January 27, 2024 • 56 mins
We live in a time where computers can beat the best humans in the world at chess, checkers, poker and video games. But these games are really just demonstrations of how intelligent our machines are growing. They’re growing more intelligent by the hour. This classic episode features a special guest, Tech Stuff's Jonathan Strickland.
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Episode Transcript
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Speaker 1 (00:00):
Hi everyone, it's Josh and for this week's select, I've
chosen our twenty eighteen episode Some Games You would Surely
Lose to a Computer. It's a philosophical discussion about AI
that's disguised as an episode on computer games. Honestly, we
didn't plan it to be like that. It just turned
out that way. We're pretty happy that it did. And
(00:22):
in light of the recent advances with machine learning like
chat GPT, a few of the things we say seemed
naively quaint now. Plus it has a doll up of
our tech stuff colleague Jonathan stricklanet and so that's a bonus.
I hope you enjoy.
Speaker 2 (00:39):
Welcome to Stuff you should know, a production of iHeartRadio.
Speaker 1 (00:49):
Hey, and welcome to the podcast. I'm Josh Clark. There's
Charles w Chuck Bryant, there's Jerry over there. I'm just
gonna come out and tell everybody making fun of me
for some weird reasons, vaguely weird ways. But I'm all right,
So check out his story for you. Okay, I'm going
to take us back to the seventeen seventies and they'll
(01:12):
swing in town of Vienna, not Virginia, not Vanna Georgia,
which you know, that's how they pronounce it, right, Yanna,
Vienna sausages, right, Vienna Austria.
Speaker 3 (01:24):
Have you ever been there?
Speaker 1 (01:26):
Vienna, Austria. No, been to Brussels. That was pretty close.
Speaker 3 (01:30):
Vienna's lovely, I'm sure.
Speaker 1 (01:32):
I think it's a lot like Brussels.
Speaker 3 (01:35):
Very clean, lovely town. I just remember it being very clean.
Speaker 1 (01:39):
Yeah, very clean, gorgeous architecture, weird little angled side streets.
They're very narrow, very pretty town. So we're in Vienna
and there is a dude skulking about going to the
royal Palace in Vienna. His name is Wolfgang von Kempelin,
(01:59):
and he's he's an inventor, he's an engineer. He's a
pretty sharp dude. And he's got with him what would
come to be known as the Turk, but he called
it the mechanical Turk or the automaton chess player, and
that's what it was. It was a wooden figure that
moved mechanically, seated at a cabinet, and on top of
(02:22):
the cabinet was a chessboard. And when he brought it
out to show to the royal court, he would it
was cool kind of but nothing they hadn't seen before,
because automata was kind of a hip thing by then.
Speaker 3 (02:38):
Yeah, people loved building these engineering these automata machines to
do various things. And people are just knocked out by
the fact that, you know, you hide these gears and
levers behind wood or a cloth, and it looks as
though there's a real well not real, but you know.
Speaker 1 (02:57):
What I mean that it's like a real machine.
Speaker 3 (03:00):
Yeah, but not. They weren't fooled anything like is that
a real man? It was, but it was for their time.
It was so advanced looking that it's like us seeing
x Makina in the movie theater.
Speaker 1 (03:12):
Sure does that make sense? Yeah, no, it does make sense.
But imagine seeing like X makinan being like I've seen
this before. This isn't anything special, okay.
Speaker 3 (03:22):
Yeah, And this thing, to be clear, looked like a
is it Zoltar or Zultan from Big Zoltar Zultan?
Speaker 1 (03:31):
I don't know. It's one of those two.
Speaker 3 (03:32):
One of those two. Like this, this guy's wearing a
turban and it's in a glass case like a bust like,
you know, like a chest up thing.
Speaker 1 (03:41):
Yeah, he's seated at this cabinet, so there's no need
for legs or anything like that.
Speaker 3 (03:45):
Yeah.
Speaker 1 (03:46):
But the thing, this is what was amazing about the Turk.
He could play chess, and he could play chess really well.
So yeah, he was like an automaton and he moved
all herky jerky or whatever. But he could play you
in chess, which was a huge, huge advance at the time. Like,
(04:07):
this is something that wouldn't come up again until the
nineteen nineties, more than two hundred years later. This thing,
this automaton could play a human being in chess and
beat them.
Speaker 3 (04:18):
Well. Yeah, and it looked like when the game started,
it would look down at the chessboard and like cock
his head, like what should my first move be?
Speaker 1 (04:25):
Right?
Speaker 3 (04:26):
And if people I love this part. If people tried
to cheat. Apparently Napoleon tried to cheat this thing because
this guy he debuted at the VI and East Court.
But then it, you know, it went on a world tour.
Speaker 1 (04:38):
Yeah, and he was even it was taken over by
a successor to the guy who toured with it.
Speaker 3 (04:43):
Even further, people went nuts for this stuff.
Speaker 1 (04:44):
They did. They loved it because they were like, this
is crazy, I can't believe what I'm seeing. Most people, though,
were not taken in by it, they're like, there's some
trick here. Sure, but von Kempelin and the guy who
came after him, I don't remember his name, they would
demonstrate you could open this happened and you could see
all the workings of the mechanical turk inside.
Speaker 3 (05:05):
Right, So what I was saying is if this thing's
since a cheater like Napoleon supposedly did it, would you
know Napoleon would move a piece out of turner illegally
or something. This dude, the turk Turk one eighty two
would pick up the chess piece, move it back as
if to say like, no, no, Napoleon, let' see what
you're doing. And then if the person attempted to move
(05:26):
it again, I don't know how many times on you
two or three times, eventually it would just go ah
and wipe his hand across the board and knock off
all the pieces. Game over, right, Which is pretty great. Yeah,
it's a nice little feature.
Speaker 1 (05:39):
Yeah it is. But it even showed even more that
this thing was thinking for itself. Yeah, that's the key here, right. Sure,
chess had been for a very long time viewed as
only something that a human would be capable of, because
it took a human intellect, and there was actually a
guy in English engineer, I think he was a mechanical engineer.
(06:03):
His name was Robert Willis. He said that chess was
in the province of intellect alone. So the idea that
there was this automaton playing chess blew people away. But
again people figured out, like, okay, there's something going on here.
We think that von Kempelin is controlling this thing remotely somehow,
(06:24):
maybe using magnets or whatever. Other people hit upon the
idea that there was a small person inside the cabinet
who would hide when the cab when the workings were shown,
when the cabinet was open to show the workings, and
then when the cabinet was closed again and the mechanical
turk started playing, the person that crawled back out and
was actually controlling it. This seems to be the case
(06:45):
that there was a person controlling it, but the idea
that it was it was a machine that could think
and beat humans in chess had like kind of unsettling implications.
Speaker 3 (06:57):
Yeah, this author Philip thick Ness, great name, British author
for sure, Philip Thickness. Yeah, he said, and you know,
people like you said, all those more complicated explanations. In
this article you sent, Astuteley points out that he followed
Occam's razor and basically said, he's got a little kid
(07:19):
in there. He's got a little a little Bobby Fisher
in there that's really good at chess, and that's what's
going on. And other people speculated that other, you know,
little people might be in there, just adults who would
fit in there. But then you know, there's the explanation
that he would open it up and shine a candle
around and say, you know, nothing to see here everyone,
(07:42):
So what should we reveal the real deal?
Speaker 1 (07:46):
Sure, I think I did already.
Speaker 3 (07:49):
Well I don't think you spelled it out as.
Speaker 1 (07:51):
Oh, we'll spell it out.
Speaker 3 (07:52):
There was a little person in there, Yeah, not just
one little person, but they would travel around and recruit people.
I guess people would get tired of being in there.
Speaker 1 (08:00):
Or they'd forget about them and they'd starve and have
to replace them.
Speaker 3 (08:03):
But it really was a trick. There was a little
person in there. They did the same thing as like
the magic acts, you know, when they saw a person
in half. It's that the lady just gets into a
tiny little ball right in one section of that box.
Speaker 1 (08:16):
But my thing is this, like, this is not a
satisfying explanation to me, Chuck.
Speaker 3 (08:20):
I think it's great.
Speaker 1 (08:21):
How did the person keep up with the board above?
Speaker 3 (08:25):
Well, I mean some I don't know if they ever
proved exactly how it was going.
Speaker 1 (08:30):
That's what I'm saying.
Speaker 3 (08:31):
Oh, okay, whether or not I think they that the
zoltar or I'm sorry, the turk was just hollowed out
and you you would just put your arms through the
arm hole turk, you would become the turk and the
churk would fuse.
Speaker 1 (08:48):
That's what some people thought. I think that's what Egar
Allen Poe thought too.
Speaker 3 (08:52):
He Kidney right.
Speaker 1 (08:54):
Other people thought that there was the the the little
person was underneath the in the cabin operating the trick
with levers and stuff like that.
Speaker 3 (09:02):
Well, there could have been a mirror or something, you know,
I guess, like a telescopic mirror.
Speaker 1 (09:07):
That's what's getting me is how would they keep up
with the game?
Speaker 3 (09:10):
Right?
Speaker 1 (09:10):
You could keep track of the game, but how could
you see where the other person moved? You would know
where you moved, but you wouldn't be able to see
where the other person moved. That's what I don't get.
Speaker 3 (09:17):
Just mirrors smoking mirrors.
Speaker 1 (09:19):
Maybe so, But the point is is it was a fake.
It was a fraud, but it raise some really big
questions about the idea of a machine beating a person
at something like chess.
Speaker 3 (09:30):
Yeah, and it really peaked the mind of one Charles
Babbage was he was a kid or young at least
at the time when he saw the Turk in person,
and a few years afterward he began work on something
called the Difference Engine, which was a machine that he
designed to calculate mathematics automatically. So some point to this
(09:54):
is kind of maybe the beginnings of humans trying to
create AI.
Speaker 1 (10:00):
Well yeah, with Babbage's differential machine or difference machine.
Speaker 3 (10:04):
Yeah, difference engine. But at the very least, what this
is is the first that I know of example of
man versus machine, even though it was really man versus
man because it was a man in the machine.
Speaker 1 (10:16):
Right. It was a fraud, Yeah, but it sparked that idea,
It definitely did. And that's something that like chess in particular,
has always been like this idea of like, if you
can teach a machine to play chess, you have really
achieved a milestone. And there's been you know, plenty of programs,
most notably Deep Blue, which we'll talk about. But there's
(10:40):
there's been this idea that like part of AI is chess,
teaching it to play chess. But they, the people who
develop AI, never set out to make a chess playing AI,
just to make a machine that can play chess. That's
not the point. Chess has always been this way to
demons straight the progress of artificial intelligence.
Speaker 3 (11:02):
Yeah, because it's a complex game that you can't just
program it, like it almost has to learn.
Speaker 1 (11:09):
Well, it depends on how you come at it at first, right,
So initially they did try to program it. Okay, there's
this from basically nineteen fifty to the about the mid
like about say nineteen fifty to twenty ten, sixty years right,
That is how they approached AI and chess is you
(11:31):
figured out how to break chess down and explain it
to a computer. Now, what if you could, ideally you
would have this computer or this AI, this artificial intelligence
be able to think about the outcome of every possible
or every possible outcome of a move before making it. Right,
(11:52):
that's just not possible. Still today we don't have computers
that can do that. Right, So what you have to
do is figure out how to create shortcuts for the machine,
give it best practices, that kind of thing. Yeah, And
that was actually laid out in nineteen fifty by a
guy named Claude Shannon who's a father of information theory,
and he wrote a paper with a pretty on the
(12:12):
nose title called Programming a Computer for Playing Chess. And
you have to say it like that when you say
the name.
Speaker 3 (12:19):
Yeah, it's got a question mark at the end, right, But.
Speaker 1 (12:21):
He laid out two big things. One is creating a
function of the different moves, and then another one is
called a mini max. And if those were the two
things that Shannon laid out, and they established about fifty
or sixty years of development in teaching an AI to
(12:42):
play chess.
Speaker 3 (12:43):
Yeah. So this evaluation function is just sort of the base,
the very basis of it all kind of where it starts,
which is you a kind of give a number to
create a numerical evaluation based on the state of the
board at that moment, right, and assign a real number
evaluation to it. So the highest number that you would
(13:06):
shoot for is obviously getting checkmate, getting a king and checkmate.
Speaker 1 (13:11):
Right, right, So what you've just done now is by
assigning a number to a state like the pieces on
a board. What you what you've done is to say,
like shoot for this number. Right, the higher the number,
like you're going to give this aither rule. Now, the
higher the number, the more desirable that this move that
could lead to that higher number. Function. Evaluation function is
(13:34):
what you want to do.
Speaker 3 (13:35):
Right, like capture the night or capture the queen. Capture
the queen would have a higher evaluation number.
Speaker 1 (13:40):
Right exactly. So that's the function. Then there's another one
called the mini max. Yeah, this is pretty great where
you want to minimize the maximum And this is another
shortcut that they taught computers.
