A List Of Games You Would Surely Lose to a Computer

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.  With special guest, Tech Stuff's Jonathan Strickland.

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Episode Transcript

Speaker 1 (00:01):
Welcome to stuff you should know from how Stuff Works
dot com. 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 reason, vaguely weird ways. But I'm

(00:26):
all right. So, Chuck, I have a story for you.
I'm gonna take us back to the seventeen seventies and
the swinging town of Vienna, not Virginia, not Vanna Georgia,
but you know that's how they pronounce it, right, Vanna
Yanna sausages, right, Vienna, Austria. You ever been there? Vienna, Austria. No,

(00:49):
I've been to Brussels. That was pretty close. Vienna's lovely,
I'm sure. I think it's a lot like Brussels. Very clean,
lovely town. I just remember to be very clean, Yeah,
very clean, gorgeous architecture, weird little angled side streets. They're
very narrow, very pretty tone. So we're in Vienna and

(01:10):
there is a dude skulking about going to the royal
Palace in Vienna. His name is Wolfgang von Kimberlin, and
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

(01:31):
Turk or the automaton chess player, and that's what it was.
It was a It was a wooden figure that moved mechanically,
seated at a cabinet and on top of the cabinet
was a chessboard. And when he brought it out to
show to the Royal Court, he would um, it was cool,

(01:53):
kind of but nothing they hadn't seen before, because automata
was kind of a hip thing by that. Yeah, people
loved building these engineering these automatum 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

(02:15):
or a cloth and it looks as though there's a
real well I'm not real, but you know what I
mean is that it's like a real machine. 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 ex machina
in the movie theater that makes sense. Yeah, no, it

(02:37):
does make sense. But imagine seeing like x Makina and
being like I've seen this before. This isn't anything special, okay. Yeah,
and this thing, to be clear, look like a is
it Zoltar or Zoltan from Big Zoltar Sultan? I don't know,
it's one of those two, one of those two. Like this,
this guy's wearing a turban and it's in a glass

(02:58):
case like bust like you know, like a chest up thing. Yeah,
he's seated at this this cabinet, so there's no need
for legs or anything like that. Yeah. 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

(03:19):
jerky or whatever. But he could play you in chess,
which was a huge, huge advance at the time. Like,
this is something that wouldn't come up again until the nineties,
more than two hundred years later. This thing, this automaton
could play a human being in chess and beat them. Well, yeah,

(03:40):
and it looked like when the game started, it would
look down at the chessboard and like cock his head
like m what should my first move be? 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 Being's court, but then it, you know,
it went on a world tour. Yeah, and he was

(04:01):
even it was taken over by his successor to the
guy who toured with it. Even further, people went nuts
for this stuff. 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. There
like there's some trick here. But von kempel In and
the guy who came after him, I don't remember his name. Um,
they would they would demonstrate. You could open this cabinet

(04:22):
and you could see all the workings of the mechanical
turk inside. 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
two would pick up the chess piece move it back

(04:43):
as if to say like, no, no, Napoleon, I see
what you're doing. And then if the person attempted to
move 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, which is pretty great,
A nice little feature, yeah it is. But it even

(05:03):
showed even more that this thing was thinking for itself.
That's the key here, right. 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 um engineer,
I think he was a mechanical engineer. His name was

(05:27):
Robert Willis. He said that um chess was in quote
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 um von Kempelin is controlling this thing
remotely somehow, maybe using magnets or whatever. Other people hit

(05:48):
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 cabin was closed again
and the mechanical turk started playing, the person had crawled
back out and it was actually controlling it. This seems
to be the case that there was a person controlling it,

(06:09):
but the idea that it was it was a machine
that could think and beat humans in chess, had like
kind of unsettling implications. Yeah, this author Philip Thickness, great name,
British author for sure, Philip Thickness. He uh, he said,

(06:30):
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 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, Um, just

(06:52):
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, just
see here everyone. Um, so what should we reveal the
real deal? Sure? I think I did already. Well, I
don't think you spelled it out is we'll spell it out.

