with this? So this is 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. And so 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. So the operator would give it another 20 seconds, it would wait, it says, I'm almost done,

I need a little bit more time. So at the end he'd get this printout and he'd be charged for like 10

times as much computer time as his job control card. And he'd say, look, I put 10 seconds,

30 seconds here, you're charging me for five minutes, I'm not going to pay for this. And

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? But I mean, he's, again, like your work, his work is, you know,

he's 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. So 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.

So 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. And if you do that, and 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 know, you've worked on Psych for 37 over that many years. Have you ever considered quitting?

I mean, has it been too much? So I'm sure there's an optimism in the early days that this is going

to be way easier. And let me ask you another way too, because I've talked to a few people on this

podcast, AI folks, that bring up Psych as an example of a project that has a beautiful vision and is a

beautiful dream, but it never really materialized. That's how it's spoken about. I suppose you could

say the same thing about neural networks and all ideas until they are. So why do you think people

say that, first of all? And 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. And 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 secondhand or thirdhand said about us.

So thank you for doing this podcast, by the way. 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%. Maybe N is maybe 90%. 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. So I think that's one reason. A, 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 and so on.

Company X here are like old companies maybe you've never even heard of like Lycos or something like

that. So 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 and we're making progress and we're now so close to done that almost all of our work is

commercial applications of our technology. So 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. So there's so many questions I want to ask. So 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. And the other way, I kind of want to ask you about your thoughts on this. So

you've had like you said in government and you had 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. So that's one way to operate and another way to operate is more in the open

where it's more consumer facing. And so you know hence something like open cycle is born at some

point or there's... No that's a misconception. Oh well this let's go there. So what is open

cycle and how is it born? Two things I want to say and I want to say each of them before the other

so it's going to be difficult. But we'll come back to open cycle in a minute. But 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

and it won't be used in inferences other than for you and so on. 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 and it will be openly available to everyone who has access to psych. So that's

an important constraint that we never went back on even when we got pushback from companies which

we often did who wanted to claim that almost everything they were telling us was proprietary.

So there's a line between very domain specific company 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 and it's not okay for them to

consider that that is proprietary and there sometimes a company will say well we're the

first ones to pay you to represent that in psych and our attitude is some polite form tough. The

deal is this take it or leave it and in a few cases they've left it and 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. 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 bothered coming to us to get access to the

full psych. But there's two parts to open psych. So one is convincing people on the idea and 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 version of TensorFlow. It's still dealing with that. They had

to basically break compatibility with the past several times and that's only over a period of

a couple 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 could develop a community that builds 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 open psych, but all the different kinds of tooling. So 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 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 will be those publications and academic work and all that kind of stuff. And also

the tooling of adding to the knowledge base, right? Like developing, you know, there's 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. 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 face 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 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 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 tradeoff 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 knowledge.

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 1001st 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 loss deficiency. 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 minimal knowledge needed. 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

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 kind of heuristic modules that really help improve the inference. How hard,

in general, is this? Because you mentioned higher order logic. In the general theorem prover sense,

it's intractable, very difficult problem. Yes. 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 the... I love that. I love to hang out with

those people actually. Oh yes, it's wonderful. But it's very much like the Eloy and the Morlocks

in H.G. Wells Time Machine. So you have the Eloy who only program in the epistemological higher

order logic language. And then you have the Morlocks who are under the ground figuring

out what the machinery is that will make this efficiently operate and so on. And so, you know,

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 in it much anymore because,

you know, 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 Syke, 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 to bytecode.

Yes.

Okay, so that's sort of that's a that that that's a, you know, it's a process that probably has to

do with the fact that when Syke was originally written, and you build up a powerful system,

like 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, this would help me understand, because my

perspective, so much of the value of everything you've done with Syke and Cycorp is the is the

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, you know, Clojure and all that. But I mean,

I'm with you. I like Lisp. I just wonder how many great programmers there are. There's 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 like, 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 about expressions written in this 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.

So 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, thank you. I appreciate it.

So I'll probably use that in the future 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 cleanly 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.

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 and the inference engine from a epistemological

philosophical aspect fascinating, but you know, 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 Axe Immunization Institute,

NAX, 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 Axe Immunization 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

at 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, commercial art, if you can copy this drawing and so on.

So yes, the analog of that.

Is there a little test to get to the core of whether you're going to 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 kind of sense of humor.

And if you're good at telling jokes and good at understanding jokes, that's one

indicator.

Like puns?

Yes.

Like dad jokes?

Yes.

Well, maybe not dad jokes, but funny jokes.

I think I'm applying to work at SACOR.

Another is if you're able to introspect.

So very often, we'll give someone a simple question and we'll say like, why is this?

And sometimes they'll just say, because it is, okay, 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 referent 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 Ph.D. 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.

It's interesting that for a lot of people, Ph.D.s, sorry, philosophy aside, that sometimes

education narrows your thinking versus expands it.

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 we're

not going to adopt it.

That's sad.

I mean, 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 the 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 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.

In terms of tests of intelligence, you know, 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 like, ultimately the metric is how long does a human want to

talk to the AI system?

So it's like if 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.

And 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?

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.

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.

So there's certain, I mean, if you look at IBM Watson and like certain impressive accomplishments

for very specific tests, almost like a demo, right?

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, right?

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 is really a battery

of tests, which together, if it passes almost all of these tests, it'd 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.

So 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?

It's like, well, 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'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 ascribe 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.

And so if a computer program, if an AI is able to exhibit all the same kinds 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, like, it's the thing that drives us.

Do you think it's important for an AI system?

You know, when Psych proposed that it's not human, and it's one of the moderators of

his contents, you know, 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 lifespans, 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 we're living in an illusion that

we constructed on the foundation of this terror.

So we're 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.

So I'm Russian, so 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, like, there...

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, there's...

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 is...

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 hero 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 worked on this problem, and it seems

like the, 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 they 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, 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 is 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 in like, it's part of the calculation here.

And it's that equation is being solved millions of times a day.

Yes.

Very effectively, that game theory, whatever that formulation is.

Absolutely.

I just 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 sort of the fear of death is just one of the quirks

of like the way our brains have evolved, but it's not a necessary feature of intelligence.

Others 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, like people in their cars tend to get irrationally angry.

That's dangerous.

But, you know, 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 commit suicide, it was front

page news that effectively said robot kills worker, because the public is just waiting

for stories about like 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 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 they're 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.

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?

Looking at all of the content out there on the web and so on, what are some possible

solutions to big problems that the world has 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 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 two, 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,