Speaker 3 (13:50):
The maximum loss that is right, Yeah.
Speaker 1 (13:52):
So what they what they taught computers to do is
so you no computer can look through an entire game
every possible outcome, but there are computers that can look
pretty far down the line at every possible outcome. And
what you can say is, Okay, you want to find
(14:13):
the evaluation function that is the worst case scenario, the
maximum loss, and then find the move that will minimize
the possibility for that outcome.
Speaker 3 (14:24):
Yeah bye, And this is you're only limited by your
programming power. But by looking not only at the state
of the board right now, but if I make this
move and I move the PAWD to this spot, what
are the next like three moves possibly that could happen
as a result of this move. And you're only limited,
like I said, by programming power. So obviously, the more
(14:45):
juice you have, the more moves ahead that you can
look exactly.
Speaker 1 (14:49):
And then they just shy away from ones with the
higher function number exactly or lower function number, depending on
how you've programmed it. But they're making these decisions based
on these rules. And then there's other things you can do,
like little shortcuts to say, if a decision tree leads
to the other players King being in checkmate, don't even
(15:13):
think about that move any further, don't evaluate any longer,
just abandon it because we would never want to make
that move. So there's all these shortcuts you can do.
And that's what they did to teach computers. That's what
Deep Blue did when it beat Gary Kasparov in nineteen
ninety seven. It was this huge, massive computer that knew
a lot of chess, a lot about chess. It had
(15:35):
a lot of rules, a lot of incredibly intricate programming
that was extremely sharp, and it actually won. It became
the first computer to beat an actual human chess grand
master in like regulation match play.
Speaker 3 (15:51):
Yeah. I mean, and I don't think Kasparov gets enough
credit for being willing to do this, because it was
a big deal for him to lose. It was in
this community and the AI community. It sent shock waves,
and everyone that was alive remembers, even if you didn't
know anything about either, one remembers Deep Blue being all
(16:12):
over the news. It was a really big deal. And
Kasparov put his name on the line and lost.
Speaker 1 (16:17):
Yeah, And I was wondering, Chuck, how how like you
would get somebody to do that.
Speaker 3 (16:22):
I'm sure the Mountain of catch.
Speaker 1 (16:25):
I guess that would probably be part of it. But
I mean, shit, I don't know.
Speaker 3 (16:29):
I bet, I bet that's out there. We just I
just didn't look it up.
Speaker 1 (16:32):
So that's possible. It's also possible that they said, look, man,
like this is chess we're talking about or whatever. But really,
what you're doing is helping advance artificial intelligence.
Speaker 3 (16:42):
Right, because we're not really trying ultimately to win chess games.
We're trying to cure cancer.
Speaker 1 (16:47):
I mean, yeah, we're going to take your title because
we're going to beat you, or our machine's going to
beat you. But even still, you're going to be helping
with cancer. Think of the cancer Casparov. That's probably what
they said.
Speaker 3 (16:59):
Should we take a break?
Speaker 1 (17:00):
Yeah?
Speaker 3 (17:00):
Uh wait, well should we tease our special guests first?
Speaker 1 (17:04):
Is he okay? I can smell him.
Speaker 3 (17:06):
I don't think we even said we're gonna have a
special guest later in the episode, Mister Jonathan Strickland of
Tech Stuff.
Speaker 1 (17:13):
Nice.
Speaker 3 (17:13):
It's just been a long time since, like years since
we had Stricken.
Speaker 1 (17:16):
The last time we had Stricken was like two thousand
and nine with the Necronomicon episode.
Speaker 3 (17:21):
What is what? Where's he been besides sitting in between
us every day?
Speaker 1 (17:24):
It's been a strickling drought, is what it's been.
Speaker 3 (17:27):
Yeah, so Strickland's coming later, but we're going to come
back after this and talk a little bit more about
a man versus machine.
Speaker 1 (17:55):
Okay, dude, So what we just described was how AI
was to play things like chess or to think like
you take something, you figure out how to break it
down into little rules and things that a computer can
think of, right, and then follow these kind of rules
to make the best decision. That's how it used to be.
(18:16):
The way that it's done now that everybody's doing now
is where you are creating a machine that teaches itself.
Speaker 3 (18:24):
Yeah, that's the jam.
Speaker 1 (18:25):
That was the breakthrough. You may have noticed back in
about ty thirteen twenty fourteen, all of a sudden, things
like Siri and Alexa got way better at what they
are doing. They got way less confused. Really, your navigation
app got a lot better. And the reason why is
(18:47):
because this type, this new type of AI, this new
type of machine learning that can teach itself and learn
on its own, just hit the scene and they just
started exploding. And one of the things that they were
first trained on was games.
Speaker 3 (19:02):
Yeah, and it makes sense. And if you thought chess
was complicated and difficult, when it comes to these new
AIS that they're teaching to teach themselves game strategy. They said,
we might as well dive in to the Chinese strategic
game Go because it has been called the most complex
game ever devised by humans. Yeah, and this was actually
(19:24):
that was actually a quote from Demi Hasabi, a neuroscientist
and the founder of deep Mind, which was deep Mind.
They were purchased by Google or were they always part
of Google.
Speaker 1 (19:39):
I don't know if they were a spun off branch
or where they were purchased, but it's one of Google's
AI outfits.
Speaker 3 (19:45):
Well, they're one of the teams, yeah, that are designing
these new programs. And to give you an idea of
how complex Go is, it deals with a board with
different stones and there are ten how do you even
say that?
Speaker 1 (20:00):
Ten to one hundred and seventieth power, So.
Speaker 3 (20:02):
That means one hundred and seventy zeros and take that
number and that's the number of possible configurations of a
go board.
Speaker 1 (20:10):
Right, So, like you say, chess is very complex and complicated,
and it's very difficult to master Go. And I've never
played Go, of you no, So it's supposedly it's easy
to learn.
Speaker 3 (20:21):
Right, but very complicated in its simplicity.
Speaker 1 (20:24):
Right, right, exactly, it's extremely difficult to master. And there
was a guy in the late nineties and I'm guessing
that he was saying this after Deep Blue beat Caspar
ofv It was an astrophysicist from Princeton. He said that
it would probably be one hundred years before a computer
beats a human a go. To give you an idea
(20:45):
of just how complex GO is that deeply would just
be caspar OFV. And this guy's saying it'll still be
one hundred years before anyone gets beat at GO by
a computer.
Speaker 3 (20:56):
And he was someone who knew about this stuff, who
was an astrophysicist. He was just some schmoe at home
and drunken as reclining.
Speaker 1 (21:03):
Is making asinine predictions.
Speaker 3 (21:07):
So and again we've said this before, but I want
to reiterate the people that I think Alpha Go is
the name of this program. The people that created this
at Deep Mind, they wanted to stress that this is
a problem solving program. We're just teaching it this game
at first, just to make it learn and to see
(21:27):
if it can get good at what it does. But
they said it is built with the idea that any
task that has a lot of data that is unstructured
and you want to find patterns in the data and
then decide what to do right, And that's kind of
like what we were talking about. It It crunches down
all these possible options aka data to decide what move
(21:48):
should I make right? And you could apply that Ideally,
they're going to apply this to Alzheimer's and cancer and
all sorts of things.
Speaker 1 (21:54):
Right, it's general purpose thinking, right, Yeah, and thinking on
the fly too, and face with novel stuff. So one
of the reasons why it's good to use games like
chess or Go or whatever, those are called perfect information
games where both players or anybody watching has all the
information that's available on it. There are definite rules or structure.
(22:18):
It's a good proving ground. But as we'll see, AI
makers are getting further and further away from those structure
games as their AI becomes more and more sophisticated, because
the structure and the limitations aren't necessarily needed anymore, because
these things are starting to be able to think on
their own in a very generalized and even creative way.
Speaker 3 (22:40):
Yeah, it's really really interesting. Yeah, the way that they're
like you said earlier before the break, that we don't
have computers that can run all the possibilities. So what
they teach in the case of Alpha Go, this program
teaches itself by playing itself in these games and Go specifically,
and the more it plays itself, the more it learns,
(23:02):
and the more ability it has during a game to
choose a move by narrowing down possibilities. So instead of like,
well there are twenty million different variations here. By playing itself,
it's able to say, well, in this scenario, they're really
only fifty different moves that I could or should make, right,
(23:22):
or that's kind of a simplified way to say it.
Speaker 1 (23:24):
But right, No, but it's true. But that's exactly right.
And what they're doing is basically the same thing that
a human does. It's going back to its memory banks, Yeah,
exact experience, huh, and saying well, I've been faced with
something like this before, and this is what I used
and it was successful. Forty out of fifty times. I'll
do this one. This is a pretty reasonable move. Yeah,
(23:45):
that is what humans do.
Speaker 3 (23:46):
Yeah. Not only I mean, boy, we screwed up the
chess episode, but I get the idea that when you're
a chess master, you don't just think what do the
numbers say and what does the book say?
Speaker 1 (23:55):
Right?
Speaker 3 (23:55):
But man, I did this move that one time and
it didn't go as the book said.
Speaker 1 (24:02):
Right.
Speaker 3 (24:02):
So that's now factored into my thinking.
Speaker 1 (24:04):
Right, except imagine being able to learn from scratch and
get to that point in eight days or eight hours. Yeah,
So that go team the alpha go the first the
first iteration of alpha go, I think they started working
on it in twenty fourteen and in twenty sixteen. At
the end of twenty sixteen, they unleashed it secretly onto
(24:28):
an Alpha Go website and it started just wiping the
floor with everybody. Yeah, everybody's like, this thing's pretty good. Oh,
it's Alpha Go. That was the end of twenty sixteen.
Speaker 3 (24:39):
Okay, so chess had already come and gone. Like, oh,
by this point, you can download a program that's like
Deep Blue, right.
Speaker 1 (24:47):
That was That's a great point. Yeah, like today the
stuff you played chess with on your laptop is even
more advanced than Deep Blue was in the nineties, and
it's just on your laptop. But this is so, this
is Go. This is the end of twenty sixteen. The
end of twenty seventeen, Alpha Go was replaced with Alpha
(25:08):
Go zero. It learned what Alpha Go had taken two
years or three years to learn in forty days by
teaching itself, and.
Speaker 3 (25:19):
It beat the master. Yeah, and finally in May of
twenty seventeen, Alpha Go took on Key g the highest
ranked Go player in the world. Don't know if he
or she still is.
Speaker 1 (25:33):
No. Lisa A. Doll is the current or was until
Alpha Alpha Go beat him?
Speaker 3 (25:39):
Oh man? Yeah, did they get knocked off and Alpha
Go is the champion? Yeah? Like that's that's not fair.
Speaker 1 (25:45):
I if it's match play and the player, the human
player is accepted a challenge from the computer, I don't
see why it wouldn't be the world champion.
Speaker 3 (25:56):
Or do they just now say on websites like human champion?
Maybe in italics? What's like a sneer?
Speaker 1 (26:03):
Right? Maybe? Yeah?
Speaker 3 (26:06):
Interesting?
Speaker 1 (26:06):
What do they call that? Wetwear? Like your brain, your
neurons and all that. What instead of hardware? It's wetwear?
Speaker 3 (26:13):
Oh? I don't know about that.
Speaker 1 (26:14):
I think that's the term for it.
Speaker 3 (26:16):
What does that mean? Though?
Speaker 1 (26:17):
It means like you you have a substrate, right your intelligence?
Your intellect is based on your neurons and they're firing
all that stuff and it's wet and squishy and meat.
Then there's hardware that you can do the same thing on,
you can build intelligence on, but it's hardware, it's not wetwear.
Speaker 3 (26:35):
Oh.
Speaker 1 (26:35):
Interesting, so that's probably it. It's the wetwear champion versus
the hardware champion. But wetwear is italicized with the sneer.
Speaker 3 (26:44):
So where things really got interesting because you were talking
earlier about what is it with the chest and go?
What do they called? What kind of games?
Speaker 1 (26:53):
Perfect information games?
Speaker 3 (26:55):
Right? Then you think And my first thought when you
said that was well, yeah, and then there's there's games
like poker like Texas Hold Them where there are a
set of rules. But poker is not about the set
of rules. It is about sitting down in front of
whatever five or six people and lying, bluffing and getting
(27:15):
away with it in your game face being bluff Like
there's so many human emotions and contextual clues and micro
expressions and all these things, like surely you could never
ever teach a machine to win at Texas Holding Poker.
Speaker 1 (27:33):
Yeah, it'll be one hundred years at least before that happens,
I predict.
Speaker 3 (27:37):
No, they did it, and more than one team has
done it.
Speaker 1 (27:42):
Yeah. I read there was one from Carnegie Mellon called
Liberatus AI Go melon Heads, Yeah, go the Thornton Melons.
Speaker 3 (27:53):
Yeah, I mean that's was The University Alberta has one
called deep Stack.
Speaker 1 (27:58):
That was the one I read about. And it actually
here's the thing, like if you read the release on it,
you're like, you don't know how this thing works?