(07:14):
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,
or they forget about them and they'd starve and have
to replace them. 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

(07:35):
gets into a tiny little ball in one section of
that box. But my thing is this, like, this is
not a satisfying explanation to me, Chuck, I think it's great.
How did the person keep up with the board above? Well?
I mean some I don't know if they ever proved
exactly how it was going. That's what I'm saying, Okay,
whether or not I think they that the zoltar or

(07:57):
I'm sorry, the turk was just hollowed out and you
you would just put your arms through the arm so
you would call up into the church. Yeah, you would
become the churk. When the churk would fuse, That's what
some people thought. I think that's what Edgar Allen Poe
thought too. He would Other people thought that there were

(08:18):
the the the little person was underneath in the cabinet
operating the turk with levers and stuff like that. Whether
it could have been a mirror or something, you know,
I guess a little telescopic mirror. That's what's getting me
is how would they keep up with the game. 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. That's what I don't get. Just mirrors

(08:40):
smoking mirrors maybe so. But the point is is it
was a fake. It was a fraud, but it raised
some really big questions about the idea of a machine
beating a person at something like chess. Yeah, and it
really peaked the mind of one Charles Babbage. He was
he was a kid or or young at least at

(09:00):
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
too to calculate mathematics automatically. So at some point to
this is kind of maybe the beginnings of humans trying
to create a I well, yeah, with Babbage's differential machine

(09:25):
or difference machine, 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.
It was a fraud, yeah, but it was it. It
sparked that idea, It definitely did. And that's something that UM,

(09:45):
like chess in particular, has always been like this idea
of like, if you can teach a machine to play chess,
you really achieved a milestone. And there's been you know,
plenty of programs, was most notably Deep Blue, which we'll
talk about, but there's there's been this idea that like
part of AI is chess, teaching it to play chess.

(10:09):
But they, the people who develop a I 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 demonstrate the progress
of artificial intelligence because it's a complex game that you
can't just program it, like it almost has to learn. Uh, well,

(10:32):
it depends on how you come at it at first, right,
So initially they did try to program it Okay, there's
this for for from basically nineteen fifty two the about
the mid like about say sixty years right. That is
how they approached AI and chess is you figured out

(10:53):
how to break chess down and explain it to a computer. Now,
what if you could, ideally you would have this this
computer or this AI, this artificial intelligence UM be able
to think about the outcome of every possible every possible
outcome of a move before making it. That's just not possible.

(11:15):
It's 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. And that was actually laid out
in by a guy named Claude Shannon, who is the
father of information theory. And he wrote a paper with
a pretty on the nose title called programming a Computer

(11:37):
for Playing Chess. And you have to say it like
that when you say the name, it's got a question
market in the end, right. But he laid out two
big things, um. 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

(11:58):
out and they established about fifty or sixty years of
development in teaching an AI to play chess. 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 ev kind of give a number to
create a numerical evaluation based on the state of the

(12:19):
board at that moment, and assign a real number evaluation
to it. So, um, the highest number that you would
shoot for is obviously getting checkmate, getting a king and
checkmate right, right, So what you've just done now is
by assigning a number to a state like the pieces

(12:40):
on a board. Um, what you what you've done is
to say, like shoot for this number. The higher the number,
like you're going to give this a either rule. Now,
the higher the number, the more desirable that this move
that could lead to that higher number. Function. Evaluation function
is what you want to do, right, like capture the
night or capture the queen. Kept of the queen would

(13:00):
have a higher evaluation number, right exactly. So that's the function.
And 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 the
maximum loss, that is right. So what they what they
talk computers do is so you know, computer can look
through an entire game every possible outcome. But what you

(13:24):
there are computers that can look pretty far down the
line at every possible outcome. And what you can say is, okay, um,
you want to find 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. Yeah.

(13:46):
By and this is your 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 the pond 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 juice

(14:07):
you have, the more moves ahead that you can look exactly.
And then they just shy away from ones with the
higher function number or lower function number, depending on how
you've programmed it. But they they they're making these decisions
based on these rules, um. And then there's other things
you can do, like little shortcuts to say, if if A,
if A decision tree leads to a the other players

(14:33):
King being in checkmate. Don't even think about that move
any further, don't evaluate any longer, just abandon it because
we would never want to make that move, right. So
there's all the shortcuts you can do. And that's what
they did to teach computers. That's what Deep Blue did
when it beat Gary Kasparo. It was this huge, massive
computer that knew a lot of chess, a lot about chess.