Speaker 3 (28:07):
Do you really?
Speaker 1 (28:08):
Yeah? And I'm pretty sure they don't fully get it,
because that's one of the problems. I actually talk about
this in the Existential Risks series.
Speaker 3 (28:15):
That's scary that is to be released right.
Speaker 1 (28:18):
That there is a type of machine learning where the
machine teaches itself but we don't really understand how it's teaching.
Probably the scariest one, right, or what it's learning, but
that's the most prevalent one. That's what a lot of
this is is like these machines. It's like here's chess,
go figure it out and they go, okay, got it.
(28:39):
How'd you do that? Wouldn't you like to know?
Speaker 3 (28:42):
So that's the scariest presentation you will see on AI
is when someone says, well, how does all this work?
Speaker 1 (28:47):
And they go, but we just know it can be
to human at poker. But the thing about deep Stack
at the University of Alberta is that it learned somehow
some sort of intuition. Yeah, because that's what's required is
not just the perfect information where you have all the
information on the board. It's with poker, you don't know
(29:08):
what the other person's cards are, and you don't know
if they're lying or bluffing or what they're doing so
that's an imperfect information game, so that would require intuition,
and apparently not one, but two different research groups taught
AI to into it.
Speaker 3 (29:24):
Yeah, Carnegie Mellon came out in January of twenty seventeen
with its Liberatus AI and they said they spent twenty
days playing one hundred and twenty thousand hands of Texas
hold them with four professional poker players and one and
smoked them. Basically got up to They weren't playing with
(29:45):
real money, obviously, but they they that would have been great.
Speaker 1 (29:48):
They were playing with skittles like me as a kid.
Speaker 3 (29:50):
Funded their next project, Liberatus was up by one point
seven million, and one of the quotes from one of
the poker players that he made to Wired magazine said,
felt like I was playing against someone who was cheating,
Like it could see my cards. I'm not accusing it
of cheating. It was just that good.
Speaker 2 (30:06):
Right.
Speaker 3 (30:07):
So that's a really interesting thing, man, that they could
teach self teach a program, or a program could teach
itself intuition. Right, it's creepy. I thought this part was interesting,
the Atari stuff. This gets pretty fun. Google deep mind.
Let it's AI wreak havoc on Atari forty nine different
(30:32):
Atari twenty six hundred games. See, they could figure out
how to win, and apparently the most difficult one was
Miss pac Man, which is a tough game. Still man,
misspac Man. They nailed it. It's still one of the
great games.
Speaker 1 (30:46):
But their their game, or their Q deep Q network
algorithm beat it.
Speaker 3 (30:54):
Yeah.
Speaker 1 (30:55):
I think it got the highest score nine nine points,
and no human or machine has ever achieved that high
score from what I.
Speaker 3 (31:03):
Understand, amazing. And the way this one does it, the
hybrid reward architecture that it uses, is really interesting. It
says here, it generates a top agent that's like a
senior manager, and then all these other one hundred and
fifty individual agents. So it's almost like they've devised this
artificial structural hierarchy of these little worker agents that go
(31:26):
out and collect I guess data and then move it
up the chain to this top agent.
Speaker 1 (31:34):
Right, and then this thing says, Okay, you know, I
think that you're probably right what these agents are probably doing.
And I don't know this is exactly true, but there's
there are models out there like this where the agent
says this is you have a ninety percent chance of
success at getting this pellet. If we take this action,
(31:57):
somebody else says, you've got an eighty two percent chance
of av this ghost if we go this way. And
then the top agent, the senior manager, can put all
this stuff together and say, well, if I listen to
this guy and this guy not only while I evade
this ghost, I'll go get this pellet. And it's based
on what confidence level that the lower agents have in
(32:19):
success in recommending these moves. And then the top agent
weighs these things.
Speaker 3 (32:24):
Wow, they should give him a little cap.
Speaker 1 (32:27):
But all this is happening like that. Oh yeah, you
know what I'm saying. This isn't like well, hold on,
hold on, everybody, what is Harvey? What do you have
to say? Well, let's get some Chinese in here and
hash it out, and everybody sits there in order some
Chinese food, and then you wait for it to come,
and then you pick up the meeting from that point on.
And then finally Harvey gives his idea, but he forgot
(32:47):
what he was talking about, so he just sits down
and eats his a roll.
Speaker 3 (32:51):
Well, here's a pretty frightening survey. There was a survey
of more than three hundred and fifty AI researchers, and
they have the following things to say, And these are
the pros that are doing this for a living. They
predicted that within ten years AI will drive better than
we do. By twenty forty nine they will be able
to write a best selling novel. AI will generate this
(33:13):
and by twenty fifty three be better at performing surgery
than humans are.
Speaker 1 (33:19):
You know. So again, one of the things that about
the field of artificial intelligence.
Speaker 3 (33:25):
You know a lot about now famous.
Speaker 1 (33:27):
It is famous for making huge predictions that did not
pan out. Sure, but you've also seen it's also famous
for beating predictions that you know have been levied against it.
But there is something in there, Chuck, that stands out
to me, and that's the idea of an AI writing
a novel. Like for a very long time I thought, well, yeah, okay,
(33:50):
you can teach a robot arm to like put a
car part or something somewhere if you wanted to just
follow these mechanical things. Or it can use in auition,
or it can use logic in reason. But to create
that's different, right. That was like the new frontier. It
used to be chess and then then was go. The
next frontier is creativity and they're starting to bang on
(34:11):
that door big time. There's a game designing AI called
Angelina out of the University of Foulmouth, which I always
want to say Foulmouth, Yeah, but we'll just call it
Foulmouth like it's supposed to. And Angelina actually comes up
with ideas for new games, not like a different level
(34:32):
or something like you should put a purple loincloth on
that player, you know, that'll look kind of cool like
new games, but whacked out games that humans would never
think of. One example I saw is in a dungeon
Battle Royale game, a player controls like ten players at once,
and some you have to sacrifice to be killed to
(34:52):
save the others, like the stuff that human wouldn't necessarily
think of. This AI is coming up with.
Speaker 3 (34:58):
Well, I mean, when you think of creatively, especially something
like writing a novel or a film, if there are
only seven stories, I mean, and that's sort of the
thinking that they're basically every every dramatic story is a
variation of one of seven things.
Speaker 1 (35:12):
Yeah. So I mean you can look at like AI
is scary, and in some ways it very much is
and can be, but there's also like, definitely a level
of excitement of the whole thing, and the idea that
there are artificial minds that are coming online or that
have come online now, that are out there that are
(35:33):
they'll they'll just naturally by definition, see things differently than
we do. Yeah, and the idea that they can come
up with stuff that we've never even thought of there
is just gonna knock our socks off, hopefully in good ways.
That's a really cool thing. And so maybe there's just
seven as far as humans know, but there's an unlimited amount.
Is if you put computer minds to thinking about these
(35:55):
kind of things, that's the premise of it.
Speaker 3 (35:57):
Right, so the robot would be like you never thought
of boy meets girl meets well trilobite. But see even
that's a variation of right.
Speaker 1 (36:09):
Just imagine something that we've never even thought of.
Speaker 3 (36:12):
Well, do you know how they should do this? If
they do do that is uh? Is not is just
release a book and not tell anyone that it was
written by an AI program because if they do that
then it's going to be so under scrutiny. Oh yeah,
they should secretly release this book and then after it's
a New York Times bestseller, say meet the Whopper, the
(36:34):
author of this.
Speaker 1 (36:36):
You know his interests are roller skating, playing Tic tac toe,
and global thermal nuclear war.
Speaker 3 (36:42):
All right, should we take a break and get strickland
in here.
Speaker 1 (36:44):
Yeah, we're going to end the Strickland drought because it
is about to rain strickland in this piece.
Speaker 3 (36:49):
You gross.
Speaker 1 (37:12):
Okay, we're back and get this. The scent of strick
has permeated our place. That's a beautiful scent.
Speaker 3 (37:20):
It smells like a soldering gun and a circuit board
and feel a lavender in a protein bar.
Speaker 2 (37:28):
That's fair. I was gonna say Draco noir that would
have been a lie.
Speaker 1 (37:32):
Is that how you said? I always called a drakar dracr.
Speaker 2 (37:36):
That's that's fair.
Speaker 3 (37:38):
I always pronounced it Benetton colors. That was what I wore.
Speaker 1 (37:43):
Oh is that what you wore?
Speaker 3 (37:44):
Yeah? During my what I call the Year of Cologne,
I had a couple of seven.
Speaker 1 (37:51):
Uh.
Speaker 2 (37:51):
This is scintillating. Why why am I here?
Speaker 1 (37:55):
So we know that you already know because we talked
via email about this, but we'll tell everybody nobody else.
We have brought you in here because you are the
master of tech and we were talking tech today, which
we've talked about without you before. But frankly, Chuck and
I and Jerry huddled and we said, this is not
quite as good with that strict so let's try something different, gotcha.
Speaker 2 (38:14):
And we're talking about games and machine versus man and
that that whole evolution and how that's gone super crazy
over the last few years.
Speaker 3 (38:24):
Games without frontiers, as Peter Gabriel would say, yeah, or
without fear.
Speaker 1 (38:29):
And we've talked, I mean, we've talked a lot about
the evolution of machine learning and how now it's starting
to take off like a rocket because they can teach themselves, right.
But one thing we haven't really talked about are solved games.
I mean, we talked about chess. Yeah, we talked about
go right, would those constitute solve games?
Speaker 3 (38:48):
Not really?
Speaker 2 (38:49):
So a solved game is the concept where if you
were to assume perfect play on either sides of the game,
you would always know how it was going to end.
Speaker 3 (38:58):
Which we always assume perfect play, right, Yeah, it's kind
of our.
Speaker 1 (39:01):
Bags, right stuff.
Speaker 2 (39:02):
You should know motto so perfect play just meaning that
no one ever makes a mistake, so very much the
way I do my work right stuff, you should know
exactly So if you were to take a game like
Tic Tac Toe and you assume perfect play on both sides,
it is always going to end in.
Speaker 3 (39:17):
A draw, which is what was in war games.
Speaker 2 (39:19):
Yes, right, the only way to win is not to
play right. Yes, so a game with like a game
like Connect four, whoever goes first is always going to win,
assuming perfect play both sides.
Speaker 3 (39:33):
Yes, what, I don't think I've played Connect for it.
That's where you drop ever a long time. That's wh
where you drop the little tokens.
Speaker 2 (39:41):
Yeah, like it kind of like Checkers.
Speaker 1 (39:42):
You did an interstitial playing Connect four. Remember I was.
Speaker 3 (39:45):
Faking it though, and you had perfect play, so I
knew it was useless.
Speaker 1 (39:48):
I was going to say that I'm so humiliated by
all the Connect four games that I've lost starting even.
Speaker 2 (39:55):
Yeah, but I mean perfect play. That's something that that
obviously only the best players typically achieve with significantly complex games.
Obviously the simpler the game, the easier it is to
play perfectly. Right tic Tac toe, if you know, once
you've mastered the basics of Tic Tac Toe and the
other person has, you're never really going to win unless
(40:17):
someone has just made a silly mistake because they weren't
paying attention.
Speaker 3 (40:21):
They put a star instead of an xer right.
Speaker 1 (40:23):
Which doesn't count automatically disqualified you. One thing I've found
that's very enjoyable is playing with little kids who haven't
figured out that tic tac toe is very easy.
Speaker 3 (40:31):
To yes and smash their face on the board and
rub it in.
Speaker 1 (40:35):
Yeah.
Speaker 2 (40:35):
I mean the same reason why I like to join
in on little league games because I can really whale
that ball out of the park. Yeah, I really missed
me feel like a man.
Speaker 3 (40:43):
That's the most tech stuffy thing you've ever said. You
really whaled that ball out of the park.
Speaker 2 (40:47):
Well, to be fair, I did just do a textuff
episode about the technology behind baseball bats, so it's so
fresh on mine.
Speaker 1 (40:53):
Nice.
Speaker 3 (40:53):
Yeah one.
Speaker 2 (40:54):
Actually, it's a lot of fun. So there have been
a lot of games that have been solved, but Checkers
will one that was recently solved back in recently by
the early nineties when it was played against a computer
called Chinook and chi in Ok.
Speaker 1 (41:11):
Yeah, like the Helicopter or the wins that blow through
Alberta exactly.
Speaker 2 (41:15):
And so there are certain games that are more easily
solved than others. You do it through an algorithm, but
other games like chess, are more complicated because you can
In chess you have multiple moves that you can do
where you can you can move a piece back the
way you win, right, it's not you're not committed to
going a specific direction with certain pieces, like with a
(41:37):
night you know you can you could go right back
to where you started on the next move if you
wanted to, and that creates more complexity. So the more
complex the game, the more difficult it is to solve.
And some games are not solvable simply because you'll never
know what the full state of the game is from
any given moment. Did you have a chance to talk
(42:00):
about the difference between perfect knowledge and imperfect knowledge in
a game.
Speaker 3 (42:04):
Yeah, yeah, we talked about that some. Yeah.
Speaker 2 (42:06):
So computers, obviously they do really well if they understand
the exact state of the game all the way through,
if they have perfect knowledge.
Speaker 1 (42:15):
All of the informations there on the board.
Speaker 2 (42:17):
Right and all players can see all information at all times.
But games like poker, which you guys talked about, obviously
you have imperfect information. You only know part of the
state of the game. That's why those games have been
more difficult, more challenging for computers to get better than
humans until relatively recently, and there have been two major
(42:38):
ways of doing that. You either throw more processing power
at it, like you get a supercomputer, or you create
neural networks, artificial neural networks, and you start teaching computers
to quote unquote learn the way people do.
Speaker 1 (42:51):
So we talked about that. Yeah, and one of the
things that we talked about was how there's this idea
that the programmers, especially say the people who are making
programs that are playing poker and or getting good at poker,
aren't exactly sure how the machines are learning to play
poker or what they're learning. They're just getting better at poker. Yeah,
(43:11):
do they know how they're learning poker? They just know
that they're learning poker and that they're good at it. Now, like,
where's the intuition? How is that being learned?
Speaker 2 (43:20):
An excellent question. The way it typically has learned, especially
with artificial neural networks, is that you set up the
computer to play millions of hands of poker that are
randomly assigned, so it's truly as random as computers can get.
That's a whole philosophical discussion that I don't think we're
ready to go into right now. But you have games
(43:44):
come up where the computer is playing itself millions upon
millions of times and learning every single time how the
statistics play out, how different betting strategies play out. It's
sort of partitioning its own mind to play against itself.
And through that process, it's as if you, as a
human player, were playing thousands of games with your friends
(44:08):
and you start to figure out, Oh, when I have
these particular cards and they're in my hand, and let's
say we're playing Texas, hold them and the community cards
are are these? Then I know that generally speaking, maybe
three times out of ten I end up winning. Maybe
I shouldn't bet. Well, the computer is doing that, but
on a scale that far dwarfs what any human can do,
(44:29):
and in a fraction in the amount of time, and
so it's sort of well, it's intuition in the sense
of it's just done it so much.
Speaker 3 (44:38):
Right, But does that mean it's completely ignoring micro expressions
and facial cues, so that doesn't even come into play.
Speaker 1 (44:47):
You should say, Strickland just nodded yet, Yeah I was.
Speaker 2 (44:49):
I was waiting for John Well. I still nod when
I do a solo show. And I do a lot
of expressive dance.
Speaker 3 (44:56):
What do you think, Jonathan, I don't know, Jonathan.
Speaker 2 (44:59):
It gets lonely in here, guys. No, but yes, what
you're saying all the tells, right, Yeah, tells that you
would use as a human player. The computer does not
pick up.
Speaker 3 (45:08):
A speypically taking data.
Speaker 2 (45:10):
Yes, typically, what it would do is it would study
the outcomes of the games from a purely statistical expression,
so that most of these poker games tend to be
computer based poker games. So it's not that it's playing
like it's not like there's a computer that says, pushed
ten more chips into the table. You know, I tick
right exactly a little it's a little winky face emoticon,
(45:33):
like I don't have good cards. No, it's it's all
usually over, sort of like internet poker, which a lot
of the people who play professional poker cut their teeth on,
especially you know, in the the more recent generations of
professional poker players.
Speaker 3 (45:48):
Kids today, Yeah, they don't know what it's like being
a smoky saloon like Moneymaker.
Speaker 2 (45:53):
When Moneymaker rose to the top a few years ago,
more than like a decade ago, now, he had come
from the world of internet poker, and so he was
using those same sort of skills in a real world setting.
But obviously there are subtle things that we humans do
in our expressions their computers do not pick up on it.
In fact, that leads us sort of into the realm
(46:14):
of games where computers don't do as well as humans.
Speaker 3 (46:19):
Yeah, is that list you sent a joke or is
it real? No, that's real.
Speaker 2 (46:23):
It does seem like it's weird, like one of the
games on there is pictionary, for example, rag or tag. Yeah,
but these are some of these are They sound silly,
But when you start to think about them in terms
of computation and robotics, you start to realize how incredibly
complex it is from a technical perspective, but incredibly easy
it is for your average human being. Okay, so with
(46:44):
humans a game of tag, once you know the basics,
it's it's all an instinct.
Speaker 1 (46:49):
You know what to do.
Speaker 2 (46:50):
You run after the person you tried to catch up
with them and tag them. But you also know push
them ind as you can. Well, if you're Josh, you
push them as hard as you can. But most of
us we tag and we're not trying to cause harm. Robots, however,
robots not so good on you said, I'm just saying
Isaac Asimov Isaac Asimov's rules of robotics. Aside, robots are
(47:13):
not very good at judging how hard they have to
hit something in order to make contact, right, They're not
as good at even your bipedal robots that walk around
like people, even the ones that can run and do
flips and stuff.
Speaker 3 (47:26):
Have you seen that one the other day that the
footage of that thing running and jumping, it's really impressive
and super creepy.
Speaker 2 (47:32):
Yeah, but even so, that's that's a clip of the
best of If you ever if you ever see the
clips where they show all the times the robots fallen over.
Speaker 3 (47:41):
Yeah, we're pouring hot coffee in someone's head.
Speaker 1 (47:44):
Yes, but they always play those clip shows, toy.
Speaker 2 (47:48):
Yes, this is true. So DARPA had its big robotics
challenge a few years ago where they had bipedal robots
tried to go through a scenario that was simulating Fukushima
nuclear disaster. So the interesting thing was the robot had
to complete a series of tasks that would have been
mundane to humans, things like open up a door and
(48:11):
walk through it and pick up a power tool and
use it against a wall, and you can watch the
footage of some of these robots doing things like being
unable to open the door because they can't tell if
they need to pull or push or they open the door,
but then immediately fall over the threshold of the door.
(48:31):
And when you see that, you realize, as advanced as
robotics is, as advanced as machine learning has become, and
as incredible as our technology has progressed, there are still
things that are fundamentally simple to your average human right
that are incredibly complicated from a technical standpoint, Like a.
Speaker 3 (48:49):
Six year old can play jinga better than a robot.
Speaker 1 (48:52):
Right right, right, Okay, But the thing is we're talking
robots here, and as we go more and more and
more online and our world becomes more and more web
based rather than reality based, doesn't the the fact that
a robot can't walk through a door matter less and less,
and the idea that that machines are learning intellect and
(49:14):
the robotivity, and you just blew my mind that that's
becoming more and more vital and important and something we
should be paying attention.
Speaker 2 (49:22):
It absolutely is something we should pay attention to. I mean,
we have robotic stock traders, the trading on thousands of
trades per second, right fast, so fast that we have
had stock market booms and crashes that last less than
a second long due to that.
Speaker 3 (49:39):
So that the robot army that will ultimately defeat us
is not something from the terminator.
Speaker 1 (49:43):
It's invisible, right, it's online, it will be online, it's.
Speaker 2 (49:48):
It's it's what's determining our retirement.
Speaker 1 (49:50):
Right, Yeah, the global economy or our municipal water supply
or whatever.
Speaker 3 (49:57):
Yeah.
Speaker 2 (49:57):
No, There's the fascinating thing to me about this is
not just that we're training machine intelligence to learn and
to perform at a level better than humans, but that
we're putting a lot of trust in those devices and
things that have real incredible impact on our lives, significant
(50:19):
enough impact where if things were to go south, it
would be really bad for us, and not in that
Terminator respect. Terminator is a terrifying dystopian science fiction story.
But then when you realize what could really happen behind
the scenes, you think, oh, robots don't have to do
any physical harm to us to really mess things up.
(50:40):
So there are certainly some cases for us to be
very vigilant in the way we deploy this artificial ants
right from the outset exactly, and too depends not necessarily.
I think I think it's I don't think it's too late,
but I think it's getting to that point of no
(51:02):
return very very quickly.
Speaker 3 (51:04):
By December this year. Yeah.
Speaker 2 (51:06):
Well, if you're if you're someone like if you're someone
like Elon Musk, you'd say, if we don't do something
now where we're totally going to plummet off the edge
of the cliff.
Speaker 1 (51:15):
But now is a window that is rapidly closing.
Speaker 2 (51:18):
Yes, yeah, yeah, the now is the now is a
time where we've got a deadline. We don't know exactly
when that deadline is going to be up, but we
know that it's not getting further out it. We're just
getting closer to that deadline. So, and a lot of
this is covered in deep conversations and the artificial intelligence
and machine learning fields that has been going on for ages,
(51:42):
to the point where you even have bodies like the
European Union that have debated on concepts like granting personhood
to artificial intelligence. So this is a really fascinating and
deep subject that and the games thing is a great
entry point into have that conversation. Uh, you know. I'm
(52:03):
lucky if I can win a game of chess against
another human being.
Speaker 1 (52:05):
Oh yeah, right, so I can't even describe chess.
Speaker 3 (52:09):
Did I did? My big thing is I do that
night thing. I call it the night shuffle. I just
move them back and forth. I just cast.
Speaker 2 (52:16):
If I can castle, then I'm so happy.
Speaker 3 (52:20):
And that's the third tech stuffiest thing. They come in threes.
Speaker 1 (52:24):
Well, Strick, thank you for stopping by.
Speaker 3 (52:26):
Should stick around for listener mail.
Speaker 1 (52:28):
I think you should too. I love to and throw
out any funny comments that you have.
Speaker 2 (52:33):
I'll throw out comments and then Jerry can decide which
ones are funny.
Speaker 1 (52:36):
Okay, all right, fair enough, all right, So if you
want to know more about AI, go listen to tech Stuff.
Strict does this every week what days.
Speaker 2 (52:45):
Monday, Tuesday, Wednesday, Thursday and Friday.
Speaker 1 (52:48):
Wow, that's amazing, buddy. And wherever you find your podcast yep, okay,
And you've been doing it for years, so if you
love this, there's a whole big backlog, nine hundred plus episode.
Speaker 3 (52:59):
You're celebrating your ten year as well, right yep, by.
Speaker 2 (53:02):
Sure am, I'll be We'll be turning ten and tex
stuff on June eleventh.
Speaker 1 (53:07):
Congratulations, well, since I said happy anniversary, it means it's
time for listener mail.
Speaker 3 (53:14):
Guys, I'm gonna call this Matt Groening and cultural relativism
about that?
Speaker 1 (53:20):
Nice?
Speaker 3 (53:20):
Hey, guys, love your podcast so much. The massive archive
makes for endless learning and entertainment. My favorite part is
you were such rad guys, including Strickland, and I could
totally imagine how did they know? I can totally imagine
myself getting a beer with you two, but without Strickland,
your Simpsons episodes were absolutely perfect. I still live in
Portland and drove on Flanders and Lovejoy Streets a lot.
Speaker 1 (53:42):
Wait, is this Matt Groening? Okay?
Speaker 3 (53:45):
Matt Granning Drew Bart in the sidewalk cement behind Lincoln
High School in downtown Portland. You can google that. I
would like to offer one interesting observation though, I've noticed
that on several episodes you guys have said that you
are cultural relativists. Is that pronounce right? Yeah? Yeah? But
then in nearly every episode I hear you pass moral
judgments on all the messed up stuff that people do,
(54:07):
whether it's racism, preak shows, or crematoriums bearing bodies on
the sly. You guys are never shy to condemn something
that deserves to be condemned. Reminds me of something I
read from Yale's sociologist Philip Gorsky, who points out that
our own relativism is rarely as radical as our theory requires.
We can't be complete relativists in our daily lives. He
(54:30):
then gives the example of how academic social scientists, where
diehard relativists, get furious and moralistic at the data fudging
of other researchers. Anyway, love the show, guys, love tech
stuff especially, and will forever be indebted to you for
your hilarity and knowledgeability. Cheers Jesse Lusco.
Speaker 1 (54:50):
Ps go tech stuff.
Speaker 2 (54:52):
That's sweet.
Speaker 1 (54:53):
Love that. Yeah, thanks a lot, Jesse. There was an
actual episode, and I don't remember which one it was,
where we a and in our cultural relativism, do you remember,
because we used to just be like, no judgment, no judgment, right,
we just can't judge, you know, And then finally we
were like, you know what, No, that's not true. We
changed our philosophy to include the idea that there are
(55:14):
moral absolutes that are universal, although sometimes we are just
judge even beyond that. Look at us, Yeah, Well, if
you want to get in touch with us, you can
send us an email to Stuff podcast at HowStuffWorks dot com.
You can send John an email to.
Speaker 2 (55:30):
Tech stuff at HowStuffWorks dot com.
Speaker 1 (55:32):
Nice, and then hang out with us at our home
on the web. Stuff youshould know dot com and just go.
Speaker 2 (55:38):
To tech Stuff. Just search it in Google. I come
up all the time.