(14:56):
It had a lot of rules, a lot of incredibly
intricate programming that was extremely sharp, and it actually wanted
became the first computer to beat an actual human chess
grand master in like regulation match play. Yeah. I mean,
and I don't think caspar Off gets enough credit for

(15:16):
like willing 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 was sent shock waves,
and everyone that was alive remembers, even if you didn't
know anything about either, one remembers Deep Blue being all
over the news. It was a really big deal. And
Casperrof put his name on the line and lost. Yeah,

(15:39):
And I was wondering, Chuck, how how like you would
get somebody to do that. I'm sure the Mountain of Catch.
I guess that would probably be part of it. But
I mean, I don't know. I bet, I bet that's
out there. We just I just didn't look it up.
So um, that's possible. It's also possible that they said, look, man,
like this is chess, we're talking about what ever, But

(16:00):
really what you're doing is helping advance artificial intelligence because
we're not at we're not really trying ultimately to win
chess games. We're trying to cure cancer. Yeah, we're gonna
take your title because we're gonna beat you, or our
machine is gonna beat you. But even still, you're gonna
be helping with cancer. Think of the cancer Casparrow. That's
probably what they said. Should we take a break? Yeah? Uh, what?

(16:23):
Well should we tease our special guests? First? Is he okay?
I can smell him. I don't think we even said
we're gonna have a special guest. Later in the episode,
Mr Jonathan Strickling of tech stuff nice. It's just been
a long time since, like years since we had stricken.
The last time we had stricken was like two thousand
nine with the Necronomicon episode what is what where has

(16:44):
he been besides sitting in between this every day? It's
been a strickling drought is It's yes, the stricklands coming later.
But we're gonna come back after this and talk a
little bit more about man versus machine. Okay, dude, So

(17:18):
what we just described was how AI was taught to
play things like chess or to think like you take something,
you figure out how to break it down into little
rules and and and things that a computer can think of, right,
and then follow these kind of rules to to make
the best decision. That's how it used to be. The

(17:39):
way that it's done now that everybody's doing now is
where you are creating a machine that teaches itself. That
was the breakthrough. You may have noticed back in about
two all of a sudden, things like Siri and alexa
um got way better at what they are doing. They

(18:02):
got way less confused. You're a navigation app got a
lot better. And the reason why is because these these
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

(18:22):
on was games. Yeah, and it makes sense. Um. And
if you thought chess was complicated and difficult, when it
comes to these new AI s 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.

(18:45):
And this was actually that was actually quote from Demi Hassabi,
neuroscientists and the founder of deep Mind, which was deep Mind.
They were purchased by Google or where they are always
part of Google. 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. Well, they're they're one of

(19:08):
the teams, yeah, that they are designing these new programs.
And to give you an idea of how complex Go is, uh,
it deals with a board with with different stones and
there are uh ten how do you even say that
tend to the hundred and seventy power So that means
a hundred and seventies zeros uh and take that number

(19:28):
and that's the number of possible configurations of a Go board. 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 now, so it's supposedly it's easy to learn, right,
but very complicated in its simplicity, right right, exactly, it's
extremely difficult to master. And there was a guy in

(19:50):
the late nineties, and I'm guessing that that he was
saying this after Deep Blue beat casper Off. Um. There
was an astrophysicists from Princeton. He said that it would
probably be a hundred years before a computer beats the
human at Go. To give you an idea of just
how complex Go is, that deeply would just be cast

(20:12):
prov And this guy is saying it will still be
a hundred years before any anyone gets beat at Go
by a computer. And he was someone who knew about
this stuff, who was an astrophysicist. He wasn't just some
schmoe at home and drunk in his recliner just making
asinine predictions. Um. So, And again we've we've said this before,
but I want to reiterate the people that, uh, I

(20:35):
think Alpha Go is the name of this program. The
people that created this a deep mind. They wanted to
stress that this is a problem solving program. We're just
teaching it this game at first, to to make it
learn and to see 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

(20:56):
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're talking about. It's
it crunches down all these possible options aka data to
decide what move should I make right, and you can
apply that. Ideally, they're gonna apply this to Alzheimer's and
cancer and all sorts of things. Right. That's general general

(21:18):
purpose thinking, right, yeah, and thinking on the fly to
faced 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 their structure. It's a good proving ground.