Speaker 1 (55:41):
Fair enough.
Speaker 2 (55:46):
Stuff you Should Know is a production of iHeartRadio. For
more podcasts my heart Radio, visit the iHeartRadio app, Apple Podcasts,
or wherever you listen to your favorite shows.
Hi everyone, it's Josh and for this week's select, I've
chosen our twenty eighteen episode Some Games You would Surely
Lose to a Computer. It's a philosophical discussion about AI
that's disguised as an episode on computer games. Honestly, we
didn't plan it to be like that. It just turned
out that way. We're pretty happy that it did. And
(00:22):
in light of the recent advances with machine learning like
chat GPT, a few of the things we say seemed
naively quaint now. Plus it has a doll up of
our tech stuff colleague Jonathan stricklanet and so that's a bonus.
I hope you enjoy.
Speaker 2 (00:39):
Welcome to Stuff you should know, a production of iHeartRadio.
Speaker 1 (00:49):
Hey, and welcome to the podcast. I'm Josh Clark. There's
Charles w Chuck Bryant, there's Jerry over there. I'm just
gonna come out and tell everybody making fun of me
for some weird reasons, vaguely weird ways. But I'm all right,
So check out his story for you. Okay, I'm going
to take us back to the seventeen seventies and they'll
(01:12):
swing in town of Vienna, not Virginia, not Vanna Georgia,
which you know, that's how they pronounce it, right, Yanna,
Vienna sausages, right, Vienna Austria.
Speaker 3 (01:24):
Have you ever been there?
Speaker 1 (01:26):
Vienna, Austria. No, been to Brussels. That was pretty close.
Speaker 3 (01:30):
Vienna's lovely, I'm sure.
Speaker 1 (01:32):
I think it's a lot like Brussels.
Speaker 3 (01:35):
Very clean, lovely town. I just remember it being very clean.
Speaker 1 (01:39):
Yeah, very clean, gorgeous architecture, weird little angled side streets.
They're very narrow, very pretty town. So we're in Vienna
and there is a dude skulking about going to the
royal Palace in Vienna. His name is Wolfgang von Kempelin,
(01:59):
and he's he's an inventor, he's an engineer. He's a
pretty sharp dude. And he's got with him what would
come to be known as the Turk, but he called
it the mechanical Turk or the automaton chess player, and
that's what it was. It was a wooden figure that
moved mechanically, seated at a cabinet, and on top of
(02:22):
the cabinet was a chessboard. And when he brought it
out to show to the royal court, he would it
was cool kind of but nothing they hadn't seen before,
because automata was kind of a hip thing by then.
Speaker 3 (02:38):
Yeah, people loved building these engineering these automata machines to
do various things. And people are just knocked out by
the fact that, you know, you hide these gears and
levers behind wood or a cloth, and it looks as
though there's a real well not real, but you know.
Speaker 1 (02:57):
What I mean that it's like a real machine.
Speaker 3 (03:00):
Yeah, but not. They weren't fooled anything like is that
a real man? It was, but it was for their time.
It was so advanced looking that it's like us seeing
x Makina in the movie theater.
Speaker 1 (03:12):
Sure does that make sense? Yeah, no, it does make sense.
But imagine seeing like X makinan being like I've seen
this before. This isn't anything special, okay.
Speaker 3 (03:22):
Yeah, And this thing, to be clear, looked like a
is it Zoltar or Zultan from Big Zoltar Zultan?
Speaker 1 (03:31):
I don't know. It's one of those two.
Speaker 3 (03:32):
One of those two. Like this, this guy's wearing a
turban and it's in a glass case like a bust like,
you know, like a chest up thing.
Speaker 1 (03:41):
Yeah, he's seated at this cabinet, so there's no need
for legs or anything like that.
Speaker 3 (03:45):
Yeah.
Speaker 1 (03:46):
But the thing, this is what was amazing about the Turk.
He could play chess, and he could play chess really well.
So yeah, he was like an automaton and he moved
all herky jerky or whatever. But he could play you
in chess, which was a huge, huge advance at the time. Like,
(04:07):
this is something that wouldn't come up again until the
nineteen nineties, more than two hundred years later. This thing,
this automaton could play a human being in chess and
beat them.
Speaker 3 (04:18):
Well. Yeah, and it looked like when the game started,
it would look down at the chessboard and like cock
his head, like what should my first move be?
Speaker 1 (04:25):
Right?
Speaker 3 (04:26):
And if people I love this part. If people tried
to cheat. Apparently Napoleon tried to cheat this thing because
this guy he debuted at the VI and East Court.
But then it, you know, it went on a world tour.
Speaker 1 (04:38):
Yeah, and he was even it was taken over by
a successor to the guy who toured with it.
Speaker 3 (04:43):
Even further, people went nuts for this stuff.
Speaker 1 (04:44):
They did. They loved it because they were like, this
is crazy, I can't believe what I'm seeing. Most people, though,
were not taken in by it, they're like, there's some
trick here. Sure, but von Kempelin and the guy who
came after him, I don't remember his name, they would
demonstrate you could open this happened and you could see
all the workings of the mechanical turk inside.
Speaker 3 (05:05):
Right, So what I was saying is if this thing's
since a cheater like Napoleon supposedly did it, would you
know Napoleon would move a piece out of turner illegally
or something. This dude, the turk Turk one eighty two
would pick up the chess piece, move it back as
if to say like, no, no, Napoleon, let' see what
you're doing. And then if the person attempted to move
(05:26):
it again, I don't know how many times on you
two or three times, eventually it would just go ah
and wipe his hand across the board and knock off
all the pieces. Game over, right, Which is pretty great. Yeah,
it's a nice little feature.
Speaker 1 (05:39):
Yeah it is. But it even showed even more that
this thing was thinking for itself. Yeah, that's the key here, right. Sure,
chess had been for a very long time viewed as
only something that a human would be capable of, because
it took a human intellect, and there was actually a
guy in English engineer, I think he was a mechanical engineer.
(06:03):
His name was Robert Willis. He said that chess was
in the province of intellect alone. So the idea that
there was this automaton playing chess blew people away. But
again people figured out, like, okay, there's something going on here.
We think that von Kempelin is controlling this thing remotely somehow,
(06:24):
maybe using magnets or whatever. Other people hit upon the
idea that there was a small person inside the cabinet
who would hide when the cab when the workings were shown,
when the cabinet was open to show the workings, and
then when the cabinet was closed again and the mechanical
turk started playing, the person that crawled back out and
was actually controlling it. This seems to be the case
(06:45):
that there was a person controlling it, but the idea
that it was it was a machine that could think
and beat humans in chess had like kind of unsettling implications.
Speaker 3 (06:57):
Yeah, this author Philip thick Ness, great name, British author
for sure, Philip Thickness. Yeah, he said, and you know,
people like you said, all those more complicated explanations. In
this article you sent, Astuteley points out that he followed
Occam's razor and basically said, he's got a little kid
(07:19):
in there. He's got a little a little Bobby Fisher
in there that's really good at chess, and that's what's
going on. And other people speculated that other, you know,
little people might be in there, just adults who would
fit in there. But then you know, there's the explanation
that he would open it up and shine a candle
around and say, you know, nothing to see here everyone,
(07:42):
So what should we reveal the real deal?
Speaker 1 (07:46):
Sure, I think I did already.
Speaker 3 (07:49):
Well I don't think you spelled it out as.
Speaker 1 (07:51):
Oh, we'll spell it out.
Speaker 3 (07:52):
There was a little person in there, Yeah, not just
one little person, but they would travel around and recruit people.
I guess people would get tired of being in there.
Speaker 1 (08:00):
Or they'd forget about them and they'd starve and have
to replace them.
Speaker 3 (08:03):
But it really was a trick. There was a little
person in there. They did the same thing as like
the magic acts, you know, when they saw a person
in half. It's that the lady just gets into a
tiny little ball right in one section of that box.
Speaker 1 (08:16):
But my thing is this, like, this is not a
satisfying explanation to me, Chuck.
Speaker 3 (08:20):
I think it's great.
Speaker 1 (08:21):
How did the person keep up with the board above?
Speaker 3 (08:25):
Well, I mean some I don't know if they ever
proved exactly how it was going.
Speaker 1 (08:30):
That's what I'm saying.
Speaker 3 (08:31):
Oh, okay, whether or not I think they that the
zoltar or I'm sorry, the turk was just hollowed out
and you you would just put your arms through the
arm hole turk, you would become the turk and the
churk would fuse.
Speaker 1 (08:48):
That's what some people thought. I think that's what Egar
Allen Poe thought too.
Speaker 3 (08:52):
He Kidney right.
Speaker 1 (08:54):
Other people thought that there was the the the little
person was underneath the in the cabin operating the trick
with levers and stuff like that.
Speaker 3 (09:02):
Well, there could have been a mirror or something, you know,
I guess, like a telescopic mirror.
Speaker 1 (09:07):
That's what's getting me is how would they keep up
with the game?
Speaker 3 (09:10):
Right?
Speaker 1 (09:10):
You could keep track of the game, but how could
you see where the other person moved? You would know
where you moved, but you wouldn't be able to see
where the other person moved. That's what I don't get.
Speaker 3 (09:17):
Just mirrors smoking mirrors.
Speaker 1 (09:19):
Maybe so, But the point is is it was a fake.
It was a fraud, but it raise some really big
questions about the idea of a machine beating a person
at something like chess.
Speaker 3 (09:30):
Yeah, and it really peaked the mind of one Charles
Babbage was he was a kid or young at least
at the time when he saw the Turk in person,
and a few years afterward he began work on something
called the Difference Engine, which was a machine that he
designed to calculate mathematics automatically. So some point to this
(09:54):
is kind of maybe the beginnings of humans trying to
create AI.
Speaker 1 (10:00):
Well yeah, with Babbage's differential machine or difference machine.
Speaker 3 (10:04):
Yeah, difference engine. But at the very least, what this
is is the first that I know of example of
man versus machine, even though it was really man versus
man because it was a man in the machine.
Speaker 1 (10:16):
Right. It was a fraud, Yeah, but it sparked that idea,
It definitely did. And that's something that like chess in particular,
has always been like this idea of like, if you
can teach a machine to play chess, you have really
achieved a milestone. And there's been you know, plenty of programs,
most notably Deep Blue, which we'll talk about. But there's
(10:40):
there's been this idea that like part of AI is chess,
teaching it to play chess. But they, the people who
develop AI, never set out to make a chess playing AI,
just to make a machine that can play chess. That's
not the point. Chess has always been this way to
demons straight the progress of artificial intelligence.
Speaker 3 (11:02):
Yeah, because it's a complex game that you can't just
program it, like it almost has to learn.
Speaker 1 (11:09):
Well, it depends on how you come at it at first, right,
So initially they did try to program it. Okay, there's
this from basically nineteen fifty to the about the mid
like about say nineteen fifty to twenty ten, sixty years right,
That is how they approached AI and chess is you
(11:31):
figured out how to break chess down and explain it
to a computer. Now, what if you could, ideally you
would have this computer or this AI, this artificial intelligence
be able to think about the outcome of every possible
or every possible outcome of a move before making it. Right,
(11:52):
that's just not possible. Still today we don't have computers
that can do that. Right, So what you have to
do is figure out how to create shortcuts for the machine,
give it best practices, that kind of thing. Yeah, And
that was actually laid out in nineteen fifty by a
guy named Claude Shannon who's a father of information theory,
and he wrote a paper with a pretty on the
(12:12):
nose title called Programming a Computer for Playing Chess. And
you have to say it like that when you say
the name.
Speaker 3 (12:19):
Yeah, it's got a question mark at the end, right, But.
Speaker 1 (12:21):
He laid out two big things. One is creating a
function of the different moves, and then another one is
called a mini max. And if those were the two
things that Shannon laid out, and they established about fifty
or sixty years of development in teaching an AI to
(12:42):
play chess.
Speaker 3 (12:43):
Yeah. So this evaluation function is just sort of the base,
the very basis of it all kind of where it starts,
which is you a kind of give a number to
create a numerical evaluation based on the state of the
board at that moment, right, and assign a real number
evaluation to it. So the highest number that you would
(13:06):
shoot for is obviously getting checkmate, getting a king and checkmate.
Speaker 1 (13:11):
Right, right, So what you've just done now is by
assigning a number to a state like the pieces on
a board. What you what you've done is to say,
like shoot for this number. Right, the higher the number,
like you're going to give this aither rule. Now, the
higher the number, the more desirable that this move that
could lead to that higher number. Function. Evaluation function is
(13:34):
what you want to do.
Speaker 3 (13:35):
Right, like capture the night or capture the queen. Capture
the queen would have a higher evaluation number.
Speaker 1 (13:40):
Right exactly. So that's the function. Then there's another one
called the mini max. Yeah, this is pretty great where
you want to minimize the maximum And this is another
shortcut that they taught computers.
Speaker 3 (13:50):
The maximum loss that is right, Yeah.