(21:41):
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. Yeah, it's really really interesting

(22:05):
the way that they're like you said um earlier and
before the break that we we don't have computers that
can run all the possibilities. So what they teach in
the case of AlphaGo, this program teaches itself by playing
itself in these in these games and go specifically, and
the more it plays itself, the more it learns, and
the more ability it has during a game to choose

(22:29):
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, there really
only fifty different moves that I could or should make.
Right or that's kind of a simplified way to say it.
But right, No, but it's true. But that's that's exactly right.
And what they're doing is basically the same thing that

(22:51):
a human does. It's and it's going back to its
memory banks, yeah, exact experience and saying well, I've 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. That is what humans do. Yeah, not only
I mean, boy, we screwed up the chess episode, but

(23:11):
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? But oh man, I did
this move that one time and it didn't go as
the book said. So that's now factored into my thinking, right,
except imagine being able to learn from scratch and get

(23:32):
to that point in eight days or eight hours. So
that Go team, the Alpha Go the first the first
iteration of Alpha Go. I think they started working on
it in two thousand fourteen and in two thousands sixteen,
at the end of two thousand and sixteen, they unleashed
it um secretly onto uh an Alpha Go website and

(23:53):
it started just wiping the floor with everybody. Everybody's like,
this thing is pretty good. Oh it's Alpha Go. Well,
here's this it. That was the end of two thousand
and sixteen. Okay, so chess had already come and gone.
Like by this point, you can download a program that's
like deep Blue, right, that was that's a great point. Yeah,
Like today the stuff you played chess with on your

(24:15):
laptop is even more advanced than Deep Blue was in
the nineties. And it's just on your laptop. Um, but
this is so, this is Go. This is the end
of two thousand and sixteen. The end of two thousand seventeen,
um Alpha Go was replaced with Alpha Go zero. It
learned what Alpha Go had taken two years or three

(24:35):
years to learn in forty days by teaching itself, and
it beat the master. And finally, in May of two
thousand seventeen, Alpha Go took on Key G, the highest
ranked Go player in the world. I don't know if
he or she still is. No Lisa A. Doll is

(24:57):
the current or was until Alpha alpha Go beat him. Man, yeah,
do they get knocked off in Alpha Go is the champion? Like,
that's that's not fair. I if it's match play and
the player, the human players accepted a challenge from the computer.
I don't see why it wouldn't be the world champions.

(25:18):
Or do they just now say on websites like human
champions maybe in italics with like a sneer maybe yeah? Interesting?
What do they call that wet wear? Like your brain,
your neurons and all that what instead of hardware, it's
wet wear. Oh, I don't know about that. I think
that's the term for it. What does that mean? Though?

(25:39):
It means like you're you have a substrate, right, you're intelligence.
You're intellect is based on your neurons and they're firing
all that stuff, and it's wet and squishy and meat.
And there's hardware that you can do the same thing on,
you can build intelligence on, but it's hardware, it's not
wet wear. Interesting, so it's probably it. It's the wet

(26:00):
champion versus the hardware champion. But wet wears italicized what
the sneer? Uh So where things really got interesting because
you were talking earlier about um, what what is it
with the chess and go? What are they called? What
kind of games? Perfect information games? Right? Then you think
And my first thought when you said that was well, yeah,

(26:22):
then then there's there's games like poker like Texas Hold Him,
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 away with it in your game face
being bluff Like there's so many human emotions and contextual

(26:44):
clues and and uh micro expressions and all these things,
like surely you could never ever teach a machine to
win at Texas Holding poker. Yeah, it'll be a hundred
years at least before that happens, I predict. No, they
did it, and more than one team has done it. Yeah.
I read um there was one from Carnegie Melon called

(27:08):
Liberatus Ai go melon heads, Yeah, go the Thornton Melons.
Uh yeah, I mean that's was. The University of Alberta
has one called deep Stack. 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

(27:28):
know how this thing works to you? Yeah, And I'm
pretty sure they don't fully get it because that's one
of the problems. Actually talked about this in the Existential
Risks series that's Scared that is to be released there, right,
that there is a 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

(27:50):
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 here's chess, go figure it out and
they go, okay, got it. How did you do that?
Wouldn't you like to know? So that's the scariest presentation
you will see on ai as when someone says, well,
how does all this work? And they go, but we

(28:12):
just know it can beat the human at poker. But
the thing about Deep Stack at the University of Alberta
is that it learned somehow some sort of intuition, 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 what the other person's cards are and

(28:32):
you don't know if they're lying or bluffing or what
they're doing. Um, so that's an imperfect information game. So
that would require intuition. And apparently not one, but two
different research groups taught AI too into it. Yeah, Carnegie
Mellon came out in uh January of two thousand seventeen
with its Liberatus AI and they said they spent twenty

(28:54):
days playing a hundred and twenty thou hands of Texas
hold them with four professional poker players and one and
smoked him. Basically got up to They weren't playing with
real money obviously, but they they would have been great funded.
Their next project, Liberatis, was up by one point seven million,

(29:17):
and one of the quotes from one of the poker
players that he made two Wired magazine said, I 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. So that's a really interesting thing. Man,
that they could teach self teach program, or a program

(29:37):
could teach itself intuition. That's creepy. I thought this part
was interesting. The Atari stuff, Um, this gets pretty fun.
Google deep mind. Let it's a i um reek havoc
on Atari forty nine different at twenty games, see they
could figure out how to win, and apparently the most

(30:00):
difficult one was Miss Pacman, which is a tough game.
Still man, Miss pac Man. They nailed it. It's still
one of the great games. But but there um their
game or their Q deep Q network algorithm beat it. Yeah.
I think it got the highest score nine hundred points,

(30:21):
and no human or machine has ever achieved that high
score from what I understand. Amazing and the way this
one does it. The hybrid reward architecture that it uses
is really interesting. It's it says here, it generates a
top agent that's like a senior manager, and then all
these other hundred and fifty individual agents. So it's almost

(30:41):
like they've devised this artificial structural hierarchy of these little
worker agents to go out and collect I guess data
and then move it up the chain to this top agent. Right,
and then this thing says, um, Okay, you know, I
think that you're probably right what these agents are probably doing.

(31:05):
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 um, you have a nine chance of
success at getting this pellet um if we take this action.
Somebody else says, you've got a two percent chance of
evading this ghost if we go this way. And then
the the top agent, the senior manager, can put all

(31:28):
this stuff together and say, well, if I listen to
this guy and this guy done, only will I evade
this ghost. I'll go get this pellet um. And it's
based on what what confidence level that the lower agents
have in success in recommending these moves, and then the
top agent ways these things. They should give him a
little a little cap But all this is happening like that,

(31:51):
you know what I'm saying. This isn't like well, hold on,
hold on, everybody, what is Harvey? Harvey? What do you
have to say? Well, let's get some Chinese in here
and 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 what he was talking about, so he
just sits down and eats his agrol. Well here's a

(32:14):
pretty frightening. Uh. Survey. Uh. There was a survey of
more than three fifty AI researchers and they had the
following things to say. And these are the pros that
are doing this for a living. They predicted that within
ten years, ay, I will drive better than we do,
they will be able to write a best selling novel,
A I will generate this, and bye be better at

(32:37):
performing surgery than humans are. You know. So again, one
of the things that about the field of artificial intelligence
a you know a lot about now famous. It is
famous for making huge predictions that did not pan out.
But you've also seen it's it's also famous for beating

(32:58):
predictions that you know been levied against it um. 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,
you can teach a robot arm to like put a
car part or something somewhere if you wanted to just
follow these these mechanical things, or it can use in

(33:21):
a wish, or it can use logic and reason. But
to create that's different, right. That was like the new frontier.
It used to be chess and then it used there
was go. The next frontier is creativity, and they're starting
to bang on that door big time. There's a game
designing AI called Angelina out of the University of foul Myth,
which I always want to say foul Mouth, but we'll

(33:43):
just call it Foulmouth like it's supposed to. And Angelina
actually comes up with ideas for new games, not um,
like a different level or something like like you should
put a purple loincloth on that player. You know that'll
look kind of like new games, but whacked out games
that humans would never think of. One example I saw

(34:06):
is in a Dungeon Battle Royal game, a player controls
like ten players at once, and some you have to
sacrifice to be killed to save the others. Like the
stuff that human wouldn't necessarily think of, this AI is
coming up with. 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 sort

(34:28):
of thinking that they're basically every every dramatic story is
a variation of one of seven things. Yeah. So I
mean you can look at like um AI is scary,
and in some ways 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