Speaker 1 (13:52):
So what they what they taught computers to do is
so you no computer can look through an entire game
every possible outcome, but there are computers that can look
pretty far down the line at every possible outcome. And
what you can say is, Okay, you want to find
(14:13):
the evaluation function that is the worst case scenario, the
maximum loss, and then find the move that will minimize
the possibility for that outcome.
Speaker 3 (14:24):
Yeah bye, And this is you're only limited by your
programming power. But by looking not only at the state
of the board right now, but if I make this
move and I move the PAWD to this spot, what
are the next like three moves possibly that could happen
as a result of this move. And you're only limited,
like I said, by programming power. So obviously, the more
(14:45):
juice you have, the more moves ahead that you can
look exactly.
Speaker 1 (14:49):
And then they just shy away from ones with the
higher function number exactly or lower function number, depending on
how you've programmed it. But they're making these decisions based
on these rules. And then there's other things you can do,
like little shortcuts to say, if a decision tree leads
to the other players King being in checkmate, don't even
(15:13):
think about that move any further, don't evaluate any longer,
just abandon it because we would never want to make
that move. So there's all these shortcuts you can do.
And that's what they did to teach computers. That's what
Deep Blue did when it beat Gary Kasparov in nineteen
ninety seven. It was this huge, massive computer that knew
a lot of chess, a lot about chess. It had
(15:35):
a lot of rules, a lot of incredibly intricate programming
that was extremely sharp, and it actually won. It became
the first computer to beat an actual human chess grand
master in like regulation match play.
Speaker 3 (15:51):
Yeah. I mean, and I don't think Kasparov gets enough
credit for being willing to do this, because it was
a big deal for him to lose. It was in
this community and the AI community. It sent shock waves,
and everyone that was alive remembers, even if you didn't
know anything about either, one remembers Deep Blue being all
(16:12):
over the news. It was a really big deal. And
Kasparov put his name on the line and lost.
Speaker 1 (16:17):
Yeah, And I was wondering, Chuck, how how like you
would get somebody to do that.
Speaker 3 (16:22):
I'm sure the Mountain of catch.
Speaker 1 (16:25):
I guess that would probably be part of it. But
I mean, shit, I don't know.
Speaker 3 (16:29):
I bet, I bet that's out there. We just I
just didn't look it up.
Speaker 1 (16:32):
So that's possible. It's also possible that they said, look, man,
like this is chess we're talking about or whatever. But really,
what you're doing is helping advance artificial intelligence.
Speaker 3 (16:42):
Right, because we're not really trying ultimately to win chess games.
We're trying to cure cancer.
Speaker 1 (16:47):
I mean, yeah, we're going to take your title because
we're going to beat you, or our machine's going to
beat you. But even still, you're going to be helping
with cancer. Think of the cancer Casparov. That's probably what
they said.
Speaker 3 (16:59):
Should we take a break?
Speaker 1 (17:00):
Yeah?
Speaker 3 (17:00):
Uh wait, well should we tease our special guests first?
Speaker 1 (17:04):
Is he okay? I can smell him.
Speaker 3 (17:06):
I don't think we even said we're gonna have a
special guest later in the episode, Mister Jonathan Strickland of
Tech Stuff.
Speaker 1 (17:13):
Nice.
Speaker 3 (17:13):
It's just been a long time since, like years since
we had Stricken.
Speaker 1 (17:16):
The last time we had Stricken was like two thousand
and nine with the Necronomicon episode.
Speaker 3 (17:21):
What is what? Where's he been besides sitting in between
us every day?
Speaker 1 (17:24):
It's been a strickling drought, is what it's been.
Speaker 3 (17:27):
Yeah, so Strickland's coming later, but we're going to come
back after this and talk a little bit more about
a man versus machine.
Speaker 1 (17:55):
Okay, dude, So what we just described was how AI
was to play things like chess or to think like
you take something, you figure out how to break it
down into little rules and things that a computer can
think of, right, and then follow these kind of rules
to make the best decision. That's how it used to be.
(18:16):
The way that it's done now that everybody's doing now
is where you are creating a machine that teaches itself.
Speaker 3 (18:24):
Yeah, that's the jam.
Speaker 1 (18:25):
That was the breakthrough. You may have noticed back in
about ty thirteen twenty fourteen, all of a sudden, things
like Siri and Alexa got way better at what they
are doing. They got way less confused. Really, your navigation
app got a lot better. And the reason why is
(18:47):
because this type, this new type of AI, this new
type of machine learning that can teach itself and learn
on its own, just hit the scene and they just
started exploding. And one of the things that they were
first trained on was games.
Speaker 3 (19:02):
Yeah, and it makes sense. And if you thought chess
was complicated and difficult, when it comes to these new
AIS that they're teaching to teach themselves game strategy. They said,
we might as well dive in to the Chinese strategic
game Go because it has been called the most complex
game ever devised by humans. Yeah, and this was actually
(19:24):
that was actually a quote from Demi Hasabi, a neuroscientist
and the founder of deep Mind, which was deep Mind.
They were purchased by Google or were they always part
of Google.
Speaker 1 (19:39):
I don't know if they were a spun off branch
or where they were purchased, but it's one of Google's
AI outfits.
Speaker 3 (19:45):
Well, they're one of the teams, yeah, that are designing
these new programs. And to give you an idea of
how complex Go is, it deals with a board with
different stones and there are ten how do you even
say that?
Speaker 1 (20:00):
Ten to one hundred and seventieth power, So.
Speaker 3 (20:02):
That means one hundred and seventy zeros and take that
number and that's the number of possible configurations of a
go board.
Speaker 1 (20:10):
Right, So, like you say, chess is very complex and complicated,
and it's very difficult to master Go. And I've never
played Go, of you no, So it's supposedly it's easy
to learn.
Speaker 3 (20:21):
Right, but very complicated in its simplicity.
Speaker 1 (20:24):
Right, right, exactly, it's extremely difficult to master. And there
was a guy in the late nineties and I'm guessing
that he was saying this after Deep Blue beat Caspar
ofv It was an astrophysicist from Princeton. He said that
it would probably be one hundred years before a computer
beats a human a go. To give you an idea
(20:45):
of just how complex GO is that deeply would just
be caspar OFV. And this guy's saying it'll still be
one hundred years before anyone gets beat at GO by
a computer.
Speaker 3 (20:56):
And he was someone who knew about this stuff, who
was an astrophysicist. He was just some schmoe at home
and drunken as reclining.
Speaker 1 (21:03):
Is making asinine predictions.
Speaker 3 (21:07):
So and again we've said this before, but I want
to reiterate the people that I think Alpha Go is
the name of this program. The people that created this
at Deep Mind, they wanted to stress that this is
a problem solving program. We're just teaching it this game
at first, just to make it learn and to see
(21:27):
if it can get good at what it does. But
they said it is built with the idea that any
task that has a lot of data that is unstructured
and you want to find patterns in the data and
then decide what to do right, And that's kind of
like what we were talking about. It It crunches down
all these possible options aka data to decide what move
(21:48):
should I make right? And you could apply that Ideally,
they're going to apply this to Alzheimer's and cancer and
all sorts of things.
Speaker 1 (21:54):
Right, it's general purpose thinking, right, Yeah, and thinking on
the fly too, and face with novel stuff. So one
of the reasons why it's good to use games like
chess or Go or whatever, those are called perfect information
games where both players or anybody watching has all the
information that's available on it. There are definite rules or structure.
(22:18):
It's a good proving ground. But as we'll see, AI
makers are getting further and further away from those structure
games as their AI becomes more and more sophisticated, because
the structure and the limitations aren't necessarily needed anymore, because
these things are starting to be able to think on
their own in a very generalized and even creative way.
Speaker 3 (22:40):
Yeah, it's really really interesting. Yeah, the way that they're
like you said earlier before the break, that we don't
have computers that can run all the possibilities. So what
they teach in the case of Alpha Go, this program
teaches itself by playing itself in these games and Go specifically,
and the more it plays itself, the more it learns,
(23:02):
and the more ability it has during a game to
choose a move by narrowing down possibilities. So instead of like,
well there are twenty million different variations here. By playing itself,
it's able to say, well, in this scenario, they're really
only fifty different moves that I could or should make, right,
(23:22):
or that's kind of a simplified way to say it.
Speaker 1 (23:24):
But right, No, but it's true. But that's exactly right.
And what they're doing is basically the same thing that
a human does. It's going back to its memory banks, Yeah,
exact experience, huh, and saying well, I've been faced with
something like this before, and this is what I used
and it was successful. Forty out of fifty times. I'll
do this one. This is a pretty reasonable move. Yeah,
(23:45):
that is what humans do.
Speaker 3 (23:46):
Yeah. Not only I mean, boy, we screwed up the
chess episode, but I get the idea that when you're
a chess master, you don't just think what do the
numbers say and what does the book say?
Speaker 1 (23:55):
Right?
Speaker 3 (23:55):
But man, I did this move that one time and
it didn't go as the book said.
Speaker 1 (24:02):
Right.
Speaker 3 (24:02):
So that's now factored into my thinking.
Speaker 1 (24:04):
Right, except imagine being able to learn from scratch and
get to that point in eight days or eight hours. Yeah,
So that go team the alpha go the first the
first iteration of alpha go, I think they started working
on it in twenty fourteen and in twenty sixteen. At
the end of twenty sixteen, they unleashed it secretly onto
(24:28):
an Alpha Go website and it started just wiping the
floor with everybody. Yeah, everybody's like, this thing's pretty good. Oh,
it's Alpha Go. That was the end of twenty sixteen.
Speaker 3 (24:39):
Okay, so chess had already come and gone. Like, oh,
by this point, you can download a program that's like
Deep Blue, right.
Speaker 1 (24:47):
That was That's a great point. Yeah, like today the
stuff you played chess with on your laptop is even
more advanced than Deep Blue was in the nineties, and
it's just on your laptop. But this is so, this
is Go. This is the end of twenty sixteen. The
end of twenty seventeen, Alpha Go was replaced with Alpha
(25:08):
Go zero. It learned what Alpha Go had taken two
years or three years to learn in forty days by
teaching itself, and.
Speaker 3 (25:19):
It beat the master. Yeah, and finally in May of
twenty seventeen, Alpha Go took on Key g the highest
ranked Go player in the world. Don't know if he
or she still is.
Speaker 1 (25:33):
No. Lisa A. Doll is the current or was until
Alpha Alpha Go beat him?
Speaker 3 (25:39):
Oh man? Yeah, did they get knocked off and Alpha
Go is the champion? Yeah? Like that's that's not fair.
Speaker 1 (25:45):
I if it's match play and the player, the human
player is accepted a challenge from the computer, I don't
see why it wouldn't be the world champion.
Speaker 3 (25:56):
Or do they just now say on websites like human champion?
Maybe in italics? What's like a sneer?
Speaker 1 (26:03):
Right? Maybe? Yeah?
Speaker 3 (26:06):
Interesting?
Speaker 1 (26:06):
What do they call that? Wetwear? Like your brain, your
neurons and all that. What instead of hardware? It's wetwear?
Speaker 3 (26:13):
Oh? I don't know about that.
Speaker 1 (26:14):
I think that's the term for it.
Speaker 3 (26:16):
What does that mean? Though?
Speaker 1 (26:17):
It means like you you have a substrate, right your intelligence?
Your intellect is based on your neurons and they're firing
all that stuff and it's wet and squishy and meat.
Then there's hardware that you can do the same thing on,
you can build intelligence on, but it's hardware, it's not wetwear.
Speaker 3 (26:35):
Oh.
Speaker 1 (26:35):
Interesting, so that's probably it. It's the wetwear champion versus
the hardware champion. But wetwear is italicized with the sneer.
Speaker 3 (26:44):
So where things really got interesting because you were talking
earlier about what is it with the chest and go?
What do they called? What kind of games?
Speaker 1 (26:53):
Perfect information games?
Speaker 3 (26:55):
Right? Then you think And my first thought when you
said that was well, yeah, and then there's there's games
like poker like Texas Hold Them where there are a
set of rules. But poker is not about the set
of rules. It is about sitting down in front of
whatever five or six people and lying, bluffing and getting
(27:15):
away with it in your game face being bluff Like
there's so many human emotions and contextual clues and micro
expressions and all these things, like surely you could never
ever teach a machine to win at Texas Holding Poker.
Speaker 1 (27:33):
Yeah, it'll be one hundred years at least before that happens,
I predict.
Speaker 3 (27:37):
No, they did it, and more than one team has
done it.
Speaker 1 (27:42):
Yeah. I read there was one from Carnegie Mellon called
Liberatus AI Go melon Heads, Yeah, go the Thornton Melons.
Speaker 3 (27:53):
Yeah, I mean that's was The University Alberta has one
called deep Stack.
Speaker 1 (27:58):
That was the one I read about. And it actually
here's the thing, like if you read the release on it,
you're like, you don't know how this thing works?
Speaker 3 (28:07):
Do you really?
Speaker 1 (28:08):
Yeah? And I'm pretty sure they don't fully get it,
because that's one of the problems. I actually talk about
this in the Existential Risks series.
Speaker 3 (28:15):
That's scary that is to be released right.
Speaker 1 (28:18):
That there is a type of machine learning where the
machine teaches itself but we don't really understand how it's teaching.
Probably the scariest one, right, or what it's learning, but
that's the most prevalent one. That's what a lot of
this is is like these machines. It's like here's chess,
go figure it out and they go, okay, got it.
(28:39):
How'd you do that? Wouldn't you like to know?
Speaker 3 (28:42):
So that's the scariest presentation you will see on AI
is when someone says, well, how does all this work?
Speaker 1 (28:47):
And they go, but we just know it can be
to human at poker. But the thing about deep Stack
at the University of Alberta is that it learned somehow
some sort of intuition. Yeah, because that's what's required is
not just the perfect information where you have all the
information on the board. It's with poker, you don't know
(29:08):
what the other person's cards are, and you don't know
if they're lying or bluffing or what they're doing so
that's an imperfect information game, so that would require intuition,
and apparently not one, but two different research groups taught
AI to into it.
Speaker 3 (29:24):
Yeah, Carnegie Mellon came out in January of twenty seventeen
with its Liberatus AI and they said they spent twenty
days playing one hundred and twenty thousand hands of Texas
hold them with four professional poker players and one and
smoked them. Basically got up to They weren't playing with
(29:45):
real money, obviously, but they they that would have been great.
Speaker 1 (29:48):
They were playing with skittles like me as a kid.
Speaker 3 (29:50):
Funded their next project, Liberatus was up by one point
seven million, and one of the quotes from one of
the poker players that he made to Wired magazine said,
felt like I was playing against someone who was cheating,
Like it could see my cards. I'm not accusing it
of cheating. It was just that good.
Speaker 2 (30:06):
Right.
Speaker 3 (30:07):
So that's a really interesting thing, man, that they could
teach self teach a program, or a program could teach
itself intuition. Right, it's creepy. I thought this part was interesting,
the Atari stuff. This gets pretty fun. Google deep mind.
Let it's AI wreak havoc on Atari forty nine different
(30:32):
Atari twenty six hundred games. See, they could figure out
how to win, and apparently the most difficult one was
Miss pac Man, which is a tough game. Still man,
misspac Man. They nailed it. It's still one of the
great games.
Speaker 1 (30:46):
But their their game, or their Q deep Q network
algorithm beat it.
Speaker 3 (30:54):
Yeah.
Speaker 1 (30:55):
I think it got the highest score nine nine points,
and no human or machine has ever achieved that high
score from what I.
Speaker 3 (31:03):
Understand, amazing. And the way this one does it, the
hybrid reward architecture that it uses, is really interesting. It
says here, it generates a top agent that's like a
senior manager, and then all these other one hundred and
fifty individual agents. So it's almost like they've devised this
artificial structural hierarchy of these little worker agents that go
(31:26):
out and collect I guess data and then move it
up the chain to this top agent.
Speaker 1 (31:34):
Right, and then this thing says, Okay, you know, I
think that you're probably right what these agents are probably doing.
And I don't know this is exactly true, but there's
there are models out there like this where the agent
says this is you have a ninety percent chance of
success at getting this pellet. If we take this action,
(31:57):
somebody else says, you've got an eighty two percent chance
of av this ghost if we go this way. And
then the top agent, the senior manager, can put all
this stuff together and say, well, if I listen to
this guy and this guy not only while I evade
this ghost, I'll go get this pellet. And it's based
on what confidence level that the lower agents have in
(32:19):
success in recommending these moves. And then the top agent
weighs these things.
Speaker 3 (32:24):
Wow, they should give him a little cap.
Speaker 1 (32:27):
But all this is happening like that. Oh yeah, you
know what I'm saying. This isn't like well, hold on,
hold on, everybody, what is Harvey? What do you have
to say? Well, let's get some Chinese in here and
hash it out, and everybody sits there in order some
Chinese food, and then you wait for it to come,
and then you pick up the meeting from that point on.
And then finally Harvey gives his idea, but he forgot
(32:47):
what he was talking about, so he just sits down
and eats his a roll.
Speaker 3 (32:51):
Well, here's a pretty frightening survey. There was a survey
of more than three hundred and fifty AI researchers, and
they have the following things to say, And these are
the pros that are doing this for a living. They
predicted that within ten years AI will drive better than
we do. By twenty forty nine they will be able
to write a best selling novel. AI will generate this
(33:13):
and by twenty fifty three be better at performing surgery
than humans are.
Speaker 1 (33:19):
You know. So again, one of the things that about
the field of artificial intelligence.
Speaker 3 (33:25):
You know a lot about now famous.
Speaker 1 (33:27):
It is famous for making huge predictions that did not
pan out. Sure, but you've also seen it's also famous
for beating predictions that you know have been levied against it.
But there is something in there, Chuck, that stands out
to me, and that's the idea of an AI writing
a novel. Like for a very long time I thought, well, yeah, okay,
(33:50):
you can teach a robot arm to like put a
car part or something somewhere if you wanted to just
follow these mechanical things. Or it can use in auition,
or it can use logic in reason. But to create
that's different, right. That was like the new frontier. It
used to be chess and then then was go. The
next frontier is creativity and they're starting to bang on
(34:11):
that door big time. There's a game designing AI called
Angelina out of the University of Foulmouth, which I always
want to say Foulmouth, Yeah, but we'll just call it
Foulmouth like it's supposed to. And Angelina actually comes up
with ideas for new games, not like a different level
(34:32):
or something like you should put a purple loincloth on
that player, you know, that'll look kind of cool like
new games, but whacked out games that humans would never
think of. One example I saw is in a dungeon
Battle Royale game, a player controls like ten players at once,
and some you have to sacrifice to be killed to
(34:52):
save the others, like the stuff that human wouldn't necessarily
think of. This AI is coming up with.
Speaker 3 (34:58):
Well, I mean, when you think of creatively, especially something
like writing a novel or a film, if there are
only seven stories, I mean, and that's sort of the
thinking that they're basically every every dramatic story is a
variation of one of seven things.
Speaker 1 (35:12):
Yeah. So I mean you can look at like AI
is scary, and in some ways it very much is
and can be, but there's also like, definitely a level
of excitement of the whole thing, and the idea that
there are artificial minds that are coming online or that
have come online now, that are out there that are
(35:33):
they'll they'll just naturally by definition, see things differently than
we do. Yeah, and the idea that they can come
up with stuff that we've never even thought of there
is just gonna knock our socks off, hopefully in good ways.
That's a really cool thing. And so maybe there's just
seven as far as humans know, but there's an unlimited amount.
Is if you put computer minds to thinking about these
(35:55):
kind of things, that's the premise of it.
Speaker 3 (35:57):
Right, so the robot would be like you never thought
of boy meets girl meets well trilobite. But see even
that's a variation of right.
Speaker 1 (36:09):
Just imagine something that we've never even thought of.
Speaker 3 (36:12):
Well, do you know how they should do this? If
they do do that is uh? Is not is just
release a book and not tell anyone that it was
written by an AI program because if they do that
then it's going to be so under scrutiny. Oh yeah,
they should secretly release this book and then after it's
a New York Times bestseller, say meet the Whopper, the
(36:34):
author of this.
Speaker 1 (36:36):
You know his interests are roller skating, playing Tic tac toe,
and global thermal nuclear war.
Speaker 3 (36:42):
All right, should we take a break and get strickland
in here.
Speaker 1 (36:44):
Yeah, we're going to end the Strickland drought because it
is about to rain strickland in this piece.
Speaker 3 (36:49):
You gross.
Speaker 1 (37:12):
Okay, we're back and get this. The scent of strick
has permeated our place. That's a beautiful scent.
Speaker 3 (37:20):
It smells like a soldering gun and a circuit board
and feel a lavender in a protein bar.
Speaker 2 (37:28):
That's fair. I was gonna say Draco noir that would
have been a lie.
Speaker 1 (37:32):
Is that how you said? I always called a drakar dracr.
Speaker 2 (37:36):
That's that's fair.
Speaker 3 (37:38):
I always pronounced it Benetton colors. That was what I wore.
Speaker 1 (37:43):
Oh is that what you wore?
Speaker 3 (37:44):
Yeah? During my what I call the Year of Cologne,
I had a couple of seven.
Speaker 1 (37:51):
Uh.
Speaker 2 (37:51):
This is scintillating. Why why am I here?
Speaker 1 (37:55):
So we know that you already know because we talked
via email about this, but we'll tell everybody nobody else.
We have brought you in here because you are the
master of tech and we were talking tech today, which
we've talked about without you before. But frankly, Chuck and
I and Jerry huddled and we said, this is not
quite as good with that strict so let's try something different, gotcha.
Speaker 2 (38:14):
And we're talking about games and machine versus man and
that that whole evolution and how that's gone super crazy
over the last few years.
Speaker 3 (38:24):
Games without frontiers, as Peter Gabriel would say, yeah, or
without fear.
Speaker 1 (38:29):
And we've talked, I mean, we've talked a lot about
the evolution of machine learning and how now it's starting
to take off like a rocket because they can teach themselves, right.
But one thing we haven't really talked about are solved games.
I mean, we talked about chess. Yeah, we talked about
go right, would those constitute solve games?
Speaker 3 (38:48):
Not really?
Speaker 2 (38:49):
So a solved game is the concept where if you
were to assume perfect play on either sides of the game,
you would always know how it was going to end.
Speaker 3 (38:58):
Which we always assume perfect play, right, Yeah, it's kind
of our.
Speaker 1 (39:01):
Bags, right stuff.
Speaker 2 (39:02):
You should know motto so perfect play just meaning that
no one ever makes a mistake, so very much the
way I do my work right stuff, you should know
exactly So if you were to take a game like
Tic Tac Toe and you assume perfect play on both sides,
it is always going to end in.
Speaker 3 (39:17):
A draw, which is what was in war games.
Speaker 2 (39:19):
Yes, right, the only way to win is not to
play right. Yes, so a game with like a game
like Connect four, whoever goes first is always going to win,
assuming perfect play both sides.
Speaker 3 (39:33):
Yes, what, I don't think I've played Connect for it.
That's where you drop ever a long time. That's wh
where you drop the little tokens.
Speaker 2 (39:41):
Yeah, like it kind of like Checkers.
Speaker 1 (39:42):
You did an interstitial playing Connect four. Remember I was.
Speaker 3 (39:45):
Faking it though, and you had perfect play, so I
knew it was useless.
Speaker 1 (39:48):
I was going to say that I'm so humiliated by
all the Connect four games that I've lost starting even.
Speaker 2 (39:55):
Yeah, but I mean perfect play. That's something that that
obviously only the best players typically achieve with significantly complex games.
Obviously the simpler the game, the easier it is to
play perfectly. Right tic Tac toe, if you know, once
you've mastered the basics of Tic Tac Toe and the
other person has, you're never really going to win unless
(40:17):
someone has just made a silly mistake because they weren't
paying attention.
Speaker 3 (40:21):
They put a star instead of an xer right.
Speaker 1 (40:23):
Which doesn't count automatically disqualified you. One thing I've found
that's very enjoyable is playing with little kids who haven't
figured out that tic tac toe is very easy.
Speaker 3 (40:31):
To yes and smash their face on the board and
rub it in.
Speaker 1 (40:35):
Yeah.
Speaker 2 (40:35):
I mean the same reason why I like to join
in on little league games because I can really whale
that ball out of the park. Yeah, I really missed
me feel like a man.
Speaker 3 (40:43):
That's the most tech stuffy thing you've ever said. You
really whaled that ball out of the park.
Speaker 2 (40:47):
Well, to be fair, I did just do a textuff
episode about the technology behind baseball bats, so it's so
fresh on mine.
Speaker 1 (40:53):
Nice.
Speaker 3 (40:53):
Yeah one.
Speaker 2 (40:54):
Actually, it's a lot of fun. So there have been
a lot of games that have been solved, but Checkers
will one that was recently solved back in recently by
the early nineties when it was played against a computer
called Chinook and chi in Ok.
Speaker 1 (41:11):
Yeah, like the Helicopter or the wins that blow through
Alberta exactly.
Speaker 2 (41:15):
And so there are certain games that are more easily
solved than others. You do it through an algorithm, but
other games like chess, are more complicated because you can
In chess you have multiple moves that you can do
where you can you can move a piece back the
way you win, right, it's not you're not committed to
going a specific direction with certain pieces, like with a
(41:37):
night you know you can you could go right back
to where you started on the next move if you
wanted to, and that creates more complexity. So the more
complex the game, the more difficult it is to solve.
And some games are not solvable simply because you'll never
know what the full state of the game is from
any given moment. Did you have a chance to talk
(42:00):
about the difference between perfect knowledge and imperfect knowledge in
a game.