(34:50):
minds that are coming online or that have come online now,
that are out there that are they'll they'll just naturally
by definition, see things differently than we do, and the
idea that they can come up with stuff that we've
never even thought of that is just gonna knock our
socks off, hopefully in good ways. Um that's that's a
really cool thing. And so maybe there's just seven as

(35:11):
far as humans know, but there's an unlimited amount if
if you put computer minds to thinking about these kind
of things, that's the premise of it. Right, So the
robot would be like you never thought of boy meets
girl meets well trilo bite. But see even that's a
variation of right, just imagine something that just we've never

(35:33):
even thought. Well, Gina, how they should do this? If
they do do that is uh is not is just
release a book and not tell anyone that was written
by an AI program, because if they do that, then
it's going to be so under scrutiny. They should secretly
release this book and then after it's a New York
Times bestseller, Say, meet the Whopper, the author of this.

(35:58):
You know his interests are rule skating, playing tic tac toe,
and global thermal nuclear war. All right, should we take
a break and get strickling in here. Yeah, we're gonna
end the strickling drought because it is about to reign
strickling in this piece. Gross. Okay, we're back and get this.

(36:37):
The scent of strick has permeated our place, and that's
a beautiful scent. It smells like a soldering gun and
a circuit board. You know, feel the lavender in a
protein bar. That's fair. I'm gonna say, Drac o' noir
that that would have been a lie. Is that how
you said? I always called it Drecar, drac Car. That's

(36:58):
that's fair, d Car. I always pronounced it Benetton colors.
I think that was what I wore. Oh is that
what you wore? Yeah? During my what I call the
Year of Cologne, I had a couple of seven. Uh,
this is scintillating. Why are you? Why am I here?
So we know that you already know because we talked

(37:20):
via email about this, but we'll tell everybody else. We
have brought you in here because you're 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
without strict so let's try something different. Got you and
and we're talking about games and machine versus man and

(37:42):
that whole whole evolution and how that's gone super crazy
over the last few years. Games without frontiers, Peter Gabriel
would say, war without fear. 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. But one thing we
haven't really talked about are solved games. And we talked

(38:04):
about chess. Yeah, we talked about go right with those
constitutes solved games, not really so 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, which we always
assume perfect play, right, that's kind of work bags stuff
should know motto. So perfect play just means that no

(38:27):
one ever makes a mistake, so very much the way
I do my work right stuff 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 a draw, which is what's in
war games. Yes, right, the only way to win is
not to play. Yes, so a game, it's a game

(38:48):
like Connect four, whoever goes first is always going to win,
assuming perfect play sides. Yes, what, I don't think I've
played Connect for it. That's where you dropped for a
long time, when where you drop the little uh tokens
kind of checkers. We did an interstitial playing Connect for Remember,
I was speaking it though, and you had perfect place,

(39:09):
so I knew it was useless. I was gonna say
that I'm so humiliated by all the Connect four games
that I've lost starting even Yeah, but but I mean
perfect play. That's something that that obviously only the the
best players typically achieve with with significantly complex games. Obviously,
the simpler the game, the easier it is to play perfectly.

(39:31):
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 someone has just
made a silly mistake because they weren't paying attention like
a star instead of an ex right, which doesn't count
automatically disqualified you. One thing I found this very enjoyable
is playing with little kids who haven't figured out that

(39:52):
tic tac toe is very easy to bring. Yes, smashed
their face on the board, rub it in. Yeah, 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, it really missed me, feel like
a man. That's the most tech stuffy thing you've ever said.
You really wail that ball out of the park. Well,
to be fair, I did just do a tech stuff
episode about the technology behind baseball bats so fresh. Actually

(40:17):
it's a lot of fun. So there have been a
lot of games that have been solved, but Checkers was
one that was recently solved back in recently by the
early nineties when it was played against a computer called
Chinook and uh h I in O okay, yeah, like
the helicopter or the winds that blow through Alberta exactly.

(40:37):
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

(40:59):
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

(41:21):
about the difference between perfect knowledge and imperfect knowledge and
a game. Yeah, yeah, we talked about that. Yeah, so
so computers obviously, they do really well if they understand
the exact state of the game all the way through.
If they if they have perfect knowledge, all of the
information is there on the board right and and all
players can see all information at all times. But games

(41:43):
like poker what 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've been two major 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,

(42:07):
and you start teaching computers to quote unquote learn the
white people do. So we talked about that, and one
of the things that we talked about was how there's
this idea that they the programmers, especially, say, the people
who are making programs that are playing poker and are
getting good at poker, aren't exactly sure how the machines

(42:28):
are learning to play poker or what they're learning. They're
just getting better at poker. Do they know how they're
learning poker? They just know that they're learning poker and
that they're good at it. Like, where's the intuition? How
is that being learned? An excellent question. The way it
typically is learned, especially with artificial neural networks, is that
you you set up the computer to play millions of

(42:52):
hands of poker that are are randomly assigned, so it's
it's truly as random as computers can get. That's the
whole philosophical discussion that I don't think we're ready to
go into right now. But you have games come up
where the computer is playing itself millions upon millions of
times and learning every single time how the statistics play out,

(43:15):
how different betting strategies play out. It's 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,
we're playing thousands of games with your friends 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

(43:35):
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.
And well, the computer is doing that, but on a
scale that far dwarfs what any human can do, and
in a in a fraction in the amount of time,
And so it's it's sort of well, it's intuition in

(43:57):
the sense of it's just done it so much, right,
But is that? Does that mean it's completely ignoring um
micro expressions and facial cues, So that didn't even come
into play? Ye, yeah, I was. I was waiting for
you've been doing this. I still nod when I do
a solo show and I do a lot of expressive dance.

(44:18):
What do you think, Jonathan, I don't know, Jonathan. It
gets lonely in here, guys. But yes, what you're saying
all the tells. It tells that you would use as
a human player. The computer does not pick up typically data. Yes, typically,
what it would do is it would study the outcomes
of the games from a purely statistical expressions. Most of

(44:40):
these poker games tend to be computer based poker games.
So it's not that it's playing like it's it's not
like there's a computer that says push ten more chips
into the table. You know, it's right, exactly, a little
it's a little winky face emoticon, like I don't have
good cards. No, it's it's all usually over of like
internet poker, which a lot of the people who play

(45:03):
professional poker cut their teeth on, especially you know in
the more recent generations of professional poker players. Yeah, they
don't know what it's like being a smokey saloon like Moneymaker.
In money Maker 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

(45:23):
using those same sort of skills in a real world setting.
But obviously there are subtle things that we humans do
in our expressions that computers do not pick up on,
and in fact that leads us sort of into the
realm of games where computers don't do as well as humans.
Is that list you sent a joke or is it really? No,

(45:44):
that's real. It does seem like it's weird, like one
of the games on there is Pictionary, for example, or tag. Yeah,
but these are some of these are 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. So with humans

(46:07):
a game of tag, once you know the basics, it's
it's all an instinct. You know what to do. You
run after the person you tried to catch up with
them and tag them. But you also know push him
in the back as hard as you can. Well, if
you're Josh, you push him 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

(46:29):
you said, and just say Isaac Asimov Isaac Asimov's rules
of robotics. Aside, robots are 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. Have you
seen that one the other day that the footage of

(46:50):
that thing running and jumping, it's really impressive and in
super creepy. 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, we'll pouring hot coffee in someone's head. Yes,
but they always play those clips shows to Yake, yes

(47:10):
that this this is true. So 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 the 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

(47:32):
a door and walk through it, and pick up a
power 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. And when you see that,

(47:54):
you realize, as advanced as robotics is, as advanced as
machine learning has become, as incredible as our technology has progressed,
there are still things that are fundamentally simple to your
average humans that are incredibly complicated from a technical standpoint,
Like a six year old can play jinga better than
a robot, right right, right, Okay, But the thing is

(48:16):
we're talking robots here, and is we go more and
more and more online in our world, becomes more and
more like 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 the robotivity, you just blew my

(48:39):
mind that that's becoming more and more vital and important
and something we should be paying attention. It absolutely is
something we should pay attention to. I mean, we have
robotic stock traders there 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

(49:00):
due to that. So the robot army that will ultimately
defeat us is not something from the terminator. It's invisible.
It's online, or it will be online. It's it's it's
it's what's determining our retirement, right, yeah, the global economy
or um our municipal waters apply or whatever. Yeah. Now
there's the fascinating thing to me about this is not

(49:24):
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, the significant
enough impact where if things were to go south, it
would be really bad for us. Uh, and not in

(49:46):
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 the robots don't have to do
any physical harm to us to really mess things up.
So there are certainly some cases for us to be