Speaker 3 (42:04):
Yeah, yeah, we talked about that some. Yeah.
Speaker 2 (42:06):
So computers, obviously they do really well if they understand
the exact state of the game all the way through,
if they have perfect knowledge.
Speaker 1 (42:15):
All of the informations there on the board.
Speaker 2 (42:17):
Right and all players can see all information at all times.
But games like poker, which you guys talked about, obviously
you have imperfect information. You only know part of the
state of the game. That's why those games have been
more difficult, more challenging for computers to get better than
humans until relatively recently, and there have been two major
(42:38):
ways of doing that. You either throw more processing power
at it, like you get a supercomputer, or you create
neural networks, artificial neural networks, and you start teaching computers
to quote unquote learn the way people do.
Speaker 1 (42:51):
So we talked about that. Yeah, and one of the
things that we talked about was how there's this idea
that the programmers, especially say the people who are making
programs that are playing poker and or getting good at poker,
aren't exactly sure how the machines are learning to play
poker or what they're learning. They're just getting better at poker. Yeah,
(43:11):
do they know how they're learning poker? They just know
that they're learning poker and that they're good at it. Now, like,
where's the intuition? How is that being learned?
Speaker 2 (43:20):
An excellent question. The way it typically has learned, especially
with artificial neural networks, is that you set up the
computer to play millions of hands of poker that are
randomly assigned, so it's truly as random as computers can get.
That's a whole philosophical discussion that I don't think we're
ready to go into right now. But you have games
(43:44):
come up where the computer is playing itself millions upon
millions of times and learning every single time how the
statistics play out, how different betting strategies play out. It's
sort of partitioning its own mind to play against itself.
And through that process, it's as if you, as a
human player, were playing thousands of games with your friends
(44:08):
and you start to figure out, Oh, when I have
these particular cards and they're in my hand, and let's
say we're playing Texas, hold them and the community cards
are are these? Then I know that generally speaking, maybe
three times out of ten I end up winning. Maybe
I shouldn't bet. Well, the computer is doing that, but
on a scale that far dwarfs what any human can do,
(44:29):
and in a fraction in the amount of time, and
so it's sort of well, it's intuition in the sense
of it's just done it so much.
Speaker 3 (44:38):
Right, But does that mean it's completely ignoring micro expressions
and facial cues, so that doesn't even come into play.
Speaker 1 (44:47):
You should say, Strickland just nodded yet, Yeah I was.
Speaker 2 (44:49):
I was waiting for John Well. I still nod when
I do a solo show. And I do a lot
of expressive dance.
Speaker 3 (44:56):
What do you think, Jonathan, I don't know, Jonathan.
Speaker 2 (44:59):
It gets lonely in here, guys. No, but yes, what
you're saying all the tells, right, Yeah, tells that you
would use as a human player. The computer does not
pick up.
Speaker 3 (45:08):
A speypically taking data.
Speaker 2 (45:10):
Yes, typically, what it would do is it would study
the outcomes of the games from a purely statistical expression,
so that most of these poker games tend to be
computer based poker games. So it's not that it's playing
like it's not like there's a computer that says, pushed
ten more chips into the table. You know, I tick
right exactly a little it's a little winky face emoticon,
(45:33):
like I don't have good cards. No, it's it's all
usually over, sort of like internet poker, which a lot
of the people who play professional poker cut their teeth on,
especially you know, in the the more recent generations of
professional poker players.
Speaker 3 (45:48):
Kids today, Yeah, they don't know what it's like being
a smoky saloon like Moneymaker.
Speaker 2 (45:53):
When Moneymaker rose to the top a few years ago,
more than like a decade ago, now, he had come
from the world of internet poker, and so he was
using those same sort of skills in a real world setting.
But obviously there are subtle things that we humans do
in our expressions their computers do not pick up on it.
In fact, that leads us sort of into the realm
(46:14):
of games where computers don't do as well as humans.
Speaker 3 (46:19):
Yeah, is that list you sent a joke or is
it real? No, that's real.
Speaker 2 (46:23):
It does seem like it's weird, like one of the
games on there is pictionary, for example, rag or tag. Yeah,
but these are some of these are They sound silly,
But when you start to think about them in terms
of computation and robotics, you start to realize how incredibly
complex it is from a technical perspective, but incredibly easy
it is for your average human being. Okay, so with
(46:44):
humans a game of tag, once you know the basics,
it's it's all an instinct.
Speaker 1 (46:49):
You know what to do.
Speaker 2 (46:50):
You run after the person you tried to catch up
with them and tag them. But you also know push
them ind as you can. Well, if you're Josh, you
push them as hard as you can. But most of
us we tag and we're not trying to cause harm. Robots, however,
robots not so good on you said, I'm just saying
Isaac Asimov Isaac Asimov's rules of robotics. Aside, robots are
(47:13):
not very good at judging how hard they have to
hit something in order to make contact, right, They're not
as good at even your bipedal robots that walk around
like people, even the ones that can run and do
flips and stuff.
Speaker 3 (47:26):
Have you seen that one the other day that the
footage of that thing running and jumping, it's really impressive
and super creepy.
Speaker 2 (47:32):
Yeah, but even so, that's that's a clip of the
best of If you ever if you ever see the
clips where they show all the times the robots fallen over.
Speaker 3 (47:41):
Yeah, we're pouring hot coffee in someone's head.
Speaker 1 (47:44):
Yes, but they always play those clip shows, toy.
Speaker 2 (47:48):
Yes, this is true. So DARPA had its big robotics
challenge a few years ago where they had bipedal robots
tried to go through a scenario that was simulating Fukushima
nuclear disaster. So the interesting thing was the robot had
to complete a series of tasks that would have been
mundane to humans, things like open up a door and
(48:11):
walk through it and pick up a power tool and
use it against a wall, and you can watch the
footage of some of these robots doing things like being
unable to open the door because they can't tell if
they need to pull or push or they open the door,
but then immediately fall over the threshold of the door.
(48:31):
And when you see that, you realize, as advanced as
robotics is, as advanced as machine learning has become, and
as incredible as our technology has progressed, there are still
things that are fundamentally simple to your average human right
that are incredibly complicated from a technical standpoint, Like a.
Speaker 3 (48:49):
Six year old can play jinga better than a robot.
Speaker 1 (48:52):
Right right, right, Okay, But the thing is we're talking
robots here, and as we go more and more and
more online and our world becomes more and more web
based rather than reality based, doesn't the the fact that
a robot can't walk through a door matter less and less,
and the idea that that machines are learning intellect and
(49:14):
the robotivity, and you just blew my mind that that's
becoming more and more vital and important and something we
should be paying attention.
Speaker 2 (49:22):
It absolutely is something we should pay attention to. I mean,
we have robotic stock traders, the trading on thousands of
trades per second, right fast, so fast that we have
had stock market booms and crashes that last less than
a second long due to that.
Speaker 3 (49:39):
So that the robot army that will ultimately defeat us
is not something from the terminator.
Speaker 1 (49:43):
It's invisible, right, it's online, it will be online, it's.
Speaker 2 (49:48):
It's it's what's determining our retirement.
Speaker 1 (49:50):
Right, Yeah, the global economy or our municipal water supply
or whatever.
Speaker 3 (49:57):
Yeah.
Speaker 2 (49:57):
No, There's the fascinating thing to me about this is
not just that we're training machine intelligence to learn and
to perform at a level better than humans, but that
we're putting a lot of trust in those devices and
things that have real incredible impact on our lives, significant
(50:19):
enough impact where if things were to go south, it
would be really bad for us, and not in that
Terminator respect. Terminator is a terrifying dystopian science fiction story.
But then when you realize what could really happen behind
the scenes, you think, oh, robots don't have to do
any physical harm to us to really mess things up.
(50:40):
So there are certainly some cases for us to be
very vigilant in the way we deploy this artificial ants
right from the outset exactly, and too depends not necessarily.
I think I think it's I don't think it's too late,
but I think it's getting to that point of no
(51:02):
return very very quickly.
Speaker 3 (51:04):
By December this year. Yeah.
Speaker 2 (51:06):
Well, if you're if you're someone like if you're someone
like Elon Musk, you'd say, if we don't do something
now where we're totally going to plummet off the edge
of the cliff.
Speaker 1 (51:15):
But now is a window that is rapidly closing.
Speaker 2 (51:18):
Yes, yeah, yeah, the now is the now is a
time where we've got a deadline. We don't know exactly
when that deadline is going to be up, but we
know that it's not getting further out it. We're just
getting closer to that deadline. So, and a lot of
this is covered in deep conversations and the artificial intelligence
and machine learning fields that has been going on for ages,
(51:42):
to the point where you even have bodies like the
European Union that have debated on concepts like granting personhood
to artificial intelligence. So this is a really fascinating and
deep subject that and the games thing is a great
entry point into have that conversation. Uh, you know. I'm
(52:03):
lucky if I can win a game of chess against
another human being.
Speaker 1 (52:05):
Oh yeah, right, so I can't even describe chess.
Speaker 3 (52:09):
Did I did? My big thing is I do that
night thing. I call it the night shuffle. I just
move them back and forth. I just cast.
Speaker 2 (52:16):
If I can castle, then I'm so happy.
Speaker 3 (52:20):
And that's the third tech stuffiest thing. They come in threes.
Speaker 1 (52:24):
Well, Strick, thank you for stopping by.
Speaker 3 (52:26):
Should stick around for listener mail.
Speaker 1 (52:28):
I think you should too. I love to and throw
out any funny comments that you have.
Speaker 2 (52:33):
I'll throw out comments and then Jerry can decide which
ones are funny.
Speaker 1 (52:36):
Okay, all right, fair enough, all right, So if you
want to know more about AI, go listen to tech Stuff.
Strict does this every week what days.
Speaker 2 (52:45):
Monday, Tuesday, Wednesday, Thursday and Friday.
Speaker 1 (52:48):
Wow, that's amazing, buddy. And wherever you find your podcast yep, okay,
And you've been doing it for years, so if you
love this, there's a whole big backlog, nine hundred plus episode.
Speaker 3 (52:59):
You're celebrating your ten year as well, right yep, by.
Speaker 2 (53:02):
Sure am, I'll be We'll be turning ten and tex
stuff on June eleventh.
Speaker 1 (53:07):
Congratulations, well, since I said happy anniversary, it means it's
time for listener mail.
Speaker 3 (53:14):
Guys, I'm gonna call this Matt Groening and cultural relativism
about that?
Speaker 1 (53:20):
Nice?
Speaker 3 (53:20):
Hey, guys, love your podcast so much. The massive archive
makes for endless learning and entertainment. My favorite part is
you were such rad guys, including Strickland, and I could
totally imagine how did they know? I can totally imagine
myself getting a beer with you two, but without Strickland,
your Simpsons episodes were absolutely perfect. I still live in
Portland and drove on Flanders and Lovejoy Streets a lot.
Speaker 1 (53:42):
Wait, is this Matt Groening? Okay?
Speaker 3 (53:45):
Matt Granning Drew Bart in the sidewalk cement behind Lincoln
High School in downtown Portland. You can google that. I
would like to offer one interesting observation though, I've noticed
that on several episodes you guys have said that you
are cultural relativists. Is that pronounce right? Yeah? Yeah? But
then in nearly every episode I hear you pass moral
judgments on all the messed up stuff that people do,
(54:07):
whether it's racism, preak shows, or crematoriums bearing bodies on
the sly. You guys are never shy to condemn something
that deserves to be condemned. Reminds me of something I
read from Yale's sociologist Philip Gorsky, who points out that
our own relativism is rarely as radical as our theory requires.
We can't be complete relativists in our daily lives. He
(54:30):
then gives the example of how academic social scientists, where
diehard relativists, get furious and moralistic at the data fudging
of other researchers. Anyway, love the show, guys, love tech
stuff especially, and will forever be indebted to you for
your hilarity and knowledgeability. Cheers Jesse Lusco.
Speaker 1 (54:50):
Ps go tech stuff.
Speaker 2 (54:52):
That's sweet.
Speaker 1 (54:53):
Love that. Yeah, thanks a lot, Jesse. There was an
actual episode, and I don't remember which one it was,
where we a and in our cultural relativism, do you remember,
because we used to just be like, no judgment, no judgment, right,
we just can't judge, you know, And then finally we
were like, you know what, No, that's not true. We
changed our philosophy to include the idea that there are
(55:14):
moral absolutes that are universal, although sometimes we are just
judge even beyond that. Look at us, Yeah, Well, if
you want to get in touch with us, you can
send us an email to Stuff podcast at HowStuffWorks dot com.
You can send John an email to.
Speaker 2 (55:30):
Tech stuff at HowStuffWorks dot com.
Speaker 1 (55:32):
Nice, and then hang out with us at our home
on the web. Stuff youshould know dot com and just go.
Speaker 2 (55:38):
To tech Stuff. Just search it in Google. I come
up all the time.
Speaker 1 (55:41):
Fair enough.
Speaker 2 (55:46):
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