(50:06):
very vigilant in the way we deploy right from the
outset exactly, and that it 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 return
very very quickly. December of this year. Yeah, Well, if

(50:28):
you're if you're someone like if you're someone like Elon Musk,
you'd say, if we don't do something now, we're we're
totally going to plummet off the edge of the cliff.
But now is a window that is rapidly closing. 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

(50:50):
not getting further out it. We're just getting closer to
that deadline. So, and a lot of this is is
it covered in deep conversations and the artificial and eligience
and machine learning fields that has been going on for ages,
to the point where you even have bodies like the
European Union that have debated on concepts like granting personhood

(51:12):
to artificial intelligence. So this is a really fascinating and
deep subject that and the games thing is a great
entry point into having that conversation. Uh, you know, I'm
lucky if I can win a game of chess against
another human being. Right, So, I can't even describe chess,
did I did. My big thing is I do that

(51:33):
night thing I called the night shuffle. I just move
them back and forth. I just castle. If I can castle,
then I'm I'm I'm so happy. And that's the third
tech stuffiest thing to come in threes. Well, strict, thank
you for stopping by. Thank you should stick around for
a listener mail. I think you should too, love to
and and throw out any funny comments that you have

(51:55):
I'll throw out comments and then Jerry can decide which
ones are funny. Okay, all right, fair, all right, So
if you want to know more about AI, go listen
to Tech Stuff. Strict does this every week, what days, Monday, Tuesday, Wednesday, Thursday,
and Friday. That's amazing, buddy, and wherever you find your
podcast Okay, and you've been doing it for years, so

(52:18):
if you love this, there's a whole big backlog, nine
plus episodes you're celebrating. You're celebrating your ten year as well,
right yep, by sure, m I'll be uh, we'll be
turning ten and Tech Stuff on June eleven, congratulating. Well,
since I said happy anniversary, it means it's time for
listener mail. Guys. I'm gonna call this, uh Matt graining

(52:39):
and cultural relativism about that. 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

(53:01):
used to live in Portland and drove on Flanders and
love Joy Streets a lot. Wait, is this Matt Greening,
Matt Graenning, Drew bart and the sidewalk cement behind Lincoln
High School in downtown Portland's 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 pronounced right? Yeah? But then

(53:23):
in nearly every episode I hear you pass moral judgments
on all the messed up stuff that people do, whether
it's racism, freak shows, or crematori ums 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 sociologists who Loop Gorsky, who points out
that our own relativism is rarely as radical as our

(53:47):
theory requires. We can't be complete relativists in our daily lives.
He then gives the example of how academic social scientists,
where die hard relativists, get furious at uh and moralistic
at the data fudging of other researchers. Anyway, love the show, guys,
love tech stuff especially, and will forever be indebted to

(54:07):
you for your hilarity and knowledgeability. Cheers Jesse Lusco ps
go tech stuff. That's that's sweet. So yeah, thanks a lot, Jesse. Um.
There was an actual episode, and I don't remember which
one it was, where we abandon our cultural relativism, do
you remember, Because we used to just be like, no judgment,
no judgment, we just can't judge, you know, and then

(54:29):
finally we're like, you know what, No, that's not true.
We changed our the our philosophy to include the idea
that there are moral absolutes that are universal, although sometimes
we are just judging even beyond that. Look at us. Yeah, well,
if you want to get in touch with us, you
can tweet to us. I'm at josh um Clark and uh,

(54:50):
John Strickland's at John Strickland. That's correct. On Twitter, Chuck's
a movie crush and uh, you have a tech stuff
hsw Twitter, right, that's right. And then we also have
s y sk podcast. We're on Facebook dot com slash
stuff you should know, slash tech stuff and slash movie Crush.
Do you have a movie crush page for Facebook? Yeah? Yeah,

(55:10):
that's actually where I spend most of my time. Oh,
I didn't know that, said it's time. And then there's
also a slash stuff you Should Know Facebook page. You
guys have a lot of Facebook and to do it
so many social medias flying around. You can send us
an email to stuff podcast at how stuff Works dot com.
You can send John an email to tex stuff at
how stuff Works dot com. Nice, and then hang out
with us at our home on the web Stuff you

(55:32):
Should Know dot com and just go to tech stuff.
Just search it in Google. I come up all the time.
Fair enough for more on this and thousands of other topics,
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